March 2025 arXiv papers — page 142

Rohit Kishan Ray

I show that for two inverse temperatures $\beta_1$ and $\beta_2$, the von Neumann entropy $S(\rho_\beta)$ of the Gibbs state $\rho_\beta$ for a given Hamiltonian $H$ satisfies $S(\rho_{\beta_1}) \geq S(\rho_{\beta_2}) \iff \beta_{1} \leq \beta_{2}$. That is, von Neumann entropy is a monotonically increasing function of temperature.

Mia Mohammad Imran, Jaydeb Sarker

Toxicity in bug report discussions poses significant challenges to the collaborative dynamics of open-source software development. Bug reports are crucial for identifying and resolving defects, yet their inherently problem-focused nature and emotionally charged context make them susceptible to toxic interactions. This study explores toxicity in GitHub bug re

Mohd Ariful Haque, Justin Williams, Sunzida Siddique, Md. Hujaifa Islam

The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop fra

Haiqin Cui, Yifu Yuan, Yan Zheng, Jianye Hao

Scaling Vision-Language-Action models for embodied manipulation demands large volumes of diverse manipulation data, yet the high cost of commercial mobile manipulators and teleoperation interfaces that are difficult to deploy at scale remain key bottlenecks. We present AhaRobot, a low-cost, fully open-source bimanual mobile manipulator tailored for Embodied-

Xiangyu Shi, Zerui Li, Wenqi Lyu, Jiatong Xia

Vision-and-Language Navigation (VLN) in continuous environments requires agents to interpret natural language instructions while navigating unconstrained 3D spaces. Existing VLN-CE frameworks rely on a two-stage approach: a waypoint predictor to generate waypoints and a navigator to execute movements. However, current waypoint predictors struggle with spatia

Han Liu, Riqiang Gao, Eileen Krieg, Sasa Grbic

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive forms of pancreatic cancer and is often diagnosed at an advanced stage due to subtle early imaging signs. To enable earlier detection and improve clinical decision-making, we propose a coarse-to-fine AI-assisted framework named PanDx for identifying PDAC on contrast-enhanced CT scans. Our

Zhikun Zou, Gan Guo, Meng Wen, Bin Liu

The significance of laser-driven polarized beam acceleration has been increasingly recognized in recent years. We propose an efficient method for generating polarized proton beams from a pre-polarized hydrogen halide gas jet, utilizing magnetic vortex acceleration enhanced by a laser-driven plasma bubble. When a petawatt laser pulse passes through a pre-pola

Abdullah Guvendi, Omar Mustafa

In this study, we analytically examine the behavior of a fermion-antifermion pair near the horizon of a static BTZ black hole using a fully covariant two-body Dirac equation with a position-dependent mass. This formulation leads to a set of four first-order equations that can be reduced to a second-order wave equation, enabling the analysis of gravitational

Damien Teney, Liangze Jiang, Florin Gogianu, Ehsan Abbasnejad

Neural architectures tend to fit their data with relatively simple functions. This "simplicity bias" is widely regarded as key to their success. This paper explores the limits of this principle. Building on recent findings that the simplicity bias stems from ReLU activations [96], we introduce a method to meta-learn new activation functions and inductive bia

Luke A. Bauer, Wenxuan Bao, Vincent Bindschaedler

We consider the problem of securely and robustly embedding covert messages into an image-based diffusion model's output. The sender and receiver want to exchange the maximum amount of information possible per diffusion sampled image while remaining undetected. The adversary wants to detect that such communication is taking place by identifying those diffusio

Ru An, Ying Wang, Yanlong Zhao, Ji-Feng Zhang

This paper addresses the one-bit consensus of controllable linear multi-agent systems (MASs) with communication noises. A consensus algorithm consisting of a communication protocol and a consensus controller is designed. The communication protocol introduces a linear compression encoding function to achieve a one-bit data rate, thereby saving communication c

Iyad Sultan

Background: The increasing use of artificial intelligence (AI) in healthcare documentation necessitates robust methods for evaluating the quality of AI-generated medical notes compared to those written by humans. This paper introduces an open-source tool, the Human Notes Evaluator, designed to assess clinical note quality and differentiate between human and

Nicholas Roberts, Niladri Chatterji, Sharan Narang, Mike Lewis

Scaling laws are a critical component of the LLM development pipeline, most famously as a way to forecast training decisions such as 'compute-optimally' trading-off parameter count and dataset size, alongside a more recent growing list of other crucial decisions. In this work, we ask whether compute-optimal scaling behaviour can be skill-dependent. In partic

