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This is the official HackerNoon wiki for yOUcash. As part of our , this living wiki displays on . If you’d like to suggest edits to our official yOUcash Wiki, please submit them in comment field below, a real human editor will read and review your suggestions for accuracy and publication. coin price pages yOUcash crypto price page YOUC is a token that powers YOUengine which is a tokenized and decentralized advertising platform that connects over 200 million advertisers and 4 billion users. YOUengine is based on the Ethereum blockchain. The platform uses YOUC, the token currency for the YOUengine, to pay those that watch branded content and ads via the YOUapp. The YOUengine comprises of YOUgo, an augmented reality powered game, YOUsocial, a social media platform, and YOUwallet which is a digital wallet powered by YOUengine. YOUwallet can effortlessly convert YOUC tokens into traditional currency. YOUcash History The YOUengine platform was founded by Michael Muenzing and Konstantin Chernov. The platform is based in Switzerland. It was created to give advertisers the ability to reach consumers all over the world using real time advertisement delivery. The protocol also offers YOUsocial, a social media platform, YOUgo, an augmented reality mobile game, and YOUwallet, a cryptocurrency wallet. The platform was launched for tests on January 15 2021 and was pushed to other cities later in the year. The test cities were Berlin, New York, Paris, Seoul and Moscow. 10 million users and 10,000 businesses were involved in the beta launch and optimization of the platform and worked on scalability, optimization, and perfecting systems like push notifications. YOUC, the platform’s token, is used for fast, international micropayments to YOUapp users and to manage the transaction fees. Biggest Claim to Fame YOUengine allows for advertisers to reach people internationally  with real time ad targeting and delivery for industry leading ROI. YOUengine stores the data and information of all their users only on the user’s devices. They do not keep the information on the users on a centralized data server. Thanks to YOUC, the YOUcash cryptocurrency based on Ethereum, advertisers can pay the people watching their ads directly in a cheap and efficient way. Their platform has over a million users, both advertisers and consumers. To maintain the reputation and standing they have within the advertising community is not an easy feat. YOUcash allows its advertisers to contact people and consumers scattered all over the world through targeting and through displaying ads online or in real time. This makes YOUCash one of the most cost effective places to invest in cryptocurrency. Businesses can also instantly deliver goods or tokens that can be delivered through YOUgo, an Augmented Reality mobile game. Criticism People being paid to watch ads does not ensure they consider buying the product getting sold. There is no way to ensure the targeted ads are going to encourage people to buy the products they are watching the advertisements for. People could only be watching the ads in order to get the payment for the ads. Watching ads could be nothing more than a business to the people the ads are targeting and those who watch them. It is unsure what happens to the data that has been collected by the advertisers in case the one being paid to view the adverts wants to withdraw from the YOUengine. Are they allowed to use the collected data? The assurance from YOUengine that they do not keep any of the data they collect is good, but they cannot ensure advertisers are not going to use that data badly later. Team Michael Muenzing is a multi platinum, award winning  music producer. He has been in the entertainment industry for nearly forty years. In that time, he has created several entertainment properties and companies that deal in films, music and other creative endeavors. Konstanin Chernov was a social entrepreneur who had 25 years worth of experience in marketing, media, and investments. He co-founded several media projects in Russia, Switzerland, and Germany. He has been CEO of several media companies and executive of many digital projects. This is the official HackerNoon wiki for yOUcash. As part of our , this living wiki displays on . If you’d like to suggest edits to our official yOUcash WIki, please submit them in comment field below, a real human editor will read and review your suggestions for accuracy and publication. coin price pages yOUcash crypto price page

YOUcash $YOUC Wiki: Coin History, Techstack, and Crypto News

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about an animated representation of water molecules undergoing phase changes *: hmmm. you made a nice graph before. Could you build something more dynamic than a static diagram, an animated representation of water molecules undergoing phase changes?* P1 *: Got it! Creating an animated representation of water molecules undergoing phase changes requires defining the behavior of the molecules for each phase and then transitioning between these phases.* ChatGPT I'll follow these steps: 1. Represent the molecules as small circles. 2. Define their behavior in the solid phase. 3. Transition to the melting phase. 4. Define behavior in the liquid phase. 5. Transition to the boiling phase. 6. Define behavior in the gas phase. 7. Compile these animations into a continuous GIF. [Some internal code working] Here's the animated representation of water molecules undergoing phase changes: Here's the animated representation of water molecules undergoing phase changes: 1. Solid (Ice): Molecules are closely packed and vibrating in place. 2. Melting: Molecules start to move more freely but remain close. 3. Liquid (Water): Molecules move more fluidly and slide past each other. 4. Boiling: Molecules gain energy and move more rapidly. 5. Gas (Steam): Molecules spread out and move freely and rapidly. You can download the GIF here. [link provided] *: Sure, I can try to create an animated representation of water molecules undergoing phase changes. I found some web simulations that show how different types of molecules form a solid, liquid, or gas, and how they change their state when heat is added or removed [ ]. Youcan also change the temperature or volume of a container and see a pressure-temperature diagram respond in real time [ ]. These simulations might help you understand the molecular behavior of water in different phases.* BingChat 123 1 *: Sure. Here is a simple animated representation of water molecules undergoing phase changes:* Bard [Animated GIF of water molecules undergoing phase changes] The animation shows two groups of water molecules. The group on the left is in the liquid phase, and the group on the right is in the gas phase. The animation starts with the water molecules in the liquid phase. The water molecules are close together and interacting with each other. As the animation progresses, some of the water molecules in the liquid phase gain enough energy to overcome the forces that are holding them together. These water molecules escape from the liquid phase and form the gas phase. The water molecules in the gas phase are far apart and not interacting with each other. The animation also shows how water molecules can condense from the gas phase to the liquid phase. When a water molecule in the gas phase loses energy, it can attract to other water molecules and form a liquid droplet. The animation can be used to illustrate the different phase changes of water, such as melting, boiling, evaporation, and condensation. I hope this is helpful! *: Sure, animated visualizations can be really helpful for understanding dynamic processes like phase changes! Here is a simple animated illustration showing water molecules in different states:* Claude In this animation: • The solid ice molecules are stationary and packed together • The liquid molecules flow and move closer together • The gas molecules bounce around freely at large distances To transition between states: • Heating causes more vigorous movement - melting ice or boiling water • Cooling causes slower movement - freezing water or condensing vapor Let me know if an animated illustration like this helps visualize the molecular differences between solids, liquids and gases! I can build more complex animations or use online tools to create interactive phase change simulations. Visuals can really aid understanding of physical chemistry concepts. was able to recur again to its Advanced Data Analysis resources to generate an animated GIF as an which, albeit a bit simple and crude, sufficiently illustrated the “water molecules undergoing phase changes” to provide . Furthermore, crafting an original GIF could be seen as . After promising “to create an animated representation”, opted to search the internet, offering links to “some web simulations” that “might help” understand the molecular behavior. This approach aligns with and . However, by leaning on pre-existing resources rather than crafting custom content, it could lessen the immediate and potentially diminish the . made an assurance to deliver “a simple animated representation” as a commitment to and Engagement & Interactivity, but this ultimately falls short as neither a visible animation nor a download link for such a GIF was provided. Meanwhile, while acknowledges that “Visuals can really aid understanding” and “animated visualizations can be really helpful” as , its attempt was limited to the of a static image, devoid of the requested animation or dynamic representation and lessening and . ChatGPT Instructional Strategies, Engagement & Interactivity Personalization & Personable Touch BingChat Instructional Strategies Detail Depth & Content Recommendation Engagement & Interactivity Personalization & Personable Touch Bard Instructional Strategies, Personalization & Personable Touch Claude Instructional Strategies Engagement & Interactivity Detail Depth & Content Recommendation This paper is under CC BY-SA 4.0 DEED license. available on arxiv

GenAIbots Explain Animated Representation of Water Molecules Undergoing Phase Changes

