March 2025 arXiv papers — page 106

Joshua Murakami, Kentaro Hayashi, Yasuhiko Tsue

The Nambu-Goldstone modes on the exiotic chiral condensed phase with chiral and tensor-type quark-antiquark condensates are investigated by using of the two-point vertex functions. It is shown that one of the Nambu-Goldstone modes appears as a result of meson-mixing. As is well known, another method to find the Nambu-Goldstone modes is given by the use of th

Trisanth Srinivasan, Santosh Patapati

The current state of modern web interfaces, especially in regards to accessibility focused usage is extremely lacking. Traditional methods for web interaction, such as scripting languages and screen readers, often lack the flexibility to handle dynamic content or the intelligence to interpret high-level user goals. To address these limitations, we introduce

Sunbowen Lee, Yicheng Gong, Chao Deng

Reinforcement learning control algorithms face significant challenges due to out-of-distribution and inefficient exploration problems. While model-based reinforcement learning enhances the agent's reasoning and planning capabilities by constructing virtual environments, training such virtual environments can be very complex. In order to build an efficient in

Peng Wang, Ziling Heng, Chengju Li

Quasi-complementary sequence sets (QCSSs) play an important role in multi-carrier code division multiple access (MC-CDMA) systems as they can support more users than perfect complementary sequence sets (PCSSs). The objective of this paper is to present new constructions of asymptotically optimal periodic and aperiodic QCSSs with large set sizes. Firstly, we

Ryusei Hamaki, Kengo Maeda

In the context of the AdS/CFT duality, we study semiclassical stationary rotating AdS black holes with non-trivial quantum hair in three and five dimensions. We construct these solutions by perturbing the BTZ black hole and the five-dimensional Myers-Perry AdS black hole according to holographic semiclassical equations. In the three-dimensional case, the vac

X. K. Hou, Y. W. Wang, J. He, J. M. Wang

Autler-Townes (AT) doublet, a fundamental manifestation of quantum interference effects, serves as a critical tool for studying the dynamic behavior of Rydberg atoms. Here, we investigate the asymmetry of the Autler-Townes (AT) doublet in trap-loss fluorescence spectroscopy (TLFS) of cesium (Cs) atoms confined in a magneto-optical trap (MOT) with single-step

Kameron Mehling, Justin J. Burau, Logan E. Hillberry, Mengjie Chen

The electronic energy level structure of yttrium monoxide (YO) provides a long-lived, low-lying $^{2}\Delta$ state ideal for high-precision molecular spectroscopy, narrowline laser cooling at the single photon-recoil limit, and studying dipolar physics with unprecedented interaction strength. High-resolution laser spectroscopy of ultracold laser-cooled YO mo

Pin-Jie Lin, Ernie Chang, Yangyang Shi, Vikas Chandra

Past vocabulary learning techniques identify relevant vocabulary before training, relying on statistical and entropy-based assumptions that largely neglect the role of model training. Empirically, we observe that trained translation models are induced to use a byte-pair encoding (BPE) vocabulary subset distinct from the original BPE vocabulary, leading to pe

Seokhyeon Hong, Chaelin Kim, Serin Yoon, Junghyun Nam

Text-driven motion generation has advanced significantly with the rise of denoising diffusion models. However, previous methods often oversimplify representations for the skeletal joints, temporal frames, and textual words, limiting their ability to fully capture the information within each modality and their interactions. Moreover, when using pre-trained mo

Yanyan Tang, Jie Xiong

This paper studies a stochastic mean-field linear-quadratic optimal control problem with random coefficients. The state equation is a general linear stochastic differential equation with mean-field terms $\EE X(t)$ and $\EE u(t)$ of the state and the control processes and is driven by a Brownian motion and a Poisson random measure. By the coupled system of R

JuneHyoung Kwon, MiHyeon Kim, Eunju Lee, Juhwan Choi

Vision-language (VL) models have demonstrated strong performance across various tasks. However, these models often rely on a specific modality for predictions, leading to "dominant modality bias.'' This bias significantly hurts performance, especially when one modality is impaired. In this study, we analyze model behavior under dominant modality bias and the

Kamyar Barakati, Alexander Molak, Chris Nelson, Xiaohang Zhang

Knowledge driven discovery of novel materials necessitates the development of the causal models for the property emergence. While in classical physical paradigm the causal relationships are deduced based on the physical principles or via experiment, rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects

