Science & Mathematics

We study randomized committee elections with additive preferences, where we seek best-of-both-worlds proportionality. That is, we strive for strong guarantees for the selection probabilities ex ante and each realized committee ex post. We focus on structured preferences and introduce antichain single-peakedness, a restricted domain generalizing single-peaked additive preferences as well as several well-studied approval domains. Our approach first computes a fractional committee and then decomposes it through a rounding rule called systematic rounding. Among profile-oblivious rounding rules, we characterize systematic rounding as the unique rule satisfying natural quota guarantees, and thereby the only promising rule for our two-step approach. We show that ex-ante core stability is incompatible with ex-post justified representation $JR$, even under single-peaked preferences. However, applying systematic rounding to certain fractional allocations that form so-called Lindahl equilibria yields ex-ante core stability and ex-post core stability up to one. Alternatively, we retain exact ex-post proportionality and approximate the ex-ante core using two parameters: $α$ raises the utility gain required for a deviation, while $β$ reduces the coalition's proportional share. We establish the tight threshold $αβ$=2. Below it, ex-post JR is incompatible even under single-peaked preferences; at and above it, adapting a fractional rule called BestRepresentative [Pierczyński and Skowron, 2022] and applying systematic rounding to its output yields ex-ante $α, β$-fractional core stability and ex-post PJR under antichain single-peaked preferences.

The growing demand for high-capacity, fast-charging, and durable rechargeable batteries has intensified the search for advanced electrode materials, with two-dimensional materials offering large surface-to-mass ratios and short ion-diffusion pathways. Here, pristine and Co-doped Janus HfSSe monolayers were investigated as anode materials for Li-ion and Na-ion batteries using first-principles density functional theory. Pristine HfSSe was found to be dynamically stable and exhibited a 0.85 eV band gap, while Co substitution shifted the Fermi level toward the valence-band region. Adsorption of the first Li or Na layer induced metallic behavior, thereby improving charge transport. Co doping strengthened alkali-metal binding, with the most favorable Li and Na adsorption energies changing from -2.6125 and -1.9800 eV to -3.7405 and -3.0529 eV, respectively. Co doping created electron-deficient sites that enhanced Li/Na binding. Co-doped HfSSe accommodated up to four Li layers and three Na layers, increasing the theoretical capacities from 555.43 to 776.19 mAh/g for Li and from 370.29 to 582.14 mAh/g for Na. The average open-circuit voltages were 0.62 V for Li and 0.29-0.30 V for Na, with minimum migration barriers of 0.2153 and 0.1044 eV, respectively. These results identified Co-doped Janus HfSSe as a promising anode material for Li-ion and Na-ion batteries.

We develop an information-theoretic method for lower bounding the prior-predictive conditional value-at-risk (CVaR) of loss in interactive statistical decision making (ISDM). A generalized Fano inequality replaces a single error event by a randomized binary statistic constructed from a bounded transform of the loss, reducing the problem to a Bernoulli $f$-divergence inequality. A bounded hinge transform yields a direct lower bound on Bayesian CVaR under a prior fixed in advance. We derive reverse-KL specializations and obtain explicit bounds for Gaussian mean estimation and Gaussian stochastic bandits. We then impose local differential privacy (LDP) at the observation stage of the same ISDM model. LDP contraction inequalities combined with the adaptive KL chain rule give explicit Bayesian CVaR lower bounds for Gaussian mean estimation with heterogeneous privacy parameters and Gaussian $K$-armed bandits.

Digital nudges have become a prominent area of research in human-centered interaction, yet little is known about how they interact when deployed together. This study examines the individual and combined effects of anchoring, salience, and social proof digital nudges on stopping behavior and outcome quality in a sequential decision task. Using a 2^3 between-subjects factorial design grounded in the secretary problem (N = 181), we find that (1) an anchor nudge significantly increased exploration rather than constraining it (b = 1.207, p = .017); (2) the salience nudge produced the smallest effect of any condition; and (3) combining the anchor and social proof nudges produced the worst outcome quality of any condition ($181.83 vs. $192.21 for anchor-only), with outcome quality declining monotonically as nudge count increased. These findings provide the first controlled evidence for a digital nudge crowding-out effect with direct implications for the ethical design of digital interfaces.

