Computer Vision
Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition
Shirong Lyu, Silu Quan, Yixuan Ding et al. (+1 more)
3D skeleton-based gait
emotion recognition faces high annotation costs, data scarcity, and poor generalization on heterogeneous data. This paper proposes SV-GCN, a single-stream multi-feature fusion framework with temporal invariance. We introduce intra-frame relative motion features to eliminate frame-rate sensitivity and embed heterogeneous cues at shallow layers, enabling early fusion without multi-stream complexity. For variable-length sequences, we design a global mask-guided valid-frame spatio-temporal graph convolution module, introducing frame-rate insensitivity for the first time in this domain. On the E-Gait
dataset, our method achieves performance comparable to state-of-the-art while demonstrating strong generalization across varying sequence lengths and frame rates, offering a viable pathway for pre-training on large-scale skeleton-based action recognition
datasets.
Read the full abstract ▾
All authors Shirong Lyu, Silu Quan, Yixuan Ding, Chengpeng Wang
Software Engineering
Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents
Ruiqing Yue, Yu Cui, Zhuoyu Sun et al. (+11 more)
Self-evolving runtime harnesses can substantially improve the capabilities of large
language model (LLM)
agents and provide a promising paradigm for optimizing
agent execution. Existing failure-driven approaches often treat observed
agent failures as direct evidence for harness modification. A key challenge in failure-driven harness
evolution is that observed failures can reflect either limitations of the underlying model or systematic deficiencies of the harness. Directly optimizing against individual failures can therefore induce model-specific accommodation and impair generalization across tasks and models. We study whether failure evidence accumulated across task instances can provide a more reliable signal for harness training. Our key insight is that failures recurring across distinct tasks provide stronger inductive evidence for systematic harness deficiencies than isolated failures. Based on this insight, we propose Ecdysis, which aggregates failure evidence across task instances before promoting recurring failure patterns into persistent harness
evolution, biasing
evolution toward repairs that are more likely to generalize beyond individual model behaviors. Ecdysis further employs collaborative failure analysis to refine modification specifications, trading additional
evolution-time
reasoning for improved modification quality. Across multiple LLMs and
benchmarks, Ecdysis improves the
reasoning accuracy of evolved harnesses by 18.56% over existing harness
evolution while achieving up to 1.84x faster harness training. Ecdysis also enables more data-efficient training. Fine-grained analysis shows that Ecdysis reduces model-specific accommodation during
evolution, while the resulting harnesses exhibit stronger cross-LLM generalization and lower inference-time
token consumption.
Read the full abstract ▾
All authors Ruiqing Yue, Yu Cui, Zhuoyu Sun, Sicheng Pan, Xianhong Xue, Tingyu Li, Ting Li, Wenzhuo Zhu, Yi Chen, Yifei Liu, Baohan Huang, Zhe Cui, Haibin Zhang, Cong Zuo
Artificial Intelligence
Geospatial AI, Dataverse Metadata, and the Study of Place-Based Government
Danny EBanks and Devika Jain
Harvard Dataverse hosts over 150,000 research
datasets, but the geographic information those
datasets carry is entered as free text by depositors and has never been assembled into a searchable structure. We construct a knowledge graph from the repository's public data and metadata, organizing 102,650
datasets within a 215,985-node network of 528,003 edges linking
datasets to keywords, publications, subjects, journals, and locations. Of those
datasets, 43,991 (42.9 percent) carry at least one geospatial field, geographic coverage, geographic unit, or a bounding box and 96.9 percent of all nodes sit in a single connected component, so
datasets remain reachable from one another even when their geospatial metadata share nothing in common. A conservative keyword search identifies 7,654 geospatially tagged
datasets (17.4 percent) as directly policy-relevant, with elections and legislatures the largest cluster, followed by government administration, health policy, transportation, and education. Five
datasets illustrate how this metadata behaves across policy domains and spatial scales, and an extended use case shows how community
language models, stance detection with geographic aggregation, and partisan
language bridging tools can attach discourse to place. The central obstacle is place resolution: the same location appears as many disconnected nodes. We argue that the graph provides a concrete setting for developing AI-driven metadata enrichment and entity resolution, and we document its coverage skew toward American, city-level data.
Read the full abstract ▾
Computer Vision
Multimodal Taxonomic Conditioning for Generative Plankton Imagery
Daniela Ivanova, Ozgu Goksu and Nicolas Pugeault
Automated plankton imaging produces severely long-tailed
datasets, where the rare taxa of greatest ecological interest have too few images to train or evaluate classifiers reliably. We generate synthetic plankton imagery conditioned on taxonomy: a CLIP encoder is adapted on a large plankton corpus with a ranked contrastive objective extended to deep, ragged taxonomies, then frozen to condition a parameter-efficient
diffusion transformer. We evaluate synthetic sample quality on distributional fidelity and downstream classifier utility.