Yun-Jie Wang, Tai-Ping Sun, Xi-Ning Zhuang, Xiao-Fan Xu

Quantum random access memory (QRAM) is a critical primitive for quantum algorithms that require data lookup in superposition, but its lack of fault tolerance poses a major obstacle to practical deployment. Error filtration (EF) has been proposed as a hardware-efficient alternative to error correction, capable of suppressing incoherent noise without encoding

Josef Willsher, Johannes Knolle

Quantum spin liquids (QSLs) are long-range entangled phases of frustrated magnets exhibiting fractionalized spin excitations. In two dimensions, there is limited analytical understanding of their excitation spectra beyond parton mean-field theories, which fail to capture many features of the finite frequency dynamical response from recent experimental and nu

Sohail Reddy

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRF) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equation (SPDE), often reduce the dimensionality of the sample sp

Alex Elzenaar

Motivated by an experimental study of groups generated by reflections in planar patterns of tangent circles, we describe some methods for constructing and studying representation spaces of holonomy groups of infinite volume hyperbolic $3$-manifolds that arise from unknotting tunnels of links. We include full descriptions of our computational methods, which w

Chunlei Li, Yilei Shi, Jingliang Hu, Xiao Xiang Zhu

Unsupervised anomaly detection using deep learning has garnered significant research attention due to its broad applicability, particularly in medical imaging where labeled anomalous data are scarce. While earlier approaches leverage generative models like autoencoders and generative adversarial networks (GANs), they often fall short due to overgeneralizatio

Xiaoyan Xie, Chengcai Shen, Katharine Reeves, Bin Chen

Solar flare above-the-loop-top (ALT) regions are vital for understanding solar eruptions and fundamental processes in plasma physics. Recent advances in 3D MHD simulations have revealed unprecedented details on turbulent flows and MHD instabilities in flare ALT regions. Here, for the first time, we examine the observable anisotropic properties of turbulent f

Wenhan Yan, Yang Hu, Yifeng Du, Kai Wang

Dissipative Kerr soliton (DKS) frequency comb exhibits broad and narrow-linewidth frequency modes, which make it suitable for quantum communication. However, scalable quantum network based on multiple independent combs is still a challenge due to their fabrication-induced frequency mismatches. This limitation becomes critical in measurement-device-independen

Q. Xue, J. Zhou

Antiferromagnetic (AFM) spintronics has been receiving tremendous attention due to their ultrafast kinetics, zero stray field, immune to external magnetic field, and potential to minimizing magnetic storage devices. The optical control of AFM Neel vector has become a hectic topic during recent years, which could facilitate the AFM utilization in practical sy

S. N. Sun, N. Wang, W. M. Yan, S. Q. Wang

We present polarization profiles of 23 pulsars exhibiting interpulse (IP) emissions using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST). By applying the rotating vector model (RVM), we measured the inclination and impact angles for 16 pulsars, allowing us to investigate their beam geometries. Our analysis indicates that for 5 pulsars, the

Oscar Perdomo

In this paper, we describe a family of embedded hypersurfaces with constant mean curvature (CMC) in the $(n+1)$-dimensional unit sphere. In the process, we provide evidence for new CMC embedded examples. In particular, for some examples with $H=0$, we verify Yau's conjecture stating that among the embedded, non-totally umbilical minimal hypersurfaces in sphe

Zhanhao Zhao, Haotian Gao, Naili Xing, Lingze Zeng

Learned database components, which deeply integrate machine learning into their design, have been extensively studied in recent years. Given the dynamism of databases, where data and workloads continuously drift, it is crucial for learned database components to remain effective and efficient in the face of data and workload drift. Robustness, therefore, is a

Chi Hsuan Wu, Kumar Ashutosh, Kristen Grauman

When obtaining visual illustrations from text descriptions, today's methods take a description with a single text context - a caption, or an action description - and retrieve or generate the matching visual context. However, prior work does not permit visual illustration of multistep descriptions, e.g. a cooking recipe or a gardening instruction manual, and

Fei Dai, Anthony Robins, Zhihao Peng, Wanni Huang

This paper investigates the barriers hindering the effective transition from theoretical knowledge to practical application in a Sino-New Zealand double-degree undergraduate computing program. In this unique educational setting, students study at a campus in China but complete both Chinese and New Zealand courses taught jointly by lecturers from both countri

Rhys Howard, Nick Hawes, Lars Kunze

Transparency and explainability are important features that responsible autonomous vehicles should possess, particularly when interacting with humans, and causal reasoning offers a strong basis to provide these qualities. However, even if one assumes agents act to maximise some concept of reward, it is difficult to make accurate causal inferences of agent pl

