{"date":"2026-07-27","schema_version":1,"captured_at":"2026-08-25T23:17:38.116Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"The Odyssey (2026 film)","views":676433,"rank":4,"extract":"The Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. 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use in external game engines","points":196,"comments":24,"url":"https://github.com/libsm64/libsm64","discussion":"https://news.ycombinator.com/item?id=49067352"},{"title":"Astronauts describe persistent 'observer' sensation after 6 month missions","points":183,"comments":111,"url":"https://spacedaily.com/sd-v-astronauts-returning-from-six-month-missions-describe-a-persistent-observer-sensation-the-feeling-of-watching-their-own-lives-from-a-half-step-outside-the-frame-weeks-after-theyr/","discussion":"https://news.ycombinator.com/item?id=49076900"},{"title":"Hamburg's Stadtpark: A Park Built to Be Used","points":171,"comments":60,"url":"https://alsterrunde.com/hamburgs-stadtpark-a-park-built-to-be-used/","discussion":"https://news.ycombinator.com/item?id=49065785"}],"research":[{"title":"Participatory Budgeting with Project Groups","abstract":"We study a generalization of the standard approval-based model of participatory budgeting (PB), in which voters are providing approval ballots over a set of predefined projects and---in addition to a global budget limit, there are several groupings of the projects, each group with its own budget limit. We study the computational complexity of identifying project bundles that maximize voter satisfaction while respecti","authors":["Jain, Pallavi","Sornat, Krzysztof","Talmon, Nimrod","Zehavi, Meirav"],"url":"https://arxiv.org/abs/2012.05213","arxiv_id":"2012.05213","published":"2026-07-27"},{"title":"Gradient flows on graphons: existence, convergence, continuity equations","abstract":"Wasserstein gradient flows on probability measures have found a host of applications in various optimization problems. They typically arise as the continuum limit of exchangeable particle systems evolving by some mean-field interaction involving a gradient-type potential. However, in many problems, such as in multi-layer neural networks, the so-called particles are edge weights on large graphs whose nodes are exchang","authors":["Oh, Sewoong","Pal, Soumik","Somani, Raghav","Tripathi, Raghavendra"],"url":"https://arxiv.org/abs/2111.09459","arxiv_id":"2111.09459","published":"2026-07-27"},{"title":"Stochastic optimization on matrices and a graphon McKean-Vlasov limit","abstract":"We consider stochastic gradient descents on the space of large symmetric matrices of suitable functions that are invariant under permuting the rows and columns using the same permutation. We establish deterministic limits of these random curves as the dimensions of the matrices go to infinity while the entries remain bounded. Under a ``small noise'' assumption the limit is shown to be the gradient flow of fun","authors":["Harchaoui, Zaid","Oh, Sewoong","Pal, Soumik","Somani, Raghav","Tripathi, Raghavendra"],"url":"https://arxiv.org/abs/2210.00422","arxiv_id":"2210.00422","published":"2026-07-27"},{"title":"MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting","abstract":"Accurate forecasting of renewable generation is crucial to facilitate the integration of Renewable Energy Sources into the power system. Focusing on photovoltaic (PV) units, forecasting methods can be divided into two main categories: physics-based and data-based strategies, with Artificial Intelligence (AI)-based models providing state-of-the-art performance. However, while these AI-based models can capture complex ","authors":["Tortora, Matteo","Conte, Francesco","Natrella, Gianluca","Soda, Paolo"],"url":"https://arxiv.org/abs/2306.10356","arxiv_id":"2306.10356","published":"2026-07-27"},{"title":"Exact Solution to Data-Driven Inverse Optimization of MILPs in Finite Time via Gradient-Based Methods","abstract":"A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed optimal-solution data, and it arises in many applications, including mixed integer linear programming (MILP). In inverse optimization for MILPs, the prediction error of the features is discontinuous with respect to the weights, so applying gradient-based optimization direct","authors":["Kitaoka, Akira"],"url":"https://arxiv.org/abs/2405.14273","arxiv_id":"2405.14273","published":"2026-07-27"},{"title":"Highly Versatile FPGA-Implemented Cyber Coherent Ising Machine","abstract":"In recent years, quantum Ising machines have drawn a lot of attention, but due to physical implementation constraints, it has been difficult to achieve dense coupling, such as full coupling with sufficient spins to handle practical large-scale applications. Consequently, classically computable equations have been derived from quantum master equations for these quantum Ising machines. Parallel implementations of these","authors":["Aonishi, Toru","Nagasawa, Tatsuya","Koizumi, Toshiyuki","Gunathilaka, Mastiyage Don Sudeera Hasaranga","Mimura, Kazushi","Okada, Masato"],"url":"https://arxiv.org/abs/2406.05377","arxiv_id":"2406.05377","published":"2026-07-27"},{"title":"Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning","abstract":"Conventional uncertainty-aware temporal difference (TD) learning often models TD errors as zero-mean Gaussian. This assumption can miss the heavy-tailed and heteroscedastic residuals induced by bootstrapping and exploration. We introduce a state-conditioned shape head based on the Generalized Gaussian Distribution (GGD) and use a numerically modified GGD loss as an online surrogate for nonstationary TD residuals. We ","authors":["Kim, Seyeon","Lee, Joonhun","Cho, Namhoon","Han, Sungjun","Hwang, Wooseop"],"url":"https://arxiv.org/abs/2408.02295","arxiv_id":"2408.02295","published":"2026-07-27"},{"title":"Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models","abstract":"Numerically solving a large parametric nonlinear dynamical system is challenging due to its high complexity and the high computational costs. In recent years, machine-learning-aided surrogates are being actively researched. However, many methods fail in accurately generalizing in the entire time interval $[0, T]$, when the training data is available only in a training time interval $[0, T_0]$, with $T_0