December 2025 arXiv papers — page 120

Angiolo Huaman, Luis Enrique Rosas-Hernandez, Salvador Barraza-Lopez

The second-order optical susceptibility of semiconductors $\chi_{ijk}^{(2)}(-2\omega;\omega,\omega)$ finds application in metrology, spectroscopy, telecommunications, material characterization, and quantum information. Pioneering calculations of $\chi_{ijk}^{(2)}(-2\omega;\omega,\omega)$ utilized non-orthogonal Gaussian orbitals centered at atoms. That formu

Ágney Lopes Roth Ferraz, Sidnei Barbieri, Murray Evangelista de Souza, Lourenço Alves Pereira Júnior

Cyber threat intelligence (CTI) encoded in STIX and structured according to the MITRE ATT&CK framework has become a global reference for describing adversary behavior. However, ATT&CK was designed as a descriptive knowledge base rather than a procedural model. We therefore ask whether its structured artifacts contain sufficient behavioral detail to support m

Xiaoyu He, Logan Post

A binary shuffle square is a binary word of even length that can be partitioned into two disjoint, identical subwords. Huang, Nam, Thaper, and the first author conjectured that as $n\rightarrow \infty$, asymptotically half of all binary words of length $2n$ are shuffle squares. We prove this conjecture in a strong form, by showing that the number of binary s

Yu-Chia Huang, Juntong Chen, Dongyu Liu, Kwan-Liu Ma

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major chal

James Munday, Valeriya Korol, Camilla Danielski, Na'ama Hallakoun

The millihertz band is densely populated by continuous gravitational-wave signals from Galactic compact binaries, dominated by double white dwarfs (DWDs; binaries of two white dwarfs) with contributions from systems containing neutron stars and black holes (Amaro-Seoane et al. 2023). As these binaries inspiral due to gravitational-wave radiation, they can re

Gregorio Pérez-Bernal, Oscar Rincón-Cardeño, Silvana Montoya-Noguera, Nicolás Guarín-Zapata

Inverse problems are extensively studied in applied mathematics, with applications ranging from acoustic tomography for medical diagnosis to geophysical exploration. Physics informed neural networks (PINNs) have emerged as a powerful tool for solving such problems, while Physics informed Kolmogorov Arnold networks (PIKANs) represent a recent benchmark that,

G. Papakonstantinou

Efficient arithmetic operations are a prerequisite for practical quantum computing. Optimization efforts focus on two primary metrics: Quantum Cost (QC), determined by the number of non-linear gates, and Logical Depth, which defines the execution speed. Existing literature identifies the HNG gate as the standard for Input-Preserving Reversible Full Adders. H

Avinash Amballa, Yashas Malur Saidutta, Chi-Heng Lin, Vivek Kulkarni

Large language models (LLMs) are increasingly being used to generate synthetic datasets for the evaluation and training of downstream models. However, prior work has noted that such generated data lacks diversity. In this paper, we propose Voyager, a novel principled approach to generate diverse datasets. Our approach is iterative and directly optimizes a ma

Takafumi Kita

A nonrelativistic proof of the spin-statistics theorem is given in terms of the field operators satisfying commutation and anticommutation relations, which are introduced here in the coordinate space as a means to build the permutation symmetry into the brackets of identical particles. An eigenvalue problem of a $\pi$-rotation for a product of two annihilati

Jie Ma, Junqing Zhang, Guanxiong Shen, Linning Peng

Radio frequency fingerprint identification (RFFI) is an emerging method for authenticating Internet of Things (IoT) devices. RFFI exploits the intrinsic and unique hardware imperfections for classifying IoT devices. Deep learning-based RFFI has shown excellent performance. However, there are still remaining research challenges, such as limited public trainin

Peichun Hua, Hao Li, Shanghao Shi, Zhiyuan Yu

Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating defenses that are both generalizable to novel threats and efficient for practical deployment. Many current strategies fall short, either targeting specific attack patterns, which limits generalization, or imposing high computational overhead.

