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is the 18-year-old Spanish co-founder and CEO of Hello, a decentralized storage network, who is betting on the future of blockchain and AI.  In this interview, Alvaro told me about his foray into the blockchain world and his ambition to become the Larry Page and Sergey Brin of web3. Read on! Alvaro Pintado Alvaro, Can you tell us how your journey to blockchain and decentralization began? I’ve always had the humanly common and innate desire of changing the world, and the main difference is that I am determined to materialize that desire into reality. Expressing this idea may appear quite simple, yet the execution thereof presents a distinct challenge. That is why I made a choice to meticulously analyze all the potential components within my grasp, aiming to see what is the greatest thing that can be done, because it is all about impact, right, and about what is the thing in essence that inherently has the capacity to wield the most significant influence on humans worldwide. As one of my favorite quotes says: "People will forget what you said, people will forget what you did, but people will never forget how you made them feel." Through an exhaustive examination of concepts and elements, delving into the crux of the matter, I achieved success. The conclusion I arrived at is that the element or technology with the greatest influence and impact on the world is not , any particular company, or platform. It is, in fact, the internet protocol—the foundational framework that connects us all across the globe. social media Blockchain is just the start of this connectivity revolution that will eradicate the IP as we presently know it. It is at this juncture that my journey with decentralized systems commences. Can you provide an overview of Hello and its mission in the decentralized data storage space? Hello is what we call a truly decentralized ecosystem, and its mission is very clear: to establish itself as the decentralized Google. In other words, we want to be the Google of . web3 We firmly believe that the trajectory of the internet is moving towards a paradigm where efficiency, sustainability, and scalability will be essential pillars of the system, the obsolescence of conventional user authentication methods and centralized solutions such as usernames and passwords is evident, and it makes no sense that 3 or 4 private companies monopolize the majority of data transmission and storage all over the internet, that is not fair nor logic for the users in terms of pricing and ownership of their own data. So Hello’s mission is to democratize data storage and to give ownership to users over their own information, and furthermore, we are going to be the world’s biggest decentralized ecosystem in less than 4 years. As a company, we hold that there are 3 fundamentals of the future, and those are efficiency, sustainability, and optimization. The decentralized concept is one of the few that inherently possesses these 3 fundamentals, so this is why decentralization is the answer. How does Hello differentiate itself from traditional centralized data storage solutions? Hello Space, which is the first app of this ecosystem (a decentralized drop-box) embraces the concept of object storage. Unlike traditional file-based storage systems, object storage treats data as discrete units, known as objects, which are accompanied by for efficient organization and retrieval. This approach enables seamless scalability and optimal utilization of resources, ensuring that data can be easily accessed, modified, and distributed across the network. metadata One crucial distinction lies in ownership. In centralized storage solutions, data ownership typically resides with the service provider, granting them control over users' information. However, the whole Hello Ecosystem operates on a decentralized model, leveraging blockchain or distributed ledger technology. This means that data ownership is decentralized, giving users more control and autonomy over their own data. Instead of relying on a single entity, the data is distributed across a network of nodes, enhancing security and resilience. Moreover, Hello Space addresses the limitations of centralized solutions that rely on passwords for user authentication. These methods have become increasingly vulnerable to breaches and identity theft. In contrast, Hello Space implements advanced cryptographic techniques, such as public-key cryptography, to authenticate and secure user access. This ensures robust protection and reduces the risk of unauthorized access to data. We are challenging the status quo of a handful of private companies monopolizing the flow and storage of information. By operating on a decentralized model, we contribute to a more equitable distribution of data control. We are allowing users to participate in the network by contributing their own storage resources, enabling a peer-to-peer exchange of storage capacity, and incentivizing users to be active participants in the ecosystem. Can you explain the incentive model for participants in the Hello network? How do they contribute to the ecosystem? Sure, but before that, it is important to understand that in a decentralized peer-to-peer (P2P) storage network, the incentive model aims to motivate participants to contribute their storage resources and actively participate in the network's operations. This model revolves around rewarding participants for their contributions while ensuring the overall efficiency and sustainability of the network. One key aspect of this incentive model is the concept of resource sharing. Participants who contribute their storage capacity to the network are incentivized by receiving rewards or tokens in return. These tokens can represent a form of or utility within the network. The more storage space a participant contributes, and the longer they maintain their resources available, the greater their potential rewards. This approach encourages individuals to share their idle storage resources and helps create a robust and scalable storage infrastructure. cryptocurrency Additionally, participants may be rewarded for other valuable contributions to the network. For example, they can earn tokens for maintaining high uptime, providing reliable data retrieval services, or participating in consensus mechanisms for network governance. These incentives promote active engagement and ensure the network's health by fostering a cooperative ecosystem where participants are rewarded for their positive contributions. Basically, the incentive model in a decentralized P2P storage network aims to align the interests of participants with the network's goals. This is achieved by offering rewards for resource sharing and meaningful involvement; the model incentivizes collaboration, reliability, and long-term sustainability, ultimately enhancing the overall functionality and efficiency of the decentralized storage ecosystem. What are the scalability challenges that Hello Storage faces in achieving widespread adoption? The truth is that we’ll face several scalability challenges that will need to be overcome in order to achieve widespread adoption of our decentralized storage network. One of the primary challenges is maintaining network performance as the user base expands. As more users join the network and data storage demands increase, the network must efficiently handle the increased traffic and maintain low latency. Scaling the infrastructure to accommodate a growing number of users and their storage needs without compromising speed and reliability is essential. Another challenge is scaling the storage capacity of the network. As more participants contribute their storage resources, it becomes crucial to ensure that the available storage capacity scales proportionally to meet the growing demand. Efficiently managing and allocating storage resources while incentivizing participants to contribute their resources requires careful planning and robust mechanisms. Consensus mechanisms, such as , also pose scalability challenges. These mechanisms are essential for maintaining the integrity of the network, but as the network grows, scaling consensus becomes complex. Also, ensuring fast transaction processing times and maintaining consensus without compromising security and decentralization requires ongoing research and optimization. blockchain What are the future plans and roadmap for Hello? Are there any upcoming features or developments that users can look forward to? Good question. Due to our insatiable ambition, we will not merely stop at Hello Space. We aspire to surpass limitations and achieve the culmination of the web3 revolution. To accomplish this, we have planned the launch of Hello Network and Hello ID in approximately one year. The Hello Network will be based on our proprietary blockchain protocol, designed to accommodate even the smallest and least powerful devices, such as tablets and smartphones. Our objective is to gradually build a vast, highly stable, and resilient network where every user becomes a direct or indirect participant. Through this approach, we aim to gradually shift power from corporations to individuals, marking the beginning of a new era. On the other hand, Hello ID will serve as the definitive replacement for traditional methods of internet authentication. It will represent a unique and globally recognized digital identity, granting access to all applications, websites, and platforms across the web. Hello ID will be a cryptographically secured identity based on blockchain technology, empowering you to maintain control over all your assets without relying on any third-party entities. And so, with the launch of these two key parts of the ecosystem on the horizon, a profound transformation awaits us. We stand on the precipice of a new era fueled by our unwavering determination to push the boundaries of technological advancement. Together, let us redefine the rules, challenge the existing condition, and carve a path toward a future where the power rests firmly in the hands of individuals. With Hello, we embark on a quest to reshape the digital landscape, fostering a realm of boundless opportunities, unrivaled security, and true digital freedom. The revolution has begun, and you are at the vanguard. Embrace the dawn of a new era, for the possibilities are infinite, and the future is ours to shape.

