March 2025 arXiv papers — page 174

Wei Dai, Alan Luo, Zane Durante, Debadutta Dash

In the rapidly evolving domain of video understanding, Video Question Answering (VideoQA) remains a focal point. However, existing datasets exhibit gaps in temporal and spatial granularity, which consequently limits the capabilities of existing VideoQA methods. This paper introduces the Multi-Object Multi-Actor Question Answering (MOMA-QA) dataset, which is

Wen Chang

We construct a bijective correspondence between the set of rigid modules over a gentle algebra and the set of admissible arc systems on the associated coordinated-marked surface. In particular, a maximal rigid module aligns with an equivalence class of admissible $5$-partial triangulations, which is an (admissible) set of simple arcs dissecting the surface i

Yanwei Wang

3D reconstruction of high-resolution target remains a challenge task due to the large memory required from the large input image size. Recently developed learning based algorithms provide promising reconstruction performance than traditional ones, however, they generally require more memory than the traditional algorithms and facing scalability issue. In thi

Ying Chen, Zhenhua Chai, Baochang Shi

This paper discusses how to develop a high-order multiple-relaxation-time lattice Boltzmann (MRT-LB) model for the general d(>=1)-dimensional diagonal-anisotropic diffusion equation. Such an MRT-LB model considers the transformation matrix constructed in a natural way and the DdQ(2d^2+1) lattice structure. A key step in developing the high-order MRT-LB model

Yuchen Mao, Hongwei Li, Yinyi Lai, Giorgos Papanastasiou

Large-scale vision models like SAM have extensive visual knowledge, yet their general nature and computational demands limit their use in specialized tasks like medical image segmentation. In contrast, task-specific models such as U-Net++ often underperform due to sparse labeled data. This study introduces a strategic knowledge mining method that leverages S

J. Wang, Y. Xu, L. J. Chen, C. Wu

Rapid identification of candidates of high-value gamma-ray bursts (GRBs), including both high-$z$ and local events, is crucial for outlining subsequent observational strategy. In this paper, we present a model that enables an on-duty astronomer to rapidly identify candidates of local GRBs prior to spectroscopy, provided that these events have been localized

William C. Quigley, Mohamed Rahouti, Gary M. Weiss

The maritime industry is governed by stringent environmental regulations, most notably the International Convention for the Prevention of Pollution from Ships (MARPOL). Ensuring compliance with these regulations is difficult due to low inspection rates and the risk of data fabrication. To address these issues, this paper proposes a secure blockchain-assisted

Murtaza Dalal

How can we build generalist robot systems? Scale may not be enough due to the significant multimodality of robotics tasks, lack of easily accessible data and the challenges of deploying on physical hardware. Meanwhile, most deployed robotic systems today are inherently modular and can leverage the independent generalization capabilities of each module to per

Panpan Li, Jing Qian, Weiping Zhang

To transport high-quality quantum state between two distant qubits through one-dimensional spin chains, the perfect state transfer (PST) method serves as the first choice, due to its natively perfect transfer fidelity that is independent of the system dimension. However, the PST requires a precise modulation of the local pulse parameters as well as an accura

Julian Alfredo Mendez, Timotheus Kampik

This paper presents the Soda language for verifying multi-agent systems. Soda is a high-level functional and object-oriented language that supports the compilation of its code not only to Scala, a strongly statically typed high-level programming language, but also to Lean, a proof assistant and programming language. Given these capabilities, Soda can impleme

Messoud Efendiev, Vitali Vougalter

The work is devoted to the global well-posedness in W^{1, (4, 2)}(R\times R^{+}) of the integro-differential problem involving the square of the one dimensional Laplace operator along with the drift term. Our proof is based on a fixed point technique. Moreover, we provide the assumption leading to the existence of the nontrivial solution for the problem unde

Dhawal Gupta, Adam Fisch, Christoph Dann, Alekh Agarwal

This work tackles the problem of overoptimization in reinforcement learning from human feedback (RLHF), a prevalent technique for aligning models with human preferences. RLHF relies on reward or preference models trained on \emph{fixed preference datasets}, and these models are unreliable when evaluated outside the support of this preference data, leading to

Gexin Huang, Ruinan Jin, Yucheng Tang, Can Zhao

Accurately visualizing and editing tumor progression in medical imaging is crucial for diagnosis, treatment planning, and clinical communication. To address the challenges of subjectivity and limited precision in existing methods, we propose SkEditTumor, a sketch-based diffusion model for controllable tumor progression editing. By leveraging sketches as stru