Shaokang Hu, Ruotong Zhao, Yihuan Liao, Derrick Wing Kwan Ng

This paper investigates the resource allocation design for a pinching antenna (PA)-assisted multiuser multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system featuring multiple dielectric waveguides. To enhance model accuracy, we propose a novel frequency-dependent power attenuation model for the dielectric waveguides in PA-assisted

Weilun Li, Bryan D. Esser, Wenming Tong, Anchal Yadav

Surface structure affects the growth, shape and properties of nanoparticles. In wet chemical syntheses, metal additives and surfactants are used to modify surfaces and guide nanocrystal growth. To understand this process, it is critical to understand how the surface structure is modified. However, measuring the type and arrangement of atoms at hard-soft inte

Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconn

Ho Hin Lee, Alberto Santamaria-Pang, Jameson Merkov, Matthew Lungren

Accurate survival prediction in oncology requires integrating diverse imaging modalities to capture the complex interplay of tumor biology. Traditional single-modality approaches often fail to leverage the complementary insights provided by radiological and pathological assessments. In this work, we introduce M4Survive (Multi-Modal Mamba Modeling for Surviva

Jameel-Un Nabi, Mavra Ishfaq, Ovidiu Nitescu, Mihail Mirea

We present the beta decay half-lives calculation for selected even even nuclei that decay through electron emission. The kinematical portion of the half-life calculation was performed using a recently introduced technique for computation of phase space factors (PSFs). The dynamical portion of our calculation was performed within the proton neutron quasiparti

Donghyun Kim, Chanyoung Kim, Hyunah Ko, Seong Jae Hwang

While 3D point clouds are widely used in vision applications, their irregular and sparse nature make them challenging to handle. In response, numerous encoding approaches have been proposed to capture the rich semantic information of point clouds. Yet, a critical limitation persists: a lack of consideration for colored point clouds, which serve as more expre

Yu-Ting Kao, Hao-Yu Lu, Yeong-Jar Chang, Darsen Lu

Analysis and verification of quantum circuits are highly challenging, given the exponential dependence of the number of states on the number of qubits. For analytical derivation, we propose a new quantum polynomial representation (QPR) to facilitate the analysis of massively parallel quantum computation and detect subtle errors. For the verification of quant

Yujun Yao, Jingjing Ye, Luming Sun, Ning Jiang

At present, there is a lack of effective probes to distinguish between mid-infrared (MIR) outbursts induced by tidal disruption events (TDEs) and changing-look active galactic nuclei (CLAGNs) based on only MIR data. Here, we propose that the time variation of MIR color (K-corrected W1-W2 after subtracting the quiescent fluxes) is a promising probe. With an o

Minje Kim, Minjun Kim, Xu Yang

Spiking Neural Networks (SNNs) present a more energy-efficient alternative to Artificial Neural Networks (ANNs) by harnessing spatio-temporal dynamics and event-driven spikes. Effective utilization of temporal information is crucial for SNNs, leading to the exploration of attention mechanisms to enhance this capability. Conventional attention operations eith

Xinyu Li

This paper analyzes the economic impact of China's retaliatory soybean tariff on U.S. soybean farmers using advanced econometric methods and comprehensive datasets including USDA reports, trade data, and historical price movements. The analysis employs a Structural Vector Autoregression (SVAR), a Difference-in-Differences (DiD) estimation, and a Dynamic Stoc

Zhenghong Hu, Tao Liu, Jin Min Yang

We consider the two-loop amplitude of $gg\rightarrow HH$ mediated by bottom quarks, which provides a correction of percent level at leading order in the low invariant mass region. In order to compute the corresponding master integrals, we perform an expansion for the bottom quark mass, using the method of differential equations and fixing the boundary consta

Zhang XueGuang

In this manuscript, an oversimplified model is proposed to test the effects of intrinsic AGN variability on expected optical quasi-periodic oscillations (QPOs) related to sub-pc binary black hole systems (BBHs) in broad line AGN. The commonly accepted CAR (Continuous AutoRegressive) process is applied to describe intrinsic AGN variability related to each BH

Ziqi Jia, Junjie Li, Xiaoyang Qu, Jianzong Wang

Multi-agent systems (MAS) have shown great potential in executing complex tasks, but coordination and safety remain significant challenges. Multi-Agent Reinforcement Learning (MARL) offers a promising framework for agent collaboration, but it faces difficulties in handling complex tasks and designing reward functions. The introduction of Large Language Model