"We Want To Be The Google of Web3": Hello Co-Founder Alvaro Pintado

The Lost Continent by Edgar Rice Burroughs, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Chapter  II CHAPTER II. What could it mean? I had left Alvarez in command. He was my most loyal subordinate. It was absolutely beyond the pale of possibility that Alvarez should desert me. No, there was some other explanation. Something occurred to place my second officer, Porfirio Johnson, in command. I was sure of it but why speculate? The futility of conjecture was only too palpable. The Coldwater had abandoned us in midocean. Doubtless none of us would survive to know why. The young man at the wheel of the power boat had turned her nose about as it became evident that the ship intended passing over us, and now he still held her in futile pursuit of the Coldwater. “Bring her about, Snider,” I directed, “and hold her due east. We can’t catch the Coldwater, and we can’t cross the Atlantic in this. Our only hope lies in making the nearest land, which, unless I am mistaken, is the Scilly Islands, off the southwest coast of England. Ever heard of England, Snider?” “There’s a part of the United States of North America that used to be known to the ancients as New England,” he replied. “Is that where you mean, sir?” “No, Snider,” I replied. “The England I refer to was an island off the continent of Europe. It was the seat of a very powerful kingdom that flourished over two hundred years ago. A part of the United States of North America and all of the Federated States of Canada once belonged to this ancient England.” “Europe,” breathed one of the men, his voice tense with excitement. “My grandfather used to tell me stories of the world beyond thirty. He had been a great student, and he had read much from forbidden books.” “In which I resemble your grandfather,” I said, “for I, too, have read more even than naval officers are supposed to read, and, as you men know, we are permitted a greater latitude in the study of geography and history than men of other professions. “Among the books and papers of Admiral Porter Turck, who lived two hundred years ago, and from whom I am descended, many volumes still exist, and are in my possession, which deal with the history and geography of ancient Europe. Usually I bring several of these books with me upon a cruise, and this time, among others, I have maps of Europe and her surrounding waters. I was studying them as we came away from the Coldwater this morning, and luckily I have them with me.” “You are going to try to make Europe, sir?” asked Taylor, the young man who had last spoken. “It is the nearest land,” I replied. “I have always wanted to explore the forgotten lands of the Eastern Hemisphere. Here’s our chance. To remain at sea is to perish. None of us ever will see home again. Let us make the best of it, and enjoy while we do live that which is forbidden the balance of our race—the adventure and the mystery which lie beyond thirty.” Taylor and Delcarte seized the spirit of my mood but Snider, I think, was a trifle sceptical. “It is treason, sir,” I replied, “but there is no law which compels us to visit punishment upon ourselves. Could we return to Pan-America, I should be the first to insist that we face it. But we know thatl’s not possible. Even if this craft would carry us so far, we haven’t enough water or food for more than three days. “We are doomed, Snider, to die far from home and without ever again looking upon the face of another fellow countryman than those who sit here now in this boat. Isn’t that punishment sufficient for even the most exacting judge?” Even Snider had to admit that it was. “Very well, then, let us live while we live, and enjoy to the fullest whatever of adventure or pleasure each new day brings, since any day may be our last, and we shall be dead for a considerable while.” I could see that Snider was still fearful, but Taylor and Delcarte responded with a hearty, “Aye, aye, sir!” They were of different mold. Both were sons of naval officers. They represented the aristocracy of birth, and they dared to think for themselves. Snider was in the minority, and so we continued toward the east. Beyond thirty, and separated from my ship, my authority ceased. I held leadership, if I was to hold it at all, by virtue of personal qualifications only, but I did not doubt my ability to remain the director of our destinies in so far as they were amenable to human agencies. I have always led. While my brain and brawn remain unimpaired I shall continue always to lead. Following is an art which Turcks do not easily learn. It was not until the third day that we raised land, dead ahead, which I took, from my map, to be the isles of Scilly. But such a gale was blowing that I did not dare attempt to land, and so we passed to the north of them, skirted Landl’s End, and entered the English Channel. I think that up to that moment I had never experienced such a thrill as passed through me when I realized that I was navigating these historic waters. The lifelong dreams that I never had dared hope to see fulfilled were at last a reality—but under what forlorn circumstances! Never could I return to my native land. To the end of my days I must remain in exile. Yet even these thoughts failed to dampen my ardor. My eyes scanned the waters. To the north I could see the rockbound coast of Cornwall. Mine were the first American eyes to rest upon it for more than two hundred years. In vain, I searched for some sign of ancient commerce that, if history is to be believed, must have dotted the bosom of the Channel with white sails and blackened the heavens with the smoke of countless funnels, but as far as eye could reach the tossing waters of the Channel were empty and deserted. Toward midnight the wind and sea abated, so that shortly after dawn I determined to make inshore in an attempt to effect a landing, for we were sadly in need of fresh water and food. According to my observations, we were just off Ram Head, and it was my intention to enter Plymouth Bay and visit Plymouth. From my map it appeared that this city lay back from the coast a short distance, and there was another city given as Devonport, which appeared to lie at the mouth of the river Tamar. However, I knew that it would make little difference which city we entered, as the English people were famed of old for their hospitality toward visiting mariners. As we approached the mouth of the bay I looked for the fishing craft which I expected to see emerging thus early in the day for their labors. But even after we rounded Ram Head and were well within the waters of the bay I saw no vessel. Neither was there buoy nor light nor any other mark to show larger ships the channel, and I wondered much at this. The coast was densely overgrown, nor was any building or sign of man apparent from the water. Up the bay and into the River Tamar we motored through a solitude as unbroken as that which rested upon the waters of the Channel. For all we could see, there was no indication that man had ever set his foot upon this silent coast. I was nonplused, and then, for the first time, there crept over me an intuition of the truth. Here was no sign of war. As far as this portion of the Devon coast was concerned, that seemed to have been over for many years, but neither were there any people. Yet I could not find it within myself to believe that I should find no inhabitants in England. Reasoning thus, I discovered that it was improbable that a state of war still existed, and that the people all had been drawn from this portion of England to some other, where they might better defend themselves against an invader. But what of their ancient coast defenses? What was there here in Plymouth Bay to prevent an enemy landing in force and marching where they wished? Nothing. I could not believe that any enlightened military nation, such as the ancient English are reputed to have been, would have voluntarily so deserted an exposed coast