Applied Physics
Determination of van der Waals thin film thickness via characteristic X-ray photon yield
Guoshun Luo, Shichao Zhao, Xinyu Liu et al. (+7 more)
Characteristic X ray generation is essential for producing X ray sources and for investigating light matter interactions. Here, we demonstrate that characteristic X ray generation can also be employed to determine the thickness of van der Waals thin films in a non destructive manner. Specifically, we establish a sixth order
polynomial model that relates film thickness to the characteristic X ray yield. The model can both predict the X ray yield of graphite films at arbitrary thicknesses and infer the film thickness from a corresponding characteristic X ray yield. We validate the model by comparing its predicted X ray yields for graphite films with those obtained from Monte Carlo simulations. Additionally, we experimentally validated the reliability and feasibility of the proposed method for extracting graphite film thickness via X ray
photon yield characterization. Our results provide a non destructive approach for determining the thickness of van der Waals thin films.
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All authors Guoshun Luo, Shichao Zhao, Xinyu Liu, Suguo Chen, Yue Wang, Ping Zhang, Zhigang Lu, Shaomeng Wang, Sunchao Huang, Yubin Gong
High Energy Physics (Experiment)
Search for charged-lepton-flavour violating $e\mu tq$ interactions at $\sqrt{s}=13$ TeV with the ATLAS detector
ATLAS Collaboration
A search for charged-
lepton-flavour violation (cLFV) in top-
quark production and decay is presented. The data analysed correspond to 140 fb$^{-1}$ of
proton-
proton collisions recorded at a centre-of-mass energy of $\sqrt{s}= 13$ TeV by the ATLAS detector at the Large
Hadron Collider. The possible cLFV contribution is studied via two mechanisms. The first is via the production of a leptonically-decaying single-top
quark in association with a pair of oppositely-charged different-flavour
leptons. The second is the production of top-
quark pairs, where one top
quark decays into a pair of oppositely-charged different-flavour
leptons and a $u$- or $c$-
quark, while the other decays into a leptonically-decaying $W$
boson and a $b$-
quark, according to the Standard Model. The signal processes contain three charged
leptons, taken to be electrons or
muons. No significant deviation from the Standard Model prediction is observed, and these results are interpreted within an Effective Field Theory framework. Upper limits are placed at 95% confidence level on six Wilson coefficients, ranging from $|C^{\mathrm{tensor}}_{e\mu tu}|/\Lambda^2 < 0.018$ TeV$^{-2}$ to $|C^{\mathrm{scalar}}_{e\mu tc}|/\Lambda^2 < 0.32$ TeV$^{-2}$ for the tensor $u$-
quark and scalar $c$-
quark couplings, respectively. These Wilson coefficient limits are translated into 95% confidence level upper limits on cLFV top-
quark decay branching ratios, ranging from $B(t \to e\mu u) < 6 \cdot 10^{-9}$ to $B(t \to e\mu c) < 2.7 \cdot 10^{-7}$ for the scalar $u$-
quark and tensor $c$-
quark couplings, respectively.
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Information Theory
Exploiting UAV Attitude for Covert Communications: Dynamics-Consistent Trajectory-Attitude Co-Design
Jinpeng Xu, Lin Zhou, Xin Xie et al. (+2 more)
Existing trajectory designs for covert communications with uncrewed aerial vehicles (
UAVs) typically rely on point-mass kinematics, which overlook the inherent coupling between flight maneuvers and
antenna orientation. Motivated by recent studies linking
UAV acceleration to attitude, we investigate dynamics-consistent trajectory and attitude co-design for rotary-wing
UAV covert communications. We consider a
UAV equipped with a directional
antenna transmitting confidential information to a legitimate ground receiver in the presence of a warden. The acceleration-induced reduced attitude of the
UAV is explicitly modeled together with its impact on the attitude-dependent directional channel gain. To ensure covertness, we derive a per-slot constraint based on the Kullback–Leibler (KL) divergence at the warden. We then formulate a joint trajectory and attitude optimization problem to maximize the average achievable covert rate subject to covertness, motion, thrust-magnitude, attitude-smoothness, and acceleration-domain safety constraints induced by roll and pitch limits. The resulting problem is non-convex because the channel gains depend jointly on position, acceleration, and attitude. To solve it, we develop a minorization–maximization (MM)-based
algorithm that constructs a concave lower bound on the transmission rate and a convex upper bound on the covert constraint at each iteration. We further extend the framework to imperfect attitude control and develop a robust design against attitude tracking errors caused by actuator limitations, sensor noise, and control delays. Numerical results demonstrate the effectiveness of the proposed dynamics-consistent design and highlight the benefit of jointly optimizing
UAV trajectory and attitude for covert communications.