Yuewen Hou, Dhanvi Bharadwaj, Gokul Subramanian Ravi

Variational Quantum Algorithms (VQAs) are promising for near- and intermediate-term quantum computing, but their execution cost is substantial. Each task requires many iterations and numerous circuits per iteration, and real-world applications often involve multiple tasks, scaling with the precision needed to explore the application's energy landscape. This

Martine S. Lenders, Carsten Bormann, Thomas C. Schmidt, Matthias Wählisch

The Internet community has taken major efforts to decrease latency on the World Wide Web with significant improvements in accelerating content transport and in compressing static content. Less attention, however, has been dedicated to compression of dynamic content. Such content is commonly provided by JSON and DNS over HTTPS. Dynamic content objects continu

Erik Larsen

Current safety evaluations of large language models rely on single-shot testing, implicitly assuming that model responses are deterministic and representative of the model's safety alignment. We challenge this assumption by investigating the stability of safety refusal decisions across random seeds and temperature settings. Testing four instruction-tuned

Efthymia Derezea, Nicky J Welton, Gabriel Rogers, Hayley E Jones

For many conditions, it is of clinical importance to know not just the ability of a test to distinguish between those with and without the disease, but also the sensitivity to detect disease at different stages: in particular, the test's ability to detect disease at a stage most amenable to treatment. In a systematic review of test accuracy, pooled stage-spe

Yueze Liu, Ajay Nagi Reddy Kumdam, Ronit Kanjilal, Hao Yang

Modern roleplaying models are increasingly sophisticated, yet they consistently struggle to capture the essence of believable, engaging characters. We argue this failure stems from training paradigms that overlook the dynamic interplay of a character's internal world. Current approaches, including Retrieval-Augmented Generation (RAG), fact-based priming, lit

Ana Anušić, Christopher Mouron

Maps $f,g\colon X\to X$ are called kin if they are forward iterates of the same map $\varphi\colon X\to X$, up to a composition with a commuting homeomorphism. Kin form an important class of commuting maps on $X$. In this paper, we characterize kin, and give an Euclidean-type algorithm which tests when two maps $f,g\colon X\to X$ are kin. Furthermore, we com

Gökberk Çelikmasat, Atay Özgövde, Fatma Başak Aydemir

Domain models are central to software engineering, as they enable a shared understanding, guide implementation, and support automated analyses and model-driven development. Yet, despite these benefits, practitioners often skip modeling because it is time-consuming and demands scarce expertise. We address this barrier by investigating whether open-weight larg

Morgan Görtz, Moritz Hauck, Axel Målqvist, Andreas Rupp

Network models are used as efficient representation of materials with complex, interconnected locally one-dimensional structures. They typically accurately capture the mechanical properties of a material, while substantially reducing computational cost by avoiding full three-dimensional resolution. Applications include the simulation of fiber-based materials

Junling Fan, George Rushevich, Giorgio Rusconi, Mengdi Zhu

Semiconductor intellectual property (IP) theft incurs estimated annual losses ranging from $225 billion to $600 billion. Despite initiatives like the CHIPS Act, many semiconductor designs remain vulnerable to reverse engineering (RE). IP Camouflage is a recent breakthrough that expands beyond the logic gate hiding of traditional camouflage through "mimetic d

Tejas Panambur, Ishan Rajendrakumar Dave, Chongjian Ge, Ersin Yumer

Modern text-to-video (T2V) diffusion models can synthesize visually compelling clips, yet they remain brittle at fine-scale structure: even state-of-the-art generators often produce distorted faces and hands, warped backgrounds, and temporally inconsistent motion. Such severe structural artifacts also appear in very low-quality real-world videos. Classical v

Luke Bhan, Hanyu Zhang, Andrew Gordon Wilson, Michael W. Mahoney

Monitoring forecasting systems is critical for customer satisfaction, profitability, and operational efficiency in large-scale retail businesses. We propose The Forecast Critic, a system that leverages Large Language Models (LLMs) for automated forecast monitoring, taking advantage of their broad world knowledge and strong ``reasoning'' capabilities. As a pr

Anja Sheppard, Chris Reale, Katherine A. Skinner

Safely landing on the lunar surface is a challenging task, especially in the heavily-shadowed South Pole region where traditional vision-based hazard detection methods are not reliable. The potential existence of valuable resources at the lunar South Pole has made landing in that region a high priority for many space agencies and commercial companies. Howeve

Rayssa B. de Andrade, Anne Egholm Høgh, Gaetana Spedalieri, Stefano Pirandola

Rapid detection of bacterial growth is crucial in clinical, food safety, and environmental contexts, yet conventional optical methods are limited by noise and require hours of incubation. Here, we present the first experimental demonstration of a quantum-enhanced photometric measurement for early bacterial detection using squeezed light. By monitoring the op

Maria Rodriguez, Minh-Tan Pham, Martin Sudmanns, Quentin Poterek

After a wildfire, delineating burned areas (BAs) is crucial for quantifying damages and supporting ecosystem recovery. Current BA mapping approaches rely on computer vision models trained on post-event remote sensing imagery, but often overlook their applicability to time-constrained emergency management scenarios. This study introduces a supervised semantic

Konstantin V. Getman, Eric D. Feigelson, Vladimir S. Airapetian, Gordon P. Garmire