Read the full abstract ▾
Networking
Unmanned Aerial Vehicle Propagation Channel over Vegetation and Lake Areas: First- and Second-Order Statistical Analysis
Deyvid L. Leite, Pablo Javier Alsina, Millena M. de M. Campos et al. (+2 more)
The use of unmanned aerial vehicles (
UAV) to provide services such as the Internet, goods delivery, and air taxis has become a reality in recent years. The use of these aircraft requires a secure communication between the control station and the
UAV, which demands the characterization of the communication channel. This paper aims to present a measurement setup using an unmanned aircraft to acquire data for the characterization of the
radio frequency channel in a propagation environment with particular vegetation (Caatinga) and a lake. This paper presents the following contributions: identification of the communication channel model that best describes the characteristics of communication; characterization of the effects of large-scale fading, such as path loss and log-normal shadowing; characterization of small-scale fading (multipath and Doppler); and estimation of the aircraft speed from the identified Doppler frequency.
Read the full abstract ▾
All authors Deyvid L. Leite, Pablo Javier Alsina, Millena M. de M. Campos, Vicente A. de Sousa Junior, Alvaro A. M. de Medeiros
Information Theory · Cross-listed from eess.SP
Leveraging Slowly Time-Varying AP-AP Channels for Interference Mitigation in Dynamic TDD
Martin Andersson, Tung T. Vu, Pål Frenger et al. (+2 more)
We address the challenge of cross-link interference in dynamic time-division duplexing (TDD) systems. Specifically, we focus on mitigating the interference caused by access points (APs) operating in downlink to APs operating in uplink. To this end, we exploit that channels between APs typically vary much more slowly over time than channels between users and APs. This observation allows us to jointly estimate the uplink user data and the AP-AP channels using a least-squares formulation over multiple coherence intervals, during which the AP-AP channels stay constant. We derive conditions for unique solvability of this least-squares problem by analyzing the rank of the regression
matrix. For cases where a unique solution does not exist, we propose to transform the problem into a uniquely solvable one by sacrificing a subset of the uplink data samples. Numerical results demonstrate that our proposed methods achieve substantial gains over baseline
algorithms. Further, we observe that one of our proposed
algorithms achieves almost perfect AP-AP interference mitigation when the AP-AP channels vary very slowly over time.
Read the full abstract ▾
All authors Martin Andersson, Tung T. Vu, Pål Frenger, Jan Åslund, Erik G. Larsson
Logic in Computer Science
Warrant Theory
Khashayar Irani
In this paper, we develop warrant theory as a philosophical discipline concerned with the inferential legitimacy of propositions within logical analysis. Warrant theory reconceptualises logic as a normative framework governing the conditions under which propositions may be introduced, accepted, rejected, and inferentially employed. Warrant is understood as inferential entitlement and is distinguished from truth, belief, and other psychological attitudes, while its relation to inferential use and meaning is examined. Warrant-theoretic analysis is then developed as a systematic method for investigating how propositions acquire inferential standing, how that standing develops, and how inferential positions interact through relations of dependence, compatibility, incompatibility, and exclusion. Acceptance and rejection provide the bilateral vocabulary for representing positive and negative inferential positions and the consequences and commitments associated with them. Finally, these elements are brought together in a warrant-theoretic definition of logic as the formal and normative study of the conditions under which propositions may be legitimately accepted or rejected and of the inferential transitions that such legitimacy warrants. On this account, logical consequence and logical failure are understood through the presence, preservation, or absence of inferential entitlement, thus locating the philosophical subject matter of logic in the systematic governance of inferential legitimacy.
Read the full abstract ▾
Robotics
Contact-Aware Incremental Model Predictive Control for an Underactuated Aerial Manipulator
Darwin Liu, Tamas Keviczky and Sihao Sun
We present a robust contact-aware control framework for aerial writing on an underactuated platform. The framework combines nonlinear model predictive control (NMPC) for accurate end-effector position and normal-force tracking at small reference penetration depths, with consistent performance across controller tunings, with whole-body incremental nonlinear dynamic inversion (INDI) for robustness to frictional and aerodynamic disturbances during contact. The proposed controllers are validated on a quadrotor-based aerial manipulator with a rigid, single-link, one-degree-of-freedom (DoF) arm in simulation and real-world experiments. The aerial writing experiments span vertical and inclined surfaces, multiple reference forces, different friction conditions, and wind disturbances. The results demonstrate that robust simultaneous five-DoF end-effector pose and contact-force tracking is achievable on a standard underactuated quadrotor with a simple, rigid, single-link arm, without requiring a fully actuated platform, a complex arm, or dedicated force/torque sensing.
Read the full abstract ▾
Artificial Intelligence
Autonomy, Social Norms, and Alignment: Towards a Developmental Framework for Autonomous Artificial Agents
Marica Notte, Ludovica Marinucci and Vieri Giuliano Santucci
In recent years, artificial intelligence has made extraordinary progress thanks to large-scale models capable of generalization and the generation of complex outputs. However, transferring this potential into
embodied agents reveals a significant limitation: the most advanced systems rely on pre-existing
datasets and human feedback strategies that are powerful but insufficient in dynamic or unknown contexts. To adapt, an
agent must acquire knowledge through direct interaction with its environment. One strategy to address this challenge involves introducing higher-level mechanisms, such as intrinsic motivations, which leverage curiosity and competence, to guide exploration and learning in complex environments. While this flexibility expands autonomy, it complicates the task of ensuring
agents remain aligned with human goals.
