Science & Mathematics

Hardware components communicate through clock-cycle-sensitive protocols. Hardware Description Languages (HDLs), however, provide limited support for expressing these protocols or for checking that independently developed components agree on them. When components disagree, the composed design can perform unsynchronized exchanges or stop making progress. We call these failures communication-safety violations. Communication safety is thus a global property, determined by interactions between components. This makes it difficult to check modularly. This paper presents Pact, a typed extension of Anvil for modular verification of communication safety in synchronous hardware. Pact types describe message-passing, control-flow, and clock-cycle behaviour. Each component is checked against its type. Compatible types guarantee that the components compose into a communication-safe system. Pact also supports incremental design. It can check a subsystem before the rest of the system exists. The check produces a summary type that captures the communication behaviour left for future compositions. To reduce annotation burden, Pact infers types by abstracting payload values while preserving communication, timing, control, and fairness. These inferred types are optimal for the checker: whenever some checker-accepted types can prove compatibility, the inferred types can prove it as well. Type inference and type checking are decidable, and compatibility is decided by a sound and complete algorithm. We implement Pact by extending the Anvil compiler and evaluate it along five dimensions: detecting communication errors in incremental design, expressiveness on ten clock-cycle-accurate translations of open-source hardware IPs, compatibility-checking time, generated hardware overhead, and annotation burden. Pact expresses realistic protocols and verifies all ten benchmarks in reasonable time.

Masked language models (MLMs) such as BERT are typically used for representation learning despite their denoising pretraining. We test whether this capability supports conditional generation without diffusion training. Our decoder repeatedly predicts masked tokens and commits high-confidence predictions using a cosine schedule. It requires no diffusion timestep or reverse-diffusion objective. Frozen BERT already exhibits substantial denoising capability, reconstructing 61.0%, 37.5%, and 24.4% of masked tokens at 15%, 45%, and 60% corruption, respectively, although generation deteriorates sharply as visible context disappears. This capability alone does not yield task-directed generation: frozen BERT reaches only 11.0% on ARC-Easy and 4.2% on MMLU. Ordinary MLM adaptation, however, rapidly converts the same architecture into a usable generator. Full adaptation reaches 28.2% and 24.6%, respectively, while parameter-efficient adaptation substantially reduces unmatched outputs. On open-ended conditional generation, full MLM adaptation reaches BLEU/ROUGE-L of 0.163/0.583 on QQP and 0.107/0.327 on Quasar-T, placing it in the same broad performance range as published BERT models trained through diffusion without explicit diffusion training. Finally, applying the same generative interface to stronger masked representations substantially improves performance: RoBERTa-large reaches 44.2% on ARC-Easy and 33.8% on MMLU. These results separate three components of masked iterative generation: pretraining supplies the representation and denoising operator, task adaptation determines what the model generates, and an iterative commitment rule supplies the decoding process. Explicit diffusion training is therefore not necessary to obtain effective iterative conditional generation from pretrained MLMs in the settings studied here.

Background: Existing dementia risk scores rely on fixed, pre-selected features and linear models, or ignore bias from informative missingness in cohort data. Here we introduce the Istanbul Technical University Dementia Risk Score (ITU-DESC), an explainable machine-learning score for incident all-cause dementia that combines broad feature selection across roughly 8,000 candidate features with explicit control of missingness bias. Results: We studied approximately 500,000 UK Biobank participants with a median follow-up of 16.6 years. We withheld missingness patterns from the model using unconditional random hot-deck imputation. We stabilized feature selection by repeating BorutaSHAP-based feature selection with ten random-imputation seeds and selecting predictors chosen in at least eight of the ten seeds, which yielded 38 predictors. We trained a CatBoost gradient-boosted decision-tree model and evaluated it on a holdout test set of 50,041 participants. For all-cause incident dementia, ITU-DESC achieved an average precision of 0.138 (95% confidence interval 0.123-0.156) and an area under the receiver operating characteristic curve (ROC AUC) of 0.853 (0.844-0.863), significantly outperforming other risk scores, including the strongest comparator, the UK Biobank Dementia Risk Prediction model (0.118 and 0.841). We identified dementia risk predictors absent from established scores, including leukocyte telomere length and liver enzymes. Conclusions: ITU-DESC is the first dementia risk score to combine broad feature selection with explicit control of missingness bias.