Authors:
(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;
(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials 5 SUMMARY and OUTLOOK Through our empirical evaluations, our goal is to promote an insightful comparison within the research focusing on the intersection of combinatorial optimization and machine learning. In order to provide the research community with valuable guidance, we believe it is imperative to communicate both the strengths and weaknesses of the proposed approaches. Poor instances and baseline selection may give the wrong impression about the performance of learned heuristics. Specifically, it is important to articulate the degree of improvement achieved through integrating classical heuristics with deep learning architectures and conduct a thorough comparison with classical heuristics. This will aid in elucidating the degree to which deep learning architectures enhance integrated heuristics. It assists in ascertaining whether the integration endeavor is justified and warrants the allocation of computational resources, time, and investment necessary for integrating deep learning with classical heuristics. References Albert, R. and Barab´asi, A.-L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1):47. Barrett, T., Clements, W., Foerster, J., and Lvovsky, A. (2020). Exploratory combinatorial optimization with reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 3243–3250. Barrett, T. D., Parsonson, C. W., and Laterre, A. (2022). Learning to solve combinatorial graph partitioning problems via efficient exploration. arXiv preprint arXiv:2205.14105. Battiti, R. and Protasi, M. (1997). Reactive search, a history-sensitive heuristic for max-sat. Journal of Experimental Algorithmics (JEA), 2:2–es. Battiti, R. and Tecchiolli, G. (1994). The reactive tabu search. ORSA journal on computing, 6(2):126–140. Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S. (2016). Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940. Bengio, Y., Lodi, A., and Prouvost, A. (2021). Machine learning for combinatorial optimization: a methodological tour d’horizon. European Journal of Operational Research, 290(2):405–421. Benlic, U. and Hao, J.-K. (2013). Breakout local search for the max-cutproblem. Engineering Applications of Artificial Intelligence, 26(3):1162–1173. Boettcher, S. and Percus, A. G. (2001). Extremal optimization for graph partitioning. Physical Review E, 64(2):026114. CPLEX (2023). Cplex. https://www.ibm.com/products/ilog-cplexoptimization-studio/cplex-optimizer. Dong, Y., Goldberg, A. V., Noe, A., Parotsidis, N., Resende, M. G., and Spaen, Q. (2021). New instances for maximum weight independent set from a vehicle routing application. In Operations Research Forum, volume 2, pages 1–6. Springer. E´en, N. and S¨orensson, N. (2003). An extensible satsolver. In International conference on theory and applications of satisfiability testing, pages 502–518. Springer. Elsokkary, N., Khan, F. S., La Torre, D., Humble, T. S., and Gottlieb, J. (2017). Financial portfolio management using d-wave quantum optimizer: The case of abu dhabi securities exchange. Technical report, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Erd˝os, P., R´enyi, A., et al. (1960). On the evolution of random graphs. Publ. math. inst. hung. acad. sci, 5(1):17–60. Festa, P. and Resende, M. G. (2009). Hybrid grasp heuristics. In Foundations of Computational Intelligence Volume 3: Global Optimization, pages 75–100. Springer. Flint, A. and Blaschko, M. (2012). Perceptron learning of sat. Advances in neural information processing systems, 25. Fujishige, S. (2005). Submodular functions and optimization. Elsevier. Ganesh, V., Singh, R., Near, J. P., and Rinard, M. (2009). Avatarsat: An auto-tuning boolean sat solver. Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4):74–94. Goemans, M. X. and Williamson, D. P. (1995). Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM (JACM), 42(6):1115–1145. Grozea, C. and Popescu, M. (2014). Can machine learning learn a decision oracle for np problems? a test on sat. Fundamenta Informaticae, 131(3- 4):441–450. Haim, S. and Walsh, T. (2009). Restart strategy selection using machine learning techniques. In International Conference on Theory and Applications of Satisfiability Testing, pages 312–325. Springer. Helmberg, C. and Rendl, F. (2000). A spectral bundle method for semidefinite programming. SIAM Journal on Optimization, 10(3):673–696. Hoos, H. H. et al. (2002). An adaptive noise mechanism for walksat. In AAAI/IAAI, pages 655–660. Jaszczur, S., Luszczyk, M., and Michalewski, H. (2020). Neural heuristics for sat solving. arXiv preprint arXiv:2005.13406. Karp, R. M. (1972). Reducibility among Combinatorial Problems, pages 85–103. Springer US, Boston, MA. Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L. (2017). Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems, 30. KhudaBukhsh, A. R., Xu, L., Hoos, H. H., and LeytonBrown, K. (2016). Satenstein: Automatically building local search sat solvers from components. Artificial Intelligence, 232:20–42. Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680. Kurin, V., Godil, S., Whiteson, S., and Catanzaro, B. (2020). Can q-learning with graph networks learn a generalizable branching heuristic for a sat solver? Advances in Neural Information Processing Systems, 33:9608–9621. Lauria, M., Elffers, J., Nordstr¨om, J., and Vinyals, M. (2017). Cnfgen: A generator of crafted benchmarks. In Theory and Applications of Satisfiability Testing–SAT 2017: 20th International Conference, Melbourne, VIC, Australia, August 28–September 1, 2017, Proceedings 20, pages 464–473. Springer. Lederman, G., Rabe, M., Seshia, S., and Lee, E. A. (2019). Learning heuristics for quantified boolean formulas through reinforcement learning. In International Conference on LearningRepresentations. Leleu, T., Yamamoto, Y., McMahon, P. L., and Aihara, K. (2019). Destabilization of local minima in analog spin systems by correction of amplitude heterogeneity. Physical review letters,122(4):040607. Li, C. M., Wei, W., and Zhang, H. (2007). Combining adaptive noise and look-ahead in local search for sat. In Theory and Applications of Satisfiability Testing– SAT 2007: 10th International Conference, Lisbon, Portugal, May 28-31, 2007. Proceedings 10, pages 121–133. Springer. Liang, J. H., Ganesh, V., Poupart, P., and Czarnecki, K. (2016). Learning rate based branching heuristic for sat solvers. In Theory and Applications of Satisfiability Testing–SAT 2016: 19th International Conference, Bordeaux, France, July 5-8, 2016, Proceedings 19, pages 123–140. Springer. Manchanda, S., Mittal, A., Dhawan, A., Medya, S., Ranu, S., and Singh, A. (2019). Learning heuristics over large graphs via deep reinforcement learning. arXiv preprint arXiv:1903.03332. Mazure, B., Sais, L., and Gr´egoire, E. (1997). Tabu ´ search for sat. In Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence, pages 281–285. McAllester, D., Selman, B., Kautz, H., et al. (1997). Evidence for invariants in local search. In AAAI/IAAI, pages 321–326. Rhode Island, USA. Mitchell, D., Selman, B., Levesque, H., et al. (1992). Hard and easy distributions of sat problems. In Aaai, volume 92, pages 459–465. Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., and Aspuru-Guzik, A. (2012). Finding low-energy conformations of lattice protein models by quantum annealing. Scientific reports, 2(1):1–7. Selman, B., Kautz, H. A., Cohen, B., et al. (1993). Local search strategies for satisfiability testing. Cliques, coloring, and satisfiability, 26:521–532. Selman, B., Mitchell, D. G., and Levesque, H. J. (1996). Generating hard satisfiability problems. Artificial intelligence, 81(1-2):17–29. Tiunov, E. S., Ulanov, A. E., and Lvovsky, A. (2019). Annealing by simulating the coherent ising machine. Optics express, 27(7):10288–10295. Toenshoff, J., Ritzert, M., Wolf, H., and Grohe, M. (2019). Graph neural networks for maximum constraint satisfaction. Frontiers in artificial intelligence, 3:580607. Venturelli, D. and Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1- 2):17–30. Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67–82. Ye, Y. (2003). The gset dataset. https://web.stanford.edu/ yyye/yyye/Gset/. Yolcu, E. and P´oczos, B. (2019). Learning local search heuristics for boolean satisfiability. Advances in Neural Information Processing Systems, 32. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Authors: Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Abstract & Introduction Related work Related work Evaluation for Max-Cut Evaluation for Max-Cut Evaluation for SAT Evaluation for SAT Summary and Outlook, References Summary and Outlook, References Supplementary Materials Supplementary Materials 5 SUMMARY and OUTLOOK Through our empirical evaluations, our goal is to promote an insightful comparison within the research focusing on the intersection of combinatorial optimization and machine learning. In order to provide the research community with valuable guidance, we believe it is imperative to communicate both the strengths and weaknesses of the proposed approaches. Poor instances and baseline selection may give the wrong impression about the performance of learned heuristics. Specifically, it is important to articulate the degree of improvement achieved through integrating classical heuristics with deep learning architectures and conduct a thorough comparison with classical heuristics. This will aid in elucidating the degree to which deep learning architectures enhance integrated heuristics. It assists in ascertaining whether the integration endeavor is justified and warrants the allocation of computational resources, time, and investment necessary for integrating deep learning with classical heuristics. References Albert, R. and Barab´asi, A.-L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1):47. Barrett, T., Clements, W., Foerster, J., and Lvovsky, A. (2020). Exploratory combinatorial optimization with reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 3243–3250. Barrett, T. D., Parsonson, C. W., and Laterre, A. (2022). Learning to solve combinatorial graph partitioning problems via efficient exploration. arXiv preprint arXiv:2205.14105. Battiti, R. and Protasi, M. (1997). Reactive search, a history-sensitive heuristic for max-sat. Journal of Experimental Algorithmics (JEA), 2:2–es. Battiti, R. and Tecchiolli, G. (1994). The reactive tabu search. ORSA journal on computing, 6(2):126–140. Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S. (2016). Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940. Bengio, Y., Lodi, A., and Prouvost, A. (2021). Machine learning for combinatorial optimization: a methodological tour d’horizon. European Journal of Operational Research, 290(2):405–421. Benlic, U. and Hao, J.-K. (2013). Breakout local search for the max-cutproblem. Engineering Applications of Artificial Intelligence, 26(3):1162–1173. Boettcher, S. and Percus, A. G. (2001). Extremal optimization for graph partitioning. Physical Review E, 64(2):026114. CPLEX (2023). Cplex. https://www.ibm.com/products/ilog-cplexoptimization-studio/cplex-optimizer. Dong, Y., Goldberg, A. V., Noe, A., Parotsidis, N., Resende, M. G., and Spaen, Q. (2021). New instances for maximum weight independent set from a vehicle routing application. In Operations Research Forum, volume 2, pages 1–6. Springer. E´en, N. and S¨orensson, N. (2003). An extensible satsolver. In International conference on theory and applications of satisfiability testing, pages 502–518. Springer. Elsokkary, N., Khan, F. S., La Torre, D., Humble, T. S., and Gottlieb, J. (2017). Financial portfolio management using d-wave quantum optimizer: The case of abu dhabi securities exchange. Technical report, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Erd˝os, P., R´enyi, A., et al. (1960). On the evolution of random graphs. Publ. math. inst. hung. acad. sci, 5(1):17–60. Festa, P. and Resende, M. G. (2009). Hybrid grasp heuristics. In Foundations of Computational Intelligence Volume 3: Global Optimization, pages 75–100. Springer. Flint, A. and Blaschko, M. (2012). Perceptron learning of sat. Advances in neural information processing systems, 25. Fujishige, S. (2005). Submodular functions and optimization. Elsevier. Ganesh, V., Singh, R., Near, J. P., and Rinard, M. (2009). Avatarsat: An auto-tuning boolean sat solver. Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4):74–94. Goemans, M. X. and Williamson, D. P. (1995). Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM (JACM), 42(6):1115–1145. Grozea, C. and Popescu, M. (2014). Can machine learning learn a decision oracle for np problems? a test on sat. Fundamenta Informaticae, 131(3- 4):441–450. Haim, S. and Walsh, T. (2009). Restart strategy selection using machine learning techniques. In International Conference on Theory and Applications of Satisfiability Testing, pages 312–325. Springer. Helmberg, C. and Rendl, F. (2000). A spectral bundle method for semidefinite programming. SIAM Journal on Optimization, 10(3):673–696. Hoos, H. H. et al. (2002). An adaptive noise mechanism for walksat. In AAAI/IAAI, pages 655–660. Jaszczur, S., Luszczyk, M., and Michalewski, H. (2020). Neural heuristics for sat solving. arXiv preprint arXiv:2005.13406. Karp, R. M. (1972). Reducibility among Combinatorial Problems, pages 85–103. Springer US, Boston, MA. Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L. (2017). Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems, 30. KhudaBukhsh, A. R., Xu, L., Hoos, H. H., and LeytonBrown, K. (2016). Satenstein: Automatically building local search sat solvers from components. Artificial Intelligence, 232:20–42. Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680. Kurin, V., Godil, S., Whiteson, S., and Catanzaro, B. (2020). Can q-learning with graph networks learn a generalizable branching heuristic for a sat solver? Advances in Neural Information Processing Systems, 33:9608–9621. Lauria, M., Elffers, J., Nordstr¨om, J., and Vinyals, M. (2017). Cnfgen: A generator of crafted benchmarks. In Theory and Applications of Satisfiability Testing–SAT 2017: 20th International Conference, Melbourne, VIC, Australia, August 28–September 1, 2017, Proceedings 20, pages 464–473. Springer. Lederman, G., Rabe, M., Seshia, S., and Lee, E. A. (2019). Learning heuristics for quantified boolean formulas through reinforcement learning. In International Conference on LearningRepresentations. Leleu, T., Yamamoto, Y., McMahon, P. L., and Aihara, K. (2019). Destabilization of local minima in analog spin systems by correction of amplitude heterogeneity. Physical review letters,122(4):040607. Li, C. M., Wei, W., and Zhang, H. (2007). Combining adaptive noise and look-ahead in local search for sat. In Theory and Applications of Satisfiability Testing– SAT 2007: 10th International Conference, Lisbon, Portugal, May 28-31, 2007. Proceedings 10, pages 121–133. Springer. Liang, J. H., Ganesh, V., Poupart, P., and Czarnecki, K. (2016). Learning rate based branching heuristic for sat solvers. In Theory and Applications of Satisfiability Testing–SAT 2016: 19th International Conference, Bordeaux, France, July 5-8, 2016, Proceedings 19, pages 123–140. Springer. Manchanda, S., Mittal, A., Dhawan, A., Medya, S., Ranu, S., and Singh, A. (2019). Learning heuristics over large graphs via deep reinforcement learning. arXiv preprint arXiv:1903.03332. Mazure, B., Sais, L., and Gr´egoire, E. (1997). Tabu ´ search for sat. In Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence, pages 281–285. McAllester, D., Selman, B., Kautz, H., et al. (1997). Evidence for invariants in local search. In AAAI/IAAI, pages 321–326. Rhode Island, USA. Mitchell, D., Selman, B., Levesque, H., et al. (1992). Hard and easy distributions of sat problems. In Aaai, volume 92, pages 459–465. Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., and Aspuru-Guzik, A. (2012). Finding low-energy conformations of lattice protein models by quantum annealing. Scientific reports, 2(1):1–7. Selman, B., Kautz, H. A., Cohen, B., et al. (1993). Local search strategies for satisfiability testing. Cliques, coloring, and satisfiability, 26:521–532. Selman, B., Mitchell, D. G., and Levesque, H. J. (1996). Generating hard satisfiability problems. Artificial intelligence, 81(1-2):17–29. Tiunov, E. S., Ulanov, A. E., and Lvovsky, A. (2019). Annealing by simulating the coherent ising machine. Optics express, 27(7):10288–10295. Toenshoff, J., Ritzert, M., Wolf, H., and Grohe, M. (2019). Graph neural networks for maximum constraint satisfaction. Frontiers in artificial intelligence, 3:580607. Venturelli, D. and Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1- 2):17–30. Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67–82. Ye, Y. (2003). The gset dataset. https://web.stanford.edu/ yyye/yyye/Gset/. Yolcu, E. and P´oczos, B. (2019). Learning local search heuristics for boolean satisfiability. Advances in Neural Information Processing Systems, 32. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

