Published: 2026-07-15 17:59:24
Authors: Atkin D. Hyatt, Mitul Dey Chowdhury, Mahir Chowdhury, Mohamed J. Ahamed, Dalziel J. Wilson
Categories: quant-ph, cond-mat.mes-hall, physics.app-ph, physics.ins-det, physics.optics
Abstract:
Strained membrane resonators have emerged as a promising platform for optomechanical accelerometry; however, the desired combination of low frequency and high $Q$-mass product requires a rethinking of their dissipation dilution engineering. Applying Bayesian optimization to a Si$_3$N$_4$ membrane, we discover a class of sail-like trampoline resonators in which the frequency is decreased by an order of magnitude while preserving the $Q$-mass product. We demonstrate centimeter-scale sails with kHz frequencies, $Q\sim10^7$ and $Q\times\text{mass}\sim$ 10 g. Vertically integrating a 7 kHz device with a nanoribbon, we realize a monolithic cavity optomechanical accelerometer with a room temperature thermal noise of $40\;\text{n}g_0/\sqrt{\text{Hz}}$, sufficient to resolve $μg_0/\sqrt{\text{Hz}}$ ambient vibration over a bandwidth of 4 kHz with a displacement imprecision of $10^{-14}\;\text{m}/\sqrt{\text{Hz}}$. Cryogenic arrays of sail membranes may be attractive for new physics searches and distributed quantum sensing experiments.
Published: 2026-07-15 17:37:25
Authors: Md Aktar Hossain, Saikat Das
Categories: cond-mat.mtrl-sci, cond-mat.mes-hall
Abstract:
Harnessing Rashba spin-orbit interaction and related spintronic functionalities has traditionally relied on metallic surfaces or interfaces containing elemental heavy metals. Here, using first-principles calculations and Cu(001)/WO$_3$(001) as a model heterostructure, we show that interfacing a light metal, Cu, with a band insulator, WO$_3$, yields an interface state that exhibits a robust Rashba spin splitting arising from the interplay between linear and cubic Rashba effects. The spin splitting is driven by the strong spin-orbit coupling of W atoms and enabled by W-Cu orbital hybridization at the interface. The cubic Rashba contribution asymptotically grows with Cu thickness and can be explained in terms of cross-coupling between the vacuum/Cu and Cu/WO$_3$ interfaces. This interfacial cross-coupling, however, diminishes at larger Cu thicknesses, allowing us to extract the intrinsic cubic Rashba parameter, which has a giant value of approximately -1.93 eV $Å^3$. In contrast, the linear Rashba parameter is only weakly affected by this cross-coupling and varies from approximately 0.30 to 0.49 eV Å. We further show that sizable linear and cubic Rashba effects persist across several interface geometries and Cu surface orientations, including (110) and (111). Our work identifies the Cu/WO$_3$ interface as a novel light-metal/heavy-element-based oxide platform for exploring the rich spectrum of Rashba physics, including linear and nonlinear spin-orbit phenomena.
Published: 2026-07-15 17:36:23
Authors: Soham Jana
Categories: math.ST, stat.ML
Abstract:
Speckle noise is a multiplicative noise commonly encountered in coherent imaging modalities such as synthetic aperture radar, optical coherence tomography, and digital holography. Although deep learning methods, in practice, have achieved state-of-the-art performance for speckle denoising, their fundamental statistical limits remain largely unexplored. Unlike additive noise models, multiplicative speckle noise makes the regression function unidentifiable from the conditional mean, rendering conventional least-squares-based deep learning approaches inapplicable.
We study the minimax estimation of smooth nonparametric regression functions using likelihood-based deep neural network (DNN) estimators under a model with both multiplicative speckle noise and additive Gaussian noise. Our framework accommodates both low-dimensional and sparse high-dimensional features. We establish finite-sample upper bounds on the estimation error of the proposed DNN estimators and derive minimax lower bounds for nonparametric function recovery under our model, showing that they match up to logarithmic factors in the sample size. Moreover, these minimax rates coincide, up to logarithmic factors, with those for nonparametric regression under additive Gaussian noise alone, demonstrating that the intrinsic difficulty of estimation remains essentially unchanged despite the challenges posed by multiplicative speckle noise. Numerical experiments further supports consistency of our DNN-based despeckling methods and demonstrate their effectiveness.
Published: 2026-07-15 17:31:40
Authors: Massimiliano Di Matteo, Maria Ferrara
Categories: math.GR
Abstract:
We construct a finite skew brace whose additive group is perfect and whose multiplicative group is non-perfect and almost simple. This gives an affirmative answer to Problem 20.109 in the twenty-first edition of the Kourovka Notebook. The additive and multiplicative groups of our example are \[
\PSU_5(64)\times A_5
\quad\text{and}\quad
\Aut(\PSU_5(64)), \] respectively. The construction combines a skew brace with additive group \(A_5\) and multiplicative group \((C_5\rtimes C_4)\times C_3\), the splitting of the automorphism extension of \(\PSU_5(64)\), and a semidirect product of skew braces.
Published: 2026-07-15 17:21:35
Authors: Maren Cosens, Patricio Schurter, Nicholas P. Konidaris, Gwen C. Rudie, Andrew B. Newman, Leon Aslan, Robert Barkhouser, Christoph Birk, Julia Brady, Tyson Hare, Stephen C. Hope, Charlie Hull, Karim Kaismoune, Daniel D. Kelson, Gerrad Killion, Alicia Lanz, Jacob McCloskey, Solange V. Ramirez, William Schoenell, Stephen A. Smee, Jason E. Williams
Categories: astro-ph.IM
Abstract:
The Magellan InfraRed Multi-Object Spectrograph (MIRMOS) is a planned next generation multi-object and integral field spectrograph for the 6.5m Magellan telescopes at Las Campanas Observatory in Chile. MIRMOS will perform $\rm R\sim3700$ spectroscopy over a simultaneous wavelength range of 0.886 - 2.404um (Y, J, H, K bands) in addition to imaging over the range of 0.7 - 0.886um. The integral field mode of operation for MIRMOS will be achieved via an image slicer style integral field unit (IFU) located on a linear stage to facilitate movement into the beam during use or storage while operating in multi-object mode. The IFU will provide an $\rm\sim18''\times26''$ field of view (FoV) made up of $\rm0.84''\times26''$ slices. This will be the largest FoV IFS operating at these wavelengths from either the ground or space, making MIRMOS an ideal instrument for a wide range of science cases including studying the high redshift circumgalactic medium and emission line tracers from ionized and molecular gas in nearby galaxies. We present here an update on the IFU design from our 2024 proceeding. Previously we utilized a re-imaging style slicer which required freeform pupil mirrors in order to achieve the required image quality while obeying significant packaging constraints near the instrument focal surface. In order to reduce the manufacturing cost and decouple the IFU from the configurable slit unit used in multi-object mode, we have moved the IFU deeper into the instrument, allowing for a switch to a virtual style IFU. This now requires a re-imaging doublet before the slicer mirrors, but removes the need for any freeform surfaces. We present here the optical design and predicted performance of the new MIRMOS IFU along with a conceptual design for the opto-mechanical system which will move the IFU between its active and stored positions.
Published: 2026-07-15 16:40:45
Authors: Alice Bernamonti, Federico Galli, Michal P. Heller, Fabio Ori, Alexandre Serantes
Categories: hep-th, cond-mat.stat-mech, gr-qc, quant-ph
Abstract:
We formulate timelike entanglement entropy and its Rényi extension in two-dimensional conformal field theory through the analytic continuation of replica twist correlators to time-ordered, timelike-separated insertions. This field-theoretic construction grounds and generalizes recent developments, and applies to temporal subregions of arbitrary extent. Within three-dimensional holography, the semiclassical boundary correlator identifies boundary-anchored complex geodesics as the relevant bulk saddles and selects the one with the smallest real part of the length. This provides a direct boundary derivation of the proposed complex extremal surface prescription and extends to Rényi index $n>1$, for which we explicitly construct the corresponding complex cosmic brane geometry in the vacuum. We develop these ideas in several representative settings, including locally and globally excited states and quantum operator quenches, making manifest the precise agreement between boundary twist correlator and bulk complex geodesic calculations. For AdS-Vaidya, our approach predicts a different result from earlier piecewise geodesic constructions, while reproducing the field theory answer. Across these examples, the operator ordering uniquely determines the imaginary part of the complex-valued entropy, which is quantized in units of $cπ/6$ and sensitive to the effective causal structure but not to the underlying dynamics.