We develop an uncertainty-aware, operator-learning-based surrogate for tendon-driven continuum robots (TDCRs) that generalizes across robot design and actuation while predicting the complete equilibrium configuration. Our central idea is a nonstationary Gaussian-process (GP) kernel whose metric is learned by a shallow neural network from mechanics-aware design coordinates. The network learns how similarity changes across design space, while the resulting kernel defines the design-to-pose operator, its posterior uncertainty, and a criterion for sequential simulator acquisition. We represent backbone position and rotational strain with spatial kernels and reconstruct orientation directly on SO(3), preserving valid material frames and tendon geometry. We use ASkotch with Nystrom preconditioning for scalable kernel operator solves and a separate Nystrom approximation for posterior covariance. Experiments show that kernel-coverage acquisition reduces mean trajectory error by approximately 15% relative to IID sampling, while posterior variance increases for increasingly unsupported designs. Compared with prior neural-operator TDCR models, the method is 30-50% more accurate using approximately 5x fewer training designs while additionally providing posterior uncertainty estimates.

Electrostatic gates on two-dimensional electron gases (2DEGs) in GaAs/AlGaAs heterostructures are essential for numerous studies of correlation driven phenomena, many performed within the fractional quantum Hall effect. However, the most commonly used type of gates, the metallic Schottky gate, has the propensity to adversely impact the electron mobility and suppress the fractional quantum Hall states. Here we demonstrate through commensurability oscillations under specially designed nanostructured superlattice gates, that strain effects are responsible for the mobility reduction. The experimental results agree well with COMSOL Multiphysics simulations. In a regular, unpatterned gate the disordered, granular nature of the metal film fills the same role as the superlattice in creating a strain field penetrating down to the 2DEG, as we show with a simplified model. These results will help in understanding, improving and finding novel methods to reduce strain-induced disorder in gated high-mobility 2DEGs.

HPC facilities collect years of job scheduler records, yet much of this operational data remains underused because accessible tools for turning it into actionable insight are scarce. We present CQTwin, an open-source digital-twin toolkit for operational analytics of HPC scheduling data. CQTwin addresses this gap through two complementary modes: offline trace replay and online event-driven simulation. Built on the Python-based CQSim discrete-event simulator, CQTwin combines a fidelity-validated, high-performance C++ engine with an interactive browser-based dashboard and a Snapshot Lab, enabling facility staff, operators, and researchers to characterize workloads, compare scheduling policies, and explore what-if scenarios. As an offline twin, CQTwin replays archived workload traces under alternative policies to show how the same workload would have unfolded under different scheduling decisions. As an online twin, it consumes job events as they arrive and projects how alternative policies would process the currently queued jobs, providing guidance for real-time decision-making. The dashboard supports the complete analysis workflow, including workload characterization, experiment configuration and monitoring, and multidimensional comparison of simulation results, allowing users to determine visually which policies best suit a workload and what trade-offs they entail. We validate CQTwin using production traces from the Argonne Leadership Computing Facility spanning 2016-2023.

Piezoelectric nanogenerators (PENGs) are an attractive platform for self-powered mechanical-energy harvesting, but their output is often limited by depolarization fields, charge leakage, and inefficient stress transfer to the active dipoles, particularly in polymer-dominated composites. Here we report HoFeO3-BaTiO3 (HFO-BTO) nanofiber heterostructures prepared by alternating sol-gel electrospinning, in which orthorhombic HoFeO3 (Pbnm) and tetragonal BaTiO3 (P4mm) coexist in intimate contact. Rietveld-refined X-ray diffraction, high-resolution transmission electron microscopy, and Raman spectroscopy indicate interfacial lattice distortion. Polarization-electric field loops show that the heterostructure retains a polarization comparable to that of BaTiO3 (16.39 vs 15.80 microcoulombs per square centimeter at zero field) while its switching-current maximum moves to a higher field (about 7 vs about 3.5 kilovolts per centimeter), consistent with strain-stabilized ferroelectric domains. When the nanofibers are encapsulated in polydimethylsiloxane (PDMS) and excited under cyclic impact, the device produces a hybrid output: the piezoelectric response of the strained BaTiO3 is supplemented by a triboelectric contribution from contact electrification. The HFO-BTO device delivers open-circuit voltage peaks of about +6 volts and -8 volts and a power density of 24.31 microwatts per cubic centimeter at a 4.7 megaohm load. This is 2.4 times the value for an identically fabricated BaTiO3 device (10.00 microwatts per cubic centimeter) and 8.7 times that of the HoFeO3 device (2.78 microwatts per cubic centimeter). These results link interface-driven lattice distortion to improved electromechanical output and identify all-oxide nanofiber heterostructures as a promising route to lead-free energy harvesting.

