Published: 2026-07-20 17:52:33
Authors: Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao, Hanwen Xu, Jaspreet Bagga, Guanghui Qin, Robert E. Kramer, Cliff Wong, Soohee Lee, Hao Qiu, Theodore Zhengde Zhao, Racheli Ben Shimol, Angela Crabtree, Kevin Matlock, Eduardo Alejandro Lozano Garcia, Naiteek Sangani, Alberto Santamaria-Pang, Jason Entenmann, Alexandra Q. Bartlett, Bill J. Wright, Bernard A. Fox, Brian Piening, Sheng Zhang, Sheng Wang, Tristan Naumann, Carlo Bifulco, Hoifung Poon
Categories: cs.CV, cs.AI
Abstract:
Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data. A growing landscape of pathology foundation models now spans diverse data sources, architectures, and downstream applications. However, most pretrained models operate only at the image-tile level, use restrictive licenses, and remain computationally expensive, limiting large-scale slide-level clinical and research use.
Here, we introduce GigaPath-Flash and GigaTIME-Flash, efficient models for whole-slide pathology AI and spatial proteomics prediction. GigaPath-Flash combines a 22M-parameter ViT-S tile encoder with a 21M-parameter LongNet slide encoder, both pretrained on large-scale real-world histopathology data. Its compact tile encoder is distilled from the billion-parameter GigaPath (ViT-g) teacher and shared by both models. GigaPath-Flash retains 97% of GigaPath's average slide-level performance with 50x less compute. GigaTIME-Flash extends this backbone to predict the tumor immune microenvironment directly from routine H&E images. It surpasses the original CNN-based GigaTIME in prediction quality while running 6x faster and using 8x less GPU memory.
Together with GigaPath and GigaTIME, these models form an open-weight, Apache-2.0-licensed family pretrained on large-scale real-world clinical data. By releasing all models and weights, we provide accessible building blocks for computational pathology, immuno-oncology, and precision health.
Published: 2026-07-20 17:49:00
Authors: Ivan Velkovsky, Carlos Camacho, Tomoki Ozawa, Hannah Price, Bryce Gadway
Categories: cond-mat.mes-hall, cond-mat.other, physics.class-ph, quant-ph
Abstract:
Non-Abelian gauge fields play a key role in describing the behavior of particles whose motion is coupled to internal degrees of freedom, such as their spin. Here, we experimentally realize a tuneable non-Abelian gauge field in an active mechanical lattice by using pairs of oscillators to encode a local pseudo-spin for each site, with inter-site spin-dependent couplings engineered via real-time measurement and feedback. We experimentally extract Wilson-loop observables in our set-up and hence demonstrate that we can create a genuinely non-Abelian gauge field. We then exploit the controllability of our mechanical lattice to engineer non-reciprocal hoppings to explore non-Hermitian non-Abelian gauge potentials. For a two-dimensional (2D) lattice, we demonstrate that the non-Hermiticity can manifest in direction-dependent Wilson loops for a single plaquette, while for a one-dimensional (1D) system, we show that a non-Abelian gauge potential can switch the localization of non-Hermitian skin modes between opposite ends of a chain. Our work establishes active mechanical lattices as a flexible and programmable platform for probing non-Abelian gauge fields and exploring their interplay with non-Hermitian dynamics.
Published: 2026-07-20 17:42:53
Authors: Richard Fitzpatrick
Categories: physics.plasm-ph
Abstract:
A recent paper [Hazeltine, et al., Phys. Plasmas 33, 072501 (2026)] has questioned whether the standard result, (ultimately) due to Ferraro, that the plasma angular velocity is approximately constant along individual equilibrium magnetic field-lines in a rotating axisymmetric magnetic mirror machine, continues to hold when the rotation becomes sonic or supersonic. In order to resolve this issue, the equilibrium of a rapidly rotating mirror is investigated, starting from first principles, using an ideal two-fluid model with anisotropic pressure. It is found that, as long as the ion gyro-radius is much less than the machine size, and the angular velocity of the plasma is much less than the ion gyro-frequency, the Ferraro result holds good.
Published: 2026-07-20 17:36:05
Authors: Benedikt Brückner, Alessio Lomuscio
Categories: cs.CV, cs.LG
Abstract:
Vision models have been found to be susceptible to perturbations such as motion blur induced at runtime by a shaking camera. This impedes their deployment in critical applications since phenomena such as slightly blurred vision might lead to failures, for example an object detector missing objects. While methods such as data augmentation or Adversarial Training can improve empirical robustness, they lack formal safety guarantees, making it difficult to identify and mitigate hidden vulnerabilities. We introduce a novel Certified Training approach that leverages an efficient encoding of convolutional perturbations to train provably robust models. Our method significantly outperforms Adversarial Training, achieving, for example, over 80% robust accuracy against motion blur of reasonable intensity on CIFAR10 while maintaining comparable standard accuracy.
Published: 2026-07-20 17:24:43
Authors: Christopher D. Long
Categories: math.PR, math.AC, math.AG
Abstract:
We give explicit complex polynomials $P,Q$ in three independent standard real Gaussian variables such that \[
{\mathbb E}(P^m)=0,\qquad {\mathbb E}(QP^m)=m!\neq0 \] for every $m\geq1$. In natural complex linear coordinates, $P$ has five terms and total degree $4$. Hence the Gaussian Moments Conjecture is false in every dimension $n\geq3$. We also give a six-term cubic example in four variables, which was found first and already proves failure for every $n\geq4$. Both examples follow from the same coefficient identity. The search was prompted by Levent Alpöge's public announcement of an explicit three-dimensional counterexample to the Jacobian Conjecture. Although the main theorem of Derksen, van den Essen, and Zhao is stated globally in dimension, its proof has fixed-dimensional content: a noninvertible cubic-homogeneous Keller map in $r$ variables forces the failure of ${\mathrm GMC}(2r)$. Tracking a standard Bass--Connell--Wright reduction of the announced map gives a conservative cubic-homogeneous counterexample in $79$ variables, and hence a route-based failure of ${\mathrm GMC}(158)$. That route is nonconstructive at the final Gaussian step and does not furnish explicit polynomials $P,Q$. The much smaller explicit failures in dimensions $4$ and $3$ below were not derived from the announced Jacobian map.
Published: 2026-07-20 17:12:53
Authors: Sergey Parnovsky, Andrey Varlamov
Categories: physics.pop-ph, physics.ed-ph
Abstract:
We analyze the physical mechanisms underlying the production of dry-cured hams, including prosciutto and jamón. Key processes such as osmosis and diffusion govern water removal and salt penetration, while enzymatic activity during long-term air curing develops flavor and texture without heat. The study quantifies diffusion depths, evaporation rates, and the effects of airflow and humidity on preventing defects like case hardening. By highlighting the physics behind salting and curing, the work provides a deeper understanding of how traditional techniques achieve safe, tender, and flavorful hams.
Published: 2026-07-20 17:12:28
Authors: Krish Agarwal, Zhuoming Chen, Yanyuan Qin, Zhenyu Gu, Atri Rudra, Beidi Chen
Categories: cs.LG
Abstract:
Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose efficient deployment requires application-specific decisions about placement, streaming, and intra-model parallelism. Existing serving systems and auto-parallelism compilers commit to limited transformations and fixed workload assumptions, so achieving high performance on a new application requires hand-crafting an efficient implementation. We present FlashRT, an agent harness that guides coding agents to lift simple developer-written reference implementations into optimized multi-GPU deployments that flexibly weigh target metrics like latency and throughput. Using a new chain-of-program paradigm, FlashRT directs a generic coding agent through a multi-pass transformation process where an agent transforms the reference into an intermediate representation (IR) to capture data dependencies and persistent-state scopes, validates this IR via a sequential interpreter, and performs static analyses to identify candidate transformations. Then, the agent iteratively implements, verifies, and benchmarks each candidate under a measurement-gated optimization loop to produce effective deployments that span different hardware budgets. Across various applications, including video world models and multimodal LLMs, FlashRT converts reference implementations into highly efficient deployments, delivering up to ~70x latency reduction and 2.8x throughput improvement on NVIDIA B200 GPUs. On AMD MI355X GPUs, FlashRT matches the peak latency reduction while increasing peak throughput improvement to 3.6x, demonstrating that agent-driven optimization can be more scalable on platforms with less mature expert optimization. In fact, for Qwen3-Omni text-to-audio inference, FlashRT reduces response latency by 65% compared to the expert vLLM-Omni implementation on AMD MI355X.