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All authors Jinpeng Xu, Lin Zhou, Xin Xie, Mengyuan Zhang, Jingjing Wang
Artificial Intelligence
DAYJOB: A Benchmark for Long-Horizon Professional Work
Stephanie Finley, Liudas Panavas, Thomas Mikkelson et al. (+12 more)
Professional work often starts with a brief request that leaves the professional to work out what is needed, which documents matter, and whether the request's premise holds. We introduce DAYJOB, a
benchmark of 130 tasks built by professionals in healthcare (50) and finance (80). The tasks are estimated to take a professional 13.6 hours on average in healthcare and 16.6 in finance. Each task is a containerized Harbor environment with an expert rubric of binary criteria (median 47.5 and 57.5 per task) that an agentic judge applies to the delivered files, and an attempt passes only if it meets every criterion. Across 30 model configurations from 13 developers, the strongest, Claude Opus 5.5, passes 24.7% of healthcare and 23.9% of finance attempts, and the median configuration passes 0.6% and 2.5%. In case studies,
agents accept premises that the record contradicts and carry wrong inputs through otherwise consistent analyses. We release all healthcare tasks, 50 of the 80 finance tasks, the evaluation harness, and the leaderboard.
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All authors Stephanie Finley, Liudas Panavas, Thomas Mikkelson, Cam Hinton, Stacey Ganss, Bradley Monton, Emily Kendall, Michelle Spradlin, Lydia Bye, Michael O'Brien, Lauren Ylvisaker, Derek Ray, Suhaas Garre, Sushant Mehta, Edwin Chen
Social and Information Networks
Beyond the Clique: Comparing Clique and Dowker Complexes for Co-occurrence Data in Learning Analytics
Koichi Yasutake, Wakana Tsuji and Hitoshi Inoue
Learning analytics increasingly represents the relations in co-occurrence data (codes in a window of discourse, participants in a thread, tags on a post) as simplicial complexes and analyses them with persistent homology. The standard approach uses the clique complex. Because its simplices are determined by the pairwise network alone, it cannot distinguish "three elements co-occurred together" from "each of the three pairs co-occurred separately". The Dowker complex, in contrast, takes as simplices the sets of entities actually observed to co-occur. We compare the two. First, we show that, on the same 1-skeleton, the Dowker complex is a subcomplex of the clique complex, the map on first homology induced by the inclusion is surjective, and its kernel is generated by phantom triangles that never co-occurred; that is, the clique construction can only erase holes. We also give a criterion for agreement that can be checked on triples of groups. We then test this on real data. On four Stack Exchange data sets, 46-91% of clique triangles are phantom and 522-1,970 holes are erased. On the example data of the learning-analytics R packages tna/Nestimate, the first Betti number $\beta_1$ of the clique complex is 0 at every threshold, whereas the Dowker complex detects holes. Against a degree-preserving null model, the Dowker complex departs strongly in five of the six data sets. This difference is invisible at the fixed thresholds used in practice. We conclude that the construction should follow the data type and that, for observed groups, the Dowker complex is the appropriate choice. Code is available at https://github.com/igu-lab/beyond-the-clique.
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Networking
Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transport
Emilio Paolini, Andrea Pinto, Flavio Esposito et al. (+1 more)
Federated LLM fine-tuning enables large models to be adapted using private and geographically distributed data at the network edge, creating recurring and deadline-sensitive communication workloads across access and transport networks. This challenge is particularly relevant in mobile RANs, where wireless variability, mobility, and device heterogeneity cause model updates to arrive asynchronously. Although these updates belong to the same learning round and share a common destination and deadline, conventional transport networks treat them as independent device-originated flows, hiding their underlying structure and limiting the ability to efficiently provision transport resources. This mismatch is particularly problematic for optical
circuit switching and all-photonics transport, which benefit from predictable and schedulable traffic demands. We argue that future RANs should act as learning-aware traffic shapers by exposing the communication structure of distributed model adaptation to the transport layer. Through in-network aggregation at the gNB, asynchronous UE updates can be transformed into fewer aggregate transfers with bounded size and delivery requirements. Once shaped in this way,
federated LLM traffic becomes a suitable candidate for selectively provisioned optical connectivity, where high-capacity paths can be established during aggregate-transfer windows and released between learning rounds. The resulting architecture combines the flexibility of packet-based mobile access with dynamically provisioned optical capacity, illustrating a broader approach for coordinating distributed AI workloads across programmable access and transport networks.
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All authors Emilio Paolini, Andrea Pinto, Flavio Esposito, Luca Valcarenghi
Neurons and Cognition
A High-Density EEG Dataset for Stimulus-Driven Auditory Attention
Ruofan Yan, Na Lu, Shu Peng et al. (+6 more)
Stimulus-driven auditory
attention determines which sound gains priority when multiple sources compete without an explicit listening goal, yet most computational studies focus either on acoustic salience or on decoding predefined attended targets. This study investigates instruction-free auditory competition using the Stimulus-driven Auditory
Attention (SAAD) paradigm and develops a neurophysiologically informed framework that integrates stimulus-derived sound priority with trial-specific
EEG evidence. Behavioral analysis using a Bradley–Terry model showed that sound priority estimated from previous competitions generalized to unseen sound pairings, improving held-out prediction from an AUC of 0.577 to 0.718.
EEG analysis further revealed mid-to-late centro-temporal lateralization associated with the reported selection side, with neural information remaining predictive beyond acoustic asymmetry. Guided by these findings, the proposed model first estimates a latent priority for each competing sound and forms relative stimulus evidence from their difference. A multi-scale
EEG pathway with complementary signed and power-based readouts then extracts trial-specific neural evidence, which is incorporated through gated decision-level integration. The framework is evaluated using mirror-constrained and pairing-held-out protocols, together with representative acoustic,
EEG,
multimodal baselines, and systematic ablations. The results support a computational account in which spontaneous auditory selection reflects the interaction between generalizable stimulus priority and trial-specific neural variability.