Neural networks that respect the symmetries of point clouds commonly compute high-order representations of the rotation group to improve their expressivity. Such models are known to be universal, and thus able to separate any pair of point clouds, when using representations of sufficiently high order, yet no precise bound on this order, as a function of the number of points, was previously known. We establish the first quadratic lower and upper degree bounds for polynomials that separate point clouds of n points up to rotations and permutations. By showing Tensor Field Networks and MACE can express all invariant polynomials of a given degree with sufficient representation order, we additionally derive the first explicit, quadratic bound on the representation order sufficient for these architectures to be complete, replacing previous existence results with concrete guarantees. We further show that, under local regularity assumptions, point cloud separation can be achieved with a message-passing-based model using invariants whose degree depends only on the size of a local neighborhood, rather than on the size of the full point cloud. Finally, we provide counterexamples showing MACE must compute high order irreducible representations to separate locally regular point clouds.

Extracting insights from large audio data sets using human transcription is expensive and time-consuming. Off-the-shelf Automatic Speech Recognition (ASR) systems offer an easy-to-use scalable alternative, but systematic transcription errors, especially in domain-specific settings, can bias downstream analyses and lead to unreliable conclusions. We investigate whether imperfect ASR transcripts can still be used to obtain statistically valid keyword frequency estimates by combining model outputs with a small set of human-labeled examples, following Prediction Powered Inference and Design-based Supervised Learning frameworks. We evaluate this approach on police radio communications from Baltimore and Chicago, a domain in which keyword distributions could provide valuable insights into police activity, but off-the-shelf ASR systems exhibit systematic errors on operationally important terminology. Using bootstrap-based confidence interval coverage as our evaluation metric, we find that our approach substantially increases the number of domain-specific keywords that can be analyzed with statistically valid confidence intervals, from 43.0% using ASR alone and 27.2% using only a small set of human-labeled transcriptions to 56.2% in the Baltimore dataset, with similar improvements observed in Chicago data. Our results demonstrate that post-hoc error correction can improve statistical validity of downstream analyses even for poor-quality transcriptions, offering an avenue for better leveraging off-the-shelf ASR models for large-scale speech analysis.

Indirect and noisy observations are difficult to invert when the forward operator smooths out the unknown signal. In this case, the unstable modes are precisely the modes that the data reveals least, thus the success of any reconstruction method depends on how prior information interacts with the geometry of the operator across regularity scales. Diffusion priors have become ubiquitous for such problems; however, the current theory still does not determine when a learned score remains informative under ill-posed forward operators. In this paper, we study posterior diffusion for linear inverse problems on Hilbert scales and derive a stability theory under an explicit score-amplification condition. Under a smoothing condition on the forward map, a source condition on the truth, and weak-norm control of score error, we derive non-asymptotic recovery bounds in \(\Hs^s\) that separate observation noise, score approximation, and reverse-time discretization. These bounds give a recoverable regularity threshold determined jointly by operator smoothing, truth regularity, score regularity, and the posterior correction. The threshold specifies the Hilbert-scale norms \(\Hs^s\) in which weak-norm score error remains controlled after propagation through the posterior reverse dynamics. For larger regularity indices \(s\), the inverse geometry magnifies the same error, and the Hilbert-scale recovery estimate no longer gives uniform stability. We also prove that a posterior correction adapted to the forward operator improves stability relative to isotropic guidance in the regularity ranges where the smoothing action of the forward map matters most. The recovery estimates give a regularity-sensitive theory of posterior diffusion and show how learned score error, operator smoothing, and reconstruction norm jointly determine the stable range predicted by the analysis.