Published: 2026-07-20 17:11:59
Authors: Kwunhang Wong, Jichang Yang, Karl M. H. Lai, Hegan Chen, Songqi Wang, Wei Xuan, Ning Lin, Han Wang, Xiaojuan Qi, Zhongrui Wang
Categories: cs.CR, cs.ET
Abstract:
Edge Artificial Intelligence of Things (AIoT) systems often collect sensitive data in situ, raising serious privacy concerns. Resistive-switching random-access memory (RRAM) is an attractive substrate for efficient AIoT thanks to its multi-bit storage and compute-in-memory (CiM) capabilities, while its inherently stochastic write behavior provides a natural source of randomness that can be leveraged for differential privacy (DP) protection. Yet how to transform this device-level randomness-typically viewed as detrimental to accuracy-into a principled randomized mechanism while preserving model utility remains underexplored. We propose RRAM-DP, a hardware-algorithm co-design that relaxes RRAM write-verify operations to inject calibrated noise for inherently (epsilon, delta)-DP with formal DP analysis; together with pretraining techniques, it renders a novel private, high-utility CiM training paradigm. On CIFAR-10/100, STS-B, and SST-2, RRAM-DP-SGD incurs at best only a 3.8% accuracy drop at (epsilon=2, delta=O(1/n))-DP relative to non-private SGD. At the same privacy level, RRAM-DP-SGD delivers up to 57x and 3.2x energy savings and 2.7x and 1.8x speedups over A100 and DiVa-GEMM, respectively. These results point toward efficient, privacy-preserving in-memory training on RRAM at the edge.
Published: 2026-07-20 17:07:41
Authors: Yi-Ping Chen, Ying-Kuan Tsai, Vispi Karkaria, Seul Lee, Daniel Apley, Wei Chen
Categories: cs.LG, cs.AI, math.ST
Abstract:
Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift. Maintaining surrogate fidelity under drift, particularly when models must also capture aleatoric uncertainty, remains an open challenge. Existing adaptive frameworks lack principled mechanisms for detecting when updates are needed, for efficiently adapting models from limited streaming data, and for certifying that updates genuinely improve predictive performance. Here we present an adaptive Digital Twin framework that integrates a Fisher score--based multivariate drift detector, Low-Rank Adaptation (LoRA) for parameter-efficient continual learning, and a Mann--Whitney $U$ test for online statistical validation. The framework monitors surrogate-model confidence via Fisher score vectors, triggers targeted fine-tuning of fewer than 1% of model parameters upon drift detection, and statistically certifies predictive improvement before deploying the updated surrogate. Applied to a stochastic linear system and a directed energy deposition additive manufacturing process as case studies, the framework successfully detects distributional shifts with short delays and restores both predictive accuracy and uncertainty quantification under abrupt and incremental drift. These results establish a statistically rigorous and computationally tractable pathway for sustaining the trustworthiness of neural-network--based Digital Twins throughout their operational life cycle.
Published: 2026-07-20 17:02:15
Authors: Jin-Xing Hou, Yan-Song Song, James Jun He, Song-Bo Zhang
Categories: cond-mat.supr-con
Abstract:
Nonreciprocal superconducting transport enables dissipationless rectification and has attracted considerable interest, yet its microscopic origin is typically sought in bulk electronic states. Here, we show that boundary-controlled chiral edge states in topological systems provide a simple yet largely overlooked mechanism for nonreciprocal superconducting transport. Focusing on chiral kagome antiferromagnets, we demonstrate that out-of-plane spin canting or spin-orbit coupling opens a high-Chern-number bulk gap, giving rise to multiple chiral edge modes. Strikingly, sublattice-dependent boundary termination selects a single-valley character for the edge states, leading to asymmetric edge spectra at opposite edges. This boundary asymmetry directly yields observable nonreciprocal signatures in Josephson junctions oriented transverse to the edges, including asymmetric Andreev spectra, Josephson diode effect, and anomalous Fraunhofer interference patterns. These findings broaden the microscopic understanding of superconducting nonreciprocity and highlight boundary engineering as a tunable route toward superconducting diode devices.
Published: 2026-07-20 16:50:15
Authors: Kaiyuan Tang, Daniel Burke, Chaoli Wang
Categories: cs.CV, cs.GR
Abstract:
Implicit neural representation (INR) methods provide continuous coordinate-to-value mappings and integrate naturally with direct volume rendering, making them attractive for representing volumetric data. However, existing INR-based approaches for volumetric data are inherently lossy, and even small reconstruction errors can propagate through rendering and downstream analysis. In this work, we explore Lossless-INR, a lossless INR framework for 3D scientific volumetric data based on bit-plane decomposition. By decomposing each voxel value into binary bit-planes, we reformulate reconstruction as per-bit binary classification, so that exact recovery reduces to predicting every bit correctly. To make this optimization tractable while keeping the representation compact, we combine an octree block-partitioning strategy that adaptively subdivides complex regions with a ternary feature-grid network whose grid entries are parameterized by a ternary set of values. Experiments on diverse volumetric datasets show that this design can achieve zero bit-error rate and bit-exact reconstruction, enabling faithful rendering and downstream analysis with a compact representation. The code is available at https://github.com/TouKaienn/Lossless-INR.
Published: 2026-07-20 16:49:30
Authors: Shyamal Y. Dharia, Stephen D. Smith, Camilo E. Valderrama
Categories: cs.LG, cs.AI
Abstract:
Real-time EEG classification on edge devices is bottlenecked by the floating-point arithmetic of conventional neural networks. We investigated Differentiable Logic Gate Networks (Diff-Logic) as a hardware-native alternative that compiles models into pure Boolean circuits executable via bitwise CPU operations. Through rigorous iso-parameter experiments across four EEG datasets spanning two classification tasks, binary dementia detection and 3-class emotion recognition, we compared Diff-Logic against matched-capacity Multi-Layer Perceptron (MLP) and Binarized Neural Network (BNN) baselines at four complexity tiers (50k-500k parameters). On dementia screening, Diff-Logic achieved 80.2% Macro F1, outperforming the MLP baseline by 6.8%. On emotion recognition, the MLP retained a moderate performance advantage but incurred a 2.3$\times$ higher latency and 14$\times$ larger model size when deployed on a power-constrained (7W) Nvidia Jetson Orin Nano CPU (Single-core). Critically, Diff-Logic inference time remained nearly constant across a 10$\times$ increase in model scale, achieving a peak speedup of 2.9$\times$ over MLPs at the largest complexity tier. Our results establish logic-based neural architectures as a practical paradigm for resource-constrained brain-computer interfaces, achieving competitive or superior performance while natively satisfying the latency and memory constraints of portable edge deployment. Code is available on GitHub: https://github.com/Shyamal-Dharia/eeg-difflogic
Published: 2026-07-20 16:33:32
Authors: Dipo Aldila, Joseph Páez Chávez, Aytül Gökçe, Thomas Götz, Burcu Gürbüz
Categories: math.DS, q-bio.PE
Abstract:
Dengue remains a major public health challenge in tropical regions, and recurring outbreaks suggest that current intervention strategies are not yet fully effective. Existing mathematical models typically assume unlimited hospital capacity and continuously applied fogging, neglecting practical constraints that strongly influence disease control. We develop a non-smooth ordinary differential equation model of dengue transmission that incorporates finite hospital capacity and a threshold-triggered fogging strategy activated when reported infections exceed a prescribed fraction of the available capacity. The model exhibits three epidemiologically relevant operating regimes, reflecting changes in hospitalization and vector-control policies as the epidemic progresses. We establish the existence and local stability of the disease-free and endemic equilibria. Numerical continuation confirms the analytical results and reveals boundary-equilibrium bifurcations at the switching thresholds, a Hopf bifurcation after hospital capacity is exceeded leading to sustained oscillatory outbreaks, and a fold bifurcation near the epidemic threshold that generates additional unstable equilibria. We further investigate periodic solutions with respect to the fogging rate and activation threshold, identifying locally optimal intervention regimes that reduce epidemic peaks while avoiding unnecessarily intensive control efforts. The results demonstrate that hospital capacity, reactive fogging, and intervention thresholds fundamentally shape dengue dynamics and provide quantitative insights for designing effective state-dependent control strategies under limited healthcare resources.