Johannes Fankhauser

Pilot wave theory endows particles with definite positions at all times governed by deterministic dynamics. However, individual particle trajectories are generically undetectable by experiment. This idea might seem to be contested in light of two proposals: (1) So-called 'weak velocity measurements', allegedly detecting Bohmian trajectories by weakly

Quang-Tuan Dang, Hoang-Son Do, Hoang Hiep Pham

The aim of this paper is to compare singularities of closed positive currents whose non-pluripolar complex Monge--Ampère masses equal. We also provide a short alternative proof for the monotonicity of non-pluripolar complex Monge--Ampère masses, generalizing results of Witt-Nyström, Darvas--Di Nezza--Lu, Lu--Nguyên and Vu.

Mohamed A. Mouamine, Fabian Mussnig

A functional analog of the Klain-Schneider theorem for vector-valued valuations on convex functions is established, providing a classification of continuous, translation covariant, simple valuations. Under additional rotation equivariance assumptions, an analytic counterpart of the moment vector is characterized alongside a new epi-translation invariant valu

Guilherme Silva, Pedro Silva, Gladston Moreira, Vander Freitas

The classification of electrocardiogram (ECG) signals is crucial for early detection of arrhythmias and other cardiac conditions. However, despite advances in machine learning, many studies fail to follow standardization protocols, leading to inconsistencies in performance evaluation and real-world applicability. Additionally, hardware constraints essential

Krzysztof Stempak

In the setting of the Grushin differential operator $G=-Δ_{x'}-|x'|^2Δ_{x''}$ with domain ${\rm Dom}\,G=C^\infty_c(\mathbb{R}^d)\subset L^2(\mathbb{R}^d)$, we define a scalar transform which is a mixture of the partial Fourier transform and a transform based on the scaled Hermite functions. This transform unitarily intertwines $G$ with a mult

Tyrone Fernando, Mohamed Darouach

This paper introduces new conditions for target output controllability and provides existence conditions for placing a specific number of poles with a target output controller. Additionally, an algorithm is presented for the design of a target output controller. Controllability of the system under consideration is not required for designing target output con

Florent Delgrange, Guy Avni, Anna Lukina, Christian Schilling

We propose a novel framework to controller design in environments with a two-level structure: a known high-level graph ("map") in which each vertex is populated by a Markov decision process, called a "room". The framework "separates concerns" by using different design techniques for low- and high-level tasks. We apply reactive synthes

Ashwinee Panda, Xinyu Tang, Milad Nasr, Christopher A. Choquette-Choo

Current techniques for privacy auditing of large language models (LLMs) have limited efficacy -- they rely on basic approaches to generate canaries which leads to weak membership inference attacks that in turn give loose lower bounds on the empirical privacy leakage. We develop canaries that are far more effective than those used in prior work under threat m

Puneet Jain

Despite the rise in affordable eXtended Reality (XR) technologies, accessibility still remains a key concern, often excluding people with disabilities from accessing these immersive XR platforms. Consequently, there has been a notable surge in HCI research on creating accessible XR solutions (also known as, assistive XR). This increased focus in assistive XR

Aref Farhadipour, Hossein Ranjbar, Masoumeh Chapariniya, Teodora Vukovic

Emotion recognition and sentiment analysis are pivotal tasks in speech and language processing, particularly in real-world scenarios involving multi-party, conversational data. This paper presents a multimodal approach to tackle these challenges on a well-known dataset. We propose a system that integrates four key modalities/channels using pre-trained models

Ibrahim Mbouandi Njiasse, Florent Ouabo Kamkumo, Ralf Wunderlich

In this paper, we address a social planner's optimal control problem for a partially observable stochastic epidemic model. The control measures include social distancing, testing, and vaccination. Using a diffusion approximation for the state dynamics of the epidemic, we apply filtering arguments to transform the partially observable stochastic optimal contr

Cécile Boulard, Sruthi Viswanathan, Wanda Fey, Thierry Jacquin

Interaction between humans and AI systems raises the question of how people understand AI systems. This has been addressed with explainable AI, the interpretability arising from users' domain expertise, or collaborating with AI in a stable environment. In the absence of these elements, we discuss designing Actionable AI, which allows non-experts to configure

Johannes Lachner, Moses C. Nah, Neville Hogan

Ensuring symmetric stiffness in impedance-controlled robots is crucial for physically meaningful and stable interaction in contact-rich manipulation. Conventional approaches neglect the change of basis vectors in curved spaces, leading to an asymmetric joint-space stiffness matrix that violates passivity and conservation principles. In this work, we derive a