Published: 2026-07-15 16:39:27
Authors: Joé Brendel, Reto Kaufmann
Categories: math.SG
Abstract:
Every even symplectic Hirzebruch surface contains a Lagrangian torus and every odd symplectic Hirzebruch surface contains a Lagrangian Klein bottle as their respective real locus. From a toric point of view, this is a visible Lagrangian which surjects to the full moment polytope under the moment map. In this paper, we prove that every Hirzebruch surface contains both a Lagrangian torus and a Lagrangian Klein bottle with this property. An interesting consequence is that the topology of visible Lagrangian submanifolds is not determined by their image under the moment map. The proof is based on a new construction, which we call the spread of a family of Hamiltonian diffeomorphisms.
Published: 2026-07-15 16:36:04
Authors: Wenxiao Wang, Priyatham Kattakinda, Soheil Feizi
Categories: cs.AI, cs.CL, cs.LG
Abstract:
Most reported gains from agent-optimization methods are one-shot: an agent is optimized against a fixed benchmark and the resulting improvement is reported as if it were a stable property of the method. This does not test the setting that matters for deployed agents, where optimization is applied recursively as new failures and new tasks appear over time. The central question this raises is whether optimizer-driven gains compound: after an agent has been optimized once, can it be optimized again on newly arrived tasks without eroding the gains the first round produced? We study this question with a two-phase continual-learning evaluation built from hard tasks in Terminal-Bench 2.0, comparing three approaches to agent-harness optimization (GEPA, Meta Harness, and RELAI's Verifiable Continual Learning, RELAI-VCL) under identical optimization budgets. All three methods improve over the baseline agent in the conventional, static, single-phase setting. However, once new tasks are introduced, the methods diverge sharply: GEPA's optimized agent transfers below the unoptimized baseline, Meta Harness transfers well but fails to improve further once given a second optimization budget, and RELAI-VCL is the only method that both transfers positively to unseen tasks and continues improving after those tasks are folded into the optimization objective, reaching the highest pass rate at every evaluated stage and the highest lifelong average pass rate overall (76.4% vs. 66.0% for GEPA, 64.6% for Meta Harness, and 58.7% for the baseline). Our key observation was that optimization gains compounded only when regression control was built into the optimization loop, providing an inductive bias against shortcut solutions that fail to generalize.
Published: 2026-07-15 16:16:42
Authors: Leitian Tao, Baolin Peng, Wenlin Yao, Tao Ge, Hao Cheng, Mike Hang Wang, Jianfeng Gao, Sharon Li
Categories: cs.LG
Abstract:
Multi-turn agents solve complex tasks through extended sequences of tool interactions before producing a final answer, making credit assignment a fundamental challenge during post-training. Outcome rewards provide reliable supervision for short-horizon reasoning, but become sparse and high-variance as trajectories grow to tens or hundreds of tool calls. They can also be misleading: a failed rollout may contain many useful actions that move the agent closer to the goal, yet outcome-only training assigns them the same negative advantage as the eventual mistake. We propose TRACE (Turn-level Reward Assignment via Credit Estimation), a dense credit-assignment method for agentic reinforcement learning. TRACE represents rollouts as state transitions at tool-call boundaries, obtains gold-answer log-probabilities from a frozen reference model, transforms them into log-ratio state values, and derives per-action rewards as Temporal-Difference changes in those values. This requires no additional critic or process-label training, and its one-step log-ratio TD component telescopes across redundant tool calls. On long-horizon complex search, TRACE substantially improves base-model tool-use ability using pure RL, without a cold-start supervised fine-tuning stage, an agentic mid-training stage, or training on live-web data. On the closed-web BrowseComp-Plus benchmark, it raises Qwen3-4B from $7.2$ to $35.6$ and Qwen3-30B-A3B from $8.4$ to $42.6$. The learned search behavior also transfers to open-web benchmarks, and the learning curves show earlier improvement and faster convergence during RL training.
Published: 2026-07-15 13:07:25
Authors: Teng Li, Hanfei Shi, Chunjiang Zhao, Ya Xiong
Categories: cs.RO
Abstract:
Selective harvesting in clustered strawberry environments is challenging because ripe fruits are often occluded by surrounding unripe fruits, making direct grasping unreliable. To address this problem, this paper proposes a hierarchical reinforcement learning framework, termed VGPA, which integrates a vision-guided decision mechanism and a Progressive Adaptive Exploration Strategy (PAES) for vision-based obstacle separation and harvesting. The task was decomposed into two sequential stages: obstacle separation and target grasping. At the high level, the vision-guided mechanism improved option selection and accelerated policy convergence. At the low level, PAES improved exploration efficiency and training stability during continuous control learning. In simulation experiments, the learned policy achieved a success rate of 96.7%. In addition, sim-to-real transfer experiments on a self-developed parallel robot showed that the proposed method achieved success rates ranging from 71.7% to 88.3%, outperforming direct picking while requiring only 1.22~s more average harvesting time. These results verified the effectiveness, generalization ability, and practical potential of the proposed method for robotic harvesting in complex clustered environments.
Published: 2026-07-15 12:46:11
Authors: Abraham Rueda Zoca
Categories: math.FA
Abstract:
We study uniform versions of the diameter two properties, that is, the uniform slice-D2P, the uniform D2P and the uniform SD2P, as the property that every ultrapower over any free ultrafilter over $\mathbb N$ has the respective diameter two property. The aim of this note is to prove that all the uniform diameter two properties are different eachother.
Published: 2026-07-15 12:41:44
Authors: Ruby J. Wright, Roosa Heiskanen, Shihong Liao, Alexander Rawlings, Peter H. Johansson, Max Mattero, Fiona H. Panther, Atte Keitaanranta
Categories: astro-ph.GA
Abstract:
Merging supermassive black holes (SMBHs) in low- and intermediate-mass galaxies are important sources for future millihertz gravitational-wave observatories such as LISA. Predicting the delay between galaxy coalescence and SMBH merger is therefore critical for modelling the observable SMBH merger population. Using the KETJU code, we perform 16 equal-mass galaxy merger simulations as part of the Resolving supermAssive Black hole Binaries In galacTic hydrodynamical Simulations (RABBITS) series to investigate SMBH binary evolution in galaxies with stellar masses below $M_{\star}\lesssim10^{10}\,{\rm M}_{\odot}$. We systematically vary the strength of stellar feedback by altering the supernova outflow velocity by a factor of $\sim4$, while still producing galaxies consistent with observed scaling relations. We find post-hardening SMBH merger time-scales spanning $\sim30$-$500\,{\rm Myr}$, with stronger stellar feedback producing systematically longer merger delays through its impact on the central stellar density of the merger remnants. Across our suite, merging time-scales vary by more than an order of magnitude, demonstrating that uncertainties in stellar feedback alone can translate into large uncertainties in SMBH merger delays. At the onset of hardening, the binary evolution remains consistent with stellar-dynamical hardening models based on the local stellar density and velocity dispersion near the binary sphere of influence. Using KETJU as a benchmark, we show that merging time-scales can be recovered with useful accuracy when these nuclear stellar properties are extrapolated from scales up to $\sim 100\,R_{\rm infl}$. These results provide a promising route for modelling SMBH mergers in cosmological simulations.
