October 2025 arXiv papers — page 224

Griffin Pitts, Anurata Prabha Hridi, Arun-Balajiee Lekshmi-Narayanan

Novice programmers benefit from timely, personalized support that addresses individual learning gaps, yet the availability of instructors and teaching assistants is inherently limited. Large language models (LLMs) present opportunities to scale such support, though their effectiveness depends on how well technical capabilities are aligned with pedagogical go

Mario Castro, Rafael Vida, Javier Galeano, José A. Cuesta

Metagenomic data has significantly advanced microbiome research by employing ecological models, particularly in personalised medicine. The generalised Lotka-Volterra (gLV) model is commonly used to understand microbial interactions and predict ecosystem dynamics. However, gLV models often fail to capture complex interactions, especially when data is limited

Sharan SK, Subin Sahayam, Umarani Jayaraman, Lakshmi Priya A

Segmenting of clinically important retinal blood vessels into arteries and veins is a prerequisite for retinal vessel analysis. Such analysis can provide potential insights and bio-markers for identifying and diagnosing various retinal eye diseases. Alteration in the regularity and width of the retinal blood vessels can act as an indicator of the health of t

Risako Katayama, Hidehiro Kaneda, Takuma Kokusho, Tsubasa Kondo

Recent near- and mid-infrared (IR) observations reveal the existence of appreciable amounts of aromatic and aliphatic hydrocarbon dust in the harsh environments of active galactic nuclei (AGNs), the origins of which are still under discussion. In this paper, we analyze the near-IR spectra of AGNs obtained with AKARI to systematically study the properties of

Matyas Barczy, Zsolt Páles

Given an nxn doubly stochastic matrix P satisfying an appropriate condition of linear algebraic-type, and a function f defined on a nonempty interval, we show that the validity of a convexity-type functional inequality for f in terms P implies that f is Jensen convex. We also prove that if f is convex, then the functional inequality in question holds for all

Nalith Udugampola, Xiaoyu Ai, Binghao Li, Henry Gong

Although LoRa is predominantly employed with the single-hop LoRaWAN protocol, recent advancements have extended its application to multi-hop mesh topologies. Designing efficient routing for LoRa mesh networks remains challenging due to LoRa's low data rate and ALOHA-based MAC. Prior work often adapts conventional protocols for low-traffic, aboveground networ

Abdalrhaman Koko, Bemin Sheen, Caitlin Green, Fionn Dunne

Despite extensive theoretical treatment of short- to long-crack transitions, direct experimental quantification of how elastic and plastic energy contributions evolve at the crack tip during arrest has remained absent. In this study, we present an in situ investigation of crack propagation in cold-worked AA-5052 using high-resolution scanning electron micros

Jahidul Arafat, Kh. M. Moniruzzaman, Shamim Hossain, Fariha Tasmin

Modern distributed systems employ aggressive optimization strategies that create latent risks - hidden vulnerabilities where exceptional performance masks catastrophic fragility when optimizations fail. Cache layers achieving 99% hit rates can obscure database bottlenecks until cache failures trigger 100x load amplification and cascading collapse. Current re

Jiaxiang Liang, Peng Xu, Minghui Du, Yifu Cheng

The nonlinear coupling between spacetime geometry and matter in the early Universe remains a frontier in theoretical cosmology. By introducing a novel gravitomagnetic-hydrodynamic framework, we reveal a fundamental analogy between magnetohydrodynamics and the co-evolution of spacetime geometry and relativistic plasma. We demonstrate that, in high-energy envi

Silpa S, Ann Eliza Joseph, Srinivas G, Harish C Barshilia

We report the development of broadband solar absorber coatings based on titanium suboxide composite thin films on aluminium substrates. The films are fabricated via scalable DC magnetron sputtering using a Ti target, followed by post-annealing in a fixed $O_2$ partial pressure of 0.45 mbar. By tuning deposition time and annealing temperature, a composite pha

Mourad Choulli

We establish uniqueness and stability inequalities for the problem of determining the higher-order coefficients of an elliptic operator from the corresponding boundary spectral data (BSD). Our analysis relies on the relationship between boundary spectral data and elliptic and hyperbolic Dirichlet to Neumann (DtN) maps. We also show how to adapt our analysis

Yongsheng Song, Zeyu Yang

This paper presents a further investigation of the properties of infinite-time mean field forward-backward stochastic differential equations (FBSDEs) and the associated elliptic master equations, which were introduced in [18] as mathematical tools for solving discounted infinite-time mean field games. By establishing the continuous dependence of the FBSDE so

