Science & Mathematics

Ammonia and hydrogen are major constituents of planets within and beyond the solar system, yet how they interact in planetary envelopes is poorly known. We perform large-scale two-phase coexistence simulations of the ammonia-hydrogen system from 500 to 4000 K and 0.1 to 100 GPa, driven by machine-learning interatomic potentials trained on density functional theory calculations. From these we map the compositions of coexisting hydrogen-rich and ammonia-rich phases, and the critical curve that separates conditions of complete miscibility from those of phase separation. The critical temperature increases with pressure, from 500 K at 0.1 GPa to 2000 K near 30 GPa, and we find no phase separation at 3000 and 4000 K up to 100 GPa. The slope of the critical curve decreases substantially between 0.3 and 30 GPa, and the curve nearly flattens above about 30 GPa and 2000 K, where molecules begin to give way to atomic species. The coexistence curves are asymmetric with an ammonia-rich critical composition. For the expected envelope compositions, the phase boundary lies close to, and intersects, published thermal profiles of Uranus and Neptune, implying ammonia may be raining out in these planets today. In contrast, in warm sub-Neptunes such as TOI-270 d, ammonia and hydrogen are likely miscible in all proportions. Together with previous studies, our results suggest that silicates, water and ammonia separate from hydrogen in sequence as planets cool, with implications for their compositional, thermal and structural evolution.

We encounter a system through its responses, yet its state concerns how the system is. Predicting further responses promises knowledge beyond what is immediately observed. What that knowledge tells us about the system's state depends on what we mean by a state in the first place. We give this notion a model-relative meaning: a state is the complete response profile over a specified query universe, and configurations agreeing on every query are regarded as the same state. From this perspective, a learned state is understood through the distinctions between these profiles that it retains. Prediction concerns selected observations; recovery concerns the profile as a whole. We examine their relationship through query ambiguity, state-space geometry, and the choice of prediction targets. Synthetic systems with known response laws and neural recordings provide complementary settings for this investigation. The aim is to clarify how prediction can become a way of knowing a system, and what it would mean for a learned state to preserve the state being studied.

How learning progresses over time and varies across individuals is a longstanding question in cognitive and behavioral science. However, a major challenge is identifying observable behavioral measures that faithfully capture learning progression. Existing approaches commonly rely on predefined behavioral features or task-level performance, while fine-scale posture and movement in freely behaving animals remain less explored. In this paper, we ask: can short-term movement trajectories reveal learning progress and individual identity? We introduce a self-supervised representation learning framework built on a simple hypothesis: behavioral characteristics associated with identity and learning remain relatively stable over nearby time intervals. Using temporal proximity as supervision, our framework learns compact representations directly from movement trajectories without manually defining behavioral states or kinematic features. Using behavior data from freely behaving rats, we show that the learned representations not only improve prediction of identity and learning-related readouts over existing methods, but also provide a tool for investigating broader scientific questions about when and how these signals appear in behavior. Specifically, trajectories as short as one second contain informative learning and identity signals. Egocentric pose dynamics are more predictive of identity, whereas allocentric motion is more predictive of learning-related variation, and both signals are strongest at decision junctions in the alternation cohort. We further use a representation-conditioned diffusion model to inspect movement patterns associated with identity and learning progress. Our findings establish short-term movement as an informative behavioral "fingerprint" for studying learning and individual differences. Our code is available at https://github.com/pizhn/TRAIL.

Reliable contact establishment remains a critical challenge for autonomous on-orbit servicing (OOS), particularly when interacting with non-cooperative targets. Gecko-inspired dry adhesives provide a lightweight, passive, and residue-free solution for robotic capture in space, yet current systems operate in open loop, without the ability to estimate their own contact or adhesion state during interaction. This paper presents a real-time interaction-awareness framework that enables sensorized gecko-inspired adhesive interfaces to estimate their contact state throughout the adhesion lifecycle using embedded capacitive sensing. Five discrete interaction states (idle, proximity, contact, engaged, and disengaged) are classified using experimentally collected and augmented sensor data, enabling a comparative evaluation of six lightweight machine learning models suitable for embedded deployment. Among the evaluated models, Random Forest achieved the highest offline classification accuracy of 95.65%. For online deployment, combining Random Forest with a rule-based state machine improved accuracy from 78.02% to 93.14%, achieving a macro F1-score of 0.9343 while maintaining a 53 ms inference cycle. These results demonstrate that incorporating physics-consistent state constraints significantly improves robustness during realtime inference, representing a step toward interaction-aware capture in future autonomous orbital servicing missions.