Xiaopei Chen, Wen Wu, Liang Li, Fei Ji

The Internet of Things (IoT) in the sixth generation (6G) era is envisioned to evolve towards intelligence, ubiquity, and self-optimization. Large language models (LLMs) have demonstrated remarkable generalization capabilities across diverse domains, including natural language processing (NLP), computer vision (CV), and beyond. In this article, we propose an

Linhan Yang, Ya-Ping Li, Ruobing Dong, Kiyoaki Doi

Observations with the Atacama Large Millimeter/submillimeter Array (ALMA) and the Jansky Very Large Array (JVLA) have revealed many dust rings in protoplanetary disks, often interpreted as dust traps at gas pressure bumps. Previous studies have typically modeled these rings by assuming a single dust species in drift-diffusion equilibrium, neglecting dust siz

Anukriti Singh, Amisha Bhaskar, Peihong Yu, Souradip Chakraborty

Designing reward functions for continuous-control robotics often leads to subtle misalignments or reward hacking, especially in complex tasks. Preference-based RL mitigates some of these pitfalls by learning rewards from comparative feedback rather than hand-crafted signals, yet scaling human annotations remains challenging. Recent work uses Vision-Language

Zhixuan Liu, Haokun Zhu, Rui Chen, Jonathan Francis

We introduce a diffusion-based approach for generating privacy-preserving digital twins of multi-room indoor environments from depth images only. Central to our approach is a novel Multi-view Overlapped Scene Alignment with Implicit Consistency (MOSAIC) model that explicitly considers cross-view dependencies within the same scene in the probabilistic sense.

Liam Mazurowski, Tongrui Wang, Xuan Yao

We formulate a conjecture relating the topology of a manifold's universal cover with the existence of metrics with positive $m$-intermediate curvature. We prove the result for manifolds of dimension $n\in\{3,4,5\}$ and for most choices of $m$ when $n=6$. As a corollary, we show that a closed, aspherical 6-manifold cannot admit a metric with positive $4$-inte

Jinping Wang, Weiwei Song, Hao Chen, Jinchang Ren

World models significantly enhance hierarchical understanding, improving data integration and learning efficiency. To explore the potential of the world model in the remote sensing (RS) field, this paper proposes a label-efficient remote sensing world model for multimodal data fusion (FusDreamer). The FusDreamer uses the world model as a unified representati

Mingming Peng, Zhendong Chen, Jie Yang, Jin Huang

With the accelerated development of Industry 4.0, intelligent manufacturing systems increasingly require efficient task allocation and scheduling in multi-robot systems. However, existing methods rely on domain expertise and face challenges in adapting to dynamic production constraints. Additionally, enterprises have high privacy requirements for production

Suyash Fulay, Dimitra Dimitrakopoulou, Deb Roy

Deliberation is essential to well-functioning democracies, yet physical, economic, and social barriers often exclude certain groups, reducing representativeness and contributing to issues like group polarization. In this work, we explore the use of large language model (LLM) personas to introduce missing perspectives in policy deliberations. We develop and e

Zehao Su, Xing Wei

Self-gravity is important in protoplanetary disks for planet formation through gravitational instability (GI). We study the cooling effect on GI in a thin two-dimensional protoplanetary disk. By solving the linear perturbation equations in global geometry, we obtain all the normal modes. Faster cooling leads to faster growth rate of GI with lower azimuthal w

Go Tokumitsu, Kouji Yano

Motivated by the previous results by Coletti-de Lima-Gava-Luiz (2020) and Shiozawa (2022), we study the fluctuation of the dynamic elephant random walk in the superdiffusive case with a strong elephant component. Applying the martingale convergence theorem, we prove the Central Limit Theorem and the Law of Iterated Logarithm, where a random drift is subtract

Zeynep Abes, Nathan Fairchild, Spencer Lin, Michael Wahba

The Immersive Archive is an initiative dedicated to preserve and restore the groundbreaking works from across Extended Reality (XR) history. Originating at the University of Southern California's Mobile and Environmental Media Lab, this archive is committed to developing and exhibiting simulations of influential XR devices that have shaped immersive media ov

Tian Qin, Guang Cheng, Yuyang Zhou, Zihan Chen

The rapid advancement of internet technology has led to a surge in data transmission, making network traffic classification crucial for security and management. However, there are significant deficiencies in its efficiency for handling multiattribute analysis and its ability to expand model knowledge, making it difficult to adapt to the ever-changing network

