March 2025 arXiv papers — page 146

Marc Illa, Martin J. Savage, Xiaojun Yao

Improved Kogut-Susskind Hamiltonians for quantum simulations of non-Abelian Yang-Mills gauge theories are developed for honeycomb (2+1D) and hyperhoneycomb (3+1D) spatial tessellations. This is motivated by the desire to identify lattices for quantum simulations that involve only 3-link vertices among the gauge field group spaces in order to reduce the compl

Uri Keshet

Faint $\gamma$-ray signatures emerge in Fermi-LAT data stacked scaled to the characteristic $R_{500}$ radii of MCXC galaxy clusters. This third paper in a series shows a $4.3\sigma$ excess of discrete 4FGL-DR4 catalog $\gamma$-ray sources at the $r<1.5R_{500}$ radii of 205 clusters, coincident with an $r\sim R_{500}$ diffuse $2.6\sigma$ excess of 1-100 GeV e

Daisuke Fujii, Mamiya Kawaguchi, Mitsuru Tanaka

We explore the confining pressure inside the nucleon and the related gravitational form factor referred to as the D-term, using the skyrmion approach based on the scale-invariant chiral perturbation theory, where the skyrmion is described as the nucleon and a scalar meson couples to the scale anomaly through the low energy theorem. Within this model framewor

Rebecca K. Leane, John F. Beacom

We present a new technique for sub-GeV dark matter (DM) searches and a new use of neutrino observatories. DM-electron scattering in an observatory can excite or ionize target molecules, which then produce light that can be detected by the photomultiplier tubes (PMTs). While individual DM scatterings are indistinguishable, the aggregate rate from many indepen

Alican Saray, Calvin Pozderac, Ari Josephson, Brian Skinner

When traffic is routed through a network that is susceptible to congestion, the self-interested decisions made by individual users do not, in general, produce the optimal flow. This discrepancy is quantified by the so-called "price of anarchy." Here we consider whether the traffic produced by self-interested users is made better or worse when users have unce

Ben Jaderberg, George Pennington, Kate V. Marshall, Lewis W. Anderson

Preparing matrix product states (MPSs) on quantum computers is an essential routine in the simulation of many-body physics. However, widely-used schemes based on staircase circuits are often too deep to execute on current hardware. Here we demonstrate that MPSs with short-range correlations can be prepared with shallow circuits by leveraging heuristics from

Orion Ning, Benjamin R. Safdi

We search for the existence of ultralight axions coupling to electrons and photons using data from the NuSTAR telescope directed toward the galaxies M82, M87, and M31. We focus on electron bremsstrahlung and Compton scattering for axion production in stars, summing over the stellar populations found in the target galaxies when computing the axion luminosity.

Chris Akers, Gracemarie Bueller, Oliver DeWolfe, Kenneth Higginbotham

A straightforward gravitational path integral calculation implies that closed universes are trivial, described by a one dimensional Hilbert space. Two recent papers by Harlow-Usatyuk-Zhao and Abdalla-Antonini-Iliesiu-Levine have sought to ameliorate this issue by defining special rules to incorporate observers into the path integral. However, the proposed ru

P. M. Sánchez-Alarcón, H. Salo, J. H. Knapen, S. Comerón

The Spitzer Survey of Stellar Structure in Galaxies (S$^4$G), together with its Early Type Galaxy (ETG) extension, stand as the most extensive dataset of deep, uniform mid-infrared (mid-IR; 3.6 and 4.5$\,\mu$m) imaging for a sample of $2817$ nearby ($d<40 \,$Mpc) galaxies. However, the velocity criterion used to select the original sample results in an addit

Keefe Mitman, Isabella Pretto, Harrison Siegel, Mark A. Scheel

Using gravitational waves to probe the geometry of the ringing remnant black hole formed in a binary black hole coalescence is a well-established way to test Einstein's theory of general relativity. However, doing so requires knowledge of when the predictions of black hole perturbation theory, i.e., quasi-normal modes (QNMs), are a valid description of the e

Alberto Salvio

This paper initiates the systematic study of thermal field theory for generic equilibrium density matrices, which feature arbitrary values not only of temperature and chemical potentials, but also of average angular momentum. The focus here is on scalar fields, although some results also apply to fields with arbitrary spins. A general technique to compute en

Itay Chachy, Guy Yariv, Sagie Benaim

Score Distillation Sampling (SDS) has emerged as an effective technique for leveraging 2D diffusion priors for tasks such as text-to-3D generation. While powerful, SDS struggles with achieving fine-grained alignment to user intent. To overcome this, we introduce RewardSDS, a novel approach that weights noise samples based on alignment scores from a reward mo