X-ray and ultraviolet (XUV) emission from young stars plays a critical role in shaping the evolution of planetary atmospheres and the conditions for habitability. To assess the long-term impact of high-energy stellar radiation, it is essential to empirically trace how X-ray luminosities and spectral hardness evolve during the first ~<1 Gyr, when atmospheric

Ali Hosseinzadeh

Speculative bubbles exhibit common statistical signatures across many financial markets, suggesting the presence of universal underlying mechanisms. We test this hypothesis in the Iranian stock market, an economy that is highly isolated, subject to capital controls, and largely inaccessible to foreign investors. Using the Log-Periodic Power Law Singularity (

Tran-Vu La, Minh-Tan Pham, Yu Li, Patrick Matgen

We investigate the application of Federated Learning (FL) for ship detection across diverse satellite datasets, offering a privacy-preserving solution that eliminates the need for data sharing or centralized collection. This approach is particularly advantageous for handling commercial satellite imagery or sensitive ship annotations. Four FL models including

Gorkem Kadir Solun, Ugur Dogrusoz

Affordable, high-quality whole-genome assemblies have made it possible to construct rich pangenomes that capture haplotype diversity across many species. As these datasets grow, they motivate the development of specialized techniques capable of handling the dense sequence variation found in large groups of related genomes. A common strategy is to encode pang

Andrew Herren, P. Richard Hahn, Jared Murray, Carlos Carvalho

stochtree is a C++ library for Bayesian tree ensemble models such as BART and Bayesian Causal Forests (BCF), as well as user-specified variations. Unlike previous BART packages, stochtree provides bindings to both R and Python for full interoperability. stochtree boasts a more comprehensive range of models relative to previous packages, including heteroskeda

Louis Boucherie

Complex systems are made up of many interacting components. Network science provides the tools to analyze and understand these interactions. Community detection is a key technique in network science for uncovering the structures that shape the behavior of these networks. This thesis introduces the Adaptive Cut, a novel method that improves clustering methods

Muddsair Sharif, Huseyin Seker

This paper presents a novel context-sensitive multi\-agent coordination for dynamic resource allocation (CAMAC-DRA) framework for optimizing smart electric vehicle (EV) charging ecosystems through the Smart2Charge application. The proposed system coordinates autonomous charging agents across networks of 250 EVs and 45 charging stations while adapting to dyna

Martín Matamala, Luciano Villarroel-Sepúlveda

In 2008, Chen and Chv\'atal conjectured that in every finite metric space of $n$ points, there are at least $n$ distinct lines, or the whole set of points is a line. This is a generalization of a classical result in the Euclidean plane. The Chen-Chv\'atal conjecture is open even in metric spaces induced by connected graphs. In 2018, it was asked by Chv\'atal

Vittorio Giammarino, Ahmed H. Qureshi

Goal-Conditioned Reinforcement Learning (GCRL) mitigates the difficulty of reward design by framing tasks as goal reaching rather than maximizing hand-crafted reward signals. In this setting, the optimal goal-conditioned value function naturally forms a quasimetric, motivating Quasimetric RL (QRL), which constrains value learning to quasimetric mappings and

Alex Liu, Lief Esbenshade, Shawon Sarkar, Zewei Tian

This report presents a comprehensive account of the Colleague AI Classroom pilot, a collaborative design (co-design) study that brought generative AI technology directly into real classrooms. In this study, AI functioned as a third agent, an active participant that mediated feedback, supported inquiry, and extended teachers' instructional reach while preserv

Christopher D. Carone

We consider the implications of asymptotic safety on two U(1) gauge extensions of the standard model that are minimal in the sense that anomaly cancellation only requires the presence of right-handed neutrinos. We study the UV fixed points of the gauge couplings taking into account kinetic mixing between hypercharge and the new U(1) gauge field. We consider

Yi Zhao, Chengyun Li, Wanzhu Tu

Hypertension is a highly prevalent condition and a major risk factor for cardiovascular disease. The landmark Systolic Blood Pressure Intervention Trial (SPRINT) showed that lowering systolic blood pressure (BP) goals from 140 mmHg to 120 mmHg leads to significantly reduced BP, cardiovascular mortality, and morbidity. However, the underlying mechanisms are n

Philipp Habicht, Lev Sorokin, Abdullah Saydemir, Ken E. Friedl

In-Car Conversational Question Answering (ConvQA) systems significantly enhance user experience by enabling seamless voice interactions. However, assessing their accuracy and reliability remains a challenge. This paper explores the use of Large Language Models (LLMs) alongside advanced prompting techniques and agent-based methods to evaluate the extent to wh