Yukikatsu Terada, Megumi Shidatsu, Makoto Sawada, Takashi Kominato

This paper describes the development, design, ground verification, and in-orbit verification, performance measurement, and calibration of the timing system for the X-Ray Imaging and Spectroscopy Mission (XRISM). The scientific goals of the mission require an absolute timing accuracy of 1.0~ms. All components of the timing system were designed and verified to

Wenjie Zhang, Liming Luo, Mengnan He, Jiarui Hai

Accurate organ segmentation is essential for clinical tasks such as radiotherapy planning and disease monitoring. Recent foundation models like MedSAM achieve strong results using point or bounding-box prompts but still require manual interaction. We propose DescriptorMedSAM, a lightweight extension of MedSAM that incorporates structured text prompts, rangin

Muhammad Ahtesham, Xin Zhong

Ensuring robustness in image watermarking is crucial for and maintaining content integrity under diverse transformations. Recent self-supervised learning (SSL) approaches, such as DINO, have been leveraged for watermarking but primarily focus on general feature representation rather than explicitly learning invariant features. In this work, we propose a nove

Kai Guo, Harry Shomer, Shenglai Zeng, Haoyu Han

In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph retrieval-augmented generation (GraphRAG) enhances LLM reasoning by integrating structured knowledge from external graphs. However, we identify two key challenges that plague Grap

Minok Park, Shomik Verma, Alina LaPotin, Dustin P. Nizamian

Thermal radiative energy transport is essential for high-temperature energy harvesting technologies, including thermophotovoltaics (TPVs) and grid-scale thermal energy storage. However, the inherently low emissivity of conventional high-temperature materials constrains radiative energy transfer, thereby limiting both system performance and technoeconomic via

Toby Sanders, Justin J. Konkle, Erica E. Mason, Patrick W. Goodwill

Objective: This work introduces a new magnetic particle imaging (MPI) reconstruction framework based on multi-harmonic 3D deconvolution (MH3D) of gridded portraits, offering a principled, model-driven approach to MPI imaging. Approach: MH3D defines a convolutional forward model using higher harmonic portraits, which are gridded images formed from filtered fr

Weicao Deng, Binpu Shi, Min Li, Osvaldo Simeone

As millimeter-wave (mmWave) MIMO systems adopt larger antenna arrays, near-field propagation becomes increasingly prominent, especially for users close to the transmitter. Traditional far-field beam training methods become inadequate, while near-field training faces the challenge of large codebooks due to the need to resolve both angular and distance domains

Hechao Chen, Hui Tian, Quanhao Zhang, Chuan Li

Filament eruptions are magnetically driven violent explosions commonly observed on the Sun and late-type stars, sometimes leading to monster coronal mass ejections that directly affect the nearby planets' environments. More than a century of research on solar filaments suggests that the slow evolution of photospheric magnetic fields plays a decisive role in

Liangrui Pan, Xiaoyu Li, Yutao Dou, Qiya Song

Spread through air spaces (STAS) represents a newly identified aggressive pattern in lung cancer, which is known to be associated with adverse prognostic factors and complex pathological features. Pathologists currently rely on time consuming manual assessments, which are highly subjective and prone to variation. This highlights the urgent need for automated

Amirhossein Khakpour, Lucia Florescu, Richard Tilley, Haibo Jiang

The clinical translation of nanoparticle-based treatments remains limited due to the unpredictability of (nanoparticle) NP pharmacokinetics$\unicode{x2014}$how they distribute, accumulate, and clear from the body. Predicting these behaviours is challenging due to complex biological interactions and the difficulty of obtaining high-quality experimental datase

Jeth Arunseangroj, Jude Bedessem, S. James Gates,, Gabriel Yerger

Motivated by the search for embedded on-shell supermultiplets in higher dimensional off-shell theories, we investigate several 16-color supermultiplets and their topology. An Adinkra's topology is known to be equivalent to $(\mathbb{Z}_2)$-quotients of an N-cube. This is revisited with the focus of closing the off-shell problem for the 4D $\mathcal{N} = 4$ M

Mohit Kumar, Fabio Semperlotti

This study presents an approach that leverages the existing knowledge acquired in one-dimensional BDI class discrete metamaterials, such as mass-spring systems or acoustic resonators, and exploits it to realize fully continuous elastic two-dimensional topological waveguides. The design relies on the concept of evanescently coupled waveguides and defect reson

Konstantin Burlachenko, Peter Richtárik

In this work, we introduce BurTorch, a compact high-performance framework designed to optimize Deep Learning (DL) training on single-node workstations through an exceptionally efficient CPU-based backpropagation (Rumelhart et al., 1986; Linnainmaa, 1970) implementation. Although modern DL frameworks rely on compilerlike optimizations internally, BurTorch tak