Jihao Zhao, Zhiyuan Ji, Zhaoxin Fan, Hanyu Wang

Retrieval-Augmented Generation (RAG), while serving as a viable complement to large language models (LLMs), often overlooks the crucial aspect of text chunking within its pipeline. This paper initially introduces a dual-metric evaluation method, comprising Boundary Clarity and Chunk Stickiness, to enable the direct quantification of chunking quality. Leverag

Karsten Jedamzik, Levon Pogosian, Tom Abel

Primordial Magnetic Fields (PMFs), long studied as relics of the early Universe, accelerate recombination and have been proposed as a way to relieve the Hubble tension. However, previous studies relied on simplified toy models. Here we use recent evaluations of recombination with PMFs, incorporating full magnetohydrodynamic (MHD) simulations and detailed Lym

Ruohao Guo, Wei Xu, Alan Ritter

As Large Language Models (LLMs) are widely deployed in diverse scenarios, the extent to which they could tacitly spread misinformation emerges as a critical safety concern. Current research primarily evaluates LLMs on explicit false statements, overlooking how misinformation often manifests subtly as unchallenged premises in real-world interactions. We curat

Sebastian Möller, Pia Knoeferle, Britta Schulte, Nils Feldhus

Machine learning techniques have conquered many different tasks in speech and natural language processing, such as speech recognition, information extraction, text and speech generation, and human machine interaction using natural language or speech (chatbots). Modern techniques typically rely on large models for representing general knowledge of one or seve

Zhehao Zhang, Yijian Zou, Timothy H. Hsieh, Sagar Vijay

We explore the universal properties of mixed quantum matter obtained from "single-shot" adaptive evolution, in which a quantum-critical ground-state is manipulated through a single round of local measurements and local unitary operations conditioned on spatially-distant measurement outcomes. The resulting mixed quantum states are characterized by altered lon

M. K. Prasad, V. Babenko, A. W. Tadbier, S. Hofmann

One of the most promising building blocks for the development of spin qubits, single-photon sources, and quantum sensors at room temperature, as well as 2D ultraviolet light-emitting diodes, are defect colour centres in 2D hexagonal boron nitride (hBN). However, a significant requirement for the realisation of such devices towards scalable technologies is th

Chenyu Li, Oscar Michel, Xichen Pan, Sainan Liu

Large-scale pre-trained video generation models excel in content creation but are not reliable as physically accurate world simulators out of the box. This work studies the process of post-training these models for accurate world modeling through the lens of the simple, yet fundamental, physics task of modeling object freefall. We show state-of-the-art video

Katrin Renz, Long Chen, Elahe Arani, Oleg Sinavski

Integrating large language models (LLMs) into autonomous driving has attracted significant attention with the hope of improving generalization and explainability. However, existing methods often focus on either driving or vision-language understanding but achieving both high driving performance and extensive language understanding remains challenging. In add

Adonai Hilário da Silva, Leonardo Kleber Castelano, Reginaldo de Jesus Napolitano

A unitary evolution in time may be treated as a curve in the manifold of the special unitary group. The length of such a curve can be related to the energetic cost of the associated computation, meaning a geodesic curve identifies an energetically optimal path. In this work, we employ sub-Riemannian geometry on the manifold of the unitary group to obtain opt

Sikai Huang, Yixin Berry Wen, Tara Adusumilli, Kusum Choudhary

Symbolic regression is essential for deriving interpretable expressions that elucidate complex phenomena by exposing the underlying mathematical and physical relationships in data. In this paper, we present an advanced symbolic regression method that integrates symbol priors from diverse scientific domains - including physics, biology, chemistry, and enginee

Cameron Strachan, Konrad Swanepoel

We present two results related to an edge-isoperimetric question for Cayley graphs on the integer lattice asked by Ben Barber and Joshua Erde [Isoperimetry of Integer Lattices, Discrete Analysis 7 (2018)]. For any (undirected) graph $G$, the edge boundary of a subset of vertices $S$ is the number of edges between $S$ and its complement in $G$. Barber and Erd

Md Mohaiminul Islam, Tushar Nagarajan, Huiyu Wang, Gedas Bertasius

Video Question Answering (VQA) in long videos poses the key challenge of extracting relevant information and modeling long-range dependencies from many redundant frames. The self-attention mechanism provides a general solution for sequence modeling, but it has a prohibitive cost when applied to a massive number of spatiotemporal tokens in long videos. Most p