Bruce W. Jordan, Kenneth A. Ribet, Anthony J. Scholl

We define a generalized Jacobian $\mathrm{J}_\mathfrak{m}(\mathit{Gr})$ and a generalized Picard group $\mathrm{P}_\mathfrak{m}(\mathit{Gr})$ of a graph $\mathit{Gr}$ with respect to a modulus $ \mathfrak{m}=\sum_{i=1}^s m_iw_i$ with $w_i$ vertices of $\mathit{Gr}$ and $m_i\geq 1$. These groups occur as the component groups of N\'{e}ron models of generalized

Won Gu, Francesca Chiaromonte, Justin D. Silverman

In genomics, differential abundance and expression analyses are complicated by the compositional nature of sequence count data, which reflect only relative-not absolute-abundances or expression levels. Many existing methods attempt to address this limitation through data normalizations, but we have shown that such approaches imply strong, often biologically

Peter Hofhansel, Alexander B. Watson

The density of states of a self-adjoint operator generalizes the eigenvalue distribution of a Hermitian matrix. We prove convergence of the density of states for a tight-binding model with a slowly-varying periodic potential to the density of states of its continuum approximation, a Mathieu-type equation.

Antonio Olivas-Martinez, Peter B. Gilbert, Andrea Rotnitzky

The proximal causal inference framework enables the identification and estimation of causal effects in the presence of unmeasured confounding by leveraging two disjoint sets of observed strong proxies: negative control treatments and negative control outcomes. In the point exposure setting, this framework has primarily been applied to estimands comparing cou

Jin-Yun Lin, Ira Z. Rothstein

We show that codimension-two defects in Fermi liquids deform the renormalization group flow via a marginally relevant coupling. The mechanism for generating the flow is distinct from the case of the Kondo problem (codimension-three defects) in that the effective particle-hole asymmetry that leads to the log running is due to the spatial anisotropy generated

Shiju Li, Younghoon Min, Hane Yie, Hoshik Kim

Sparse General Matrix-Matrix Multiplication (SpGEMM) is a fundamental operation in numerous scientific computing and data analytics applications, often bottlenecked by irregular memory access patterns. This paper presents Hash based Multi-phase SpGEMM on GPU and the Acceleration of Indirect Memory Access (AIA) technique, a novel custom near-memory processing

Bitop Maitra, Ozgur B. Akan

Molecular communication (MC) enables information transfer using particles inspired by biological systems. Volatile Organic Compounds (VOCs) are one of the most abundant and diverse classes of signaling molecules used by living or non-living objects. VOC-based MC holds great promise in developing long-range, bio-compatible communication systems capable of int

Jay Anderson

Most discussion of charge-transfer-efficiency (CTE) losses involves the parallel transfer of charge in the y-direction down the chip columns (y-CTE). Serial charge-transfer efficiency (x-CTE) refers to the horizontal transer of charge. Serial CTE losses were first assessed in WFC3/UVIS in 2014 (WFC3/ISR 2014-02), where it was found that bright stars were shi

Rachmiel Klein

Mapping class groups of locally finite graphs are the analogue of those of infinite-type surfaces, and serve as a "big" version of $\text{Out}(F_n)$. In this paper, we investigate which of these mapping class groups have a dense conjugacy class. We obtain a complete classification for self-similar locally finite graphs, and show that a large class of mapping

Eddy de Leon, Joerg Frauendiener, Christian Klein

Physical aspects of a static solution to the Einstein equations with two black holes are studied via ray tracing. The exact solution for this double Schwarzschild solution is known in explicit form. The black holes are separated by a singularity called \emph{Weyl strut}. The effect of this strut on null geodesics is shown to be defocusing in contrast to the

Javad Zahedi Moghaddam, Aria Nosratinia

This paper studies the exact recovery threshold subject to preserving the privacy of connections in $h$-uniform hypergraphs. Privacy is characterized by the $(\epsilon, \delta)$-hyperedge differential privacy (DP), an extension of the notion of $(\epsilon, \delta)$-edge DP in the literature. The hypergraph observations are modeled through a $h$-uniform stoch

Shreekanth S. Yuvarajan, Vincent Iglesias-Cardinale, David Hucul, Herbert F. Fotso

A variety of photon-mediated operations are critical to the realization of scalable quantum information processing platforms and their accurate characterization is essential for the identification of optimal regimes and their experimental realizations. Such light-matter interactions are often studied with a broad variety of analytical and computational metho