Alignment, already a challenge for artificial systems in general, becomes even more complex in unstructured and dynamic contexts where predefined rules prove insufficient. To be effective and adaptable, norms must be rooted in experience through an epistemological process that starting from simple, situated principles allows for the gradual construction of more complex rules through experience, autonomous learning, and cooperation with other moral
agents. Similarly to children learning social norms by exploring their environment and participating in collective practices, artificial
agents must also be educated toward
alignment. Following Dennett, the status of a moral
agent is not innate but is attributed gradually based on the ability to responsibly manage increasing degrees of freedom. From this perspective, the regulatory sandboxes can be viewed as pedagogical environments for AI: dynamic spaces where
alignment develops as a formative process, progressively shaping autonomous behaviors through interaction and cooperation in scenarios of increasing complexity.
Read the full abstract ▾
Machine Learning
Learnware and AI Model Management System
Zhi-Hua Zhou
The transition from file storage to database management systems transformed stored data into managed resources. AI now faces an analogous transition from AI model storage to AI model management. Existing model pools essentially serve as AI model storage systems. What is needed instead are AI model management systems that enable models trained by different developers, for different tasks, with different data, and under different objectives to be identified, reused, and even assembled to address future user tasks. Because AI model developers are generally unwilling to share their training data, such systems should operate without accessing the training data of model developers and, ideally, without accessing raw data of future users. This requirement poses a fundamental challenge: the functionality of a modern AI model may not be fully understood even by the developer who trained it. How, then, can a system identify which models are useful for a given user task, let alone assemble models developed independently for different purposes? At first glance, this objective may appear unattainable. It becomes possible, however, by upgrading the basic unit of management from a machine learning model to a learnware. Learnware = Model + Specification. The specification, whose assignment transforms a trained model into a learnware, is generated with the help of a machine learning process without disclosing the training data of the developer and has a theoretically established data-preservation property. The Learnware Dock System (LDS) provides a path toward powerful AI model management systems. Because specifications are generated according to a published reference and are comparable across models, they can also serve as an AI model collaboration protocol through which independently developed models, including intelligent
agents, can collaborate.
Read the full abstract ▾
Machine Learning
Musec: MomentUm SpEctral Clipping for Stable Muon-type Training
Zhuanghua Liu, Menglian Wang and Luo Luo
Muon has emerged as a highly effective optimizer for large
language model training, often achieving superior convergence and performance compared with the widely adopted Adam and AdamW optimizers. Nevertheless,
Muon is prone to training instability due to its spectral flattening, manifested by loss spikes and unbounded growth of model weights. Existing approaches primarily rely on weight or
attention-logit clipping, which require architecture-specific modifications and do not directly address instability across all model components. We propose MomentUm SpEctral Clipping (Musec), which replaces
Muon's spectral flattening with spectral clipping: rather than setting all singular values of the momentum
matrix to approximately one, Musec clips singular values that exceed a threshold while preserving the underlying spectral structure of the momentum. Our strategy provides an optimizer-level, architecture-agnostic mechanism for stabilizing
Muon training. Theoretically, we establish convergence guarantees for Musec in nonconvex nonsmooth
stochastic optimization. Practically, we develop Soft Musec, an efficient implementation that uses a smooth spectral saturation function approximated by coupled Newton-Schulz iterations. Empirically, Soft Musec consistently improves training stability over existing
Muon variants across a wide range of learning rates and model sizes, remaining stable in settings where existing
Muon variants diverge while matching their performance under well-tuned configurations.
Read the full abstract ▾
Systems and Control
Elementwise Positivity of the Solution to Lyapunov Equation for Hurwitz Companion Matrices
Miaomiao Wang, Patrizio Colaneri and Jie Chen
We prove, with the aid of AI, that for every real symmetric forcing
matrix $Q\succeq0$, the unique solution of a continuous-time Lyapunov equation is entrywise nonnegative whenever the state
matrix is a real Hurwitz companion
matrix. This proves an earlier
conjecture. The proof makes no assumption on the spectrum of the state
matrix, and is made possible by means of using Horner
polynomial matrices. We also show that discrete-time counterpart of the
conjecture is false: Counterexamples can be readily constructed to show that Schur companion
matrices, including one generated by a
polynomial with all positive coefficients, yield solutions with negative off-diagonal entries even with identity forcing.