Published: 2026-07-20 14:59:44
Authors: Yinchen Liu, Quanyu Tang, Shengtong Zhang
Categories: math.CO
Abstract:
Let $s^+(G)$ and $s^-(G)$ denote the sums of the squares of the positive and negative adjacency eigenvalues of a graph $G$, respectively. We prove the conjecture of Elphick, Farber, Goldberg, and Wocjan that every connected graph $G$ on $n$ vertices satisfies $$
\min\{s^+(G),s^-(G)\}\ge n-1. $$ The proof introduces a new framework for square-energy estimates, in which the Hadamard squares of positive semidefinite matrices that encode these spectral quantities are relaxed to the full doubly nonnegative cone.
Published: 2026-07-20 14:10:53
Authors: Shigui Li, Delu Zeng
Categories: cs.LG
Abstract:
The prevailing inference framework for diffusion models formulates generation fundamentally as a problem of numerical integration. This perspective casts the model as an exact estimator, neglecting the inherent statistical uncertainty of the denoising process. In this work, we propose Forward-Process Aligned Diffusion prediction (\textbf{DiFA}), a training-free framework that reframes inference-time data prediction refinement as a sequential state estimation problem. Rather than reusing past outputs solely for numerical integration, DiFA treats iterative data predictions along the reverse trajectory as correlated observations to build a forward-aligned temporal consensus. Inspired by Kalman filtering, this consensus aggregates historical predictions according to structural consistency and noise-level compatibility. To counteract the over-smoothing tendency of temporal consensus, we introduce a deviation guidance mechanism to adaptively preserve residual details. Empirically, DiFA yields significant improvements on CIFAR-10 and ImageNet across the evaluated metrics, including FID, IS, and FD-DINOv2, demonstrating that aligning inference with the forward statistical structure substantially improves generative fidelity.
Published: 2026-07-20 14:10:19
Authors: Kento Kawaharazuka, Yoshiki Obinata, Hirokazu Ishida, Jihoon Oh, Temma Suzuki, Shintaro Inoue, Keita Yoneda, Ayumu Iwata, Kei Okada
Categories: cs.RO
Abstract:
The global competition for developing robotic foundation models is intensifying. Among the data collection systems used for dual-arm robots, ALOHA is representative of being low-cost and open-source, and is widely adopted by researchers as a de facto standard. However, due to its limited ability to generate high forces and speeds, it is difficult to handle heavy objects or perform fast manipulations. To address this, we developed MEVION, a low-cost and open-source dual-arm robot data collection system capable of generating greater force and speed. All parts of this robot can be sourced through e-commerce, and by extensively utilizing sheet metal welding, its large body structure is constructed with a small number of components at low cost, while also simplifying assembly. MEVION is equipped with four 6-DoF arms with parallel grippers. Each arm weighs 7.0 kg and has a maximum torque of 60 Nm, and the entire system can be constructed for about USD 14,000. The elbow joint adopts a closed-link mechanism similar to those used in quadruped robots, which reduces the distal mass and enables higher force and speed output at the end-effector. We demonstrate that MEVION enables data collection for object manipulation tasks not previously possible and supports imitation learning-based motion generation. All hardware and software of this work are included in the Supplementary Materials or https://github.com/haraduka/mevion.
Published: 2026-07-20 14:09:41
Authors: Biagio Ricceri
Categories: math.AP
Abstract:
In this paper, on a bounded domain $Ω\subset {\bf R}^n$, we consider a nonlocal problem of the type $$\cases{-Δu=Q(u,λ)f(u) & in $Ω$ \cr & \cr u=0 & on $\partialΩ$\cr}$$ where $Q:H^1_0(Ω)\times {\bf R}\to {\bf R}$, proving, under suitable assumptions, the existence of at least three weak solutions for each $λ$ running in a suitable interval.
Published: 2026-07-20 13:59:23
Authors: Hyojung Jang, Rotana Radwan, Malcolm Risk, Yao Lee, Jiang Bian, Xu Shi, Serena Guo, Lili Zhao
Categories: stat.AP
Abstract:
Electronic health record (EHR) networks provide unprecedented opportunities to study treatment mechanisms at scale, but mediation analyses across institutions are often hindered by privacy and governance constraints that restrict sharing of patient-level data. We developed a privacy-preserving federated mediation framework that enables estimation of natural direct and indirect effects without exchanging individual-level records across participating sites. The proposed approach integrates renewable learning with counterfactual causal mediation analysis, allowing institutions to collaboratively investigate treatment mechanisms using only low-dimensional summary statistics. Both simulation studies and the real-world application demonstrated that the federated estimator closely reproduced pooled-data results while preserving patient privacy. We applied the method to 32,146 patients in the Indiana Network for Patient Care to evaluate the extent to which body mass index (BMI) mediates the effect of GLP-1 receptor agonist on glycated hemoglobin (HbA1c) reduction. The BMI-mediated pathway accounted for only a small proportion of the overall treatment effect, suggesting that most glycemic improvement occurred through mechanisms other than weight loss.
Published: 2026-07-20 13:55:20
Authors: Julio Flores, Eva Primo, Daniel Rodríguez, Miguel Romance
Categories: cs.SI
Abstract:
Inspired by the dynamical PageRank framework of Gleich and Rossi in 2012, we introduce a continuous-time PageRank model in which the personalization vector evolves as a weighted average of its past values, with the weights determined by a memory function. The resulting dynamics are formulated as an initial value problem for an integro-differential equation, where the initial condition is a probability vector. We investigate how the choice of memory function influences the long-time behavior of the PageRank vector. In particular, for strongly connected networks $\mathcal{G}$, we prove that broad classes of memory functions lead to convergence toward a stationary state that is independent of the initial condition. In contrast, when the memory function is exponential-oscillatory, $ω(t)=e^{at}\cos(bt)$ for $t\geq0$ with $a,b>0$, we show that the PageRank dynamics exhibit asymptotically periodic behavior, revealing that oscillatory memory can fundamentally alter the qualitative evolution of the ranking process. To establish these results, we first prove the existence and uniqueness of solutions using standard results from the theory of integro-differential equations and show that the solution remains a probability vector for all times, thereby preserving the essential properties of the PageRank model.
Published: 2026-07-20 13:49:51
Authors: Stefano Blando, Emanuele Guerrazzi, Riccardo Porcedda, Giuseppe Squillace, Max Tschaikowski, Andrea Vandin
Categories: cs.AI, cs.MA
Abstract:
Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents. Recent advances in large language models (LLMs) make it tempting to replace, enrich, or perturb these rules with LLM-based agentic capabilities. However, this raises a methodological question: how does introducing LLM-driven decisions affect the reliability, computational cost, and behavior of ABM simulations? We investigate this for Mesa ABM models, a popular Python library for ABMs, analyzed by statistical model checking. Building on Mesa's integration with the statistical model checker MultiVeStA, we extend the classical Schelling segregation model with a hybrid population: ordinary agents classify neighbors using the standard symbolic rule, while one agent delegates this task to an LLM through tool calls. The LLM-enabled agent receives natural-language descriptions of neighboring agents and invokes tools that increment counters of similar/different neighbors; these counters determine its happiness according to the original Schelling dynamics. This provides a minimal but controlled setting where the semantic, operational, and computational behavior of LLM-based decisions can be studied inside an otherwise standard ABM. We report preliminary experiments with locally served LLMs of different sizes, showing that smaller models may fail simple semantic classification experiments or become operationally unusable during repeated tool-call generation, while larger tested models pass these preliminary checks. We discuss how statistical model checking can estimate classical ABM observables and quantify the impact of introducing agentic LLM components into simulation models.