Dahmane Dechicha

This paper is devoted to the hydrodynamic limit for the linear Boltzmann equation, in the case of a heavy tail equilibrium and a cross section which depends on the space variable and which degenerates for large velocities, without symmetry assumptions. For an appropriate time scale, a macroscopic equation with an elliptic operator, which is equivalent to the

Adrien Abgrall

For G, H two finite collections of finitely generated subgroups of a right-angled Artin group A, the untwisted McCool group U(A; G, Ht) is the subgroup of untwisted outer automorphisms of A preserving the conjugacy class of each element of G and acting trivially up to conjugacy on each element of H. We prove that when the elements of G are standard subgroups

Nannan Wu, Zhuo Kuang, Zengqiang Yan, Ping Wang

Despite the potential of federated learning in medical applications, inconsistent imaging quality across institutions-stemming from lower-quality data from a minority of clients-biases federated models toward more common high-quality images. This raises significant fairness concerns. Existing fair federated learning methods have demonstrated some effectivene

Andrew Crossman, Andrew R. Plummer, Chandra Sekharudu, Deepak Warrier

We present Auspex - a threat modeling system built using a specialized collection of generative artificial intelligence-based methods that capture threat modeling tradecraft. This new approach, called tradecraft prompting, centers on encoding the on-the-ground knowledge of threat modelers within the prompts that drive a generative AI-based threat modeling sy

Brendan Cross, Boleslaw K. Szymanski

Here, we introduce a new tool for community detection, a generator of networks, which uses parameters to control the structure of created networks. Typically, network scientists designing novel community detection algorithms use synthetically generated benchmarks with community structures that they intend to detect and scale the benchmark networks across siz

Hong-Yan Zhang, Haoting Liu, Zhi-Qiang Feng, Ci-Fei Dong

The symplectic geometry mode decomposition (SGMD) is a powerful method for analyzing time sequences. The SGMD is based on the upper conversion via embedding and down conversion via diagonal averaging principle (DAP) inherited from the singular spectrum analysis (SSA). However, there are two defects in the DAP: it just hold for the time delay $\tau=1$ in the

Changxiao Cai, Gen Li

Score-based diffusion models have become a foundational paradigm for modern generative modeling, demonstrating exceptional capability in generating samples from complex high-dimensional distributions. Despite the dominant adoption of probability flow ODE-based samplers in practice due to their superior sampling efficiency and precision, rigorous statistical

Alexander A. Gaifullin

In 2014 the author showed that in the three-dimensional spherical space, alongside with three classical types of flexible octahedra constructed by Bricard, there exists a new type of flexible octahedra, which was called exotic. In the present paper we give a geometric construction for exotic flexible octahedra, describe their configuration spaces, and calcul

Harald Garcke, Kei Fong Lam, Robert Nürnberg, Andrea Signori

The Cahn-Hilliard model with reaction terms can lead to situations in which no coarsening is taking place and, in contrast, growth and division of droplets occur which all do not grow larger than a certain size. This phenomenon has been suggested as a model for protocells, and a model based on the modified Cahn-Hilliard equation has been formulated. We intro

Tianai Yin, Zhenning Cai, Yanli Wang

We introduce a fast Fourier spectral method to compute linearized collision operators of the Boltzmann equation for variable hard-sphere gases. While the state-of-the-art method provides a computational cost O(MN^4 log N), with N being the number of modes in each direction and M being the number of quadrature points on a hemisphere, our method reduces the co

Yingfa Chen, Yutong Wu, Chenyang Song, Zhen Leng Thai

Grouped-Query Attention (GQA) is a widely adopted strategy for reducing the computational cost of attention layers in large language models (LLMs). However, current GQA configurations are often suboptimal because they overlook how context length influences inference cost. Since inference cost grows with context length, the most cost-efficient GQA configurati

Ryota Inagaki, Tanya Khovanova, Austin Luo

Chip-firing is a combinatorial game on a graph, in which chips are placed and dispersed among its vertices until a stable configuration is achieved. We specifically study a chip-firing variant on an infinite, rooted, directed $k$-ary tree where we place $k^n$ chips labeled $0,1,\dots, k^n-1$ on the root for some nonnegative integer $n$, and we say a vertex $

Philippe Chlenski, Kaizhu Du, Dylan Satow, Raiyan R. Khan

We present Manify, an open-source Python library for non-Euclidean representation learning. Leveraging manifold learning techniques, Manify provides tools for learning embeddings in (products of) non-Euclidean spaces, performing classification and regression with data that lives in such spaces, estimating the curvature of a manifold, and more. Manify aims to