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All authors Ruofan Yan, Na Lu, Shu Peng, Wenlong You, Zhige Chen, Yuxuan Yan, Yan Liu, Kay Chen Tan, Jibin Wu
Computer Vision
STAGE: Subspace-Targeted Affine Generative Erasure for Text-to-3D Models
Karol Dziekan, Przemysław Spurek and Dawid Malarz
Concept erasure suppresses a target concept while preserving behavior on unrelated inputs. Existing closed-form methods were designed for 2D image
diffusion and assume a single generative pathway, so one edit must cover geometry and texture at once. Native 3D generators, which synthesize structured 3D representations directly rather than by lifting 2D samples, violate this assumption. We show that shape and object concepts must be erased in the structural stage of the pipeline and material concepts in the appearance stage. We therefore formulate erasure in native text-to-3D as a stage-aware editing problem and introduce STAGE, a training-free, closed-form framework. STAGE confines each edit to the low-dimensional subspace spanned by the differences between erase and anchor
embeddings, and relaxes the norm-preserving (orthogonal) constraint of prior editors into a least-squares affine correction that maps target activations onto safe anchors subject to a penalty on the displacement of retained
prompts. The correction applies to the structural stage, the appearance stage, or both. We find that the stage an edit must reach is determined by concept type. On TRELLIS, the standard open native 3D generator, across 15 shape, material, and object concepts, STAGE reaches 66.7 on a composite score that balances forgetting the target concept against preserving everything else, aggregating CLIP-based semantic and physical metrics, versus 53.2 for the strongest adapted baseline. Code: https://github.com/gmum/STAGE/ Project Page https://gmum.github.io/STAGE/
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Robotics
Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations
Giulio Schiavi, Andrei Cramariuc, Michael Pantic et al. (+1 more)
Most current grasp synthesis systems are trained offline and remain fixed during deployment. While this works well when deployment conditions resemble the training data, performance can degrade when
robots encounter conditions they have not seen before, such as unfamiliar objects. In this work, we present a continual-learning framework for single-view 6-DoF grasp synthesis for a parallel-jaw gripper in cluttered scenes. Rather than finetuning a large parametric model, our method adapts through
memory in a learned
embedding space: grasp outcomes update future grasp scores, while optional user demonstrations are recalled and transferred to new scenes as additional candidate grasps. We evaluate our method in simulation and in extensive real-world experiments comprising over 1500 grasp trials. We show that our method matches the performance of existing 6-DoF grasping baselines even before adaptation, improves online on unseen objects from categories absent or underrepresented during training, and supports long-horizon continual learning with limited forgetting. In real-world experiments, our method reaches over 90% success rates on several challenging object categories after only 50 online grasp attempts.
Videos and code at https://giuschio.github.io/cl_grasping/.
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All authors Giulio Schiavi, Andrei Cramariuc, Michael Pantic, Roland Siegwart
Cryptography and Security
A Systematization of Knowledge on DeFi Vaults: Architectures, Curation Mechanisms, and Strategy Design
Davide Mancino and Luca Pennella
Decentralized finance (DeFi) vaults are smart-contract-based asset management systems that pool deposits, execute programmable strategies, and mint tokenized shares representing claims on underlying assets and strategy performance. As vault designs have evolved from early yield aggregators to modular, actively managed systems, a new control layer, curation, has emerged to select strategies, configure risk parameters, and coordinate operational execution, introducing principal-
agent dynamics and new failure modes. This paper systematizes DeFi vault architectures and curator-mediated control planes through (i) a unified system model and formal definitions for share accounting, roles, and operational dependencies, and (ii) three complementary taxonomies covering vault exposures and objectives, curator governance and accountability mechanisms, and strategy execution patterns together with their failure modes. We further map a representative set of production protocols to the proposed dimensions. The frameworks in this work aim to support rigorous analysis and safer design of blockchain-based financial applications.
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Classical Analysis and ODEs
Stability of concentration in the Paley-Wiener space
Denis Zelent
We prove stability of concentration in the Paley-Wiener space for each fixed time-bandwidth product. The concentration deficit controls the squared $L^2(\mathbb R)$-distance from a translated first prolate spheroidal wave function and the squared normalized symmetric difference from an interval at the same center. The obtained exponent for the function distance is optimal. The main idea is the introduction of the set discrepancy weighted by the best optimizers in place of the symmetric difference. The proof then combines elementary
Hilbert space arguments with classical properties of the prolate spheroidal wave functions.
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Probability
Higher-Order Fluctuations of the Log-Partition Function for Ising Models on Inhomogeneous Random Graphs
Sanchayan Bhowal and Somabha Mukherjee
Classical central limit
theorems (CLTs) for log-partition functions of Ising models on Erdős-Rényi random graphs lead to degenerate limits in the high-temperature regime when self-loops are absent. In this paper, we fill this surprising gap in the more general context of Ising models on inhomogeneous random graphs. In particular, we show that if $G(N,W)$ is an inhomogeneous random graph without self-loops generated by a graphon $W$, then, in the high-temperature regime, the log-partition function of the corresponding Ising model satisfies a CLT on the $N$-scale, with a limiting variance that incorporates contributions from cycles of all orders. This behavior contrasts sharply with that of the corresponding model in which self-loops are allowed, where the analogous Gaussian fluctuations occur on the $\sqrt{N}$-scale, with their asymptotic variance determined entirely by the diagonal profile of $W$. Our analysis yields, as a byproduct of independent interest, a CLT for the log-determinant of the associated resolvent
matrix, established by combining the Lindeberg CLT for triangular arrays with suitable bounds on higher-order trace terms.
