May 2025 arXiv papers — page 93

Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He

Memory is a critical component in large language model (LLM)-based agents, enabling them to store and retrieve past executions to improve task performance over time. In this paper, we conduct an empirical study on how memory management choices impact the LLM agents' behavior, especially their long-term performance. Specifically, we focus on two fundamental m

Zhixu Silvia Tao, Kasper Vinken, Hao-Wei Yeh, Avi Cooper

Mixing datasets for fine-tuning large models (LMs) has become critical for maximizing performance on downstream tasks. However, composing effective dataset mixtures typically relies on heuristics and trial-and-error, often requiring multiple fine-tuning runs to achieve the desired outcome. We propose a novel method, $\textit{Merge to Mix}$, that accelerates

Andrew P. Berg, Qian Zhang, Mia Y. Wang

Unmanned aerial vehicle (UAV) usage is expected to surge in the coming decade, raising the need for heightened security measures to prevent airspace violations and security threats. This study investigates deep learning approaches to UAV classification focusing on the key issue of data scarcity. To investigate this we opted to train the models using a total

Ruijie Xi, He Ba, Hao Yuan, Rishu Agrawal

Embedding-Based Retrieval (EBR) is an important technique in modern search engines, enabling semantic match between search queries and relevant results. However, search logging data on platforms like Facebook Marketplace lacks the diversity and details needed for effective EBR model training, limiting the models' ability to capture nuanced search patterns. T

Alexander Ponomarenko

This paper addresses the challenge of merging hierarchical navigable small world (HNSW) graphs, a critical operation for distributed systems, incremental indexing, and database compaction. We propose three algorithms for this task: Naive Graph Merge (NGM), Intra Graph Traversal Merge (IGTM), and Cross Graph Traversal Merge (CGTM). These algorithms differ in

Tehseen Adel, Maria F. Munoz, Thuc T. Mai, Charlezetta E. Wilson-Stokes

Angle-resolved Raman spectroscopy (ARRS) is an effective method to analyze the symmetry of phonons and other excitations in molecules and solid-state crystals. While there are several configurations of ARRS instruments, the measurement system detailed here utilizes two pairs of linear polarizers and superachromatic half-wave plates. After the orientations of

Danissa Sandykbayeva, Valeriya Kostyukova, Aditya Shekhar Nittala, Zhanat Kappassov

The perception and recognition of the surroundings is one of the essential tasks for a robot. With preliminary knowledge about a target object, it can perform various manipulation tasks such as rolling motion, palpation, and force control. Minimizing possible damage to the sensing system and testing objects during manipulation are significant concerns that p

Yu Zhang

We investigate the impacts of NLP research published in top-tier conferences (i.e., ACL, EMNLP, and NAACL) from 1979 to 2024. By analyzing citations from research articles and external sources such as patents, media, and policy documents, we examine how different NLP topics are consumed both within the academic community and by the broader public. Our findin

Shangding Gu, Donghao Ying, Ming Jin, Yu Joe Lu

We introduce Model Feedback Learning (MFL), a novel test-time optimization framework for optimizing inputs to pre-trained AI models or deployed hardware systems without requiring any retraining of the models or modifications to the hardware. In contrast to existing methods that rely on adjusting model parameters, MFL leverages a lightweight reverse model to

Charles Koll, Preston Tan Hang, Mike Rosulek, Houssam Abbas

In distributed Cyber-Physical Systems and Internet-of-Things applications, the nodes of the system send measurements to a monitor that checks whether these measurements satisfy given formal specifications. For instance in Urban Air Mobility, a local traffic authority will be monitoring drone traffic to evaluate its flow and detect emerging problematic patter

Mars Liyao Gao, J. Nathan Kutz, Bernat Font

Identifying the governing equations of a dynamical system is one of the most important tasks for scientific modeling. However, this procedure often requires high-quality spatio-temporal data uniformly sampled on structured grids. In this paper, we propose mesh-free SINDy, a novel algorithm which leverages the power of neural network approximation as well as

Jingcong Liang, Siyuan Wang, Miren Tian, Yitong Li

Mixture-of-Experts (MoE) enables efficient scaling of large language models (LLMs) with sparsely activated experts during inference. To effectively deploy large MoE models on memory-constrained devices, many systems introduce *expert offloading* that caches a subset of experts in fast memory, leaving others on slow memory to run on CPU or load on demand. Whi

Patanjali Maithani, Aliasghar Arab, Farshad Khorrami, Prashanth Krishnamurthy

In collaborative human-robot environments, the unpredictable and dynamic nature of human motion can lead to situations where collisions become unavoidable. In such cases, it is essential for the robotic system to proactively mitigate potential harm through intelligent control strategies. This paper presents a hierarchical control framework based on Control B