Published: 2026-07-20 13:42:14
Authors: Wenqing Hu
Categories: math.PR
Abstract:
We study the emergence of (cross-scale) fluxes associated with energy and enstrophy in a stochastic version of Zeitlin's $SU(N)$ approximation of 2-d Euler dynamics. Motivated by the Euler-Arnold formulation, we interpret the nonlinear transport as motion along coadjoint orbits, reflecting an underlying symmetry that preserves all Casimir invariants. We introduce a fast-slow stochastic framework in which rapid mixing is modeled by a structured fast stochastic forcing acting along selected directions. We show that when the fast dynamics preserves the full coadjoint orbit symmetry given by the natural symplectic structure, the averaged system exhibits no nontrivial flux. In contrast, when this symmetry is broken by tangential non-Hamiltonian vector fields on the coadjoint orbit, we identify conditions that yield nonzero fluxes carried by the Euler-Arnold nonlinearity. Thus, coadjoint symmetry suppresses averaged nonlinear flux, whereas its breaking can select a preferred direction of cross-scale transfer.
Published: 2026-07-20 13:40:55
Authors: Simone Vinciguerra, Nicolas Martinet, Marco Gatti
Categories: astro-ph.CO
Abstract:
Simulation-based inference (SBI) has become a major tool for extracting cosmological information from weak-lensing (WL) surveys, particularly from non-Gaussian observables. We compare its two main paradigms: explicit likelihood inference (ELI), based on a Gaussian likelihood built from an emulator and covariance matrix, and likelihood-free inference (LFI), which learns the likelihood directly from simulations using neural density estimators. Using Gaussian random field mocks representative of the non-tomographic final Euclid data release, we analyse shear two-point correlation functions (shear-2PCFs), compressed with linear or non-linear methods, together with a fundamentally different map-level convolutional neural network (CNN) statistic, focusing on $Ω_{\rm m}$ and $S_8$. We deploy posterior calibration diagnostics developed for LFI, including the test of accuracy with random points (TARP), showing that ELI becomes strongly miscalibrated under emulation inaccuracies or likelihood non-Gaussianity, whereas LFI remains well calibrated. These effects drive substantial disagreement between ELI and LFI, which largely vanishes once addressed. We further show that the compression scheme can significantly degrade ELI while leaving LFI largely unaffected. Although shear-2PCFs should capture all the information in Gaussian fields, finite compression and non-Gaussian likelihoods cause ELI constraints to differ by up to a factor of two from those inferred with the CNN, while the discrepancy drops to $\approx 30\%$ for LFI, underscoring the robustness of the deep-learning probe. Overall, our results indicate that in our simple setup, which neglects systematic biases, LFI provides a more robust and better-calibrated framework, while highlighting accurate non-Gaussian likelihood modelling and posterior calibration diagnostics as essential for future ELI analyses.
Published: 2026-07-20 13:38:20
Authors: M. Calvo-Schwarzwalder, A. Valverde, A. Cuesta López, A. Cabrera-Codony, U. Thorat, T. G. Myers
Categories: math-ph, physics.app-ph, physics.chem-ph
Abstract:
We present a column adsorption model that couples a Pseudo-First-Order (PFO) kinetic formulation with the Sips isotherm framework. Using a traveling wave approximation, we derive analytical solutions for specific operating conditions. Qualitatively, these solutions deviate significantly from their pure Sips counterparts: instead of a smooth, continuous increase in concentration at the column outlet, the PFO-Sips model predicts an abrupt, sudden breakthrough. We validate these analytical solutions against diverse experimental datasets from the literature. The results reveal that the PFO-based model consistently underperforms compared to the original Sips formulation. Furthermore, this validation exposes fundamental inconsistencies within the PFO framework. We demonstrate that despite its widespread use in the literature for almost a century, the PFO model is inherently flawed and structurally unfit for describing column adsorption dynamics.
Published: 2026-07-20 13:29:53
Authors: Che Cheng, Daniel Mock, Peter Rossmanith
Categories: cs.DS, cs.CC, cs.DM
Abstract:
We extend the algorithmic framework of progressive exploration [Fabiański et al., STACS 2019], which yields simple, yet surprisingly general and efficient parameterized algorithms for Dominating Set, Independent Set, and some of their variants. While they identified stability and the Helly property as necessary for their approach, we show that -- with a simple change -- in the case of Dominating Set, one can get rid of the stability requirement. This yields a fixed-parameter tractable algorithm on exactly those graph classes which do not contain long co-matchings or double-ladders as semi-induced subgraphs. Lifting one of these two restrictions makes Dominating Set W[1]-hard on these classes. Our algorithm generalizes results on weakly $γ$-closed graphs, and results from Sparsity theory, e.g., nowhere dense and biclique-free classes. At the same time, we match the time complexity of the previously known algorithms on those classes. We demonstrate that this technique can easily be applied to the Distance-$r$ Dominating Set and the Set Cover problem.
Published: 2026-07-20 13:27:31
Authors: Julian Adamek, Øyvind Christiansen
Categories: astro-ph.IM, astro-ph.CO
Abstract:
High-performance computing is increasingly dominated by hardware acceleration using Graphics Processing Units (GPUs). To take advantage of this long-term trend, we implement a major overhaul of the parallelisation approach in the relativistic particle-mesh N-body code gevolution. The new version 2.0 of gevolution employs three layers of parallelisation: MPI for scalability on a distributed memory system, shared memory parallelisation on each MPI rank using OpenMP, and offloading all compute intensive tasks to GPUs using CUDA. The code also includes many new features that have been developed over the past years. We provide an overview of the code structure and show key performance benchmarks. The public release of gevolution 2.0 can be found at https://github.com/gevolution-code/gevolution-2.0. We also release a GPU-ready version of the LATfield2 library which provides the parallelisation backend, available at https://github.com/gevolution-code/LATfield2.
Published: 2026-07-20 13:19:58
Authors: Wen Qiu, Zhiqiang He, Wei Zhao, Hiroshi Masui
Categories: cs.MA, cs.LG, cs.NI
Abstract:
Most reinforcement learning controllers for these networks assume stationary conditions, and the few that handle change react to the external environment while leaving the network's internal state unexamined. We show that sustained non-stationarity damages this internal state directly: as objectives shift, neurons progressively fall dormant and the shared policy loses the capacity to learn. The obvious remedy, resetting dormant neurons, is unsafe under shared-parameter multi-agent training: many neurons that appear inactive are still receiving strong training gradients, and whether a neuron appears dormant depends on which agent's observations it processes. PRIME (Plasticity Recovery In Multi-agent Environments) therefore verifies both directions before intervening. Extending the bidirectional Silent Neuron framework to cooperative multi-agent reinforcement learning, it aggregates activation and gradient statistics over the full team batch, reads the backward signal from the gradient the training loss has already deposited , not from a hand-crafted proxy, and reinitializes only neurons that are simultaneously activation-dormant and gradient-silent. Useful representations are preserved while learning capacity is restored. On a phase-switching UAV emergency communication simulator, PRIME improves interquartile mean return by 24.9\% over MAPPO and holds dormant neuron fractions at 10--20\% versus 40--45\%; ablations attribute the gains to the gradient signal and team-level aggregation rather than to the specific reset operator. A dynamic regret bound shows that the perturbation cost scales with the small silent-subspace dimension rather than the full parameter count.
Published: 2026-07-20 13:16:04
Authors: Shotaro Kawano, Keiichiro Toda, Miu Tamamitsu, Haruyuki Sakurai, Kuniaki Konishi, Takuro Ideguchi
Categories: physics.optics
Abstract:
Femtosecond laser ablation redistributes optically deposited energy across electronic, structural, mechanical, and thermal degrees of freedom over timescales from femtoseconds to milliseconds. However, these coupled processes are usually measured in separate temporal ranges and through different observables, limiting quantitative comparison between early transient dynamics, residual heating, and final morphology. Here we introduce pump-probe holographic imaging that reconstructs amplitude- and phase-resolved optical fields across this full temporal range under matched imaging conditions. Applied to deep-ultraviolet femtosecond ablation of BK7 glass, the method captures the transition from early excitation and removal-stage dynamics to residual substrate heating and permanent modification. Differential phase analysis isolates sub-nanosecond evolution of the transient ablating layer and microsecond residual heating after material removal. Above the ablation threshold, crater depth increases with fluence, whereas the residual thermal signal saturates, indicating that additional absorbed energy is preferentially partitioned into material removal and ablation-related processes rather than retained as substrate heat. These results identify fluence-dependent energy partitioning as a dynamical basis of low-heat-affected femtosecond processing and establish holographic imaging as a route to tracking laser-driven nonequilibrium material transformations.