Read the full abstract ▾
Computer Vision
Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits
John Bonnici, Matthew Baugh, Aleksandra Kulbaka et al. (+3 more)
Accurate identification of end-diastole (ED) and end-systole (ES) in echocardiography underpins the quantification of ventricular function, yet manual selection of these key frames is subjective and introduces clinically significant inter-operator variability. Recent self-supervised methods either prescribe strict periodic trajectories or learn an unconstrained low-dimensional motion subspace from reconstruction or registration objectives. The former offers interpretability but imposes restrictive assumptions on temporal progression, whereas the latter leaves cardiac phase implicit and ED/ES must be recovered through post-hoc
geometric processing of the learned trajectory. We translate the physiological observation that cardiac phase is a one-dimensional signal into a prior by constraining the latent motion component to a single-parameter latent orbit, i.e., a global linear trajectory in latent space indexed by a bounded scalar phase variable. Mapping this variable through a sinusoidal nonlinearity yields an oscillatory motion signal with consistent temporal ordering, enabling direct identification of ED and ES from the learned phase signal. This inductive bias allows the model to capture an interpretable representation of the cardiac cycle, while maintaining flexibility to capture irregular heartbeats. Trained on EchoNet-Dynamic without annotations, our minimal single-parameter cardiac phase model learns an effective latent orbit, significantly improves upon the previous state of the art in ED localisation and matches it in ES localisation while using a more constrained representation and fewer training epochs. This demonstrates that a principled physiological inductive bias can match or exceed the performance of more complex representations. Code is available at: https://github.com/BonniciJ/OrbitalEcho/
Read the full abstract ▾
All authors John Bonnici, Matthew Baugh, Aleksandra Kulbaka, Sarah Cechnicka, Bernhard Kainz, Alberto Gomez
Emerging Technologies
From Grid to Chip: Power Architecture, Stability, and Flexibility of AI Data Centers
Yubo Song, Rui Kong, Takuro Umihara et al. (+3 more)
The rapid growth of artificial intelligence (AI) computing is transforming data centers into large, dynamic electrical loads. Their deployment is primarily constrained by energy availability and grid-connection capacity, which is further aggravated by the ability of power-delivery architectures, control systems, and computing workloads to operate reliably during fast grid disturbances. This article presents a technological perspective on AI data centers as grid-interactive computing systems. First, it reviews grid-integration bottlenecks, evolving connection policies, grid-code requirements, which has fostered new technological trends via spatio-temporal flexibility available through workload orchestration, cooling systems, on-site resources, and energy storage. Second, it maps the
evolution of power-delivery architectures from medium-voltage grid interfaces to chip-level, discussing higher-voltage DC distribution, solid-state
transformers, wide-bandgap devices, advanced chip-level power delivery, and
liquid cooling. Third, it establishes a three-level stability framework spanning rack-level DC-bus dynamics, facility-level converter interactions, and system-level grid-coupled behavior. The framework connects dominant instability mechanisms, including constant power load effects, impedance interactions, forced oscillations, and operating-mode transitions, with suitable modeling, assessment, and mitigation approaches. Synthesizing these topics, this article highlights grid-to-chip co-design as a central requirement for scalable AI infrastructure, linking computing workloads, power-delivery systems, energy buffers, and grid operation.
Read the full abstract ▾
All authors Yubo Song, Rui Kong, Takuro Umihara, Pooya Davari, Frede Blaabjerg, Subham Sahoo
Machine Learning
RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
Ramiro Valdes Jara, David Chapman and Adam Meyers
Multivariate time series imputation (MTSI) aims to recover missing values in temporal data composed of multiple interdependent variables. This problem is central to real-world applications such as healthcare monitoring, traffic networks, and energy systems. Recent
diffusion-based approaches have shown strong potential for probabilistic imputation by learning to generate missing values through iterative denoising. However, most existing approaches perform
diffusion directly in the original data space, requiring the denoising network to simultaneously capture global structure, temporal dynamics, and
stochastic variability. This makes the generative task unnecessarily complex, especially when modern deterministic imputers can already provide accurate initial reconstructions. To address this limitation, we propose RDDMPI, a conditional residual
diffusion framework that operates directly in residual space. Instead of modeling the full missing signal directly, we reformulate probabilistic imputation as a baseline-residual decomposition, where a pretrained model captures the dominant signal and a
diffusion process models the residual uncertainty. To better exploit deterministic guidance, \model{} conditions the reverse denoising process on both the baseline-completed signal and its latent representation, while a reliability-aware conditioning mechanism adaptively controls the influence of baseline information during residual generation. This formulation simplifies the
diffusion learning objective, enabling it to focus on structured correction terms rather than reconstructing the full signal. Experiments on multiple
benchmark datasets demonstrate that RDDMPI consistently improves both reconstruction accuracy and uncertainty quantification.
Read the full abstract ▾
Information Retrieval
Your Retriever Already Knows: Distribution-Shape QPP for RAG Retrieval Sufficiency
Matyáš Veselý, Michal Průšek and Jiří Franc
Standard
Retrieval-Augmented Generation (RAG) pipelines often provide no reliable inference-time signal of whether
retrieval succeeded; on ambiguous or out-of-scope queries, generation may then hallucinate. Motivated by a Czech
nuclear-regulator deployment where data sensitivity precludes third-party LLM APIs, we compare three Query Performance Prediction (QPP) paradigms for
retrieval sufficiency in RAG: score-based features, a content-based LLM judge, and a hybrid. On the eight ViDoRe
vision domains (14,514 queries), our 24 non-lexical features (GeneralQPP; 15 distribution-shape, 5 query-surface, 4 global) reach a weighted-average AUROC of 0.856 at 2 ms per query, ahead of a classic-QPP literature pool (Classic Full, 0.835) and well above a local Qwen3.5 LLM judge (0.649, +0.207 gap; $\sim$3000$\times$ faster and cheaper per query). Adding the LLM judgment as one feature (hybrid) matches S1 on ViDoRe (0.863) but gains a statistically significant edge on SÚJB (AUROC 0.911 at Hit@5,
adversarial-detection 0.954; 1,510 queries, 500 synthetic
adversarial), at LLM latency. Rankings agree across
datasets (Spearman $\rho = 0.90$). Under Leave-One-Domain-Out, S1 drops to 0.706; a 13-feature LODO-stepwise subset (S1-Lean) recovers to 0.719 (+0.032 over the literature pool).