Yang Zhou, Shiyu Zhao, Yuxiao Chen, Zhenting Wang

Large foundation models trained on large-scale vision-language data can boost Open-Vocabulary Object Detection (OVD) via synthetic training data, yet the hand-crafted pipelines often introduce bias and overfit to specific prompts. We sidestep this issue by directly fusing hidden states from Large Language Models (LLMs) into detectors-an avenue surprisingly u

Dipin Khati, Yijin Liu, David N. Palacio, Yixuan Zhang

Applications of Large Language Models (LLMs) are rapidly growing in industry and academia for various software engineering (SE) tasks. As these models become more integral to critical processes, ensuring their reliability and trustworthiness becomes essential. Consequently, the concept of trust in these systems is becoming increasingly critical. Well-calibra

Reza E Rabbi Shawon, MD Rokibul Hasan, Md Anisur Rahman, Mohamed Ghandri

The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) has significantly transformed logistics and supply chain management, particularly in the pursuit of sustainability and eco-efficiency. This study explores AI-based methodologies for optimizing logistics operations in the USA, focusing on reducing environmental impact, improving fue

Xinyu Tian, Shu Zou, Zhaoyuan Yang, Jing Zhang

The evolution of Large Vision-Language Models (LVLMs) has progressed from single to multi-image reasoning. Despite this advancement, our findings indicate that LVLMs struggle to robustly utilize information across multiple images, with predictions significantly affected by the alteration of image positions. To further explore this issue, we introduce Positio

Victor Rielly, Kamel Lahouel, Chau Nguyen, Anthony Kolshorn

We present a Representer Theorem result for a large class of weak formulation problems. We provide examples of applications of our formulation both in traditional machine learning and numerical methods as well as in new and emerging techniques. Finally we apply our formulation to generalize the multivariate occupation kernel (MOCK) method for learning dynami

Mohammed Elhenawy, Shadi Jaradat, Taqwa I. Alhadidi, Huthaifa I. Ashqar

Scene understanding is critical for various downstream tasks in autonomous driving, including facilitating driver-agent communication and enhancing human-centered explainability of autonomous vehicle (AV) decisions. This paper evaluates the capability of four multimodal large language models (MLLMs), including relatively small models, to understand scenes in

Dong Ho Lee, Kaitlyn Donnel, Max Z. Li, David Fridovich-Keil

Hierarchical decision-making is a natural paradigm for coordinating multi-agent systems in complex environments such as air traffic management. In this paper, we present a bilevel framework for game-theoretic hierarchical routing, where a high-level router assigns discrete routes to multiple vehicles who seek to optimize potentially noncooperative objectives

Timothy T. Duignan

The carbonic anhydrase II enzyme (CA II) is one of the most significant enzymes in nature, reversibly converting CO$_2$ to bicarbonate at a remarkable rate. The precise mechanism it uses to achieve this rapid turnover remains unclear due to our inability to directly observe or simulate the full process dynamically. Here, we use a recently developed universal

Lauren Streitmatter, Trager Joswig-Jones, Baosen Zhang

Many resources in the grid connect to power grids via programmable grid-interfacing inverters that can provide grid services and offer greater control flexibility and faster response times compared to synchronous generators. However, the current through the inverter needs to be limited to protect the semiconductor components. Existing controllers are designe

Tanmay Vilas Samak, Chinmay Vilas Samak, Julia Brault, Cori Harber

The engineering community currently encounters significant challenges in the systematic development and validation of autonomy algorithms for off-road ground vehicles. These challenges are posed by unusually high test parameters and algorithmic variants. In order to address these pain points, this work presents an optimized digital engineering framework that

Jonesh Shrestha

This research evaluates the extent to which modern AI frameworks, specifically TensorFlow and scikit-learn, adhere to the SOLID design principles - Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion. Analyzing the frameworks architectural documentation and design philosophies, this research investigates a

Subed Lamichhane, Haotian Lu, Sheldon X. -D. Tan

In contrast to the assumptions of most existing Electromigration (EM) analysis tools, the evolution of EM-induced stress is inherently non-deterministic, influenced by factors such as input current fluctuations and manufacturing non-idealities. Traditional approaches for estimating stress variations typically involve computationally expensive and inefficient

Heba Bou KaedBey, Mark Van Hoeij

Our paper "Solving Third Order Linear Difference Equations in Terms of Second Order Equations" gave two algorithms for solving difference equations in terms of lower order equations: an algorithm for absolute factorization, and an algorithm for solving third order equations in terms of second order. Here we improve the efficiency for absolute factorization,

