arXiv:2607.12325v1 Announce Type: new
Abstract: Hyperspectral photoluminescence (PL) of moir\'e transition-metal dichalcogenide heterobilayers encodes spatially varying exciton landscapes, but extracting that information is hampered by the ambiguity of multi-peak spectral decomposition. Here we develop a peak-decomposition-free inverse framework for quantitative optical metrology of effective disorder coordinates. From the raw cube $I(x,y,E)$ we construct physically motivated descriptor maps -- centroid energy, dominant emission energy, spectral width, low/high spectral-weight ratio, and dominant--centroid offset. Their spatial autocorrelation hierarchy and covariance structure form a robust descriptor fingerprint of multi-scale disorder-sensitive spectral statistics. By matching these descriptor summary statistics to a minimal smooth-plus-trap generative model through a grid-Bayesian inverse, we infer effective disorder coordinates $\Thetaeff=\{\Ws,\xis,\Wt,\nt\}$ with explicit uncertainties. Using synthetic PL cubes generated from controlled multi-scale landscapes, we recover the well-identified smooth-disorder coordinates and constrain the trap sector up to a strength--density degeneracy. We report this degeneracy explicitly as an intrinsic identifiability limit rather than a deficiency of the method, and map four canonical disorder regimes onto a disorder-coordinate diagram. The descriptor statistics are stable against shot noise and pixel pitch, and behave predictably under optical-resolution and energy-window changes. The same pipeline ingests experimental cubes without modification, making it directly applicable to two-dimensional and moir\'e materials. Our results establish descriptor-based hyperspectral PL as a practical, minimal-assumption route to optical disorder diagnostics and provide the validated core of a reusable analysis workflow (\texttt{HyperPL-Diag}) for moir\'e exciton systems.
Science Journals
arXiv:2506.10125v3 Announce Type: replace
Abstract: As one of the key tools in many security tasks, decompilers reconstruct human-readable source code from binaries. Yet, despite recent advances, their outputs often suffer from syntactic and semantic errors and remain difficult to read. Recently, with the advent of large language models (LLMs), researchers began to explore the potential of LLMs to refine decompiler output. Nevertheless, our study of these approaches reveals their problems, such as introducing new errors and relying on unreliable accuracy validation.
In this paper, we present D-LIFT, an enhanced decompiler-LLM pipeline with a fine-tuned LLM using code quality-aware reinforcement learning. Unlike prior work that overlooks preserving accuracy, D-LIFT adheres to a key principle for enhancing the quality of decompiled code: preserving accuracy while improving readability. Central to D-LIFT, we propose D-Score, an integrated code quality assessment system to score the decompiled source code from multiple aspects, and use it to guide reinforcement learning fine-tuning and to select the best output during inference. In line with our principle, D-Score assigns low scores to any inaccurate output and only awards higher scores for readability to code that passes the accuracy check. Our implementation, based on Ghidra and a range of LLMs, demonstrates significant improvements for the accurate decompiled code from the coreutils and util-linux projects. Compared to baseline LLMs without D-Score-driven fine-tuning, our trained LLMs produce 55.3% more improved decompiled functions, as measured by D-Score. Overall, D-LIFT improves the quality of 68.2% of all the functions produced by the native decompiler.
arXiv:2506.12339v2 Announce Type: replace
Abstract: We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language instructions. In this paper, we introduce a hierarchical agentic system consisting of three specialized agents: Manager Agent that decomposes complex user instructions into subtasks; an Action Agent that translates these into structured commands using a Backus-Naur Form (BNF) grammar; and a Reflection Agent that validates alignment between generated actions and the user's original intent. We evaluate SheetMind on the 221-task SheetCopilot Benchmark with GPT-3.5-Turbo. SheetMind achieved 100% execution success and 54.8% functional correctness, exceeding SheetCopilot (44.3%) while maintaining perfect execution reliability. We also conduct ablation study on a separately curated dataset to confirm that the full three-agent configuration consistently outperforms all partial variants. Lastly, we integrate our system into Google Sheets via a Workspace extension.