Read the full abstract ▾
Sound
ZipCodec: Ultra-Low-Frame-Rate Streaming Speech Coding
Luca Della Libera, Cem Subakan and Mirco Ravanelli
Neural audio codecs are a fundamental component of modern
speech generation systems. While recent codecs achieve increasingly low bitrates, reducing frame rate remains challenging, as each
token must preserve more information while maintaining reconstruction quality. We present ZipCodec, a streaming neural
speech codec operating at 6.25 Hz and 0.80 kbps with a theoretical latency of 160 ms. Our approach combines large-scale WavLM
distillation with a redesigned
transformer-based architecture, a scalar spherical quantizer, and a latency-aware streaming decoder. Experiments show that ZipCodec substantially outperforms existing streaming codecs at comparable bitrates in both reconstruction and downstream tasks, while operating at a significantly lower frame rate. Despite its 842M parameters, ZipCodec achieves real-time single-stream inference on a consumer-grade CPU. Demo samples, code and checkpoints are available at https://lucadellalib.github.io/zipcodec-web/.
Read the full abstract ▾
Machine Learning
LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics
Mariia Baranova, Adrien Petralia, Etienne Le Naour et al. (+3 more)
The energy transition is reshaping residential electricity consumption through the increasing adoption of distributed generation, electrified appliances, and demand-response programs. Understanding these evolving behaviors requires access to granular smart-meter data for applications such as load
forecasting, appliance detection, and demand-side flexibility analysis. However, such data are subject to strict access restrictions and data-protection regulations. Thus, realistic synthetic alternatives are necessary. In this paper, we introduce LoaDiff, a
diffusion-based generative model for year-long, sub-hourly smart-meter load curves. LoaDiff supports flexible conditioning on static household attributes, such as appliance ownership, and dynamic contextual variables, including calendar information and outdoor temperature. We evaluate the model against multiple generative baselines on three residential electricity-consumption
datasets. Our experiments assess four complementary dimensions: fidelity and diversity, training-record memorization risk, downstream utility for load
forecasting and appliance detection, and conditional controllability under alternative temperature conditions. The results show that LoaDiff generates realistic and diverse load profiles, achieves a favorable trade-off between generation quality and limited evidence of memorization, preserves information useful for downstream energy applications, and responds coherently to changes in conditioning variables.
Read the full abstract ▾
All authors Mariia Baranova, Adrien Petralia, Etienne Le Naour, Nathan Etourneau, Guillaume Hofmann, Themis Palpanas
Computer Vision
Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation
Jintao Zhang, Kai Jiang, Jintao Chen et al. (+32 more)
We present Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time
video editing model. Moreover, we explore the feasibility of real-time spatial
video generation for both Vidu S2-Avatar and Vidu S2-Editing. Compared with Vidu S1, Vidu S2-Avatar supports real-time 720p
video generation, generation with dynamic references that can be updated at any moment, and stronger instruction following, such as dancing. Vidu S2-Editing supports editing a
video stream in real time, including style rendering, clothing replacement, character replacement, and background replacement. Experiments show that Vidu S2 outperforms all baselines. A playable online demo is available at https://vidu.com/vidu-stream.
Read the full abstract ▾
All authors Jintao Zhang, Kai Jiang, Jintao Chen, Xu Wang, Deyuan Liu, Jungang Li, Dechuang Chen, Ming Lin, Jingjiang Zhou, Haopeng Jin, Qi Jia, Xiaohang Wang, Yaole Wang, Zhanqiang Zhang, Ran Li, Zhengkun Huang, Shuyue Xiong, Yuji Wang, Zikun Dai, Hui He, Yang Luo, Mang Ning, Weiqi Feng, Chengyang Ye, Xinyue Lin, Min Zhao, Hongzhou Zhu, Hengkai Tan, Zeyuan Wang, Chendong Xiang, Kaiwen Zheng, Zhijie Deng, Fan Bao, Jianfei Chen, Jun Zhu
Cryptography and Security · Cross-listed from quant-ph
Certifying Adversarial Robustness of Quantum Classifiers under Known-Readout Query Access
Ji Guan and Mingyu Huang
A
quantum classifier assigns labels by evolving an input
quantum state and measuring the output, so repeated executions reveal only a distribution over labels. We study certified
adversarial robustness for such classifiers under known-readout query access (KRQA), where an evaluator can prepare inputs, knows the
quantum measurement, and observes finite-shot outcomes but cannot inspect the internal
evolution, parameters, or gradients. We give a measurement-only framework that returns two complementary guarantees for each input: a lower bound ruling out untargeted errors within a radius, and an attack-independent upper bound witnessing an
adversarial state within a radius. Both are estimable from the known readout measurement and sampled outcomes, require no
tomography or
circuit description, and have finite-sample control of probability-estimation error. The upper bound uses gap operators induced by the
quantum measurement; the lower bound relaxes state-space search to an efficient optimization over outcome distributions with operator-spectrum constraints, yielding certificates that are never weaker than prior probability-only certificates and can be strictly stronger when the spectral constraints are active. On tractable instances, we compare the lower bound with numerical white-box reference estimates; across multiple classifiers, the upper bound remains informative when standard attacks fail. We further demonstrate real-device feasibility on IBM
Quantum hardware: from 40 executions of two 8-
qubit quantum neural networks, our method estimates both bounds, with the expected lower-upper ordering on every tested input. Taken together, these results show that robustness claims for
quantum classifiers can be audited directly from observable statistics under KRQA.
