February 2025 arXiv papers — page 27

Rob Burns

We examine the relationship between the Chung-Graham and Zeckendorf representations of an integer using the software package {\tt Walnut}.

Ankita Kakoti, Mrinal Kumar Das

Left-Right Symmetric Model(LRSM) in this work is extended with a sterile fermion per generation, the lightest of the same is considered to be a suitable dark matter candidate for the study and analysis of the associated properties. The model has been realized using $A_{4}$ modular symmetry, the advantage being the non-requirement of the use of extra fields,

J. C. Geromel, E. V. L. Nunes, L. Hsu

This paper presents a novel procedure for robust control design of linear time-invariant systems using a Multivariable Generalized Super-Twisting Algorithm (MGSTA). The proposed approach addresses robust stability and performance conditions, considering convex bounded parameter uncertainty in all matrices of the plant state-space realization and Lipschitz ex

Akhil Penta, Vaibhav Adwani, Ankush Chopra

Accurate skier tracking is essential for performance analysis, injury prevention, and optimizing training strategies in alpine sports. Traditional tracking methods often struggle with occlusions, dynamic movements, and varying environmental conditions, limiting their effectiveness. In this work, we used STARK (Spatio-Temporal Transformer Network for Visual T

Vsevolod Gubarev, Alexander Panasenko

We describe all Rota-Baxter operators of any weight on the space of matrices from $M_2(F)$ considered under the product $a\circ b = (ab + ba)/2$ and usually denoted as $M_2(F)^{(+)}$. This algebra is known to be a simple Jordan one. We introduce symmetrized Rota-Baxter operators of weight $\lambda$ and show that every Rota-Baxter operator of weight 0 on $M_2

Ruben Gonzalez, Conrad M Albrecht, Nassim Ait Ali Braham, Devyani Lambhate

Hyperspectral imaging provides detailed spectral information, offering significant potential for monitoring greenhouse gases like CH4 and NO2. However, its application is constrained by limited spatial coverage and infrequent revisit times. In contrast, multispectral imaging delivers broader spatial and temporal coverage but lacks the spectral granularity re

Shi Fu, Yingjie Wang, Yuzhu Chen, Xinmei Tian

High-quality data is essential for training large generative models, yet the vast reservoir of real data available online has become nearly depleted. Consequently, models increasingly generate their own data for further training, forming Self-consuming Training Loops (STLs). However, the empirical results have been strikingly inconsistent: some models degrad

Junxiao Ma, Jingjing Wang, Jiamin Luo, Peiying Yu

Prior studies on Video Anomaly Detection (VAD) mainly focus on detecting whether each video frame is abnormal or not in the video, which largely ignore the structured video semantic information (i.e., what, when, and where does the abnormal event happen). With this in mind, we propose a new chat-paradigm \textbf{M}ulti-scene Video Abnormal Event Extraction a

Jacob Dunefsky, Arman Cohan

Steering vectors (SVs) have emerged as a promising approach for interpreting and controlling LLMs, but current methods typically require large contrastive datasets that are often impractical to construct and may capture spurious correlations. We propose directly optimizing SVs through gradient descent on a single training example, and systematically investig

Bich Ngoc, Doan, Joseph Seering

Volunteer moderators use various strategies to address online harms within their communities. Although punitive measures like content removal or account bans are common, recent research has explored the potential for restorative justice as an alternative framework to address the distinct needs of victims, offenders, and community members. In this study, we t

Md Mehrab Tanjim, Ryan A. Rossi, Mike Rimer, Xiang Chen

Conversational assistants often require a question rewriting algorithm that leverages a subset of past interactions to provide a more meaningful (accurate) answer to the user's question or request. However, the exact rewriting approach may often depend on the use case and application-specific tasks supported by the conversational assistant, among other const