arXiv:2607.13026v1 Announce Type: new
Abstract: Reliable operation of resonant acoustic levitators requires knowledge of the acoustic field state because the optimum transducer-reflector distance and resonant operating condition shift with wavelength, temperature, object insertion, and mechanical alignment. Existing adjustment methods are limited, especially for compact closed-loop operation and architectures without a passive reflector. Here, we investigate transducer-mounted microelectromechanical system (MEMS) microphones as off-axis external sensors that acquire relative acoustic signals without placing sensors inside the levitation cavity. Using a linear microphone configuration, we performed transducer-reflector distance sweeps over resonance modes n = 5-8 and compared microphone amplitude with acoustic radiation force measured by a precision balance and with peak-to-peak transducer current. The channel-mean microphone-voltage maxima occurred within two sampled distance increments, or at most 30 micrometers, of the force maxima. At the microphone-derived peak positions, at least 98.3% of the corresponding maximum force was retained. Microphone amplitude localized the force maximum more sharply than peak-to-peak transducer current. In one frequency-shift experiment, microphone phase provided a proof of principle for correction-direction estimation, while envelope modulation captured channel-resolved field changes during object oscillation. Ring measurements showed channel-dependent responses as transducer-reflector tilt was varied, but did not provide a calibrated or unique tilt estimate. These results show the potential of external MEMS microphones as relative acoustic observables for resonance-related field-state assessment and provide a basis for compact transducer-side feedback. The principle may also be transferable to transducer-transducer and array-based architectures.
arXiv:2506.07073v2 Announce Type: replace
Abstract: The ultimate purpose of generative music AI is music production. The studio-lab, a social form within the art-science branch of cross-disciplinarity, is a way to advance music production with AI music models. During a studio-lab experiment involving researchers, music producers, and an AI model for music generating bass-like audio, it was observed that the producers used the model's output to convey two or more pitches with a single harmonic complex tone, which in turn revealed that the model had learned to generate structured and coherent simultaneous melodic lines using monophonic sequences of harmonic complex tones. These findings prompt a reconsideration of the long-standing debate on whether humans can perceive harmonics as distinct pitches and highlight how generative AI can not only enhance musical creativity but also contribute to a deeper understanding of music.
arXiv:2506.07473v5 Announce Type: replace
Abstract: Music information retrieval distinguishes between low- and high-level descriptions of music. Current generative AI models rely on text descriptions that are higher level than the controls familiar to studio musicians. Pitch strength, a low-level perceptual parameter of contemporary popular music, may be one feature that could make such AI models more suited to music production. Signal and perceptual analyses suggest that pitch strength (1) varies significantly across and inside songs; (2) contributes to both small- and large-scale structure; (3) contributes to the handling of polyphonic dissonance; and (4) may be a feature of upper harmonics made audible in a perspective of perceptual richness.
arXiv:2607.09760v2 Announce Type: replace-cross
Abstract: Radio frequency fingerprint identification (RFFI) exploits transmitter-specific hardware imperfections as physicallayer identity cues for Internet of Things (IoT) devices, but deep models often degrade across acquisition environments. In multi-antenna reception, antenna topology and frequencyoffset dynamics structure receiver observations, while capturedependent variation distorts target embeddings and misaligns source-trained decision boundaries. This article proposes physicsinformed structure anchoring with capture-aware prototype calibration (PISA-CAPC) to address both representation and decision mismatches. The two stages separate source representation construction from target decision correction. During source training, PISA organizes antenna tokens through a topology-guided graph, conditions propagation on CFO-derived acquisition dynamics, and applies bounded contextual residual suppression to preserve identity evidence. At deployment, unlabeled capture-aware prototype calibration (U-CAPC) estimates capture-local prototypes and recalibrates target decision scores while keeping the representation and source classifier fixed. Thus, calibration uses neither target labels nor target-domain backbone updates. On a measured WiFi benchmark with four receive antennas and ten transmitters, PISA-CAPC achieves a mean target-domain Macro-F1 of 0.9257 under a balanced transductive setting. Component ablations support complementary roles for topology-guided anchoring, CFO-conditioned modulation, reliability-aware token aggregation, contextual suppression, and capture-aware calibration. These results indicate that physically motivated representation learning can be combined with labelfree decision calibration to improve cross-environment RFFI under the evaluated protocol without changing the deployed backbone.