Published: 2026-07-15 12:31:19
Authors: Wenxuan Miao, Haosong Liu, Weiming Hu, Zihan Liu, Aiyue Chen, Jianlin Yu, Yiwu Yao, Yiming Gan, Jieru Zhao, Jingwen Leng, Minyi Guo, Yu Feng
Categories: cs.AR, cs.AI
Abstract:
Video diffusion transformers (vDiTs) generate high quality video but introduce extremely high compute cost due to the long diffusion timesteps and self attention computation. As diffusion timesteps are reduced, the computation cost of self attention becomes the dominant bottleneck. Existing acceleration approaches largely inherit sparse attention techniques from large language models, which fail to consider the unique spatiotemporal correlation of video data.
This paper presents Kaleido, an algorithm hardware codesign that accelerates all operations in vDiTs by exploiting channel-wise spatiotemporal correlations in latent space. Based on this insight, we propose a lightweight channelwise reuse algorithm that skips redundant computations by reusing partial results while preserving higher generative quality than prior methods (>17 dB). To efficiently support this algorithm, we design a systolic array like accelerator with reconfigurable processing elements and a lightweight data dispatcher to mitigate irregular sparsity and data access patterns introduced by our reuse algorithm. Evaluations across three mainstream vDiT models show that Kaleido achieves up to 5.9x speedup and 16.0x energy savings over state of the art accelerators.
Published: 2026-07-15 12:22:36
Authors: Charilaos Papaioannou, Ioannis Tsantilas, Dimitris Giannakakos, Vasilis Michalakopoulos, Sotiris Pelekis, Vangelis Marinakis, Arsam Aryandoust, Antonello Monti, Ricardo J. Bessa, Perdo P. Vergara, Jochen Cremer, Elissaios Sarmas
Categories: cs.LG, cs.AI
Abstract:
Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-distribution error degrade the most under topology shift. We term this topology overfitting: the tendency of task-specific gradient signals to encode relational structure particular to the training topologies rather than the underlying physics, causing models to fail on unseen grids despite strong in-distribution performance. To expose and address this failure mode, we introduce MxGPS (Multiplex GPS), a multiplex graph transformer that runs K task-specialised GPS branches over a shared node encoder, jointly trained on Static State Estimation (SSE) and AC Power Flow (PF) via a self-supervised pre-training and multi-task fine-tuning protocol, with a cross-branch attention module evaluated in ablation. The joint SSE+PF objective forces the shared encoder to simultaneously satisfy complementary gradient signals, preventing it from overfitting to topology-specific relational structure. Under a 3-fold sliding-window cross-validation spanning four unseen topologies (14-, 24-, 162-, and 300-bus), MxGPS attains 0% boundary violation rate (BVR) on all four zero-shot Power Flow topologies. Critically, models with substantially lower in-distribution PF error degrade by 190% to 1400% under topology shift, whereas MxGPS degrades by only 39%, an inversion that directly implicates topology overfitting as the failure mechanism rather than insufficient model capacity. With only 1.6M parameters (12x fewer than the GridFM reference baseline), MxGPS demonstrates that multi-task joint training is a principled and parameter-efficient mechanism for topology-agnostic generalisation in power grid foundation models.
Published: 2026-07-15 12:21:26
Authors: Didier Concordet
Categories: math.ST
Abstract:
We study likelihood-ratio tests for the hypothesis that a positive-semidefinite matrix has rank at most a prescribed value. The null hypothesis is stratified: points of maximal allowed rank lie on a regular boundary stratum, whereas lower-rank points are singular. Consequently, the usual chi-bar-square calibration on the top stratum does not by itself describe the whole composite null, especially along sequences whose rank changes at the local $n^{-1/2}$ scale.
After profiling regular nuisance parameters, we derive a common reduced Gaussian experiment for every fixed null rank and for all admissible local rank transitions. On the top stratum, the classical chi-bar-square law is recovered. At lower ranks, the limit generally involves projection onto a nonconvex rank-constrained semidefinite set.
Our main calibration result shows that, under isotropy, the top-stratum law is least favourable over all fixed null strata and all local null rank transitions. We also prove the corresponding transition dominance under arbitrary anisotropy when the active corank is one. Finally, on the top stratum, we obtain a conditional shape derivative for the limiting distribution and its critical value. Gaussian covariance models and finite-sample experiments illustrate nuisance profiling, rank transitions, anisotropy, and orientation sensitivity.
Published: 2026-07-15 12:12:37
Authors: Shuhao Li, Guodong Du, Anhao Zhao, Wanyu Lin, Tianyu Yuan, Xiaoyu Shen
Categories: cs.CL
Abstract:
Large language models have made strong reasoning gains through supervised fine-tuning, reinforcement learning, and on-policy distillation, yet these post-training methods are usually evaluated only by final-answer accuracy. We study how they reshape confidence during reasoning. We introduce a three-stage calibration framework that evaluates confidence before, during, and after chain-of-thought generation, corresponding to difficulty estimation, early termination, and answer aggregation. Through a controlled comparison on mathematical reasoning benchmarks, we find that OPD provides the most useful pre-reasoning confidence, SFT gives the strongest online signal for early stopping, and RL produces the most reliable trace-level signal for aggregation. We further show that confidence reliability is position-dependent: RL confidence becomes informative after a path-commitment phase, while OPD confidence is useful early but can become inversely calibrated later. Based on this observation, we propose PosConf, a position-aware confidence strategy that uses confidence only from reliable relative-position intervals. PosConf improves RL answer aggregation by 6.1 points over majority voting and consistently improves OPD early stopping under tight token budgets, with gains up to 4.3 points by avoiding its later inverse-calibration region, showing that \emph{confidence in reasoning models should be used both stage-wise and position-awarely}. Our code is available at https://github.com/EIT-NLP/Post-Training-Calibration.
Published: 2026-07-15 12:07:31
Authors: Elia Brué, Maria Colombo, Guido De Philippis, Carl Johan Peter Johansson
Categories: math.AP
Abstract:
The purpose of this note is twofold. First, we prove quantitative estimates for the Ambrosio-Trevisan commutator which implies propagation of logarithmic Sobolev regularity. Second, we prove a similar estimate for the DiPerna-Lions commutator by relying on quantitative differentiation.
Published: 2026-07-15 12:05:03
Authors: Bernd A. Kniehl, Oleg L. Veretin
Categories: hep-lat, hep-ex
Abstract:
We review perturbative matching between the regularization-invariant symmetric MOM (RI/SMOM) and MS schemes at the symmetric subtraction point, which is relevant for lattice QCD simulations. For bilinear operators we summarize three-loop conversion factors for local quark currents and for the n=2,3 twist-two moments of structure functions. For three-quark operators we summarize two-loop RI/SMOM matching for N=0 baryonic operators and three-loop anomalous dimensions together with two-loop matching for the N=1 Mellin moment, enabling improved lattice studies of baryon distribution amplitudes. All numerical results quoted here are given in Landau gauge.
Published: 2026-07-15 12:00:54
Authors: Adam Suski, Elina Spyrou, Jacob Mays, Richard Green
Categories: eess.SY
Abstract:
Long-duration energy storage (LDES) is increasingly regarded as essential for reliability in decarbonized power systems. To encourage investment, policymakers introduce contracts, such as cap-and-floor schemes. So far, these schemes have only been evaluated using exogenous revenue or price distributions. This paper develops a two-stage stochastic equilibrium model to evaluate how LDES cap-and-floor design affects investment and market outcomes. This model endogenously captures the interactions among contract design, investment capacity, and cost of capital. Results for a stylized Great Britain case study show that market incompleteness substantially suppresses LDES investment. Centrally administered zero-premium contracts can restore the risk-neutral investment level by reducing downside risk, but doing so requires substantial expected transfers from consumers to investors and produces outcomes that are sensitive to the cap, floor, and sharing parameters. Bilaterally negotiated contracts largely eliminate expected transfers and reduce sensitivity to those parameters, but provide weaker investment incentives.