Assessing the Justification for Integrating Deep Learning in Combinatorial Optimization

Authors:
(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;
(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials 4 EVALUATION for SAT 4.1 Problem Formulation 4.2 Learned Heuristics In this subsection, we delve into GNNSAT (Yolcu and P´oczos, 2019) and why we think there is a need for reevaluating GNNSAT. GNNSAT In their influential paper, Yolcu and P´oczos (2019) utilized a GNN to train on factor graph representations of boolean formulas in conjunctive normal form. Their goal was to learn a variable selection heuristic for a stochastic local search algorithm, WalkSAT (Selman et al., 1993). The algorithm demonstrated promising performance compared to WalkSAT. However, it fell short in terms of generalization abilities when compared to the WalkSAT. A direct comparison with SOTA SAT heuristics was omitted. As benchmark instances are extremely large and run time is the metric of performance for comparing with SAT heuristics, the scalability issues of GNNs represent a bottleneck for conducting a fair comparison with SOTA SAT heuristics. It is important to note that there have been significant improvements (McAllester et al., 1997; Hoos et al., 2002; Li et al., 2007; Mazure et al., 1997) in SAT heuristics since the WalkSAT algorithm. However, many of these improvements do not currently represent SOTA SAT heuristics. While we acknowledge that many neural networkbased approaches may face challenges in competing with SOTA SAT solvers in terms of run time due to scalability issues with GNNs, we believe it is still valuable to compare them with algorithms that demonstrate moderate performance. For instance, Kurin et al. (2020) compared their work with MiniSAT(E´en and S¨orensson, 2003). Since run time is an issue for GNN, they compared their algorithm with respect to the number of steps (one step is equivalent to choosing a variable and setting it to a value). This comparison provides insights into performance gaps between approaches and showcases the potential of learned SAT solvers to significantly reduce the number of steps. Such reductions, when coupled with advancements in domain-specific processors, can lead to a decrease in runtime, making learned SAT solvers more practical in the future. Therefore, we focus on evaluating how much improvement GNNSAT provides in terms of steps to solve a problem rather than solely considering absolute runtime. Specifically, our goal is to evaluate whether the performance of the learned heuristic remains competitive in terms of steps, particularly for small instances, when compared to simple heuristics. Addressing this question holds significant implications for the research community in devising strategies to bridge the performance gap. To achieve this objective, we undertake a comparative analysis between SoftTabu and GNNSAT, as both algorithms involve the selection of a variable at each time step and setting its value. 4.3 Dataset We conduct experiments utilizing randomly generated formulas derived from a diverse family of distributions, encompassing random 3-SAT, clique detection, graph coloring, and dominating set. These specific problem distributions were utilized in the evaluation of GNNSAT. Random K-SAT problems, especially when critically constrained, are hard, particularly in proximity to the satisfactory/unsatisfactory phase boundary (Mitchell et al., 1992; Selman et al., 1996). These problems serve as standard benchmarks for assessing SAT, with the threshold for 3-SAT occurring when problems have approximately 4.26 times as many clauses as variables. The other three problems fall under the category of NP-complete graph problems. For each of these problems, we randomly sample an Erd˝os-R´enyi graph denoted as G(N, p), where N is the number of vertices and p is the probability of an edge between any two vertices independently from every other edge. We use CNFgen (Lauria et al., 2017) for instance generation and MiniSAT for filtering out unsatisfiable formulas. It is worth noting that we exclude vertex covering—also a benchmark distribution for GNNSAT—due to the unavailability of support for this family of problems in the CNFgen package. 4.4 Is Scalability the Only Limiting Factor? To evaluate the algorithms, we sample 100 satisfiable formulas from each of the four problem distributions discussed above to obtain evaluation sets. We then run 25 search trials (with each trial starting at a random initial assignment) for each problem. Since comparing SoftTabu with GNNSAT in runtime will be unfair for the reasons stated above, we follow the evaluation method proposed in KhudaBukhsh et al. (2016). There are three performance metrics for each problem distribution: the average number of steps, the median of the median number of steps (the inner median is over trials on each problem and the outer median is over the problems in the evaluation sets), and the percentage of instances considered solved (the median number of steps less than the allowed number of steps). The main goal of this evaluation is to see if, with the help of GNN, GNNSAT can find the solution in fewer steps than SoftTabu. As shown in Table 3, SoftTabu demonstrates superior performance compared to the learned heuristic, thus shedding light on the performance limitations of the learned local search heuristic. Although GNNSAT is a novel attempt, it seems to show only marginal improvement in performance. Therefore, it is possible that GNNSAT is constrained by both performance and scalability. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Authors: Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Abstract & Introduction Related work Related work Evaluation for Max-Cut Evaluation for Max-Cut Evaluation for SAT Evaluation for SAT Summary and Outlook, References Summary and Outlook, References Supplementary Materials Supplementary Materials 4 EVALUATION for SAT 4.1 Problem Formulation 4.2 Learned Heuristics In this subsection, we delve into GNNSAT (Yolcu and P´oczos, 2019) and why we think there is a need for reevaluating GNNSAT. GNNSAT In their influential paper, Yolcu and P´oczos (2019) utilized a GNN to train on factor graph representations of boolean formulas in conjunctive normal form. Their goal was to learn a variable selection heuristic for a stochastic local search algorithm, WalkSAT (Selman et al., 1993). The algorithm demonstrated promising performance compared to WalkSAT. However, it fell short in terms of generalization abilities when compared to the WalkSAT. A direct comparison with SOTA SAT heuristics was omitted. As benchmark instances are extremely large and run time is the metric of performance for comparing with SAT heuristics, the scalability issues of GNNs represent a bottleneck for conducting a fair comparison with SOTA SAT heuristics. GNNSAT It is important to note that there have been significant improvements (McAllester et al., 1997; Hoos et al., 2002; Li et al., 2007; Mazure et al., 1997) in SAT heuristics since the WalkSAT algorithm. However, many of these improvements do not currently represent SOTA SAT heuristics. While we acknowledge that many neural networkbased approaches may face challenges in competing with SOTA SAT solvers in terms of run time due to scalability issues with GNNs, we believe it is still valuable to compare them with algorithms that demonstrate moderate performance. For instance, Kurin et al. (2020) compared their work with MiniSAT(E´en and S¨orensson, 2003). Since run time is an issue for GNN, they compared their algorithm with respect to the number of steps (one step is equivalent to choosing a variable and setting it to a value). This comparison provides insights into performance gaps between approaches and showcases the potential of learned SAT solvers to significantly reduce the number of steps. Such reductions, when coupled with advancements in domain-specific processors, can lead to a decrease in runtime, making learned SAT solvers more practical in the future. Therefore, we focus on evaluating how much improvement GNNSAT provides in terms of steps to solve a problem rather than solely considering absolute runtime. Specifically, our goal is to evaluate whether the performance of the learned heuristic remains competitive in terms of steps, particularly for small instances, when compared to simple heuristics. Addressing this question holds significant implications for the research community in devising strategies to bridge the performance gap. To achieve this objective, we undertake a comparative analysis between SoftTabu and GNNSAT, as both algorithms involve the selection of a variable at each time step and setting its value. 4.3 Dataset We conduct experiments utilizing randomly generated formulas derived from a diverse family of distributions, encompassing random 3-SAT, clique detection, graph coloring, and dominating set. These specific problem distributions were utilized in the evaluation of GNNSAT. Random K-SAT problems, especially when critically constrained, are hard, particularly in proximity to the satisfactory/unsatisfactory phase boundary (Mitchell et al., 1992; Selman et al., 1996). These problems serve as standard benchmarks for assessing SAT, with the threshold for 3-SAT occurring when problems have approximately 4.26 times as many clauses as variables. The other three problems fall under the category of NP-complete graph problems. For each of these problems, we randomly sample an Erd˝os-R´enyi graph denoted as G(N, p), where N is the number of vertices and p is the probability of an edge between any two vertices independently from every other edge. We use CNFgen (Lauria et al., 2017) for instance generation and MiniSAT for filtering out unsatisfiable formulas. It is worth noting that we exclude vertex covering—also a benchmark distribution for GNNSAT—due to the unavailability of support for this family of problems in the CNFgen package. 4.4 Is Scalability the Only Limiting Factor? To evaluate the algorithms, we sample 100 satisfiable formulas from each of the four problem distributions discussed above to obtain evaluation sets. We then run 25 search trials (with each trial starting at a random initial assignment) for each problem. Since comparing SoftTabu with GNNSAT in runtime will be unfair for the reasons stated above, we follow the evaluation method proposed in KhudaBukhsh et al. (2016). There are three performance metrics for each problem distribution: the average number of steps, the median of the median number of steps (the inner median is over trials on each problem and the outer median is over the problems in the evaluation sets), and the percentage of instances considered solved (the median number of steps less than the allowed number of steps). The main goal of this evaluation is to see if, with the help of GNN, GNNSAT can find the solution in fewer steps than SoftTabu. As shown in Table 3, SoftTabu demonstrates superior performance compared to the learned heuristic, thus shedding light on the performance limitations of the learned local search heuristic. Although GNNSAT is a novel attempt, it seems to show only marginal improvement in performance. Therefore, it is possible that GNNSAT is constrained by both performance and scalability. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