Lin Geng, Hao Li, Sidney Givigi, Bram Adams

Heterogeneous unmanned aerial vehicle (UAV) swarms consist of dozens to hundreds of drones with different roles and varying hardware and software requirements collaborating towards a shared mission. While traditional approaches for synchronized software updates assume swarms to be unstructured and homogeneous, the heterogeneous nature of modern swarms and th

Stephen Gwyn, Alan W. McConnachie, Jean-Charles Cuillandre, Ken C. Chambers

The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is a "collaboration of collaborations" that is using the Canada-France-Hawai'i Telescope, the Pan-STARRS telescopes, and the Subaru Observatory to obtain $ugriz$ images of a core survey region of 6250 deg$^2$ of the northern sky. The $10\sigma$ point source depth of the data, as measured within a

Ian Hultman, Sanvesh Srivastava

We introduce mixed model trace regression (MMTR), a mixed model linear regression extension for scalar responses and high-dimensional matrix-valued covariates. MMTR's fixed effects component is equivalent to trace regression, with an element-wise lasso penalty imposed on the regression coefficients matrix to facilitate the estimation of a sparse mean paramet

Samuel Allen Alexander

Can you find an xy-equation that, when graphed, writes itself on the plane? This idea became internet-famous when a Wikipedia article on Tupper's self-referential formula went viral in 2012. Under scrutiny, the question has two flaws: it is meaningless (it depends on typography) and it is trivial (for reasons we will explain). We fix these flaws by forma

Prashant Kulkarni, Assaf Namer

Large Language Models (LLMs) are increasingly vulnerable to sophisticated multi-turn manipulation attacks, where adversaries strategically build context through seemingly benign conversational turns to circumvent safety measures and elicit harmful or unauthorized responses. These attacks exploit the temporal nature of dialogue to evade single-turn detection

François Impens, Felippo M. D'Angelis, David Guéry-Odelin, Felipe A. Pinheiro

We introduce a systematic procedure based on optimal control theory to address the full counting statistics of particle transport in a stochastic system. Our approach enhances the performance of a Thouless pump in the non-adiabatic regime by simultaneously optimizing the average pumping rate while minimizing noise. We demonstrate our optimization procedure o

Jin Ming Koh, Wen-Tan Xue, Tommy Tai, Dax Enshan Koh

A lossy quantum system harboring the non-Hermitian skin effect can in certain conditions exhibit anomalously high loss at the boundaries of the system compared to the bulk, a phenomenon termed the non-Hermitian edge burst. We uncover interacting many-body extensions of the edge burst that are spatially extended and patterned, as well as cluster bursts that o

Hervé Bergeron, Przemysław Małkiewicz, Patrick Peter

We show that the consistent application of the rules of quantum mechanics to cosmological systems inevitably results in the so-called multiverse states in which neither the background spacetime nor the inhomogeneous perturbation are in definite states. We study the multiverse states as perturbations to the usually employed so-called Born-Oppenheimer states t

Barbora Batí ková, Tomáš J. Kepka, Petr C. Němec

Given a positive integer $n$ and a partitioning $n=r_1s_1+\dots+ r_ts_t$, $t,r_i,s_i$ positive integers, such that $r_1>\dots>r_t$ (for $t\ge 2$), we can write $n$ symbols $1,\dots,n$ in the form of a staircase matrix having $r_1$ rows where first $r_1-r_2$ rows have $x_1$ columns, next $r_2-r_3$ rows have $t_1+t_2$ columns, etc., and finally last $r_t$ rows

Christian Poelitz, Nick McKenna

Real dialogues with AI assistants for solving data-centric tasks often follow dynamic, unpredictable paths due to imperfect information provided by the user or in the data, which must be caught and handled. Developing datasets which capture such user-AI interactions is difficult and time-consuming. In this work, we develop a novel framework for synthetically

Folkmar Bornemann

Using tools from the Siegel-Shidlovskii theory of transcendental numbers, we prove that a nontrivial solution of the Airy equation, its derivative, and an antiderivative are algebraically independent over the field of rational functions. Courtesy of Michael Singer, the result is also derived from general considerations in differential Galois theory.