Published: 2026-07-20 12:55:22
Authors: Xinran Ji, Junxian Wang, Tianchi Zhang, Xuan Zhao, Di Wu, Zixuan Wei, Yizhi Chen, Jialong Wang, Lei Bi
Categories: cond-mat.mtrl-sci, physics.optics
Abstract:
Laser annealing (LA) technique has emerged as an effective method for localized crystallization of magneto-optical (MO) garnet thin films on semiconductor substrates. However, no studies have explored the crystallization and magneto-optical (MO) properties of cerium-substituted yttrium iron garnet (Ce:YIG, Ce1Y2Fe5O12) thin films for integrated photonic device applications using LA technique. In this study, we provide a comprehensive investigation into the laser annealing of Ce:YIG films deposited on SiO2 substrates and silicon nitride photonic waveguides for integrated nonreciprocal photonic device applications. Garnet phase was successfully observed in films grown on SiO2 substrates, and SiN waveguides with laser annealing of sputtered Ce:YIG films on top of a laser annealed Y3Fe5O12 seed layer. The magneto-optical (MO) properties of Ce:YIG films on oxidized Si substrates were found to be comparable to those prepared by rapid thermal annealing (RTA). A Mach-Zehnder Interferometer (MZI) type optical isolator based on Ce:YIG film on SiN was fabricated, exhibiting a saturation Faraday rotation of -2317.7 deg/cm and propagation loss of 188.2 dB/cm. Isolation ratio of 27.1 dB and insertion loss of 10.1 dB were achieved at 1552.7 nm wavelength.
Published: 2026-07-20 12:38:31
Authors: Souvik Mondal, N Bisai, Abhijit Sen, Indranil Bandyopadhyay
Categories: physics.plasm-ph
Abstract:
In this work, we investigate the nonlinear dynamics of isolated current-carrying edge-localized mode (ELM) filaments using a reduced electromagnetic fluid model in slab geometry. Numerical simulations show that unidirectional parallel current significantly suppresses radial filament velocity and reduces the outward propagation velocity by weakening the curvature-driven interchange force. The reduction in radial velocity is found to follow a modified scaling relation, demonstrating that increasing current progressively weakens outward filament propagation. Analysis of the vorticity equation shows that the electromagnetic current source changes from a dipolar structure to a remarkable spiral pattern, and overcomes the conventional curvature drive in the nonlinear phase. This current-driven source directly imprints its topology on the vorticity field, resulting in spiral vorticity, enhanced angular momentum, increased rotational energy, and localized shear layers. The filament therefore undergoes a transition from a conventional propagating state to a rotationally self-organized electromagnetic structure. These findings demonstrate that parallel current acts as an effective electromagnetic vorticity source and provides new insight into the nonlinear dynamics of ELM filaments in tokamak edge plasmas.
Published: 2026-07-20 12:30:27
Authors: Zhiyi Huang, Qinpei Lou, Tao Xiao
Categories: cs.DS
Abstract:
Mixture-of-Experts (MoE) models route each token to only a few expert networks, distributing the serving load across experts whose popularity shifts over time. A serving system must therefore dynamically decide how many GPUs to assign to each expert, trading off service latency against the cost of reconfiguring the assignment. We introduce a formal model of MoE Serving and initiate a principled study of online and offline algorithms for it. Our main result is a polynomial-time $O(\sqrt{\log k})$-competitive online algorithm, where $k$ is the number of GPUs beyond one per expert. We complement it with a matching $Ω(\sqrt{\log k})$ barrier for the online dual problem underlying our analysis. In the offline setting, we give a constant-factor approximation, show that MoE Serving is NP-hard, and rule out an FPTAS assuming ETH.
Published: 2026-07-20 12:18:02
Authors: Maria Gragera Garces, Sabina Drăgoi, Lirandë Pira
Categories: quant-ph, cs.DC, cs.ET, cs.LG
Abstract:
Circuit cutting promises to scale quantum computations beyond current hardware, but variational quantum advantage also requires low cutting overhead, classical hardness, and trainability. We show that these properties are strongly constrained by entanglement geometry. Matrix product state (MPS) and tree tensor network (TTN) circuits with constant seam bond dimension can be cut with \(O(1/\varepsilon^2)\) sampling overhead, but remain efficiently classically simulable, ruling out asymptotic quantum advantage within these families. By independently controlling seam and intra-block entanglement, we construct a two-block circuit family that remains cheaply cuttable while requiring a super-polynomial global MPS bond dimension, as supported numerically up to \(n=100\). However, MPS hardness and trainability require incompatible depth regimes, \(d=ω(\log n)\) and \(d=O(\log n)\), respectively. Using magic rather than entanglement as the hardness resource avoids this conflict: shallow Clifford+\(T\) circuits remain cuttable and trainable while their stabiliser-simulation cost grows exponentially with the \(T\)-count.
Published: 2026-07-20 12:15:30
Authors: Hiroshi Imai, Kohei Kurahara, Huib J. van Langevelde, Maria J. Rioja, Richard Dodson
Categories: astro-ph.GA
Abstract:
SKA-VLBI astrometry will enable us to measure up to thousands of three dimensional motions of OH masers associated with circumstellar envelopes (CSEs) of OH/IR stars in the Nuclear Stellar Disk (NSD) and sites of high mass star formation in the Central Molecular Zone (CMZ) of the Galactic Center (GC). It is expected that the spatio-kinematical distribution of those OH masers should indicate the existence of a ring structure in the NSD, which has formed as a result of outward propagation of star-formation activities in the GC. This is likely visualized clearly by a group of OH/IR stars, some of which should have stellar pulsation periods of >400 days and the corresponding ages of <500 Myr, and some sites of ongoing star formation. These OH/IR stars should host 1612-MHz OH masers, some of which should become targets of huge-sample VLBI astrometry, in moderate accuracy, in SKA-MID Band 2 (~1.6 GHz). The data of maser source proper motions will exhibit a stream motion in the stellar ring structure. Furthermore, the information of accurate distances (error <100 pc) of the maser sources are necessary to directly find the major-axis direction of a possible elliptical ring of stars at ~8 kpc. These distances may be yielded through trigonometric parallaxes measurable in SKA-MID Band 5a (5--7 GHz) and/or photometric parallaxes derived from the pulsation period--luminosity relation of long period variable stars hosting the maser sources.
Published: 2026-07-20 12:14:20
Authors: Zacharias Roupas
Categories: astro-ph.HE, astro-ph.GA
Abstract:
During the formation of a star cluster a spin-mass correlation of stellar black holes is generated as they grow via accretion of the residual gas. Moreover, the black hole spin tends to be anti-aligned with its orbital angular momentum in the cluster. We show that GW231123, reported by the LIGO-Virgo-KAGRA (LVK) collaboration, lies on our predicted high-mass, high-spin plateau of the spin-mass correlation with positive Bayesian evidence over the LVK prior. Furthermore, vector resonant relaxation (VRR) equilibrium is favored over the isotropic LVK prior in reproducing the distribution of the relative spin tilt. The joint Bayes factor suggests strong evidence for the favored cluster models. GW231123 is thus consistent with our proposed channel that generates correlated black hole masses and spins, and drives the spins' orientations.
Published: 2026-07-20 12:05:14
Authors: Guangyao Cui, Amit Sigawi, Michael Karp
Categories: physics.flu-dyn, math-ph
Abstract:
Global linear stability analysis of bluff body wake flows is performed using the matrix-forming method based on finite-difference discretization. Particular emphasis is placed on the influence of outflow boundary conditions, with the aim of minimizing the required computational domain size without degrading accuracy or inducing spurious oscillations near the outlet. This study focuses on incompressible wakes behind bluff bodies such as cylinders and airfoils at high angle of attack, especially in regimes where global modes exhibit downstream spatial amplification. It is shown that below the critical Reynolds number -- where the global mode remains linearly stable -- significant spatial growth can persist far downstream, even when the wake is nearly absent. This behavior underscores the importance of imposing a physical boundary condition at the outlet. Several commonly used outflow boundary conditions are evaluated, including Dirichlet, Neumann, extrapolation, stress-free, sponge layer, and the Robin condition that incorporates predictions from local linear stability analysis at the outlet. The results demonstrate that, for different $Re$ cases, the Robin condition enables robust convergence of global modes within substantially truncated domains, thereby improving the efficiency of global stability analysis. These findings highlight the broader applicability of the matrix-forming approach for complex stability analyses, including Floquet analysis of time-periodic flows and extensions to compressible configurations.