Shuchi Chawla, Trung Dang, Zhiyi Huang, Yifan Wang

We consider a combinatorial auction setting where buyers have fractionally subadditive (XOS) valuations over the items and the seller's objective is to maximize the social welfare. A prophet inequality in this setting bounds the competitive ratio of sequential allocation (often using item pricing) against the hindsight optimum. We study the dependence of the

Jan Tönshoff, Martin Grohe

Boolean Satisfiability (SAT) solvers are foundational to computer science, yet their performance typically hinges on hand-crafted heuristics. This work introduces Reinforcement Learning from Algorithm Feedback (RLAF) as a paradigm for learning to guide SAT solver branching heuristics with Graph Neural Networks (GNNs). Central to our approach is a novel and g

Jia Liang, William Colocho, Franz-Josef Decker, Ryan Humble

Anomalies in radio-frequency (RF) stations can result in unplanned downtime and performance degradation in linear accelerators such as SLAC's Linac Coherent Light Source (LCLS). Detecting these anomalies is challenging due to the complexity of accelerator systems, high data volume, and scarcity of labeled fault data. Prior work identified faults using beam-b

Dongze Wu, David I. Inouye, Yao Xie

Predicting potential and counterfactual outcomes from observational data is central to individualized decision-making, particularly in clinical settings where treatment choices must be tailored to each patient rather than guided solely by population averages. We propose PO-Flow, a continuous normalizing flow (CNF) framework for causal inference that jointly

G. A. Bridi, F. L. Marquezino, C. M. H. de Figueiredo

Graph pebbling is a problem in which pebbles are distributed across the vertices of a graph and moved according to a specific rule: two pebbles are removed from a vertex to place one on an adjacent vertex. The goal is to determine the minimum number of pebbles required to ensure that any target vertex can be reached, known as the pebbling number. Computing t

Dongyoung Park, Gaby G. Dagher

In the contemporary digital landscape, cybersecurity has become a critical issue due to the increasing frequency and sophistication of cyber attacks. This study utilizes a non-zero-sum game theoretical framework to model the strategic interactions between cyber attackers and defenders, with the objective of identifying optimal strategies for both. By definin

Philipp D. Siedler

We introduce a novel dataset designed to benchmark the physical and spatial reasoning capabilities of Large Language Models (LLM) based on topology optimization, a method for computing optimal material distributions within a design space under prescribed loads and supports. In this dataset, LLMs are provided with conditions such as 2D boundary, applied force

Peter Norwood, Christina Yau, Denise Wolf, Philip Beineke

The I-SPY2 phase 2 clinical trial is a long-running platform trial that evaluates neoadjuvant treatments for locally advanced breast cancer, assigning subjects to novel agents using response-adaptive randomization. Recently, I-SPY2 was reconfigured as a sequential multiple assignment randomized trial (SMART), with up to three stages of therapy. At the first

Mark Embree

This manuscript is designed to introduce students in applied mathematics and data science to the concept of regularization for ill-posed inverse problems. Construct a mathematical model that describes how an image gets blurred. Convert a calculus problem into a linear algebra problem by discretization. Inverting the blurring process should sharpen up an imag

Gowtham Premananth, Philip Resnik, Sonia Bansal, Deanna L. Kelly

Studies on schizophrenia assessments using deep learning typically treat it as a classification task to detect the presence or absence of the disorder, oversimplifying the condition and reducing its clinical applicability. This traditional approach overlooks the complexity of schizophrenia, limiting its practical value in healthcare settings. This study shif

Dongyoung Park, Gaby G. Dagher

Defending against sophisticated cyber threats demands strategic allocation of limited security resources across complex network infrastructures. When the defender has limited defensive resources, the complexity of coordinating honeypot placements across hundreds of nodes grows exponentially. In this paper, we present a multi-attacker Bayesian Stackelberg fra

Andrew P. Berg, Qian Zhang, Mia Y. Wang

As unmanned aerial vehicles (UAVs) become increasingly prevalent in both consumer and defense applications, the need for reliable, modality-specific classification systems grows in urgency. This paper addresses the challenge of data scarcity in UAV audio classification by expanding on prior work through the integration of pre-trained deep learning models, pa

David Rytz, Suyoung Choi, Wanming Yu, Wolfgang Merkt

This article presents Platform Adaptive Locomotion (PAL), a unified control method for quadrupedal robots with different morphologies and dynamics. We leverage deep reinforcement learning to train a single locomotion policy on procedurally generated robots. The policy maps proprioceptive robot state information and base velocity commands into desired joint a