arXiv:2607.12398v1 Announce Type: new
Abstract: Freehand 3-D ultrasound (US) imaging has attracted increasing attention owing to its intuitive volumetric visualization, ease of use, and low cost. However, accurate 3-D reconstruction critically depends on stable probe pose estimation, yet existing trackerless methods remain susceptible to accumulated pose errors, particularly over long scanning trajectories. To address this limitation, we propose a global-to-local pose estimation framework that exploits external camera observations for globally stable localization and B-mode US images for anatomy-aware local refinement. Specifically, the framework comprises a dual-camera branch that performs contextual feature aggregation across camera views and temporal observations to estimate a globally consistent probe trajectory, and a B-mode branch that performs anatomical feature aggregation from sequential US images to capture tissue-dependent local motion cues. A cross-modal fusion module subsequently integrates the contextual camera features and anatomical US features to predict pose residuals and refine the camera-derived estimates in the transformation space. Furthermore, a multi-scale pose loss constrains relative motion over multiple temporal horizons to suppress accumulated drift during extended scans. The proposed framework is validated on phantom and in vivo datasets. On two in-house datasets (FUSION-J and FUSION-L) collected using different machines, the proposed US + Dual-Cam model reduces average trajectory drift to 1.67 mm and 1.29 mm, representing improvement of 16.50% and 27.12%, respectively, over a strong dual-camera baseline, while substantially outperforming US-only pose estimation (>13 mm drift). In in vivo forearm arteries reconstruction, it achieves Hausdorff distances of 1.58 mm, demonstrating the effectiveness of the proposed method on real clinical scenarios.
arXiv:2607.12867v1 Announce Type: new
Abstract: We consider a quiz show game in which a contestant is presented with a sequence of questions. Each time the contestant answers a question correctly, she receives a prize and proceeds to the next question; the probability of answering each question correctly is given. If the contestant answers a question incorrectly, she receives a consolation prize and the game ends. The contestant's problem of determining the optimal order in which to answer questions, for known fixed parameters, is a classic one studied in Kadane (1969), and admits a simple index-based solution. We consider game-theoretic versions of this problem in which a game show host can choose how to allocate a fixed prize budget. Our models are motivated by operational search problems in national security involving reconnaissance missions and inspecting for evidence of nuclear enrichment, as well as certain scheduling problems. We study three variants of the game, corresponding to different ways in which the host can distribute the prize money. For the first variant, we provide complete closed-form solutions, including equilibrium strategies and the value of the game. We reduce the second variant to a game solved in the literature. For the third variant, we obtain partial results by analyzing a more general game with a geometric structure.
arXiv:2602.10892v5 Announce Type: replace
Abstract: Smart contracts are stateful programs deployed on blockchains; they secure over a trillion dollars in transaction value per year. High-stakes smart contracts often rely on timely alerts about external events, but prior work has not analyzed their resilience to an attacker suppressing alerts via bribery. We formalize this challenge in a cryptoeconomic setting as the \emph{alerting problem}, giving rise to a game between a bribing adversary and~$n$ rational participants, who pay a penalty if they are caught deviating from the protocol. We establish a quadratic, i.e.,~$O(n^2)$, upper bound, whereas a straightforward alerting protocol only achieves~$O(n)$ bribery cost.
