YOLCA

yolca

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

Uni-OVSeg: A Step Towards Efficient and Bias-Resilient Vision Systems

Investigating Ad Targeting on YouTube: Implications and Legal Analysis

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?

Die Grenzen erlernter Heuristiken für die lokale Suche aufdecken: Sind Sie der Mächtigste unter den Sanftmütigen?

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?

학습된 지역 검색 휴리스틱의 한계 공개: 당신은 가장 온유한 사람입니까?

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). 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