Bharat Srikishan, Daniel O'Malley, Mohamed Mehana, Nicholas Lubbers

Modeling the evolution of physical systems is critical to many applications in science and engineering. As the evolution of these systems is governed by partial differential equations (PDEs), there are a number of computational simulations which resolve these systems with high accuracy. However, as these simulations incur high computational costs, they are i

Wenjie Li, Heng Guo, Yuefeng Hou, Guangwei Gao

Lightweight image super-resolution (SR) aims to reconstruct high-resolution images from low-resolution images under limited computational costs. We find that existing frequency-based SR methods cannot balance the reconstruction of overall structures and high-frequency parts. Meanwhile, these methods are inefficient for handling frequency features and unsuita

Juha-Matti Runtti, Usman Virk, Pekka Kyosti, Lassi Hentil

6G radio access architecture is envisioned to contain a network of short-range in-X subnetworks with enhanced capabilities to provide efficient and reliable wireless connectivity. Short-range communications in industrial environments are actively researched at the so-called mid-bands or FR3, e.g., in the EU SNS JU 6G-SHINE project. In this paper, we analyze

Meng Wang, Zi Yang, Ruifeng Zhao, Yaoting Jiang

The integration of Internet of Things (IoT) technology in pulmonary nodule detection significantly enhances the intelligence and real-time capabilities of the detection system. Currently, lung nodule detection primarily focuses on the identification of solid nodules, but different types of lung nodules correspond to various forms of lung cancer. Multi-type d

Károly J. Böröczky, Ágnes Kovács, Stephanie Mui, Gaoyong Zhang

This paper studies the general Lp dual curvature density equation under a group symmetry assumption. This geometric partial differential equation arises from the general Lp dual Minkowski problem of prescribing the Lp dual curvature measure of convex bodies. It is a Monge-Ampere type equation on the unit sphere. If the density function of the dual curvature

Wenjie Li, Heng Guo, Yuefeng Hou, Zhanyu Ma

Image super-resolution (SR) aims to recover low-resolution images to high-resolution images, where improving SR efficiency is a high-profile challenge. However, commonly used units in SR, like convolutions and window-based Transformers, have limited receptive fields, making it challenging to apply them to improve SR under extremely limited computational cost

Ziyue Wang, Yurui Dong, Fuwen Luo, Minyuan Ruan

The rapid advancing of Multimodal Large Language Models (MLLMs) has spurred interest in complex multimodal reasoning tasks in the real-world and virtual environment, which require coordinating multiple abilities, including visual perception, visual reasoning, spatial awareness, and target deduction. However, existing evaluations primarily assess the final ta

Jiachi Chen, Zhenzhe Shao, Shuo Yang, Yiming Shen

In recent years, the Ethereum platform has witnessed a proliferation of smart contracts, accompanied by exponential growth in total value locked (TVL). High-TVL smart contracts often require complex numerical computations, particularly in mathematical financial models used by many decentralized applications (DApps). Improper calculations can introduce numeri

Dongik Lee, Valentin Stanev, Xiaohang Zhang, Mijeong Kang

Delineating the superconducting order parameters is a pivotal task in investigating superconductivity for probing pairing mechanisms, as well as their symmetry and topology. Point-contact Andreev reflection (PCAR) measurement is a simple yet powerful tool for identifying the order parameters. The PCAR spectra exhibit significant variations depending on the t

Yuzheng Bi

For $\beta\in(1,2]$ let $T_\beta: [0,1)\to[0,1); x\mapsto \beta x\pmod 1$. In this paper we study the periodic points in the open dynamical system $([0,1), T_\beta)$ with a hole $[0,t)$. For $p\in\mathbb{N}$ we characterize the largest $t$, denoted by $S_\beta(p)$, in which the survivor set $K_\beta(t)$ has a periodic point of smallest period $p$. More preci

Wardah Mahmood, Muhammad Hammad Raza Gardezi, Muddasir Naeem, Ammar Ahmed Khan

We synthesize MoS$_{2}$ atomic layer flakes at different growth conditions to tailor S-terminated and Mo-terminated edge defect states that are investigated for their ferromagnetic response. We leverage quantum weak measurement principles to construct a spin Hall effect of light-based magneto-optic Kerr effect (SHEL-MOKE) setup to sense the ultra-small magne

Sina Malakouti, Adriana Kovashka

Text-to-image (T2I) diffusion models exhibit impressive photorealistic image generation capabilities, yet they struggle in compositional image generation. In this work, we introduce RoleBench, a benchmark focused on evaluating compositional generalization in action-based relations (e.g., "mouse chasing cat"). We show that state-of-the-art T2I models and comp