We present a \emph{simultaneous game} that asymptotically achieves the quadratic upper bound and thus asymptotically-optimal bribery resistance. We then present two protocols that implement our simultaneous game: The first leverages a strong network synchrony assumption. The second relaxes this strong assumption and instead takes advantage of trusted hardware and blockchain proof-of-publication to establish a timed commitment scheme. These two protocols are constant-time but incur a linear storage overhead on the blockchain. We analyze a third, \emph{sequential alerting} protocol that optimistically incurs no on-chain storage overhead, at the expense of~$O(n)$ worst-case execution time. All three protocols achieve asymptotically-optimal bribery costs, but with different resource and performance tradeoffs. Together, they illuminate a rich design space for practical solutions to the alerting problem.
arXiv:2510.01841v2 Announce Type: replace
Abstract: Person search aims to jointly perform person detection and re-identification by localizing and identifying a query person within a gallery of uncropped scene images. Existing methods predominantly utilize ImageNet pre-trained backbones, which may be suboptimal for capturing the complex spatial context and fine-grained identity cues necessary for person search. Moreover, they rely on a shared backbone feature for both person detection and re-identification, leading to suboptimal features due to conflicting optimization objectives. In this paper, we propose DiffPS (Diffusion Prior Knowledge for Person Search), a novel framework that leverages a pre-trained diffusion model while eliminating the optimization conflict between two sub-tasks. We analyze key properties of diffusion priors and propose three specialized modules: (i) Diffusion-Guided Region Proposal Network (DGRPN) for enhanced person localization, (ii) Multi-Scale Frequency Refinement Network (MSFRN) to mitigate shape bias, and (iii) Semantic-Adaptive Feature Aggregation Network (SFAN) to leverage text-aligned diffusion features. DiffPS sets a new state-of-the-art on CUHK-SYSU and PRW.
arXiv:2601.21140v2 Announce Type: replace-cross
Abstract: We establish efficient algorithms for weakly-interacting quantum spin systems at arbitrary temperature. In particular, we obtain a fully polynomial-time approximation scheme for the partition function and an efficient approximate sampling scheme for the thermal distribution over a classical spin space. Our approach is based on the cluster expansion method and a standard reduction from approximate sampling to approximate counting.
arXiv:2511.04689v3 Announce Type: replace
Abstract: Evaluating large language models (LLMs) typically requires thousands of benchmark items, making the process expensive, slow, and increasingly impractical at scale. Existing evaluation protocols rely on average accuracy over fixed item sets, treating all items as equally informative despite substantial variation in difficulty and discrimination. We introduce ATLAS, an adaptive testing framework based on Item Response Theory (IRT) that estimates model ability using Fisher information-guided item selection. ATLAS reduces the number of required items by up to 90% while maintaining measurement precision. For instance, it matches whole-bank ability estimates using only 41 items (0.157 MAE) on HellaSwag (5,600 items). We further reconstruct accuracy from ATLAS's ability estimates and find that reconstructed accuracies closely match raw accuracies across all five benchmarks, indicating that ability ${\theta}$ preserves the global performance structure. At the same time, ${\theta}$ provides finer discrimination within accuracy-equivalent models: among more than 3,000 evaluated models, 23-31% shift by more than 10 rank positions, and models with identical accuracies receive meaningfully different ability estimates. Code and calibrated item banks are available at https://github.com/Peiyu-Georgia-Li/ATLAS.git.
arXiv:2511.18240v5 Announce Type: replace
Abstract: The rapid expansion of the Internet of Things (IoT) has intensified cybersecurity challenges, particularly in detecting and mitigating Distributed Denial-of-Service (DDoS) attacks at the network edge. Traditional Intrusion Detection Systems (IDSs) remain limited by static signatures, dependence on labeled data, poor adaptability to evolving and zero-day attacks, and high computational overhead on resource-constrained edge gateways. Moreover, most Deep Reinforcement Learning (DRL)-based IDS studies prioritize detection performance while overlooking energy consumption and carbon impact. To address these limitations, this paper proposes two carbon-aware DRL-based IDS frameworks: DeepEdgeIDS, a label-free Autoencoder-DQN architecture for anomaly-guided online mitigation, and AutoDRL-IDS, a supervised LSTM-DQN model for temporally informed detection and response. Both systems incorporate multi-objective reward functions that jointly consider security performance, response latency, energy consumption, memory utilization, and estimated carbon emissions, using learning-paradigm-specific detection feedback. AutoDRL-IDS employs ground-truth-dependent detection metrics during supervised training, whereas DeepEdgeIDS relies on anomaly confidence and post-mitigation traffic stabilization for label-free online learning. The proposed systems are theoretically analyzed and experimentally evaluated on physical IoT edge gateways under DDoS traffic. The results show that AutoDRL-IDS achieves 94 percent detection accuracy, while DeepEdgeIDS attains 98 percent offline evaluation accuracy and demonstrates stronger adaptability to previously unseen attack patterns.