Jingtao Zhan, Jiahao Zhao, Jiayu Li, Yiqun Liu

Intelligence is a crucial trait for species to find solutions within a limited number of trial-and-error attempts. Building on this idea, we introduce Survival Game as a framework to evaluate intelligence based on the number of failed attempts in a trial-and-error process. Fewer failures indicate higher intelligence. When the expectation and variance of fail

Boyang Zhang, Zhe Li, Zhongju Wang, Yang Yu

Single-photon avalanche detectors (SPADs) have enabled various applications in emerging photonic quantum information technologies in recent years. However, despite many efforts to improve SPAD's performance, the design of SPADs remained largely an iterative and time-consuming process where a designer makes educated guesses of a device structure based on empi

Yan-Rong Li, Jinyi Shangguan, Jian-Min Wang, Ric Davies

The geometric distances of active galactic nuclei (AGNs) are challenging to measure because of their exceptionally compact structure yet vast cosmic distances. A combination of spectroastrometry and reverberation mapping (SARM) of broad-line regions (BLRs) constitutes a novel means to probe the geometric distance of AGNs, which has recently become practicall

Hongjun Heo, Wan Choi

Accurate beam alignment is a critical challenge in XL-MIMO systems, especially in the near-field regime, where conventional far-field assumptions no longer hold. Although 2D grid-based codebooks in the polar domain are widely accepted for capturing near-field effects, they often suffer from high complexity and inefficiency in both time and computational reso

Junfeng Lu, Hao Wang, Yangshuai Wang

Concurrent multiscale methods play an important role in modeling and simulating materials with defects, aiming to achieve the balance between accuracy and efficiency. Atomistic-to-continuum (a/c) coupling methods, a typical class of concurrent multiscale methods, link atomic-scale simulations with continuum mechanics. Existing a/c methods adopt the classic s

Soobin Park, Hankyung Kim, Youn-kyung Lim

Image-generative AI provides new opportunities to transform personal data into alternative visual forms. In this paper, we illustrate the potential of AI-generated images in facilitating meaningful engagement with personal data. In a formative autobiographical design study, we explored the design and use of AI-generated images derived from personal data. Inf

Christopher Zerafa

Full Waveform Inversion (FWI) reconstructs high-resolution subsurface models via multi-variate optimization but faces challenges with solver selection and data availability. Deep Learning (DL) offers a promising alternative, bridging data-driven and physics-based methods. While FWI in DL has been explored in the time domain, the pseudo-spectral approach rema

Jungin Kim, Shinwoo Park, Yo-Sub Han

Identifying LLM-generated code through watermarking poses a challenge in preserving functional correctness. Previous methods rely on the assumption that watermarking high-entropy tokens effectively maintains output quality. Our analysis reveals a fundamental limitation of this assumption: syntax-critical tokens such as keywords often exhibit the highest entr

Jongbae Hong

We investigate a quantum phase transition (QPT) in quantum point contacts by analyzing the gate-voltage-dependent quasiparticle energy at the Fermi level at zero temperature. This energy is computed using the local density of states at the site of the localized spin, which is extracted from the replicated gate-voltage-dependent differential conductance shape

Lei Li, Chen Wang

We study in this paper the convergence of the random splitting method for Allen-Cahn equation in a background flow that plays as a simplified model for phase separation in multiphase flows. The model does not own the gradient flow structure as the usual Allen-Cahn equation does, and the random splitting method is advantageous due to its simplicity and better

Kerem Zaman, Shashank Srivastava

Large Language Models (LLMs) offer natural language explanations as an alternative to feature attribution methods for model interpretability. However, despite their plausibility, they may not reflect the model's true reasoning faithfully. While several faithfulness metrics have been proposed, they are often evaluated in isolation, making principled compariso

Anay Majee, Maria Xenochristou, Wei-Peng Chen

Handling heterogeneous data in tabular datasets poses a significant challenge for deep learning models. While attention-based architectures and self-supervised learning have achieved notable success, their application to tabular data remains less effective over linear and tree based models. Although several breakthroughs have been achieved by models which tr