Learned priors are used to reconstruct regions that measurements barely reach, and to report uncertainty there. They can fail silently: the reported interval excludes the truth while every ground-truth-free diagnostic stays within its noise. We call such errors the blind set and ask whether they can be predicted before the target data are seen. For a correctly specified linear-Gaussian model averaged over the prior, the risk of a silent failure factorises exactly into non-detection and non-coverage, which does not hold for learned systems in general; we build a score that approximates this reference by linearising a VAE prior. In a prospective study on real X-ray CT objects (2DeteCT) under simulated restricted access, with predictions committed by hash before any trial, the score ranked the realised blindness of two learned inference systems (latent Laplace and ensemble Kalman inversion) with Spearman $ρ= 0.79$ and 0.84 over 240 pre-specified errors. Simple structural predictors would also have passed these tests. Sequential post-hoc analyses, each specified before its own computation, show that under bounded reach the learned score gave no detectable improvement over a physics-based baseline, in rankings or trial-level log scores. At the weakest attenuation studied, prior information improved rankings over the physics-based baseline, most clearly for ensemble Kalman inversion, but a Gaussian prior did as well and the gain was not detectable at the trial level. The score is not a calibrated probability. Silent failures can be anticipated here, but their structure follows mainly from measurement access and the decision quantity rather than from the learned prior.

We study the worst-case, over priors, Bayesian conditional value-at-risk (CVaR) of cumulative pseudo-regret in time-homogeneous finite-horizon episodic Markov decision processes. A high-probability guarantee for UCBVI-BF yields a prior-predictive upper bound. A lower bound follows by a three-state MDP and applying a reverse-KL inequality. For a constant number of states, the bounds have the same polynomial dependence on horizon, actions, and episodes, with a logarithmic gap in the risk level.

Programmable Logic Controller (PLC) coding remains a manual, IDE-centric task that demands constant context switching between documentation, project files, and code editors. Recent work has demonstrated that retrieval-augmented large language models can generate vendor-compliant Structured Text at high compilation rates, yet these systems treat code generation as a one-shot pipeline: the engineer submits a query and receives code, with no persistent knowledge of the specific plant. We present a plant-aware conversational copilot that extends this paradigm in three directions: plant-specific context ingestion, a chat-first interaction model for explanation and debugging, and a graduated tool-access ladder from chat to deployment. The system is designed for real plant infrastructure and keeps the engineer in control of each step that produces real-world effects.

Effective manipulation requires a policy to capture not only about the current scene geometry, but also about how that geometry will evolve under interaction. Existing approaches either augment policies with geometric information from current observations or introduce future prediction as auxiliary supervision, leaving open how predictive geometry should be represented for action generation. Therefore, we propose a two-stage framework, which learns complementary predictive representations from multi-view video clips. During pretraining, a frozen 3D foundation model provides dense geometric features that are compressed into predictive geometric representations and optimized to preserve scene structure and its temporal evolution. Action supervision is then applied to geometric transitions to extract a complementary motor representation that emphasizes action-relevant transitions. During downstream post-training, predictive geometry is transferred through geometry queries, while the motor representation guides intermediate features along the action-generation pathway. Future observations are used only as privileged supervision during training and are not required at inference time. Across LIBERO, LIBERO-Plus, SimplerEnv, and real-world bimanual and single-arm tasks, our approach consistently improves downstream policies, including an absolute gain of 10.4 percentage points on the WidowX evaluation. We further demonstrate transfer across multiple VLA architectures and WAMs, with improvements in both future-world and action prediction. The code will be available at https://github.com/xuxiaoxxxx/CPR.

Vision-Language-Action (VLA) policies increasingly reason before acting, but existing reasoning models compress this intermediate decision into a single representation. When several plans are valid, this commitment hides alternatives that could otherwise be selected or revised. We introduce the Generative Plan Latent (GPL), which represents the reasoning state as a low-dimensional conditional distribution over plans, such as which object, grasp, or route to use. This makes the reasoning state addressable: without retraining the VLA, plans can be sampled (coverage), selected on request (steering), and reweighted by deployment outcomes (outcome adaptation). We test all three on SimplerEnv manipulation with $π_0$, and extend the analysis to RoboCasa, LIBERO, and a maze domain. Steering the plan raises the fraction of executions that follow the request from 0.48 to 0.94, while the same guidance on the native action distribution shows no reliable gain. Finally, we show that an explicit plan is most useful when it carries decision information not already determined by the observation.