Takao Morinari, Hibiki Takegami

We investigate the effect of a magnetic field on the Kitaev model using the equation of motion approach for the spin Green's function, considering both the case of suppressed magnetization ($m = 0$) and finite magnetization ($m \neq 0$). When magnetization is suppressed, the specific heat exhibits a clear $60^\circ$ periodicity in its angular dependence, wit

Hritik Bansal, Clark Peng, Yonatan Bitton, Roman Goldenberg

Large-scale video generative models, capable of creating realistic videos of diverse visual concepts, are strong candidates for general-purpose physical world simulators. However, their adherence to physical commonsense across real-world actions remains unclear (e.g., playing tennis, backflip). Existing benchmarks suffer from limitations such as limited size

Eugen Mihailescu, Radu B. Munteanu

We introduce a new notion of local inverse metric entropy along backward trajectories for ergodic measures preserved by endomorphisms (non-invertible maps) on a compact metric space. A second notion of inverse measure entropy is defined by using measurable partitions. Our notions have several useful applications. Inverse entropy can distinguish between isomo

Christopher S. Yang, Sylvester J. Gates, Dulara De Zoysa, Jaehoon Choe

Traditional artificial neural networks take inspiration from biological networks, using layers of neuron-like nodes to pass information for processing. More realistic models include spiking in the neural network, capturing the electrical characteristics more closely. However, a large proportion of brain cells are of the glial cell type, in particular astrocy

Sabeen Ahmed, Nathan Parker, Margaret Park, Evan W. Davis

Cancer cachexia is a multifactorial syndrome characterized by progressive muscle wasting, metabolic dysfunction, and systemic inflammation, leading to reduced quality of life and increased mortality. Despite extensive research, no single definitive biomarker exists, as cachexia-related indicators such as serum biomarkers, skeletal muscle measurements, and me

Lidia Al-Zogbi, Deepak Raina, Vinciya Pandian, Thorsten Fleiter

Femoral artery access is essential for numerous clinical procedures, including diagnostic angiography, therapeutic catheterization, and emergency interventions. Despite its critical role, successful vascular access remains challenging due to anatomical variability, overlying adipose tissue, and the need for precise ultrasound (US) guidance. Needle placement

Yizheng Sun, Hao Li, Chang Xu, Hongpeng Zhou

Vision-Language Models (VLMs) are powerful yet computationally intensive for widespread practical deployments. To address such challenge without costly re-training, post-training acceleration techniques like quantization and token reduction are extensively explored. However, current acceleration evaluations primarily target minimal overall performance degrad

Guoqing Xia, Pei Xiao, Bohan Li, Yue Zhang

Compressed sensing (CS)-based techniques have been widely applied in the grant-free non-orthogonal multiple access (NOMA) to a single-antenna base station (BS). In this paper, we consider the multi-antenna reception at the BS for uplink grant-free access for the massive machine type communication (mMTC) with limited channel resources. To enhance the overload

Haozhe An, Connor Baumler, Abhilasha Sancheti, Rachel Rudinger

We examine LLM representations of gender for first names in various occupational contexts to study how occupations and the gender perception of first names in LLMs influence each other mutually. We find that LLMs' first-name gender representations correlate with real-world gender statistics associated with the name, and are influenced by the co-occurrence of

Xiao Wang, Lu Dong, Sahana Rangasrinivasan, Ifeoma Nwogu

The social robot's open API allows users to customize open-domain interactions. However, it remains inaccessible to those without programming experience. In this work, we introduce AutoMisty, the first multi-agent collaboration framework powered by large language models (LLMs), to enable the seamless generation of executable Misty robot code from natural lan

Yan Wang, Shijie Zhao, Kexin Zhang, Junlin Li

Although recent research applying text-to-image (T2I) diffusion models to real-world super-resolution (SR) has achieved remarkable progress, the misalignment of their targets leads to a suboptimal trade-off between inference speed and detail fidelity. Specifically, the T2I task requires multiple inference steps to synthesize images matching to prompts and re

Farah Basarić, Vladan Brajović, Gerrit Behner, Kristof Moors

The realization of various qubit systems based on high-quality hybrid superconducting quantum devices, is often achieved using semiconductor nanowires. For such hybrid devices, a good coupling between the superconductor and the conducting states in the semiconductor wire is crucial. GaAs/InAs core/shell nanowires with an insulating core, and a conductive InA