Eadom Dessalene, Pavan Mantripragada, Michael Maynord, Yiannis Aloimonos

We introduce EmbodiSwap - a method for producing photorealistic synthetic robot overlays over human video. We employ EmbodiSwap for zero-shot imitation learning, bridging the embodiment gap between in-the-wild ego-centric human video and a target robot embodiment. We train a closed-loop robot manipulation policy over the data produced by EmbodiSwap. We make

Yulin Chen, Haoran Li, Yuan Sui, Yangqiu Song

With the development of technology, large language models (LLMs) have dominated the downstream natural language processing (NLP) tasks. However, because of the LLMs' instruction-following abilities and inability to distinguish the instructions in the data content, such as web pages from search engines, the LLMs are vulnerable to prompt injection attacks. The

Michael Spannowsky

This chapter provides an introduction to collider phenomenology, explaining how theoretical concepts are translated into experimental analyses at the Large Hadron Collider (LHC). Beginning with the principles of collider operation and detector design, it outlines how collisions of protons are modelled through parton distribution functions, hard matrix elemen

Yanan Chu, Yan Wang

Gallai's conjecture asserts that every connected graph on $n$ vertices can be decomposed into $\frac{n+1}{2}$ paths. For general graphs (possibly disconnected), it was proved that every graph on $n$ vertices can be decomposed into $\frac{2n}{3}$ paths. This is also best possible (consider the graphs consisting of vertex-disjoint triangles). Lov\'{a}sz showed

The CTAO-LST Project, :, K. Abe, S. Abe

Cherenkov Telescope Array Observatory (CTAO) is the next-generation ground-based gamma-ray observatory operating in the energy range from 20 GeV up to 300 TeV, with two sites in La Palma (Spain) and Paranal (Chile). It will consist of telescopes of three sizes, covering different parts of the large energy range. We report on the performance of Large-Sized Te

Kanoko Goto, Takumi Hirose, Mahiro Ukai, Shuhei Kurita

Referring expression comprehension (REC) aims to localize the target object described by a natural language expression. Recent advances in vision-language learning have led to significant performance improvements in REC tasks. However, localizing extremely small objects remains a considerable challenge despite its importance in real-world applications such a

Seungseop Lim, Gibaeg Kim, Hyunkyung Lee, Wooseok Han

An accurate differential diagnosis (DDx) is essential for patient care, shaping therapeutic decisions and influencing outcomes. Recently, Large Language Models (LLMs) have emerged as promising tools to support this process by generating a DDx list from patient narratives. However, existing evaluations of LLMs in this domain primarily rely on flat metrics, su

Carmen Arana, Matěj Stehlík

The Lov\'asz complex $L(G)$ of a graph $G$ is a deformation retract of its neighborhood complex, equipped with a canonical $Z_2$-action. We show that, under mild assumptions, $L(G)$ is homeomorphic to a surface if and only if $G$ is a non-bipartite quadrangulation of the orbit space $L(G)/Z_2$ in which every $4$-cycle is facial. This yields a classification

Benjamin Marsh, Paolo Serafino

We introduce a modified Schnorr signature scheme to allow for time-bound signatures for transaction fee auction bidding and smart contract purposes in a blockchain context, ensuring an honest producer can only validate a signature before a given block height. The immutable blockchain is used as a source of universal time for the signature scheme. We show the

Deepak Babu Piskala, Sharlene Chen, Udita Patel, Parul Kalra

Evaluating the quality of multi-turn chatbot interactions remains challenging, as most existing methods assess interactions at the turn level without addressing whether a user's overarching goal was fulfilled. A ``goal'' here refers to an information need or task, such as asking for policy information or applying for leave. We propose a comprehensive framewo

Xuancong He

In this paper, I present some sufficient conditions for projective hypersurfaces to be GIT (semi-)stable. These conditions will be presented in terms of dimension and degree of the hypersurfaces, dimension of the singular locus and multiplicities of the singular points. When singularities of the hypersurface are isolated and all have multiplicity 2, we can j

Frances Isabel Allen

The focused ion beam (FIB) microscope is well established as a high-resolution machining instrument capable of site-selectively removing material down to the nanoscale. Beyond subtractive processing, however, the FIB can also add material using a technique known as focused ion beam induced deposition (FIBID), enabling the direct-write of complex nanostructur

Shaharyar Alam Ansari, Mohammad Luqman, Aasim Zafar, Savir Ali

Rapid urbanization has intensified traffic congestion, environmental strain, and inefficiencies in transportation systems, creating an urgent need for intelligent and adaptive traffic management solutions. Conventional systems relying on static signals and manual monitoring are inadequate for the dynamic nature of modern traffic. This research aims to develo