Read the full abstract ▾
Artificial Intelligence
Kesheng Chen, Yamin Hu and Wenjian Luo
Domain practitioners understand their business constraints but may lack operations-research expertise or dedicated support. LLM-based optimization
agents translate natural-
language requirements into models or solver programs that established optimization tools can execute. This progress makes optimization more accessible, but real-world operations are dynamic: changing demand, resources, and priorities require updates to data, constraints, and objectives. Methods centered on isolated requests offer limited support for rapid adaptation that preserves earlier decisions and reuses useful search results. We introduce MAPLE (
Memory-Augmented Planning with
Language and
Evolution), an
agent for maintaining optimization problems through successive natural-
language requests. MAPLE combines
language-based problem construction with mathematical programming and evolutionary search. It retains the optimization program, accepted plans, earlier updates, and candidate solutions for subsequent requests. We introduce NLDO, a
benchmark of 15 trajectories and 180 updates spanning selection, scheduling, rostering, routing, and cloud-resource placement. In the main evaluation, MAPLE completes all trajectories and achieves online scalar quality of 0.951 and a Pareto hypervolume ratio of 0.875. Controlled comparisons further show that maintaining executable state improves update validity and can preserve useful search information across substantial revisions.
Read the full abstract ▾
Information Retrieval
FedHUR: Learning Hierarchical Utility-Guided Client Relations for Personalized Federated Recommendation
Mingzhe Han, Jiahao Liu, Dongsheng Li et al. (+5 more)
Federated recommendation enables collaborative model training while keeping user interaction data on local clients. A central problem in
federated recommendation is how to aggregate useful information across clients for personalized recommendation. Existing personalized aggregation methods usually construct client relations from predefined parameter-based assumptions, such as parameter similarity or complementarity, and use these relations to determine aggregation weights. However, such methods construct a single global relation, which is insufficient to capture the hierarchical and multi-granularity nature of user relations in recommendation. Moreover, these predefined relations cannot directly reflect whether the related clients can improve prediction performance after aggregation. To address these limitations, we propose FedHUR, a
federated recommendation framework for learning hierarchical utility-guided client relations. FedHUR takes item-item filters as the object for relation construction and aggregation. Specifically, it first aggregates and clusters each client's local information to obtain global hierarchical information. Each client computes hierarchical utility signals based on its local information and the global hierarchical information, indicating which collaborative information is useful for improving its prediction. The server uses these utility signals to retrieve clients that are useful to that client for further personalized aggregation. Extensive experiments on five real-world
datasets show that FedHUR consistently outperforms existing
federated recommendation baselines, demonstrating the effectiveness of hierarchical utility-guided client relation learning. Code is available at https://github.com/Mingzhe-Han/FedHUR.
Read the full abstract ▾
All authors Mingzhe Han, Jiahao Liu, Dongsheng Li, Jiankui Zhou, Hansu Gu, Peng Zhang, Ning Gu, Tun Lu
Artificial Intelligence · Cross-listed from physics.plasm-ph
Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices
J. Gallego, P. Protopapas, A. Bustos et al. (+6 more)
In this work, we develop an inverse Physics-Informed Neural Network (PINN) framework to infer the dependence of the scrape-off layer (SOL) perpendicular heat conductivity on
plasma density and temperature, $\kappa_\perp(n,T)$. The method combines radial profile measurements of electron density and temperature with the residual of a reduced one-dimensional SOL transport equation, so that the inferred conductivity is constrained by both the measurements and the underlying transport model. Three neural networks are trained simultaneously: two reconstruct the temperature and density profiles as functions of the radial coordinate and transported power, while a third represents the effective conductivity as a function of the local density and temperature. The framework is first validated using synthetic data generated from a prescribed conductivity function, allowing the inferred $\kappa_\perp(n,T)$ to be compared directly with the ground truth. The model recovers the imposed functional dependence with errors below $10~\%$ in the data-constrained region. Bootstrap resampling is shown to provide a practical indicator of prediction reliability and consistency. A scan in the number of
plasma profiles used for training and the number of radial measurement positions per profile identifies a practical trade-off between reconstruction accuracy and data availability. Finally, the method is applied to an experimental
dataset from the TJ-II stellarator obtained with the helium-beam diagnostic. This exploratory application provides an initial estimate of the effective SOL conductivity and illustrates the potential of inverse PINNs for extracting transport information from
plasma edge measurements.