Viktor Dorfler, Giles Cuthbert

We paraphrase Descartes' famous dictum in the area of AI ethics where the "I doubt and therefore I am" is suggested as a necessary aspect of morality. Therefore AI, which cannot doubt itself, cannot possess moral agency. Of course, this is not the end of the story. We explore various aspects of the human mind that substantially differ from AI, which includes

Stephen Lepp, Rebecca G. Martin, Stephen H. Lubow

Observations of hierarchical triple star systems show that misalignments are common both between the angular momentum vector of the inner binary and the outer companion orbit, and between the outer binary orbit and a circumtriple gas disk. With analytic methods and n-body simulations we explore the dynamics of circumtriple orbits around a misaligned hierarch

Ryoki Endo, Xuefeng Liu

The Dirichlet eigenvalues of the Laplacian on a triangle that collapses into a line segment diverge to infinity. In this paper, to track the behavior of the eigenvalues during the collapsing process of a triangle, we establish a quantitative error estimate for the Dirichlet eigenvalues on collapsing triangles. As an application, we solve the open problem con

Benjamin Dowling, Britta Hale, Xisen Tian, Bhagya Wimalasiri

As reliance on space systems continues to increase, so does the need to ensure security for them. However, public work in space standards have struggled with defining security protocols that are well tailored to the domain and its risks. In this work, we investigate various space networking paradigms and security approaches, and identify trade-offs and gaps.

Tianyi Zhang, Weiming Zhi, Joshua Mangelson, Matthew Johnson-Roberson

This paper tackles the problem of generating representations of underwater 3D terrain. Off-the-shelf generative models, trained on Internet-scale data but not on specialized underwater images, exhibit downgraded realism, as images of the seafloor are relatively uncommon. To this end, we introduce DreamSea, a generative model to generate hyper-realistic under

Travis Barrett, Amit Kumar Mishra

In this paper we present the statistical analysis of data from inexpensive sensors. We also present the performance of machine learning algorithms when used for automatic calibration such sensors. In this we have used low-cost Non-Dispersive Infrared CO$_2$ sensor placed at a co-located site at Cape Point, South Africa (maintained by Weather South Africa). T

Bernard Bercu, Stefano Favaro

The Ewens-Pitman model is a probability distribution for random partitions of the set $[n]=\{1,\ldots,n\}$, parameterized by $\alpha\in[0,1)$ and $\theta>-\alpha$, with $\alpha=0$ corresponding to the Ewens model in population genetics. The goal of this paper is to provide an alternative and concise proof of the Feng-Hoppe large deviation principle for the n

Mauro Fava, Aldo H. Romero, Eric Bousquet

A phase transition can drive the spontaneous emergence of chiral orders in crystals below a critical temperature. However, selecting either a right- or a left-handed phase with the aid of electromagnetic fields is challenging, particularly when intrinsic polar and axial moments are lacking. In this work we show that \textit{purely} chiral phases with opposit

Yufei Li, John Nham, Ganesh Jawahar, Lei Shu

Generic text rewriting is a prevalent large language model (LLM) application that covers diverse real-world tasks, such as style transfer, fact correction, and email editing. These tasks vary in rewriting objectives (e.g., factual consistency vs. semantic preservation), making it challenging to develop a unified model that excels across all dimensions. Exist

Max Meynig

New non-perturbative results on the eigenvalues of the spheroidal equation are presented. The results, found using an all orders WKB analysis, include a perturbative/non-perturbative (P/NP) relation as well as the first exponential correction to the perturbative series which is valid in certain regions of parameters. The quantum periods are also computed.

Kai Ren, Heejin Ahn, Maryam Kamgarpour

We tackle safe trajectory planning under Gaussian mixture model (GMM) uncertainty. Specifically, we use a GMM to model the multimodal behaviors of obstacles' uncertain states. Then, we develop a mixed-integer conic approximation to the chance-constrained trajectory planning problem with deterministic linear systems and polyhedral obstacles. When the GMM mome

Feng Gu, Zongxia Li, Carlos Rafael Colon, Benjamin Evans

Event annotation is important for identifying market changes, monitoring breaking news, and understanding sociological trends. Although expert annotators set the gold standards, human coding is expensive and inefficient. Unlike information extraction experiments that focus on single contexts, we evaluate a holistic workflow that removes irrelevant documents,

Travis Barrett, Amit Kumar Mishra

In this paper, we describe the design of an inexpensive and agile climate sensor system which can be repurposed easily to measure various pollutants. We also propose the use of machine learning regression methods to calibrate CO2 data from this cost-effective sensing platform to a reference sensor at the South African Weather Service's Cape Point measurement