Data selection plays a central role in modern large language model training, as the quality of training data strongly influences downstream performance. A common approach is to curate a development set that encodes the desired skills (e.g., reasoning or multilinguality) and select data that are similar to them. Existing methods compute similarity using cosine distance over either the model's hidden states or gradients. However, we observe that this max-similarity strategy induces a mode-seeking bias: the selected data clusters tightly around the development samples. To address this, we propose ICLHerd, a data selection framework that leverages the von Mises-Fisher kernel to balance mode-seeking with mode-averaging. We further introduce in-context learning as a validation proxy, enabling efficient hyperparameter search over the balance trade-off. Results show that ICLHerd outperforms existing LLM data selection algorithms, highlighting the importance of balancing mode-seeking and mode-averaging.

Rising temperatures due to climate change will reduce power system transmission capacity. This reduction will interact with an ongoing shift from static to time-varying transmission rating standards. Previous research has yet to disentangle the effects of changing rating standards and climates on investment decisions. We quantify transmission investment needs for the Western U.S. power system (WECC) through mid-century under climate change and three rating standards: static ratings (SLR), ambient-adjusted ratings (AAR), and dynamic line ratings (DLR). By embedding alternative rating standards and high-resolution future weather data into a generation and transmission expansion model, PyPSA-USA, we find WECC will lose 1-3% of its transmission capacity across potential future climates under static ratings by mid-century, necessitating a 1-5% increase in transmission investment. Adoption of AAR or DLR eliminates most (81-99%) of the adaptation-driven transmission capacity needs, and further reduces aggregate transmission investments (by up to 31%). Additionally, we demonstrate that planning with time-varying ratings does not increase investment variability for individual lines under climate uncertainty relative to planning with static ratings. Our results emphasize the value and practicality of considering alternative rating standards in climate adaptation planning.

Runtime Verification typically assumes a fixed alphabet of observable actions. This assumption becomes fragile in evolving software systems, where updates, reconfigurations, or architectural changes may rename, split, or fuse actions while preserving the system's high-level intent. In these cases, previously valid monitors and specifications may become misaligned with the running system. In this paper, we study runtime semantic discovery, namely the problem of identifying whether newly observed actions play the same operational role as previously known ones, without reconstructing a global behavioral model. To this end, we propose a local and runtime-computable notion of action similarity based on the neighbor contexts in which actions occur within execution traces. We define a family of k-neighbor equivalences and use them to compare systems whose observable alphabets differ. Our approach is intentionally lightweight: it does not aim to fully characterize behavioral preservation, but rather to identify candidate semantic correspondences between actions in evolving systems. We discuss the expressive power and limitations of neighbor-based equivalence and show how inferred correspondences can support the adaptation of runtime monitors and specifications under selected classes of alphabet evolution.

Few-step trajectory distillation aims to learn accurate global updates from dense local supervision along a teacher's sampling trajectory. Existing approaches construct within-step dynamics using specific trajectory parameterizations or discrete interval predictions, tying the learned update to a prescribed temporal structure. We propose \method, which directly learns a continuous trajectory primitive representing cumulative displacement along the teacher trajectory. By supervising finite differences of the primitive with local teacher velocities, \method learns the accumulated motion directly, while obtaining global updates through endpoint differences. This formulation avoids prescribing a temporal basis or fixed query grid and instead learns the temporal evolution of cumulative displacement from local supervision. We further establish a local-to-global error bound to characterize how local regression errors affect full-interval displacement accuracy, and introduce endpoint absorption to eliminate the auxiliary primitive branch while preserving one-network-evaluation inference. Experiments on large-scale text-to-image generation show that \method achieves a T-FID of 20.92 and an HPSv2 score of 28.35 at one NFE, improving both metrics over a trajectory-distillation baseline. After endpoint absorption, it retains comparable performance, achieving a T-FID of 20.86 and an HPSv2 score of 28.33.