Asmaa Abada, Antonio Enrique Cárcamo Hernández, Salvador Urrea

We propose an economical model in which the tiny active neutrino masses arise from an interplay of linear and inverse seesaw mechanisms. The Standard Model is extended by a local $U(1)'$ gauge symmetry and discrete $\mathbb{Z}_{3}\otimes\mathbb{Z}_{4}$ symmetries, together with gauge-singlet scalars and neutral leptons. Owing to the preserved discrete symmet

Ricard Riba

By results of Morita, Pitsch and, more recently, Faes, it is known that any integral homology 3-sphere can be constructed as a Heegaard splitting with a gluing map an element of the fourth Johnson subgroup. In this work we prove that the equivalence relation on the fourth Johnson subgroup induced by this construction admits an intrinsic description in terms

Madison Bochard, Tim Conser, Alyssa Duran, Lazaro Martull

High enrollment in STEM-related degree programs has created increasing demand for scalable tutoring support, as universities experience a shortage of qualified instructors and teaching assistants (TAs). To address this challenge, LeafTutor, an AI tutoring agent powered by large language models (LLMs), was developed to provide step-by-step guidance for studen

Pei-Jun Huang, Ian Dell'Antonio, Philip LaDuca, Zacharias Escalante

We present a new measurement of the half-light radius of the Tucana Dwarf galaxy, based on the combination of wider, deeper imaging conducted as part of the Local Volume Complete Cluster Survey (LoVoCCS) and HST archival images. We obtain a stellar density profile within the Tucana Dwarf field based on elliptical fitting. After background subtraction, we fit

Jialin Zheng, Haoyu Wang, Yangbin Zeng, Han Xu

Model Predictive Control (MPC) is a powerful control strategy for power electronics, but it highly relies on manually-derived and topology-specific analytical models, which is labor-intensive and time-consuming in practical designs. To overcome this bottleneck, this paper introduces a Digital-Twin-based MPC (DT-MPC) framework for generic power converters tha

N. Bhathi, J. S. Randhawa, R. Kanungo, J. Refsgaard

Model-observation comparisons of type-I X-ray bursts (XRBs) can reveal the properties of accreting neutron star systems, including the neutron star compactness. XRBs are powered by nuclear burning and a handful of reactions have been shown to impact the model results. Reactions in the NiCu cycles, featuring a competition between $^{59}$Cu($p$,$\gamma$)$^{60}

Nuno Macedo, Hugo Pacheco

Many properties related to security or concurrency must be encoded as so-called hyperproperties, temporal properties that allow reasoning about multiple traces of a system. However, despite recent advances on model checking hyperproperties, there is still a lack of higher-level specification languages that can effectively support software engineering practit

Julia L. Gonski, Peter W. Graham, Surjeet Rajendran, Harikrishnan Ramani

Particles at the TeV scale with lifetimes of a year or longer could have been abundantly produced at the LHC yet escaped detection because of backgrounds, and could still be trapped within detector materials. With gluinos in split-supersymmetry as a working example, we show that these trapped particles can be recovered from detector materials once prepared i

Kaichuang Zhang, Wei Yin, Jinghao Yang, Ping Xu

Decentralized federated learning (DFL) has emerged as a promising paradigm that enables multiple clients to collaboratively train machine learning models through iterative rounds of local training, communication, and aggregation, without relying on a central server. Nevertheless, DFL systems continue to face a range of challenges, including fairness and Byan

Xincheng Cao, Haochong Chen, Bilin Aksun-Guvenc, Levent Guvenc

Parking a vehicle in tight spaces is a challenging task to perform due to the scarcity of feasible paths that are also collision-free. This paper presents a strategy to tackle this kind of maneuver with a modified Hybrid-A* path-planning algorithm that combines the feasibility guarantee inherent in the standard Hybrid A* algorithm with the addition of static

Hunter Ellis, Bobby G. Duersch, Botong Li, Imteaz Rahaman

NiO is a promising p-type material for photovoltaics and power electronics, but its temperature limits remain unclear. Using in situ high-temperature X-ray diffraction (HT-XRD) from 30 to 1100 C, we track the structural evolution of NiO thin films in air. The film crystallizes from an amorphous phase to cubic NiO between 300 and 400 C, where the emergence an

T. Forrest Kieffer, Jakob Cupp, John S. Van Dyke, Paraj Titum

We consider nonlinear partial differential equations (PDEs) for advection-diffusion processes which are augmented by an auxiliary parameter $\delta$ such that $\delta=0$ corresponds to linear advection-diffusion. We derive potentially non-perturbative series expansions in $\delta$ that provide a process to obtain the solution of the nonlinear PDE through sol