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Artificial Intelligence
Questionnaire-Guided Disaggregation of Energy Appliance Use for Domestic Smart Meter Data
Achal Nanjundamurthy, Rupam Misra, Suzanne Little et al. (+1 more)
Ireland's smart metering programme records electricity use at 30-minute resolution, with smart meters installed in over 80% of households as of late 2025. While this is useful for billing of smart, time-of-use tariffs, it is too coarse to capture use of domestic appliances. We present a label-free disaggregation system that breaks usage data into 9 appliance categories by combining event detection for high-power loads with questionnaire-guided estimation. Our evaluation draws on four
datasets: a calibration household with a commercial comparator, two public
benchmarks (UK-DALE and REFIT) with per-appliance sub-metering, and a smart meter
dataset of more than 4,800 years of use from 2,968 Irish consumers. Compared against two independently developed disaggregation systems our hybrid method combining analysis of usage data with questionnaire results, achieves the lowest whole-decomposition error on all buildings across the
datasets, with better month-level performance over 54 paired months ($p<0.001$, Holm-corrected). Our method provides useful advice on a household's energy consumption patterns and advice on how to reduce or shift usage on some appliances in order to reduce costs.
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All authors Achal Nanjundamurthy, Rupam Misra, Suzanne Little, Alan F. Smeaton
Artificial Intelligence
ITC-MoE: Importance-guided Token-aware Compression for MoE Diffusion Language Models
Lianjun Liu, Shipeng Li, You Huang et al. (+5 more)
Mixture-of-Experts (MoE)
Diffusion Language Models (DLMs) offer flexible parallel decoding and increased model capacity, but their large number of expert parameters incurs substantial computation and storage costs. Existing low-rank MoE compression methods largely rely on static factorization and fixed rank allocation, which overlook the distinctive properties of MoE DLMs. Specifically, we identify two properties: cross-mode non-uniform redundancy, where parameter redundancy and sensitivity to rank truncation vary across the input, output, and expert modes, and
token-wise utilization variation, where hot and cold
tokens exhibit distinct spectral characteristics and expert activation patterns. To address these challenges, we propose ITC-MoE, an Importance-guided
Token-aware Compression framework for MoE DLMs. ITC-MoE consists of two complementary components. First, Importance-guided Adaptive Tucker Compression (IATC) incorporates activation and gradient importance into expert weight transformation, jointly factorizes expert weights across multiple modes, and adaptively allocates ranks under a fixed parameter budget. Second,
Token-aware Compensation and Routing (TCR) applies lightweight low-rank compensation to compression-sensitive hot
tokens and restricts the candidate expert set for cold
tokens with concentrated routing patterns. By jointly adapting compression capacity and inference execution to both parameter redundancy and
token-wise variation, ITC-MoE substantially reduces the computation and storage costs of MoE DLMs while preserving their generation quality. For example, on SDAR-30B-A3B-Chat-b32, ITC-MoE maintains an accuracy of 96.33% on MultiArith under a 30% compression budget, while achieving up to a 7.22x end-to-end speedup. The code is publicly available at https://github.com/lianjunl13-sudo/ITC-MoE.
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All authors Lianjun Liu, Shipeng Li, You Huang, Weiqi Yan, Mingte Qiu, Huazhong Liu, Xiaofeng Zhu, Yunshan Zhong
Optimization and Control
Petrov-Galerkin operator inference with application to stability-encouraging identification
Johannes Rettberg, Jonas Nicodemus, Harsh Sharma et al. (+3 more)
Data-driven model order reduction methods such as operator inference enable the efficient construction of reduced-order models directly from high-dimensional time-domain data. Standard operator inference typically seeks a Galerkin-type reduced model in a prescribed low-dimensional subspace by identifying its reduced operators from projected snapshot data. The resulting inference problem is formulated as a least-squares problem admitting an efficient closed-form solution. However, it is well known from intrusive model order reduction for linear time-invariant systems that Petrov-Galerkin projections can additionally preserve important system properties such as stability and passivity. To overcome the limitations of standard operator inference, we extend the framework for linear time-invariant systems to incorporate Petrov-Galerkin projections and provide explicit error expressions and bounds between the intrusive and nonintrusive reduced operators, thus generalizing results from the literature. We demonstrate the proposed approach in the context of dissipative and port-
Hamiltonian systems. Furthermore, we introduce a novel convex optimization formulation that explicitly enforces the port-
Hamiltonian structure on the inferred operators. The effectiveness of the proposed methods is demonstrated on several well-established
benchmark problems, including the CD player, an atmospheric model, a mass-spring-damper system, and a poroelasticity system.