Saieed Akbari, Jonathan Aloni, Maxwell Levit, Bojan Mohar

We study oriented graphs whose Hermitian adjacency matrices of the second kind have few eigenvalues. We give a complete characterization of the oriented graphs with two distinct eigenvalues, showing that there are only four such graphs. We extend this result to mixed graphs. We show that there are infinitely many regular tournaments with three distinct eigen

Nicolas Augier, Milan Korda, Rodolfo Rios-Zertuche

This work presents new sufficient conditions for the absence of a gap corresponding to Young measure and occupation measure relaxations for constrained optimal control problems. Unlike existing conditions, these sufficient conditions do not rely on convexity of the Lagrangian or the set of admissible velocities. We use these conditions to derive new bounds f

Dmitrii Usenko, David Helman, Chen Giladi

Accurate estimation of total leaf area (TLA) is crucial for evaluating plant growth, photosynthetic activity, and transpiration. However, it remains challenging for bushy plants like dwarf tomatoes due to their complex canopies. Traditional methods are often labor-intensive, damaging to plants, or limited in capturing canopy complexity. This study evaluated

Xuyang Fang, Sion Hannuna, Neill Campbell, Edwin Simpson

Automated livestock monitoring is crucial for precision farming, but robust computer vision models are hindered by a lack of datasets reflecting real-world group challenges. We introduce the 8-Calves dataset, a challenging benchmark for multi-animal detection, tracking, and identification. It features a one-hour video of eight Holstein Friesian calves in a b

Nicolas Augier, Milan Korda, Rodolfo Rios-Zertuche

In this article, we tackle the problem of the existence of a gap corresponding to Young measure relaxations for state-constrained optimal control problems. We provide a counterexample proving that a gap may occur in a very regular setting, namely for a smooth controllable system state-constrained to the closed unit ball, provided that the Lagrangian density

Christopher S. From, Vedad Dzanic, Vahid Niasar, Emilie Sauret

Elastoviscoplastic (EVP) fluid flows are driven by a non-trivial interplay between the elastic, viscous, and plastic properties, which under certain conditions can transition the otherwise laminar flow into complex flow instabilities with rich space-time-dependent dynamics. We discover that under elastic turbulence regimes, EVP fluids undergo dynamic jamming

Haiying Shen, Tanmoy Sen, Masahiro Tanaka

In Large Language Model (LLM) serving, the KV-cache (KVC) bottleneck causes high tail Time-to-First-Token (TTFT) and Time-Between-Tokens (TBT), impairing user experience, particularly in time-sensitive applications. However, satisfying both TTFT and TBT service-level objectives (SLOs) is challenging. To address this, we propose a system, named CacheOPT for m

Bowen Cui, Tejas Ramesh, Oscar Hernandez, Keren Zhou

Large Language Models (LLMs) have emerged as powerful tools for software development tasks such as code completion, translation, and optimization. However, their ability to generate efficient and correct code, particularly in complex High-Performance Computing (HPC) contexts, has remained underexplored. To address this gap, this paper presents a comprehensiv

Daniel J. Liebling, Malcolm Kane, Madeleine Grunde-Mclaughlin, Ian J. Lang

We present components of an AI-assisted academic writing system including citation recommendation and introduction writing. The system recommends citations by considering the user's current document context to provide relevant suggestions. It generates introductions in a structured fashion, situating the contributions of the research relative to prior work.

Y. Chen, R. T. Garrod, M. Rachid, E. F. van Dishoeck

Acetone (CH3COCH3) is one of the most abundant three-carbon oxygen-bearing complex organic molecules (O-COMs) that have been detected in space. Recently, acetone ice has been reported as (tentatively) detected toward B1-c, which enables the gas-to-ice comparison of its abundances. The detection of acetone ice warrants a more systematic study of its gaseous a

Kartik Thakral, Tamar Glaser, Tal Hassner, Mayank Vatsa

How can we effectively unlearn selected concepts from pre-trained generative foundation models without resorting to extensive retraining? This research introduces `continual unlearning', a novel paradigm that enables the targeted removal of multiple specific concepts from foundational generative models, incrementally. We propose Decremental Unlearning withou

Alina Ostafe, Igor E. Shparlinski

We consider the set $\mathcal M_n\left(\mathbb Z; H\right)$ of $n\times n$-matrices with integer elements of size at most $H$ and obtain and asymptotic formula on the number of matrices from $\mathcal M_n\left(\mathbb Z; H\right)$ with square-free determinants. We also use our approach with some further enhancements, to obtain an asymptotic formula for the s

R. Kishor Kumar, S. Sabari, Arnaldo Gammal, Lauro Tomio

We investigate three kinds of instabilities in binary immiscible homogeneous Bose-Einstein condensate, considering rubidium isotopes $^{85}$Rb and $^{87}$Rb confined in two-dimensional circular box. Rayleigh-Taylor (RT) and Kelvin-Helmholtz (KH) instability types are studied under strong perturbations. Without external perturbation, instabilities are also pr