Ilya Mandel, Om Sharan Salafia, Andrew Levan, Paul Disberg

We analytically derive, and illustrate with a population synthesis model, the maximum offset of binary neutron star mergers ejected from their host galaxies. This approximate maximum offset is 300 kpc $\times\ (v_\mathrm{esc} / 500\ \mathrm{km}\ \mathrm{s}^{-1})^{-7}$, where $v_\mathrm{esc}$ is the escape velocity from the host galaxy. Massive hosts with hig

Haoyu Wang, Junwei Liu, Jialin Zheng, Yangbin Zeng

Multi-active bridge (MAB) converters, the core of the state-of-the-art medium-voltage power electronic transformers, can flexibly connect multiple DC ports among distributed DC grids and loads, but suffer from hard switching under conventional single phase-shift control, especially under unbalanced voltage conversion ratios and light load conditions. Althoug

Mevan Wijewardena, Kamiar Asgari, Michael J. Neely

This paper considers the problem of obtaining bounded time-average expected queue sizes in a single-queue system with a partial-feedback structure. Time is slotted; in slot $t$ the transmitter chooses a rate $V(t)$ from a continuous interval. Transmission succeeds if and only if $V(t)\le C(t)$, where channel capacities $\{C(t)\}$ and arrivals are i.i.d. draw

Ekrem Aydıner, Tekin Dereli, İzzet Sakallı, Erdem Sucu

We investigate the thermodynamic and optical properties of Einstein-Born-Infeld-Anti-de Sitter (EBI-AdS) black holes (BHs). Our study derives the Hawking temperature using standard surface gravity methods and examines quantum corrections through both the Generalized Uncertainty Principle (GUP) and exponential entropy modifications, showing enhanced thermal r

Noah Piemontese-Fischer

In this article, we study scaling laws for singularly perturbed two-well energies with prescribed Dirichlet boundary data in settings where the wells and/or the boundary data are incompatible. Our main focus is the geometrically linear two-well problem, for which we characterize the energy scaling in two dimensions for nearly all combinations of linear bound

Matthew Mark Romano, JungHyun Bae, Paul Cantonwine

We show that machine learning methods produce superior particle position reconstruction accuracy in scintillation-based detectors.

Senhao Gao, Junqing Zhang, Luoyu Mei, Shuai Wang

Human activity recognition (HAR) requires extracting accurate spatial-temporal features with human movements. A mmWave radar point cloud-based HAR system suffers from sparsity and variable-size problems due to the physical features of the mmWave signal. Existing works usually borrow the preprocessing algorithms for the vision-based systems with dense point c

Antonio Guillen-Perez

The development of robust Autonomous Vehicles (AVs) is bottlenecked by the scarcity of "Long-Tail" training data. While fleets collect petabytes of video logs, identifying rare safety-critical events (e.g., erratic jaywalking, construction diversions) remains a manual, cost-prohibitive process. Existing solutions rely on coarse metadata search, which lacks p

Denise Gonzalez-Cruz, Genesis Encarnacion, Kaili Martinez-Beasley, Robin Wilson

Government actions, such as the Medina v. Planned Parenthood South Atlantic Supreme Court ruling and the passage of the Big Beautiful Bill Act, have aimed to restrict or prohibit Medicaid funding for Planned Parenthood Healthcare Centers (PPHCs) at both the state and national levels. These funding cuts are particularly harmful in states like California, whic

Hongrui Chen, Cambyse Rouzé, Jielun Chen, Jiaqing Jiang

We propose a randomized algorithm to compute the log-partition function of weakly interacting fermions with polynomial runtime in both the system size and precision. Although weakly interacting fermionic systems are considered tractable for many computational methods such as the diagrammatic quantum Monte Carlo, a mathematically rigorous proof of polynomial

Marc Uphues, Sebastian Thöne, Herbert Kuchen

Quantum computing promises a remarkable performance boost for certain applications, including computational intensive problems addressed by enterprise systems. However, software architectures of enterprise systems must consider specific characteristics and quality attributes when collaborating with quantum computing services. Hence, this paper presents a mod

Minghui Liu, Aadi Palnitkar, Tahseen Rabbani, Hyunwoo Jae

Large language models (LLMs) have demonstrated remarkable performance on long-context tasks, but are often bottlenecked by memory constraints. Namely, the KV cache, which is used to significantly speed up attention computations, grows linearly with context length. A suite of compression algorithms has been introduced to alleviate cache growth by evicting uni

Amrita Acharyya

It is known when we call a poset P, a $\mathcal{P}$-chain permutational poset, given a subset of permutations $\mathcal{P}$ of the symmetric group $S_{n}$. In this work, we use the same idea to study subsets of words of length $n$, that are not necessarily permutations, for example: especially when they are certain classes of restricted growth functions indu