Kai Ren, Giulio Salizzoni, Mustafa Emre Gürsoy, Maryam Kamgarpour

We address safe multi-robot interaction under uncertainty. In particular, we formulate a chance-constrained linear quadratic Gaussian game with coupling constraints and system uncertainties. We find a tractable reformulation of the game and propose a dual ascent algorithm. We prove that the algorithm converges to a feedback generalized Nash equilibrium of th

Z. Stuchlík, A. Zhidenko

Gravitational perturbations of higher-dimensional black holes in the Einstein-Gauss-Bonnet theory, proposed by Boulware and Deser, have been extensively studied in numerous works, primarily focusing on the fundamental mode. These studies have shown that for sufficiently small black holes, comparing to the Gauss-Bonnet coupling parameter, a dynamical instabil

Abdullah Al Hossain Newaz, Refat Jahan

This paper explores the production of a specified object using a combination of machining processes, including milling, shaping, and drilling, while emphasizing the critical role of fixture design in ensuring precision repeatability, and efficiency. The study outlines the systematic approach to transforming raw materials into a finished product through an op

Benjamin Beaudett, Shenyuan Liang, Anuj Srivastava

Despite high-dimensionality of images, the sets of images of 3D objects have long been hypothesized to form low-dimensional manifolds. What is the nature of such manifolds? How do they differ across objects and object classes? Answering these questions can provide key insights in explaining and advancing success of machine learning algorithms in computer vis

Mayte Y. Li-Gomez, Taras Hrushevskyi, Kayla McArthur, Pablo Yepiz-Graciano

Entanglement is a key resource in quantum technologies, enhancing precision and resolution in imaging and sensing by leveraging the cross-correlation of photon pairs. This correlation enables precise time synchronization of photons reaching the photodetectors, effectively suppressing environmental noise and improving measurement accuracy. Building on this co

Peizheng Li, Adnan Aijaz

The convergence of robotics, advanced communication networks, and artificial intelligence (AI) holds the promise of transforming industries through fully automated and intelligent operations. In this work, we introduce a novel co-working framework for robots that unifies goal-oriented semantic communication (SemCom) with a Generative AI (GenAI)-agent under a

Simon Nguyen, Kentaro Hoffman, Tyler McCormick

Collecting labeled data for machine learning models is often expensive and time-consuming. Active learning addresses this challenge by selectively labeling the most informative observations, but when initial labeled data is limited, it becomes difficult to distinguish genuinely informative points from those appearing uncertain primarily due to noise. Ensembl

Xiujin Liu

This paper proposes an innovative solution to the growing issue of greenhouse gas emissions: a closed photobioreactor (PBR) facade system to mitigate greenhouse gas (GHG) concentrations. With digital fabrication technology, this study explores the transition from traditional, single function building facades to multifunctional, integrated building systems. I

Juhi Singh, Jan A. P. Reuter, Tommaso Calarco, Felix Motzoi

Ultracold atoms trapped in optical lattices have emerged as a scalable and promising platform for quantum simulation and computation. However, gate speeds remain a significant limitation for practical applications. In this work, we employ quantum optimal control to design fast, collision-based two-qubit gates within a superlattice based on a Fermi-Hubbard de

Zihang Wei, Yang Zhou, Yunlong Zhang, Mihir Kulkarni

This study proposes a coordinated ramp metering control framework in large networks based on scalable nonlinear traffic dynamics model discovery. Existing coordinated ramp metering control methods often require accurate traffic dynamics models in real time, however, for large-scale highway networks, since these models are always nonlinear, they are extremely

Guoqing Xia, Pei Xiao, Qu Luo, Bing Ji

This paper presents a fundamental performance analysis of joint location and velocity estimation in a cell-free (CF) MIMO integrated sensing and communication (ISAC) system. Unlike prior studies that primarily rely on continuous-time signal models, we consider a more practical and challenging scenario in the discrete-time digital domain. Specifically, we fir

Shusuke Takayama, Ian Frank

We compare the effectiveness of zero-shot Chain-of-Thought (CoT) prompting in Japanese and English using ChatGPT-3.5 and 4o-mini. The technique of zero-shot CoT, which involves appending a phrase such as "Let's think step by step" to a prompt to encourage reasoning before answering, has been shown to offer LLM performance improvements in mathematical and rea