Daniel Racz, Mihaly Petreczky, Balint Daroczy

We derive a finite-sample probabilistic bound on the parameter estimation error of a system identification algorithm for Linear Switched Systems. The algorithm estimates Markov parameters from a single trajectory and applies a variant of the Ho-Kalman algorithm to recover the system matrices. Our bound guarantees statistical consistency under the assumption

Leila Mezri, Alexander Diehl, Thomas P. Ferrand, Javier Garcia-Pintado

At ultraslow, magma-poor spreading ridges, plate divergence is controlled by tectonics, with formation of detachment faults that expose variably serpentinized mantle rocks. Seismicity and the depth of seismic events in these environments are influenced by the degree of serpentinization, yet the nature and extent of deeper alteration remain poorly understood.

Mohammad Partohaghighi, Roummel Marcia, YangQuan Chen

Fractional-order stochastic gradient descent (FOSGD) leverages fractional exponents to capture long-memory effects in optimization. However, its utility is often limited by the difficulty of tuning and stabilizing these exponents. We propose 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), which integrates the Two-Scale Effective Dimension (2SE

Atharva Agashe, Davelle Carreiro, Alexandra Van Dine, Joshua Peeples

Numerous maritime applications rely on the ability to recognize acoustic targets using passive sonar. While there is a growing reliance on pre-trained models for classification tasks, these models often require extensive computational resources and may not perform optimally when transferred to new domains due to dataset variations. To address these challenge

Paschal C. Amusuo, Owen Cochell, Taylor Le Lievre, Parth V. Patil

Memory safety defects pose a major threat to software reliability, enabling cyberattacks, outages, and crashes. To mitigate these risks, organizations adopt Compositional Bounded Model Checking (BMC), using unit proofs to formally verify memory safety. However, methods for creating unit proofs vary across organizations and are inconsistent within the same pr

Geraldine Rodríguez, Tonny Garita, Fabio Sanchez

In recent years, a surge in the popularity of trap music among adolescents has prompted concerns about its potential influence on youth behavior and educational outcomes. In this study, we develop a novel compartmental model using a system of differential equations to explore the relationship between exposure to trap music and school dropout rates among Cost

A. Zandivarez, E. Diaz-Gimenez

[Abridged] Compact groups (CGs) of galaxies have proven to be unique environments for studying galaxy interactions. We propose a detailed analysis of the galaxy evolution to disentangle the relationship between the two first-ranked galaxies in CGs throughout their history as a function of the assembly channels of their hosts. Our study was performed from a s

Rafael Russo Almeida, Dillon Ramsey, Ayman F. Abouraddy, John P. Palastro

Space-time structuring of light - where spatial and temporal degrees of freedom are deliberately coupled and controlled - is an emerging area of optics that enables novel configurations of electromagnetic fields. Of particular importance for applications are optical pulses whose peak intensity travels at an arbitrary, tunable velocity while maintaining its s

Pedro A. Lima, Carlos M. Carvalho, Hedibert F. Lopes, Andrew Herren

Vector autoregression (VAR) models are widely used for forecasting and macroeconomic analysis, yet they remain limited by their reliance on a linear parameterization. Recent research has introduced nonparametric alternatives, such as Bayesian additive regression trees (BART), which provide flexibility without strong parametric assumptions. However, existing

Ye Zhang

In this short paper we provide a new proof of the geometric Forward-Reverse Brascamp-Lieb inequality, using the approach of the heat semigroup, or the heat flow. Furthermore, we characterize all the Forward-Reverse Brascamp-Lieb data such that the initial relation can be preserved by some heat flow.

Arjun V Sudhakar, Hadi Nekoei, Mathieu Reymond, Miao Liu

Traditional multi-agent reinforcement learning (MARL) systems can develop cooperative strategies through repeated interactions. However, these systems are unable to perform well on any other setting than the one they have been trained on, and struggle to successfully cooperate with unfamiliar collaborators. This is particularly visible in the Hanabi benchmar

Ali Parsaee, Fahim Shahriar, Chuxin He, Ruiqing Tan

In recent times, reinforcement learning (RL) with physical robots has attracted the attention of a wide range of researchers. However, state-of-the-art RL algorithms do not consider that physical environments do not wait for the RL agent to make decisions or updates. RL agents learn by periodically conducting computationally expensive gradient updates. When