and an excellent harbor to the mercies of an enemy. I found myself becoming more and more deeply involved in quandary. The puzzle which confronted me I could not unravel. We had landed, and I now stood upon the spot where, according to my map, a large city should rear its spires and chimneys. There was nothing but rough, broken ground covered densely with weeds and brambles, and tall, rank grass. Had a city ever stood there, no sign of it remained. The roughness and unevenness of the ground suggested something of a great mass of debris hidden by the accumulation of centuries of undergrowth. I drew the short cutlass with which both officers and men of the navy are, as you know, armed out of courtesy to the traditions and memories of the past, and with its point dug into the loam about the roots of the vegetation growing at my feet. The blade entered the soil for a matter of seven inches, when it struck upon something stonelike. Digging about the obstacle, I presently loosened it, and when I had withdrawn it from its sepulcher I found the thing to be an ancient brick of clay, baked in an oven. Delcarte we had left in charge of the boat; but Snider and Taylor were with me, and following my example, each engaged in the fascinating sport of prospecting for antiques. Each of us uncovered a great number of these bricks, until we commenced to weary of the monotony of it, when Snider suddenly gave an exclamation of excitement, and, as I turned to look, he held up a human skull for my inspection. I took it from him and examined it. Directly in the center of the forehead was a small round hole. The gentleman had evidently come to his end defending his country from an invader. Snider again held aloft another trophy of the search—a metal spike and some tarnished and corroded metal ornaments. They had lain close beside the skull. With the point of his cutlass Snider scraped the dirt and verdigris from the face of the larger ornament. “An inscription,” he said, and handed the thing to me. They were the spike and ornaments of an ancient German helmet. Before long we had uncovered many other indications that a great battle had been fought upon the ground where we stood. But I was then, and still am, at loss to account for the presence of German soldiers upon the English coast so far from London, which history suggests would have been the natural goal of an invader. I can only account for it by assuming that either England was temporarily conquered by the Teutons, or that an invasion of so vast proportions was undertaken that German troops were hurled upon the England coast in huge numbers and that landings were necessarily effected at many places simultaneously. Subsequent discoveries tend to strengthen this view. We dug about for a short time with our cutlasses until I became convinced that a city had stood upon the spot at some time in the past, and that beneath our feet, crumbled and dead, lay ancient Devonport. I could not repress a sigh at the thought of the havoc war had wrought in this part of England, at least. Farther east, nearer London, we should find things very different. There would be the civilization that two centuries must have wrought upon our English cousins as they had upon us. There would be mighty cities, cultivated fields, happy people. There we would be welcomed as long-lost brothers. There would we find a great nation anxious to learn of the world beyond their side of thirty, as I had been anxious to learn of that which lay beyond our side of the dead line. I turned back toward the boat. “Come, men!” I said. “We will go up the river and fill our casks with fresh water, search for food and fuel, and then tomorrow be in readiness to push on toward the east. I am going to London.” 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. Edgar Rice Burroughs (1994). The Lost Continent. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/149/pg149-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 www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

I had left Alvarez in command

This is paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Leah Chrestien, Czech Technical University in Prague; (2) Tomá˘s Pevný, Czech Technical University in Prague, and Gen Digital, Inc.; (3) Stefan Edelkamp, Czech Technical University in Prague; (4) Antonín Komenda, Czech Technical University in Prague. Table of Links Abstract & Introduction Preliminaries Conditions on strictly optimally efficient heuristic Loss functions Related Work Experiments Conclusion and References Appendix 7 Conclusion The motivation of this paper stemmed from the observation that even the cost-to-goal, considered to be an optimal heuristic, fails to guarantee a strictly optimally efficient search. Since a large body of existing research optimizes this quantity, we are effectively lost with respect to what should be optimized. To fill this gap, we have stated the necessary and sufficient conditions guaranteeing the forward search to be strictly optimally efficient. These conditions show that the absolute value of the heuristic is not important, but that the ranking of states in the Open list is what controls the efficiency. Ranking can be optimized by minimizing the ranking loss functions, but its concrete implementation needs to correspond to a variant of the forward search. In case of mismatch, the resulting heuristic can perform poorly, which has been demonstrated when the heuristic optimized for BGFS search was used with A* search. The other benefit of ranking losses is that from the point of view of statistical learning theory, they solve a simpler problem than ubiquitous regression in estimating the cost-to-goal. We do not question the existence of a strictly optimally efficient heuristic. Given our experiments, we believe that if the space of heuristic functions over which the loss is optimized is sufficiently rich, the result will be sufficiently close to the optimal for the needs of the search. 7.1 Acknowledgements This work has been supported by project numbers 22-32620S and 22-30043S from Czech Science Foundation and OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765 "Research Center for Informatics". This work has also received funding from the European Union’s Horizon Europe Research and Innovation program under the grant agreement TUPLES No 101070149. References [1] Forest Agostinelli, Stephen McAleer, Alexander Shmakov, and Pierre Baldi. Solving the rubik’s cube with deep reinforcement learning and search. Nature Machine Intelligence, 1(8):356–363, 2019. [2] Thomas Anthony, Zheng Tian, and David Barber. Thinking fast and slow with deep learning and tree search. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 5360–5370, 2017. [3] Shahab Jabbari Arfaee, Sandra Zilles, and Robert C Holte. Learning heuristic functions for large state spaces. Artificial Intelligence, 175(16-17):2075–2098, 2011. [4] Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B Shah. Julia: A fresh approach to numerical computing. SIAM review, 59(1):65–98, 2017. [5] Mohak Bhardwaj, Sanjiban Choudhury, and Sebastian Scherer. Learning heuristic search via imitation. In Conference on Robot Learning, pages 271–280. PMLR, 2017. [6] Blai Bonet and Héctor Geffner. Planning as heuristic search. Artificial Intelligence. 