Siddhant Agarwal, Ali Can Bekar, Christian Hüttig, David S. Greenberg

Mantle convection simulations are an essential tool for understanding how rocky planets evolve. However, the poorly known input parameters to these simulations, the non-linear dependence of transport properties on pressure and temperature, and the long integration times in excess of several billion years all pose a computational challenge for numerical solve

Kazuma Ohara

Let $F$ be a non-archimedean local field with residue characteristic $p$ and $G$ be a connected reductive group defined over $F$. In earlier joint works with Jeffrey D. Adler, Jessica Fintzen, and Manish Mishra, we proved that the Hecke algebras attached to types constructed by Kim and Yu are isomorphic to the Hecke algebras attached to depth-zero types. Not

Shuvashis Sarker, Shamim Rahim Refat, Faika Fairuj Preotee, Shifat Islam

The brain is a highly complex organ that manages many important tasks, including movement, memory and thinking. Brain-related conditions, like tumors and degenerative disorders, can be hard to diagnose and treat. Magnetic Resonance Imaging (MRI) serves as a key tool for identifying these conditions, offering high-resolution images of brain structures. Despit

Jefferson Sousa, Matthew D. Sievert, Jacquelyn Noronha-Hostler, Patrick Carzon

We propose a new class of charge-conjugation-odd flow observables and use them to investigate the dynamics of conserved currents in simulations of relativistic heavy-ion collisions. Inspired by the success of the initial energy and momentum distributions at predicting final-state anisotropic flow, we construct systematically-improvable initial-state estimato

Asterios Tsiourvas, Wei Sun, Georgia Perakis

LLM routing aims to select the most appropriate model for each query, balancing competing performance metrics such as accuracy and cost across a pool of language models. Prior approaches typically adopt a decoupled strategy, where the metrics are first predicted and the model is then selected based on these estimates. This setup is prone to compounding error

Yıldırım Özen, Burak Erinç Çetin, Kaan Engür, Elif Naz Demiryılmaz

Generative large language models present significant potential but also raise critical ethical concerns, including issues of safety, fairness, robustness, and reliability. Most existing ethical studies, however, are limited by their narrow focus, a lack of language diversity, and an evaluation of a restricted set of models. To address these gaps, we present

Aayushi Dangol, Runhua Zhao, Robert Wolfe, Trushaa Ramanan

The integration of generative Artificial Intelligence (genAI) into everyday life raises questions about the competencies required to critically engage with these technologies. Unlike visual errors in genAI, textual mistakes are often harder to detect and require specific domain knowledge. Furthermore, AI's authoritative tone and structured responses can crea

Faika Fairuj Preotee, Shuvashis Sarker, Shamim Rahim Refat, Tashreef Muhammad

Leaf diseases are harmful conditions that affect the health, appearance and productivity of plants, leading to significant plant loss and negatively impacting farmers' livelihoods. These diseases cause visible symptoms such as lesions, color changes, and texture variations, making it difficult for farmers to manage plant health, especially in large or remote

Kathryn Linehan, Radu Balan

The singular value decomposition (SVD) is commonly used in applications requiring a low rank matrix approximation. However, the singular vectors cannot be interpreted in terms of the original data. For applications requiring this type of interpretation, e.g., selection of important data matrix columns or rows, the approximate CUR matrix factorization can be

Aayushi Dangol, Robert Wolfe, Runhua Zhao, JaeWon Kim

As artificial intelligence (AI) advances in reasoning capabilities, most recently with the emergence of Large Reasoning Models (LRMs), understanding how children conceptualize AI's reasoning processes becomes critical for fostering AI literacy. While one of the "Five Big Ideas" in AI education highlights reasoning algorithms as central to AI decision-making,

Alexander Rudikov, Vladimir Fanaskov, Sergei Stepanov, Buzheng Shan

Neural operators (NOs) struggle with high-contrast multiscale partial differential equations (PDEs), where fine-scale heterogeneities cause large errors. To address this, we use the Generalized Multiscale Finite Element Method (GMsFEM) that constructs localized spectral basis functions on coarse grids. This approach efficiently captures dominant multiscale f

Shichao Li, Peiliang Li, Qing Lian, Peng Yun

Perceiving pedestrians in highly crowded urban environments is a difficult long-tail problem for learning-based autonomous perception. Speeding up 3D ground truth generation for such challenging scenes is performance-critical yet very challenging. The difficulties include the sparsity of the captured pedestrian point cloud and a lack of suitable benchmarks f