The reliable and cost-effective integration of large-scale offshore wind farms into power systems is largely influenced by maintenance operations. To date, power producers primarily rely on farm-level information (e.g., health signals, power generation, weather conditions) for maintenance planning of wind turbine fleets. We propose to consider additional grid-level information in maintenance operations, such as generation curtailment and market pricing. Periods of high generation curtailment or negative electricity pricing can be viewed as opportunities for preventive maintenance, since assets are fully or partially shut down due to grid-imposed constraints. We first analytically establish that access to market information has non-negative economic value for maintenance scheduling under a stylized setting, providing a theoretical foundation for treating market and grid information as a maintenance opportunity. We then formulate a grid-informed opportunistic maintenance scheduling model as a receding-horizon mixed integer linear program that jointly leverages both farm- and grid-level signals under realistic conditions to determine a maintenance plan for offshore wind turbines. Considering a 600-MW offshore wind farm in the United States, our proposed method reduces annual maintenance costs by up to 10%, while enhancing asset availability and resource utilization, relative to advanced maintenance models that solely rely on farm-level information.

Accurate and efficient document layout analysis is critical for modern document parsing systems. However, although the YOLO framework represents a leading paradigm for the real-time detector, its adaptation to heterogeneous documents remains underexplored. To address this gap, we propose DocLayout-YOLO, a document-specialized real-time detector that integrates diverse synthetic document pretraining and page-axis context modeling. From the data perspective, we introduce MeshLayout-1M, a synthetic pretraining corpus generated by the proposed Mesh-candidate BestFit algorithm, which provides large-scale and diverse document layouts for pretraining and improves adaptation to heterogeneous document scenarios. From the model perspective, we propose Page-Axis Layout Attention (PALA), which captures long-range dependencies along the horizontal and vertical page axes while remaining computationally efficient. Extensive experiments demonstrate that DocLayout-YOLO achieves state-of-the-art performance on four heterogeneous document benchmarks, obtaining 72.4%/75.5%/96.0%/73.1% mAP50:95 on D4LA, M6Doc, HJDataset, and PRIMA-LAD, respectively. Compared with existing real-time detectors, DocLayout-YOLO establishes a superior accuracy-efficiency trade-off in document layout analysis. Code, model, and data are available at https://github.com/opendatalab/DocLayout-YOLO/tree/release-v2.

Large Language Models (LLMs) depend fundamentally on the diversity, depth, and factual rigor of their pre-training corpora. However, contemporary foundational pre-training datasets (such as The Pile, S2ORC, and Dolma) exhibit a severe structural omission: African scientific, medical, legal, and agricultural scholarship constitutes less than 0.5% of global pre-training tokens. Consequently, state-of-the-art models suffer from systematic failure modes when reasoning about tropical epidemiology, localized smallholder agro-ecologies, regional economic governance, and indigenous linguistic structures. To resolve this representation deficit and advance African intellectual sovereignty in artificial intelligence, we introduce Africa-Papers, the largest curated multi-domain Pan-African academic pre-training corpus to date. Comprising over 844,807 full-text papers spanning 10.15 billion tokens across 879 Parquet shards, Africa-Papers unifies four primary scientific streams: (1) continental peer-reviewed journals indexed in DOAJ across 31 African nations, (2) the CGIAR CGSpace agricultural repository, (3) African-authored and African-focused STEM/AI preprints on arXiv, and (4) open-access international publications by scholars affiliated with African institutions indexed via OpenAlex. The entire corpus is processed into layout-aware, clean Markdown preserving section hierarchies, mathematical equations, and tabular data, accompanied by exact BPE token counts and a standardized 8-domain taxonomy. We release Africa-Papers openly on the Hugging Face Hub under the AfriSpeech organization to accelerate continual pre-training and domain evaluation for foundation models globally.