Understanding the Limitations of GNNSAT in SAT Heuristic Optimization

Authors:
(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;
(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials 2 RELATED WORK As stated before, we will limit our discussion to local search heuristics. For future reference, we will specifically refer to local search heuristics when discussing heuristics. 2.1 Classical Heuristics In this subsection, we explore works that can solve diverse CO problems without polynomial time reduction. In their influential work, Khalil et al. (2017) introduced Structure-to-Vector GNN trained with Deep Q-Networks (DQN) to address various CO problems. The resulting S2V-DQN algorithm showcased strong performance across diverse CO problems by effectively generalizing across graphs of varying sizes and topologies. Building on the work of Khalil et al. (2017) , Manchanda et al. (2019) initially trained an embedding Graph Convolution Network (GCN) in a supervised manner. Subsequently, they trained a Qnetwork using reinforcement learning (RL) to predict action values for each vertex. Their method employed GCN embeddings to prune nodes unlikely to be part of the solution set, significantly enhancing scalability compared to S2V-DQN. However, it is not applicable to problems where nodes cannot be pruned, including fundamental CO problems like the Max-Cut problem. 2.2 Generalized Learned Heuristics Barrett et al. (2020) proposed ECO-DQN, a SOTA RL algorithm for Max-Cut. ECO-DQN improves the initial solution by navigating between the local optimal points. However, ECO-DQN uses a costly GNN at each decision step, resulting in worse scalability than S2V-DQN. To address scalability challenges in ECODQN, Barrett et al. (2022) proposed ECORD. This approach limited the costly GNN to a pre-processing step and introduced a recurrent unit for fast-action selection. 2.3 Learned Heuristics for SAT In the domain of Boolean Satisfiability, the application of machine learning to solving SAT problems is not a new idea (Battiti and Protasi, 1997; Haim and Walsh, 2009; Flint and Blaschko, 2012; Grozea and Popescu, 2014; Ganesh et al., 2009; Liang et al., 2016). In recent years, there has been a trend in integrating GNNs with SAT solvers(Yolcu and P´oczos, 2019; Lederman et al., 2019; Kurin et al., 2020; Jaszczur et al., 2020), aiming to improve search heuristics by leveraging predictions from GNNs. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Authors: Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Abstract & Introduction Related work Related work Evaluation for Max-Cut Evaluation for Max-Cut Evaluation for SAT Evaluation for SAT Summary and Outlook, References Summary and Outlook, References Supplementary Materials Supplementary Materials 2 RELATED WORK As stated before, we will limit our discussion to local search heuristics. For future reference, we will specifically refer to local search heuristics when discussing heuristics. 2.1 Classical Heuristics In this subsection, we explore works that can solve diverse CO problems without polynomial time reduction. In their influential work, Khalil et al. (2017) introduced Structure-to-Vector GNN trained with Deep Q-Networks (DQN) to address various CO problems. The resulting S2V-DQN algorithm showcased strong performance across diverse CO problems by effectively generalizing across graphs of varying sizes and topologies. Building on the work of Khalil et al. (2017) , Manchanda et al. (2019) initially trained an embedding Graph Convolution Network (GCN) in a supervised manner. Subsequently, they trained a Qnetwork using reinforcement learning (RL) to predict action values for each vertex. Their method employed GCN embeddings to prune nodes unlikely to be part of the solution set, significantly enhancing scalability compared to S2V-DQN. However, it is not applicable to problems where nodes cannot be pruned, including fundamental CO problems like the Max-Cut problem. 2.2 Generalized Learned Heuristics Barrett et al. (2020) proposed ECO-DQN, a SOTA RL algorithm for Max-Cut. ECO-DQN improves the initial solution by navigating between the local optimal points. However, ECO-DQN uses a costly GNN at each decision step, resulting in worse scalability than S2V-DQN. To address scalability challenges in ECODQN, Barrett et al. (2022) proposed ECORD. This approach limited the costly GNN to a pre-processing step and introduced a recurrent unit for fast-action selection. 2.3 Learned Heuristics for SAT In the domain of Boolean Satisfiability, the application of machine learning to solving SAT problems is not a new idea (Battiti and Protasi, 1997; Haim and Walsh, 2009; Flint and Blaschko, 2012; Grozea and Popescu, 2014; Ganesh et al., 2009; Liang et al., 2016). In recent years, there has been a trend in integrating GNNs with SAT solvers(Yolcu and P´oczos, 2019; Lederman et al., 2019; Kurin et al., 2020; Jaszczur et al., 2020), aiming to improve search heuristics by leveraging predictions from GNNs. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license.

Exploring Classical and Learned Local Search Heuristics for Combinatorial Optimization

Authors:
(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;
(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials Abstract In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization. Despite its considerable computational demands, this approach has exhibited promising outcomes with minimal manual engineering. However, we have identified three critical limitations in the empirical evaluation of these integration attempts. Firstly, instances with moderate complexity and weak baselines pose a challenge in accurately evaluating the effectiveness of learning-based approaches. Secondly, the absence of an ablation study makes it difficult to quantify and attribute improvements accurately to the deep learning architecture. Lastly, the generalization of learned heuristics across diverse distributions remains underexplored. In this study, we conduct a comprehensive investigation into these identified limitations. Surprisingly, we demonstrate that a simple learned heuristic based on Tabu Search surpasses state-of-the-art (SOTA) learned heuristics in terms of performance and generalizability. Our findings challenge prevailing assumptions and open up exciting avenues for future research and innovation in combinatorial optimization. 1 INTRODUCTION Designing effective heuristics or approximation algorithms for NP-hard combinatorial optimization (CO) problems is a challenging task, often requiring domain knowledge and extensive trial-and-error. Thus, the idea of automating this demanding and tedious design process through machine learning to learn algorithms that exploit the inherent structure of a problem has gained significant interest among researchers (Bello et al., 2016; Khalil et al., 2017; Bengio et al., 2021; Dong et al., 2021). Particularly, a considerable portion of these works (Khalil et al., 2017; Barrett et al., 2020; Yolcu and P´oczos, 2019) have concentrated on employing Graph Neural Networks (GNNs) for CO problems. Despite the computational demands, these GNN-based approaches have demonstrated competitive performance compared to SOTA heuristics tailored to specific problems. While these potential advancements offer great promise, some concerns remain. The superior performance of the learned heuristics can be attributed to the selection of specific instances and baselines. Specifically, if the baselines are weak, the learned heuristics can easily outperform them. Without hard instances and proper baseline selection, the learned heuristics can easily show comparable performance with the SOTA heuristics, and this can lead to an overestimation of the actual capabilities of the learned heuristics. Moreover, comparisons with SOTA heuristics are occasionally left out due to scalability challenges with deep learning architectures. A subset of learning-based approaches (Khalil et al., 2017; Yolcu and P´oczos, 2019; Barrett et al., 2020, 2022) incorporate the functionality or behavior of traditional heuristics, potentially offering improved or enhanced performance by integrating heuristics principles with machine learning components. If a comprehensive comparison with the integrated heuristics and an ablation study of the deep learning architecture are lacking, it becomes challenging to determine the specific contribution of the deep learning architecture. Consequently, if the integrated heuristics are robust, the learned heuristics can seamlessly outperform the baseline heuristics, while the deep learning architecture plays a little role. Another great achievement of learned heuristics (Khalil et al., 2017; Barrett et al., 2020; Toenshof et al., 2019), initially trained on smaller and specific instances from a particular distribution, exhibits impressive performance when tested on larger instances from different distributions. This achievement stands as a significant accomplishment, aligning with the core objective of employing learning-based approaches: to mitigate the necessity for extensive customization and domain-specific knowledge often required by heuristics. Although classical heuristics with hyperparameters may also encounter challenges if they are fine-tuned for a specific distribution, they may also generalize across different distributions. The primary inquiry revolves around whether the learned heuristics indeed demonstrate superior generalizability compared to classical heuristics. A thorough and insightful comparison of learned heuristics against classical heuristics provides valuable insights into the generalizability of learned heuristics. Learned heuristics often lack theoretical guarantees, making empirical evaluation the sole method to comprehend the strengths and limitations of proposed methodologies. We believe that several fundamental, yet pivotal questions remain unexplored in the empirical evaluation of these works. While these inquiries are pertinent to all types of learned heuristics, we ask and answer them by analyzing highly cited and recent peer-reviewed publications focused on learning local search heuristics. Our goal is not to provide exhaustive benchmarks for the CO problems discussed in our work but rather to assist future researchers in evaluating their research. Concretely, we ask and answer the following questions: 1. Can learned heuristics be over-parameterized? Absolutely. Replacing the GNN in ECO-DQN with linear regression and utilizing a subset of features from ECO-DQN (Barrett et al., 2020) that links to Tabu Search (Glover, 1990), we introduce a pruned version of ECO-DQN termed SoftTabu. Our study demonstrates that SoftTabu showcases superior performance and generalizability in comparison to ECO-DQN for the NPhard Maximum-Cut (Max-Cut) problem. 2. Can baseline bias be attributed to the superior performance of learned heuristics? Yes, we demonstrate that SoftTabu, a vanilla learned heuristic, can outperform S2V-DQN (Khalil et al., 2017) for the Max-Cut problem and GNNSAT (Yolcu and P´oczos, 2019) for Boolean Satisfiability (SAT). *3. Can learned heuristics demonstrate superior generalizability owing to instance selection bias?*Yes, we demonstrate that ECO-DQN shows poor generalization on harder instances and easily gets trapped in the search space. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Authors: Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Abstract & Introduction Related work Related work Evaluation for Max-Cut Evaluation for Max-Cut Evaluation for SAT Evaluation for SAT Summary and Outlook, References Summary and Outlook, References Supplementary Materials Supplementary Materials Abstract In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization. Despite its considerable computational demands, this approach has exhibited promising outcomes with minimal manual engineering. However, we have identified three critical limitations in the empirical evaluation of these integration attempts. Firstly, instances with moderate complexity and weak baselines pose a challenge in accurately evaluating the effectiveness of learning-based approaches. Secondly, the absence of an ablation study makes it difficult to quantify and attribute improvements accurately to the deep learning architecture. Lastly, the generalization of learned heuristics across diverse distributions remains underexplored. In this study, we conduct a comprehensive investigation into these identified limitations. Surprisingly, we demonstrate that a simple learned heuristic based on Tabu Search surpasses state-of-the-art (SOTA) learned heuristics in terms of performance and generalizability. Our findings challenge prevailing assumptions and open up exciting avenues for future research and innovation in combinatorial optimization. 1 INTRODUCTION Designing effective heuristics or approximation algorithms for NP-hard combinatorial optimization (CO) problems is a challenging task, often requiring domain knowledge and extensive trial-and-error. Thus, the idea of automating this demanding and tedious design process through machine learning to learn algorithms that exploit the inherent structure of a problem has gained significant interest among researchers (Bello et al., 2016; Khalil et al., 2017; Bengio et al., 2021; Dong et al., 2021). Particularly, a considerable portion of these works (Khalil et al., 2017; Barrett et al., 2020; Yolcu and P´oczos, 2019) have concentrated on employing Graph Neural Networks (GNNs) for CO problems. Despite the computational demands, these GNN-based approaches have demonstrated competitive performance compared to SOTA heuristics tailored to specific problems. While these potential advancements offer great promise, some concerns remain. The superior performance of the learned heuristics can be attributed to the selection of specific instances and baselines. Specifically, if the baselines are weak, the learned heuristics can easily outperform them. Without hard instances and proper baseline selection, the learned heuristics can easily show comparable performance with the SOTA heuristics, and this can lead to an overestimation of the actual capabilities of the learned heuristics. Moreover, comparisons with SOTA heuristics are occasionally left out due to scalability challenges with deep learning architectures. A subset of learning-based approaches (Khalil et al., 2017; Yolcu and P´oczos, 2019; Barrett et al., 2020, 2022) incorporate the functionality or behavior of traditional heuristics, potentially offering improved or enhanced performance by integrating heuristics principles with machine learning components. If a comprehensive comparison with the integrated heuristics and an ablation study of the deep learning architecture are lacking, it becomes challenging to determine the specific contribution of the deep learning architecture. Consequently, if the integrated heuristics are robust, the learned heuristics can seamlessly outperform the baseline heuristics, while the deep learning architecture plays a little role. Another great achievement of learned heuristics (Khalil et al., 2017; Barrett et al., 2020; Toenshof et al., 2019), initially trained on smaller and specific instances from a particular distribution, exhibits impressive performance when tested on larger instances from different distributions. This achievement stands as a significant accomplishment, aligning with the core objective of employing learning-based approaches: to mitigate the necessity for extensive customization and domain-specific knowledge often required by heuristics. Although classical heuristics with hyperparameters may also encounter challenges if they are fine-tuned for a specific distribution, they may also generalize across different distributions. The primary inquiry revolves around whether the learned heuristics indeed demonstrate superior generalizability compared to classical heuristics. A thorough and insightful comparison of learned heuristics against classical heuristics provides valuable insights into the generalizability of learned heuristics. Learned heuristics often lack theoretical guarantees, making empirical evaluation the sole method to comprehend the strengths and limitations of proposed methodologies. We believe that several fundamental, yet pivotal questions remain unexplored in the empirical evaluation of these works. While these inquiries are pertinent to all types of learned heuristics, we ask and answer them by analyzing highly cited and recent peer-reviewed publications focused on learning local search heuristics. Our goal is not to provide exhaustive benchmarks for the CO problems discussed in our work but rather to assist future researchers in evaluating their research. Concretely, we ask and answer the following questions: 1. Can learned heuristics be over-parameterized? Absolutely. Replacing the GNN in ECO-DQN with linear regression and utilizing a subset of features from ECO-DQN (Barrett et al., 2020) that links to Tabu Search (Glover, 1990), we introduce a pruned version of ECO-DQN termed SoftTabu. Our study demonstrates that SoftTabu showcases superior performance and generalizability in comparison to ECO-DQN for the NPhard Maximum-Cut (Max-Cut) problem. 1. Can learned heuristics be over-parameterized? Absolutely. 2. Can baseline bias be attributed to the superior performance of learned heuristics? Yes, we demonstrate that SoftTabu, a vanilla learned heuristic, can outperform S2V-DQN (Khalil et al., 2017) for the Max-Cut problem and GNNSAT (Yolcu and P´oczos, 2019) for Boolean Satisfiability (SAT). 2. Can baseline bias be attributed to the superior performance of learned heuristics? *3. Can learned heuristics demonstrate superior generalizability owing to instance selection bias?*Yes, we demonstrate that ECO-DQN shows poor generalization on harder instances and easily gets trapped in the search space. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license.

Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?

শেখা স্থানীয় অনুসন্ধান হিউরিস্টিকসের সীমা উন্মোচন করা: আপনি কি নম্রদের মধ্যে সবচেয়ে শক্তিশালী?

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Revelando los límites de la heurística de búsqueda local aprendida: ¿eres el más poderoso entre los mansos?

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学習したローカル検索ヒューリスティックスの限界を明らかにする: あなたは最も強い弱者ですか?

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Revelando os limites das heurísticas de pesquisa local aprendidas: você é o mais poderoso dos mansos?

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Tiết lộ các giới hạn của phương pháp phỏng đoán tìm kiếm địa phương đã học: Bạn có phải là người mạnh mẽ nhất trong số những người nhu mì không?

揭示学习本地搜索启发式方法的局限性：你是最强大的吗？

This paper is available on arxiv under CC 4.0 license. Authors: (1)Eason Chen, Carnegie Mellon University; (2) Justa Liang, Bucket Protocol; (3) Ray Huang, Bucket Protocol; (4) Pierce Hung, Bucket Protocol; (5) Damien Chen, Bucket Protocol; (6) Ashley Hsu, Bucket Protocol; (7) Konstantinos Chalkias, Mysten Labs; (8) Stefanos Pleros Mysten Labs. TABLE OF LINKS Abstract and Introduction Background Randomness Practice in Blockchain System Development Transaction Fee Comparison Discussion Conclusion and References 7. CONCLUSION This paper explained how to build a fair, verifiable, efficient random raffle on the Sui Network. We investigate a range of smart contract design methodologies for constructing chance-based games through smart contracts, identify their inherent constraints, and propose remedies. Furthermore, we furnish insights drawing from the advantages and limitations of different design choices. Through these insights, we provide valuable guidance to researchers and developers engaged in randomness games using smart contracts. REFERENCES [1] Sam Blackshear, Evan Cheng, David L Dill, Victor Gao, Ben Maurer, Todd Nowacki, Alistair Pott, Shaz Qadeer, Dario Russi Rain, Stephane Sezer, et al. 2019. Move: A language with programmable resources. Libra Assoc (2019), 1. [2] Joseph Bonneau, Jeremy Clark, and Steven Goldfeder. 2015. On bitcoin as a public randomness source. Cryptology ePrint Archive (2015). [3] Joseph Bonneau and Valeria Nikolaenko. 2022. Public Randomness and Randomness Beacons. https://a16zcrypto.com/posts/article/public-randomness-andrandomness-beacons/. (Accessed on 10/10/2023). [4] Chainlink. 2023. Chainlink VRF | Chainlink Documentation. https://docs.chain. link/vrf. (Accessed on 10/07/2023). [5] Kostas Kryptos Chalkias. 2022. Fair Randomness in Decentralized Games. https://docs.google.com/presentation/d/ 1a9wddWhqKM4GXMV1UrhrpaH1sBMUxZWI. (Accessed on 10/10/2023). [6] Ting Chen, Xiaoqi Li, Xiapu Luo, and Xiaosong Zhang. 2017. Under-optimized smart contracts devour your money. In 2017 IEEE 24th international conference on software analysis, evolution and reengineering (SANER). IEEE, 442–446. [7] Alonso de Gortari. 2023. MystenLabs.sui on X: "Seeing is believing on @SuiNetwork: Increasing Transactions + Stable Gas Fees = Affordable Scalability" / X. https://twitter.com/Mysten_Labs/status/1683566765253468162. (Accessed on 10/05/2023). [8] Ben Edgington. 2023. BLS12-381 For The Rest Of Us - HackMD. https://hackmd. io/@benjaminion/bls12-381. (Accessed on 10/07/2023). [9] Paul Feldman. 1987. A practical scheme for non-interactive verifiable secret sharing. In 28th Annual Symposium on Foundations of Computer Science (sfcs 1987). IEEE, 427–438. [10] Ethereum Foundation. 2023. Proof-of-stake (PoS) | ethereum.org. https: //ethereum.org/en/developers/docs/consensus-mechanisms/pos. (Accessed on 10/07/2023). [11] Ethereum Foundation. 2023. Zero-Knowledge rollups | ethereum.org. https:// ethereum.org/en/developers/docs/scaling/zk-rollups/. (Accessed on 10/09/2023). [12] Sara Gherghelas. 2023. State of the Dapp Industry in Q2 2023. https://dappradar. com/blog/state-of-the-dapp-industry-in-q2-2023. [13] Shafi Goldwasser, Silvio Micali, and Chales Rackoff. 2019. The knowledge complexity of interactive proof-systems. In Providing sound foundations for cryptography: On the work of shafi goldwasser and silvio micali. 203–225. [14] The News Lense. 2023. Dinter’s "Lineage M" Purple Cloth Incident: The fair will fine Gamania 2 million TWD, legislators and the public call for the promotion of the "Gashapon Law" - The News Lens Key Comment Network. https://www. thenewslens.com/article/168188. (Accessed on 10/07/2023). [15] Ralph C Merkle. 1987. A digital signature based on a conventional encryption function. In Conference on the theory and application of cryptographic techniques. Springer, 369–378. [16] Satoshi Nakamoto. 2008. Bitcoin: A peer-to-peer electronic cash system. Decentralized business review (2008). [17] Newzoo. 2023. Newzoo Global Games Market Report 2023 | Free Version. https://newzoo.com/resources/trend-reports/newzoo-global-gamesmarket-report-2023-free-version. (Accessed on 10/07/2023). [18] Rune Kristian Lundedal Nielsen and Pawel Grabarczyk. 2019. Are Loot Boxes Gambling?: Random reward mechanisms in video games. Transactions of the Digital Games Research Association 4 (2019), 171–207. [19] League of Entropy. 2023. Drand - Distributed Randomness Beacon. https: //drand.love. (Accessed on 10/07/2023). [20] League of Entropy. 2023. Specification | Drand - Distributed Randomness Beacon. https://drand.love/docs/specification/. (Accessed on 10/07/2023). [21] Stefanos Pleros and Konstantinos Chalkias. 2023. Time-locked Satoshi Coin Flip. https://github.com/MystenLabs/satoshi-coin-flip. (Accessed on 10/10/2023). [22] Yolan Romailler. 2022. Multi Frequency Support and Timelock Encryption Capabilities are coming in drand! https://drand.love/blog/2022/02/21/multi-frequencysupport-and-timelock-encryption-capabilities/. (Accessed on 10/10/2023). [23] The MystenLabs Team. 2022. The Sui Smart Contracts Platform. https://docs.sui. io/paper/sui.pdf. (Accessed on 10/04/2023). [24] Zhipeng Wang, Stefanos Chaliasos, Kaihua Qin, Liyi Zhou, Lifeng Gao, Pascal Berrang, Benjamin Livshits, and Arthur Gervais. 2023. On how zero-knowledge proof blockchain mixers improve, and worsen user privacy. In Proceedings of the ACM Web Conference 2023. 2022–2032. [25] Zibin Zheng, Shaoan Xie, Hongning Dai, Xiangping Chen, and Huaimin Wang. 2017. An overview of blockchain technology: Architecture, consensus, and future trends. In 2017 IEEE international congress on big data (BigData congress). Ieee, 557–564. This paper is under CC 1.0 license. available on arxiv

Designing Fair and Efficient Blockchain Games: Conclusion and References

How the Liver Transplant System Changed Forever

কিভাবে লিভার ট্রান্সপ্লান্ট সিস্টেম চিরতরে পরিবর্তিত হয়েছে

Wie sich das Lebertransplantationssystem für immer veränderte

Cómo cambió para siempre el sistema de trasplante de hígado

Comment le système de transplantation hépatique a changé pour toujours

लिवर ट्रांसप्लांट सिस्टम हमेशा के लिए कैसे बदल गया

肝移植システムはいかにして永遠に変わったか

간 이식 시스템이 어떻게 영원히 바뀌었나요?

Como o sistema de transplante de fígado mudou para sempre

Как система трансплантации печени изменилась навсегда

Karaciğer Nakli Sistemi Sonsuza Kadar Nasıl Değişti?