Shuvashis Sarker, Shamim Rahim Refat, Faika Fairuj Preotee, Tanvir Rouf Shawon

Advanced diagnostic instruments are crucial for the accurate detection and treatment of lung diseases, which affect millions of individuals globally. This study examines the effectiveness of deep learning and transfer learning models using a hybrid dataset, created by merging four individual datasets from Bangladesh and global sources. The hybrid dataset sig

Qinmei Xu, Yiheng Li, Xianghao Zhan, Ahmet Gorkem Er

Foundation models leveraging vision-language pretraining have shown promise in chest X-ray (CXR) interpretation, yet their real-world performance across diverse populations and diagnostic tasks remains insufficiently evaluated. This study benchmarks the diagnostic performance and generalizability of foundation models versus traditional convolutional neural n

Simon Langenscheidt

We present an improved notion of internal tetrad shifts in 4 dimensions which is always integrable in the presence of corners. This allows us to study the fully extended corner symmetry algebra of gauge charges, which is a deformation of $ISO(1,3)^S$ involving spacetime curvature. We argue this implies corner noncommutativity of the spin connection $\omega$.

Wen Wen, Yaohong Wu, Yue Sheng, Neil Birkbeck

Video quality assessment (VQA) is a challenging research topic with broad applications. Traditional hand-crafted and discriminative learning-based VQA models mainly focus on pixel-level distortions and lack contextual understanding, while recent multimodal large language models (MLLMs) struggle with sensitivity to small distortions or handle quality scoring

Weiguo Gao, Ming Li

Diffusion trajectory distillation accelerates sampling by training a student model to approximate the multi-step denoising trajectories of a pretrained teacher model using far fewer steps. Despite strong empirical results, the trade-off between distillation strategy and generative quality remains poorly understood. We provide a theoretical characterization b

Sheshera Mysore, Debarati Das, Hancheng Cao, Bahareh Sarrafzadeh

As large language models (LLMs) are used in complex writing workflows, users engage in multi-turn interactions to steer generations to better fit their needs. Rather than passively accepting output, users actively refine, explore, and co-construct text. We conduct a large-scale analysis of this collaborative behavior for users engaged in writing tasks in the

Wei Liu, Siya Qi, Xinyu Wang, Chen Qian

Recent advances such as DeepSeek R1-Zero highlight the effectiveness of incentive training, a reinforcement learning paradigm that computes rewards solely based on the final answer part of a language model's output, thereby encouraging the generation of intermediate reasoning steps. However, these methods fundamentally rely on external verifiers, which limit

Braden M. Weight, Yu Zhang

Hybrid light-matter polaritonic states have shown great promise for altering already known and enabling novel chemical reactions and controlling photophysical phenomena. This field has recently become one of the most prominent and active areas of research that connects the communities of chemistry and quantum optics. The ab initio modeling of such polaritoni

Chen-Huan Wu

We investigate the Holstein mechanism in a single-electron (one-site) system, where unitary evolution intrinsically involves both fermion and boson operators under nonadiabatic conditions. The resulting unitary dynamics and boson-frequency dependence reveal a quantum phase transition, evidenced by distinct short-time (power-law decay) and long-time (exponent

Shaobo Li, Ben Sherwood

This paper considers equity premium prediction, for which mean regression can be problematic due to heteroscedasticity and heavy-tails of the error. We show advantages of quantile predictions using a novel penalized quantile regression that offers a model for a full spectrum analysis on the equity premium distribution. To enhance model interpretability and a

Soyeon Kim, Namhee Kim, Yeonwoo Jeong

Effective evaluation of web data record extraction methods is crucial, yet hampered by static, domain-specific benchmarks and opaque scoring practices. This makes fair comparison between traditional algorithmic techniques, which rely on structural heuristics, and Large Language Model (LLM)-based approaches, offering zero-shot extraction across diverse layout

Zaher Hani, Yao Li, Andrea Nahmod, Gigliola Staffilani

Motivated by the central phenomenon of energy cascades in wave turbulence theory, we construct non-equilibrium statistical steady states (NESS), or invariant measures, for a simplified model derived from the nonlinear Schr\"odinger (NLS) equation with external forcing and dissipation. This new perspective to studying energy cascades, distinct from traditiona

Mariia Seleznova, Hung-Hsu Chou, Claudio Mayrink Verdun, Gitta Kutyniok

We introduce GradPCA, an Out-of-Distribution (OOD) detection method that exploits the low-rank structure of neural network gradients induced by Neural Tangent Kernel (NTK) alignment. GradPCA applies Principal Component Analysis (PCA) to gradient class-means, achieving more consistent performance than existing methods across standard image classification benc

Jack Van Sambeek, Jessica Dong, Anton V. Ievlev, Tao Cai

We present the electronic transport properties of $\mathrm{BaZrS_3}$ (BZS) thin films grown epitaxially by gas-source molecular beam epitaxy (MBE). We observe n-type behavior in all samples, with carrier concentration ranging from $4 \times 10^{18}$ to $4 \times 10^{20} \mathrm{cm^{-3}}$ at room temperature (RT). We observe a champion RT Hall mobility of 11.