Gongxiaohui Chen, Philippe Piot, John Power, Chunguang Jing

This paper presents a study of the radiofrequency (RF) characteristics and beam dynamics of an X-band photogun (Xgun) operating in the transient state. The photoinjector is designed to operate with short RF pulses (9 ns) to achieve high accelerating gradients. Short-pulse operation potentially reduces breakdown risks, as experimentally demonstrated by achiev

Lorenzo Torricelli

A Thorin process is a stochastic process with independent and stationary increments whose laws are weak limits of finite convolutions of gamma distributions. Many popular L\'evy processes fall under this class. The Thorin class can be characterized by a representing triplet that conveys more information on the process compared to the L\'evy triplet. In this

Marianne Arriola, Aaron Gokaslan, Justin T. Chiu, Zhihan Yang

Diffusion language models offer unique benefits over autoregressive models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeling and are limited to fixed-length generation. In this work, we introduce a class of block diffusion language models that interpolate between discrete denoising diffusion and autor

Jonathan Zheng, Sauvik Das, Alan Ritter, Wei Xu

Probabilistic reasoning is a key aspect of both human and artificial intelligence that allows for handling uncertainty and ambiguity in decision-making. In this paper, we introduce a new numerical reasoning task under uncertainty for large language models, focusing on estimating the privacy risk of user-generated documents containing privacy-sensitive inform

Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim, Suhong Moon

Large language models (LLMs) have shown remarkable advancements in enabling language agents to tackle simple tasks. However, applying them for complex, multi-step, long-horizon tasks remains a challenge. Recent work have found success by separating high-level planning from low-level execution, which enables the model to effectively balance high-level plannin

Veronica Calvo Cortes, Hadleigh Frost, Bernd Sturmfels

We study stratifications of regions in the space of symmetric matrices. Their points are Mandelstam matrices for momentum vectors in particle physics. Kinematic strata in these regions are indexed by signs and rank two matroids. Matroid strata of Lorentzian quadratic forms arise when all signs are non-negative. We characterize the posets of strata, for massl

Guido Da Re, Keefe Mitman, Leo C. Stein, Mark A. Scheel

Understanding the characteristics of the remnant black hole formed in a binary black hole merger is crucial for conducting gravitational wave astronomy. Typically, models of remnant black holes provide information about their mass, spin, and kick velocity. However, other information related to the supertranslation symmetries of the BMS group, such as the mem

Marcello Barylli, Joyaditya Saha, Tineke E. Buffart, Jan Koster

Recent advances in single cell sequencing and multi-omics techniques have significantly improved our understanding of biological phenomena and our capacity to model them. Despite combined capture of data modalities showing similar progress, notably single cell transcriptomics and proteomics, simultaneous multi-omics level probing still remains challenging. A

Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng

Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) characteristics, which enhance reasoning abilities and enable the

Elisa Calì, Tommaso Fulcini, Riccardo Coppola, Lorenzo Laudadio

Achieving web accessibility is essential to building inclusive digital experiences. However, accessibility issues are often identified only after a website has been fully developed, making them difficult to address. This paper introduces a Visual Studio Code plugin that integrates calls to a Large Language Model (LLM) to assist developers in identifying and

Lingmin Ran, Mike Zheng Shou

The development of video diffusion models unveils a significant challenge: the substantial computational demands. To mitigate this challenge, we note that the reverse process of diffusion exhibits an inherent entropy-reducing nature. Given the inter-frame redundancy in video modality, maintaining full frame rates in high-entropy stages is unnecessary. Based

Zixiang Chen, Greg Yang, Qingyue Zhao, Quanquan Gu

Despite deep neural networks' powerful representation learning capabilities, theoretical understanding of how networks can simultaneously achieve meaningful feature learning and global convergence remains elusive. Existing approaches like the neural tangent kernel (NTK) are limited because features stay close to their initialization in this parametrization,

Sven Witthaus, Atoosa Parsa, Dong Wang, Nidhi Pashine

Under an externally applied load, granular packings form force chains that depend on the contact network and moduli of the grains. In this work, we investigate packings of variable modulus (VM) particles, where we can direct force chains by changing the Young's modulus of individual particles within the packing on demand. Each VM particle is made of a silico

Shan Xu, Steven C. Hauser, Saad S. Nagi, James A. Jablonski

Interpersonal touch is an important channel of social emotional interaction. How these physical skin-to-skin touch expressions are processed in the peripheral nervous system is not well understood. From microneurography recordings in humans, we evaluated the capacity of six subtypes of cutaneous mechanoreceptive afferents to differentiate human-delivered soc