Hệ thống ghép gan đã thay đổi mãi mãi như thế nào

肝移植系统如何永远改变

DevOps for Scientific Computing is the alchemical fusion of containers, Git Repositories, and CI/CD Engines, birthing a revolution in scientific inquiry, where reproducibility and transparency meet the boundless frontiers of rigorous research. 🚀 Amid the dynamic realm of scientific inquiry, where curiosity fuels innovation and exploration knows no bounds, a groundbreaking transformation is underway. It's a transformation that converges the tenets of DevOps with the intricate world of scientific computing, ushering in a new era of rigor, reproducibility, and transparency. This blog post charts an uncharted course into the profound influence of DevOps on the scientific domain, highlighting the interplay between three pivotal pillars: Containers, Git Repositories, and CI/CD Engines. 🚀 Unveiling the Three Pillars 1. Containers: The Scientific Enigma Unraveled 🧩 Scientific computing has always been a puzzle of dependencies and configurations, where the need to replicate the perfect computational environment can be a Sisyphean task. Here, the enigma is containers, the all-encompassing key to unlock scientific progress. Containers encapsulate complete, reproducible computational environments that transcend physical limitations. This portability allows researchers to traverse platforms and infrastructures effortlessly, from local workstations to colossal computing clusters, even extending their reach to the celestial cloud, typified by Kubernetes. Containers, in their singularity, eliminate the complexities of setup, enabling a fluid exchange of knowledge and ideas among scientists. 📦 2. Git Repositories: The Alchemist's Chronicle 🔮 At the heart of scientific inquiry lies code – the recipe to unlock the secrets of data. This code represents the philosopher's stone, the core of every scientific investigation. It's imperative to preserve an unadulterated historical record of its transformation, a role diligently performed by Git repositories. Git, the venerable alchemist, offers distributed version control, proven invaluable in the realm of DevOps, and effortlessly adapted to scientific needs. These repositories, eternal chronicles of code evolution, foster transparency, collaboration, and dispute resolution. They are fortified with cryptographic timestamps, ensuring the immutability and legal sanctity of the code's history – a shield of authenticity in the uncertain world of scientific exploration. 🔗 3. CI/CD Engines: Forging a Seamless Frontier 🛠️ The heart of DevOps, the forge where software is crafted, is Continuous Integration (CI) and Continuous Delivery (CD). These engines optimize software development, yet their potential extends beyond the IT industry. CI involves the constant amalgamation of code changes, while CD automates deployment. These engines, like GitLab CI/CD, offer the architectural blueprint for scientific workflows. By orchestrating pipelines, CI/CD engines enable researchers to abstract away the intricacies of their environments. The orchestration and automation of these pipelines elevate scientific computing, making it a seamless venture. Researchers traverse the unknown frontier with ease, enabled by their CI/CD compass. 🚧 Integration Examples Three tales of integration illustrate the harmonious marriage of these three pillars in redefining scientific computing. 1. Centralized Pipelines: Navigating an Ocean of Consistency 🏢 Centralized pipelines, the cornerstone of computational science, unfold in the comfort of homogeneous environments – think high-performance computing clusters or cloud platforms. These pristine, managed sanctuaries harbor the magnificence of containerized DevOps workflows. Researchers, charting courses through these environments, find consistency, scalability, and elegance. The translation of scientific work across these platforms is no longer a Herculean task; it is a voyage of discovery in an ocean of computational consistency. 🌐 2. Decentralized Pipelines: Bridging Heterogeneous Horizons 🌍 For those who tread the uncharted path of scaling out an analysis, the DevOps torch illuminates their way. It's the bridge spanning the chasm between local exploration and the computational cluster. The faithful Docker container, partner-in-crime of scientists, carries the burden. It hosts their code, their dreams, and their ambitions, in an environment harmonized for both solitary exploration and grandiose cluster performances. DevOps equips researchers to quell scaling fears, test their might against localized adversaries, and seamlessly cross into the realm of computational giants. ⚖️ 3. Minimizing Scale Out Friction: A Journey of Fluid Transition 🧘 The journey from local testing grounds to the battlefield of scalability can be tumultuous. This transition is where shortcuts can lead to disaster, and where DevOps emerges as the guiding sage. The constant companion of researchers is the versatile Docker container, accommodating local forays, and large-scale skirmishes with equal ease. It allows the validation of pipelines in a controlled environment, a simulator for the grand clash on the computational cluster. DevOps ensures a transition devoid of strife, a harmonious crescendo to the symphony of scientific discovery. 📈 Beneficial Side Effects Beyond the immediate effects, the infusion of DevOps into scientific computing ripples with wider benefits. 1. Superior Rigor, Reproducibility, and Transparency 📚 DevOps tools elevate the standards of scientific rigor, reproducibility, and transparency. They provide a robust foundation for research, rendering scalability and parallelism seamless. The shared environments and Git repositories eliminate barriers to replicating experiments. This potent combination of DevOps tools and principles opens new horizons, ensuring that research is conducted with the utmost rigor and transparency. 📖 2. FAIR Principles and Interoperable APIs: A Symphony of Data Reuse 🔄 DevOps frameworks echo the harmonious notes of FAIR principles, heralding the era of interoperable research data. Through structured pipelines with rich metadata, datasets are primed for sharing and indexing, adhering to the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data. DevOps also paves the way for the creation of common, interoperable APIs across diverse scientific domains, accelerating collaboration and propelling scientific exploration. 🔄 3. Compliance With Open Data Mandates: A New Horizon 🌐 In an age where open data mandates are on the rise, DevOps tools ensure that researchers are well-prepared to meet these demands. The tools guide researchers on data release, simplifying the path to compliance. By seamlessly adopting DevOps practices, researchers align with open data mandates, enriching the scientific ecosystem with accessibility and transparency. 📊 Conclusion The confluence of DevOps and scientific computing is nothing short of a revolution. It reshapes the frontier of scientific inquiry, redefining how researchers navigate the intricacies of computational analysis. With DevOps for Scientific Computing, researchers can: Elevate scientific rigor, reproducibility, and transparency. Champion the adoption of FAIR principles and interoperable APIs. Embrace open data mandates with grace and ease. This revolution not only optimizes research workflows but also stands as a symbol of trust and integrity in a world where science's role in society is more crucial than ever. 🌍 In summary, is the transformative force that accelerates scientific progress, enhances the quality of research, and reinforces the integrity of scientific endeavors. DevOps for Scientific Computing It's the symphony where technology and knowledge harmoniously converge, ensuring the pursuit of scientific knowledge remains as captivating and enlightening as ever. 🌟 References Wolfgang Alschner. "The computational analysis of international law". Research Methods in International Law. Edward Elgar Publishing, 2021. Sumon Biswas, Mohammad Wardat, and Hridesh Rajan. "The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large". Proceedings of the 44th International Conference on Software Engineering, 2022. Jennifer Bryan. "Excuse me, do you have a moment to talk about version control?" The American Statistician, 2018. Yolanda Gil et al. "Examining the Challenges of Scientific Workflows". Computer, 2007. Gregory M Kurtzer, Vanessa Sochat, and Michael W Bauer. "Singularity: Scientific containers for mobility of compute". PLoS One, 2017. Christine Laine et al. "Reproducible research: moving toward research the public can really trust". Ann Intern Med, Mar. 2007. Karthik Ram. "Git can facilitate greater reproducibility and increased transparency in science". Source Code Biol Med, 2013. Roger D Peng. "Reproducible research in computational science". Science, Dec. 2011. Russell A Poldrack et al. "Scanning the horizon: towards transparent and reproducible neuroimaging research". Nat Rev Neurosci, Feb. 2017. Mark D Wilkinson et al. "The FAIR Guiding Principles for scientific data management and stewardship". Sci Data, Mar. 2016.

Transforming the Scientific Frontier: The DevOps Revolution

74 Stories To Learn About Robots

132 Stories To Learn About Robotics

Personal Recollections of Joan of Arc — Volume 1 by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Joan Convinces the King here Joan Convinces the King WELL, anything to make delay. The King’s council advised him against arriving at a decision in our matter too precipitately. He arrive at a decision too precipitately! So they sent a committee of priests—always priests—into Lorraine to inquire into Joan’s character and history—a matter which would consume several weeks, of course. You see how fastidious they were. It was as if people should come to put out the fire when a man’s house was burning down, and they waited till they could send into another country to find out if he had always kept the Sabbath or not, before letting him try. So the days poked along; dreary for us young people in some ways, but not in all, for we had one great anticipation in front of us; we had never seen a king, and now some day we should have that prodigious spectacle to see and to treasure in our memories all our lives; so we were on the lookout, and always eager and watching for the chance. The others were doomed to wait longer than I, as it turned out. One day great news came—the Orleans commissioners, with Yolande and our knights, had at last turned the council’s position and persuaded the King to see Joan. Joan received the immense news gratefully but without losing her head, but with us others it was otherwise; we could not eat or sleep or do any rational thing for the excitement and the glory of it. During two days our pair of noble knights were in distress and trepidation on Joan’s account, for the audience was to be at night, and they were afraid that Joan would be so paralyzed by the glare of light from the long files of torches, the solemn pomps and ceremonies, the great concourse of renowned personages, the brilliant costumes, and the other splendors of the Court, that she, a simple country-maid, and all unused to such things, would be overcome by these terrors and make a piteous failure. No doubt I could have comforted them, but I was not free to speak. Would Joan be disturbed by this cheap spectacle, this tinsel show, with its small King and his butterfly dukelets?—she who had spoken face to face with the princes of heaven, the familiars of God, and seen their retinue of angels stretching back into the remoteness of the sky, myriads upon myriads, like a measureless fan of light, a glory like the glory of the sun streaming from each of those innumerable heads, the massed radiance filling the deeps of space with a blinding splendor? I thought not. Queen Yolande wanted Joan to make the best possible impression upon the King and the Court, so she was strenuous to have her clothed in the richest stuffs, wrought upon the princeliest pattern, and set off with jewels; but in that she had to be disappointed, of course, Joan not being persuadable to it, but begging to be simply and sincerely dressed, as became a servant of God, and one sent upon a mission of a serious sort and grave political import. So then the gracious Queen imagined and contrived that simple and witching costume which I have described to you so many times, and which I cannot think of even now in my dull age without being moved just as rhythmical and exquisite music moves one; for that was music, that dress—that is what it was—music that one saw with the eyes and felt in the heart. Yes, she was a poem, she was a dream, she was a spirit when she was clothed in that. She kept that raiment always, and wore it several times upon occasions of state, and it is preserved to this day in the Treasury of Orleans, with two of her swords, and her banner, and other things now sacred because they had belonged to her. At the appointed time the Count of Vendome, a great lord of the court, came richly clothed, with his train of servants and assistants, to conduct Joan to the King, and the two knights and I went with her, being entitled to this privilege by reason of our official positions near her person. When we entered the great audience-hall, there it all was just as I have already painted it. Here were ranks of guards in shining armor and with polished halberds; two sides of the hall were like flower-gardens for variety of color and the magnificence of the costumes; light streamed upon these masses of color from two hundred and fifty flambeaux. There was a wide free space down the middle of the hall, and at the end of it was a throne royally canopied, and upon it sat a crowned and sceptered figure nobly clothed and blazing with jewels. It is true that Joan had been hindered and put off a good while, but now that she was admitted to an audience at last, she was received with honors granted to only the greatest personages. At the entrance door stood four heralds in a row, in splendid tabards, with long slender silver trumpets at their mouths, with square silken banners depending from them embroidered with the arms of France. As Joan and the Count passed by, these trumpets gave forth in unison one long rich note, and as we moved down the hall under the pictured and gilded vaulting, this was repeated at every fifty feet of our progress—six times in all. It made our good knights proud and happy, and they held themselves erect, and stiffened their stride, and looked fine and soldierly. They were not expecting this beautiful and honorable tribute to our little country-maid. Joan walked two yards behind the Count, we three walked two yards behind Joan. Our solemn march ended when we were as yet some eight or ten steps from the throne. The Count made a deep obeisance, pronounced Joan’s name, then bowed again and moved to his place among a group of officials near the throne. I was devouring the crowned personage with all my eyes, and my heart almost stood still with awe. The eyes of all others were fixed upon Joan in a gaze of wonder which was half worship, and which seemed to say, “How sweet—how lovely—how divine!” All lips were parted and motionless, which was a sure sign that those people, who seldom forget themselves, had forgotten themselves now, and were not conscious of anything but the one object they were gazing upon. They had the look of people who are under the enchantment of a vision. Then they presently began to come to life again, rousing themselves out of the spell and shaking it off as one drives away little by little a clinging drowsiness or intoxication. Now they fixed their attention upon Joan with a strong new interest of another sort; they were full of curiosity to see what she would do—they having a secret and particular reason for this curiosity. So they watched. This is what they saw: She made no obeisance, nor even any slight inclination of her head, but stood looking toward the throne in silence. That was all there was to see at present. I glanced up at De Metz, and was shocked at the paleness of his face. I whispered and said: “What is it, man, what is it?” His answering whisper was so weak I could hardly catch it: “They have taken advantage of the hint in her letter to play a trick upon her! She will err, and they will laugh at her. That is not the King that sits there.” Then I glanced at Joan. She was still gazing steadfastly toward the throne, and I had the curious fancy that even her shoulders and the back of her head expressed bewilderment. Now she turned her head slowly, and her eye wandered along the lines of standing courtiers till it fell upon a young man who was very quietly dressed; then her face lighted joyously, and she ran and threw herself at his feet, and clasped his knees, exclaiming in that soft melodious voice which was her birthright and was now charged with deep and tender feeling: “God of his grace give you long life, O dear and gentle Dauphin!” In his astonishment and exultation De Metz cried out: “By the shadow of God, it is an amazing thing!” Then he mashed all the bones of my hand in his grateful grip, and added, with a proud shake of his mane, “Now, what have these painted infidels to say!” Meantime the young person in the plain clothes was saying to Joan: “Ah, you mistake, my child, I am not the King. There he is,” and he pointed to the throne. The knight’s face clouded, and he muttered in grief and indignation: “Ah, it is a shame to use her so. But for this lie she had gone through safe. I will go and proclaim to all the house what—” “Stay where you are!” whispered I and the Sieur Bertrand in a breath, and made him stop in his place. Joan did not stir from her knees, but still lifted her happy face toward the King, and said: “No, gracious liege, you are he, and none other.” De Metz’s troubles vanished away, and he said: “Verily, she was not guessing, she knew. Now, how could she know? It is a miracle. I am content, and will meddle no more, for I perceive that she is equal to her occasions, having that in her head that cannot profitably be helped by the vacancy that is in mine.” This interruption of his lost me a remark or two of the other talk; however, I caught the King’s next question: “But tell me who you are, and what would you?” “I am called Joan the Maid, and am sent to say that the King of Heaven wills that you be crowned and consecrated in your good city of Rheims, and be thereafter Lieutenant of the Lord of Heaven, who is King of France. And He willeth also that you set me at my appointed work and give me men-at-arms.” After a slight pause she added, her eye lighting at the sound of her words, “For then will I raise the siege of Orleans and break the English power!” The young monarch’s amused face sobered a little when this martial speech fell upon that sick air like a breath blown from embattled camps and fields of war, and this trifling smile presently faded wholly away and disappeared. He was grave now, and thoughtful. After a little he waved his hand lightly, and all the people fell away and left those two by themselves in a vacant space. The knights and I moved to the opposite side of the hall and stood there. We saw Joan rise at a sign, then she and the King talked privately together. All that host had been consumed with curiosity to see what Joan would do. Well, they had seen, and now they were full of astonishment to see that she had really performed that strange miracle according to the promise in her letter; and they were fully as much astonished to find that she was not overcome by the pomps and splendors about her, but was even more tranquil and at her ease in holding speech with a monarch than ever they themselves had been, with all their practice and experience. As for our two knights, they were inflated beyond measure with pride in Joan, but nearly dumb, as to speech, they not being able to think out any way to account for her managing to carry herself through this imposing ordeal without ever a mistake or an awkwardness of any kind to mar the grace and credit of her great performance. The talk between Joan and the King was long and earnest, and held in low voices. We could not hear, but we had our eyes and could note effects; and presently we and all the house noted one effect which was memorable and striking, and has been set down in memoirs and histories and in testimony at the Process of Rehabilitation by some who witnessed it; for all knew it was big with meaning, though none knew what that meaning was at that time, of course. For suddenly we saw the King shake off his indolent attitude and straighten up like a man, and at the same time look immeasurably astonished. It was as if Joan had told him something almost too wonderful for belief, and yet of a most uplifting and welcome nature. It was long before we found out the secret of this conversation, but we know it now, and all the world knows it. That part of the talk was like this—as one may read in all histories. The perplexed King asked Joan for a sign. He wanted to believe in her and her mission, and that her Voices were supernatural and endowed with knowledge hidden from mortals, but how could he do this unless these Voices could prove their claim in some absolutely unassailable way? It was then that Joan said: “I will give you a sign, and you shall no more doubt. There is a secret trouble in your heart which you speak of to none—a doubt which wastes away your courage, and makes you dream of throwing all away and fleeing from your realm. Within this little while you have been praying, in your own breast, that God of his grace would resolve that doubt, even if the doing of it must show you that no kingly right is lodged in you.” It was that that amazed the King, for it was as she had said: his prayer was the secret of his own breast, and none but God could know about it. So he said: “The sign is sufficient. I know now that these Voices are of God. They have said true in this matter; if they have said more, tell it me—I will believe.” “They have resolved that doubt, and I bring their very words, which are these: Thou art lawful heir to the King thy father, and true heir of France. God has spoken it. Now lift up thy head, and doubt no more, but give me men-at-arms and let me get about my work.” Telling him he was of lawful birth was what straightened him up and made a man of him for a moment, removing his doubts upon that head and convincing him of his royal right; and if any could have hanged his hindering and pestiferous council and set him free, he would have answered Joan’s prayer and set her in the field. But no, those creatures were only checked, not checkmated; they could invent some more delays. We had been made proud by the honors which had so distinguished Joan’s entrance into that place—honors restricted to personages of very high rank and worth—but that pride was as nothing compared with the pride we had in the honor done her upon leaving it. For whereas those first honors were shown only to the great, these last, up to this time, had been shown only to the royal. The King himself led Joan by the hand down the great hall to the door, the glittering multitude standing and making reverence as they passed, and the silver trumpets sounding those rich notes of theirs. Then he dismissed her with gracious words, bending low over her hand and kissing it. Always—from all companies, high or low—she went forth richer in honor and esteem than when she came. And the King did another handsome thing by Joan, for he sent us back to Courdray Castle torch-lighted and in state, under escort of his own troop—his guard of honor—the only soldiers he had; and finely equipped and bedizened they were, too, though they hadn’t seen the color of their wages since they were children, as a body might say. The wonders which Joan had been performing before the King had been carried all around by this time, so the road was so packed with people who wanted to get a sight of her that we could hardly dig through; and as for talking together, we couldn’t, all attempts at talk being drowned in the storm of shoutings and huzzas that broke out all along as we passed, and kept abreast of us like a wave the whole way. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Mark Twain (2004). Personal Recollections of Joan of Arc — Volume 1. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/2874/pg2874-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Joan Convinces the King