J. Ignacio Alvarez-Hamelin, Juan I. Giribet, Ignacio Mas, J. Francisco Presenza

This article presents new contributions to the study of graph rigidity and its interplay with fundamental graph invariants. Recently, a quantitative measure of graph rigidity in $\mathbb{R}^d$, termed the generalized algebraic connectivity, was introduced. This development extends the notion of algebraic connectivity---the second-smallest eigenvalue of the L

Urvashi Verma, Margaret Lewis, Jordan Lehman, Rana D. Parshad

The soybean aphid (\emph{Aphis glycines}) is an invasive insect pest that continues to cause large-scale damage to soybean crops in the North Central United States. The current manuscript proposes several mathematical models for the top-down bio-control of the aphid, as well as control via pesticides and neonicotinoids. The models are motivated empirically,

Igor Razgon

Decomposable Negation Normal Forms \textsc{dnnf} [Darwiche, 'Decomposable Negation Normal Form', JACM, 2001] is a landmark Knowledge Compilation (\textsc{kc}) model, highly important both in \textsc{ai} and Theoretical Computer Science. Numerous restrictions of the model have been studied. In this paper we consider the restriction where all the gates are $\a

Bradley Coles, Yahya Hmaiti, Joseph J. LaViola

We present a comprehensive survey of perception-based redirected walking (RDW) techniques in virtual reality (VR), presenting a taxonomy that serves as a framework for understanding and designing RDW algorithms. RDW enables users to explore virtual environments (VEs) larger than their physical space, addressing the constraints of real walking in limited home

Pijush Patra, Anubhab Roy, J. S. Wettlaufer

We study the dynamics of collisions between a pair of uncharged conducting droplets under the influence of a uniaxial compressional flow and an external electric field. The near-field asymptotic expression for the electric-field-induced attractive force demonstrate that surface-to-surface contact in finite time is facilitated by overcoming lubrication resist

Tung Le, B. G. Rodrigues

In this manuscript, for $q:=2^n$ with $n\geq2$, we study two primitive maximal subgroups of the alternating group ${\sf A}_{q-1}$. These subgroups are the full automorphism groups of $2$-designs which are constructed from algebraic curves over the finite field ${\mathbb F}_q$.

Wenrui Yu, Yiyi Chen, Johannes Bjerva, Sokol Kosta

We propose LAGO - Language Similarity-Aware Graph Optimization - a novel approach for few-shot cross-lingual embedding inversion attacks, addressing critical privacy vulnerabilities in multilingual NLP systems. Unlike prior work in embedding inversion attacks that treat languages independently, LAGO explicitly models linguistic relationships through a graph-

Jinjin Gu

This position paper argues that the image processing community should broaden its focus from purely model-centric development to include agentic system design as an essential complementary paradigm. While deep learning has significantly advanced capabilities for specific image processing tasks, current approaches face critical limitations in generalization,

Alexei Kniazev, Simon Pustilnik

The dwarf galaxy Peekaboo was recently identified as a Local Volume (LV) gas-rich and 'eXtremely Metal-Poor' (XMP) dIrr. Its gas metallicity is Z~Zsun/50, with +/-1$\sigma$ uncertainty range of [Zsun/72-Zsun/35]). Its the "Tip of Red Giant Stars" (TRGB)-based distance is of 6.8$\pm$0.7 Mpc. HST data for its individual stars reveal that its older RGB stars co

Yu-Ting Wang, Chao Liu, Zhi-Yu Zhang

High-velocity clouds (HVCs) are interstellar gas clouds whose velocities are incompatible with Galactic rotation. Since the first discovery of HVCs in 1963, their origins have been debated for decades but are still not settled down, because of the lack of vital parameters of HVCs, e.g., the distance. In this work, we determined the distance to the high-veloc

Aaron J. Li, Suraj Srinivas, Usha Bhalla, Himabindu Lakkaraju

Sparse autoencoders (SAEs) are commonly used to interpret the internal activations of large language models (LLMs) by mapping them to human-interpretable concept representations. While existing evaluations of SAEs focus on metrics such as the reconstruction-sparsity tradeoff, human (auto-)interpretability, and feature disentanglement, they overlook a critica