Sami Boulebnane, James Sud, Ruslan Shaydulin, Marco Pistoia

The quantum approximate optimization algorithm (QAOA) and quantum annealing are two of the most popular quantum optimization heuristics. While QAOA is known to be able to approximate quantum annealing, the approximation requires QAOA angles to vanish with the problem size $n$, whereas optimized QAOA angles are observed to be size-independent for small $n$ an

Chen-Wei Wang, Wen-Jun Tan, Shao-Lin Xiong, Rahim Moradi

The prompt emission of Gamma-Ray Bursts (GRBs) could be composed of different spectral components, such as a dominant non-thermal Band component in the keV-MeV range, a subdominant quasi-thermal component, and an additional hard non-thermal component extending into the GeV range. The existence and evolutionary behaviors of these components could place strong

Yan He, Chih-Chun Chien

We present a theory of two-component atomic Fermi superfluid with tunable pairing interaction in a thin spherical shell subject to spin-orbit coupling (SOC). By incorporating SOC into the Fermi superfluid in the BCS-Bose Einstein condensation (BEC) crossover, we obtain the energy spectrum and equations of state. While the order parameter and chemical potenti

Prince Frederick Kwao, Srivathsan Poyyapakkam Sundar, Brajesh Gupt, Ayush Asthana

Solving challenging problems in quantum chemistry is one of the most promising applications of quantum computers. Within the quantum algorithms proposed for problems in excited state quantum chemistry, subspace-based quantum algorithms, including quantum subspace expansion (QSE), quantum equation of motion (qEOM) and quantum self-consistent equation-of-motio

Thomas Kleine Buening, Jiarui Gan, Debmalya Mandal, Marta Kwiatkowska

We study Reinforcement Learning from Human Feedback (RLHF) in settings where multiple labelers may strategically misreport feedback to steer the learned policy toward their own preferences. We show that existing RLHF algorithms, including recent pluralistic methods, are not strategyproof, and that even a single strategic labeler can cause arbitrarily large m

Jiahao Xia, Yutao Hu, Yaolei Qi, Zhenliang Li

Solving medical imaging data scarcity through semantic image generation has attracted growing attention in recent years. However, existing generative models mainly focus on synthesizing whole-organ or large-tissue structures, showing limited capability in reproducing fine-grained anatomical details. Due to the stringent requirement of topological consistency

Shijie Chen, Yiwei Chen, Amir Aghabiglou, Motahare Torki

We introduce interlaced R2D2 (iR2D2), a DNN series paradigm for scalable image reconstruction from accelerated non-Cartesian k-space acquisitions in MRI with sensitivity map self-calibration. While unrolled DNN architectures provide robust image formation, embedding non-uniform fast Fourier transform operators within the backpropagation graph becomes impract

Paul-Hermann Balduf, Simone Hu

For a given graph $G$, Budzik, Gaiotto, Kulp, Wang, Williams, Wu, Yu, and the first author studied a ''topological'' differential form $\alpha_G$, which expresses violations of BRST-closedness of a quantum field theory along a single topological direction. In a seemingly unrelated context, Brown, Panzer, and the second author studied a ''Pfaffian'' different

Sangwon Jang, June Suk Choi, Jaehyeong Jo, Kimin Lee

Text-to-image diffusion models have achieved remarkable success in generating high-quality contents from text prompts. However, their reliance on publicly available data and the growing trend of data sharing for fine-tuning make these models particularly vulnerable to data poisoning attacks. In this work, we introduce the Silent Branding Attack, a novel data

Arohi Jain, Jiayang Yan, Jacob R. Pierce, Tanner T. Simpson

We present a novel approach for generating collider-quality electron bunches using a plasma photoinjector. The approach leverages recently developed techniques for the spatiotemporal control of laser pulses to produce a moving ionization front in a nonlinear plasma wave. The moving ionization front generates an electron bunch with a current profile that bala

Tim Büchner, Christoph Anders, Orlando Guntinas-Lichius, Joachim Denzler

The relationship between muscle activity and resulting facial expressions is crucial for various fields, including psychology, medicine, and entertainment. The synchronous recording of facial mimicry and muscular activity via surface electromyography (sEMG) provides a unique window into these complex dynamics. Unfortunately, existing methods for facial analy