Zhitao Wang, Zhe Chen, Mingyang Jiang, Tong Qin

Autonomous parking has become a critical application in automatic driving research and development. Parking operations often suffer from limited space and complex environments, requiring accurate perception and precise maneuvering. Traditional rule-based parking algorithms struggle to adapt to diverse and unpredictable conditions, while learning-based algori

Zichuan Fu, Wentao Song, Yejing Wang, Xian Wu

Recent advances in transformer-based Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their quadratic computational complexity concerning sequence length remains a significant bottleneck for processing long documents. As a result, many efforts like sparse attention and state space models have been proposed

Yunmei Huang, Songlin Hou, Zachary Nelson Horve, Songlin Fei

The precise identification of tree species is fundamental to forestry, conservation, and environmental monitoring. Though many studies have demonstrated that high accuracy can be achieved using bark-based species classification, these models often function as "black boxes", limiting interpretability, trust, and adoption in critical forestry applications. Att

An-Jun Liu, Bryan K. Clark

The ground state of second-quantized quantum chemistry Hamiltonians is key to determining molecular properties. Neural quantum states (NQS) offer flexible and expressive wavefunction ansatze for this task but face two main challenges: highly peaked ground-state wavefunctions hinder efficient sampling, and local energy evaluations scale quartically with syste

Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Domae Yukiyasu

This paper introduces a novel pipeline to enhance the precision of object masking for robotic manipulation within the specific domain of masking products in convenience stores. The approach integrates two advanced AI models, CLIP and SAM, focusing on their synergistic combination and the effective use of multimodal data (image and text). Emphasis is placed o

Gibson Nkhata, Usman Anjum, Justin Zhan

Sentiment Analysis (SA) or opinion mining is analysis of emotions and opinions from any kind of text. SA helps in tracking peoples viewpoints and it is an important factor when it comes to social media monitoring product and brand recognition customer satisfaction customer loyalty advertising and promotions success and product acceptance. That is why SA is o

Yuhong Wang, Xinyao Wang, Chen Shen, Jianquan Liao

In scenarios where high penetration of renewable energy sources (RES) is connected to the grid over long distances, the output of RES exhibits significant fluctuations, making it difficult to accurately characterize. The intermittency and uncertainty of these fluctuations pose challenges to the stability of the power system. This paper proposes a distributio

Arthur Delarue, Kleanthis Karakolios

Interference between treated and untreated units is a source of bias in marketplace experiments. In this paper, we specifically consider pricing interventions, in which a platform seeks to adjust base pricing levels at the marketplace level in order to increase demand. In a matching marketplace, this type of experiment leads to a crucial design question: sho

Erik Lötstedt, Kaoru Yamanouchi

We compare four different encoding schemes for the quantum computing of spin chains with a spin quantum number $S>1/2$: a compact mapping, a direct (or one-hot) mapping, a Dicke mapping, and a qudit mapping. The three different qubit encoding schemes are assessed by conducting Hamiltonian simulation for $1/2 \le S \le 5/2$ using a trapped-ion quantum compute

Wenshuai Liu

According to the giant impact theory, the Moon formed through accreting the debris disk produced by a collision between Theia and the proto-Earth, and the predicted lunar orbital inclination relative to the Earth's equatorial plane is about within one degree when Moon formed. However, the current lunar orbital inclination with five degrees relative to the Ea

Longling Geng, Edward Y. Chang

This benchmark suite provides a comprehensive evaluation framework for assessing both individual LLMs and multi-agent systems in Real-world planning and scheduling scenarios. The suite encompasses 14 designed planning and scheduling problems that progress from basic to highly complex, incorporating key aspects such as multi-agent coordination, inter-agent de

Srikireddy Dhanunjay Reddy, Tharun Kumar Reddy Bollu

Stress became a common factor in the busy daily routines of all academic and corporate working environments. Everyone checks for efficient stress-buster alternatives to calm down from work pressure. Instead of investing time in unnecessary efforts, this work shows the stress relief scenario of subjects by listening to Raag Darbari music notes as a simple add