Abdullah Kahraman, Michael Sachs, Soumen Ghosh, Benjamin I. Poulter

Proton-coupled electron transfer (PCET) is foundational to catalysis, bioenergetics, and energy conversion, yet capturing and disentangling the coupled motions of electrons, protons, and solvent has remained a major experimental challenge. We combine femtosecond optical spectroscopy, site-specific ultrafast soft X-ray absorption spectroscopy, and time-resolv

Hidekazu Yoshioka

The objective of this paper is to provide a new mathematical tool for fish migration that has not been studied well. McKean-Vlasov stochastic differential equations (MVSDEs) have broad potential applications in science and engineering, but remain insufficiently explored. We consider a non-negative McKean-Vlasov diffusion bridge, a diffusion process pinned at

Zehua Liu, Han Wu, Xiaojin Fu, Shuqi Liu

Optimizers are crucial for the efficient training of Large Language Models (LLMs). While AdamW is the de facto standard, recent structure-aware optimizers like Muon have emerged, which regularize gradient updates by operating on entire weight matrices. The Muon optimizer balances the gradient updates along all the directions. However, Muon's reliance on the

Zhengyi Liu, Xinrui Wang, Xianyong Fang, Zhengzheng Tu

RGB-T salient object detection (SOD) aims to segment attractive objects by combining RGB and thermal infrared images. To enhance performance, the Segment Anything Model has been fine-tuned for this task. However, the imbalance convergence of two modalities and significant gradient difference between high- and low- activations are ignored, thereby leaving roo

G. S. Karapetian, A. P. Mahtessian, L. E. Byzalov, M. A. Hovhannisyan

Recent observations with the James Webb Space Telescope (JWST) of massive galaxies at ages below 1 Gyr pose a challenge to standard models of galaxy formation, which predict significantly longer assembly timescales. One possible explanation is that active galactic nuclei (AGN) drive large scale outflows that accelerate galaxy growth. To test this scenario in

Yue Huang, Yanyuan Chen, Dexuan Xu, Chenzhuo Zhao

Medical problem-solving demands expert knowledge and intricate reasoning. Recent studies of large language models (LLMs) attempt to ease this complexity by introducing external knowledge verification through retrieval-augmented generation or by training on reasoning datasets. However, these approaches suffer from drawbacks such as retrieval overhead and high

Mohammadjavad Abbaspour, Mukund R. Shukla, Praveen K. Saxena, Shivam Saxena

Indoor farming enables year-round food production but its reliance on artificial lighting significantly increases energy consumption, peak load charges, and energy costs for growers. Recent studies indicate that plants are able to tolerate interruptions in light, enabling the design of 24-hour lighting schedules (or "recipes") with strategic light modulation

Nikitin Nikita

The introduction of machine learning methods has led to significant advances in automation, optimization, and discoveries in various fields of science and technology. However, their widespread application faces a fundamental limitation: the transfer of models between data domains generally lacks a rigorous mathematical justification. The key problem is the l

Johannes Mehrer, Ben Lonnqvist, Anna Mitola, Abdulkadir Gokce

Brain stimulation is a powerful tool for understanding cortical function and holds promise for therapeutic interventions in neuropsychiatric disorders. Initial visual prosthetics apply electric microstimulation to early visual cortex which can evoke percepts of simple symbols such as letters. However, these approaches are fundamentally limited by hardware co

Nisar Hussain, Amna Qasim, Gull Mehak, Muhammad Zain

The use of derogatory terms in languages that employ code mixing, such as Roman Urdu, presents challenges for Natural Language Processing systems due to unstated grammar, inconsistent spelling, and a scarcity of labeled data. In this work, we propose a QLoRA based fine tuning framework to improve offensive language detection in Roman Urdu-English text. We tr

Linghao Zhang, Jiawang Nie, Tingting Tang

Activation functions are crucial for deep neural networks. This novel work frames the problem of training neural network with learnable polynomial activation functions as a polynomial optimization problem, which is solvable by the Moment-SOS hierarchy. This work represents a fundamental departure from the conventional paradigm of training deep neural network

Bumjun Kim, Dongjae Jeon, Dueun Kim, Wonje Jeung

Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive models, offering flexible generation orders and strong performance on complex reasoning tasks. However, instruction-tuned dLLMs exhibit a critical vulnerability we term \texttt{<eos>} overflow: as allocated sequence length increases, responses paradoxically beco