To balance investment incentives, transfers, and social welfare, policymakers should jointly consider contract and institutional design.
Published: 2026-07-15 11:55:59
Authors: Jing Yang, Zhuo Chen, Xue-Ning Bai
Categories: astro-ph.EP, astro-ph.IM
Abstract:
Dust plays a crucial role in protoplanetary disks (PPDs) evolution and planet formation, influencing disk dynamics through gas-dust coupling, regulating disk temperature by dominating continuum opacity, and altering disk ionization fraction by capturing free electrons. In this work, we develop a high-order discontinuous Galerkin (DG) method-based open-source code GRACE-DG to solve the collision-induced coagulation-fragmentation equations. In particular, we have derived a new conservative formulation for the non-linear fragmentation term, which enables the DG method to capture the mass transfer process. The new solver exhibits good convergence in coupled aggregation and breakage simulations, making it highly suitable for future integration into hydrodynamic codes.
Published: 2026-07-15 11:47:06
Authors: Ankit Singh, Valeriy V. Ginzburg, Alessio Zaccone
Categories: cond-mat.soft, cond-mat.dis-nn, cond-mat.mtrl-sci, physics.app-ph
Abstract:
We develop a microscopic constitutive theory for the nonlinear deformation of metallic and polymer glasses based on nonaffine elasticity coupled to irreversible many-body relaxation. The theory predicts the full stress--strain response, from linear elasticity through stress overshoot and yielding to steady plastic flow. We show that stress overshoot originates from the competition between a nonaffine elastic instability induced by strain-driven loss of mechanical connectivity at the atomic/molecular level, and viscous dissipation associated with structural relaxation. For polymer glasses, finite chain extensibility naturally accounts for strain hardening at large deformation. The stretched-exponential relaxation exponent is obtained independently from stress or modulus relaxation measurements and provides the primary dynamical input to the theory. Using a small set of physically meaningful parameters, the model quantitatively reproduces experimental stress--strain curves for metallic glasses, polycarbonate, PMMA, and epoxy resins over a broad range of strain rates. These results establish a unified microscopic framework linking relaxation dynamics, yielding, plastic flow, and strain hardening in amorphous solids.
Published: 2026-07-15 11:46:28
Authors: Vinícius Miranda
Categories: math.FA
Abstract:
We solve [4, Question 4.2] left open by Dantas, Jung and Martínez-Cervantes by providing a complete characterization for the pairs $(L_p([0,1]),L_q([0,1]))$ having the weak maximizing property. More precisely, we prove that, for $1
Published: 2026-07-15 11:43:40
Authors: Eduardo Reyes, Tianqi Wang
Categories: math.GR, math.DS, math.GT
Abstract:
We establish several results about Patterson--Sullivan measures for relatively Anosov groups. First, we prove that these measures are exact dimensional with respect to visual metrics induced by Gromov models in the Groves--Manning quasi-isometry class. Under the additional assumption that the group is relatively Morse, we show that the associated scalar Cartan metric is Gromov hyperbolic and that the corresponding boundary premetric is a visual metric to which the exact-dimensionality theorem applies. Second, we prove that their Manhattan manifolds are $C^1$-regular, from which we deduce that the growth indicator is $C^1$-regular and strictly concave on the interior of the limit cone. This extends the case of Anosov representations by Kim--Oh--Wang. Our methods are dynamical, and we exploit the fact due to Kim--Oh and Blayac--Canary--Zhu--Zimmer that Bowen--Margulis--Sullivan measures for relatively Anosov groups are finite and mixing.
Published: 2026-07-15 11:42:07
Authors: Swann Bessa, Pierre Fernandez, Gergely Szilvasy, Matthijs Douze, Hervé Jégou
Categories: cs.IR, stat.ML
Abstract:
Large-scale approximate nearest neighbor search commonly relies on partitions for indexing: database vectors are partitioned into clusters, and for each query a probing function selects the clusters to be scanned. The query probing function and the database partition are rarely treated as separate entities: most techniques assign queries with the same assignment function as the database vectors, which is suboptimal especially when database and query distributions differ. This paper introduces CwA (Cluster with Auctions), which addresses this limitation by jointly learning a balanced database partition and a neural probing function. CwA optimizes search performance directly for the query distribution. It minimizes its objective by alternating two steps: (i) gradient descent on the neural network of the probing function, and (ii) a large-scale combinatorial optimization of the cluster assignment for the database vectors. We solve the latter with a parallelizable auction algorithm that balances the partition by design. To further scale CwA, we extend the method to a Cartesian product of clusters that increases the partition's granularity. When database and query distributions differ, CwA achieves up to 4.7$\times$ throughput over the state-of-the-art at equal recall. In the in-distribution (ID) setting, even a simple linear probing function trained with CwA outperforms competing deep neural methods.
Published: 2026-07-15 11:40:25
Authors: R A Udaya Rakshith, Inavamsi Enaganti, Umang J Gala
Categories: cs.HC
Abstract:
Understanding how visitors engage with interactive exhibits usually calls for either labour-intensive manual observation or invasive multimodal sensing -- eye-tracking, cameras, wearables -- that few science centres can deploy at scale. We ask how much can be learned instead from the handful of fields that most touch-enabled exhibits already log by default: a session's start time, end time, and press count. Analysing 2,816 visitor sessions across eight exhibits at two venues of a science experience centre in Bengaluru, India, we derive interaction density -- presses per second -- as a simple behavioural signature, and use it to distinguish fast-paced games from slower, deliberate quizzes. Density does so cleanly (Mann-Whitney r=0.556) and predicts exhibit type on its own with a cross-validated AUC=0.778. But the data complicates the obvious story: games are not just more intense, visitors also dwell on them longer (r=0.172), reversing the intuitive trade-off between intensity and duration -- traced to exhibits whose escalating difficulty creates open-ended re-engagement loops rather than fixed endpoints. Density is not a universal replacement for existing metrics either: raw press count alone explains far more variance in dwell time (R^2=0.527) than density does (R^2=0.081), though combining both improves on either alone (R^2=0.667). Exhibit-level anomalies, a cross-venue replication check, and a session-length censoring artefact further stress-test rather than simply confirm these results. The broader case we make is methodological: minimal, privacy-preserving interaction logs -- not additional sensors -- can already support rigorous, falsifiable behavioural research at any science centre with touch-enabled exhibits.
Published: 2026-07-15 11:37:23
Authors: Stephen McIntosh, Reuben Smit, Daisuke Saito, Nobuaki Minematsu, Herman Kamper
Categories: cs.CL, eess.AS
Abstract:
L2 speech assessment has traditionally focused on phonetic assessment, leaving the scoring of suprasegmental features such as rhythm and intonation underexplored. Moreover, assessment methods often require training with labeled L2 speech data, making them difficult to apply in low-resource settings. We investigate whether DTW over self-supervised WavLM representations can provide a text-free framework for assessing phonetic accuracy, rhythm, and intonation in English and Japanese L2 speech. Results show that a basic DTW-based approach that compares learner speech to native templates exceeds human agreement on holistic and sentence-level phonetic scoring. For rhythm, we introduce methods that measure the degree of warping in the DTW alignment path; our best method approaches human-level performance. For intonation, we combine DTW distance over prosodic residuals with pitch and intensity features, but performance remains more modest on some tasks. Our results point to self-supervised representations as a promising, text-free basis for multi-aspect pronunciation assessment.