Hexiang Pan, Shaofeng Cai, Tien Tuan Anh Dinh, Yuncheng Wu

Concurrency control (CC) algorithms are important in modern transactional databases, as they enable high performance by executing transactions concurrently while ensuring correctness. However, state-of-the-art CC algorithms struggle to perform well across diverse workloads, and most do not consider workload drifts. In this paper, we propose NeurCC, a novel l

Songpeng Pei, Nataly Ospina, Xiaowan Zhang, Qiang Li

In this study, we present a detailed analysis of public archival soft X-ray data on the classical nova V339 Delphini (Nova Del 2013) during its outburst, obtained using the {\it Chandra} High-Resolution Camera Spectrometer (HRC-S) and Low Energy Transmission Grating (LETG), as well as {\it XMM-Newton} in 2013. The observations, spanning from day 85.2 to day

Hao Xiang, Zhaoliang Zheng, Xin Xia, Seth Z. Zhao

Cooperative perception enabled by Vehicle-to-Everything (V2X) communication holds significant promise for enhancing the perception capabilities of autonomous vehicles, allowing them to overcome occlusions and extend their field of view. However, existing research predominantly relies on simulated environments or static datasets, leaving the feasibility and e

Sara Rahmani

Based on the helicity method, formulae for the semileptonic transition of $\Lambda_b^0 \to \Lambda_c^+ \ell^- \bar \nu_\ell$ including lepton mass effects are derived. In order to calculate the form factors of the $\Lambda_b$ baryon transition matrix element, we employ the phase-moment parameterization and perform fits to the Lattice QCD data. With the help

Chang-Ock Lee, Byungeun Ryoo

Extreme learning machines (ELMs), which preset hidden layer parameters and solve for last layer coefficients via a least squares method, can typically solve partial differential equations faster and more accurately than Physics Informed Neural Networks. However, they remain computationally expensive when high accuracy requires large least squares problems to

Naai-Jung Shih

How does AI connect to the past in conservation? What can 17 years old photos be helpful in a renewed effort of preservation? This research aims to use AI to connect both in a seamless 3D reconstruction of heritage from imagery data taken from Gongfan Palace, Yunlin Taiwan. AI-assisted 3D modeling was used to reconstruct correspondent details across differen

Benniu Zhang, Liangshuo Zhang, Xiaodong Wu, Jigang Yu

Structural fatigue failures account for most of catastrophic metal component failures, annually causing thousands of accidents, tens of thousands of casualties, and $100 billion in global economic losses. Current detection methods struggle to identify early-stage fatigue damage characterized by sub-nanometer atomic displacements and localized bond rupture. H

Chen Liang, Yuxuan Liu, Martez Mott, Anhong Guo

Hand interactions are increasingly used as the primary input modality in immersive environments, but they are not always feasible due to situational impairments, motor limitations, and environmental constraints. Speech interfaces have been explored as an alternative to hand input in research and commercial solutions, but are limited to initiating basic hand

Marco Arnold, Lukas Hildebrandt, Kaspar Janssen, Efe Ongan

The autonomous transportation of materials over challenging terrain is a challenge with major economic implications and remains unsolved. This paper introduces LEVA, a high-payload, high-mobility robot designed for autonomous logistics across varied terrains, including those typical in agriculture, construction, and search and rescue operations. LEVA uniquel

Xiao Pan, Sina Tavasoli, T. Y. Yang, Sina Poorghasem

Rapid response to natural disasters such as earthquakes is a crucial element in ensuring the safety of civil infrastructures and minimizing casualties. Traditional manual inspection is labour-intensive, time-consuming, and can be dangerous for inspectors and rescue workers. This paper proposed an autonomous inspection approach for structural damage inspectio

Lingdi Meng, Tian-Cai Peng, Zhi Hu, Siqi Xu

Within the basis light-front quantization framework, we compute the masses and light-front wave functions of the $\Lambda_b$ baryon and its isospin triplet counterparts $\Sigma_b^+$, $\Sigma_b^0$, and $\Sigma_b^-$ using a light-front effective Hamiltonian in the leading Fock sector. These wave functions are obtained as eigenstates of the effective Hamiltonia

M. Farino, A. Tan, A. Apponi, M. Betti

To resolve the effective neutrino mass $m_\beta$ with an energy resolution of 50~meV, the PTOLEMY experiment has proposed a novel transverse electromagnetic filtering process. Substantially reducing the kinetic energy of tritium $\beta$-decay electrons by counteracting motion from ${\bf E}$ $\times$ ${\bf B}$ and $\nabla{\rm B}$ drift, the PTOLEMY filter req