Junhua Chen, Zixi Zhang, Hantao Zhong, Rika Antonova

We introduce Group Policy Gradient (GPG), a family of critic-free policy-gradient estimators for general MDPs. Inspired by the success of GRPO's approach in Reinforcement Learning from Human Feedback (RLHF), GPG replaces a learned value function with a group-based Monte Carlo advantage estimator, removing the memory, compute, and hyperparameter costs of trai

Zhao Song, Shenghao Xie, Samson Zhou

This paper studies the computational challenges of large-scale attention-based models in artificial intelligence by utilizing importance sampling methods in the streaming setting. Inspired by the classical definition of the $\ell_2$ sampler and the recent progress of the attention scheme in Large Language Models (LLMs), we propose the definition of the atten

Salim Rezvani, Ammar Jaleel Mahmood, Robin Chhabra

Robots with internal visual self-models promise unprecedented adaptability, yet existing autonomous modeling pipelines remain fragile under realistic sensing conditions such as noisy imagery and cluttered backgrounds. This paper presents the first systematic study quantifying how visual degradations--including blur, salt-and-pepper noise, and Gaussian noise-

Yongqiang Cai, Yifei Duan

A significant connection exists between the controllability of dynamical systems and the approximation capabilities of neural networks, where residual networks and vanilla feedforward neural networks can both be regarded as numerical discretizations of the flow maps of dynamical systems. Leveraging the expressive power of neural networks, prior works have ex

Siva Sai, Saksham Gupta, Vinay Chamola, Rajkumar Buyya

The integration of Diffusion Models into Intelligent Transportation Systems (ITS) is a substantial improvement in the detection of accidents. We present a novel hybrid model integrating guidance classification with diffusion techniques. By leveraging fine-tuned ExceptionNet architecture outputs as input for our proposed diffusion model and processing image t

Wen-Ya Tian, Neng-Chang Wei, Yu-Fei Wang, Fei Huang

In our previous work [Phys. Rev. C {\bf 101}, 014003 (2020)], we have analyzed all available data on differential cross sections and spin density matrix elements for the $\gamma p \to K^{\ast +} \Lambda$ reaction using an effective Lagrangian approach. There, the $t$-channel $K$, $K^*$, and $\kappa$ exchanges, the $u$-channel $\Lambda$, $\Sigma$, and $\Sigma

Patrick Letendre

Let $1=d_{1}<d_{2}< \cdots < d_{\tau(n)}=n$ denote the ordered sequence of the positive divisors of an integer $n$. We are interested in estimating the arithmetic function $$ V(n) := \prod_{1 \le i < j \le \tau(n)}(d_{j}-d_{i}) \quad (n \ge 1). $$

Laura Pierson

Promotion has been well-studied for rectangular standard Young tableaux, in which case the orbit lengths divide the total number of boxes and are described by a cyclic sieving phenomenon (CSP), but little is known about the orbit lengths for tableaux of general shape. We approach this problem by building a stable sequence of tableaux where we fix the bottom

Wenlong Deng, Yi Ren, Yushu Li, Boying Gong

Reinforcement learning with verifiable rewards has significantly advanced the reasoning capabilities of large language models, yet how to explicitly steer training toward exploration or exploitation remains an open problem. We introduce Token Hidden Reward (THR), a token-level metric that quantifies each token's influence on the likelihood of correct respons

Selidji Caroline Tossou

In this paper, I investigate the 2017 labor market reform in Benin, which reduced firing costs and allowed firms to renew short-term contracts indefinitely. Using micro-data from the Harmonized Household Living Standards Surveys and a two-way fixed effect approach with nearby countries as the control group, I assess the reform's impact on employment, worker

Jessica Y. Bo, Majeed Kazemitabaar, Mengqing Deng, Michael Inzlicht

Sycophancy, the tendency of LLM-based chatbots to express excessive agreement with their users, even when inappropriate, is emerging as a significant risk in human-AI interactions. However, the extent to which this affects human-LLM collaboration in complex problem-solving tasks is not well quantified, especially among novices who are prone to misconceptions

Jiang Wu, Sichao Wu, Yinsong Ma, Guangyuan Yu

Industrial accidents, particularly in high-risk domains such as surface and underground mining, are frequently caused by unsafe worker behaviors. Traditional manual inspection remains labor-intensive, error-prone, and insufficient for large-scale, dynamic environments, highlighting the urgent need for intelligent and automated safety monitoring. In this pape

Erik Sverdrup, James Yang, Michael LeBlanc

Random survival forests are widely used for estimating covariate-conditional survival functions under right-censoring. Their standard log-rank splitting criterion is typically recomputed at each candidate split. This O(M) cost per split, with M the number of distinct event times in a node, creates a bottleneck for large cohort datasets with long follow-up. W