Published: 2026-07-15 11:26:18
Authors: Tahereh Ramezani, Nikola Faltova, Prapti Mondal, Katerina Neumannova, Ernst Paunzen, Johana Supikova, Gabriel Szasz
Categories: astro-ph.GA, astro-ph.IM, astro-ph.SR
Abstract:
Reliable membership determination is a fundamental step in the study of star clusters. With the advent of $Gaia$ astrometry, a wide range of statistical and machine-learning techniques has been developed to assign membership probabilities. However, the current situation of membership lists is very unsatisfactory. This review summarises the main methodologies, compares their strengths and limitations, and discusses future directions. The aim is to provide a comprehensive overview and to lead to a more efficient and reliable approach for the forthcoming $Gaia$ DR4. Basically, we know of spatial, classical kinematic, and photometric methods, as well as maximum likelihood and Bayesian statistical methods, and machine learning and clustering algorithms. These different methods come with many modifications and flavours. We assessed all the advantages and disadvantages of the known methods to determine cluster membership probabilities. Although nowadays most methods are based on poor statistical numerics, the more robust algorithms should still be taken into account. It is important to apply and compare several methods. The next step must be to define a list of standard star clusters to test and verify all known methods. The list must cover the complete grid of cluster parameters (age, distance, reddening, and metallicity) and total masses.
Published: 2026-07-15 11:15:16
Authors: Gayathri Hegde, Pradeep Kayshap
Categories: astro-ph.SR
Abstract:
This work aims to investigate magnetohydrodynamic (MHD) waves in solar plages at five distinct heights, spanning from photosphere to the upper chromosphere, using spectroscopic observations provided by Interface Region Imaging Spectrograph (IRIS). The dominant period is found to not change within the plages, while, in pores, the dominant period decreases linearly from 4.20 to 3.20 minutes from the photosphere to the upper chromosphere. Furthermore, in the solar plages, cross-wavelet analysis reveals that periods from 2.0 to 6.0 minutes propagate till the upper chromosphere from the photosphere. The periods beyond 6 minutes have a zero/constant phase difference in the photosphere and in the middle chromosphere. Thus, the 6.0-minute period would be considered the cutoff period at these heights. Next, the propagation speeds of MHD waves above solar plages are estimated in the photosphere and chromosphere. Within the limit of uncertainties, the propagation speed in solar plage is close to the sound speed; hence, the waves are slow magnetoacoustic in nature. Lastly, with the help of phase difference analysis, we found that formation heights of Mg ii k2r and Mg ii k3 are underestimated in solar plage, while the formation height of Mn i is overestimated. In case of pores, the formation heights of Mg ii k2r, Mg ii k3, and Mn i are overestimated. Interestingly, in the quiet-Sun (QS), the formation heights of Mn i and Fe i are nearly the same, and also the formation height of Mg ii k3 is similar to the formation height of Mg ii k2r. In conclusion, some important findings are reported in this work, namely, (1) dominant periods at five different heights between the photosphere and chromosphere, (2) estimation of cutoff periods in the photosphere, middle chromosphere, and the upper chromosphere in the plages, and (3) variations in the formation heights of these spectral lines in pore, plage, and QS.
Published: 2026-07-15 10:45:59
Authors: Ntumba Elie Nsampi, Adarsh Djeacoumar, Hans-Peter Seidel, Tobias Ritschel, Thomas Leimkühler
Categories: cs.GR
Abstract:
Volumetric inverse rendering seeks to recover the optical properties of participating media from images. Existing approaches either rely on differentiable stochastic light transport simulation, which require substantial algorithmic effort, or use simplified models that fail to capture global illumination. We propose a formulation that reconciles physically complete light transport with general-purpose neural optimization. The optical properties of the medium and the full light field are represented as neural fields and estimated through a joint optimization process. Global illumination is enforced via a residual objective derived from the Radiative Transfer Equation in local differential form, complemented by a volume rendering term along primary viewing rays to mitigate \rev{low-frequency} bias. We demonstrate reconstruction of spatially varying, color-resolved scattering, absorption, and phase function parameters from multi-view images. Beyond reconstruction, the same framework supports learning generative models of participating media with physical optical properties under global illumination.
Published: 2026-07-15 10:43:16
Authors: Qionglei Chen, Yaowei Xie
Categories: math.AP
Abstract:
We prove norm inflation, in the sense of strong ill-posedness, for the two-dimensional Boussinesq system in supercritical Besov spaces. For the inviscid system, norm inflation holds in \(\dot B^β_{p,q}(\mathbb R^2)\times \dot B^β_{p,r}(\mathbb R^2)\) for \(β\neq0\), \(1
Published: 2026-07-15 10:42:07
Authors: Andrea Giovannini, Lorenzo Mario Amorosa, Vittorio Todisco, Claudia Campolo, Antonella Molinaro, Su Hongjia, Alessandro Bazzi
Categories: cs.NI
Abstract:
Connected and automated vehicles (CAVs) are expected to increasingly rely on 5G and future 6G ultra-reliable and low-latency communication (URLLC) services to support safety-critical and time-sensitive applications. Since wireless link conditions can vary rapidly in urban vehicular environments, proactively adapting service parameters based on future channel conditions is essential to maintain service continuity and reliability. In this paper, we investigate the use of machine learning (ML) techniques for channel quality prediction in vehicular URLLC scenarios. Specifically, we evaluate deep neural network (DNN) and long short-term memory (LSTM) models to forecast future channel conditions and enable proactive service adaptation with minimized performance degradation. The analysis is conducted using realistic simulations combining the SUMO traffic simulator and the Sionna-RT ray-tracing framework in a real urban environment reconstructed from OpenStreetMap data. Results show that ML-based prediction significantly outperforms approaches relying solely on past channel measurements and achieves performance close to the ideal case in which future channel conditions are perfectly known in advance. These findings demonstrate the potential of ML-driven prediction techniques to enhance the reliability and robustness of URLLC services for connected vehicular systems.
Published: 2026-07-15 10:24:48
Authors: Yuuga Takasu, Satoru Hayami
Categories: cond-mat.str-el
Abstract:
Magnetization control via magnetic octupole injection has recently been proposed for a new class of centrosymmetric antiferromagnets, namely $d$-wave altermagnets, where the magnetic octupole is the lowest-rank magnetic multipole allowed by symmetry and serves as an alternative carrier to spin injection. In contrast, in noncentrosymmetric antiferromagnets, the magnetic quadrupole (MQ) constitutes the lowest-rank symmetry-allowed magnetic multipole, suggesting that MQ currents can provide an efficient route toward magnetization control through MQ injection. Here, we establish the symmetry conditions for MQ-current generation by constructing the multipole representation of the MQ conductivity tensor and show that MQ currents are generically allowed in noncentrosymmetric crystallographic point groups. As a representative example, we demonstrate MQ-current generation in the linear-response regime associated with symmetry lowering from the centrosymmetric point group ($mmm$) to its noncentrosymmetric subgroup ($mm2$). Furthermore, we reveal MQ accumulation near sample edges, analogous to spin accumulation induced by the spin Hall effect. This edge accumulation provides direct evidence of MQ-current generation and constitutes a key prerequisite for realizing MQ injection and MQ-based magnetization control in noncentrosymmetric antiferromagnets.
Published: 2026-07-15 10:21:52
Authors: Yongren Shi, Wenyi Gong
Categories: cs.AI, cs.HC
Abstract:
AI agents are joining human teams, raising a basic question: when an automated agent becomes a regular participant, does group organization strengthen or weaken? We study this question in open-source software, where bots open pull requests, review code, and merge changes alongside people, leaving a public record of every interaction. Treating bots as participants rather than tools, we examine 2,991 GitHub projects for two years before and after each adopted its first bot. We measure three capabilities that institutional theory links to durable coordination - repeated engagement, social memory, and role differentiation - and two outcomes: conflict cascades and output distinctiveness. Bot adoption is followed by more repeated collaboration, greater recognition of specific bots in discussion, fewer conflict cascades, and more distinctive outputs. These changes cluster around adoption rather than accumulating gradually. Because we lack an untreated comparison group, we interpret the results as precisely timed associations, not causal effects. Two patterns are difficult for alternative explanations to account for: capabilities predict outcomes according to their function - coordination versus differentiation - rather than whether humans or bots provide them, and human-side capabilities account for the bot-conflict association but not the bot-distinctiveness association. The findings are consistent with a specific interpretation: predictable, rule-based agents can become part of a community's social infrastructure. The bot is the occasion; social organization is the mechanism.