Xiaoyong Xi, Chong Shen, Dongsheng Zhao

A topological space is domain-representable (or, has a domain model) if it is homeomorphic to the maximal point space $\mbox{Max}(P)$ of a domain $P$ (with the relative Scott topology). We first construct an example to show that the set of maximal points of an ideal domain $P$ need not be a $G_{\delta}$-set in the Scott space $\Sigma P$, thereby answering an

Jinghao Jia, Ruowen Qin, Milo Craun, Egor Lukiyanov

Safe kernel extensions have gained significant traction, evolving from simple packet filters to large, complex programs that customize storage, networking, and scheduling. Existing kernel extension mechanisms like eBPF rely on in-kernel verifiers to ensure safety of kernel extensions by static verification using symbolic execution. We identify significant us

Shuai Wang, Shihao Zhang, Jiaqi Wu, Zijian Tian

Clear monitoring images are crucial for the safe operation of coal mine Internet of Video Things (IoVT) systems. However, low illumination and uneven brightness in underground environments significantly degrade image quality, posing challenges for enhancement methods that often rely on difficult-to-obtain paired reference images. Additionally, there is a tra

Volker Perlick

It is the purpose of this paper to give a characterisation of circular light rays in a plasma on an axially symmetric and stationary spacetime. We restrict to the case of an unmagnetised, pressure-free electron-ion plasma and we assume that the plasma shares the symmetry of the spacetime. As a main tool we use two potentials, one for prograde and one for ret

Ziqi Yao, Mingsong Chen, Cheng Chen

We explore the problem of approximate matrix multiplication (AMM) within the sliding window model, where algorithms utilize limited space to perform large-scale matrix multiplication in a streaming manner. This model has garnered increasing attention in the fields of machine learning and data mining due to its ability to handle time sensitivity and reduce th

Callum Gassner, Jitraporn Vongsvivut, Meguya Ryu, Soon Hock Ng

Anisotropy plays a critical role in governing the mechanical, thermal, electrical, magnetic, and optical properties of materials, influencing their behavior across diverse applications. Probing and quantifying this directional dependence is crucial for advancing materials science and biomedical research, as it provides a deeper understanding of structural or

Shavindra Wickramathilaka, John Grundy, Kashumi Madampe, Omar Haggag

The use of diverse mobile applications among senior users is becoming increasingly widespread. However, many of these apps contain accessibility problems that result in negative user experiences for seniors. A key reason is that software practitioners often lack the time or resources to address the broad spectrum of age-related accessibility and personalisat

Chen Yang, YuBin Zhang, Heng Lu, Ce Zhang

Graphene nanomechanical resonators are well suited for the study of parametric oscillations. Their large frequency tunability and their pronounced nonlinearities enable an efficient modulation of their resonant frequencies. Here, we present measurements of the response of few-layer graphene nanomechanical resonators, each driven by a large parametric pump at

Yuxiao Wen

In combinatorial semi-bandits, a learner repeatedly selects from a combinatorial decision set of arms, receives the realized sum of rewards, and observes the rewards of the individual selected arms as feedback. In this paper, we extend this framework to include \emph{graph feedback}, where the learner observes the rewards of all neighboring arms of the selec

Mayank Sharma, Nahum Arav, Qinyuan Zhao, Maryam Dehghanian

Measuring the distance of quasar outflows from the central source ($R$) is essential for determining their importance for AGN feedback. There are two methods to measure $R$: 1) A direct determination using spatially resolved Integral Field Spectroscopy (IFS) of the outflow in emission. 2) An indirect method which uses the absorption troughs from ionic excite

Qinyuan Zhao, Luming Sun, Lu Shen, Guilin Liu

Quasar feedback is routinely invoked as an indispensable ingredient in galaxy formation models. Galactic outflows are a crucial agent of quasar feedback that frequently manifest themselves in absorption and emission lines. Measuring the size and energetics of outflows based on absorption lines remains a challenge, and integral-field spectroscopy (IFS) mappin