arXiv:2607.12520v1 Announce Type: new
Abstract: What a frontier model recalls about a person or tool from its own weights -- before any retrieval step -- often shapes the first description a human sees, making that parametric corpus presence a measurement problem. Citations explain about a third of whether a model recognizes a researcher; we target the residual and build NameRank, a [0,1] recognition score: each of 4,685 entities in 54 cohorts is probed with one open-ended question across 36 models, and an independent judge returns a binary verdict against a curated gold -- did the model state a specific, non-guessable fact about this exact entity? -- so hallucination, context echo, and guesses earn nothing. Synthetic-null entities hold the floor near zero, and verdicts track the entity, not the model. One thesis organizes the findings: recognition is paid to named, indexable artifacts, not to credentials or titles. Every Olympic-style credential sits below a working-researcher baseline, because no named artifact ships with the medal, yet the ranking inverts at the marquee tier, where Nobel, Turing, and Fields laureates saturate the panel. For independent creators the tool out-ranks its maker, and the credential that does propagate is a named method or awarded paper. Being one of many named contributors to a celebrated artifact, by contrast, earns almost nothing -- the authors listed on a flagship model report or system card sit near the recognition floor -- because recognition attaches to the artifact's own distinctive name, not to the roster behind it. No bibliometric predicts recognition well; top-density institutions out-recognize peers at matched citations; and on 258 news events recognition loads on peak salience, not persistence. A self-report probe shows introspection reads a corpus prior, not its own knowledge.
arXiv:2407.19692v5 Announce Type: replace
Abstract: Self-supervised learning (SSL) has recently attracted significant attention in the field of recommender systems. Contrastive learning (CL) stands out as a major SSL paradigm due to its robust ability to generate self-supervised signals. Mainstream graph contrastive learning (GCL)-based methods typically implement CL by creating contrastive views through various data augmentations. Despite these methods are effective, we argue that there still exist several challenges. i) Data augmentation requires additional graph convolution (GCN) or modeling operations, significantly increasing time costs. Moreover, graph augmentation disrupts the intrinsic properties of the user-item graph by randomly removing nodes/edges, while feature augmentation applies noise to all nodes, neglecting their unique characteristics. ii) Existing GCL-based methods use traditional CL objectives to capture self-supervised signals. However, few studies have explored obtaining more beneficial CL objectives from more perspectives and have attempted to fuse the varying self-supervised signals from these CL objectives to enhance recommendation performance.
To overcome these challenges, we propose Adaptive Fusion Graph Contrastive Learning (AFGCL) for recommendation. AFGCL exploits structural information naturally produced during graph propagation to construct contrastive representations. Specifically, we introduce an adaptive fusion strategy that estimates the contributions of different propagation depths to the primary recommendation task and adaptively combines their representations. Furthermore, we construct an explicit representation for each observed user--item interaction and propose a fused contrastive objective. Experimental results on three public datasets demonstrate the superior recommendation performance and training efficiency of AFGCL compared with state-of-the-art baselines.