Yuyan Wu, Sanjay Giridharan, Leixin Ma, Hae Young Noh

Underwater marine animal monitoring is essential for assessing biodiversity, evaluating ecosystem health, and understanding the effects of offshore structures. Traditional approaches such as tagging, sonar, and camera systems are often invasive, energy-intensive, or limited by poor visibility and water turbidity. Inspired by the hydrodynamic sensing of seal

Xiangyu Peng, Can Qin, Zeyuan Chen, Ran Xu

Multimodal retrieval-augmented Generation (MM-RAG) is a key approach for applying large language models (LLMs) and agents to real-world knowledge bases, yet current evaluations are fragmented -- focusing on either text or images in isolation, or simplified multimodal setup, failing to capture document-centric multimodal use cases. In this paper, we introduce

Jijie Zhou, Niloofar Mireshghallah, Tianshi Li

The rapid deployment of large language models (LLMs) in consumer applications has led to frequent exchanges of personal information. To obtain useful responses, users often share more than necessary, increasing privacy risks via memorization, context-based personalization, or security breaches. We present a framework to formally define and operationalize dat

Ran Gu, Enhui Ding, Shigui Ma

Vaccines play a crucial role in the prevention and control of infectious diseases. However, the vaccine supply chain faces numerous challenges that hinder its efficiency. To address these challenges and enhance public health outcomes, many governments provide subsidies to support the vaccine supply chain. This study analyzes a government-subsidized, three-ti

Mohammadjavad Javadi, Charlie Wadds, Robin Chhabra

Untethered soft robots are essential for advancing the real-world deployment of soft robotic systems in diverse and multitasking environments. Inspired by soft-bodied inchworm, we present a fully untethered soft robot with a curved, flexible structure actuated by magnetic forces. The robot has a total mass of 102.63 g and demonstrates multimodal locomotion,

Xu Wang, Yan Hu, Benyou Wang, Difan Zou

Sparse Autoencoders (SAEs) are widely used to steer large language models (LLMs), based on the assumption that their interpretable features naturally enable effective model behavior steering. Yet, a fundamental question remains unanswered: does higher interpretability indeed imply better steering utility? To answer this question, we train 90 SAEs across thre

Selidji Caroline Tossou

This paper evaluates the causal effect of the access to Benin's free cesarean section policy on females and their children. I use a large sample of Demographic and Health Surveys (DHS) for West African countries and analyze how the exemption of the cesarean section user fees for females in Benin directly impacts maternal and infant mortality, family size dec

Aymeric Fabre

In electricity markets around the world, the ability to anticipate price movements with precision can be the difference between profit and loss, especially for fast-acting assets like battery energy storage systems (BESS). As grid volatility increases due to renewables and market decentralisation, operators and forecasters alike face growing pressure to tran

Vladimir Dzhunushaliev, Vladimir Folomeev, Nurzada Beissen, Adilet Nurmukhamedov

Within general relativity, we study spherically symmetric configurations with wormhole topology consisting of spinor fields and a Maxwell electric field. For such a system, we construct complete families of regular asymmetric solutions describing wormholes connecting two identical Minkowski spacetimes. The physical properties of such systems are completely d

Xiaoxin Wang, Kai Kang, Leyi Zhang, Changjing Zhuge

Viral infections trigger complex immune responses with heterogeneous outcomes shaped by nonlinear feedbacks. An ordinary differential equation model is developed to investigate immune response dynamics during viral infection, incorporating six modules: viral load, innate immunity, cellular immunity, humoral immunity, immune suppression, and IL-6 levels. Bifu

Igor P. Chunchuzov, Oleg E. Popov, Elizabeth A. Silber, Segey N. Kulichkov

We investigate the influence of a fine-scale (FS) layered structure in the atmosphere on the propagation of infrasound signals generated by fragmenting meteoroids. Using a pseudo-differential parabolic equation (PPE) approach, we model broadband acoustic signals from point sources at altitudes of 35-100 km. The presence of FS fluctuations in the stratosphere

Nicholas Macsai, August Mendelsohn, David Harrison, Russell Mammei

We report on the conversion of the Manitoba II mass spectrometer into a versatile low-energy proton beam facility. This infrastructure is adaptable to any detector-under-test (DUT), and has proven itself effective with the characterization of silicon detectors used in subatomic beyond-the-StandardModel (BSM) searches, namely the Nab experiment. A pencil beam

