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2,173 papers
This study focuses on diagnosis-related features and compares five feature selection paradigms for opioid use disorder prediction: recurrence enrichment, NTK-motivated early gradient sensitivity, LightGBM-SHAP, Elastic Net, and large language model (LLM)-guided semantic selection.
Magnetic components in high-frequency, high-power-density converters are increasingly driven by non-sinusoidal flux-density waveforms with fast transitions, minor-loop operation, dc bias, and temperature variation. Under these conditions, steady-state core-loss formulas and single-valued material curves cannot fully capture transient magnetization responses. This work proposes the Physics-Informed Hybrid Neural Operator (PI-HNO), a compact material-specific neural model with B-H energy-consistency regularization for core-loss-oriented transient magnetization prediction. Given the measured B(t)-H(t) history, the input B(t) series over the prediction interval and operating-condition information, PI-HNO predicts the H(t) series and the corresponding reconstructed B-H trajectory. The model integrates a local recurrent branch for boundary-state representation and rate-dependent response evolution with a Preisach-inspired global branch that extracts waveform-level hysteresis context. Evaluation on the MagNetX transient database using material-specific models for 14 ferrite materials demonstrates that PI-HNO achieves a compact trade-off between sequence accuracy and B(t)-H(t) energy consistency, with the mean and 95th percentile B(t)-H(t) energy consistency errors of 1.92% and 7.60%, respectively, using only 4777 trainable parameters per model. Ablation studies further demonstrate that the local, global, and energy-aware regularized components provide distinct contributions to transient magnetization prediction.
This work investigates the expressiveness of general query/key rotations and finds that any normalized continuous positive-definite attention modulation function can be approximated by random rotations induced by its own Fourier transform, which is term Random Fourier Rotations.
GEqTrain is presented, a configuration-driven framework that separates dataset semantics, model composition, and training objectives, and GEqDiff, a generative extension based on equivariant flow matching that aims to make equivariant modeling more reproducible, extensible, and reusable.
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See, a two-stage data synthesis framework consisting of an efficient exploration stage that builds an explicit UI transition graph over screens and elements, and a graph-based synthesis stage that composes diverse multi-step trajectories via planning and controlled sampling, yields reproducible and explainable data generation.
The results suggest that low-dimensional topology can be a useful tool to guide designs of AI architectures, and generalize the results from $d = 3$ to arbitrary $d>3$.
It is shown that the asymptotic decay behavior of f is not fixed by the architecture and emerges from the coupling between the state dynamics and parameter dynamics, settling into either a collapsed regime (fast, exponential forgetting) or an extended, anti-collapsed regime (slow, power-law forgetting).
A suite of interpretability case studies are conducted, uncovering initial evidence of novel diffusion-specific phenomena such as non-chronological reasoning, token and sequence smearing, and intermediate-context reasoning, and monitorability is found, which finds that DiffusionGemma is similarly monitorable to Gemma 4.
The Standard Interpretable Model is introduced, a general theory grounded in Lagrangian mechanics that enables the deductive design of interpretable methods and offers pedagogical grounding for interpretability curricula and may shift the scientific community's perspective of a discipline that has long been fragmented.
Experimental results fully validated the high-precision screening capability of the Mos-Gen framework, a motif-aware generative collaborative framework that couples the pretrained molecular representation model Uni-Mol with a variational autoencoder (VAE) specifically tailored for the design of disulfide-containing allicin derivatives as mosquito insecticides.
Can neural networks learn algebraic rules from visual inputs, or do they merely fit observed patterns? We study this question using MNIST (or EMNIST letters) as states and modular arithmetic operations as actions in a JEPA-style world model. Standard supervised and JEPA baselines with operation embeddings achieve high accuracy on seen operations but fail to extrapolate reliably to unseen operations. We propose BRo-JEPA, a world model with a block-rotation predictor that represents arithmetic operations as rotations, resulting in the cyclic structure of modular arithmetic in latent space. By applying actions as rotations, the BRo-JEPA predictor learns the rotation angles to align the latent representations with the underlying modular structure which enables strict zero-shot operation generalization. While our best block-rotation supervised baseline reaches only 54.54% zero-shot accuracy on MNIST and 25.13% on EMNIST, BRo-JEPA with a ResNet-18 encoder achieves 99.44% and 94.35% respectively, despite being trained only on the primitive operations $\pm$1. Our results suggest that world models can learn algebraic rules when the latent transformations encode the underlying modular structure. Code is available \href{https://github.com/DL-World-Models/brojepa}{here}.
Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding and reliable decision-making. Causal discovery foundation models (CDFMs) seek to amortize this problem by mapping a dataset directly to a causal graph in a single forward pass, avoiding per-dataset testing, search, or optimization. However, existing CDFMs remain limited, often failing to consistently match strong classical methods, and we find that a key bottleneck is how causal pretraining tasks are constructed. Based on this observation, we propose TabCausal, a data-driven CDFM trained with broad causal pretraining over diverse graph priors, structural mechanisms, noise models, dimensions, sample sizes, and intervention regimes. A dynamic task construction strategy composes these causal environments into varied discovery tasks, enabling more transferable structural learning from observational and mixed-interventional data. On large-scale synthetic benchmarks, TabCausal achieves better macro-averaged performance than a diverse set of causal discovery baselines. To further bridge abstract synthetic generators and realistic causal reasoning scenarios, we introduce a protocol-guided and LLM-audited semantic causal environment benchmark, where domain-grounded SCMs generate interpretable observational and interventional datasets for out-of-distribution analysis. Across both synthetic and semantic environments, TabCausal demonstrates robust structure recovery, especially under interventional evidence, highlighting broad causal pretraining as a key ingredient for transferable amortized causal discovery.
General Science
Climate & Sustainability
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
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