arXiv:2510.23745v2 Announce Type: replace Abstract: Quantifying the uncertainty in the output of a neural network is essential for deployment in scientific or engineering applications where decisions must be made under limited or noisy data. Bayesian neural networks (BNNs) provide a framework for this purpose by constructing a Bayesian posterior distribution over the network parameters. However, the prior, which is of key importance in any Bayesian setting, is rarely meaningful for BNNs. This is because the complexity of the […]

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Many machine learning classification tasks involve imbalanced datasets, which are often subject to over-sampling techniques aimed at improving model performance. However, these techniques are prone to generating unrealistic or infeasible samples. Furthermore, they often function as black boxes, lacking interpretability in their procedures. This opacity makes it difficult to track their effectiveness and provide necessary adjustments, and they may ultimately fail to yield significant performance improvements. To bridge this gap, we introduce the Decision Predicate Graphs for Data […]

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arXiv:2602.06142v1 Announce Type: new Abstract: The phase ordering problem has been a long-standing challenge since the late 1970s, yet it remains an open problem due to having a vast optimization space and an unbounded nature, making it an open-ended problem without a finite solution, one can limit the scope by reducing the number and the length of optimizations. Traditionally, such locally optimized decisions are made by hand-coded algorithms tuned for a small number of benchmarks, often requiring significant […]

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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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This is a section from a nonfiction book I’m writing (…I guess this public announcement will now pressure me to finish it and find a publisher 😬). Thanks Anna Pryslopska for initiating the interesting Twitter discussion! It’s common knowledge that Americans use the unique date format mm-dd-yyyy, in which the month appears before the day, differently from the rest of the world. There is no clear answer to why that is, but some hypothesize that this format was […]

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arXiv:2601.17036v1 Announce Type: new Abstract: ArXiv recently prohibited the upload of unpublished review papers to its servers in the Computer Science domain, citing a high prevalence of LLM-generated content in these categories. However, this decision was not accompanied by quantitative evidence. In this work, we investigate this claim by measuring the proportion of LLM-generated content in review vs. non-review research papers in recent years. Using two high-quality detection methods, we find a substantial increase in LLM-generated content across […]

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In the training of neural networks, adaptive moment estimation (Adam) typically converges fast but exhibits suboptimal generalization performance. A widely accepted explanation for its defect in generalization is that it often tends to converge to sharp minima. To enhance its ability to find flat minima, we propose its new variant named inverse Adam (InvAdam). The key improvement of InvAdam lies in its parameter update mechanism, which is opposite to that of Adam. Specifically, it computes element-wise multiplication of […]

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arXiv:2602.21212v1 Announce Type: new Abstract: Natural disasters such as earthquakes, torrential rainfall, floods, and volcanic eruptions occur with extremely low frequency and affect limited geographic areas. When individuals face disaster situations, they often experience confusion and lack the domain-specific knowledge and experience necessary to determine appropriate responses and actions. While disaster information is continuously updated, even when utilizing RAG search and large language models for inquiries, obtaining relevant domain knowledge about natural disasters and experiences similar to one’s […]

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Differentiable simulators enable gradient-based optimization of soft robots over material parameters, control, and morphology, but accurately modeling real systems remains challenging due to the sim-to-real gap. This issue becomes more pronounced when geometry is itself a design variable. System identification reduces discrepancies by fitting global material parameters to data; however, when constitutive models are misspecified or observations are sparse, identified parameters often absorb geometry-dependent effects rather than reflect intrinsic material behavior. More expressive constitutive models can improve accuracy […]

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