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All authors Johannes Rettberg, Jonas Nicodemus, Harsh Sharma, Boris Kramer, Jörg Fehr, Benjamin Unger
Cryptography and Security
GNSS Spoofing in Mobile Devices: A Survey on Impact and Countermeasures
Robert Argo, Andrea Nardin, Alex Minetto et al. (+1 more)
Smartphones rely on Global
Navigation Satellite System (GNSS)-based positioning for many of the functions they execute everyday. The GNSS receivers embedded in smartphones are susceptible to anthropogenic
radio frequency interference attacks in the forms of jamming and spoofing due to the low-power and open-architecture signals they receive from the satellite constellations. While jamming is a practice that denies a GNSS receiver the ability to form a position, velocity, and time (PVT) solution, spoofing represents a more insidious threat by using forged satellite signals that aim at causing the victim receiver to compute a false PVT solution. The ubiquity of smartphones and the sensitive geolocation data they hold make them a primary target for malicious spoofing. However, their hardware constraints and the lack of deep visibility into the GNSS receiver processing chain create significant hurdles for effective countermeasures. Existing surveys comprehensively explore general spoofing countermeasures but fail to address these mobile-specific limitations. This article fills that gap with a novel survey focused on techniques viable within the unique constraints of smartphone architectures. Specifically, we establish a taxonomy for defining GNSS spoofing attack effects and countermeasures, provide a historical review of smartphone vulnerability characterization, and provide an overview of techniques proposed to detect and counteract smartphone spoofing threats, offering a comparative framework to weigh their respective pros and cons on mobile platforms.
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All authors Robert Argo, Andrea Nardin, Alex Minetto, Pau Closas
Sound
AudioJev: Direct Audio Decisions with Order-Calibrated Probabilities
Sihan Lv, Zhen Li, Zhiqi Cao et al. (+4 more)
Audio decisions often depend on evidence that a transcript does not preserve, while their probability estimates can depend on how answer options are ordered. AudioJev maps a waveform, question and supplied alternatives directly to a candidate distribution through one shared full-parameter model. We define order calibration as preserving an answer's probability under meaning-preserving option permutations. Random-derangement SKL training pairs each question with a reordered view in which every alternative changes position, supervises both answers, and aligns the two distributions before applying a symmetric KL penalty. Inference retains a single candidate-scoring forward, with no calibration head or order ensemble. Across three training seeds, AudioJev reaches 68.88%/55.33% mean accuracy on complete MMAU/MMAR and reduces random-order SKL by 43.8%/60.5% relative to the single-view removal ablation. The same model handles intent, environmental sound, note properties,
speech activity and conversational transitions. Paired removal ablations and multi-order evaluation measure predictive accuracy and probability stability together, establishing a direct audio interface whose calibration objective acts on candidate meaning rather than presentation position. Inference code and model weights are available at https://github.com/SihanLv/AudioJev-Inference and https://huggingface.co/shlv/AudioJev.
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All authors Sihan Lv, Zhen Li, Zhiqi Cao, Jinshan Zhang, Ying Li, Meng Xi, Jianwei Yin
High Energy Physics (Theory)
Gravity and generalised geometry from a Lie 2-algebroid perspective
Athanasios Chatzistavrakidis, Chris Hull, Larisa Jonke et al. (+2 more)
We revisit the problem of defining natural torsion and
curvature tensors for generalised connections and of using them to reconstruct the effective action for the massless modes of the closed string from generalised geometry. The starting point is the description of a Courant algebroid as a differential graded (dg)
symplectic manifold. A systematic procedure for split dg
manifolds singles out a generalised Lie bracket as part of a larger Lie 2-algebroid structure, with the right properties to unambiguously define torsion and Riemann
curvature tensors together with their corresponding Bianchi identities. This framework eliminates the structural circularity and ambiguity problems of previous approaches in the literature. We show that a unique generalised metric compatible generalised connection with fixed (con)torsion exists for any choice of ordinary connection that splits the dg
manifold, while at the same time all Lie 2-algebroids obtained in this way are equivalent up to $L_{\infty}$ quasi-isomorphisms. For a given Lie 2-algebroid we demonstrate that the unique compatible generalised connection reproduces the effective closed string action from a
geometric action based on the new generalised
Ricci tensor upon making a canonical choice of torsion.
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All authors Athanasios Chatzistavrakidis, Chris Hull, Larisa Jonke, Sylvain Lavau, Peter Schupp
Computer Vision
ODDR: One-Step Deshadow Diffusion via Reward Guidance
Junseong Shin, Kijun Kim, Minseong Kim et al. (+2 more)
Recent advances in deep learning for shadow removal have significantly enhanced image quality and realism. However, most approaches rely on real-world paired
datasets, which are costly to collect and often limited in scene diversity, leading to limited generalization. To address these limitations, we propose One-step Deshadow
Diffusion via Reward guidance (ODDR), a new framework that achieves efficient and high-fidelity shadow removal without relying on real-world paired supervision. Our method begins with One-step Deshadow
Diffusion (ODD), a baseline model trained on synthetic shadow data for efficient one-step shadow-free reconstruction. We further adapt ODD into ODDR using ShadowReward. In contrast to traditional, annotation-heavy approaches, ShadowReward is the first reward model for shadow removal trained entirely without human annotation. It learns to mimic human perceptual judgments by ranking synthetically generated images with controlled degradations, such as texture distortion and boundary artifacts. This reward-guided fine-tuning enables ODDR to close the synthetic-to-real domain gap. Extensive experiments show that ODD achieves strong performance without relying on real-world paired supervision, and ODDR further improves the results, narrowing the gap to fully supervised methods trained on real-world paired data while maintaining higher computational efficiency as a single-step model.