2001 Jun; 129 (1-2): 5-33., 2001. [7] Vladimir Cherkassky, Xuhui Shao, Filip M Mulier, and Vladimir N Vapnik. Model complexity control for regression using vc generalization bounds. IEEE transactions on Neural Networks, 10(5):1075–1089, 1999. [8] Leah Chrestien, Tomáš Pevný, Antonín Komenda, and Stefan Edelkamp. Heuristic search planning with deep neural network using imitation, attention and curriculum learning. In PRLIJCAI workshop, 2021. [9] Lukás Chrpa. Combining learning techniques for classical planning: Macro-operators and entanglements. In ICTAI, pages 79–86. IEEE Computer Society, 2010. [10] Rina Dechter and Judea Pearl. The optimality of A* revisited. In Michael R. Genesereth, editor, AAAI, pages 95–99. AAAI Press, 1983. [11] Edsger W. Dijkstra. A note on two problems in connexion with graphs. Numerische Mathematik, 1:269–271, 1959. [12] Stefan Edelkamp. Automated creation of pattern database search heuristics. In Model Checking and Artificial Intelligence, pages 35–50, 2006. [13] Stefan Edelkamp and Stefan Schrödl. Heuristic Search - Theory and Applications. Academic Press, 2012. [14] Ariel Felner, Uzi Zahavi, Robert Holte, Jonathan Schaeffer, Nathan Sturtevant, and Zhifu Zhang. Inconsistent heuristics in theory and practice. Artificial Intelligence, 175(9):1570–1603, 2011. [15] Dieqiao Feng, Carla P. Gomes, and Bart Selman. Solving hard AI planning instances using curriculum-driven deep reinforcement learning. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 2198–2205. ijcai.org, 2020. [16] Patrick Ferber, Malte Helmert, and Jörg Hoffmann. Neural network heuristics for classical planning: A study of hyperparameter space. In ECAI, pages 2346–2353. IOS Press, 2020. [17] Santiago Franco, Álvaro Torralba, Levi H. S. Lelis, and Mike Barley. On creating complementary pattern databases. In IJCAI, pages 4302–4309, 2017. [18] Caelan Reed Garrett, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. Learning to rank for synthesizing planning heuristics. page 3089–3095, 2016. [19] Edward Groshev, Aviv Tamar, Maxwell Goldstein, Siddharth Srivastava, and Pieter Abbeel. Learning generalized reactive policies using deep neural networks. 2018. [20] Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, and David Silver. Learning to search with mctsnets. In International conference on machine learning, pages 1822–1831. PMLR, 2018. [21] Peter E Hart, Nils J Nilsson, and Bertram Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics, 4(2):100–107, 1968. [22] Patrik Haslum, Adi Botea, Malte Helmert, Blai Bonet, and Sven Koenig. Domain-independent construction of pattern database heuristics for cost-optimal planning. In AAAI, pages 1007–1012, 2007. [23] Malte Helmert and Gabriele Röger. How good is almost perfect? In AAAI, pages 944–949. AAAI Press, 2008. [24] Robert C. Holte and Sandra Zilles. On the optimal efficiency of cost-algebraic A. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, pages 2288–2295. AAAI Press, 2019. [25] Michael Katz, Shirin Sohrabi, Horst Samulowitz, and Silvan Sievers. Delfi: Online planner selection for cost-optimal planning. IPC-9 planner abstracts, pages 57–64, 2018. [26] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2014. [27] Donald E Knuth and Ronald W Moore. An analysis of alpha-beta pruning. Artificial intelligence, 6(4):293–326, 1975. [28] Richard E. Korf. Iterative-deepening-A*: An optimal admissible tree search. In IJCAI, pages 1034–1036. Morgan Kaufmann, 1985. [29] Richard E Korf. Macro-operators: A weak method for learning. Artificial intelligence, 26(1):35–77, 1985. [30] Šimon Mandlík, Matej Ra ˇ cinsk ˇ y, Viliam Lis ` y, and Tomáš Pevn ` y. Jsongrinder. jl: automated ` differentiable neural architecture for embedding arbitrary json data. Journal of Machine Learning Research, 23(298):1–5, 2022. [31] Drew V. McDermott. A heuristic estimator for means-ends analysis in planning. In AIPS, pages 142–149. AAAI, 1996. [32] David Ménager, Dongkyu Choi, Mark Roberts, and David W. Aha. Learning planning operators from episodic traces. In 2018 AAAI Spring Symposia. AAAI Press, 2018. [33] Ionut Moraru, Stefan Edelkamp, Santiago Franco, and Moisés Martínez. Simplifying automated pattern selection for planning with symbolic pattern databases. In KI, pages 249–263, 2019. [34] Christian Muise. Collection of pddl files of classical problems. https://github.com/AI-Planning/classical-domains, 2023. [35] XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan. On surrogate loss functions and f-divergences. The Annals of Statistics, pages 876–904, 2009. [36] Laurent Orseau, Levi Lelis, Tor Lattimore, and Théophane Weber. Single-agent policy tree search with guarantees. Advances in Neural Information Processing Systems, 31, 2018. [37] Laurent Orseau and Levi HS Lelis. Implementation of the algorithms described in "policyguided heuristic search with guarantees" by l. orseau and l. lelis, published at aaai’21. https://github.com/levilelis/h-levin, 2021. [38] Laurent Orseau and Levi HS Lelis. Policy-guided heuristic search with guarantees. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 12382–12390, 2021. [39] Arthur L Samuel. Some studies in machine learning using the game of checkers. ii—recent progress. IBM Journal of research and development, 11(6):601–617, 1967. [40] Max-Philipp B. Schrader. gym-sokoban. https://github.com/mpSchrader/gym-sokoban, 2018. [41] Jendrik Seipp, Florian Pommerening, and Malte Helmert. New optimization functions for potential heuristics. In ICAPS, 2015. [42] Jendrik Seipp and Silvan Sievers. Fast downward stone soup 2023. [43] Jendrik Seipp, Álvaro Torralba, and Jörg Hoffmann. PDDL generators. https://doi.org/10.5281/zenodo.6382173, 2022. [44] William Shen. Strips-hgn. https://github.com/williamshen-nz/STRIPS-HGN, 2021. [45] William Shen, Felipe Trevizan, and Sylvie Thiébaux. Learning domain-independent planning heuristics with hypergraph networks. In ICAPS, volume 30, pages 574–584, 2020. [46] David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. nature, 550(7676):354–359, 2017. [47] Kristian Smith. Maze creator / solver. https://github.com/ravenkls/Maze-Generator-and Solver, 2022. [48] Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezny, Steven Schockaert, and Ondrej Kuzelka. Lifted relational neural networks: Efficient learning of latent relational structures. Journal of Artificial Intelligence Research, 62:69–100, 2018. [49] Simon Ståhlberg, Blai Bonet, and Hector Geffner. Learning general optimal policies with graph neural networks: Expressive power, transparency, and limits. In Akshat Kumar, Sylvie Thiébaux, Pradeep Varakantham, and William Yeoh, editors, Proceedings of the Thirty-Second International Conference on Automated Planning and Scheduling, ICAPS 2022, Singapore (virtual), June 13-24, 2022, pages 629–637. AAAI Press, 2022. [50] Ingo Steinwart. Consistency of support vector machines and other regularized kernel classifiers. IEEE transactions on information theory, 51(1):128–142, 2005. [51] Takeshi Takahashi, He Sun, Dong Tian, and Yebin Wang. Learning heuristic functions for mobile robot path planning using deep neural networks. In ICAPS, volume 29, pages 764–772, 2019. [52] Alvaro Torralba, Vidal Alcázar, Daniel Borrajo, Peter Kissmann, and Stefan Edelkamp. SymBA*: A symbolic bidirectional A* planner. In International Planning Competition, pages 105–108, 2014. [53] Sam Toyer, Sylvie Thiébaux, Felipe Trevizan, and Lexing Xie. Asnets: Deep learning for generalised planning. Journal of Artificial Intelligence Research, 68:1–68, 2020. [54] Vladimir Vapnik. Principles of risk minimization for learning theory. Advances in neural information processing systems, 4, 1991. [55] Marin Vlastelica, Michal Rolinek, and Georg Martius. Neuro-algorithmic policies enable fast combinatorial generalization. In International Conference on Machine Learning, pages 10575– 10585. PMLR, 2021. [56] Xuemei Wang. Learning planning operators by observation and practice. In Kristian J. Hammond, editor, AIPS, pages 335–340. AAAI, 1994. [57] Christopher Wilt and Wheeler Ruml. When does weighted A* fail? In International Symposium on Combinatorial Search, volume 3, 2012. [58] Christopher Makoto Wilt and Wheeler Ruml. Effective heuristics for suboptimal best-first search. J. Artif. Intell. Res., 57:273–306, 2016. [59] Yuehua Xu, Alan Fern, and Sungwook Yoon. Learning linear ranking functions for beam search with application to planning. Journal of Machine Learning Research, 10(7), 2009. [60] Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, and Asako Kanezaki. Path planning using neural A* search. In International Conference on Machine Learning, pages 12029–12039. PMLR, 2021. [61] Ziqi Zhang and Florian Geißer. Extending graph neural networks for generalized stochastic planning. In Bridging the Gap Between AI Planning and Reinforcement Learning. 2021. [62] Tan Zhi-Xuan. PDDL. jl: An Extensible Interpreter and Compiler Interface for Fast and Flexible AI Planning. PhD thesis, Massachusetts Institute of Technology, 2022. [63] Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar C Tatikonda, Nicha Dvornek, Xenophon Papademetris, and James Duncan. Adabelief optimizer: Adapting stepsizes by the belief in observed gradients. Advances in neural information processing systems, 33:18795–18806, 2020.

Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal: Conclusion and References

This paper is available on under CC 4.0 license. Arxiv Authors: (1) Md Masud Rana, Department of Mathematics, University of Kentucky; (2) Duc Duy Nguyen, Department of Mathematics, University of Kentucky & ducnguyen@uky.edu. Table of Links Abstract & Introduction Datasets and Results Methods Conclusion, Data and Software Availability, Competing interests, Acknowledgments & References 4 Conclusion The study of protein-protein interactions (PPIs) and the prediction of mutation-induced binding free energy changes are of great importance in understanding the molecular basis of biological processes. The application of geometric graph theory and atom-level graph coloring techniques provides a powerful framework for analyzing biomolecules and capturing their intricate relationships. By utilizing the concept of geometric subgraphs and constructing multi-scale weighted colored geometric subgraphs (MWCGS), we can effectively represent the structural and functional properties of PPIs. The site-specific MWCGS features allow us to extract meaningful patterns and characteristics, shedding light on the effects of mutations and the underlying molecular interactions. In this work, we developed a mutation-induced binding free energy change predictor, called GGL-PPI, by incorporating site-specific MWCGS features for PPIs and gradient-boosting trees. Our method demonstrates superior performance compared to existing methods. The model was validated on three datasets: AB-Bind S645, SKEMPI 1.0 S1131, and SKEMPI 2.0 S4169 and S8338, showcasing its robustness and effectiveness. Furthermore, GGL-PPI was evaluated on a blind test set, the Ssym dataset. To prevent data leakage between the test and training sets, the model was trained on a homology-reduced balanced training set Q3488. This approach ensures the reliability and fairness of the evaluation process. GGL-PPI exhibits the most unbiased and superior performance in predicting binding free energy changes for both direct and reverse mutations, outperforming other existing methods, particularly for reverse mutations. Overall, the results highlight the potential of the GGL-PPI approach in accurately predicting mutation-induced binding free energy changes in protein-protein interactions, providing valuable insights into the molecular mechanisms underlying protein-protein interactions and facilitating drug design and discovery efforts. 5 Data and Software Availability The source code is available at Github: https://github.com/NguyenLabUKY/GGL-Mutation. 6 Competing interests No competing interest is declared. 7 Acknowledgments This work is supported in part by funds from the National Science Foundation (NSF: # 2053284, # 2151802, and # 2245903), and the University of Kentucky Startup Fund. References [1] Dana Chuderland and Rony Seger. Protein-protein interactions in the regulation of the extracellular signal-regulated kinase. Mol. Biotechnol., 29:57–74, 2005. [2] Harry Jubb, Tom L Blundell, and David B Ascher. Flexibility and small pockets at protein–protein interfaces: new insights into druggability. Prog. Biophys. Mol., 119(1):2–9, 2015. [3] Mileidy W Gonzalez and Maricel G Kann. Chapter 4: Protein interactions and disease. PLoS Comput. Biol., 8(12):e1002819, 2012. [4] Cunliang Geng, Li C Xue, Jorge Roel-Touris, and Alexandre MJJ Bonvin. Finding the δδg spot: are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? Wiley Interdiscip. Rev. Comput. Mol. Sci., 9(5):e1410, 2019. [5] Rocco Moretti, Sarel J Fleishman, Rudi Agius, Mieczyslaw Torchala, Paul A Bates, Panagiotis L Kastritis, Joao PGLM Rodrigues, Mika¨el Trellet, Alexandre MJJ Bonvin, Meng Cui, et al. Community-wide evaluation of methods for predicting the effect of mutations on protein–protein interactions. Proteins: Struct. Funct. Genet., 81(11):1980–1987, 2013. [6] Jeffrey R Brender and Yang Zhang. Predicting the effect of mutations on protein-protein binding interactions through structure-based interface profiles. PLoS Comput. Biol., 11(10):e1004494, 2015. [7] Ning Zhang, Yuting Chen, Haoyu Lu, Feiyang Zhao, Roberto Vera Alvarez, Alexander Goncearenco, Anna R Panchenko, and Minghui Li. Mutabind2: predicting the impacts of single and multiple mutations on protein-protein interactions. Iscience, 23(3):100939, 2020. [8] Carlos HM Rodrigues, Yoochan Myung, Douglas EV Pires, and David B Ascher. mcsm-ppi2: predicting the effects of mutations on protein–protein interactions. Nucleic Acids Res., 47(W1):W338–W344, 2019. [9] Minghui Li, Marharyta Petukh, Emil Alexov, and Anna R Panchenko. Predicting the impact of missense mutations on protein–protein binding affinity. J. Chem. Theory Comput., 10(4):1770–1780, 2014. [10] Hafumi Nishi, Manoj Tyagi, Shaolei Teng, Benjamin A Shoemaker, Kosuke Hashimoto, Emil Alexov, Stefan Wuchty, and Anna R Panchenko. Cancer missense mutations alter binding properties of proteins and their interaction networks. PloS one, 8(6):e66273, 2013. [11] Mu Gao, Hongyi Zhou, and Jeffrey Skolnick. Insights into disease-associated mutations in the human proteome through protein structural analysis. Structure, 23(7):1362–1369, 2015. [12] Alessia David, Rozami Razali, Mark N Wass, and Michael JE Sternberg. Protein–protein interaction sites are hot spots for disease-associated nonsynonymous snps. Hum. Mutat., 33(2):359–363, 2012. [13] H Billur Engin, Jason F Kreisberg, and Hannah Carter. Structure-based analysis reveals cancer missense mutations target protein interaction interfaces. PLoS One, 11(4):e0152929, 2016. [14] Amelie Stein, Douglas M Fowler, Rasmus Hartmann-Petersen, and Kresten Lindorff-Larsen. Biophysical and mechanistic models for disease-causing protein variants. Trends Biochem. Sci., 44(7):575–588, 2019. [15] Stephanie Portelli, Jody E Phelan, David B Ascher, Taane G Clark, and Nicholas Furnham. Understanding molecular consequences of putative drug resistant mutations in mycobacterium tuberculosis. Sci. Rep., 8(1):1–12, 2018. [16] Sundeep Chaitanya Vedithi, Sony Malhotra, Madhusmita Das, Sheela Daniel, Nanda Kishore, Anuja George, Shantha Arumugam, Lakshmi Rajan, Mannam Ebenezer, David B Ascher, et al. Structural implications of mutations conferring rifampin resistance in mycobacterium leprae. Sci. Rep., 8(1):5016, 2018. [17] Vytautas Gapsys, Servaas Michielssens, Daniel Seeliger, and Bert L de Groot. Accurate and rigorous prediction of the changes in protein free energies in a large-scale mutation scan. Angew. Chem., Int. Ed., 55(26):7364–7368, 2016. [18] Elizabeth H Kellogg, Andrew Leaver-Fay, and David Baker. Role of conformational sampling in computing mutationinduced changes in protein structure and stability. Proteins: Structure, Function, and Bioinformatics, 79(3):830–838, 2011. [19] Brian J Bender, Alberto Cisneros III, Amanda M Duran, Jessica A Finn, Darwin Fu, Alyssa D Lokits, Benjamin K Mueller, Amandeep K Sangha, Marion F Sauer, Alexander M Sevy, et al. Protocols for molecular modeling with rosetta3 and rosettascripts. Biochemistry, 55(34):4748–4763, 2016. [20] Kurt S Thorn and Andrew A Bogan. Asedb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics, 17(3):284–285, 2001. [21] MD Shaji Kumar and M Michael Gromiha. Pint: protein–protein interactions thermodynamic database. Nucleic Acids Res., 34(suppl 1):D195–D198, 2006. [22] MD Shaji Kumar, K Abdulla Bava, M Michael Gromiha, Ponraj Prabakaran, Koji Kitajima, Hatsuho Uedaira, and Akinori Sarai. Protherm and pronit: thermodynamic databases for proteins and protein–nucleic acid interactions. Nucleic Acids Res., 34(suppl 1):D204–D206, 2006. [23] Iain H Moal and Juan Fern´andez-Recio. Skempi: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models. Bioinformatics, 28(20):2600–2607, 2012. [24] Justina Jankauskait˙e, Brian Jim´enez-Garc´ıa, Justas Dapk¯unas, Juan Fern´andez-Recio, and Iain H Moal. Skempi 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics, 35(3):462–469, 2019. [25] Sarah Sirin, James R Apgar, Eric M Bennett, and Amy E Keating. Ab-bind: antibody binding mutational database for computational affinity predictions. Protein Sci., 25(2):393–409, 2016. [26] Cunliang Geng, Anna Vangone, and Alexandre MJJ Bonvin. Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes. Protein Eng. Des. Sel., 29(8):291–299, 2016. [27] Sherlyn Jemimah, K Yugandhar, and M Michael Gromiha. Proximate: a database of mutant protein–protein complex thermodynamics and kinetics. Bioinformatics, 33(17):2787–2788, 2017. [28] Douglas EV Pires, David B Ascher, and Tom L Blundell. mcsm: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics, 30(3):335–342, 2014. [29] Cunliang Geng, Anna Vangone, Gert E Folkers, Li C Xue, and Alexandre MJJ Bonvin. isee: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations. Proteins: Structure, Function, and Bioinformatics, 87(2):110–119, 2019. [30] Sherlyn Jemimah, Masakazu Sekijima, and M Michael Gromiha. Proaffimuseq: sequence-based method to predict the binding free energy change of protein–protein complexes upon mutation using functional classification. Bioinformatics, 36(6):1725–1730, 2020. [31] Guangyu Zhou, Muhao Chen, Chelsea JT Ju, Zheng Wang, Jyun-Yu Jiang, and Wei Wang. Mutation effect estimation on protein–protein interactions using deep contextualized representation learning. NAR genom. bioinform., 2(2):lqaa015, 2020. [32] Alexey Strokach, Tian Yu Lu, and Philip M Kim. Elaspic2 (el2): combining contextualized language models and graph neural networks to predict effects of mutations. J. Mol. Biol., 433(11):166810, 2021. [33] Xianggen Liu, Yunan Luo, Pengyong Li, Sen Song, and Jian Peng. Deep geometric representations for modeling effects of mutations on protein-protein binding affinity. PLoS Comput. Biol., 17(8):e1009284, 2021. [34] Piero Fariselli, Pier Luigi Martelli, Castrense Savojardo, and Rita Casadio. Inps: predicting the impact of nonsynonymous variations on protein stability from sequence. Bioinformatics, 31(17):2816–2821, 2015. [35] Grant Thiltgen and Richard A Goldstein. Assessing predictors of changes in protein stability upon mutation using self-consistency. PLoS One, 7(10):e46084, 2012. [36] Fabrizio Pucci, Katrien V Bernaerts, Jean Marc Kwasigroch, and Marianne Rooman. Quantification of biases in predictions of protein stability changes upon mutations. Bioinformatics, 34(21):3659–3665, 2018. [37] Dinara R Usmanova, Natalya S Bogatyreva, Joan Ari˜no Bernad, Aleksandra A Eremina, Anastasiya A Gorshkova, German M Kanevskiy, Lyubov R Lonishin, Alexander V Meister, Alisa G Yakupova, Fyodor A Kondrashov, et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics, 34(21):3653–3658, 2018. [38] Duc D Nguyen, Tian Xiao, Menglun Wang, and Guo-Wei Wei. Rigidity strengthening: A mechanism for protein– ligand binding. J. Chem. Inf. Model., 57(7):1715–1721, 2017. [39] Md Masud Rana and Duc Duy Nguyen. Geometric graph learning with extended atom-types features for proteinligand binding affinity prediction. Comput. Biol. Med., 164:107250, 2023. [40] Jian Jiang, Rui Wang, and Guo-Wei Wei. Ggl-tox: geometric graph learning for toxicity prediction. J. Chem. Inf. Model., 61(4):1691–1700, 2021. [41] Menglun Wang, Zixuan Cang, and Guo-Wei Wei. A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation. Nat. Mach. Intell., 2(2):116–123, 2020. [42] Duan Chen, Zhan Chen, Changjun Chen, Weihua Geng, and Guo-Wei Wei. Mibpb: a software package for electrostatic analysis. J. Comput. Chem., 32(4):756–770, 2011. [43] Xiang Liu, Huitao Feng, Jie Wu, and Kelin Xia. Hom-complex-based machine learning (hcml) for the prediction of protein–protein binding affinity changes upon mutation. J. Chem. Inf. Model., 62(17):3961–3969, 2022. [44] Douglas EV Pires and David B Ascher. mcsm-ab: a web server for predicting antibody–antigen affinity changes upon mutation with graph-based signatures. Nucleic Acids Res., 44(W1):W469–W473, 2016. [45] Peng Xiong, Chengxin Zhang, Wei Zheng, and Yang Zhang. Bindprofx: assessing mutation-induced binding affinity change by protein interface profiles with pseudo-counts. J. Mol. Biol., 429(3):426–434, 2017. [46] Quanya Liu, Peng Chen, Bing Wang, Jun Zhang, and Jinyan Li. dbmpikt: a database of kinetic and thermodynamic mutant protein interactions. BMC bioinformatics, 19(1):1–7, 2018. [47] Bian Li, Yucheng T Yang, John A Capra, and Mark B Gerstein. Predicting changes in protein thermodynamic stability upon point mutation with deep 3d convolutional neural networks. PLoS Comp. Bio., 16(11):e1008291, 2020. [48] Lijun Quan, Qiang Lv, and Yang Zhang. Strum: structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics, 32(19):2936–2946, 2016. [49] Leo Breiman. Random forests. Machine learning, 45:5–32, 2001. [50] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20:273–297, 1995. [51] Duc Duy Nguyen, Kaifu Gao, Menglun Wang, and Guo-Wei Wei. Mathdl: mathematical deep learning for d3r grand challenge 4. J. Comput. Aided. Mol. Des., 34(2):131–147, 2020. [52] Jerome H Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232, 2001. This paper is available on under CC 4.0 license. Arxiv

PPIs and the Prediction of Mutation-Induced Binding Free Energy Changes: What It All Means

Authors: (1) Dong Jiang, School of Internet, Anhui University, National Engineering Research Center of Agro-Ecological Big Data Analysis and Application, Anhui University & jiangd@nju.edu.cn; (2) Zhen Yuan, School of Internet, Anhui University; (3) Wen-xin Li, School of Internet, Anhui University; (4) Liang-liang Lu, Key Laboratory of Optoelectronic Technology of Jiangsu Province, Nanjing Normal University, National Laboratory of Solid State Microstructures, Nanjing University, Nanjing & lianglianglu@nju.edu.cn. Table of Links Abstract & Introduction Strategy Description Encryption Speed Evaluation Statistical Evaluation Security Analysis Parameter Setup Comparison To Previous Works Conclusions Acknowledgments & References Acknowledgments This work is financially supported by the National Key Research and Development Program of China (2017YFA0303 704), the National Natural Science Foundation of China (12274233, 62273001), the Major Scientific Research Project of Anhui Province (KJ2021ZD0005), the Compiled Scientific Research Plan Project of Anhui Province (2022AH040020), the Outstanding Research and Innovation Team Project of Anhui Province (2022AH010005), the University Collaborative Innovation Project of Anhui Province (GXXT-2021-091). References [1] Pareek NK, Patidar V, Sud KK. Image encryption using chaotic logistic map. Image Vision Comput 2006; 24:926-934. [2] Fang P, Liu H, Wu C, Liu M. A survey of image encryption algorithms based on chaotic systesm. Visual Comput 2022; 2022: 1-29. [3] Zhang Y, Wang B. Optical encryption based on interference. Opt Lett 2008; 33: 2443-2445. [4] Zhang Y, Wang X. A symmetric image encryption algorithm based on mixed linear–nonlinear coupled map lattice. Inform Sciences 2014; 273: 329-351. [5] Tedmori S, AI-Najdawi N. Image cryptographic algorithm based on the Haar wavelet transform. Inform Sci 2014; 269: 21-34. [6] Chai x, Gan Z, Chen Y, Zhang Y. A visually secure image encryption scheme based on compressive sensing. Signal Process 2017; 134:35-51. [7] Luo Y, Zhou R, Liu J, Cao Y, Ding X. A parallel image encryption algorithm based on the piecewise linear chaotic map and hyper-chaotic map. Nonlinear Dynam 2018; 93:1165-1181. [8] Zia