Published: 2026-07-20 12:00:56
Authors: Tomasz Brzeziński, Krzysztof Radziszewski, Brais Ramos Pérez
Categories: math.RA, math.QA
Abstract:
An affinization of the notion of a Poisson algebra is presented. This is termed a Poisson affgebra and consists of an affine space together with an associative bi-affine multiplication and a bi-affine Lie bracket that acts as an affine derivation for the associative product. The constructive relation between Poisson affgebras and Poisson algebras is described and several low-dimensional examples are studied in detail.
Published: 2026-07-20 11:46:13
Authors: G. D. Alexeev, M. G. Alexeev, C. Alice, A. Amoroso, V. Andrieux, V. Anosov, K. Augsten, W. Augustyniak, C. D. R. Azevedo, B. Badelek, R. Beck, J. Beckers, Y. Bedfer, J. Bernhard, F. Bradamante, A. Bressan, W. -C. Chang, C. Chatterjee, M. Chiosso, S. -U. Chung, A. Cicuttin, M. L. Crespo, D. D'Ago, S. Dalla Torre, S. S. Dasgupta, S. Dasgupta, F. Delcarro, I. Denisenko, O. Yu. Denisov, S. V. Donskov, N. Doshita, Ch. Dreisbach, W. Dunnweber, R. R. Dusaev, D. Ecker, P. Faccioli, M. Faessler, M. Finger, M. jr Finger, H. Fischer, K. J. Flothner, W. Florian, J. M. Friedrich, V. Frolov, L. G. Garcia Ordonez, O. P. Gavrichtchouk, S. Gerassimov, J. Giarra, D. Giordano, A. Grasso, A. Gridin, M. Grosse Perdekamp, B. Grube, M. Gruner, A. Guskov, P. Haas, D. von Harrach, M. Hoffmann, N. d'Hose, C. -Y. Hsieh, S. Ishimoto, A. Ivanov, T. Iwata, V. Jary, R. Joosten, E. Kabuss, F. Kaspar, A. Kerbizi, B. Ketzer, G. V. Khaustov, J. H. Koivuniemi, V. N. Kolosov, K. Kondo Horikawa, I. Konorov, A. Yu. Korzenev, A. M. Kotzinian, O. M. Kouznetsov, A. Koval, F. Kunne, K. Kurek, R. P. Kurjata, G. Kurten, A. Kveton, K. Lavickova, S. Levorato, Y. -S. Lian, J. Lichtenstadt, P. -J. Lin, R. Longo, V. E. Lyubovitskij, A. Maggiora, N. Makke, G. K. Mallot, A. Maltsev, A. Martin, J. Marzec, J. Matousek, T. Matsuda, C. Menezes Pires, F. Metzger, W. Meyer, M. Mikhasenko, E. Mitrofanov, D. Miura, Y. Miyachi, R. Molina, A. Moretti, A. Nagaytsev, D. Neyret, M. Niemiec, J. Novy, W. -D. Nowak, G. Nukazuka, A. G. Olshevsky, M. Ostrick, D. Panzieri, B. Parsamyan, S. Paul, H. Pekeler, J. -C. Peng, M. Pesek, D. V. Peshekhonov, M. Peskova, S. Platchkov, J. Pochodzalla, V. A. Polyakov, C. Quintans, G. Reicherz, C. Riedl, D. I. Ryabchikov, A. Rychter, A. Rymbekova, V. D. Samoylenko, A. Sandacz, S. Sarkar, I. A. Savin, G. Sbrizzai, H. Schmieden, A. Selyunin, S. Seriubin, L. Sinha, D. Spulbeck, A. Srnka, M. Stolarski, M. Sulc, H. Suzuki, S. Tessaro, F. Tessarotto, A. Thiel, F. Tosello, A. Townsend, V. Tskhay, B. Valinoti, B. M. Veit, J. F. C. A. Veloso, A. Vijayakumar, M. Virius, M. Wagner, S. Wallner, K. Zaremba, M. Zavertyaev, M. Zemko, E. Zemlyanichkina, M. Ziembicki, for the COMPASS Collaboration, C. Fernandez-Ramirez, M. Mikhasenko, L. Bibrzycki, G. Foti, N. Hammoud, V. Mathieu, G. Montana, R. J. Perry, A. Pilloni, A. Rodas, V. Shastry, W. A. Smith, A. P. Szczepaniak, D. Winney
Categories: hep-ex
Abstract:
We present new \compass high-statistics measurements of the peripheral production of $ηπ^-$ and $η^\prime π^-$ pairs in the reactions $π^- p \to η^{(\prime)}π^- p$. For the first time, we perform an unbinned analysis of the high-mass region of the $ηπ^-$ and $η^\primeπ^-$ systems, which allows us to disentangle the exchange mechanisms governing their production. We report the first observation in high-energy scattering of an exotic Reggeon with high significance exceeding $5\,σ$ for both channels, which is, most likely, related to the exotic $π_1(1600)$.8
Published: 2026-07-20 11:32:23
Authors: Zhaoyan Hong, Yishen Sun, Xinyi Zhang, Zhentao Han, Jinhao Dong, Wei Lu, Kai Xu, Liu Tang, Qi Liu, Xiaoyong Du
Categories: cs.DB
Abstract:
Modern DBMSs expose multiple configurable components (e.g., knobs, query hints, and indexes) that jointly determine query performance. Multi-component tuning is challenging due to the large combinatorial search space and the difficulty of learning effective tuning policies under limited feedback. Existing approaches still rely on blind search over the configuration space and interaction-heavy policy learning, leading to high tuning overhead and limited performance gains. Recent advances in large language models (LLMs) enable knowledge-driven tuning, but existing LLM-based methods fail to effectively exploit online feedback and historical observations, often converging prematurely to suboptimal configurations.
In this paper, we present EvoTune, a memory-aware evolution framework for multi-component DBMS tuning. EvoTune first localizes a query-specific high-impact subspace via collaborative diagnosis, which combines lightweight pattern learning with LLM-based reasoning. It further introduces a utility-aware retrieval policy that selects informative observations based on their resulting long-term performance improvement, instead of similarity-based retrieval. To support continual improvement, EvoTune organizes tuning feedback into a hierarchical memory and incrementally refines both subspace localization and tuning policies without requiring LLM fine-tuning. Extensive experiments show that EvoTune consistently outperforms state-of-the-art baselines, achieving up to 44.5% performance improvement under the same tuning budget and reaching the best competing baseline's final performance up to 3.9X faster.
Published: 2026-07-20 11:17:19
Authors: Md. Asaduzzaman Shuvo
Categories: cs.CL
Abstract:
Many Bangla words are at once personal names and culturally loaded common nouns, "Maya" is both a girl's name and a word for affectionate compassion. Choosing the right reading demands cultural knowledge that is scarce in the pretraining data of modern language models. We introduce Culturally Entangled Homograph (CEH) disambiguation and build a Bangla benchmark of 1,516 expert-verified sentences (3,032 labelled occurrences) in which one word appears twice with two distinct readings, each labelled with a culturally grounded category and an explanation of the reasoning behind it. Across open- and closed-source models, we find a systematic dominant-meaning bias: models default to the common-noun sense and overlook the name. A Bangla-specific model fails under every prompting regime we test, showing that language-specific pretraining alone does not confer cultural grounding. We further show that contrastive chain-of-thought prompting can sharply reduce this bias without training, and that distilling cultural explanations teaches small (1-3B) models to reason toward the correct reading rather than memorise labels, cutting dominant-meaning bias from as high as 100% to under 5% and turning the failed Bangla-specific model into our strongest system. Dataset and code are available at https://github.com/ashuvo25/BanglaCEH.