Roland Daynauth, Christopher Clarke, Krisztian Flautner, Lingjia Tang

The LLM-as-a-Judge paradigm offers a scalable, reference-free approach for evaluating language models. Although several calibration techniques have been proposed to better align these evaluators with human judgment, prior studies focus primarily on narrow, well-structured benchmarks. As a result, it remains unclear whether such calibrations generalize to rea

Sasha Boguraev, Christopher Potts, Kyle Mahowald

Language Models (LMs) have emerged as powerful sources of evidence for linguists seeking to develop theories of syntax. In this paper, we argue that causal interpretability methods, applied to LMs, can greatly enhance the value of such evidence by helping us characterize the abstract mechanisms that LMs learn to use. Our empirical focus is a set of English f

Qiang Zhu, Kuan Lu, Menghao Huo, Yuxiao Li

Image-to-image translation aims to learn a mapping between a source and a target domain, enabling tasks such as style transfer, appearance transformation, and domain adaptation. In this work, we explore a diffusion-based framework for image-to-image translation by adapting Diffusion Transformers (DiT), which combine the denoising capabilities of diffusion mo

Mehrdad Ghassabi, Pedram Rostami, Hamidreza Baradaran Kashani, Amirhossein Poursina

The rapid advancement of language models has demonstrated the potential of artificial intelligence in the healthcare industry. However, small language models struggle with specialized domains in low-resource languages like Persian. While numerous medical-domain websites exist in Persian, no curated dataset or corpus has been available making ours the first o

Michael G. Taylor

This paper describes the passage of light through a system of waveplates mathematically in terms of quaternions, an extension of the complex numbers, instead of the more usual Jones vectors and Jones matrices. Both the light beam and the waveplate are represented by a quaternion. It is possible to manipulate the quaternion expression more readily than the Jo

Mehran Zoravar, Shadi Alijani, Homayoun Najjaran

Exploring the trustworthiness of deep learning models is crucial, especially in critical domains such as medical imaging decision support systems. Conformal prediction has emerged as a rigorous means of providing deep learning models with reliable uncertainty estimates and safety guarantees. However, conformal prediction results face challenges due to the ba

Yaman Güçlü, Francesco Patrizi, Martin Campos Pinto

We propose a novel projection-based approach to derive structure-preserving Finite Element Exterior Calculus (FEEC) discretizations using standard tensor-product splines on domains with a polar singularity. This approach follows the main lines of broken-FEEC schemes which define stable and structure-preserving operators in non-conforming discretizations of t

Rachel Bowens-Rubin, James Mang, Mary Anne Limbach, Aarynn L. Carter

NIRCam and MIRI coronagraphy have successfully demonstrated the ability to directly image young sub-Jupiter mass and mature gas-giant exoplanets. However, these modes struggle to reach the sensitivities needed to find the population of cold giant planets that are similar to our own Solar System's giant planets ($T_{\rm eff} = 60 - 125$ K; $a=5 - 30$ AU). For

Roni Edwin

Motivated by inequalities in Fourier analysis, we present an improvement on the lower bound for the sign uncertainty principle of Bourgain, Clozel and Kahane in high dimensions. Additionally, our methods can be used to match the existing Torquato-Stillinger lower bounds for the Cohn-Elkies linear program for sphere packing.

Anirudh Maiya, Razan Alghamdi, Maria Leonor Pacheco, Ashutosh Trivedi

The success of Large Language Models (LLMs) in human-AI collaborative decision-making hinges on their ability to provide trustworthy, gradual, and tailored explanations. Solving complex puzzles, such as Sudoku, offers a canonical example of this collaboration, where clear and customized explanations often hold greater importance than the final solution. In t

Hamed Hasibi, Neerja Mhaskar, W. F. Smyth

Finding an Approximate Longest Common Substring (ALCS) within a given set $S=\{s_1,s_2,\ldots,s_m\}$ of $m \ge 2$ strings is a key problem in computational biology, such as identifying related mutations across multiple genetic sequences. We study several variants of ALCS problems that, given integers $k$ and $t \le m$, seek the longest string $u$ -- or the l

Derek Frydel

Statistical-mechanical models often exhibit a dimension-dependent solvability: in 1D, exact solutions are straightforward; in 2D, solutions are exact but require nontrivial derivations; and in 3D, closed-form solutions are typically unavailable. This logic is repeated for a simple model of self-propelled particles, run-and-tumble particles (RTP) in a harmoni

Juan Manuel Cornejo, Andrés Gallardo, Luiz F. Monteiro, Ignacio Viglizzo

Five-valued Nelson algebras are those satisfying the condition: $((x\to z)\to y)\to(((y \to x)\to y)\to y)=1$. We give alternative equations defining these algebras, and determine the structure and number of elements of the free five-valued Nelson algebra with a finite number of free generators.