Benjamin J. Vaughan, Yuhan Wang, Cody J. Duell, Jason Austermann

The CCAT Observatory is a ground-based submillimeter to millimeter experiment located on Cerro Chajnantor in the Atacama Desert, at an altitude of 5,600 meters. CCAT features the 6-meter Fred Young Submillimeter Telescope (FYST), which will cover frequency bands from 210 GHz to 850 GHz using its first-generation science instrument, Prime-Cam. The detectors u

Mahmood Hasani, Atena Shircharandabi, Masiha Rabiei, Mobin Motaharifar

Photonic Integrated Circuits (PICs) are fundamental for optical computing, communication, quantum information processing, and precision sensing. However, traditional numerical simulations for designing PIC components are computationally intensive and struggle with high-dimensional parameter spaces. This paper introduces XG-Attention-WGAN PIC, a novel framewo

Amir Sadikov

Low-discrepancy point sets and digital sequences underpin quasi-Monte Carlo (QMC) methods for high-dimensional integration. We cast two long-standing QMC design problems as program synthesis and solve them with an LLM-guided evolutionary loop that mutates and selects code under task-specific fitness: (i) constructing finite 2D/3D point sets with low star dis

Diba Behnoudfar, Kyle E. Niemeyer

Accurately simulating coupled physical processes under uncertainty is essential for reliable modeling and design in performance-critical applications such as combustion systems. Ablative heat shield design, as a specific example of this class, involves modeling multi-physics interactions between reacting flows and a porous material. Repeatedly evaluating the

Huijing Zhang, Muyang Cao, Linshan Jiang, Xin Du

Continuous learning of novel classes is crucial for edge devices to preserve data privacy and maintain reliable performance in dynamic environments. However, the scenario becomes particularly challenging when data samples are insufficient, requiring on-device few-shot class-incremental learning (FSCIL). Although existing work has explored parameter-efficient

Takuma Yoshihara, Masayuki Ohzeki

We propose a quantum-classical hybrid method for solving large-scale mixed-integer quadratic problems (MIQP). Although extended Benders decomposition is effective for MIQP, its master problem which handles the integer and quadratic variables often becomes a computational bottleneck. To address this challenge, we integrate the D-Wave CQM solver into the decom

Alireza Aghasi, Jeongyeol Kwon, Saeed Ghadimi

In this paper, we develop zeroth-order algorithms with provably (nearly) optimal sample complexity for stochastic bilevel optimization, where only noisy function evaluations are available. We propose two distinct algorithms: the first is inspired by Jacobian/Hessian-based approaches, and the second builds on using a penalty function reformulation. The Jacobi

Guy Blanc, Caleb Koch, Jane Lange, Carmen Strassle

We give new evidence that quantum circuits are substantially more powerful than classical circuits. We show, relative to a random oracle, that polynomial-size quantum circuits can sample distributions that subexponential-size classical circuits cannot approximate even to TV distance $1-o(1)$. Prior work of Aaronson and Arkhipov (2011) showed such a separatio

Mohammadjavad Javadi, Robin Chhabra

Cosserat rod theory is the popular approach to modeling ferromagnetic soft robots as 1-Dimensional (1D) slender structures in most applications, such as biomedical. However, recent soft robots designed for locomotion and manipulation often exhibit a large width-to-length ratio that categorizes them as 2D shells. For analysis and shape-morphing control purpos

Nicholas Carter, Arkaprava Gupta, Prateek Ganguli, Benedikt Dietrich

Deep Brain Stimulation (DBS) is a highly effective treatment for Parkinson's Disease (PD). Recent research uses reinforcement learning (RL) for DBS, with RL agents modulating the stimulation frequency and amplitude. But, these models rely on biomarkers that are not measurable in patients and are only present in brain-on-chip (BoC) simulations. In this work,

Yihang Jiang, Xiaoyang Li, Guangxu Zhu, Xiaowen Cao

To support the development of low altitude economy, the air-ground integrated sensing and communication (ISAC) networks need to be constructed to provide reliable and robust communication and sensing services. In this paper, the sensing capabilities in the cooperative air-ground ISAC networks are evaluated in terms of area radar detection coverage probabilit

Satoshi Masuda, Satoshi Kouzawa, Kyousuke Sezai, Hidetoshi Suhara

Currently, generating high-level test cases described in natural language from requirement documents is performed manually. In the industry, including companies specializing in software testing, there is a significant demand for the automatic generation of high-level test cases from requirement documents using Large Language Models (LLMs). Efforts to utilize