arXiv:2506.22993v2 Announce Type: replace
Abstract: How much of children's educational attainment remains predictable from the wider social contexts in which they grow up, once parental background is known? Sociological research places households, schools, neighborhoods, and extended kin at the center of intergenerational reproduction, yet whether these contexts add predictive information beyond parental background is rarely tested directly. This matters because the added value of social contexts helps distinguish whether they operate as independent sources of inequality or as channels through which parental advantage is reproduced. Using population-scale administrative data from Statistics Netherlands, we construct a network linking a full cohort of children aged 11-12 to parents, extended kin, classmates, household members, and neighbors, and predict university completion at ages 24-25. We compare logistic regression and gradient boosting, which use individual-level aggregates of these contexts, with graph neural networks (GNNs) operating directly on the network, interpreting differences in out-of-sample performance as prediction gaps. Parental socioeconomic background captures most predictable variation; even GNNs add little once parents are known. Prediction gaps are largest among children without a registered father, especially girls and those with less-educated mothers. Methodologically, we argue that prediction gaps can support sociological theory-building: small gaps show where current theory-based models already explain what can be measured; large gaps identify where targeted mechanism-focused research is warranted.
arXiv:2607.12437v1 Announce Type: new
Abstract: In recent years, much attention has been devoted to the study of optimization problems under norm-based objectives coming from the rich class of monotone, symmetric norms (and their generalizations). This work has however almost exclusively focused on covering problems, wherein one seeks to minimize the norm of the cost vector induced by a solution.
We introduce and study the class of {\em norm-budgeted packing problems}, which are packing problems where the resource constraints underlying the packing problem are modeled via a {\em norm budget constraint} involving a {\em monotone, symmetric norm}. Formally, we have some elements with associated rewards and sizes, a downwards-closed collection of feasible solutions, and a budget $B$. Each solution induces a size vector, and the goal is to maximize the total reward subject to the norm-budget constraint $f(\text{size vector})\leq B$. The versatility of monotone, symmetric norms implies that a variety of classical packing problems can be captured under the umbrella of norm-budgeted packing problems. Moreover, the closure properties of monotone, symmetric norms, also enable one to encode multiple different norm-budget constraints via a single monotone, symmetric norm.
We consider the norm-budgeted versions of a variety of canonical packing problems, including knapsack, matching, maximum-weight independent set in a $k$-set system, maximum generalized assignment problem (MaxGAP), and $k$-facility location, and develop a framework that allows us to obtain {\em constant-factor approximation guarantees} for these problems, and {\em PTASes for knapsack, and MaxGAP on identical and related machines}.
We also develop constant-factor approximation algorithms for the {\em submodular} versions of some norm-budgeted packing problems, wherein the reward function is now specified by a monotone, submodular function.
arXiv:2607.12626v1 Announce Type: new
Abstract: Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when carried over to a larger domain. We recently showed that such policies are also prone to saturated bang-bang actuations that collapse into standing streamwise waves whose scale is set by the computational box rather than by the near-wall cycle, and proposed architectural fixes that avoid these degeneracies. Here, we employ Evolution Strategy (ES) to optimise a recurrent closed-loop controller directly on a large turbulent channel at $\mathit{Re}_{\tau}\simeq180$, evaluating policy performance over full flow episodes using an energy-aware criterion and processing candidate policies in parallel. To our knowledge, this is the first application of an evolution strategy to the control of a turbulent flow. The ES controller reduces the skin friction by about $26\%$, exceeding the gradient-based multi-agent controller of Cavallazzi et al. (2026), GRU-MARL, trained on a minimal box ($17\%$), and marginally exceeding classic opposition control (OC, $22\%$). A wall-normal decomposition of the friction, Reynolds-stress profiles and anisotropy invariants show that the ES and opposition-controlled flows follow separate trajectories through the buffer layer, reaching comparable drag reduction by different reorganisations of the near-wall turbulence. In particular, the ES actuation correlates predominantly with the streamwise velocity fluctuations rather than with the wall-normal velocity that classical OC targets.