Carter Adams, Caleb Carter, Jackson Simmons

Early detection of suicide risk from social media text is crucial for timely intervention. While Large Language Models (LLMs) offer promising capabilities in this domain, challenges remain in terms of interpretability and computational efficiency. This paper introduces Evidence-Driven LLM (ED-LLM), a novel approach for clinical marker extraction and suicide

Guoqing Diao

Motivated by the controversy in the chess community, where Hikaru Nakamura, a renowned grandmaster, has posted multiple impressive winning streaks over the years on the online platform chess.com, we derive the probabilities of various types of streaks in online chess and/or other sports. Specifically, given the winning/drawing/losing probabilities of individ

Huangyuan Su, Aaron Walsman, Daniel Garces, Sham Kakade

In this project, our goal is to determine how to leverage the world-knowledge of pretrained large language models for efficient and robust learning in multiagent decision making. We examine this in a taxi routing and assignment problem where agents must decide how to best pick up passengers in order to minimize overall waiting time. While this problem is sit

Dung V. Nguyen, Minh H. Nguyen, Luc Q. Nguyen, Rachel S. Y. Teo

Existing methods for merging experts during model training and fine-tuning predominantly rely on Euclidean geometry, which assumes a flat parameter space. This assumption can limit the model's generalization ability, especially during the pre-training phase, where the parameter manifold might exhibit more complex curvature. Curvature-aware merging methods ty

Kouji Yano, Mingdong Zhao

As an analogue to the explicit formula in the stable case, the asymptotic behavior at the origin of the renormalized zero resolvent of one-dimensional L\'evy processes is studied under certain regular variation conditions on the L\'evy-Khinchin exponent and the L\'evy measure.

Yao Wang, Fujun Du, Youxin Wang, Hongchi Wang

From observations, column density ratios or integrated intensity ratios between some species exhibit monotonic increase or decrease along with the evolution of high-mass star-forming regions (HMSFRs). Such ratios are defined as chemical clocks, which can be used to constrain the evolutionary stage. We performed chemical simulations to reproduce the observed

Ryo B. Tanaka, Goro Oohata, Takayuki Uozumi

We theoretically investigated the mechanism of quantum entanglement between the spin of photoelectrons and linear polarization of emitted X-ray photons in the 3$d\rightarrow\ $2$p$ XEPECS process for $\rm Ti_{2}O_{3}$. In the calculation, we used a realistic $\rm TiO_{6}$-type cluster model with the full multiplet structure of the Ti ion and the charge-trans

Shuliang Liu, Xinze Li, Zhenghao Liu, Yukun Yan

Retrieval-Augmented Generation (RAG) has proven its effectiveness in alleviating hallucinations for Large Language Models (LLMs). However, existing automated evaluation metrics cannot fairly evaluate the outputs generated by RAG models during training and evaluation. LLM-based judgment models provide the potential to produce high-quality judgments, but they

Archana Saxena, Praveen C. Srivastava

In the present work, we have done a systematic study of beta decay properties such as electron spectral-shapes, shape factors, and log$ft$ values for the higher forbidden non-unique $\beta^{-}$ transitions in the mass region A=85-123. We have performed the nuclear shell model (SM) calculations to explore the sensitivity of the electron spectral-shapes for di

Dayver Daza-Salgado, Edgar Medina-Segura, Valeria Rodriguez-Fajardo, Benjamin Perez-Garcia

The Higher-Order Poincar\'e Sphere (HOPS) provides a powerful geometrical tool for representing vector beams as points on the surface of a unitary sphere. Since a particular position on the surface represents any spatial mode regardless of its shape, this representation cannot be used to discern between the spatial modes geometries of vector modes. For insta