Published: 2026-07-20 10:56:17
Authors: Song Son Ha, Florian Foerster, Henry Beuster, Eduard Zeller, Dominik Merli, Gerd Scholl
Categories: cs.CR
Abstract:
OPC Unified Architecture (OPC UA) encryption conceals application-layer semantics and restricts intrusion detection to residual communication structure. Although machine learning-based intrusion detection systems (IDSs) can detect attacks in encrypted OPC UA traffic, the relationship between residual structural observability and attack detectability remains insufficiently understood. This paper presents an explanatory framework combining a structural observability profile, the Structural Leakage Score (SLS), controlled within-family and cross-family comparisons, phase-specific analysis, and dimension-ablation analysis. Jensen--Shannon divergence is used to characterize transport, temporal, and protocol-lifecycle dimensions, while the SLS summarizes the residual structural magnitude. Evaluation on an industrial private 5G testbed covers four attack families with progressively reduced nominal activity. SLS generally tracks within-family recall trends but does not reproduce cross-family detectability ordering. Interpreting these mismatches also requires temporal prevalence, inter-burst persistence, predictive utility, unique contribution, and redundancy. The framework complements conventional IDS metrics by relating detection outcomes to the magnitude, temporal distribution, and predictive role of observable structural evidence.
Published: 2026-07-20 10:54:39
Authors: Lijie Liu, Mingbo Dou, Xu Chen, Xianjie Wang, M. Ye. Zhuravlev, A. V. Nikolaev, L. L. Tao
Categories: cond-mat.other
Abstract:
Spin relaxation results in the spin decoherence and a finite spin lifetime, which are detrimental to spintronic devices. To achieve a long spin lifetime desirable for spintronic devices, elucidating the spin relaxation mechanism and factors influencing the spin lifetime is of vital importance. Here, we investigate the spin relaxation in $X$-wave magnets ($X=p, d, f, g, i$) with Rashba spin-orbit coupling within the framework of D'yakonov-Perel' mechanism. We calculate the general matrix of the spin relaxation time for an arbitrary Néel vector direction of the $X$-wave magnet. As an illustration, we study the spin relaxation for the Néel vector along the $[001]$ direction. It is found that the reciprocal spin-relaxation-time matrices are anisotropic and diagonal for the $d$-, $f$-, $g$- and $i$-wave magnets. For the $p$-wave magnet, we derive the analytical expressions for the temporal evolution of spins. Moreover, the spin relaxation rate is proportional to the momentum relaxation time, Rashba and altermagnetic spin-split strengths for all $X$-wave magnets. Our results shine more light on the fundamental understanding of the spin relaxation mechanism in $X$-wave magnets.
Published: 2026-07-20 10:29:50
Authors: Maria Groyne, Cedric Baijot, Michaël De Becker
Categories: astro-ph.IM
Abstract:
Under cryogenic interstellar conditions, the amorphous structure of interstellar ice results in binding-energy distributions (BEDs) per species. However, only few studies attempted their inclusion in astrochemical models. This paper introduces DESTINY, a deterministic astrochemical framework designed to incorporate BEDs while self-consistently accounting for the competition among activated surface processes. The framework is currently constrained to a monolayer. Surface processes initiated by surface species are reformulated using a trial-frequency-capped formalism represented through branched absorbing Markov chains. The ordinary differential equations (ODE) system is redefined based on normalized effective probabilities. Preliminary results based on a reduced surface network are discussed. To isolate the effects of the probabilistic reformulation from those induced by BED discretizations, DESTINY is benchmarked against Nautilus, a single-BE rate-equation based open source code. In the single-BE limit, DESTINY reproduces the behavior of Nautilus for most species. The largest deviations are obtained for CH$_{x = [2,4]}$ ; these are traced to a different treatment of the H$_2$ encounter effect, impacting both H$_2$ surface exploration and desorption efficiencies within the DESTINY framework. Introducing BEDs redistributes species among adsorption sites of different depths, altering the balance between diffusion, desorption, and reactions. Significant effects are found for H, H$_2$, NH$_x$, NO, CH$_x$, CO and H$_x$CO. Preliminary results showed that the self-consistent treatment of the H$_2$ encounter effect coupled with the explicit treatment of BEDs can substantially modify grain-surface chemistry. Further framework extensions are expected in the near future.
Published: 2026-07-20 10:29:01
Authors: Tengyang Liu, Ruifeng Zhang
Categories: math.AP, math-ph
Abstract:
We investigate the relation between minimizers and weak solutions for a class of singular functionals arising from Born--Infeld type theories $\mathcal{L}(s)$. In the setting of an electrostatic field $s=\frac{1}{2}|\nablaφ|^2$, $\mathcal{L}(s)$ satisfies $\lim_{s\to(1/2)^-}\mathcal{L}(s)=+\infty$, which naturally enforces the finite gradient bound $|\nablaφ|\le 1$, also called the truncation threshold. For a prescribed extended charge density $ρ$, we consider the relation between the weak solution of the system \begin{equation} \begin{cases} -{\rm div}\left(b\left(\frac12|\nablaφ|^2\right)\nablaφ\right)=ρ,& \text{in }\mathbb{R}^N,\\ b(s)=\mathcal{L}'(s),\quad\lim_{s\to\frac12^-} b(s)=+\infty,\\ \lim_{|x|\to\infty}φ(x)=0 \end{cases} \end{equation} and the minimizer $φ_0$ of the singular functional. We propose a monotonic approximation method to handle the intrinsic singularities of $\mathcal{L}(s)$. We prove that the gradient of the minimizer never touches the singular boundary $|\nablaφ|^2=1$; this structural result yields a key integrability property, the existence and uniqueness of the minimizer, and the corresponding variational inequality. Under the additional assumption that $ρ$ is radially distributed, we show that the minimizer is the unique weak solution. Furthermore, we establish the $C^1$ and $C^2$ regularity of the minimizer under suitable integrability conditions on $ρ$, and provide a uniform estimate for the strict spacelikeness condition $|\nablaφ_0|\le 1-ε$, where the parameter $ε>0$ is explicitly characterized in terms of the spatial dimension, the spatial region, and $ρ$. Our results extend the classical Born--Infeld theory to a general class of singular Born--Infeld type theories, thereby providing a unified framework for the variational analysis and regularity of such singular functionals and systems.
Published: 2026-07-20 10:22:53
Authors: Xiaozhong Lyu, Gen Li, Zhiyin Qian, Xucong Zhang, Marc Pollefeys, Siyu Tang
Categories: cs.CV, cs.AI
Abstract:
Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment. A holistic and efficient multimodal model capable of reconstructing this 4D representation is therefore highly desirable. However, existing approaches often rely on auxiliary inputs such as pre-computed camera trajectories, treat scene perception and human ego-motion modeling as separate problems despite their strong interdependency, and suffer from slow inference time. To address these limitations, we present ReViV, the first unified framework for holistic egocentric 4D reconstruction that extracts both viewer and view dynamics from a single monocular RGB video. We formulate the task as learning the full joint probability distribution over multimodal signals, including RGB video, camera trajectory, gaze direction, full-body motion, hand motion, and depth. Powered by a Masked Generative Egocentric Transformer, ReViV operates within a single feed-forward architecture to simultaneously reconstruct the temporally consistent 4D reconstruction across the viewer and the view with fast inference speed. Extensive experiments on diverse benchmarks, including HoloAssist, HOT3D, ARCTIC, Aria Digital Twin, and TACO, demonstrate that ReViV achieves state-of-the-art accuracy and efficiency across holistic ego-body, hand, and gaze reconstruction, camera tracking, while maintaining highly competitive egocentric depth estimation without relying on heavy task-specific priors. Code and models are fully open-sourced: https://reviv4d.github.io/.
Published: 2026-07-20 10:15:02
Authors: Moona Mazher, Abdul Qayyum, Steven A. Niederer, Daniel C. Alexander
Categories: cs.CV, cs.AI
Abstract:
Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data. However, existing foundation models for neuroimaging remain limited by task-specific training, slice-based learning strategies, or relatively small pretraining datasets, restricting their generalizability across diverse brain MRI applications. In this work, we present BrainNext, a general-purpose self-supervised foundation model for volumetric brain MRI analysis. BrainNext combines masked autoencoder (MAE) pretraining with a native three-dimensional Bi-Directional xLSTM-UNet architecture to learn rich anatomical representations from 60,551 unlabeled brain MRI examinations spanning multiple MRI modalities. The pretrained model is subsequently adapted to downstream tasks through lightweight task-specific fine-tuning. We evaluate BrainNext on the Foundation Models for Medical Imaging (FOMO) 2025 Method Track, encompassing classification, segmentation, and brain-age estimation, where it achieved second place overall and ranked first in the meningioma segmentation task on the official FOMO 2025 challenge leaderboard, demonstrating strong transferability across heterogeneous neuroimaging tasks. These results highlight the potential of large-scale self-supervised pretraining to learn robust and transferable volumetric representations, establishing BrainNext as a scalable foundation model for diverse brain MRI applications.