Ryu Ueno

The class of statistical manifolds with divisible cubic forms arises from affine differential geometry. We examine the geodesic connectedness of affine connections on this class of statistical manifolds. In information geometry, the geodesic connectedness of the affine connections are often assumed, as in the generalized Pythagorean theorem. In Riemannian ge

Stephanie Hu, Xiaolu Guo

An important step in understanding how children acquire languages is studying how infants learn word segmentation. It has been established in previous research that infants may use statistical regularities in speech to learn word segmentation. The research of Goldwater et al., demonstrated that incorporating context in models improves their ability to learn

Jiwon Sung, Jinseok Choi

Time-interleaved ADCs (TI-ADCs) achieve high sampling rates by interleaving multiple sub-ADCs in parallel. Mismatch errors between the sub-ADCs, however, can significantly degrade the signal quality, which is a main performance bottleneck. This paper presents a hybrid calibration approach by interpreting the mismatch problem as a tracking problem, and uses t

Guanyu Chen, Shengze Xu, Dong Ni, Tieyong Zeng

We propose a general framework for the Discontinuous Galerkin-induced Neural Network (DGNN), inspired by the Interior Penalty Discontinuous Galerkin Method (IPDGM). In this approach, the trial space consists of piecewise neural network space defined over the computational domain, while the test function space is composed of piecewise polynomials. We demonstr

Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah

Federated Learning (FL) is a promising approach for privacy-preserving collaborative learning. However, it faces significant challenges when dealing with domain shifts, especially when each client has access only to its source data and cannot share it during target domain adaptation. Moreover, FL methods often require high communication overhead due to multi

Izumi Hachisu, Mariko Kato

Millinovae are a new class of transient supersoft X-ray sources with no clear signature of mass ejection. They show similar triangle shapes of $V/I$ band light curves with thousand times fainter peaks than typical classical novae. Maccarone et al. regarded the prototype millinova, ASASSN-16oh, as a dwarf nova and interpreted the supersoft X-rays to originate

Liang-Chung Hsia, Hongming Nie, Chenxi Wu

Let $K$ be a complete non-archimedean field of characteristic $0$ equipped with a discrete valuation. We establish the rationality of the Artin-Mazur zeta function on the Julia set for any subhyperbolic rational map defined over $K$ with a compact Julia set. Furthermore, we conclude that the topological entropy on the Julia set of such a map is given by the

Jingxing Li, Yongjae Lee, Abhay Kumar Yadav, Cheng Peng

Image matching is a key component of modern 3D vision algorithms, essential for accurate scene reconstruction and localization. MASt3R redefines image matching as a 3D task by leveraging DUSt3R and introducing a fast reciprocal matching scheme that accelerates matching by orders of magnitude while preserving theoretical guarantees. This approach has gained s

Natsuki Ueno, Shoichi Koyama

The spatial information of sound plays a crucial role in various situations, ranging from daily activities to advanced engineering technologies. To fully utilize its potential, numerous research studies on spatial audio signal processing have been carried out in the literature. Sound field estimation is one of the key foundational technologies that can be ap

Berk Iskender, Sushan Nakarmi, Nitin Daphalapurkar, Marc L. Klasky

Dynamic imaging involves the reconstruction of a spatio-temporal object at all times using its undersampled measurements. In particular, in dynamic computed tomography (dCT), only a single projection at one view angle is available at a time, making the inverse problem very challenging. Moreover, ground-truth dynamic data is usually either unavailable or too

Yirui Zheng, Juntai Shen, Xufen Wu, Bin-Hui Chen

Bar structures can form internally due to the instability of their host galaxies or externally due to perturbations from other galaxies. We systematically quantify the growth timescales ($\tau_\mathrm{bar}$) of bars formed through these two mechanisms with a series of controlled $N$-body simulations. In galaxies susceptible to bar instability, tidally induce

Lingchan Bao, Tong Wei, Yuanyu Wan

We revisit multi-agent asynchronous online optimization with delays, where only one of the agents becomes active for making the decision at each round, and the corresponding feedback is received by all the agents after unknown delays. Although previous studies have established an $O(\sqrt{dT})$ regret bound for this problem, they assume that the maximum dela

Yusuke Tajima, Junichi Shiogai, Masayuki Ochi, Kazutaka Kudo

Epitaxial thin-film growth is a versatile and powerful technique for achieving a precise control of composition, stabilizing non-equilibrium phases, tailoring growth orientation, as well as forming heterointerfaces of various quantum materials. For synthesis of highly crystalline thin films, in-depth understanding of epitaxial relationship between the desire