Victor Gondret, Clothilde Lamirault, Rui Dias, Charlie Leprince

We study the entanglement properties of two-mode bosonic Gaussian states based on their multi-mode counting statistics. We exploit the idea that measuring high-order correlations of particle numbers can reveal entanglement without making any assumptions about the coherence of the fields. We show that the two- and four-body number correlations are sufficient

E. Yelton, C. P. Larson, K. Dodge, K. Okubo

Throughout multiple cooldowns we observe a power-law reduction in time for the rate of multi-qubit correlated poisoning events, while the rate of shifts in qubit offset-charge remains constant; evidence of a non-ionizing source of pair-breaking phonon bursts for superconducting qubits. We investigate different types of sample packaging, some of which are sen

G. Barontini, V. Naniyil, J. P. Stinton, D. Reid

Cosmic rays are deemed to be generated by a process known as ``Fermi acceleration", in which charged particles scatter against magnetic fluctuations in astrophysical plasmas. The process itself is however universal, has both classical and quantum formulations, and is at the basis of dynamical systems with interesting mathematical properties, such as the cele

Ryan Lamy

Let $n,k\in\mathbb{N}$ and let $S$ be the closed surface of genus $nk$. A copy of the braid group on $2k+2$ strands modulo its center is found inside $\mathrm{Mod}(S)$, provided $n\geq 3$. In particular, for $k=1$ the class of the half-twist braid inside $B_4/Z(B_4)$ is identified with a hyperelliptic involution inside $\mathrm{Mod}(S)$. As a consequence, we

Abraham Loeb

I show that the small differences between the orbital parameters of the dark comet 2005 VL1 and the Venera 2 spacecraft (reported in arXiv:2503.07972) are of the magnitude expected from gravitational deflection by a close encounter of Venera 2 with Venus.

Christopher Leon, Petr M. Anisimov, Nikolai Yampolsky, Alexander Scheinker

Calculating the effects of Coherent Synchrotron Radiation (CSR) is one of the most computationally expensive tasks in accelerator physics. Here, we use convolutional neural networks (CNN's), along with a latent conditional diffusion (LCD) model, trained on physics-based simulations to speed up calculations. Specifically, we produce the 3D CSR wakefields gene

Evita Nestoridi

We prove that the limit profile of a sequence of reversible Markov chains exhibiting total variation cutoff is a continuous function, under a computable condition involving the spectrum of the transition matrix and the cutoff window.

Fabio Cassini, Chiara Segala

In this manuscript, we study the turnpike property in stochastic discrete-time optimal control problems for interacting agents. Extending previous deterministic results, we show that the turnpike effect persists in the presence of noise under suitable dissipativity and controllability conditions. To handle the possible stiffness in the system dynamics, we em

Tattwamasi Amrutam, Yongle Jiang

Let $\Gamma$ be a countable discrete group. We say that $\Gamma$ has $C^*$-invariant subalgebra rigidity (ISR) property if every $\Gamma$-invariant $C^*$-subalgebra $\mathcal{A}\le C_r^*(\Gamma)$ is of the form $C_r^*(N)$ for some normal subgroup $N\triangleleft\Gamma$. We show that all torsion-free, non-amenable (cylindrically) hyperbolic groups with proper

L. E. Pirogov, P. M. Zemlyanukha, E. M. Dombek

Studies of the structure and kinematics of cores associated with the regions of massive star and star cluster formation are necessary for constructing scenario for the evolution of these objects. We analyzed spectral maps of the massive cores of G012.418+00.506, G326.472+00.888, G328.567--00.535, G335.586--00.289 and G343.127--00.063 from the MALT90 survey i

Mumuksh Tayal, Yogesh Simmhan

Edge devices like Nvidia Jetson platforms now offer several on-board accelerators -- including GPU CUDA cores, Tensor Cores, and Deep Learning Accelerators (DLA) -- which can be concurrently exploited to boost deep neural network (DNN) inferencing. In this paper, we extend previous work by evaluating the performance impacts of running multiple instances of t

Annu Bura, Tanmoy Samanta, Alphonse Sterling, Yajie Chen

Recent high-resolution solar observations have unveiled the presence of small-scale loop-like structures in the lower solar atmosphere, often referred to as unresolved fine structures, low-lying loops, and miniature hot loops. These structures undergo rapid changes within minutes, and their formation mechanism has remained elusive. In this study, we conducte

Alberto Pozanco, Marianela Morales, Daniel Borrajo, Manuela Veloso

Identifying the specific actions that achieve goals when solving a planning task might be beneficial for various planning applications. Traditionally, this identification occurs post-search, as some actions may temporarily achieve goals that are later undone and re-achieved by other actions. In this paper, we propose a compilation that extends the original p