Published: 2026-07-20 10:06:47
Authors: Oliver Aleksander Larsen, Tiziano Santilli, Francesco Daghero, Mahyar T. Moghaddam
Categories: cs.SE, cs.AI, cs.HC
Abstract:
Cyber-physical systems built on deterministic edge inference, such as on-vehicle flood detection for agricultural fields, produce structured decision logs that must be interpreted differently by heterogeneous stakeholders. Pairing such systems with large language models (LLMs) to generate stakeholder-specific reports introduces a tension: the generative layer is non-deterministic, while the edge plane must remain replayable and auditable. We propose an architectural pattern resting on two invariants: unidirectional consumption, in which the generative layer is a strict read-only consumer of the deterministic plane and never writes back, and persona-as-configuration, in which stakeholder adaptation is a versioned prompt-template artifact rather than runtime improvisation. We instantiate the pattern as a context-aware dashboard layer over the JSON decision logs of a previously published edge-based standing-water detection system, and analyse how the integration boundary admits standard generative-reliability mitigations as configuration- or middleware-level extension points. A structured expert review rated the pattern favourably across five ISO/IEC 25010-aligned quality dimensions, with strongest agreement on separation of concerns. End-user evaluation with agricultural stakeholders is planned for future work.
Published: 2026-07-20 10:04:01
Authors: Xiaoning Bai, Shangzhi Zeng, Jin Zhang
Categories: math.OC
Abstract:
Value-function-type reformulations have generated a broad class of methods for bilevel optimization. However, the corresponding value-function-type constraints are inherently degenerate and generally fail to satisfy standard constraint qualifications, so the associated multiplier sequences may be unbounded and bounded-multiplier convergence analyses become inapplicable. We study this issue for the regularized gap-function reformulation of bilevel problems with constrained convex lower-level programs. We prove that accumulation points of approximate stationary sequences are C-stationary for the corresponding Karush-Kuhn-Tucker-based mathematical program with complementarity constraints (MPCC), even when the multiplier sequence associated with the regularized gap-function constraint is unbounded. The result holds under Mangasarian-Fromovitz constraint qualification (MFCQ) for the upper- and lower-level constraint systems and MPCC-MFCQ at the limiting MPCC point, without any constraint qualification on the regularized gap-function constraint itself. We further provide an example showing that approximate stationary points of the standard regularized gap-function reformulation may converge to a point that is C-stationary but not M-stationary. To guarantee M-stationarity, we introduce a slack-based two-parameter penalty formulation preserving exact multiplier-slack complementarity and establish M-stationarity under a domination condition on the penalty parameters. We develop an inexact slack-penalty method with adaptive penalty updates and feasibility correction, whose accumulation points are M-stationary under the stated assumptions.
Published: 2026-07-20 10:02:18
Authors: Nabeela Khan, Bowen Wu, Runwu Shi, Benjamin Yen, Takeshi Ashizawa, Carlos Toshinori Ishi, Takashi Minato, Kazuhiro Nakadai
Categories: cs.RO, cs.CV, cs.HC
Abstract:
Recent sign language generation (SLG) systems increasingly output dense 3D body representations, which better preserve full-body kinematics and geometry for downstream embodiment on humanoid robots. However, these generated motions frequently exhibit self-intersections such as hand-hand and hand-torso penetration. While such artifacts may be tolerated in offline rendering, they become critical in humanoid execution as they lead to infeasible inverse-kinematics (IK) solutions, collisions, and unstable retargeted trajectories. We present a system-level framework that bridges SLG outputs to humanoid joint-space execution via two components. First, we introduce a volumetric SMPL-X collision-mitigation module that projects generated signing motions toward physically plausible configurations while minimally deviating from the original trajectory. Second, we propose a vision-language-guided retargeting algorithm built on an IK backbone: a VLM serves as a visual critic over rendered humanoid motion, identifies embodiment-specific failure modes, and triggers targeted task-space corrections. Our results highlight collision handling and perception-guided refinement as key missing components for reliable humanoid signing.
Published: 2026-07-20 09:51:02
Authors: Ziyi Liu, Grace Zhang
Categories: cs.LG, cs.AI, cs.RO
Abstract:
Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations. However, real-world tasks often exhibit substantial natural variations (e.g., picking up mugs with varying shapes), making it impractical to collect demonstrations that fully specify a new task under every possible scenario. In practice, while demonstrations for the target task are limited, it is often easier to obtain datasets of heterogeneous but related behaviors. This motivates the problem of few-shot IRL with multi-task demonstrations (FM-IRL), where an agent must learn a new task with substantial variations from only a limited number of target-task demonstrations, together with sufficient demonstrations of related tasks and online agent experience. To do so, we must both recover the expert distribution of the new task and provide guidance when the agent deviates from it. We introduce Multitask discriminator Proximity-Guided IRL (MPG), which learns two complementary reward components: (1) a generalizable discriminator that transfers shared structure across related tasks to identify expert behavior in a new task, and (2) a proximity function that measures how far a state deviates from expert behavior and provides corrective guidance during exploration. We demonstrate the effectiveness of our method on multiple challenging navigation and manipulation tasks under significant variations (e.g., object configurations, table layouts, and initial robot poses), achieving an average success rate of 81.2%, outperforming the strongest per-task baseline by an average of 24.7 percentage points.
Published: 2026-07-20 09:50:20
Authors: Xiaozheng Fan, Panshi Jing, Chuanguang Zhang, Junshuai Wang, Chunlan Ma, Shijing Gong, Chuanxi Zhao, Tianxing Wang, Yipeng An
Categories: cond-mat.supr-con
Abstract:
First-principles studies of superconductivity often neglect anharmonic effects (AHE), despite their crucial role in achieving quantitative accuracy in many materials. To bridge this gap, we introduce a general computational approach, termed anharmonic superconducting density functional theory (ah-SCDFT) which systematically incorporates anharmonic corrections into standard SCDFT. This approach allows for high-fidelity predictions of superconducting properties with only a modest increase in computational cost for a limited number of superconducting calculation convergence steps. We demonstrate the effectiveness and reliability of ah-SCDFT by applying it to the prototypical superconductor MgB2, accurately reproducing its superconducting behavior under both ambient conditions and applied pressure in excellent agreement with experiment. Our results establish ah-SCDFT as a powerful, efficient, and broadly applicable approach for quantitatively reliable studies of superconductivity and a promising tool for the prediction of new superconducting materials.
Published: 2026-07-20 09:47:45
Authors: Poojan Angiras, Sachin Rana, Md. Kaosor Ali Mondal, Shakir Eqbal, Amal Sarkar
Categories: hep-ex
Abstract:
Gas Electron Multipliers (GEMs) are essential detector components in modern high-energy physics experiments, where precise and stable detection of charged particles over a broad energy range is required. We present a comprehensive Garfield$^{++}$ and ANSYS-based study of conventional bi-conical and optimized single-conical GEM detectors to investigate the performance of the GEM detector for muons ($μ$), pions ($π$), kaons ($K$), and protons ($P$), which constitute the dominant charged particles measured directly in collider-based experiments. The aim is to examine the impact of particle-dependent ionization characteristics on charge amplification and ion backflow, and to evaluate the potential of an optimized GEM configuration for different leptons and hadrons. The conventional bi-conical GEM design does not always operate at optimal efficiency, as ion backflow can lead to space-charge accumulation and electric field distortions, ultimately limiting performance in high-rate environments. Thus, geometrical optimization is essential to address these limitations and enhance detector performance. A single-conical hole geometry is introduced and systematically compared with the conventional bi-conical design. For both of these configurations, the results exhibit clear and systematic variations in the detector performance with the particle type and the incident energy. The optimized geometry improves the balance between effective gain and ion backflow, demonstrating its potential for future high-rate MPGD applications.