Yirui Luo, Yong Liang Guan, Yao Ge, Chau Yuen

The recently proposed multi-chirp waveform, affine frequency division multiplexing (AFDM), is regarded as a prospective candidate for integrated sensing and communication (ISAC) due to its robust performance in high-mobility scenarios and full diversity achievement in doubly dispersive channels. However, the insufficient Doppler resolution caused by limited

Jia Wei He, Shi Long Li, Yong Zhou

We investigate the maximal $L_p$-regularity in J.L. Lions' problem involving a time-fractional derivative and a non-autonomous form $a(t;\cdot,\cdot)$ on a Hilbert space $H$. This problem says whether the maximal $L_p$-regularity in $H$ hold when $t \mapsto a(t ; u, v)$ is merely continuous or even merely measurable. We prove the maximal $L_p$-regularity res

Bowen Zhang, Pengcheng Luo, Genke Yang, Boon-Hee Soong

With the rise of artificial intelligence (AI), applying large language models (LLMs) to mathematical problem-solving has attracted increasing attention. Most existing approaches attempt to improve Operations Research (OR) optimization problem-solving through prompt engineering or fine-tuning strategies for LLMs. However, these methods are fundamentally const

Abu Musa Patoary, Amit Vikram, Victor Galitski

Shor's algorithm for the prime factorization of numbers provides an exponential speedup over the best known classical algorithms. However, nontrivial practical applications have remained out of reach due to experimental limitations. The bottleneck of the experimental realization of the algorithm is the modular exponentiation operation. In this paper, based o

Xin Liu, Xudong Wang, Pei Liu, Guoming Tang

The linear growth of key-value (KV) cache memory and quadratic computational in attention mechanisms complexity pose significant bottlenecks for large language models (LLMs) in long-context processing. While existing KV cache optimization methods address these challenges through token pruning or feature merging, they often incur irreversible information loss

Youngkyung Lee, Doyoung Chung

Quantum computing is rapidly advancing toward cloud-based services, raising significant concerns about the privacy and security of computations outsourced to untrusted quantum servers. Universal Blind Quantum Computation (UBQC) protocols enable clients with limited quantum resources to delegate computations while concealing both inputs and circuit details. H

Mahmoud Abdelgalil, Jorge I. Poveda

We study a novel class of algorithms for solving model-free feedback optimization problems in dynamical systems. The key novelty is the introduction of \emph{persistent resetting learning integrators} (PRLI), which are integrators that are reset at the same frequency at which the plant is dithered using exploratory signals for model-free optimization. It is

Yongqi Li, Xiaowei Zhang

Training deep neural networks is challenging. To accelerate training and enhance performance, we propose PadamP, a novel optimization algorithm. PadamP is derived by applying the adaptive estimation of the p-th power of the second-order moments under scale invariance, enhancing projection adaptability by modifying the projection discrimination condition. It

Panagiotis Typaldos, Andreas A. Malikopoulos

In this paper, we present a hierarchical framework that integrates upper-level routing with low-level optimal trajectory planning for connected and automated vehicles (CAVs) traveling in an urban network. The upper-level controller efficiently distributes traffic flows by utilizing a dynamic re-routing algorithm that leverages real-time density information a

Shiwon Kim, Dongjun Hwang, Sungwon Woo, Rita Singh

Class-incremental learning (CIL) aims to adapt to continuously emerging new classes while preserving knowledge of previously learned ones. Few-shot class-incremental learning (FSCIL) presents a greater challenge that requires the model to learn new classes from only a limited number of samples per class. While incremental learning typically assumes restricte

Pjotr Buys, Jan van den Heuvel, Ross J. Kang

Given $d>0$ and a positive integer $n$, let $G$ be a triangle-free graph on $n$ vertices with average degree $d$. With an elegant induction, Shearer (1983) tightened a seminal result of Ajtai, Koml\'os and Szemer\'edi (1980/1981) by proving that $G$ contains an independent set of size at least $(1+o(1))\frac{\log d}{d}n$ as $d\to\infty$. By a generalisation

Song Jiang, Chunhui Zhou

We study the existence and zero viscous limit of smooth solutions to steady compressible Navier-Stokes equations near plane shear flow between two moving parallel walls. Under the assumption $0<L\ll1$, we prove that for any plane supersonic shear flow $\mathbf{U}^0=(\mu(x_2),0)$, there exist smooth solutions near $\mathbf{U}^0$ to steady compressible Navier-

Shu Wang, Yanbo Gao, Shuai Li, Chong Lv

This paper presents MetricGrids, a novel grid-based neural representation that combines elementary metric grids in various metric spaces to approximate complex nonlinear signals. While grid-based representations are widely adopted for their efficiency and scalability, the existing feature grids with linear indexing for continuous-space points can only provid