Published: 2026-07-15 10:21:46
Authors: Pengxuan Gao, Kai Ying, Botao Wu, Jianhua Mo, Qingsong Wen
Categories: eess.SP
Abstract:
Low-altitude unmanned aerial vehicles (UAVs) are emerging as key platforms for wireless intelligence tasks. However, practical low-altitude wireless systems usually operate in complex urban environments, where visual occlusion, sparse geometric observations, multipath propagation, and sensor failures may degrade the reliability of single-modality models. To address these challenges, this paper proposes M3F-UAV, a missing-modality multimodal foundation model for low-altitude wireless sensing. The proposed framework learns a unified multimodal representation from visual, geometric, and wireless observations. Specifically, modality-specific pretrained feature extractors are adopted for RGB/depth images, LiDAR point clouds, and CSI matrices, respectively. Through cross-modal fusion and missing-modality-aware pretraining with feature-level masked reconstruction and UAV localization objectives, M3F-UAV can extract fixed-size features from different modality combinations and adapt them to downstream low-altitude wireless tasks with lightweight task heads. Experiments on the LAMBDA dataset show that M3F-UAV outperforms single-modality baselines and maintains robust performance under missing-modality settings.
Published: 2026-07-15 10:18:36
Authors: Yi-Jun Chang, Yi-Xuan Lee, Meng-Tsung Tsai
Categories: cs.DC
Abstract:
Local certification is a framework for verifying global graph properties using only local information. In this model, a prover assigns short labels, called certificates, to the vertices of a graph. Each vertex then exchanges certificates with its neighbors and performs a purely local check to determine whether the graph satisfies the desired property. This line of research has led to efficient certification schemes for a broad range of graph classes, including minor-closed families, topological graph classes, and graphs defined by forbidden subgraphs.
In this paper, we study the local certification of graph connectivity. Prior work by Bousquet, Feuilloley, and Pierron (JPDC 2024) showed that $2$-vertex-connectivity, $2$-edge-connectivity, and $3$-vertex-connectivity admit $O(\log n)$-bit certificates, leveraging structural characterizations such as ear decompositions. We go substantially beyond these cases and investigate general $k$-vertex-connectivity and $k$-edge-connectivity. We develop new approaches that exploit connections between connectivity and combinatorial structures, including branchings, Eulerian subgraphs, and independent spanning trees.
For $k$-edge-connectivity, we obtain an $O_k(\log n)$-bit certification scheme and prove a matching $Ω_k(\log n)$ lower bound for every $k\ge 3$. The lower bound also applies to $k$-vertex-connectivity. For $k$-vertex-connectivity, we obtain $\tilde{O}_k(\sqrt{n})$-bit certificates for every $k$ under a conjecture of Itai and Zehavi. We further show that, for $k=2$, the logarithmic barrier can be broken on sparse graph classes: $2$-edge-connectivity admits constant-size certificates in bounded-expansion graphs, and $2$-vertex-connectivity admits constant-size certificates in bounded-degree graphs. In contrast, for $2$-vertex-connectivity in general graphs, we prove an $Ω(\log(\log^\ast n))$-bit lower bound.
Published: 2026-07-15 10:15:05
Authors: Zehan Liu, Yage He, Xianwu Gong
Categories: cs.CV, cs.RO
Abstract:
Existing iterative stereo matching methods primarily adopt two types of correspondence representation: explicit matching search via correlation volumes and local residual refinement via warped features, yet the two remain separately modeled. We propose WAVE-Stereo, built on a core insight: correlation volumes and feature warping provide complementary matching cues. \textbf{GeoWarp Correspondence Encoder (GWCE)} encodes matching search, residual alignment, and disparity prior in parallel at the ConvGRU input. To mitigate matching degradation in textureless regions, we propose \textbf{Periodic Global Context Propagation (PGCP)}, which propagates global spatial information in a periodic manner. On five real-world benchmarks -- Middlebury, ETH3D, KITTI 2012, KITTI 2015, and Booster -- WAVE-Stereo achieves competitive zero-shot generalization accuracy without any external foundation model prior, achieving 3.18\% D1-all on KITTI 2015, 4.42\% Bad-2.0 on Booster, and 66ms real-time inference, striking a favorable balance between accuracy and efficiency. Our code is available at https://github.com/yamanoko-do/WAVE-Stereo.
Published: 2026-07-15 10:13:42
Authors: Tituan Allain, Jean-Philippe Berger, Guillaume Bourdarot, Carlo Sirtori, Hugues Guillet De Chatellus
Categories: astro-ph.IM, physics.ins-det, physics.optics
Abstract:
The task of imaging complex dust environments such as the inner astronomical units of a planet-forming disks requires dedicated mid-infrared (MIR) interferometric facilities with kilometric baselines and a large number of telescopes. Extrapolating technologies from current facilities is not straightforward. We aim to demonstrate the feasibility of using MIR heterodyne interferometry with a photonic correlation approach to recombine MIR signals from distant telescopes. We want to determine the current technological limits of such systems are. We developed a laboratory demonstration bench that can correlate MIR signals at 10 um with a photonic correlator. The photonic correlator uses commercially available telecom components at 1.5 um to transport and correlate heterodyne signals that could go up to 10 GHz in bandwidth, directly extendable to 40 GHz. We used the demonstration bench to study the noise levels and detection limits of a heterodyne interferometer with a photonic correlation. We confirm the correlation exhibited by wideband MIR signals with the photonic correlator, along with a characterization of the performance of the system and analyzed the noise levels. We show that the photonic correlator does not limit the detection and that it can be used to compensate for free-space delays at 10 um with a fiber delay at 1.5 um. With our current sub-optimal commercial infrared (IR) detectors, we have derived a detection limit of 130 Jy that is coherent flux for 8 m class telescopes with 1 h of incoherent integration. We discuss the possibility of lowering the detection limit down to typical T-Tauri stars (approximately 1 Jy) using new detectors and coherent integration based upon local oscillator synchronization with telecom fiber links.
Published: 2026-07-15 10:05:53
Authors: Zijie Yu, Gaowen Liu, Ramana Rao Kompella, Philip S. Yu, Yue Song
Categories: cs.LG
Abstract:
Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyperspherical geometry. However, existing probabilistic interpretations typically rely on Gaussian assumptions, which fail to capture this directional and multimodal structure. We propose a principled density model for the CLIP latent space based on Mixtures of von Mises-Fisher (MovMF) distributions defined on the unit hypersphere. Using the Expectation-Maximization (EM) algorithm, we efficiently learn a probabilistic model in which each mixture component corresponds to a coherent semantic concept. This formulation yields a closed-form likelihood naturally aligned with hyperspherical geometry, enabling accurate and interpretable density estimation. Empirically, our model significantly improves long-tailed and out-of-distribution detection and provides a natural semantic decomposition, representing each embedding as a sparse probabilistic combination of interpretable concepts. These results suggest that CLIP latent space is more faithfully characterized as a hyperspherical semantic mixture rather than an isotropic Gaussian, establishing a simple and geometrically consistent probabilistic framework for modeling and understanding multimodal representations. Project page is available at https://xiaoyuzhizi.github.io/movmf-clip/.