This mostly expository note studies the sample-noise regimes in which the testing problem for noisy $k$-XOR in dimension $n$ can be efficiently solved. Concretely, we ask what pairs $(β, δ)$ admit algorithms running in time $n^{O(k)}$ where $m = n^β, β\in [0,k]$ is the number of samples and $\mathsf{Bern}(n^{-δ}),δ\in (0,+\infty)$ is the noise distribution. The current state-of-the-art algorithms are: I. A folklore algorithm when $β> 1, δ>1$ based on Gaussian elimination; II. A folklore algorithm when $β\ge k/2$ based on the birthday paradox; III. A recent algorithm in the regime $β> δ+ k(1-δ), δ\in (0,1)$ based on a reduction to the first algorithm [DJ24,CSZ24]. IV. Extending [AF09], we strictly improve the last regime to $β> δ+ (k-1)(1-δ), δ\in (0,1).$ These algorithms correspond to efficiently finding short cycles in uniform hypergraphs and can be equivalently phrased as algorithmic Moore bounds.

As language-model agents grow more capable, they can carry more of a task without asking anyone for help. Falling reliance on a human partner is easy to read as a failure of collaboration when it may instead be a correct division of labor, and the rate of human involvement alone cannot tell the two apart. We study this systematically by placing an agent and a person under a shared objective while varying who holds the advantage: the human has a clear advantage, neither does, or the agent does. To obtain this control, we introduce a shared-control maze, in which an agent-controlled and a human-controlled avatar share the objective of reaching a common target in as few team turns as possible. The agent allocates each team turn between acting itself and handing the move to the person, and initial placement sets the human's shortest-path advantage while the task is otherwise unchanged. We evaluate GPT~5.6~Sol, Command~A+, Qwen~3.8~27B, and Command~R, first measuring solo capability with a single avatar and full-map observability, then running co-play with human participants across the three advantage conditions. Across the four models, stronger solo performance is associated with selective, route-conditioned human involvement: the models that solve the maze reliably on their own act for themselves almost without exception when the human holds no advantage, and involve the human on roughly $80\%$ of initial decisions when the human route is shorter. Command R, the model with substantially weaker solo performance, shows no such selectivity.

Health-related discussions on social media are increasingly shaped by influencers who offer diverse, and often competing perspectives on the same issue. Yet, such content is typically evaluated in isolation, without considering the broader discourse in which it appears. We introduce GRID (Grounded Reasoning over Interconnected Discourse), a framework for evaluating influencer content through collective inference over the surrounding discourse. GRID represents each video as an argumentation graph connecting claims to its central message, or takeaway, links related claims and takeaways across videos, grounds claims in scientific evidence, and jointly infers credibility over the resulting network. We evaluate GRID on an end-to-end health-related content validation task using 569 influencer videos grounded in expert assessments. Our results show that accounting for the broader discourse improves influencer content validation. Finally, we use GRID-inferred credibility to demonstrate how argumentative strategies and moral framing differ between low- and high-credibility content.

As two most prevalent neurodegenerative diseases (NDDs), Alzheimer's disease (AD) and Parkinson's disease (PD) shared commonalities both in symptomology and pathophysiology. However, to what extent and in what way these two distinct disorders overlap remain underexplored. In this study, we propose an explicit, interpretable and spatially adaptive transfer learning framework to elucidate the morphometric biomarkers linked to AD and PD. We applied such high-dimensional machine learning model to two most popular open-source neuroimaging datasets, i.e., Alzheimer's Disease Neuroimaging Initiative (ADNI) and Parkinson's Progression Markers Initiative (PPMI). On the one hand, our cross-disorder classifier revealed neuro-anatomical alteration patterns well aligning with previous neuroimaging research findings and underscoring patients' clinical presentations. On the other hand, the spatially adaptive trained classifier using shared prior achieved statistically superior diagnostic performance compared to respective individual models, which further highlights the cross-disorder framework's efficacy on modeling limited-size datasets across disease domains.