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All authors Junseong Shin, Kijun Kim, Minseong Kim, Dongjin Kim, Tae Hyun Kim
Materials Science
Pressure induced structural transitions, coordination crossover, and optoelectronic response in divalent metavanadate CaV$_2$O$_6$
Bhagyashri Giri, Asish Kumar Mishra, Bidisha Mukherjee et al. (+3 more)
High-pressure synchrotron X-ray diffraction, Raman spectroscopy, photoluminescence (PL), and UV-Vis absorption measurements were carried out on the divalent metavanadate CaV$_2$O$_6$ up to 31 GPa at room temperature. Ambient CaV$_2$O$_6$ crystallizes in a centrosymmetric pseudo-brannerite monoclinic structure ($C2/m$), where vanadium atoms reside in fivefold-coordinated VO$_5$ trigonal bipyramids. Under hydrostatic compression, the system undergoes a structural phase transition starting at $\sim$1 GPa to a lower-
symmetry, non-centrosymmetric monoclinic phase ($C2$), which becomes the single stable phase above 5.2 GPa. This transition involves an increase in vanadium coordination from VO$_5$ bipyramids to edge-sharing distorted VO$_6$ octahedra. High-pressure Raman scattering corroborates the structural phase boundaries, exhibiting mode softening ($\gamma_3 = -0.42$, $\gamma_9 = -0.46$) within the mixed-phase regime (1.0 - 4.4 GPa) and sharp anomalies in mode frequencies and linewidths across 5.2 GPa. The optical measurements show complete PL quenching near 1 GPa due to non-radiative recombination pathways introduced by local polyhedral distortions, alongside a non-monotonic
evolution of the indirect optical band gap. Above 20 GPa, an anomaly in polyhedral distortion and internal strain drives an isosymmetric structural modification within the $C2$ phase, marked by an alteration in bulk compressibility and stabilization of the optical gap. Comparative high-pressure band-gap measurements on the sister compound MgV$_2$O$_6$ validate the explicit coupling between cation coordination changes and optoelectronic response in divalent metavanadates.
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All authors Bhagyashri Giri, Asish Kumar Mishra, Bidisha Mukherjee, Debabrata Samanta, Nico Giordano, Goutam Dev Mukherjee
Nuclear Theory
Hadronization into Nuclei: Does Size Matter?
Anton Andronic, Peter Braun-Munzinger, Hjalmar Brunßen et al. (+1 more)
We investigate an extension of the statistical hadronization model (SHM) that takes (hyper)
nucleus sizes into account. The spatial extent of
nuclear wave functions relative to the fireball volume is used to calculate a size correction, which describes the suppression with respect to a point-like treatment of the (hyper)
nucleus at hadronization. For several species of light
nuclei (d, ${}^{3}\mathrm{H}$, ${}^{3}\mathrm{He}$) and for the hypernucleus ${}^{3}_{\Lambda}\mathrm{H}$, the predicted effects of the size correction on SHM yields are compared to yield measurements in pp, p-Pb, and Pb-Pb collisions in the ALICE experiment. The experimental data suggest that this size correction, based on applying final-state wave functions at chemical freeze-out, does not give a consistent description of (hyper)
nucleus production in
nuclear and hadronic collisions. By contrast, the SHM without a
nuclear-size correction provides a substantially better description of the data. This result lends further support to the interpretation that such
nuclear states are formed from initially compact (multi-
quark) configurations which after hadronization expand to their final-state wave functions.
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All authors Anton Andronic, Peter Braun-Munzinger, Hjalmar Brunßen, Johanna Stachel
Robotics
Interaction-Stiffness-Guided Basis Allocation in Dynamic Movement Primitives for Efficient Skill Transfer
Chan Xu, Silu Chen, Dehao Wang et al. (+6 more)
Dynamic Movement Primitives (DMPs) provide a compact and stable formulation for trajectory representation and generalization in
robot skill learning. However, their predefined basis layout limits the allocation of approximation capacity according to stage-dependent precision requirements. To address this issue, this article proposes Stage-Criticality-Guided Dynamic Movement Primitives (SC-DMPs) with adaptive basis allocation for precision-critical skill learning. Operator-
robot interaction stiffness and a trajectory-consistency cue derived from cross-demonstration task-space variability are integrated to construct a stage-criticality index. Guided by this index, basis centers are redistributed in normalized time through inverse cumulative criticality and mapped to the canonical phase domain, while their bandwidths are refined to adjust local approximation support. This enables denser and more flexible representation at high-criticality stages while retaining sparser allocation elsewhere. Experiments on handwriting trajectories and three real-
robot tasks show that the inferred criticality is concentrated in geometrically demanding and task-constrained regions. Comparisons with DMPs, ProMPs, ProDMP, GP-MP, and KMP demonstrate improved trajectory reproduction, endpoint generalization, and task-critical accuracy while retaining a compact model and the stable structure of classical DMPs.