arXiv:2607.12473v1 Announce Type: new
Abstract: This study extends variable-lattice-density optimization to high-Reynolds-number flows for the design of periodically arranged pin-fin heat sinks. Effective permeability and drag coefficient are identified from unit-cell Reynolds-averaged Navier--Stokes analyses of cylindrical pin-fin arrays and incorporated into a reduced model based on the Darcy--Forchheimer law for macroscopic design exploration. To enable stable optimization under high-Reynolds-number conditions, a dual-mesh framework is introduced, in which the flow field and sensitivities are evaluated on a fine mesh, whereas the design variables are updated on a coarse mesh corresponding to the unit-cell arrangement. For the base condition, the $L_2$ norm of the temperature deviation from the area-averaged temperature is decreased from 4.38 K to 1.00 K in the reduced model, and geometry-resolved analysis of the reconstructed design confirms a reduction from 5.98 K to 1.17 K. Additional calculations with higher inlet velocities and modified outlet locations show that the proposed method captures the dominant flow-redistribution trends under different operating and geometric conditions, although the thermal objective became less accurate when local solid-temperature variations became pronounced. These results indicate that the proposed approach is useful as practical design-exploration for identifying candidate pin-fin heat sink configurations under high-Reynolds-number conditions.
arXiv:2607.12481v1 Announce Type: new
Abstract: This paper develops a scenario-free uncertainty-aware bilevel optimization framework for coordinated electric vehicle (EV) charging and reactive power support in distribution networks using distribution locational marginal prices (DLMPs). The upper-level EV aggregator jointly schedules active and reactive charging power to minimize charging costs, while the lower-level energy management system performs network-constrained economic dispatch and determines DLMPs subject to feeder and voltage constraints. To capture uncertainties in load demand and photovoltaic (PV) generation, a compact robust counterpart (RC) reformulation is developed that avoids the computational burden of large-scale stochastic programming and conventional robust optimization. Unlike existing robust counterpart methods that primarily assume Gaussian uncertainties, the proposed approach derives a deterministic reformulation for net-demand uncertainty modeled by a normal-minus-beta distribution, providing a more realistic representation of asymmetric load and renewable variability. An exactness lemma preserves the economic interpretation of DLMPs after KKT reformulation and Big-M linearization. EV chargers also provide reactive power support through non-unity power factor operation to improve voltage regulation. Simulation results on the IEEE 33-bus distribution system demonstrate improved voltage security, effective uncertainty-aware EV coordination, and significantly lower computational complexity than conventional stochastic and robust optimization approaches.
arXiv:2607.12500v1 Announce Type: new
Abstract: Deep learning models for online handwriting recognition have been shown effective and are increasingly deployed in practical applications. However, their vulnerability to adversarial attacks is still a challenge. Existing adversarial methods are predominantly designed for image-based inputs and typically rely on additive spatial perturbations. When applied to online handwriting, which is inherently represented as a time series of pen trajectories, such perturbations often introduce high-frequency jitter and visibly unnatural stroke artifacts. In this work, we propose a novel adversarial attack framework for online handwriting recognition based on salience-guided temporal editing. Instead of adding noise, the proposed method generates adversarial examples by inserting and deleting points at time steps selected according to temporal salience, preserving the shape and smoothness of the original handwriting. Temporal salience is estimated using gradient-based activation mapping, which guides edits toward time steps that strongly support the original class prediction. We evaluate the proposed approach on the Unipen and CASIA-OLHWDB datasets under both white-box and one-shot black-box attack settings. Experimental results demonstrate that while conventional image-based attacks achieve strong white-box performance, they exhibit poor transferability across models. In contrast, the proposed temporal editing attack achieves stronger one-shot black-box transferability while preserving the visual structure of the handwriting. These results indicate that temporal editing is a relevant threat model for online handwriting recognition, particularly in one-shot black-box transfer settings.
arXiv:2607.12504v1 Announce Type: new
Abstract: Post-Quantum Cryptography (PQC) is increasingly being integrated into TLS 1.3 to enhance resilience against quantum-enabled attacks. However, the additional computational and communication overhead introduced by PQC primitives during the handshake phase may also amplify the impact of TLS handshake exhaustion attacks, leading to more severe Distributed Denial-of-Service (DDoS) threats.
In this study, we establish an empirical testbed consisting of one PQC-enabled TLS server and ten attacking nodes, generating over 16.5 GB of mixed traffic data that includes both legitimate browsing behavior and high-intensity handshake exhaustion attacks.