Neural networks (NNs) are increasingly deployed on edge devices such as mobile and IoT platforms, offering low latency and improved privacy compared to cloud-based deployment. However, on-device models embedded in mobile applications can be unpacked and exposed to adversaries, enabling cross-node attacks in centrally distributed ecosystems, where adversarial examples crafted on one compromised device transfer effectively to other nodes sharing the same models. To address this threat, we propose VeilNN, a defense framework that reduces adversarial transferability across on-device models via model obfuscation. The core idea of VeilNN is to introduce controlled model diversity while preserving original task functionality. Specifically, VeilNN applies stealthy and irreversible obfuscation at both the architectural and parameter levels through network cell obfuscation and layer assembling, and is applicable to diverse NN architectures, including MLPs, CNNs, and Vision Transformers. Extensive experiments demonstrate that VeilNN effectively defends against cross-node attacks, achieving an average defense success of 49.19% and up to 81.19% across four adversarial attack types, with more than 33.16% improvement over existing defenses.

Accurate multi-horizon photovoltaic (PV) power forecasting is essential for grid stability, energy scheduling, and the efficient operation of PVbatterygrid systems. However, existing models often fail to jointly capture short-term variability and long-term temporal patterns, while lacking unified multi-horizon capability and interpretability. To address these limitations, this study proposes a novel hybrid deep learning framework that integrates a Temporal Fusion Transformer (TFT) and N-BEATS within an attention-based fusion architecture. The proposed model leverages TFT to learn short-term temporal dependencies and dynamic feature importance, while N-BEATS captures long-term trend and seasonal components through interpretable basis decomposition. An adaptive attention mechanism dynamically fuses both branches, enabling balanced multi-scale learning. The framework simultaneously generates forecasts for 30-minute, 2-hour, 6-hour, 12-hour, and 24-hour horizons within a single unified pipeline. The model is evaluated using multi-year real-world datasets from the Desert Knowledge Australia Solar Centre (DKASC). Experimental results demonstrate superior performance over statistical, machine learning, and deep learning baselines. The proposed model achieves RMSE values of 42.35, 46.27, 52.29, and 48.11 across the 20162017, 2023, 2024, and 20252026 datasets, respectively, corresponding to a 3540% reduction compared to the best baseline (TFT). Additionally, MAPE remains below 5% across all datasets, indicating high predictive accuracy and stability. The model also provides interpretability through attention visualization and trendseasonality decomposition, and is deployed on a cloud-based platform for real-time forecasting. These results demonstrate that the proposed framework is robust, interpretable, and suitable for practical renewable energy applications.

Object: Motion artefacts remain a major limitation of routine two-dimensional T2-weighted turbo spin-echo (TSE) brain MRI, reducing diagnostic confidence and often necessitating repeated acquisitions. MoSAIK (Motion Search with AI in K-space) retrospectively estimates in-plane rigid-body motion directly from standard multi-coil TSE k-space. Without a motion-free reference, external navigators, or additional acquisitions, MoSAIK infers motion from k-space self-consistency violations and incorporates predicted parameters into model-based, motion-aware reconstruction rather than synthesizing corrected images. Materials and Methods: Selected complex coil data were represented as log-magnitude and phase channels. A decoupled convolutional embedding and Transformer encoder estimated phase-encoding-line-indexed rotation and two-dimensional translations. Training used motion-free clinical acquisitions with simulated motion; four held-out subjects were evaluated. Component-wise errors assessed estimation; SSIM and PSNR in silico and entropy focus criterion (EFC) in vivo assessed reconstruction. Results: Motion estimation errors were low for both rotation (MSE $