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All authors Chan Xu, Silu Chen, Dehao Wang, Xiyu Chen, Dexin Jiang, Chi Zhang, Guilin Yang, Chenguang Yang, Zaojun Fang
Soft Condensed Matter
Ideal gas of active particles in a box
Siran Li, Ralf Eichhorn and Lennart Dabelow
Extending a fundamental concept of thermodynamics to the nonequilibrium realm of active matter, we derive an ideal gas law for a suspension of noninteracting active particles confined to a finite rectangular box. The law is exact for run-and-tumble particles (RTPs) in one dimension and established by a systematic analytical expansion of the steady-state probability density for active Ornstein-Uhlenbeck particles in any dimension. It also agrees well with numerical results for active Brownian particles and RTPs in higher dimensions and recovers previously known relations for the limit of large system sizes. Together, these results demonstrate the applicability of the ideal active gas law to the three most widely studied theoretical models of colloidal active matter. As an application, we analyze thermodynamic processes involving active matter on a macroscopic level, without recourse to microscopic particle trajectories or self-propulsion fluctuations.
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Computer Vision
Dyna3: VLM-Guided Training-Free 4D Reconstruction via Depth Foundation Models
Xinhao Xiang, Weiyang Li, Zhijie Zheng et al. (+2 more)
Recent depth foundation models like Depth Anything 3 (DA3) achieve remarkable multi-view depth estimation but assume static 3D scenes, limiting their applicability to real-world dynamic environments. Existing training-free 4D methods like Easi3R and VGGT4D rely on correspondence-trained backbones whose
attention encodes cross-frame matching, a property absent in depth-only models like DA3. We present Dyna3, a training-free framework that extends DA3 for 4D dynamic scene reconstruction without any fine-tuning. Our key insight is that DA3's cross-view features, though trained only for depth consistency, implicitly encode motion-discriminative signals when combined with best-match feature search across frames. Its static surfaces find consistent matches globally, while dynamic objects cannot. We further adopt
vision-
language models (VLM) to automatically generate scene-specific semantic
prompts for SAM 3, enabling precise instance-level
segmentation that distinguishes which objects move from what objects exist. For reconstruction, we decouple the scene into a cross-frame aligned static background and per-frame dynamic point clouds. Experiments on four
datasets demonstrate that Dyna3 surpasses correspondence-trained methods with +5.5pp J-Mean over state-of-the-art VGGT4D on dynamic object
segmentation, while achieving up to 13x faster pose estimation and 3x faster 4D reconstruction with 4 to 8x lower
memory. Dyna3 could therefore enable much denser temporal sampling that prior methods cannot support.
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All authors Xinhao Xiang, Weiyang Li, Zhijie Zheng, Abhijeet Rastogi, Jiawei Zhang
Signal Processing
CSI-Free Positioning of Movable Antennas for IoT Networks: A Compositional Kernelized Bandit
Wonseok Choi, Yunseob Tae, Jeongjae Lee et al. (+1 more)
Movable
antenna (MA) arrays reshape the propagation channel by mechanically changing the element positions, which suits Internet-of-Things access points whose devices cannot adapt on their own. Existing position optimization assumes the channel is known at every candidate configuration. In practice a configuration can be evaluated only after the array has moved there, over several slots limited by the actuator speed, during which the channel changes. We formulate the positioning of M MAs serving K < M devices without channel state information as a nonstationary kernelized bandit over the joint configuration space, driven by a single scalar rate feedback per slot under a reachability constraint and an actuation-energy cost. Since the information gain of a standard kernel grows exponentially with M , and the multiuser sum rate depends on the positions through a sum of per-
antenna terms, we design a compositional kernel that retains interactions up to
antenna pairs. On this kernel we propose CoMoveUCB, which selects a target configuration over the whole feasible set by coordinate ascent and retains it under a persistence rule. We prove sublinear dynamic regret with a
polynomial dependence on M . Simulations show that CoMoveUCB outperforms fixed, measure-then-optimize, and reachability-confined
benchmarks across loadings, confirming the value of repositioning an MA array under realistic actuation limits.
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All authors Wonseok Choi, Yunseob Tae, Jeongjae Lee, Songnam Hong
Machine Learning
Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research
Mehmet Baygin, Sengul Dogan and Turker Tuncer
Model validation estimates the performance of a complete learning procedure on new data. However, an invalid split can produce an optimistic and stable result. This tutorial reviews hold-out validation, train/validation/test designs, repeated random subsampling, k-fold and repeated stratified cross-validation, leave-one-out and leave-p-out schemes, group-aware validation, and nested group cross-validation. General machine-learning principles are linked to
EEG epochs, paired-eye OCT images, repeated clinical measurements, and multicenter data. Eight controlled scenarios compare flawed and leakage-safe designs: seven use locked confusion
matrices with auditable metrics, and one uses a reproducible repeated-study simulation. The scenarios cover global feature selection, normalization leakage, dependent records, center mixing, repeated test-set use, and
estimator instability. Bias, variance, metric aggregation, uncertainty, and computational cost are also examined. A data-size
matrix, a decision tree, and reporting checklists are provided. Reproducible MATLAB templates and scikit-learn counterparts are included. The results show that no validation method is universally best. The independent unit must match the intended deployment target. Every data-dependent operation must also exclude the observations used for performance estimation.