Taofeek A. O. Yusuf, Sigurd S. Petersen, Puchu Li, Jian Ren

In the era of Industry 4.0, precise indoor localization is vital for automation and efficiency in smart factories. Reconfigurable Intelligent Surfaces (RIS) are emerging as key enablers in 6G networks for joint sensing and communication. However, RIS faces significant challenges in Non-Line-of-Sight (NLOS) and multipath propagation, particularly in localizat

S. Khrapak, A. Khrapak

The original Rosenfeld-Tarazona (RT) scaling of the excess energy in simple dense fluids predicts a $\propto T^{3/5}$ thermal correction to the fluid Madelung energy. This implies that the excess isochoric heat capacity scales as $C_{\rm v}^{\rm ex}\propto T^{-2/5}$. Careful examination performed in this paper demonstrates that the exponent $-2/5$ is not alw

Sangwon Shin, Mehmet C. Vuran

The increasing congestion of the radio frequency spectrum presents challenges for efficient spectrum utilization. Cognitive radio systems enable dynamic spectrum access with the aid of recent innovations in neural networks. However, traditional real-valued neural networks (RVNNs) face difficulties in low signal-to-noise ratio (SNR) environments, as they were

Alex Brown, Joscha Fregin, Thomas Bendall, Thomas Melvin

This paper investigates the application of a fast-wave slow-wave spectral deferred correction time-stepping method (FWSW-SDC) to the compressible Euler equations. The resulting model achieves arbitrary order accuracy in time, demonstrating robust performance in standard benchmark idealised test cases for dynamical cores used for numerical weather prediction.

Yamin Arefeen, Brett Levac, Bhairav Patel, Chang Ho

Purpose: Magnetic Resonance Imaging (MRI) enables non-invasive assessment of brain abnormalities during early life development. Permanent magnet scanners operating in the neonatal intensive care unit (NICU) facilitate MRI of sick infants, but have long scan times due to lower signal-to-noise ratios (SNR) and limited receive coils. This work accelerates in-NI

K. C. Santana, F. M. Maccagni, R. Deane, J. Healy

The detailed study of gas flows in local Active Galactic Nuclei (AGN) is essential for understanding the regulation of star formation and black hole growth, which are fundamental to galaxy evolution. One such AGN case study is NGC 5643, a nearby ($D_{L}\sim17.3$ Mpc) star-forming, late-type, Seyfert galaxy, where inflows and outflows have been observed in de

Daniel Sabi Takou, Assimiou Yarou Mora, Gabriel Y. H. Avossevou

In this paper, we examine the thermodynamic behavior of a quantum harmonic oscillator with a position-dependent mass (PDM), where spatial inhomogeneity is modeled through a deformation parameter {\alpha}. Based on the exact energy spectrum, we explore the resulting thermodynamic quantities and superstatistics. Our findings reveal that increasing {\alpha} lea

Konstantinos Boutivas, Dimitrios Katsinis, Ioannis Papadimitriou, Georgios Pastras

We study the entanglement entropy of a free massive scalar field at its ground state in (3+1)-dimensional AdS space in global coordinates. We consider spherical entangling surfaces centered at the origin of AdS. We determine the structure of the UV-divergent terms in the entanglement entropy and compute the numerical values of the respective coefficients. We

Michal Jablonowski

The maximum length of the shortest path from a leaf to the root of a skein tree for knots and links gives a measure of the complexity of computing link polynomials by the skein relation (the Jones polynomial, the Alexander-Conway polynomial, and more generally HOMFLY-PT polynomial). In this paper, we prove the new upper bound on the skein tree depth of a lin

Lesley Wheat, Martin v. Mohrenschildt, Saeid Habibi

The Bayes Error Rate (BER) is the fundamental limit on the achievable generalizable classification accuracy of any machine learning model due to inherent uncertainty within the data. BER estimators offer insight into the difficulty of any classification problem and set expectations for optimal classification performance. In order to be useful, the estimators

Zeynab Kaseb, Matthias Moller, Pedro P. Vergara, Peter Palensky

This study further explores reformulating power flow (PF) analysis as a discrete combinatorial optimization problem, proposed in our earlier study using the Adiabatic Quantum Power Flow (AQPF) algorithm, which can be executed on Ising machines, including quantum and quantum-inspired hardware. This approach provides a new representation of the underlying equa