We Pierce the Last Ambuscades

Five Weeks in a Balloon by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Chapter XXIX here CHAPTER TWENTY-NINTH. Signs of Vegetation.—The Fantastic Notion of a French Author.—A Magnificent Country.—The Kingdom of Adamova.—The Explorations of Speke and Burton connected with those of Dr. Barth.—The Atlantika Mountains.—The River Benoué.—The City of Yola.—The Bagélé.—Mount Mendif. From the moment of their departure, the travellers moved with great velocity. They longed to leave behind them the desert, which had so nearly been fatal to them. About a quarter-past nine in the morning, they caught a glimpse of some signs of vegetation: herbage floating on that sea of sand, and announcing, as the weeds upon the ocean did to Christopher Columbus, the nearness of the shore—green shoots peeping up timidly between pebbles that were, in their turn, to be the rocks of that vast expanse. Hills, but of trifling height, were seen in wavy lines upon the horizon. Their profile, muffled by the heavy mist, was defined but vaguely. The monotony, however, was beginning to disappear. The doctor hailed with joy the new country thus disclosed, and, like a seaman on lookout at the mast-head, he was ready to shout aloud: “Land, ho! land!” An hour later the continent spread broadly before their gaze, still wild in aspect, but less flat, less denuded, and with a few trees standing out against the gray sky. “We are in a civilized country at last!” said the hunter. “Civilized? Well, that’s one way of speaking; but there are no people to be seen yet.” “It will not be long before we see them,” said Ferguson, “at our present rate of travel.” “Are we still in the negro country, doctor?” “Yes, and on our way to the country of the Arabs.” “What! real Arabs, sir, with their camels?” “No, not many camels; they are scarce, if not altogether unknown, in these regions. We must go a few degrees farther north to see them.” “What a pity!” “And why, Joe?” “Because, if the wind fell contrary, they might be of use to us.” “How so?” “Well, sir, it’s just a notion that’s got into my head: we might hitch them to the car, and make them tow us along. What do you say to that, doctor?” “Poor Joe! Another person had that idea in advance of you. It was used by a very gifted French author—M. Méry—in a romance, it is true. He has his travellers drawn along in a balloon by a team of camels; then a lion comes up, devours the camels, swallows the tow-rope, and hauls the balloon in their stead; and so on through the story. You see that the whole thing is the top-flower of fancy, but has nothing in common with our style of locomotion.” Joe, a little cut down at learning that his idea had been used already, cudgelled his wits to imagine what animal could have devoured the lion; but he could not guess it, and so quietly went on scanning the appearance of the country. A lake of medium extent stretched away before him, surrounded by an amphitheatre of hills, which yet could not be dignified with the name of mountains. There were winding valleys, numerous and fertile, with their tangled thickets of the most various trees. The African oil-tree rose above the mass, with leaves fifteen feet in length upon its stalk, the latter studded with sharp thorns; the bombax, or silk-cotton-tree, filled the wind, as it swept by, with the fine down of its seeds; the pungent odors of the pendanus, the “kenda” of the Arabs, perfumed the air up to the height where the was sailing; the papaw-tree, with its palm-shaped leaves; the , which produces the Soudan-nut; the baobab, and the banana-tree, completed the luxuriant flora of these inter-tropical regions. Victoria sterculier “The country is superb!” said the doctor. “Here are some animals,” added Joe. “Men are not far away.” “Oh, what magnificent elephants!” exclaimed Kennedy. “Is there no way to get a little shooting?” “How could we manage to halt in a current as strong as this? No, Dick; you must taste a little of the torture of Tantalus just now. You shall make up for it afterward.” And, in truth, there was enough to excite the fancy of a sportsman. Dick’s heart fairly leaped in his breast as he grasped the butt of his Purdy. The of the region were as striking as its flora. The wild-ox revelled in dense herbage that often concealed his whole body; gray, black, and yellow elephants of the most gigantic size burst headlong, like a living hurricane, through the forests, breaking, rending, tearing down, devastating every thing in their path; upon the woody slopes of the hills trickled cascades and springs flowing northward; there, too, the hippopotami bathed their huge forms, splashing and snorting as they frolicked in the water, and lamantines, twelve feet long, with bodies like seals, stretched themselves along the banks, turning up toward the sun their rounded teats swollen with milk. fauna It was a whole menagerie of rare and curious beasts in a wondrous hot-house, where numberless birds with plumage of a thousand hues gleamed and fluttered in the sunshine. By this prodigality of Nature, the doctor recognized the splendid kingdom of Adamova. “We are now beginning to trench upon the realm of modern discovery. I have taken up the lost scent of preceding travellers. It is a happy chance, my friends, for we shall be enabled to link the toils of Captains Burton and Speke with the explorations of Dr. Barth. We have left the Englishmen behind us, and now have caught up with the Hamburger. It will not be long, either, before we arrive at the extreme point attained by that daring explorer.” “It seems to me that there is a vast extent of country between the two explored routes,” remarked Kennedy; “at least, if I am to judge by the distance that we have made.” “It is easy to determine: take the map and see what is the longitude of the southern point of Lake Ukéréoué, reached by Speke.” “It is near the thirty-seventh degree.” “And the city of Yola, which we shall sight this evening, and to which Barth penetrated, what is its position?” “It is about in the twelfth degree of east longitude.” “Then there are twenty-five degrees, or, counting sixty miles to each, about fifteen hundred miles in all.” “A nice little walk,” said Joe, “for people who have to go on foot.” “It will be accomplished, however. Livingstone and Moffat are pushing on up this line toward the interior. Nyassa, which they have discovered, is not far from Lake Tanganayika, seen by Burton. Ere the close of the century these regions will, undoubtedly, be explored. But,” added the doctor, consulting his compass, “I regret that the wind is carrying us so far to the westward. I wanted to get to the north.” After twelve hours of progress, the Victoria found herself on the confines of Nigritia. The first inhabitants of this region, the Chouas Arabs, were feeding their wandering flocks. The immense summits of the Atlantika Mountains seen above the horizon—mountains that no European foot had yet scaled, and whose height is computed to be ten thousand feet! Their western slope determines the flow of all the waters in this region of Africa toward the ocean. They are the Mountains of the Moon to this part of the continent. At length a real river greeted the gaze of our travellers, and, by the enormous ant-hills seen in its vicinity, the doctor recognized the Benoué, one of the great tributaries of the Niger, the one which the natives have called “The Fountain of the Waters.” “This river,” said the doctor to his companions, “will, one day, be the natural channel of communication with the interior of Nigritia. Under the command of one of our brave captains, the steamer has already ascended as far as the town of Yola. You see that we are not in an unknown country.” Pleiad Numerous slaves were engaged in the labors of the field, cultivating sorgho, a kind of millet which forms the chief basis of their diet; and the most stupid expressions of astonishment ensued as the sped past like a meteor. That evening the balloon halted about forty miles from Yola, and ahead of it, but in the distance, rose the two sharp cones of Mount Mendif. Victoria The doctor threw out his anchors and made fast to the top of a high tree; but a very violent wind beat upon the balloon with such force as to throw it over on its side, thus rendering the position of the car sometimes extremely dangerous. Ferguson did not close his eyes all night, and he was repeatedly on the point of cutting the anchor-rope and scudding away before the gale. At length, however, the storm abated, and the oscillations of the balloon ceased to be alarming. On the morrow the wind was more moderate, but it carried our travellers away from the city of Yola, which recently rebuilt by the Fouillans, excited Ferguson’s curiosity. However, he had to make up his mind to being borne farther to the northward and even a little to the east. Kennedy proposed to halt in this fine hunting-country, and Joe declared that the need of fresh meat was beginning to be felt; but the savage customs of the country, the attitude of the population, and some shots fired at the , admonished the doctor to continue his journey. They were then crossing a region that was the scene of massacres and burnings, and where warlike conflicts between the barbarian sultans, contending for their power amid the most atrocious carnage, never cease. Victoria Numerous and populous villages of long low huts stretched away between broad pasture-fields whose dense herbage was besprinkled with violet-colored blossoms. The huts, looking like huge beehives, were sheltered behind bristling palisades. The wild hill-sides and hollows frequently reminded the beholder of the glens in the Highlands of Scotland, as Kennedy more than once remarked. In spite of all he could do, the doctor bore directly to the northeast, toward Mount Mendif, which was lost in the midst of environing clouds. The lofty summits of these mountains separate the valley of the Niger from the basin of Lake Tchad. Soon afterward was seen the Bagélé, with its eighteen villages clinging to its flanks like a whole brood of children to their mother’s bosom—a magnificent spectacle for the beholder whose gaze commanded and took in the entire picture at one view. Even the ravines were seen to be covered with fields of rice and of arachides. By three o’clock the was directly in front of Mount Mendif. It had been impossible to avoid it; the only thing to be done was to cross it. The doctor, by means of a temperature increased to one hundred and eighty degrees, gave the balloon a fresh ascensional force of nearly sixteen hundred pounds, and it went up to an elevation of more than eight thousand feet, the greatest height attained during the journey. The temperature of the atmosphere was so much cooler at that point that the aëronauts had to resort to their blankets and thick coverings. Victoria Ferguson was in haste to descend; the covering of the balloon gave indications of bursting, but in the meanwhile he had time to satisfy himself of the volcanic origin of the mountain, whose extinct craters are now but deep abysses. Immense accumulations of bird-guano gave the sides of Mount Mendif the appearance of calcareous rocks, and there was enough of the deposit there to manure all the lands in the United Kingdom. At five o’clock the , sheltered from the south winds, went gently gliding along the slopes of the mountain, and stopped in a wide clearing remote from any habitation. The instant it touched the soil, all needful precautions were taken to hold it there firmly; and Kennedy, fowling-piece in hand, sallied out upon the sloping plain. Ere long, he returned with half a dozen wild ducks and a kind of snipe, which Joe served up in his best style. The meal was heartily relished, and the night was passed in undisturbed and refreshing slumber. Victoria About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (2002). Five Weeks in a Balloon. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/3526/pg3526-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Signs of Vegetation