U, McCartney M, Scotney B, Martinez J, AbuTair M, Memon J. Survey on image encryption techniques using chaotic maps in spatial, transform and spatiotemporal domains. Int J Inf Secur 2022; 21:917-935. [9] Chen J, Zhu Z, Fu C, Yu H. A fast image encryption scheme with a novel pixel swapping-based confusion approach. Nonlinear Dynam 2014; 77:1191-1207. [10] Wong K, Kwok B, Yuen C. An efficient diffusion approach for chaosbased image encryption. Chaos Soliton Fract 2009; 41:2652-2663. [11] Matthews R. On the derivation of a “chaotic” encryption algorithm. Cryptologia 1989; 13:29-42. [12] Massoudi A, Lefebvre F, De Vleeschouwer C, Macq B, Quisquater JJ. Overview on selective encryption of image and video: challenges and perspectives. Eurasip J Inf Secur 2008; 2008:179290. [13] Liu F, Koenig H. A survey of video encryption algorithms. Comput Secur 2010; 29: 3-15. [14] Chiaraluce F, Ciccarelli L, Gambi E, Pierleoni P, Reginelli M. A new chaotic algorithm for video encryption. IEEE T Consum Electr 2002; 48:838-844. [15] Lian S, Sun J, Wang J, Wang Z. A chaotic stream cipher and the usage in video protection. Chaos Soliton Fract 2007; 34:851-859. [16] Yang S, Sun S. A video encryption method based on chaotic maps in DCT domain. Prog Nat Sci 2008; 18:1299-1304. [17] Valli D, Ganesan K. Chaos based video encryption using maps and Ikeda time delay system. Eur Phys J Plus 2017; 132:1-18. [18] Kumar RR, Ganeshkumar D, Suresh A, Manigandan K. A new one round video encryption scheme based on 1D chaotic maps. in: 2019 5th Int Conf Adv Comput Commun Syst; 2019:439-444. [19] Li x, Yu H, Zhang H, Jin X, Sun H, Liu J. Video encryption based on hyperchaotic system. Multimed Tools Appl 2020; 79:23995-24011. [20] Yasser I, Mohamed A, Samra AS, Khalifa F. A chaotic-based encryption/decryption framework for secure multimedia communications. Entropy 2020; 22:1253. [21] Hosny KM, Zaki MA, Lashin NA, Hamza HM. Fast colored video encryption using block scrambling and multi-key generation. Visual Comput 2022; 2022:1-32. [22] Dua M, Makhija D, Manasa PYL, Mishra P. 3D chaotic map-cosine transformation based approach to video encryption and decryption. Open Comput Sci 2022; 12:37-56. [23] Hamidouche W, Farajallah M, Sidaty N, Assad S, Deforges O. Real-time selective video encryption based on the chaos system in scalable HEVC extension. Signal Process: Image Commun 2017; 58:73-86. [24] Chen H, Tanougast C, liu Z, Blondel W, Hao B. Optical hyperspectral image encryption based on improved Chirikov mapping and gyrator transform. Opt Laser Eng 2018; 107:62-70. [25] Fu C, Chen J, Zou H, Meng W, Zhan Y, Yu Y. A chaos-based digital image encryption scheme with an improved diffusion strategy. Opt Express 2012; 20:2363-2378. [26] Zhong J, Jiang D, Huang Q, Cao Y. A self-updating digital chaotic stream cipher. Int J Mod Phys 2018; 29:1850074. [27] Hua Z, Zhou Y. Image encryption using 2D Logistic-adjusted-Sine map. Inform Sci 2016; 339:237-253. [28] Li c, Lin D, Feng B, Lv J, Hao F. Cryptanalysis of a chaotic image encryption algorithm based on information entropy. IEEE Access 2018; 6: 75834-75842. [29] Man Z, Li J, Di X, Sheng Y, Liu Z. Double image encryption algorithm based on neural network and chaos. Chaos Soliton Fract 2021; 152:111318. [30] Kwok HS, Tang WKS. A fast image encryption system based on chaotic maps with finite precision representation. Chaos Soliton Fract 2007; 32:1518-1529. [31] Zhang X, Zhao Z, Wang J. Chaotic image encryption based on circular substitution box and key stream buffer. Singal Process 2014; 29:902-913. [32] Ye G, Wong KW. An efficient chaotic image encryption algorithm based on a generalized Arnold map. Nonlinear Dynam 2012; 69:2079-2087. [33] Rhouma R, Meherzi S, Belghith S. OCML-based colour image encryption. Chaos Soliton Fract 2009; 40:309-318. [34] Belazi A, Khan M, El-Latif AAA, Belghith S. Efficient cryptosystem approaches: S-boxes and permutation-substitution-based encryption. Nonlinear Dynam 2018; 87:337-361. [35] Wu Y, Zhou Y, Saveriades G, Agaian S, Noonan JP, Natarajan P. Local Shannon entropy measure with statistical tests for image randomness. Inform Sci 2013; 222:323-342. [36] Artiles JAP, Chaves DPB, Pimentel C. Image encryption using block cipher and chaotic sequences. Signal Process 2019; 79:24-31. [37] Xian Y, Wang X. Fractal sorting matrix and its application on chaotic image encryption. Infrom Sci 2021; 547:1154-1169. [38] Rukhin A,et al. A statistical test suite for random and pseudorandom number generators for cryptographic applications. NIST Special Publication 800-22, Revision 1.a 2010. [39] Wang X, Teng L, Qin X. A novel colour image encryption algorithm based on chaos. Signal Process 2012; 92:1101-1108. [40] Alvarez G, Li S. Some basic cryptographic requirements for chaos-based cryptosystems. Int J Bifurcat Chaos 2006; 16:2129-2151. [41] Khan M, Massod F. A novel chaotic image encryption technique based on multiple discrete dynamical maps. Multimed Tools Appl 2019; 78:26203- 26222. [42] Luo Y, Yu J, Lai W, Liu L. A novel chaotic image encryption algorithm based on improved baker map and logistic map. Multimed Tools Appl 2019; 78: 22023-22043. [43] Mansouri A, Wang X. A novel one-dimensional sine powered chaotic map and its application in a new image encryption scheme. Inform Sci 2020; 520:46-62. [44] Talhaoui MZ, Wang X. A new fractional one dimensional chaotic map and its application in high-speed image encryption. Infrom Sci 2021; 550:13- 26. [45] Chen H, Bai E, Jiang X, Wu Y. A Fast Image Encryption Algorithm Based on Improved 6-D Hyper-Chaotic System. IEEE Access 2022; 10:116031- 116044. [46] Azzeh J, Zahran B, Alqadi Z. Salt and pepper noise: Effects and removal. JOIV: Int J Inform Visual 2018; 2:252-256. [47] Gan Z, Chai X, Han D, Chen Y. A chaotic image encryption algorithm based on 3-D bit-plane permutation. Neural Comput Appl 2019; 31:7111- 7130.

Acknowledgments and References 

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References APPENDIX A. EXCLUDED WAVELENGTH REGIONS Here, we explicitly define the wavelength regions that were excluded from our analysis for having severe telluric contamination or insufficient signal-to-noise ratios (especially at the edges of the spectral orders). The excluded regions for the IGRINS data are shown in Table 3. REFERENCES Airapetian, V. S., Glocer, A., Khazanov, G. V., et al. 2017, ApJL, 836, L3, doi: 10.3847/2041-8213/836/1/L3 Artigau, E., Kouach, D., Donati, J.-F., et al. 2014, in ´ Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K. Ramsay, I. S. McLean, & H. Takami, 914715, doi: 10.1117/12.2055663 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f Batalha, N. E., Mandell, A., Pontoppidan, K., et al. 2017, PASP, 129, 064501, doi: 10.1088/1538-3873/aa65b0 Berta-Thompson, Z. K., Irwin, J., Charbonneau, D., et al. 2015, Nature, 527, 204, doi: 10.1038/nature15762 Birkby, J. L. 2018, arXiv e-prints, arXiv:1806.04617. https://arxiv.org/abs/1806.04617 Bolmont, E., Selsis, F., Owen, J. E., et al. 2017, MNRAS, 464, 3728, doi: 10.1093/mnras/stw2578 Bourrier, V., de Wit, J., Bolmont, E., et al. 2017a, AJ, 154, 121, doi: 10.3847/1538-3881/aa859c Bourrier, V., Ehrenreich, D., Wheatley, P. J., et al. 2017b, A&A, 599, L3, doi: 10.1051/0004-6361/201630238 Brogi, M., Line, M., Bean, J., D´esert, J. M., & Schwarz, H. 2017, ApJL, 839, L2, doi: 10.3847/2041-8213/aa6933 Brogi, M., & Line, M. R. 2019, AJ, 157, 114, doi: 10.3847/1538-3881/aaffd3 Brogi, M., Snellen, I. A. G., de Kok, R. J., et al. 2013, ApJ, 767, 27, doi: 10.1088/0004-637X/767/1/27 Caballero, J. A., Gonzalez-Alvarez, E., Brady, M., et al. 2022, arXiv e-prints, arXiv:2206.09990. https://arxiv.org/abs/2206.09990 Carnall, A. C. 2017, arXiv e-prints, arXiv:1705.05165. https://arxiv.org/abs/1705.05165 Chiavassa, A., & Brogi, M. 2019, A&A, 631, A100, doi: 10.1051/0004-6361/201936566 de Wit, J., Wakeford, H. R., Gillon, M., et al. 2016, Nature, 537, 69, doi: 10.1038/nature18641 de Wit, J., Wakeford, H. R., Lewis, N. K., et al. 2018, Nature Astronomy, 2, 214, doi: 10.1038/s41550-017-0374-z Deibert, E. K., de Mooij, E. J. W., Jayawardhana, R., et al. 2021a, AJ, 161, 209, doi: 10.3847/1538-3881/abe768 —. 