Mostafa Emam, Matthias Gerdts

Ensuring safety in autonomous vehicles necessitates advanced path planning and obstacle avoidance capabilities, particularly in dynamic environments. This paper introduces a bi-level control framework that efficiently augments road boundaries by incorporating time-dependent grid projections of obstacle movements, thus enabling precise and adaptive path plann

Liming Wang, Junrui Ni, Kai-Wei Chang, Saurabhchand Bhati

Training speech recognizers with unpaired speech and text -- known as unsupervised speech recognition (UASR) -- is a crucial step toward extending ASR to low-resource languages in the long-tail distribution and enabling multimodal learning from non-parallel data. However, existing approaches based on phones often rely on costly resources such as grapheme-to-

Jialin Liu, Lisang Ding, Stanley Osher, Wotao Yin

Implicit models, an emerging model class, compute outputs by iterating a single parameter block to a fixed point. This architecture realizes an infinite-depth, weight-tied network that trains with constant memory, significantly reducing memory needs for the same level of performance compared to explicit models. While it is empirically known that these compac

Yuri Latushkin, Alin Pogan

We prove general representation formulas for strongly continuous cosine and sine operator families in terms of scattering resonances of their generators. This generalizes known results related to decay, growth and oscillatory behavior of solutions of abstract wave equations to a wide class of non-self-adjoint operators in Banach spaces. Inspired by the class

Rabeya Amin Jhuma, Mostafa Mohaimen Akand Faisal

This study explored how in-context learning (ICL) in large language models can be disrupted by data poisoning attacks in the setting of public health sentiment analysis. Using tweets of Human Metapneumovirus (HMPV), small adversarial perturbations such as synonym replacement, negation insertion, and randomized perturbation were introduced into the support ex

Alice Smith, Bob Johnson, Xiaoyu Zhu, Carol Lee

Augmented Reality (AR) applications often require robust real-time tracking of objects in the user's environment to correctly overlay virtual content. Recent advances in computer vision have produced highly accurate deep learning-based object trackers, but these models are typically too heavy in computation and memory for wearable AR devices. In this paper,

An Vuong, Susan Gauch

Accurately predicting short-term stock price movement remains a challenging task due to the market's inherent volatility and sensitivity to investor sentiment. This paper discusses a deep learning framework that integrates emotion features extracted from tweet data with historical stock price information to forecast significant price changes on the following

Jiaxi Li, Yucheng Shi, Xiao Huang, Jin Lu

Tree search has become as a representative framework for test-time reasoning with large language models (LLMs), exemplified by methods such as Tree-of-Thought and Monte Carlo Tree Search. However, it remains difficult to provide instant and reliable quantitative assessments of intermediate reasoning step quality, and extensive path exploration is computation

Saleh Darzi, Saif Eddine Nouma, Kiarash Sedghighadikolaei, Attila Altay

With advances in wireless communication and growing spectrum scarcity, Spectrum Access Systems (SASs) offer an opportunistic solution but face significant security challenges. Regulations require disclosure of location coordinates and transmission details, exposing user privacy and anonymity during spectrum queries, while the database operations themselves p

Xiluo He, Alexander Polok, Jesús Villalba, Thomas Thebaud

An increasingly common training paradigm for multi-talker automatic speech recognition (ASR) is to use speaker activity signals to adapt single-speaker ASR models for overlapping speech. Although effective, these systems require running the ASR model once per speaker, resulting in inference costs that scale with the number of speakers and limiting their prac

Furkan Eris, Ulysse McConnell, Can Firtina, Onur Mutlu

Nanopore sequencing technologies continue to advance rapidly, offering critical benefits such as real-time analysis, the ability to sequence extremely long DNA fragments (up to millions of bases in a single read), and the option to selectively stop sequencing a molecule before completion. Traditionally, the raw electrical signals generated during sequencing

Aidi Zhang, Da Yang, Hing Ong, Zhihong Tan

Atmospheric rivers (ARs) often cause damaging winds, rainfall, and floods. However, the physical mechanisms governing their evolution remain poorly understood. To close this gap, we perform a global Vapor Kinetic Energy (VKE) budget analysis. Using two formulations of VKE, we show that ARs are governed by similar mechanisms regardless of ocean basins. ARs in

Jun Tong

Significant studies on communications over doubly selective channels have utilized on-grid DD channel models, which are previously investigated in Bello's seminar paper in 1963. The DD grid is typically specified by the bandwidth and time duration of the transmission frames. However, the physical channels are determined by the propagation environments and th

Javier Nieto, Joachim Neu, Ling Ren

Proof-of-stake blockchains require consensus protocols that support Dynamic Availability and Reconfiguration (which we call the DAR model). Here, Dynamic Availability means that the consensus protocol should remain live even if a large number of nodes temporarily crash, and Reconfiguration means it should be possible to change the set of operating nodes over

