arXiv:2602.07057v1 Announce Type: new Abstract: Urban design profoundly impacts public spaces and community engagement. Traditional top-down methods often overlook public input, creating a gap in design aspirations and reality. Recent advancements in digital tools, like City Information Modelling and augmented reality, have enabled a more participatory process involving more stakeholders in urban design. Further, deep learning and latent diffusion models have lowered barriers for design generation, providing even more opportunities for participatory urban design. Combining state-of-the-art latent diffusion […]

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There has been a growing interest in using neural networks, especially message-passing neural networks (MPNNs), to solve hard combinatorial optimization problems heuristically. However, existing learning-based approaches for hard combinatorial optimization tasks often rely on supervised training data, reinforcement learning, or gradient estimators, leading to significant computational overhead, unstable training, or a lack of provable performance guarantees. In contrast, classical approximation algorithms offer such performance guarantees under worst-case inputs but are non-differentiable and unable to adaptively exploit structural regularities […]

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arXiv:2601.15500v3 Announce Type: replace Abstract: In recent years, Rectified flow (RF) has gained considerable popularity largely due to its generation efficiency and state-of-the-art performance. In this paper, we investigate the degree to which RF automatically adapts to the intrinsic low dimensionality of the support of the target distribution to accelerate sampling. We show that, using a carefully designed choice of the time-discretization scheme and with sufficiently accurate drift estimates, the RF sampler enjoys an iteration complexity of order […]

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arXiv:2601.20909v1 Announce Type: new Abstract: Domain-Driven Design (DDD) is a key framework for developing customer-oriented software, focusing on the precise modeling of an application’s domain. Traditionally, metamodels that describe these domains are created manually by system designers, forming the basis for iterative software development. This paper explores the partial automation of metamodel generation using generative AI, particularly for producing domain-specific JSON objects. By training a model on real-world DDD project data, we demonstrate that generative AI can produce […]

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We apply several machine learning algorithms to the problem of anomaly detection in operational data for large-scale, high-voltage electric power grids. We observe important differences in the performance of the algorithms. Neural networks typically outperform classical algorithms such as k-nearest neighbors and support vector machines, which we explain by the strong contextual nature of the anomalies. We show that unsupervised learning algorithm work remarkably well and that their predictions are robust against simultaneous, concurring anomalies.

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arXiv:2512.01384v3 Announce Type: replace-cross Abstract: We present CLAPS, a posterior-aware conformal regression method that pairs a Last-Layer Laplace Approximation with split-conformal calibration. From the resulting Gaussian posterior, CLAPS defines a simple two-sided posterior CDF score that aligns the conformity metric with the full predictive shape, not just a point estimate. This alignment can yield substantially narrower prediction intervals at a fixed target coverage, particularly on small to medium tabular datasets where data are scarce and uncertainty modeling is […]

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The following workflow is a conclusion of the video: from Mu Li of Amazon flowchart TB next(“another paper”) subgraph p1 [“Pass 1(Selection)”] direction LR title[Title] abs[Abstract] conc[Conclusion] FaT[“Figures and Tablets”] title–abs–conc– FaT end p1.- dec{Decision}.-|”stop(low quality ornlow relation)”| next dec.-|”go on”| p2 subgraph p2 [“Pass 2(Selection)”] direction TB int_cit[“Introduction:nSome important Citations” ] method_ME[“Method:nwithout Math and Engineeringnfocus on figures and tablets”] end p2.-dec2{Decision} dec2.-|”too hard to understand,n go to one ofn the citationsnin introduction”

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Neural operators have emerged as powerful surrogates for the solution of partial differential equations (PDEs), yet their ability to handle general, highly variable boundary conditions (BCs) remains limited. Existing approaches often fail when the solution operator exhibits strong sensitivity to boundary forcings. We propose a general framework for conditioning neural operators on complex non-homogeneous BCs through function extensions. Our key idea is to map boundary data to latent pseudo-extensions defined over the entire spatial domain, enabling any standard […]

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