2021b, ApJL, 919, L15, doi: 10.3847/2041-8213/ac2513 Demory, B.-O., Gillon, M., de Wit, J., et al. 2016, Nature, 532, 207, doi: 10.1038/nature17169 Esteves, L. J., de Mooij, E. J. W., Jayawardhana, R., Watson, C., & de Kok, R. 2017, AJ, 153, 268, doi: 10.3847/1538-3881/aa7133 Gandhi, S., Brogi, M., & Webb, R. K. 2020, MNRAS, 498, 194, doi: 10.1093/mnras/staa2424 Giacobbe, P., Brogi, M., Gandhi, S., et al. 2021, Nature, 592, 205, doi: 10.1038/s41586-021-03381-x Gibson, N. P., Nugroho, S. K., Lothringer, J., Maguire, C., & Sing, D. K. 2022, MNRAS, 512, 4618, doi: 10.1093/mnras/stac091 Gibson, N. P., Merritt, S., Nugroho, S. K., et al. 2020, MNRAS, 493, 2215, doi: 10.1093/mnras/staa228 Gressier, A., Mori, M., Changeat, Q., et al. 2022, A&A, 658, A133, doi: 10.1051/0004-6361/202142140 Grimm, S. L., & Heng, K. 2015, The Astrophysical Journal, 808, 182, doi: 10.1088/0004-637X/808/2/182 Grimm, S. L., Malik, M., Kitzmann, D., et al. 2021, ApJS, 253, 30, doi: 10.3847/1538-4365/abd773 Hargreaves, R. J., Gordon, I. E., Rey, M., et al. 2020, ApJS, 247, 55, doi: 10.3847/1538-4365/ab7a1a Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2 Helling, C. 2019, Annual Review of Earth and Planetary Sciences, 47, 583, doi: 10.1146/annurev-earth-053018-060401 Herman, M. K., de Mooij, E. J. W., Nugroho, S. K., Gibson, N. P., & Jayawardhana, R. 2022, AJ, 163, 248, doi: 10.3847/1538-3881/ac5f4d Hirano, T., Kuzuhara, M., Kotani, T., et al. 2020, Publications of the Astronomical Society of Japan, 72, doi: 10.1093/pasj/psaa085 Jindal, A., de Mooij, E. J. W., Jayawardhana, R., et al. 2020, AJ, 160, 101, doi: 10.3847/1538-3881/aba1eb Jones, A., Noll, S., Kausch, W., Szyszka, C., & Kimeswenger, S. 2013, A&A, 560, A91, doi: 10.1051/0004-6361/201322433 Kempton, E. M. R., Bean, J. L., Louie, D. R., et al. 2018, PASP, 130, 114401, doi: 10.1088/1538-3873/aadf6f Khalafinejad, S., Molaverdikhani, K., Blecic, J., et al. 2021, A&A, 656, A142, doi: 10.1051/0004-6361/202141191 Kite, E. S., & Schaefer, L. 2021, ApJL, 909, L22, doi: 10.3847/2041-8213/abe7dc Kotani, T., Tamura, M., Nishikawa, J., et al. 2018, in Ground-based and Airborne Instrumentation for Astronomy VII, ed. C. J. Evans, L. Simard, & H. Takami, Vol. 10702, International Society for Optics and Photonics (SPIE), 296 – 306, doi: 10.1117/12.2311836 Kreidberg, L. 2015, PASP, 127, 1161, doi: 10.1086/683602 Kreidberg, L., Koll, D. D. B., Morley, C., et al. 2019, Nature, 573, 87, doi: 10.1038/s41586-019-1497-4 Kuzuhara, M., Hirano, T., Kotani, T., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10702, Ground-based and Airborne Instrumentation for Astronomy VII, ed. C. J. Evans, L. Simard, & H. Takami, 1070260, doi: 10.1117/12.2311832 Lee, J.-J., & Gullikson, K. 2016, plp: v2.1 alpha 3, v2.1-alpha.3, Zenodo, doi: 10.5281/zenodo.56067 Libby-Roberts, J. E., Berta-Thompson, Z. K., Diamond-Lowe, H., et al. 2022, AJ, 164, 59, doi: 10.3847/1538-3881/ac75de Mace, G., Sokal, K., Lee, J.-J., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10702, Ground-based and Airborne Instrumentation for Astronomy VII, ed. C. J. Evans, L. Simard, & H. Takami, 107020Q, doi: 10.1117/12.2312345 Madhusudhan, N., Ag´undez, M., Moses, J. I., & Hu, Y. 2016, SSRv, 205, 285, doi: 10.1007/s11214-016-0254-3 Miguel, Y. 2019, MNRAS, 482, 2893, doi: 10.1093/mnras/sty2803 Molli`ere, P., & Snellen, I. A. G. 2019, A&A, 622, A139, doi: 10.1051/0004-6361/201834169 Molli`ere, P., Wardenier, J. P., van Boekel, R., et al. 2019, A&A, 627, A67, doi: 10.1051/0004-6361/201935470 Morris, B. M., Delrez, L., Brandeker, A., et al. 2021, A&A, 653, A173, doi: 10.1051/0004-6361/202140892 Mugnai, L. V., Modirrousta-Galian, D., Edwards, B., et al. 2021, AJ, 161, 284, doi: 10.3847/1538-3881/abf3c3 Mugnai, L. V., Modirrousta-Galian, D., Edwards, B., et al. 2021, AJ, 161, 284, doi: 10.3847/1538-3881/abf3c3 Neves Ribeiro do Amaral, L., Barnes, R., Segura, A., & Luger, R. 2022, arXiv e-prints, arXiv:2203.10127. https://arxiv.org/abs/2203.10127 Noll, S., Kausch, W., Barden, M., et al. 2012, A&A, 543, A92, doi: 10.1051/0004-6361/201219040 Nugroho, S. K., Kawahara, H., Gibson, N. P., et al. 2021, ApJL, 910, L9, doi: 10.3847/2041-8213/abec71 Oliva, E., Origlia, L., Scuderi, S., et al. 2015, A&A, 581, A47, doi: 10.1051/0004-6361/201526291 Ortenzi, G., Noack, L., Sohl, F., et al. 2020, Scientific Reports, 10, 10907, doi: 10.1038/s41598-020-67751-7 Owen, J. E., Shaikhislamov, I. F., Lammer, H., Fossati, L., & Khodachenko, M. L. 2020, SSRv, 216, 129, doi: 10.1007/s11214-020-00756-w Park, C., Jaffe, D. T., Yuk, I.-S., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K. Ramsay, I. S. McLean, & H. Takami, 91471D, doi: 10.1117/12.2056431 Peacock, S., Barman, T., Shkolnik, E. L., et al. 2020, ApJ, 895, 5, doi: 10.3847/1538-4357/ab893a Quirrenbach, A., Amado, P. J., Caballero, J. A., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K. Ramsay, I. S. McLean, & H. Takami, 91471F, doi: 10.1117/12.2056453 Ramirez, R. M., & Kaltenegger, L. 2014, ApJL, 797, L25, doi: 10.1088/2041-8205/797/2/L25 Ridden-Harper, A. R., Snellen, I. A. G., Keller, C. U., et al. 2016, A&A, 593, A129, doi: 10.1051/0004-6361/201628448 Rogers, J. G., & Owen, J. E. 2021, MNRAS, 503, 1526, doi: 10.1093/mnras/stab529 Rogers, L. A., Bodenheimer, P., Lissauer, J. J., & Seager, S. 2011, ApJ, 738, 59, doi: 10.1088/0004-637X/738/1/59 Rousselot, P., Lidman, C., Cuby, J. G., Moreels, G., & Monnet, G. 2000, A&A, 354, 1134 Schaefer, L., & Fegley, B. 2009, ApJL, 703, L113, doi: 10.1088/0004-637X/703/2/L113 Shkolnik, E. L., & Barman, T. S. 2014, AJ, 148, 64, doi: 10.1088/0004-6256/148/4/64 Sim, C. K., Le, H. A. N., Pak, S., et al. 2014, Advances in Space Research, 53, 1647, doi: 10.1016/j.asr.2014.02.024 Sing, D. K., Fortney, J. J., Nikolov, N., et al. 2016, Nature, 529, 59, doi: 10.1038/nature16068 Snellen, I. A. G., Brandl, B. R., de Kok, R. J., et al. 2014, Nature, 509, 63, doi: 10.1038/nature13253 Snellen, I. A. G., de Kok, R. J., de Mooij, E. J. W., & Albrecht, S. 2010, Nature, 465, 1049, doi: 10.1038/nature09111 Snellen, I. A. G., de Kok, R. J., le Poole, R., Brogi, M., & Birkby, J. 2013, ApJ, 764, 182, doi: 10.1088/0004-637X/764/2/182 Swain, M. R., Estrela, R., Roudier, G. M., et al. 2021, AJ, 161, 213, doi: 10.3847/1538-3881/abe879 Tamura, M., Suto, H., Nishikawa, J., et al. 2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 8446, Ground-based and Airborne Instrumentation for Astronomy IV, ed. I. S. McLean, S. K. Ramsay, & H. Takami, 84461T, doi: 10.1117/12.925885 Tamuz, O., Mazeh, T., & Zucker, S. 2005, MNRAS, 356, 1466, doi: 10.1111/j.1365-2966.2004.08585.x Thompson, M. A., Telus, M., Schaefer, L., et al. 2021, Nature Astronomy, 5, 575, doi: 10.1038/s41550-021-01338-8 Trifonov, T., Caballero, J. A., Morales, J. C., et al. 2021, Science, 371, 1038, doi: 10.1126/science.abd7645 Tsiaras, A., Rocchetto, M., Waldmann, I. P., et al. 2016, ApJ, 820, 99, doi: 10.3847/0004-637X/820/2/99 Vanderspek, R., Huang, C. X., Vanderburg, A., et al. 2019, ApJL, 871, L24, doi: 10.3847/2041-8213/aafb7a Vidal-Madjar, A., Lecavelier des Etangs, A., D´esert, J. M., et al. 2003, Nature, 422, 143, doi: 10.1038/nature01448 Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2 Wakeford, H. R., Lewis, N. K., Fowler, J., et al. 2019, AJ, 157, 11, doi: 10.3847/1538-3881/aaf04d Woitke, P., Helling, C., Hunter, G. H., et al. 2018, A&A, 614, A1, doi: 10.1051/0004-6361/201732193 Wunderlich, F., Godolt, M., Grenfell, J. L., et al. 2019, A&A, 624, A49, doi: 10.1051/0004-6361/201834504 Yurchenko, S. N., & Tennyson, J. 2014, MNRAS, 440, 1649, doi: 10.1093/mnras/stu326 Zhang, M., Knutson, H. A., Wang, L., et al. 2021, AJ, 161, 181, doi: 10.3847/1538-3881/abe382 Zhang, Z., Zhou, Y., Rackham, B. V., & Apai, D. 2018, AJ, 156, 178, doi: 10.3847/1538-3881/aade4f

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