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Computer Vision
ShelfChange3D: Object-Level 3D Change Detection for Retail Shelf Monitoring
Lingyi Zhou, Yunke Wang, Mengyu Zheng et al. (+3 more)
Reliable shelf monitoring is an important capability for retail automation, yet existing out-of-stock detection methods mainly operate in image space and lack metric 3D localization for downstream robotic systems. We formulate shelf monitoring as object-level 3D change detection: given two RGB-D observations captured at different times, the goal is to identify changed products and localize each change with a 3D bounding box. To support this task, we introduce ShelfChange3D, comprising 145K synthetic and 5K real-world paired RGB-D observations with object-level 3D change annotations. We further propose ChangeBox, an end-to-end framework that jointly reasons over paired observations and predicts object-level 3D change boxes. To improve localization accuracy, we introduce a geometry-based refinement stage that exploits depth and
gravity prior to estimate relative pose and refine predicted boxes. Experiments show that ChangeBox outperforms existing change detection baselines, with further gains from refinement and effective transfer from synthetic to real-world observations.
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All authors Lingyi Zhou, Yunke Wang, Mengyu Zheng, Wenbo Wang, Zijian Wang, Chang Xu
Artificial Intelligence
Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics
Antonio Emanuele Cinà, Giovanni Scodeller, Cecilia Caterina Pasquale et al. (+5 more)
The rapid
evolution of Intelligent Transportation Systems and Logistics (ITS&L) has become a cornerstone of the modern social economy, relying heavily on the integration of Data, Artificial Intelligence (AI), and, more specifically, Machine Learning (ML). This paper provides a comprehensive review of Trustworthy Data and Machine Learning Operations (DataOps and MLOps) in the ITS&L domain, underscoring their importance in improving efficiency, reliability, and decision-making precision within transportation and logistics services. We begin by identifying gaps in current literature, offering clear context for our contribution. Subsequently, we explore the complexities of DataOps and MLOps, discussing their necessity, key components, available tools, practical insights, and case studies relevant to ITS&L. Additionally, we address the critical issue of Trustworthiness in AI applications, examining methods and tools designed to strengthen confidence in AI systems - especially in real-world ITS&L scenarios. The paper concludes with a discussion of persisting challenges and future prospects in this rapidly advancing field, aiming to serve as a vital resource for researchers, industry practitioners, and policy makers. Overall, this work not only establishes a foundational understanding of DataOps and MLOps in ITS&L but also charts a path for further research and innovation in developing more efficient, sustainable, and trustworthy intelligent transportation and logistics systems.
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All authors Antonio Emanuele Cinà, Giovanni Scodeller, Cecilia Caterina Pasquale, Silvia Siri, Davide Anguita, Fabio Roli, Simona Sacone, Luca Oneto
Soft Condensed Matter
Inclined surfaces bias bacterial swarming expansion and edge organization
Pengxi Gu, Zixiang Lin, Hui Ren et al. (+5 more)
Bacterial swarming is the collective expansion of dense populations of motile
cells across hydrated soft surfaces, yet
gravity is usually treated as a fixed background condition in standard horizontal assays. We used surface inclination to examine how agar-surface orientation relative to
gravity affects the expansion of Enterobacter sp. SM3 swarms. At macroscopic scales, we found that colony expansion depended non-monotonically on inclination, with the strongest spreading at intermediate inclinations and weaker expansion under shallow or near-inverted conditions. Paired up-facing acute and down-facing obtuse configurations with the same tilt magnitude showed different expansion responses, suggesting that swarm expansion depended on both tilt magnitude and which side of the agar surface faced upward relative to
gravity. Inclination also produced downhill-biased colony morphologies. For microscopic scales, particle image velocimetry (PIV) analysis showed generally lower mean activity under inclination than in the flat references, with a non-monotonic angular response. The downhill tip showed slower motion, shorter spatial correlations and smaller
vortex cores than the upper region. To interpret the macroscopic trends, we extended a two-phase thin-film model by introducing an effective
gravity coupling projected normal and tangential to the agar surface. Simulations suggest that the surface-tangential component mainly drives lateral redistribution, whereas the surface-normal component modifies film pressure and helps distinguish up-facing from down-facing configurations.
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All authors Pengxi Gu, Zixiang Lin, Hui Ren, Jin Zhu, Anqi Li, Omar Said, Weijie Chen, Zijie Qu
Analysis of PDEs
The anisotropic Calderón problem for Riemannian metrics at high fixed frequency
Mihajlo Cekić, Katya Krupchyk, Suman Kumar Sahoo et al. (+1 more)
We study an inverse boundary value problem for the Helmholtz equation on a smooth compact non-trapping Riemannian
manifold with strictly convex boundary. We prove that, given two such metrics, for sufficiently large but fixed frequency $\lambda$, equality of their Dirichlet-to-Neumann maps implies equality of their lens data, up to a smooth boundary-fixing diffeomorphism. This establishes a high-frequency bridge between the Calderón problem and lens
rigidity. We also prove a quantitative version of this result uniformly valid in a suitable bounded set of Riemannian metrics. Crucially, we show that Gaussian beam type solutions concentrating on maximal geodesics, split near the boundary into outgoing/incoming parts, such that the phase of the outgoing part contains information on exit point, direction, and travel time. The recovery of lens data then proceeds by a careful stationary phase analysis and a boundary integral identity.
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All authors Mihajlo Cekić, Katya Krupchyk, Suman Kumar Sahoo, Gunther Uhlmann