Dario Antolini, Edoardo Ballico, Alessandro Oneto

We study projective surfaces in $\mathbb{P}^3$ which can be written as Hadamard product of two curves. We show that quadratic surfaces which are Hadamard product of two lines are smooth and tangent to all coordinate planes, and such tangency points uniquely identify the quadric. The variety of such quadratic surfaces corresponds to the Zariski closure of the

Jhh-Jing Hong, Feng-Li Lin

Quantum thermal states are known to be passive, as required by the second law of thermodynamics. This paper investigates the potential for work extraction by coupling a thermal bath to a qubit of either spin, fermionic, or topological type, which acts as a quantum thermal state at different temperatures. The amount of work extraction is derived from the work

Yun-Tak Oh, Hyun-Yong Lee

The 1-form symmetries in two-dimensional topological systems are ``shadowed'' as global symmetries in their one-dimensional quantum transfer matrices. In this work, we introduce a distinct shadow effect arising from the pair-creation of anyons, which manifests as a local symmetry of the quantum transfer matrix. The interplay between these two shadow effects

Weipeng Jiang, Juan Zhai, Shiqing Ma, Ziyan Lei

In recent years, Large Language Models (LLMs) have faced increasing demands to selectively remove sensitive information, protect privacy, and comply with copyright regulations through unlearning, by Machine Unlearning. While evaluating unlearning effectiveness is crucial, existing benchmarks are limited in scale and comprehensiveness, typically containing on

Charles L. Epstein, Manas Rachh, Yuguan Wang

This paper provides an explicit formula for the approximate solution of the static London equations. These equations describe the currents and magnetic fields in a Type-I superconductor. We represent the magnetic field as a 2-form and the current as a 1-form, and assume that the superconducting material is contained in a bounded, connected set, $\Omega,$ wit

Xinyang Liu, Hengrong Du, Wei Deng, Ruqi Zhang

Hutchinson estimators are widely employed in training divergence-based likelihoods for diffusion models to ensure optimal transport (OT) properties. However, this estimator often suffers from high variance and scalability concerns. To address these challenges, we investigate Hutch++, an optimal stochastic trace estimator for generative models, designed to mi

Zhaoyi Joey Hou, Adriana Kovashka, Xiang Lorraine Li

Evaluating creativity is challenging, even for humans, not only because of its subjectivity but also because it involves complex cognitive processes. Inspired by work in marketing, we attempt to break down visual advertisement creativity into atypicality and originality. With fine-grained human annotations on these dimensions, we propose a suite of tasks spe

Ruifeng Tan, Weixiang Hong, Jiayue Tang, Xibin Lu

Battery Life Prediction (BLP), which relies on time series data produced by battery degradation tests, is crucial for battery utilization, optimization, and production. Despite impressive advancements, this research area faces three key challenges. Firstly, the limited size of existing datasets impedes insights into modern battery life data. Secondly, most d

Di-Lun Yang, Xiaojun Yao

Recent measurements of polarization phenomena in relativistic heavy ion collisions have aroused a great interest in understanding dynamical spin evolution of the QCD matter. In particular, the spin alignment signature of $J/\psi$ has been recently observed in Pb-Pb collisions at LHC, which may infer nontrivial spin transport of quarkonia in quark gluon plasm

Eden Hartman, Erel Segal-Halevi, Biaoshuai Tao

The classic notion of \emph{truthfulness} requires that no agent has a profitable manipulation -- an untruthful report that, for \emph{some} combination of reports of the other agents, increases her utility. This strong notion implicitly assumes that the manipulating agent either knows what all other agents are going to report, or is willing to take the risk

Sami Mabrouk, Sergei Silvestrov, Fatma Zouaidi

In this paper, we first introduce the notion of twisted $\mathcal O$-operators on a Hom-Lie-Yamaguti algebra by a given $(2,3)$-cocycle with coefficients in a representation. We show that a twisted $\mathcal O$-operator induces a Hom-Lie-Yamaguti structure. We also introduce the notion of a weighted Reynolds operator on a Hom-Lie-Yamaguti algebra, which can