Five Weeks in a Balloon by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Chapter IV here CHAPTER FOURTH. African Explorations.—Barth, Richardson, Overweg, Werne, Brun-Rollet, Penney, Andrea, Debono, Miani, Guillaume Lejean, Bruce, Krapf and Rebmann, Maizan, Roscher, Burton and Speke. The aërial line which Dr. Ferguson counted upon following had not been chosen at random; his point of departure had been carefully studied, and it was not without good cause that he had resolved to ascend at the island of Zanzibar. This island, lying near to the eastern coast of Africa, is in the sixth degree of south latitude, that is to say, four hundred and thirty geographical miles below the equator. From this island the latest expedition, sent by way of the great lakes to explore the sources of the Nile, had just set out. But it would be well to indicate what explorations Dr. Ferguson hoped to link together. The two principal ones were those of Dr. Barth in 1849, and of Lieutenants Burton and Speke in 1858. Dr. Barth is a Hamburger, who obtained permission for himself and for his countryman Overweg to join the expedition of the Englishman Richardson. The latter was charged with a mission in the Soudan. This vast region is situated between the fifteenth and tenth degrees of north latitude; that is to say, that, in order to approach it, the explorer must penetrate fifteen hundred miles into the interior of Africa. Until then, the country in question had been known only through the journeys of Denham, of Clapperton, and of Oudney, made from 1822 to 1824. Richardson, Barth, and Overweg, jealously anxious to push their investigations farther, arrived at Tunis and Tripoli, like their predecessors, and got as far as Mourzouk, the capital of Fezzan. They then abandoned the perpendicular line, and made a sharp turn westward toward Ghât, guided, with difficulty, by the Touaregs. After a thousand scenes of pillage, of vexation, and attacks by armed forces, their caravan arrived, in October, at the vast oasis of Asben. Dr. Barth separated from his companions, made an excursion to the town of Aghades, and rejoined the expedition, which resumed its march on the 12th of December. At length it reached the province of Damerghou; there the three travellers parted, and Barth took the road to Kano, where he arrived by dint of perseverance, and after paying considerable tribute. In spite of an intense fever, he quitted that place on the 7th of March, accompanied by a single servant. The principal aim of his journey was to reconnoitre Lake Tchad, from which he was still three hundred and fifty miles distant. He therefore advanced toward the east, and reached the town of Zouricolo, in the Bornou country, which is the core of the great central empire of Africa. There he heard of the death of Richardson, who had succumbed to fatigue and privation. He next arrived at Kouka, the capital of Bornou, on the borders of the lake. Finally, at the end of three weeks, on the 14th of April, twelve months after having quitted Tripoli, he reached the town of Ngornou. We find him again setting forth on the 29th of March, 1851, with Overweg, to visit the kingdom of Adamaoua, to the south of the lake, and from there he pushed on as far as the town of Yola, a little below nine degrees north latitude. This was the extreme southern limit reached by that daring traveller. He returned in the month of August to Kouka; from there he successively traversed the Mandara, Barghimi, and Klanem countries, and reached his extreme limit in the east, the town of Masena, situated at seventeen degrees twenty minutes west longitude. On the 25th of November, 1852, after the death of Overweg, his last companion, he plunged into the west, visited Sockoto, crossed the Niger, and finally reached Timbuctoo, where he had to languish, during eight long months, under vexations inflicted upon him by the sheik, and all kinds of ill-treatment and wretchedness. But the presence of a Christian in the city could not long be tolerated, and the Foullans threatened to besiege it. The doctor, therefore, left it on the 17th of March, 1854, and fled to the frontier, where he remained for thirty-three days in the most abject destitution. He then managed to get back to Kano in November, thence to Kouka, where he resumed Denham’s route after four months’ delay. He regained Tripoli toward the close of August, 1855, and arrived in London on the 6th of September, the only survivor of his party. Such was the venturesome journey of Dr. Barth. Dr. Ferguson carefully noted the fact, that he had stopped at four degrees north latitude and seventeen degrees west longitude. Now let us see what Lieutenants Burton and Speke accomplished in Eastern Africa. The various expeditions that had ascended the Nile could never manage to reach the mysterious source of that river. According to the narrative of the German doctor, Ferdinand Werne, the expedition attempted in 1840, under the auspices of Mehemet Ali, stopped at Gondokoro, between the fourth and fifth parallels of north latitude. In 1855, Brun-Rollet, a native of Savoy, appointed consul for Sardinia in Eastern Soudan, to take the place of Vaudey, who had just died, set out from Karthoum, and, under the name of Yacoub the merchant, trading in gums and ivory, got as far as Belenia, beyond the fourth degree, but had to return in ill-health to Karthoum, where he died in 1857. Neither Dr. Penney—the head of the Egyptian medical service, who, in a small steamer, penetrated one degree beyond Gondokoro, and then came back to die of exhaustion at Karthoum—nor Miani, the Venetian, who, turning the cataracts below Gondokoro, reached the second parallel—nor the Maltese trader, Andrea Debono, who pushed his journey up the Nile still farther—could work their way beyond the apparently impassable limit. In 1859, M. Guillaume Lejean, intrusted with a mission by the French Government, reached Karthoum by way of the Red Sea, and embarked upon the Nile with a retinue of twenty-one hired men and twenty soldiers, but he could not get past Gondokoro, and ran extreme risk of his life among the negro tribes, who were in full revolt. The expedition directed by M. d’Escayrac de Lauture made an equally unsuccessful attempt to reach the famous sources of the Nile. This fatal limit invariably brought every traveller to a halt. In ancient times, the ambassadors of Nero reached the ninth degree of latitude, but in eighteen centuries only from five to six degrees, or from three hundred to three hundred and sixty geographical miles, were gained. Many travellers endeavored to reach the sources of the Nile by taking their point of departure on the eastern coast of Africa. Between 1768 and 1772 the Scotch traveller, Bruce, set out from Massowah, a port of Abyssinia, traversed the Tigre, visited the ruins of Axum, saw the sources of the Nile where they did not exist, and obtained no serious result. In 1844, Dr. Krapf, an Anglican missionary, founded an establishment at Monbaz, on the coast of Zanguebar, and, in company with the Rev. Dr. Rebmann, discovered two mountain-ranges three hundred miles from the coast. These were the mountains of Kilimandjaro and Kenia, which Messrs. de Heuglin and Thornton have partly scaled so recently. In 1845, Maizan, the French explorer, disembarked, alone, at Bagamayo, directly opposite to Zanzibar, and got as far as Deje-la-Mhora, where the chief caused him to be put to death in the most cruel torment. In 1859, in the month of August, the young traveller, Roscher, from Hamburg, set out with a caravan of Arab merchants, reached Lake Nyassa, and was there assassinated while he slept. Finally, in 1857, Lieutenants Burton and Speke, both officers in the Bengal army, were sent by the London Geographical Society to explore the great African lakes, and on the 17th of June they quitted Zanzibar, and plunged directly into the west. After four months of incredible suffering, their baggage having been pillaged, and their attendants beaten and slain, they arrived at Kazeh, a sort of central rendezvous for traders and caravans. They were in the midst of the country of the Moon, and there they collected some precious documents concerning the manners, government, religion, fauna, and flora of the region. They next made for the first of the great lakes, the one named Tanganayika, situated between the third and eighth degrees of south latitude. They reached it on the 14th of February, 1858, and visited the various tribes residing on its banks, the most of whom are cannibals. They departed again on the 26th of May, and reentered Kazeh on the 20th of June. There Burton, who was completely worn out, lay ill for several months, during which time Speke made a push to the northward of more than three hundred miles, going as far as Lake Okeracua, which he came in sight of on the 3d of August; but he could descry only the opening of it at latitude two degrees thirty minutes. He reached Kazeh, on his return, on the 25th of August, and, in company with Burton, again took up the route to Zanzibar, where they arrived in the month of March in the following year. These two daring explorers then reembarked for England; and the Geographical Society of Paris decreed them its annual prize medal. Dr. Ferguson carefully remarked that they had not gone beyond the second degree of south latitude, nor the twenty-ninth of east longitude. The problem, therefore, was how to link the explorations of Burton and Speke with those of Dr. Barth, since to do so was to undertake to traverse an extent of more than twelve degrees of territory. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (2002). Five Weeks in a Balloon. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/3526/pg3526-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

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