Zisheng Chen, Chunwei Wang, Runhui Huang, Hongbin Xu

In this paper, we introduce SemHiTok, a unified image Tokenizer via Semantic-Guided Hierarchical codebook that provides consistent discrete representations for multimodal understanding and generation. Recently, unified image tokenizers have sparked exploration within the research community, which is designed to capture high-level semantic features for unders

Angelo Borsotti, Luca Breveglieri, Stefano Crespi Reghizzi, Angelo Morzenti

Regular expression (RE) matching is a very common functionality that scans a text to find occurrences of patterns specified by an RE; it includes the simpler function of RE recognition. Here we address RE parsing, which subsumes matching by providing not just the pattern positions in the text, but also the syntactic structure of each pattern occurrence, in t

Abdelaziz Bouzidi, Hamid Laga, Hazem Wannous, Ferdous Sohel

Neural fields have emerged as a powerful framework for representing continuous multidimensional signals such as images and videos, 3D and 4D objects and scenes, and radiance fields. While efficient, achieving high-quality representation requires the use of wide and deep neural networks. These, however, are slow to train and evaluate. Although several acceler

Antonín Jaroš, Michael S. Seifner, Johann Toyfl, Benjamin Czasch

We present a novel method that combines spin resonance spectroscopy with transmission electron microscopy (TEM), enabling localized in-situ detection of microwave (MW)-driven spin excitations. Our approach utilizes continuous wave MW excitation at GHz frequencies, while employing the free-space electron beam as a signal receiver to sense spin precession. Spi

Hirofumi Noda, Mio Aoyagi, Koji Mori, Hiroshi Tomida

The Soft X-ray Imager (SXI) is an X-ray CCD camera of the Xtend system onboard the X-Ray Imaging and Spectroscopy Mission (XRISM), which was successfully launched on September 7, 2023 (JST). During ground cooling tests of the CCDs in 2020/2021, using the flight-model detector housing, electronic boards, and a mechanical cooler, we encountered an unexpected i

Hung Q. Vo, Samira Zare, Son T. Ly, Lin Wang

Achieving health equity in Artificial Intelligence (AI) requires diagnostic models that maintain reliability across diverse populations. However, breast cancer screening systems frequently suffer from domain overfitting, degrading significantly when deployed to varying demographics. While Invariant Learning algorithms aim to mitigate this by suppressing site

Paul B. Larson, Chris Lambie-Hanson

Using a variation of Woodin's $\mathbb{P}_{\mathrm{max}}$ forcing, we force over a model of the Axiom of Determinacy to produce a model of ZFC containing a very strongly increasing sequence of length $\omega_{2}$ consisting of functions from $\omega$ to $\omega$. We also show that there can be no such sequence of length $\omega_{4}$.

Chih H. Huang, Pranav Jadhav, Brian Plancher, Zachary Kingston

Sampling-based motion planning algorithms, like the Rapidly-Exploring Random Tree (RRT) and its widely used variant, RRT-Connect, provide efficient solutions for high-dimensional planning problems faced by real-world robots. However, these methods remain computationally intensive, particularly in complex environments that require many collision checks. To im

Hao Luo, Yu Zhang, Ahmed Alkhateeb

Near-field communication with large antenna arrays promises significant beamforming and multiplexing gains. These communication links, however, are very sensitive to user mobility as any small change in the user position may suddenly drop the signal power. This leads to critical challenges for the robustness of these near-field communication systems. In this

S. Ali, A. Ahmidouch, G. R. Ambrose, A. Asaturyan

The Super High Momentum Spectrometer (SHMS) has been built for Hall C at the Thomas Jefferson National Accelerator Facility (Jefferson Lab). With a momentum capability reaching 11 GeV/c, the SHMS provides measurements of charged particles produced in electron-scattering experiments using the maximum available beam energy from the upgraded Jefferson Lab accel

Taosha Guo, Fabio Pasqualetti

In this paper, we study a transfer learning framework for Linear Quadratic Regulator (LQR) control, where (i) the dynamics of the system of interest (target system) are unknown and only a short trajectory of impulse responses from the target system is provided, and (ii) impulse responses are available from $N$ source systems with different dynamics. We show

Ewa A. Drzazga-Szczȩśniak, Adam Z. Kaczmarek, Marta Kielak, Shivam Gupta

In this study, the cumulative effect of the empirical probability distribution of a random variable is identified as a factor that amplifies the occurrence of extreme events in datasets. To quantify this observation, a corresponding information measure is introduced, drawing upon Shannon entropy for joint probabilities. The proposed approach is validated usi