Kun Zhao, Haoke Zhang, Jiayi Wang, Yifei Lou

Robust Principal Component Analysis (RPCA) aims to recover a low-rank structure from noisy, partially observed data that is also corrupted by sparse, potentially large-magnitude outliers. Traditional RPCA models rely on convex relaxations, such as nuclear norm and $\ell_1$ norm, to approximate the rank of a matrix and the $\ell_0$ functional (the number of n

Maraz Mia, Mir Mehedi A. Pritom

Explainable Artificial Intelligence (XAI) has aided machine learning (ML) researchers with the power of scrutinizing the decisions of the black-box models. XAI methods enable looking deep inside the models' behavior, eventually generating explanations along with a perceived trust and transparency. However, depending on any specific XAI method, the level of t

Balázs Boros, Gheorghe Craciun, Oskar Henriksson, Jiaxin Jin

Dynamical systems with polynomial right-hand sides are very important in various applications, e.g., in biochemistry and population dynamics. The mathematical study of these dynamical systems is challenging due to the possibility of multistability, oscillations, and chaotic dynamics. One important tool for this study is the concept of reaction systems, which

Xu-Jie Peng, Ling-Xuan Kong, He Lu

The polarization-entangled photon source (PEPS) at non-degenerated wavelengths is pivotal to connect quantum systems working at different wavelengths, with the assistance of quantum teleportation. Here, a compact Sagnac-type photon source is designed and demonstrated, in which two photons with wavelengths at 810 and 1550~nm are highly entangled in polarizati

Xiao-Xu Fang, Guoliang Shentu, He Lu

Broadband nonlinear optical devices play a critical role in both classical and quantum optics. Here, we design and fabricate a 6.82-mm-long step-chirped periodically poled lithium niobate~(CPPLN) waveguide on lithium niobate on insulator, which enables quasi-phase matching over a broad bandwidth for second-harmonic generation~(SHG) and spontaneous parametric

Qi-Tao Duan, Teng Li, Si-Qi Chen, Shengshi Pang

The diamond sensor has emerged as a promising platform for quantum sensing, enabling the estimation of physical quantities -- such as microwave~(MW) field -- with precision unattainable by classical counterpart. However, traditional diamond sensors suffer severe precision degradation when the signal MW is not resonant with the sensor transition frequency. He

Alice Yang, Michael Beasley, Catherine Taylor, Hu Guo

Augmented and mixed reality (MR) systems have the potential to improve surgical precision by overlaying digital guidance directly onto the operative field. This paper presents a novel MR guidance system using the Magic Leap head-mounted display to assist surgeons in executing precise scalpel movements during liver surgery. The system projects holographic cue

Oliver Polachini, Fabricio Marques

This work seeks to present an investigation about the trajectories followed by rays of light passing through refractive index gradients that was entirely carried by high school students. Such trajectories are curved, therefore contradicting the common sense that light should always travel along straight lines. This fact causes the formation of distorted and

Christopher Solinas, Radovan Haluska, David Sychrovsky, Finbarr Timbers

We present Neural Bayesian Filtering (NBF), an algorithm for maintaining distributions over hidden states, called beliefs, in partially observable systems. NBF is trained to find a good latent representation of the beliefs induced by a task. It maps beliefs to fixed-length embedding vectors, which condition generative models for sampling. During filtering, p

Meenakshi Manikandan, Leilani Gilpin

This paper introduces Hill-ADAM. Hill-ADAM is an optimizer with its focus towards escaping local minima in prescribed loss landscapes to find the global minimum. Hill-ADAM escapes minima by deterministically exploring the state space. This eliminates uncertainty from random gradient updates in stochastic algorithms while seldom converging at the first minimu

Tanqiu Jiang, Min Bai, Nikolaos Pappas, Yanjun Qi

Vision-language model (VLM)-based web agents increasingly power high-stakes selection tasks like content recommendation or product ranking by combining multimodal perception with preference reasoning. Recent studies reveal that these agents are vulnerable against attackers who can bias selection outcomes through preference manipulations using adversarial pop

Raquib Bin Yousuf, Aadyant Khatri, Shengzhe Xu, Mandar Sharma

Recently proposed evaluation benchmarks aim to characterize the effective context length and the forgetting tendencies of large language models (LLMs). However, these benchmarks often rely on simplistic 'needle in a haystack' retrieval or continuation tasks that may not accurately reflect the performance of these models in information-dense scenarios. Thus,