Robin Vassantlal, Hasan Heydari, Bernardo Ferreira, Alysson Bessani

It is well known that encryption alone is not enough to protect data privacy. Access patterns, revealed when operations are performed, can also be leveraged in inference attacks. Oblivious RAM (ORAM) hides access patterns by making client requests oblivious. However, existing protocols are still limited in supporting concurrent clients and Byzantine fault to

Marcus Kasdorf, Diego Simpson-Ochoa, Abdelrahman Bekhit, Mauro S. Ferreira

Randomly self-assembled nanowire networks (NWNs) are dynamical systems in which junctions between two nanowires can be modelled as memristive units viewed as adaptive resistors with memory. Various memristive models have been proposed to capture the complex mechanics of these junctions. Here, we showcase a novel computational framework named Memristive Nanow

Troy Allen

Large language models (LLMs) are increasingly deployed locally for privacy and accessibility, yet users lack tools to measure their resource usage, environmental impact, and efficiency metrics. This paper presents EnviroLLM, an open-source toolkit for tracking, benchmarking, and optimizing performance and energy consumption when running LLMs on personal devi

Deepak Sah, Manoranjan P. Singh

We investigate the creation of electron-positron pairs (EPPs) in a sequence of alternating-sign, time-dependent electric field pulse trains by solving the quantum Vlasov equations. Specifically, we focus on Sauter-like pulse trains with random time delays between successive pulses, drawn from a Gaussian distribution wherein the extent of fluctuations is cont

Jie Ma, Junqing Zhang, Guanxiong Shen, Alan Marshall

Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits deep learning models to extract hardware impairments to uniquely identify wireless devices. Recent studies show deep learning-based RFFI is vulnerable to adversarial attacks. However, effect

Mohammad E. Heravifard, Kazem Hejranfar

Physics-Informed Neural Networks (PINNs) and more recently Physics-Informed Kolmogorov-Arnold Networks (PIKANs) have emerged as promising approaches for solving partial differential equations (PDEs) without reliance on extensive labeled data. In this work, we propose a novel multi-resolution Hybrid Wavelet-Fourier-Enhanced Physics-Informed Kolmogorov-Arnold

Y. Alipour Fakhri

We develop a thermally coupled Ginzburg-Landau theory on \emph{Weakly Non-Tonelli (WNT) Finsler manifolds}, extending classical vortex analysis beyond the Tonelli convexity paradigm. The WNT framework weakens global $1$-homogeneity and strict convexity while preserving superlinearity and local ellipticity, enabling a geometric treatment of superconductors wh

Wei Xiao, Anni Li

This paper proposes a novel Taylor-Lagrange Control (TLC) method for nonlinear control systems to ensure the safety and stability through Taylor's theorem with Lagrange remainder. To achieve this, we expand a safety or stability function with respect to time along the system dynamics using the Lie derivative and Taylor's theorem. This expansion enables the c

Glenn Zhang, Treasure Mayowa, Jason Fan, Yicheng Fu

Producing trustworthy and reliable Large Language Models (LLMs) has become increasingly important as their usage becomes more widespread. Calibration seeks to achieve this by improving the alignment between the model's confidence and the actual likelihood of its responses being correct or desirable. However, it has been observed that the internal confidence

Anfeng Peng, Ajesh Koyatan Chathoth, Stephen Lee

System logs are a critical resource for monitoring and managing distributed systems, providing insights into failures and anomalous behavior. Traditional log analysis techniques, including template-based and sequence-driven approaches, often lose important semantic information or struggle with ambiguous log patterns. To address this, we present EnrichLog, a

Chris Calger

An isolated point on an algebraic curve is a closed point not belonging to a collection of points of the same degree parametrized by $\mathbf{P}^1$ or a positive rank abelian subvariety of the curve's Jacobian. We study the sets of $j$-invariants, in extensions of bounded degree, that arise as the $j$-invariant of an isolated point on a modular curve. We obt

Arijit Bishnu, Debarshi Chanda, Buddha Dev Das, Arijit Ghosh

We present a simple nonadaptive randomized algorithm that estimates the number of edges in a simple, unweighted, undirected graph, possibly containing isolated vertices, using only degree and random edge queries. For an $n$-vertex graph, our method requires only $\widetilde{O}(\sqrt{n})$ queries, achieving sublinear query complexity. The algorithm independen

Ling-Hong Hung, Ka Yee Yeung

Open-source scientific software is effectively closed to modification by its complexity. With recent advances in technology, an agentic AI team led by a single human can now rapidly and robustly modify large codebases and re-open science to the community which can review and vet the AI generated code. We demonstrate this with a case study, STAR-Flex, which i