Read the full abstract ▾
All authors J. Gallego, P. Protopapas, A. Bustos, A. Alonso, S. Barquero, A. Baciero, I. Rivera, J. A. Moríñigo, R. Mayo-García
Networking
Comparative Performance Analysis of OTFS and OFDM Modulations for Mobile Wireless Communications
J. Marcos Leal B. Filho, Danilo de S. Pena, Marília C. Muniz et al. (+2 more)
This paper provides a quantitative performance comparison between Orthogonal Time Frequency Space (OTFS) and Orthogonal Frequency-Division Multiplexing (OFDM) modulation schemes, focusing on mobile wireless communication scenarios. We evaluate and compare both schemes based on critical communication scenarios and configurations such as mobility levels, modulation orders, multipath environments, and equalizers. The study systematically identifies conditions where OTFS and OFDM each exhibit optimal performance. Results from simulations demonstrate that OTFS outperforms OFDM consistently for high mobility scenarios and multipath environments. Depending on the modulation order, the performance gap between OTFS and OFDM might be very close or many orders of magnitude. Moreover, at low and mid values of SNR, the non-linear equalizer performs better than traditional linear equalizers for OTFS.
Read the full abstract ▾
All authors J. Marcos Leal B. Filho, Danilo de S. Pena, Marília C. Muniz, Álvaro A. M. de Medeiros, Vicente A. de Sousa Jr
Data Structures and Algorithms
A Reusable Framework for Robust Approximation Algorithms in the Interval Uncertainty Model
Ralf Klasing, Tobias Mömke and Émile Naquin
Robust optimization under interval uncertainty aims to compute solutions that perform well on a range of scenarios that are described by interval-constrained costs. In this paper, we revisit a framework introduced by Ganesh, Maggs and Panigrahi in 2020 to study the robust optimization of NP-hard problems under interval uncertainty. We start by generalizing a result in the $\ell=0$ case, which transforms a category of approximation
algorithms into a robust approximation
algorithm. Furthermore, in the general case, we provide a
theorem that turns any local search-based approximation
algorithm into a robust approximation
algorithm under three newly formalized conditions over the moves of the local search
algorithm. We then use this result to present the first robust approximation
algorithm for Weighted $k$-Set Cover, the third NP-hard problem known to admit a robust approximation, and the first since the publication of Ganesh, Maggs and Panigrahi's paper.
Read the full abstract ▾
Computation and Language
A Training-Free, Alignment-Free Approach to Corporate Intelligence: Application to SEC Filings
Jean-François Delpech
High-dimensional dense text
embeddings and large
language models face real obstacles in financial-disclosure analysis: context-window limits,
hallucination risk, high computational cost, and the arbitrary rotation of vector spaces across independently trained models. We present a training-free,
alignment-free framework for corporate intelligence built on deterministic sparse seed vectors. Hashing word strings into a fixed high-dimensional basis places all documents and all temporal epochs in a common coordinate system by construction, removing any need for training or
alignment. Accumulating these seed vectors across sentence contexts yields corpus-specific semantic signatures that compose linearly, supporting sub-second document comparison, issuer fingerprinting, tracking of how an issuer's vocabulary shifts between filings, and thematic sentence extraction, all on ordinary CPU hardware. Demonstrating the approach on a multi-year corpus of SEC filings (10-K, 10-Q, 8-K), we show how material corporate events, among them Boeing's 737 MAX crisis, Intel's supply-chain disruptions, and Bunge's acquisition of Viterra, emerge as distinct, interpretable semantic profiles, each traceable to the exact source sentences that produced it, with no domain-specific training and no LLM inference.
Read the full abstract ▾
Computer Vision
LangStreet: Persistent Language Fields for Anchor-Decoded Street Gaussians
Runyi Yang, Deheng Zhang, Xiaoye Wang et al. (+6 more)
Language Gaussian fields implicitly assume that the primitive carrying semantics remains identifiable across views. This assumption breaks in scalable anchor-decoded representations, where persistent anchors generate view-conditioned child Gaussians whose geometry and appearance vary with the camera. We introduce Ours, a persistent
language field for such structured Gaussian scenes. Our key idea is semantic ownership: transient children route observations, while persistent decoder slots and their parent anchors own the
language field. We use alpha-compositing responsibilities to accumulate additive directional evidence at slots; these statistics marginalize exactly to anchors. We then complete weakly supported slots with anchor-aligned evidence while preserving the anchor direction, and represent slot detail through low-rank residuals in anchor-relative semantic coordinates. Our primary model, Ours (base), stores anchor features together with compact slot residuals. Ours (light) retains only anchor features, whereas Ours (max) stores the full-dimensional completed slot features explicitly. Without scene-specific semantic optimization, Ours (base) nearly matches Ours (max) across KITTI, Virtual KITTI, and Waymo. On KITTI, it achieves 34.19 2D mIoU with a 2.72 GiB effective feature footprint, compared with 34.20 mIoU and 12.90 GiB for Ours (max). The same accuracy-storage trend holds on Virtual KITTI and Waymo. These results show that
language fields on view-conditioned splats require persistent semantic ownership, conserved evidence, and a hierarchy that balances stability, detail, and representation cost. Our code, checkpoints, and
benchmark suite will be publicly available.