Llorenç Escolà-Farràs, Florian Speelman

Quantum position verification (QPV) aims to verify an untrusted prover's location by timing communication with them. To reduce uncertainty, it is desirable for this verification to occur in a single round. However, previous protocols achieving one-round secure QPV had critical drawbacks: attackers pre-sharing an EPR pair per qubit could perfectly break them,

Oskar van der Wal, Pietro Lesci, Max Muller-Eberstein, Naomi Saphra

The stability of language model pre-training and its effects on downstream performance are still understudied. Prior work shows that the training process can yield significantly different results in response to slight variations in initial conditions, e.g., the random seed. Crucially, the research community still lacks sufficient resources and tools to syste

Aman Kushwaha, Raghavendra Tripathi

In 1959, Marcus and Ree proved that any bistochastic matrix $A$ satisfies $\Delta_n(A):= \max_{\sigma\in S_n}\sum_{i=1}^{n}A(i, \sigma(i))-\sum_{i, j=1}^n A(i, j)^2 \geq 0$. Erd\H{o}s asked to characterize the bistochastic matrices satisfying $\Delta_n(A)=0$. This problem remains largely open, and very recently, a complete list of such matrices was obtained

Jorge Álvaro González, Ana María Saiz García, Victor Monzon Baeza

In a globalized and interconnected world, interoperability has become a key concept for advancing tactical scenarios. Federated Coalition Networks (FCN) enable cooperation between entities from multiple nations while allowing each to maintain control over their systems. However, this interoperability necessitates the sharing of increasing amounts of informat

Jialiang Geng, George Michailidis

The paper studies the problem of detecting and locating change points in multivariate time-evolving data. The problem has a long history in statistics and signal processing and various algorithms have been developed primarily for simple parametric models. In this work, we focus on modeling the data through feed-forward neural networks and develop a detection

Anwar Ibrahim, Denis Derkach, Alexey Petrenko, Fedor Ratnikov

Optimizing accelerator control is a critical challenge in experimental particle physics, requiring significant manual effort and resource expenditure. Traditional tuning methods are often time-consuming and reliant on expert input, highlighting the need for more efficient approaches. This study aims to create a simulation-based framework integrated with Rein

Giulio Del Zanna, Supriya Hebbur Dayananda

Accurate atomic models for astrophysical plasma can be very complex, requiring thousands of states. However, for a variety of applications such as large-scale forward models of the Stokes parameters of a spectral line in the solar corona, it is necessary to build much reduced atomic models. We present two examples of such models, focused on the two near-infr

Kunal Mozumdar, Herbert F. Fotso, Jong E. Han

Quenched disorder in a solid state system can result in Anderson localization, where electrons are exponentially localized and the system behaves like an insulator. By solving exactly a disordered electronic lattice model out of equilibrium, we investigate the effect of a DC electric field on Anderson localization in an open system, and provide a minimal pla

Mingyang Liu, Gabriele Farina, Asuman Ozdaglar

We study equilibrium finding in polymatrix games under differential privacy constraints. Prior work in this area fails to achieve both high-accuracy equilibria and a low privacy budget. To better understand the fundamental limitations of differential privacy in games, we show hardness results establishing that no algorithm can simultaneously obtain high accu

Shuokang Huang, Julie A. McCann

Human pose estimation (HPE) detects the positions of human body joints for various applications. Compared to using cameras, HPE using radio frequency (RF) signals is non-intrusive and more robust to adverse conditions, exploiting the signal variations caused by human interference. However, existing studies focus on single-domain HPE confined by domain-specif

Minjae Chung, Jong Bum Won, Ganghyun Kim, Yujin Kim

Although Vision Transformers (ViTs) have recently demonstrated superior performance in medical imaging problems, they face explainability issues similar to previous architectures such as convolutional neural networks. Recent research efforts suggest that attention maps, which are part of decision-making process of ViTs can potentially address the explainabil

Partha Patra, Sumit Mukherjee, A. K. Pan

Conventional wisdom asserts that measurement incompatibility is necessary for revealing the non-locality and contextuality. In contrast, a recent work [Phys. Rev. Lett. 130, 230201 (2023)] demonstrates the generalized contextuality without measurement incompatibility by using a five-outcome qubit measurement. In this paper, we introduce a two-party prepare-m

Nguyen Thach, Fei Liu, Houyu Zhou, Hau Chan

Designing strategyproof mechanisms for multi-facility location that optimize social costs based on agent preferences had been challenging due to the extensive domain knowledge required and poor worst-case guarantees. Recently, deep learning models have been proposed as alternatives. However, these models require some domain knowledge and extensive hyperparam