A. Rodríguez-Ardila, M. A. Fonseca-Faria, L. G. Dahmer-Hahn, A. Prieto

We investigated by means of MUSE/VLT observations the true size of the coronal line region (CLR) in a local sample of nine active galactic nuclei known for displaying prominent coronal emission. Our analysis show that the CLR is extended from several hundred parsecs to a few kiloparsecs in the lines of [Fe VII] (IP=99 eV) and [Fe X] (IP=235 eV). In all cases

Erik García Neefjes, Stuart C. Hawkins

We numerically investigate the sensitivity of the scattered wave field to perturbations in the shape of a scattering body illuminated by an incident plane wave. This study is motivated by recent work on the inverse problem of reconstructing a scatterer shape from measurements of the scattered wave at large distances from the scatterer. For this purpose we co

Song He, Yi Li, Hao Ouyang, Yuan Sun

This review explores recent advances in the theory of $T\bar{T}$ deformation, an irrelevant yet solvable deformation of quantum field theories defined via the quadratic form of the energy-momentum tensor. It addresses classical and quantum aspects, highlighting significant developments across various fields, including field theory, holography, and string the

Yuan-Xin Zheng, Gang Li, Shi-Dong Liu, Jia-Jun Wu

The BESIII collaboration recently observed a charged hidden-charm structure with strangeness in the recoil mass spectrum of $K^+$ in the processes $e^+e^- \to D^-_s D^{*0} K^+$ or $D^{*-}_s D^0 K^+$, named as $Z_{cs}(3985)^-$. Within the energy region around the peak of $Z_{cs}(3985)$, various box diagrams are present. A systematic study of these box diagram

Sang-Ho Kim

The photoproduction of $J/\psi$ meson off the nucleon is investigated within a dynamical model approach based on a Hamiltonian which describes the reaction mechanisms of the Pomeron exchange, meson exchange, and direct $J/\psi$ radiation terms.To shed light on the low-energy mechanism, we scrutinize the role of light-meson [$\pi^0(135)$, $\eta(548)$, $\eta'(

Yunxiao Wang, Meng Liu, Wenqi Liu, Xuemeng Song

Video large language models have achieved remarkable performance in tasks such as video question answering, however, their temporal understanding remains suboptimal. To address this limitation, we curate a dedicated instruction fine-tuning dataset that focuses on enhancing temporal comprehension across five key dimensions. In order to reduce reliance on cost

JunYong Choi, Min-Cheol Sagong, SeokYeong Lee, Seung-Won Jung

We propose a diffusion-based inverse rendering framework that decomposes a single RGB image into geometry, material, and lighting. Inverse rendering is inherently ill-posed, making it difficult to predict a single accurate solution. To address this challenge, recent generative model-based methods aim to present a range of possible solutions. However, finding

Minghao Yang, Zhi-An Huang, Zhihang Zheng, Yuqiao Liu

Hi-C technology measures genome-wide interaction frequencies, providing a powerful tool for studying the 3D genomic structure within the nucleus. However, high sequencing costs and technical challenges often result in Hi-C data with limited coverage, leading to imprecise estimates of chromatin interaction frequencies. To address this issue, we present a nove

Kouichi Hirotani, Hsien Shang, Ruben Krasnopolsky, Kenichi Nishikawa

We describe a post-processing radiative transport code for computing the spectra, the coreshift, and the surface-brightness distribution of special relativistic jets with arbitrary optical thickness. The jet consists of an electron-positron pair plasma and an electron-proton normal plasma. Electrons and positrons are relativistic and composed of thermal and

Qi-yue Yu, Shi-wen Lin, Ting-wei Yang

A finite-field multiple-access (FFMA) system separates users within a finite field by utilizing different element-pairs (EPs) as virtual resources. The Cartesian product of distinct EPs forms an EP code, which serves as the input to a finite-field multiplexing module (FF-MUX). This allows the FFMA technique to reorder the channel coding and multiplexing modu

Gayathrini Premawardhana, Deven P. Bowman, Jacob M. Taylor

We investigate how to entangle an atom interferometer and a macroscopic mechanical oscillator in order to create non-classical states of the oscillator. We propose an entanglement witness, from whose violation, the generation of entanglement can be determined. We do this for both the noiseless case and when including thermal noise. Thermal noise can arise fr