Narges Golmohammadi, Madan Mohan Rayguru, Sabur Baidya

The evolution of 5G wireless technology has revolutionized connectivity, enabling a diverse range of applications. Among these are critical use cases such as real time teleoperation, which demands ultra reliable low latency communications (URLLC) to ensure precise and uninterrupted control, and enhanced mobile broadband (eMBB) services, which cater to data-i

Marco Olivieri

We consider a dilute gas in 3D composed of two species of bosons interacting through positive inter-species and intra-species pairwise potentials. We prove a second order expansion for the energy density in the thermodynamic limit. For the case of compactly supported, integrable potentials, we derive the correct second order of the expansion. If we make the

Alan Ta, Nilsu Salgin, Mustafa Demir, Kala Phillips Reindel

College students are increasingly affected by stress, anxiety, and depression, yet face barriers to traditional mental health care. This study evaluated the efficacy of a mobile health (mHealth) intervention, Mental Health Evaluation and Lookout Program (mHELP), which integrates a smartwatch sensor and machine learning (ML) algorithms for real-time stress de

Hyungjun Doh, Jingyu Shi, Rahul Jain, Heesoo Kim

Storytelling in AR has gained attention due to its multi-modality and interactivity. However, generating multi-modal content for AR storytelling requires expertise and efforts for high-quality conveyance of the narrator's intention. Recently, Generative-AI (GenAI) has shown promising applications in multi-modal content generation. Despite the potential benef

Haojin Guo, Zongyi Guo, Jianguo Guo, Tiago Roux Oliveira

Extremum Seeking (ES) is an effective real-time optimization method for PDE systems in cascade with nonlinear quadratic maps. To address PDEs in the feedback loop, a boundary control law and a re-design of the additive probing signal are mandatory. The latter, commonly called "trajectory generation" or "motion planning," involves designing perturbation signa

Tejaswi Polimetla, Katy Ilonka Gero, Elena Leah Glassman

As generative AI tools become embedded in creative practice, questions of ownership in co-creative contexts are pressing. Yet studies of human-AI collaboration often invoke "ownership" without definition: sometimes conflating it with other concepts, and other times leaving interpretation to participants. This inconsistency makes findings difficult to compare

Matthew Lyle Olson, Musashi Hinck, Neale Ratzlaff, Changbai Li

The ImageNet hierarchy provides a structured taxonomy of object categories, offering a valuable lens through which to analyze the representations learned by deep vision models. In this work, we conduct a comprehensive analysis of how vision models encode the ImageNet hierarchy, leveraging Sparse Autoencoders (SAEs) to probe their internal representations. SA

Hannah Friedman, Serkan Hoşten

Grassmann and flag varieties lead many lives in pure and applied mathematics. Here we focus on the algebraic complexity of solving various problems in linear algebra and statistics as optimization problems over these varieties. The measure of the algebraic complexity is the amount of complex critical points of the corresponding optimization problem. After an

V. Carruba, R. Sfair, R. A. Araujo, O. C. Winter

Currently, 20 co-orbital asteroids of Venus are known, with only one with an eccentricity below 0.38. This is most likely caused by observational biases since asteroids with larger eccentricities may approach the Earth and are easier to detect. We aim to assess the possible threat that the yet undetected population of Venus co-orbitals may pose to Earth, and

Vitali Vougalter, Vitaly Volpert

The work deals with establishing the solvability of a system of integro-differential equations in the situation of the double scale anomalous diffusion. Each equation of such system involves the sum of the two negative Laplace operators raised to two distinct fractional powers in the space of three dimensions. The proof of the existence of solutions is based

Haozhe Wang, Alex Su, Weiming Ren, Fangzhen Lin

Chain-of-thought reasoning has significantly improved the performance of Large Language Models (LLMs) across various domains. However, this reasoning process has been confined exclusively to textual space, limiting its effectiveness in visually intensive tasks. To address this limitation, we introduce the concept of reasoning in the pixel-space. Within this

Gowtham Premananth, Vinith Kugathasan, Carol Espy-Wilson

Advancements in AI-driven speech-based applications have transformed diverse industries ranging from healthcare to customer service. However, the increasing prevalence of non-native accented speech in global interactions poses significant challenges for speech-processing systems, which are often trained on datasets dominated by native speech. This study inve

Shujun Liu, Siyuan Wang, Zejun Li, Jianxiang Wang

Large vision-language models (LVLMs) remain vulnerable to hallucination, often generating content misaligned with visual inputs. Although recent training-based approaches aim to mitigate hallucination, they typically rely on predefined or randomly edited negative samples that do not reflect actual model errors, thus limiting training efficacy. In this work,