Rodrigo A. Fontenele, Natanael C. Costa, Thereza Paiva, Raimundo R. dos Santos

The attractive Hubbard model plays a paradigmatic role in the study of superconductivity (superfluidity) and has become directly realizable in ultracold atom experiments on optical lattices. However, the critical temperatures, $T_c$'s, remain lower than the lowest temperatures currently achievable in experiments. Here, we explore a possible route to enhance

Swayamsiddha Maharana, Fil Simovic, Ioannis Soranidis, Daniel R. Terno

Most distinguishing features of black holes and their mimickers are concentrated near the horizon. In contrast, astrophysical observations and theoretical considerations primarily constrain the far-field geometry. In this work we develop tools to effectively describe both, using the two-point Pad\'e approximation to construct interpolating metrics connecting

Max Buckley, Konstantinos Papathanasiou, Andreas Spanopoulos

This paper addresses the challenge of solving Constrained Markov Decision Processes (CMDPs) with $d > 1$ constraints when the transition dynamics are unknown, but samples can be drawn from a generative model. We propose a model-based algorithm for infinite horizon CMDPs with multiple constraints in the tabular setting, aiming to derive and prove sample compl

Tursunali Xamidov, Sanjar Shaymatov, Bobomurat Ahmedov, Tao Zhu

In this study, we explore the influence of the quantum correction parameter $\xi$ on the motion of particles and the properties of quasiperiodic oscillations (QPOs) around a quantum-corrected black hole (QCBH). We first analyze the geodesics of a test particle and derive weak-field constraints on parameter $\xi$ from the perihelion precession of orbits, usin

Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao

DeepSeek-R1-Zero has successfully demonstrated the emergence of reasoning capabilities in LLMs purely through Reinforcement Learning (RL). Inspired by this breakthrough, we explore how RL can be utilized to enhance the reasoning capability of MLLMs. However, direct training with RL struggles to activate complex reasoning capabilities such as questioning and

Hantao Zhang, Yuhe Liu, Jiancheng Yang, Weidong Guo

Accurate medical image segmentation is crucial for precise anatomical delineation. Deep learning models like U-Net have shown great success but depend heavily on large datasets and struggle with domain shifts, complex structures, and limited training samples. Recent studies have explored diffusion models for segmentation by iteratively refining masks. Howeve

Diego Bolliger, Lorenz Zauter, Robert Ziegler

In this paper, we devise three actor-critic algorithms with decentralized training for multi-agent reinforcement learning in cooperative, adversarial, and mixed settings with continuous action spaces. To this goal, we adapt the MADDPG algorithm by applying a networked communication approach between agents. We introduce surrogate policies in order to decentra

Ka Chun Shum, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung

Diffusion models have shown great promise in synthesizing visually appealing images. However, it remains challenging to condition the synthesis at a fine-grained level, for instance, synthesizing image pixels following some generic color pattern. Existing image synthesis methods often produce contents that fall outside the desired pixel conditions. To addres

Dany Moshkovich, Hadar Mulian, Sergey Zeltyn, Natti Eder

The rise of agentic AI systems, where agents collaborate to perform diverse tasks, poses new challenges with observing, analyzing and optimizing their behavior. Traditional evaluation and benchmarking approaches struggle to handle the non-deterministic, context-sensitive, and dynamic nature of these systems. This paper explores key challenges and opportuniti

Rui Song, Chenwei Liang, Yan Xia, Walter Zimmer

Dynamic scene rendering opens new avenues in autonomous driving by enabling closed-loop simulations with photorealistic data, which is crucial for validating end-to-end algorithms. However, the complex and highly dynamic nature of traffic environments presents significant challenges in accurately rendering these scenes. In this paper, we introduce a novel 4D

Cheng Huang, Weizheng Xie, Tsengdar J. Lee, Jui-Kai Wang

Structural changes in main retinal blood vessels serve as critical biomarkers for the onset and progression of glaucoma. Identifying these vessels is vital for vascular modeling yet highly challenging. This paper proposes X-GAN, a generative AI-powered unsupervised segmentation model designed for extracting main blood vessels from Optical Coherence Tomograph

André Beauducel, Norbert Hilger, Anneke C. Weide

Previous research has shown that ignoring individual differences of factor loadings in conventional factor models may reduce the determinacy of factor score predictors. Therefore, the aim of the present study is to propose a heterogeneous regression factor score with larger determinacy than the conventional regression factor score when individuals have diffe