Read the full abstract ▾
All authors Runyi Yang, Deheng Zhang, Xiaoye Wang, Mengjiao Ma, Lei Sun, Kanzhi Wu, Ajad Chhatkuli, Luc Van Gool, Danda Pani Paudel
Artificial Intelligence
Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models
Andreas Schwung, Steve Yuwono, Sofiene Lassoued et al. (+1 more)
This paper presents a novel approach for data-driven self-learning control of highly flexible, modular manufacturing systems. Specifically, we employ a novel framework for model-based reinforcement learning which introduces approximate inverse process models within the training of reinforcement policies. This approach disentangles the learning of actuation dynamics and the dynamics in state space, resulting in RL-based training solely within the task space. We propose a lightweight feedforward architecture for approximate inverse models and integrate them within the policy network of standard RL
algorithms. We apply the approach to a laboratory modular production testbed with heterogeneous production modules. The results underline the efficiency improvements for modular manufacturing units in terms of both performance and training speed, particularly for off-policy
algorithms.
Read the full abstract ▾
All authors Andreas Schwung, Steve Yuwono, Sofiene Lassoued, Dorothea Schwung
Cryptography and Security
PHAT: PHotonic Accelerator for TFHE
Guowei Yang, Farbin Fayza, Beren Aydoğan et al. (+3 more)
Fully Homomorphic Encryption (FHE) enables secure computation on encrypted data, making it a promising solution for
privacy-preserving applications in the cloud. Among various FHE schemes, FHE over the
Torus (TFHE) stands out due to its support for arbitrary operations. However, its high computation and communication overhead, particularly in the Fast
Fourier Transform (FFT) operations required during bootstrapping, limits its practicality for real-world applications. Conventional electronic accelerators struggle to achieve sufficient throughput due to the limitations of technology scaling and the
memory-wall problem. To address these challenges, we propose PHAT, a PHotonic Accelerator for TFHE leveraging Optically-addressed Phase-Change
Memory (OPCM). OPCM-based processing-in-
memory systems offer high computation and communication throughput, making them well-suited for accelerating FFT operations in TFHE. However, directly mapping FFT to OPCM presents challenges such as high-precision analog computation and the high latency and energy cost of programming OPCM
cells. To overcome these challenges, we introduce a novel electro-photonic accelerator architecture optimized for TFHE, featuring OPCM-based FFT units, a twiddle-stationary dataflow tailored for OPCM, and a scheduling mechanism to maximize the utilization of the FFT units. PHAT delivers $2.14\times$–$5.10\times$ speedup across four real-world TFHE workloads against the state-of-the-art ASIC accelerator. Our approach significantly enhances the performance of TFHE applications, paving the way for practical and efficient homomorphic encryption in cloud computing.
Read the full abstract ▾
All authors Guowei Yang, Farbin Fayza, Beren Aydoğan, Carlos A. Ríos Ocampo, Ayse K. Coskun, Ajay Joshi
Computers and Society
Who Bears the Risk When Generative AI Enters Transport? A Distributional Sociotechnical Audit of Algorithmic Equity, Synthetic-Data Validity, and Public Trust
Amir Rafe and Subasish Das
Generative artificial intelligence is entering transportation through traveler-facing advisories, synthetic crash-record generation, and policy decision support. Existing governance frameworks lack transport-specific statistical tools to measure distributional risks across heterogeneous populations. We develop a Distributional Sociotechnical Audit (DSA) that integrates algorithmic equity, synthetic-data validity, and public-attitude heterogeneity into one empirical pipeline. The audit analyzes 5,760 persona-controlled queries to four LLM families across 12 demographic cues and four transport topics, uses two cross-family judges and a Wasserstein-2 Equity Dispersion Index, tests three FARS crash-record generators with conditional projected maximum mean discrepancy (cpMMD), fits a
Bayesian ordered-logit model to Pew American Trends Panel Wave 152 (N = 4,538), and combines the signals into a continuous Sociotechnical Risk Index. Congestion-pricing advice has the highest persona-based dispersion (mean EDI = 1.96; highest direct EDI = 2.20). CART synthetic crash records fail all conditional tests (p < 0.001), while the Gaussian copula has borderline conditional stress (p = 0.105) despite passing marginal checks. Attitudes to AI vary across demographic strata. Distributional audits and continuous risk indices with sensitivity reporting offer a more defensible basis for transport GenAI governance than categorical approval tiers, which show a 75% assignment flip rate under weight perturbation.
Read the full abstract ▾