Experimental results show that PQC-TLS can prolong periods of sustained high CPU utilization on the server by up to 88 times, significantly amplifying the effectiveness of such attacks. Furthermore, we evaluate state-of-the-art deep learning-based Intrusion Detection Systems (IDS) and observe a substantial decline in attack detection performance under PQC traffic conditions.
In particular, exosphere achieves only around 50% recall, while HyperVision's AU-ROC degrades to near-random levels (0.49), revealing critical detection blind spots in existing IDS when operating in PQC environments.
The main contributions of this work are threefold: (1) we systematically quantify and analyze the root causes of IDS detection blind spots in PQC settings; (2) we publicly release a comprehensive PQC-DDoS hybrid traffic dataset, including precise attack timestamps and server-side resource monitoring data; and (3) we open-source all experimental code and AWS deployment scripts, enabling a fully reproducible cloud-based testing environment.
These resources aim to support both academia and industry in developing next-generation PQC-aware intrusion detection systems.
arXiv:2607.12754v1 Announce Type: new
Abstract: Reconfigurable optical and optoelectronic devices require compact tuning mechanisms capable of reshaping electronic, excitonic, polaritonic, and photonic responses without rebuilding the underlying nanostructure. Against this backdrop, twist has emerged as a powerful geometric degree of freedom that reconfigures interlayer coupling, momentum matching, symmetry, radiation channels, and chiral response by simply rotating adjacent two-dimensional layers or photonic lattices. In this Review, we survey twist-engineered optical and optoelectronic devices spanning van der Waals materials and photonic platforms. We first review the current landscape of twist-angle metrology, classifying existing characterization approaches into three complementary categories: direct structural imaging, methods based on moir\'e periodicity and morphological features, and techniques that infer the twist angle from spectroscopic or electronic responses. We then survey the principal technological routes for twist-angle control, including deterministic transfer and growth strategies, atomic force microscopy (AFM)-assisted manipulation, quantum twisting microscopy (QTM), microelectromechanical systems (MEMS), and emerging non-contact approaches, highlighting their respective capabilities, limitations, and prospects for programmable and scalable moir\'e photonic platforms. Finally, we discuss the future evolution of twist engineering from the fabrication of individual twisted structures toward dynamically reconfigurable, feedback-controlled, and manufacturable photonic systems. We further highlight MEMS-based rotation, piezoelectric actuation, and micro-LiDAR as representative enabling technologies and emerging applications within this broader landscape.
arXiv:2607.00011v2 Announce Type: replace
Abstract: Reusable agent skills are emerging as a service-oriented capability layer for Large Language Model (LLM) agents. Unlike plain retrieval items, a skill exposes functional capabilities, input-output assumptions, tool dependencies, context cost, and risk metadata. Selecting skills is particularly challenging for small LLM agents, which can load only a few capability units under restricted context, tool availability, and risk tolerance. Existing fixed Top-k methods rank skills by textual relevance and overlook requirement satisfaction, deliverability, and operational constraints. We present SkillSelect-Serve, a QoS-aware, budget-constrained Skill Service recommendation framework. Raw skills are profiled as structured Skill Services, the task is converted into a structured requirement object, and candidates discovered from a large-scale registry are ranked by a calibrated task-conditioned suitability estimator and packed by a constrained projection enforcing token-budget, aggregated-risk, and tool-availability constraints, using only deployment-observable features. On a registry of 35,353 skills with pooled multi-positive relevance judgments verified by two independent assessors, the unconstrained top-5 recommendation fits a realistic 4,000-token context for only 9.1% of tasks; the constrained projection restores 100% deliverability at a cost of only 1.14 points of hit rate, outperforming retrieve-and-rerank, budget truncation, and diversity-based selection under identical budgets. The same mechanism halves delivered risk exposure and eliminates the 44-81% tool-violation rates of tool-agnostic recommendation. At an identical three-service budget, hit rate improves from 0.8864 to 0.9091 over fixed Top-3 retrieval. The results support managing reusable agent skills as discoverable, comparable, and constraint-aware service units instead of plain retrievable documents.