Published: 2026-07-15 09:58:37
Authors: Shaoru Sun, Xingtao Wang, Zihan Ma, Wenrui Li, Jiantao Zhou, Debin Zhao, Xiaopeng Fan
Categories: cs.CV
Abstract:
While 3D Gaussian Editing (3DGE) has seen substantial progress, text-driven 3D human garment editing remains largely underexplored. Existing 3DGE works typically follow a paradigm that applies 2D editing techniques to multi-view rendered images and updates 3D Gaussians based on the modified images. Extending such methods to 3D human garment editing suffers from low-fidelity outcomes, caused by introduced distortions and garment inconsistencies. A promising breakthrough opportunity arises from the SMPL eXpressive (SMPL-X) model that embodies rich prior information for virtual humans. Motivated by this insight, we propose a text-driven 3D human garment editor termed T3HG-Editor, which delivers high-fidelity and garment consistent results by leveraging geometry and joint priors embedded in SMPL-X. Specifically, T3HG-Editor contains three stages, namely obtainment of editable Gaussians, garment consistent editing, and Gaussian updating with overflow pruning. The obtainment of editable Gaussians begins with seeding Gaussians along SMPL-X normals to generate sufficient near surface Gaussians, followed by a 2D mask constraint that precisely localizes the target Gaussians to be edited. The garment consistent editing aggregates tokens corresponding to the same SMPL-X vertex across multiple views and propagates them to their original views, enforcing garment consistency without requiring additional training. Gaussian updating with overflow pruning employs a Signed Distance Function (SDF) defined on SMPL-X to construct a human distance field, which is then integrated with a 2D semantic mask to prune overflowing Gaussians, thus preventing contamination of non-target regions. Experiments on multiple subjects and diverse garment types demonstrate that T3HG-Editor outperforms state-of-the-art methods in both editing quality and garment consistency.
Published: 2026-07-15 09:49:48
Authors: Yue Jiet Chong, Yimin Wang, Wei Zhang, Xuanyao Fong
Categories: cs.AR
Abstract:
LLM impose significant computational and memory demands, creating challenges for energy-efficient inference across platforms ranging from data centers to power-constrained edge devices. Weight precision plays a critical role in balancing inference accuracy, throughput, and energy consumption, while modern LLM workloads exhibit pronounced heterogeneity and tolerance that favors adaptive precision execution. This paper presents CIMERA, a reconfigurable-precision LLM inference accelerator that integrates compute-in-interconnect and memory to mitigate the memory wall and enable precision-aware execution. Compared to Nvidia H100, CIMERA delivers up to $25\times$ and $10\times$ higher energy efficiency for 1B and 13B models, respectively.
Published: 2026-07-15 09:44:25
Authors: Tianshun Han, Ziyu Shi, Lijian Liu, Ajian Liu, Benjia Zhou, Hugo Jair Escalante, Yanyan Liang, Sergio Escalera, Zhen Lei, Jun Wan
Categories: cs.CV, cs.AI
Abstract:
Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenarios. They often produce unstable geometry, inaccurate limb articulation and unreliable predictions under depth ambiguity or self-occlusion. A key reason is that existing datasets still lack the combination of high-resolution images, high-precision annotations and diverse whole-body motions required to support robust reconstruction. To address this gap, we present Human4K, a large-scale 4K multi-view whole-body human reconstruction dataset with mocap-accurate SMPL-X annotations. Human4K contains over six million 4K images captured by an eight-view high-resolution camera system synchronized with a professional Vicon motion capture setup, covering 11 subjects performing complex, highly articulated and strongly self-occluded full-body motions. All sequences are processed by a Motion-Retargeting and Refinement Module (MRRM) to ensure precise alignment for the full body and extremities. Experimental results show that training with Human4K consistently improves whole-body reconstruction on standard benchmarks, with particularly large gains for hands, feet and depth-ambiguous limb configurations.
Published: 2026-07-15 09:30:26
Authors: Cervane Grimaud, Denys Malyshev, Emmanuel Moulin
Categories: astro-ph.HE, astro-ph.CO, hep-ph
Abstract:
Axion-Like-Particles (ALPs) are pseudo-scalar particles actively searched as light dark matter candidates. ALPs can couple to photons which give rise to the possibility of oscillations with photons in an external magnetic field. If sufficiently strong, this coupling can imprint distinctive spectral irregularities in the gamma ray spectrum of astrophysical sources. We present a prospective study on the sensitivity of probing ALP-photon interactions using stacked observations of selected active galactic nuclei (AGNs) located behind galaxy clusters. The ALP-photon conversion in cluster magnetic fields produces absorption-like features in AGN spectra that are difficult to predict for individual sources. To address this, we apply a stacking analysis of multiple AGN-cluster pairs, yielding a controlled prediction of the expected ALP induced spectral patterns and enhancing the sensitivity to such irregularities. Using simulated data for selected hard-spectrum Fermi/LAT AGNs that can be observed by Imaging Atmospheric Cherenkov Telescopes such as H.E.S.S., we evaluate the performance of this method. The combination of mock IACT observations with our stacking approach enable exploration of the previously uncharted ALP dark matter parameter space in the neV mass range.
Published: 2026-07-15 09:28:58
Authors: Zhenghang Xu, Minghao Yin, Jumping Zhou, Jean-Marie Lagniez
Categories: cs.DC
Abstract:
Propositional Model Counting ($\#\mathsf{SAT}$) is essential for probabilistic reasoning but faces scalability limits on single cores. Existing distributed approaches struggle with high initialization overheads (static decomposition) or rigid architecture. We propose a novel, generic framework for distributed \emph{exact} model counting. Leveraging C++ templates, our architecture decouples parallel orchestration from solving logic, enabling state-of-the-art solvers to be parallelized with minimal modification. We implement an adaptive work-stealing strategy that ensures effective load balancing. Experiments on competition benchmarks show that our approach achieves near-linear scalability and significantly outperforms existing distributed solvers.
Published: 2026-07-15 09:25:26
Authors: Chinonso Onah, Stuart Hadfield, Kristel Michielsen
Categories: quant-ph, cs.CC, cs.CG, math-ph
Abstract:
We study the separation of geometric effects from quantum interference in quantum optimization algorithms. Constrained optimization problems such as routing, assignment, and scheduling are often encoded as product spaces of local variables, together with global feasibility penalties. The central algorithmic question we address is how a constraint-preserving mixing operator transports quantum amplitude across an exponential search space in the presence of local and global constraints. We develop a framework that separates three effects that are usually intermixed: amplitude transport, coherent interference among transported amplitudes, and problem-dependent classical postprocessing. We show that the mixing operator alone does not have a target-seeking ability. Concretely, the normalized distribution induced by its amplitude transport moves toward the distance profile of a uniformly random configuration. Thus, quantum sampling advantage may only arise when the phases of the many computational paths reaching a target configuration are sufficiently aligned for their amplitudes to reinforce. We show that, when the cost phases are engineered so that these paths add coherently, a number of circuit alternations growing only logarithmically with problem size suffices to convert the sum of their absolute contributions into a lower bound on the target amplitude, yielding a certified success probability independent of the ambient Hilbert-space dimension, the search-space size, or the feasible-set cardinality. We develop applications to problem-specific transpilation diagnostics, scalable hardware probes, constraint-induced classical maps of quantum-generated samples, the attribution of solution quality between the quantum distribution and classical post-processing in hybrid quantum-classical workflows and connections to distance-partitioned product spaces from classical coding theory.
Published: 2026-07-15 09:12:45
Authors: Shengjie Yu, Laurent Sanchez-Palencia
Categories: cond-mat.quant-gas
Abstract:
Rydberg and Rydberg-dressed atomic gases have recently emerged as a promising quantum simulator for a variety of models in condensed matter physics. Here we investigate one-dimensional bosons with soft-core Rydberg-dressed interactions using exact path-integral quantum Monte Carlo simulations. The finite-range and the negative Fourier component of the interaction potential generate a roton mode at finite momentum, while particle-hole backscattering processes enhance the susceptibility of one-dimensional systems at twice the Fermi momentum. The competition between the corresponding length scales yields a rich phase diagram, featuring a conventional Tomonaga-Luttinger liquid (TLL) regime, a beyond-TLL regime, and commensurate cluster phases. In the TLL regime, the system transitions from Lieb-Liniger-like bnehavior with Luttinger parameter $K>1$ to hard-rod-like behavior with $K<1$, with a quasi-supersolid phase emerging for $K < 1/2$. For strong interactions and high densities, deviations from TLL theory appear as precursors for the onset of cluster phases, where particles aggregate into stable clusters of several particles. The properties of each phase is discussed in detail.