Liam L. H. Lau, Andreas Gleis, Daniel Kaplan, Premala Chandra

We examine the interplay between electron correlations and phonons in an Anderson-Holstein impurity model with an Einstein phonon. When the phonons are slow compared to charge fluctuations (frequency $\omega_0 \ll U/2$, the onsite Coulomb scale $U/2$), we demonstrate analytically that the expected phonon-mediated reduction of interactions is completely suppr

Fasil Hussain Rather, Firdous Haidar, Muhammed Jaffar A., Ipsita Mandal

We compute the magnetoelectric conductivity for ideal nodal-line semimetals (NLSMs), with a finite but tiny mass-gap, in distinct planar-Hall set-ups. Each differing configuration results from the relative orientation of the nodal-ring's plane with respect to the plane spanned by the electric ($\mathbf E $) and magnetic ($\mathbf B$) fields. The net conducti

Zijian Zhao, Xuming Zhang, Jiayu Wen, Mingwen Liu

In financial trading, return prediction is one of the foundation for a successful trading system. By the fast development of the deep learning in various areas such as graphical processing, natural language, it has also demonstrate significant edge in handling with financial data. While the success of the deep learning relies on huge amount of labeled sample

Jie Li, Anusha Withana, Alexandra Diening, Kai Kunze

As human augmentation technologies evolve, the convergence of AI, robotics, and extended reality (XR) is redefining human potential -- enhancing cognition, perception, and physical abilities. However, these advancements also introduce ethical dilemmas, security risks, and concerns over loss of control. This workshop explores both the transformative potential

Rohan Bhatnagar, Ling Liang, Krish Patel, Haizhao Yang

Motivated by the remarkable success of artificial intelligence (AI) across diverse fields, the application of AI to solve scientific problems, often formulated as partial differential equations (PDEs), has garnered increasing attention. While most existing research concentrates on theoretical properties (such as well-posedness, regularity, and continuity) of

Qiang Zhang, Jiahang Cao, Jingkai Sun, Yecheng Shao

In recent years, quadruped robotics has advanced significantly, particularly in perception and motion control via reinforcement learning, enabling complex motions in challenging environments. Visual sensors like depth cameras enhance stability and robustness but face limitations, such as low operating frequencies relative to joint control and sensitivity to

Alessandro Grecucci, Parisa Ahmadi Ghomroudi, Carmen Morawetz, Valerie Lesk

Emotion regulation plays a crucial role in mental health, and difficulties in regulating emotions can contribute to psychological disorders. While reappraisal and suppression are well-studied strategies, the combined contributions of gray matter (GM) and white matter (WM) to these strategies remain unclear due to methodological limitations in previous studie

I. M. Buchinskiy, M. V. Kotov, A. V. Treier

In this paper, we investigate the computational complexity of the knapsack problem and subset sum problem for the following tropical algebraic structures. We consider the semigroup of square matrices of size $k \times k$ with non-negative entries over the max-plus algebra and the semigroup square matrices of size $k \times k$ with positive entries over the m

Deb Roy, Lawrence Lessig, Audrey Tang

Picture a community torn over a proposed zoning law. Some are angry, others defensive, and misunderstandings abound. On social media, they broadcast insults at one another; every nuanced perspective is reduced to a viral soundbite. Yet, when they meet face-to-face and start speaking, something changes: residents begin listening more than speaking, and people

Wen Zhao, Junlong Tian, Jie Peng

Various light-matter interactions lead to diverse phase diagram structures in superradiant phase transition (SPT) studies. Such systems consist of multiqubit and multimode with anisotropic couplings, one- and two-photon interactions, Stark shifts, inter-cavity hoppings, qubit-qubit interactions and so on. We find a general phase diagram feature that the orig

Yanwei Jia, Du Ouyang, Yufei Zhang

Stochastic policies (also known as relaxed controls) are widely used in continuous-time reinforcement learning algorithms. However, executing a stochastic policy and evaluating its performance in a continuous-time environment remain open challenges. This work introduces and rigorously analyzes a policy execution framework that samples actions from a stochast

Alexei V. Tkachenko

How much of the energy consumed by artificial neural networks is set by physics rather than by implementation? For irreversible digital hardware the reference is Landauer&#39;s principle, which charges $k_B T\ln 2$ per erased bit. We map a generic feedforward network onto a physical Hamiltonian in which each layer relation is an elastic compatibility constra

Aniruddha Acharya, Kaitlin Hopkins, Tatum Simms

Silicon has striking similarity with carbon and is found in plant cells. However, there is no specific role that has been assigned to silicon in the life cycle of plants. The amount of silicon in plant cells is species specific and can reach levels comparable to macronutrients. Silicon is the central element for artificial intelligence, nanotechnology and di