Marta Adamska, Daria Smirnova, Hamid Nasiri, Zhengxin Yu

Large Language Models (LLMs) have become widely used across various domains spanning search engines, code generation, and text creation. However, a major concern associated with their adoption is the high cost of inference, impacting both their sustainability and financial feasibility. In this study, we empirically study how different prompt and response cha

Penglin Hu, Chunhui Zhao, Quan Pan

In practical application, the pursuit-evasion game (PEG) often involves multiple complex and conflicting objectives. The single-objective reinforcement learning (RL) usually focuses on a single optimization objective, and it is difficult to find the optimal balance among multiple objectives. This paper proposes a three-objective RL algorithm based on fuzzy Q

Vsevolod Skorokhodov, Nikita Durasov, Pascal Fua

Gaussian Splatting has become a popular technique for various 3D Computer Vision tasks, including novel view synthesis, scene reconstruction, and dynamic scene rendering. However, the challenge of natural-looking object insertion, where the object's appearance seamlessly matches the scene, remains unsolved. In this work, we propose a method, dubbed D3DR, for

Elena Caviglia, Amartya Goswami, Luca Mesiti

We introduce and study $\mu$-elements, that generalize a lattice-theoretic abstraction (namely, essential elements) of essential ideals of rings, essential submodules of modules, and dense subsets of topological spaces. Exploring several examples, we show that $\mu$-elements are indeed a genuine extension of essential elements. We study preservation of $\mu$

Simone Cavazzoni, Berihu Teklu, Matteo G. A. Paris

The frequency of a quantum harmonic oscillator cannot be determined through static measurement strategies on a prepared state, as the eigenstates of the system are independent of its frequency. Therefore, dynamic procedures must be employed, involving measurements taken after the system has evolved and encoded the frequency information. This paper explores t

Bhisham Dev Verma, Rameshwar Pratap

This work suggests faster and space-efficient index construction algorithms for LSH for Euclidean distance (\textit{a.k.a.}~\ELSH) and cosine similarity (\textit{a.k.a.}~\SRP). The index construction step of these LSHs relies on grouping data points into several bins of hash tables based on their hashcode. To generate an $m$-dimensional hashcode of the $d$-d

Daniel Morton, Marco Pavone

Safe real-time control of robotic manipulators in unstructured environments requires handling numerous safety constraints without compromising task performance. Traditional approaches, such as artificial potential fields (APFs), suffer from local minima, oscillations, and limited scalability, while model predictive control (MPC) can be computationally expens

Mirko Poljak

Non-Abelian anyons such as Majorana zero modes (MZMs) have the potential to enable fault-tolerant quantum computing through topological protection. Experimentally reported InSb topological superconductor nanowires (TSNW) are investigated theoretically and numerically to evaluate their suitability to host MZMs. We employ eigenspectra analysis and quantum tran

Mahdi Zakizadeh, Mohammad Taher Pilehvar

Gender bias in pretrained language models (PLMs) poses significant social and ethical challenges. Despite growing awareness, there is a lack of comprehensive investigation into how different models internally represent and propagate such biases. This study adopts an information-theoretic approach to analyze how gender biases are encoded within various encode

Jun Wang, Suyi Li

Soft robots have become increasingly popular for complex manipulation tasks requiring gentle and safe contact. However, their softness makes accurate control challenging, and high-fidelity sensing is a prerequisite to adequate control performance. To this end, many flexible and embedded sensors have been created over the past decade, but they inevitably incr

Márton Á. Goda, Helen Badge, Jasmeen Khan, Yosef Solewicz

Objective. Large vessel occlusion (LVO) stroke presents a major challenge in clinical practice due to the potential for poor outcomes with delayed treatment. Treatment for LVO involves highly specialized care, in particular endovascular thrombectomy, and is available only at certain hospitals. Therefore, prehospital identification of LVO by emergency ambulan

Ninad Jayesh Gandhi, Moparthy Venkata Subrahmanya Sri Harsha

Private machine learning introduces a trade-off between the privacy budget and training performance. Training convergence is substantially slower and extensive hyper parameter tuning is required. Consequently, efficient methods to conduct private training of models is thoroughly investigated in the literature. To this end, we investigate the strength of the

Ansi Bai, Zhi-Hao Zhang

In their study of Levin-Wen models [Commun. Math. Phys. 313 (2012) 351-373], Kitaev and Kong proposed a weak Hopf algebra associated with a unitary fusion category $\mathcal{C}$ and a unitary left $\mathcal{C}$-module $\mathcal{M}$, and sketched a proof that its representation category is monoidally equivalent to the unitary $\mathcal{C}$-module functor cate