Large Language Models (LLMs) often struggle with temporally grounded reasoning because their static parametric memory is misaligned with the dynamic evolution of real-world knowledge. Although RAG and Parameter-Efficient Fine-Tuning (PEFT) provide practical ways to enhance LLMs, they remain insufficient for temporal reasoning. Existing retrieval methods typically prioritize semantic relevance while overlooking temporal validity, event order, and evolutionary dependencies, resulting in evidence that is relevant in isolation but temporally fragmented. Meanwhile, conventional PEFT methods usually employ fixed and input-agnostic low-rank updates, making it difficult to capture time-dependent entity semantics and relation drift. To address these limitations, we propose Temporal Evidence-Parameter Co-Alignment (TEPCA), a framework for LLM reasoning over TKGs. TEPCA aligns LLM reasoning with temporal knowledge at both the evidence and parameter levels. At the evidence level, it introduces time-constrained Dynamic Relevance-Guided Path (DRGP) pruning to preserve globally coherent temporal paths, together with Mutual-Information (MI)-based pruning to retain query-informative local facts under temporal constraints. At the parameter level, it integrates Dynamic-Gated LoRA (DG-LoRA), a timestamp-conditioned low-rank adaptation mechanism that dynamically modulates the effective adaptation subspace according to temporal context. This design enables LLMs to better capture fine-grained entity evolution and time-dependent relation semantics while preserving parameter efficiency. Experimental results on two TKG benchmarks demonstrate that TEPCA achieves state-of-the-art performance. Additional evaluations of DG-LoRA on eight commonsense QA datasets further verify its general input-adaptive capacity, scalability across LLM backbones, and interpretability.

Electricity distribution planning depends on peak-demand coincidence within customer groups, yet similarity-based clustering of smart-meter data does not optimize it directly. We formulate customer grouping to maximize the average of group coincidence factors, weighted by each group's daily share of summed individual peak demand. We prove that jointly optimizing memberships and group peak times is strongly NP-hard even for two groups. We therefore develop a fixed-peak refinement procedure applicable to any feasible partition. It alternates between identifying group peak times and exactly reassigning customers under minimum group-size constraints. Each polynomial-time reassignment maximizes a lower bound tight at the current partition, so accepted updates strictly improve weighted coincidence. We initialize refinement by clustering individual peak-time distributions and evaluate the method on four public smart-meter panels (27,654 customers) using chronological holdouts and 200 paired replicates with complete refitting. Refinement consistently improves weighted coincidence during fitting and reduces sensitivity to initialization. On held-out days, the method outperforms random grouping and five standalone benchmarks, with mean paired gains of 1.08-3.48 percentage points over the strongest benchmark. Requiring nearly equal group sizes lowers test weighted coincidence only modestly. The groups retain recognizable peak-time patterns over time, suggesting applications in peak-aware customer aggregation.

Multi-parametric MRI is the gold standard for neuro-oncology, yet acquiring complete 4-modality protocols may be unfeasible in real-world clinical practice. When handling missing modalities, existing automated systems force a critical compromise: robust segmenters operate as uninterpretable "black boxes" that bypass image generation, while standard synthesis models often produce blurry reconstructions lacking diagnostic reliability. To address this issue, we propose CAPS, a Clinically-Verifiable Anatomy-Prioritized Synthesis framework that reconciles computational precision with clinical transparency. Our multi-modal generative architecture is driven by a hybrid objective function (L_1 + L_AFP + L_DualDice) that explicitly enforces the preservation of deep anatomical features and strict topological boundaries, rather than relying solely on pixel-wise optimization. Evaluated comprehensively on the BraTS Adult Glioma dataset, the framework achieves superior synthesis fidelity across intensity-based metrics, and demonstrates robust downstream segmentation performance (82.38% mean Dice), successfully restoring verifiable visual context. Furthermore, the framework exhibits exceptional zero-shot cross-domain generalization, maintaining high structural fidelity across completely unseen Meningioma, Pediatric, and Sub-Saharan African cohorts without domain-specific adaptation. Finally, a blinded radiological assessment confirms that our anatomy-aware approach significantly enhances both perceived image quality and clinical utility, providing clinicians with the reliable visual evidence required to trust downstream automated workflows.