Mehmet Zor

The problem of representing laminated structures by an equivalent volume and determining the elastic constants of this equivalent volume from the layer properties is a fundamental issue in the analysis of composite and multilayered systems. In the literature, the most widely used approach for this purpose is the Voigt-type volume-weighted averaging method. A

José-Ramón Vidal, Vicent Pla, Luis Guijarro, Israel Leyva-Mayorga

Recent developments in cyber-physical systems have increased the importance of maximizing the freshness of the information about the physical environment. However, optimizing the access policies of Internet of Things devices to maximize the data freshness, measured as a function of the Age-of-Information (AoI) metric, is a challenging task. This work introdu

Grace M. Forrey, Raymond C. Simons, Jonathan R. Trump, Lu Shen

We introduce a new non-parametric technique to quantify the spatially-resolved relationship between the local star-formation rate (SFR) and dust attenuation. We then apply it to 14 star-forming galaxies at $1.0<z<2.5$ using JWST/NIRISS slitless spectroscopy from the NGDEEP survey. First, we construct spatially resolved ($\sim$1~kpc per pixel) Balmer decremen

Jad Al Aaraj, Athina Markopoulou

Camera-equipped mobile devices, such as phones, smart glasses, and AR headsets, pose a privacy challenge for bystanders, who currently lack effective real-time mechanisms to control the capture of their picture, video, including their face. We present BlindSpot, an on-device system that enables bystanders to manage their own privacy by signaling their privac

Edward Lue Chee Lip, Anthony Channg, Diana Kim, Aaron Sandoval

As AI capabilities advance, we increasingly rely on powerful models to decompose complex tasks $\unicode{x2013}$ but what if the decomposer itself is malicious? Factored cognition protocols decompose complex tasks into simpler child tasks: one model creates the decomposition, while other models implement the child tasks in isolation. Prior work uses trusted

Xianghui Xie, Bowen Wen, Yan Chang, Hesam Rabeti

Accurate capture of human-object interaction from ubiquitous sensors like RGB cameras is important for applications in human understanding, gaming, and robot learning. However, inferring 4D interactions from a single RGB view is highly challenging due to the unknown object and human information, depth ambiguity, occlusion, and complex motion, which hinder co

Philippe Voyer, Simon Tartakovsky, Steven J. Benton, William C. Jones

This paper presents an attitude estimation and yaw-rate control framework for balloon-borne payloads using pivot-only actuation, motivated by the Taurus experiment. Taurus is a long-duration balloon instrument designed for rapid azimuthal scanning at approximately 30 deg/s using a motorized pivot at the flight-train connection, without a reaction wheel. We m

Minseon Kim, Lucas Caccia, Zhengyan Shi, Matheus Pereira

User prompts to large language models (LLMs) are often ambiguous or under-specified, and subtle contextual cues shaped by user intentions, prior knowledge, and risk factors strongly influence what constitutes an appropriate response. Misinterpreting intent or risks may lead to unsafe outputs, while overly cautious interpretations can cause unnecessary refusa

Keduse Worku, Tiger Yu-Yang Hsiao, Dan Coe, Abdurro'uf

We present a catalog of 57 high-redshift $z>6$ galaxy candidates, including 14 spectroscopic confirmations ($z = 6.10$ -- 9.25), 2 Little Red Dots ($z = 4.77$, 5.81), and 2 interlopers ($z = 3.23$, 3.72), based on \JWST\ NIRCam imaging (7 filters), NIRSpec spectroscopy (PRISM and G395H), and archival \HST\ imaging (17 filters) of the strong lensing galaxy cl

Alireza Joonbakhsh, Alireza Rostami, AmirMohammad Kamalinia, Ali Nazeri

The rapid proliferation of artificial intelligence (AI) models and methods presents growing challenges for research software engineers and researchers who must select, integrate, and maintain appropriate models within complex research workflows. Model selection is often performed in an ad hoc manner, relying on fragmented metadata and individual expertise, w

Nat Sothanaphan

Stanley sequences starting from the set $\{0, n\}$ where $n$ is a positive integer have long been conjectured to be divided into two types: the "regular" type where the growth rate is $\Theta(n^{\log_2(3)})$, and the "irregular" type where the growth rate is thought to be $\Theta(n^2/\log n)$. A paradigmatic case of a candidate irregular type is $n=4$, altho

Claudia de Rham, Sumer Jaitly, Greg Kaplanek

In theories with multiple particle species standard fixed-t positivity bounds do not directly apply to 2-to-2 definite species scattering amplitudes when the initial and final state are not the same (inelastic processes). These inelastic amplitudes are nevertheless constrained by positivity bounds indirectly, by considering scattering states which are arbitr