We present multilayer social network data from 50 communities, located in 29 countries around the world, gathered as part of the "ENDOW" project. The communities show exceptional diversity, from fishers in Bangladesh and Vanuatu, to horticulturalists in Fiji and Bolivia, to pastoralists in Namibia and Argentina, to farmers and wage labourers in India and Colombia. Data collection embraced this variation, while still aiming for comparability. Respondents answered ten network prompts on behalf of their "sharing unit" (household), covering different types of material and immaterial support. From these, we construct networks representing flows of support between sharing units in each community. We compare nomination patterns and network summary statistics across the ENDOW communities and to other benchmark datasets. The summaries exhibit considerable heterogeneity, suggesting a need to revisit existing ``rules of thumb'' about social network structure. These measures contribute novel reference points for research on the diversity of human sociality and social structure.

Sleep physiology evolves throughout the night, with changes in sleep cycles, respiratory stability, and cardiovascular dynamics. However, self-supervised learning (SSL) methods for sleep typically process short, fixed-duration windows without explicitly modeling where each window occurs within the full sleep session. We introduce Global Time-Aware Conditioning (GTC), a mechanism that complements local positional encodings by conditioning latent representations on the global time-of-sleep of each input window. We integrate GTC into three representative SSL frameworks and evaluate them on five clinically relevant tasks with extreme class imbalance across two independent pediatric cohorts. GTC generally improves F1 and AUPRC across architectures and tasks, with gains persisting under cross-cohort transfer, highlighting the value of global temporal context for sleep representation learning.

The past two years have seen considerable interest in protocols for managing how AI agents interact with human users, external tools, and each other. In many ways, these protocols, including the Model Context Protocol (MCP) and Agent2Agent (A2A), mirror the suite of protocols that underlie the modern internet. Motivated by this parallel, this paper draws lessons from the development, history, and governance of internet protocols with the aim of informing the responsible development of AI agent protocols. Based on our analysis, we make two conclusions. First, if AI agent protocols do not sufficiently prioritise security in the early stages of development, they risk accumulating a security deficit that becomes increasingly costly to address over time due to practical and economic challenges. Second, current governance structures for AI agent protocols centralise decision-making power in a small number of individuals and companies, raising important questions regarding legitimacy, especially if they become critical digital infrastructure. We conclude with a discussion and implications for policymakers and developers of AI agent protocols.

Reconstructing human activity from egocentric video requires more than recovering individual 3D components: the observer's motion, surrounding geometry, and hand-object interactions must fit together in a consistent world. Achieving this coherence requires both joint reconstruction and aligned supervision, yet existing methods and datasets offer limited support for either. We present EgoXperience, a full-stack paradigm that addresses these needs through data collection, automatic annotation, unified modeling, and benchmarking. Central to EgoXperience is an automatic annotation engine, EgoX-Engine, that couples geometrically verified object tracking with SLAM-based world alignment, yielding consistent supervision for the camera, scene, hands, and objects. This yields EgoX-Data-6M, a 50-hour dataset of task-driven manipulation and locomotion within and across indoor and outdoor environments, with second-level task captions. We carefully select one hour across these activity categories as EgoX-Bench to evaluate joint reconstruction. To translate this supervision into reconstruction, we introduce EgoX, trained with EgoX-Data-6M and complementary annotations from additional public datasets. Given monocular egocentric video and first-frame object masks, our model achieves joint metric reconstruction of the camera, scene, hands, and objects in a shared world coordinate frame with a single forward pass. Extensive experiments demonstrate state-of-the-art performance across scene estimation and world-space hand-object pose recovery. Against the strongest evaluated baselines, EgoX reduces camera, hand, and depth errors by 68.9%, 65.6%, and 42.9%, and improves object pose scores by 28.0%.

We present the tool VoxLogicA2, a spatial model checker aimed at Computer Vision, with applications in high-resolution 2D and 3D medical imaging. The new version of the tool combines logical primitives, non-logical primitives, and training and prediction primitives of state-of-the-art neural networks, freely interleaved with the symbolic tool functionality. The primary application field is neuro-symbolic medical image analysis. VoxLogicA2 is a fast, parallelising spatial model checker with a declarative domain-specific language, capable of describing both the learning procedures and the spatial logic procedures of an analysis in one program. We validate our approach by verifying its improved efficiency, and by using the new constructs of the language to describe case studies of previous work, which were based on Python, with a specification that uses VoxLogicA2 only