Adam Karvonen, Can Rager, Johnny Lin, Curt Tigges

Sparse autoencoders (SAEs) are a popular technique for interpreting language model activations, and there is extensive recent work on improving SAE effectiveness. However, most prior work evaluates progress using unsupervised proxy metrics with unclear practical relevance. We introduce SAEBench, a comprehensive evaluation suite that measures SAE performance

Samare Rostami, Matteo Giantomassi, Xavier Gonze

The Quasi-harmonic Approximation (QHA) is a widely used method for calculating the temperature dependence of lattice parameters and the thermal expansion coefficients from first principles. However, applying QHA to anisotropic systems typically requires several dozens or even hundreds of phonon band structure calculations, leading to high computational costs

Pan Xu

We revisit the classical analysis of Karp, Vazirani, and Vazirani (KVV, STOC~1990), which established the well-known upper bound of $1 - 1/e$ as the limiting proportion of vertices that can be matched by any online procedure in a canonical bipartite structure. Although foundational, the original analysis contains several inaccuracies, including a fundamental

M. Kitatani, T. Schäfer, A. A. Katanin, A. Toschi

We study quantum criticality in the doped two-dimensional periodic Anderson model with the hybridization acting as a tuning parameter. Employing the dynamical vertex approximation we find two distinct quantum critical behaviors. One is a quantum critical point between the antiferromagnetically ordered and the Kondo state, both metallic with itinerant $f$ ele

Alexia Yavicoli, Han Yu

In DOI:10.1017/etds.2022.2 the author proved that for each integer $k$ there is an implicit number $M > 0$ such that if $b_1, \cdots , b_k$ are multiplicatively independent integers greater than $M$, there are infinitely many integers whose base $b_1, b_2, \cdots , b_k$ expansions all do not have zero digits. In this paper we don't require the multiplicative

Peng Chen, Pi Bu, Yingyao Wang, Xinyi Wang

Recent advances in Vision-Language-Action models (VLAs) have expanded the capabilities of embodied intelligence. However, significant challenges remain in real-time decision-making in complex 3D environments, which demand second-level responses, high-resolution perception, and tactical reasoning under dynamic conditions. To advance the field, we introduce Co

Raquel Ana Magalhães Bush

This paper examines the small-world properties of a Spotify artist feature collaboration network, focusing on clustering and diameter. We analyze the giant component and subgraphs based on genres, country-specific charts, and detected communities to assess their small-world characteristics. Results indicate that the network is scale-free and follows a power-

Leo Zanotti

The complexity of continuous piecewise affine (CPA) functions can be measured by the number of pieces $p$ or the number of distinct affine functions $n$. For CPA functions on $\mathbb{R}^d$, this paper shows an upper bound of $p=O(n^{d+1})$ and constructs a family of functions achieving a lower bound of $p=\Omega(n^{d+1-\frac{c}{\sqrt{\log_2(n)}}})$.

Bin Shen, Feng Du, Franziska Breitner, Victoria A. Ginga

Strange metallicity with $T$-linear electrical resistance preceding high-$T_c$ superconductivity remains an enigmatic, yet crucial, signature of correlation physics. Using electrical transport and magnetization measurements up to 50 GPa, we show that such a strange-metal phase is formed in pressurized kagome ferromagnet CrNiAs. In contrast to other kagome ma

Haiyan Wei, Hangrui Xu, Bingxu Zhu, Yulian Geng

Virtual stain transfer leverages computer-assisted technology to transform the histochemical staining patterns of tissue samples into other staining types. However, existing methods often lose detailed pathological information due to the limitations of the cycle consistency assumption. To address this challenge, we propose STNHCL, a hypergraph-based patch-wi

Zak Buzzard

Extending deep Q-learning to cooperative multi-agent settings is challenging due to the exponential growth of the joint action space, the non-stationary environment, and the credit assignment problem. Value decomposition allows deep Q-learning to be applied at the joint agent level, at the cost of reduced expressivity. Building on past work in this direction

Lucas Finazzi, Mariano Barella, Fernando Gomez Marlasca, Lucas Sambuco Salomone

LabOSat-01 is a payload designed to perform characterization experiments on electronic devices in hostile environments. Both Commercial-Off-The-Shelf components and custom nano and micro devices were studied in the last decade with this platform. The Total Ionizing Dose (TID) received by small satellites in Low Earth Orbit was measured with LabOSat-01 using