Published: 2026-07-15 09:10:10
Authors: Yongjian Zhu, Yusen Tao, Feitian Zhang
Categories: cs.RO
Abstract:
Miniature hybrid underwater gliders have attracted increasing attention for long-endurance ocean observation and confined-space inspection. Large-range wing reconfiguration offers a promising yet largely unexplored approach for simultaneously enhancing maneuverability and shape adaptability in constrained underwater environments. However, such morphing introduces substantial challenges in mechanical integration, dynamic modeling, and hydrodynamic characterization. This paper presents FoDeGlider, a miniature hybrid underwater glider equipped with two independently actuated wings capable of large-range folding and deflection. To capture configuration-dependent variations in mass distribution, center-of-geometry location, and hydrodynamic loading, a multibody dynamics model is developed by treating wing configuration as a structural variable. A composite rigid body algorithm (CRBA)-based projection formulates the composite inertia, wrench transformations, and component-level hydrodynamics into a unified Fossen-form dynamic model applicable to arbitrary wing configurations. A sequential parameter-identification framework is further proposed to estimate fuselage and wing hydrodynamic coefficients, resulting in an open benchmark dataset for model identification and validation. Extensive experiments are conducted, the results of which demonstrate accurate dynamic modeling and parameter identification across diverse morphing configurations. Gate traversal experiments further validate FoDeGlider's ability to actively reconfigure its morphology during locomotion, enabling enhanced navigation in confined underwater environments.
Published: 2026-07-15 09:09:25
Authors: Kun Yu, Jianhua Yang, Yixiang Chen, Changwei Wang, Hongyuan Yu, Yan Huang, Fushuo Huo, Ya Jing, Zhumin Chen, Keji He
Categories: cs.AI
Abstract:
Language-guided human following is an important capability for embodied agents, but existing benchmarks typically assume that the target person is visible at the start of an episode. This setting simplifies the problem and overlooks a more realistic requirement: an agent often needs to first find a language-described target and then persistently follow that target in a dynamic environment. While recent work has started to study human search, existing settings are typically evaluated in task-specific scenarios and often rely on stronger prior knowledge of the environment. Moreover, they usually treat searching and following as separate tasks and still lack a unified benchmark for systematic evaluation. To address these limitations, we introduce the Unified Embodied Seeking and Following Benchmark (UESF-Bench), a large-scale and diverse benchmark for embodied human seeking and following. The benchmark requires agents to handle semantic-guided exploration, reliable behavior switching and recovery, and delayed identity grounding. To this end, we propose SeekFollow-VLA, a vision-language-action framework with a task-driven routing mechanism for latent phase inference and transition modeling between seeking and following. Experimental results show that SeekFollow-VLA achieves clear improvements over both single-head and dual-head baselines across single-person and multi-person environments, establishing a baseline for unified embodied seek-and-follow.
Published: 2026-07-15 09:07:34
Authors: Luca Maraschi, Matteo Collina
Categories: cs.SE, cs.DC
Abstract:
Durable workflow engines reconstruct execution state by deterministically replaying an immutable event log, coupling every in-flight run to the code version that produced its history: a new deployment can invalidate the replay of runs started under the old version, silently corrupting state or halting progress. Existing mitigations -- pinning, patch gates, side-by-side deployment -- treat every change as maximally dangerous and drain old versions, untenable for workflows that sleep for weeks.
We present a closed-form probabilistic model that quantifies the risk of upgrading in-flight runs from workflow version $V_1$ to $V_2$ using only a static structural diff and telemetry the protocol already persists -- event logs, step payloads, historical paths -- with no dry-run, sandbox, or shadow execution. Risk decomposes along three axes (protocol, interface, state migration) and combines an exact backward (rehydration) term, computed on recorded prefixes modulo trace equivalence of concurrent completions, with a probabilistic forward term from hitting probabilities in an empirically estimated Markov model of control flow. Estimation is Bayesian throughout, so the Workflow Upgrade Risk (WUR) score carries a credible interval and thin telemetry surfaces as uncertainty. We prove that a zero backward-risk verdict certifies safe rehydration under the new version, and derive a policy partitioning runs into migrate, review, and pin classes. Finally we drop the inter-run independence assumption: coupling through hooks, hierarchy, and shared resources is captured by an empirical coupling graph, fleet risk becomes the least fixpoint of a failure-contagion operator, and the coupling-aware migrate/pin partition is computed exactly as a minimum s-t cut.
Published: 2026-07-15 08:59:36
Authors: Fabio Arnez, Alexandra Gomez-Villa
Categories: cs.LG, cs.AI
Abstract:
Joint-Embedding Predictive Architectures (JEPAs) are the dominant design for latent world models, yet they are usually justified by empirical performance rather than a normative principle. We show that the choice of anti-collapse regulariser determines whether a JEPA's training objective, a prediction loss plus a weighted embedding regulariser, is a valid Active Inference (AIF) variational free energy. We organise four non-contrastive regularisers (VICReg, LogDet, PairDist, and SIGReg) into an entropy-estimator hierarchy indexed by a prior-miscalibration gap, and show that the gap's sign, whether the estimator bounds the latent entropy from above or below, decides whether the AIF surprise bound survives: VICReg and LogDet are unsafe upper bounds, PairDist a safe lower bound, and SIGReg eliminates the gap. We then prove a correspondence theorem: under the standard constant-noise encoder model and successful SIGReg enforcement (isotropic-Gaussian embeddings), the gap vanishes, the objective becomes an exact information bottleneck, the surprise bound is preserved, and the latent goal cost becomes an exact proxy for AIF pragmatic value, whereas VICReg leaves an irreducible second-order anisotropy term. We extend the correspondence to multi-step expected free energy, ensemble epistemic value, and a learned-policy regime, and we identify the one AIF term no current JEPA world model computes: the state-epistemic value, a future-state coverage signal. The predictions differ in kind, not degree, and are stated here as theoretical consequences left for empirical test in separate work; full proofs are in Appendix A, and the algebraic core of every result is machine-verified in Lean 4 (Appendix D).
Published: 2026-07-15 08:58:43
Authors: Dongrui Jiang, Jochen Garcke, Okan Akca, Jeremias Hollnagel, Bernhard Klaassen, Mehrnaz Anvari, Joachim Müller-Kirchenbauer
Categories: cs.CE
Abstract:
Large-scale gas-network scenario evaluation is a computational bottleneck in integrated energy-system planning, particularly when gas infrastructure interacts with power, heat, hydrogen, and sector-coupling pathways. Conventional nonlinear hydraulic solvers provide reliable feasibility assessment but are costly for stochastic screening, whereas unconstrained learning-based surrogates may produce hydraulically infeasible states. This study develops a physics-informed graph neural network surrogate for steady-state gas-network simulation and feasibility screening. The model uses an edge-centric architecture to predict pipe-level squared-pressure differences and flows. A differentiable projection layer enforces nodal mass conservation on predicted flows, while a Laplacian reconstruction maps edge pressure differences to topologically consistent nodal pressures.
The framework is evaluated on GasLib-134, GasLib-135, and GasLib-582 using stochastically generated operating scenarios. On the meshed 582-node benchmark trained with 5000 scenarios, the surrogate achieves a pressure mean absolute error of 1.05~bar, corresponding to 1.3\% of the realized pressure range, with $R^2 = 0.981$. Projected-flow predictions reach $R^2 = 0.972$, and mass-balance residuals are reduced to numerical precision, on the order of $10^{-5}$--$10^{-4}$~Nm$^3$/s. Compared with the MYNTS reference solver, inference is reduced from seconds to milliseconds, with the largest benchmark evaluated in less than 40~ms. Loadability and out-of-distribution stress-test evaluations demonstrate robust feasibility screening under high-load conditions, while strongly localized demand concentrations are identified as cases requiring solver-based verification near feasibility limits. The framework provides a physically constrained planning accelerator for high-volume scenario screening and prioritization.