Bin Xie, Yingfei Liu, Tiancai Wang, Jiale Cao

The generation and simulation of diverse real-world scenes have significant application value in the field of autonomous driving, especially for the corner cases. Recently, researchers have explored employing neural radiance fields or diffusion models to generate novel views or synthetic data under driving scenes. However, these approaches suffer from unseen

Carrie Wang, Sihem Amer-Yahia, Laks V. S. Lakshmanan, Reynold Cheng

We study Aggregation Queries over Nearest Neighbors (AQNN), which compute aggregates over the learned representations of the neighborhood of a designated query object. For example, a medical professional may be interested in the average heart rate of patients whose representations are similar to that of an insomnia patient. Answering AQNNs accurately and eff

Tatsuya Aoyama, Ethan Wilcox

LMs' alignment with human reading behavior (i.e. psychometric predictive power; PPP) is known to improve during pretraining up to a tipping point, beyond which it either plateaus or degrades. Various factors, such as word frequency, recency bias in attention, and context size, have been theorized to affect PPP, yet there is no current account that explains w

Rundong Li, Zheng Liu, Hairuo Wei, Yixi Cai

Point cloud bundle adjustment is critical in large-scale point cloud mapping. However, it is both computationally and memory intensive, with its complexity growing cubically as the number of scan poses increases. This paper presents BALM3.0, an efficient and distributed large-scale point cloud bundle adjustment method. The proposed method employs the majoriz

Yu-Ting Huang

In this article, we establish the existence of a good minimal model for a compact K\"ahler klt pair $(X, B)$ when the Albanese map of $X$ is a projective morphism and the general fiber of $(X, B)$ has a good minimal model.

Itsuki Kasai, Itsuki Takagi, Kazutaka G. Nakamura

Pump-probe reflectivity measurements have been performed on a single crystal of tungsten diselenide (WSe$_2$) using ultrashort near-infrared pulses. The behavior is well reproduced in simulations superimposing three oscillations (7.45, 7.49 and 7.7 THz) with different phases. The Fourier transform spectrum features small peaks at 4.0 and 11.5 THz along with

Gyeongje Cho, Yeonkyoung So, Jaejin Lee

Recent findings raise concerns about whether the evaluation of Multiple-Choice Question Answering (MCQA) accurately reflects the comprehension abilities of large language models. This paper explores the concept of choice sensitivity, which refers to the tendency for model decisions to be more influenced by the answer options than by a genuine understanding o

Zhengjiao Liu, Tao Wang, Xiaojing Yang

A graph is \emph{$(\mathcal{I}, \mathcal{F})$-partitionable} if its vertex set can be partitioned into two parts such that one part $\mathcal{I}$ is an independent set, and the other $\mathcal{F}$ induces a forest. A graph is \emph{$k$-degenerate} if every subgraph $H$ contains a vertex of degree at most $k$ in $H$. Bernshteyn and Lee defined a generalizatio

Albert Countryman, H. A. Vinutha, Fabiola Diaz Ruiz, Xiaoming Mao

We demonstrate that the spatial correlations of microscopic stresses in 2D model colloidal gels obtained in computer simulations can be quantitatively described by the predictions of a theory for emergent elasticity of pre-stressed solids (vector charge theory). By combining a rigidity analysis with the characterization provided by the stress correlations, w

Tomoyuki Hanawa, Patrick D. Mullen

High order algorithms have emerged in numerical astrophysics as a promising avenue to reduce truncation error (proportional to a power of the linear resolution $\Delta x$) with only a moderate increase to computational expense. Significant effort has been placed in the development of finite volume algorithms for (magneto)hydrodynamics, however, state-of-the-

Zhiyuan Peng, Xin Yin, Rui Qian, Peiqin Lin

Large language models (LLMs) have transformed code generation. However, most existing approaches focus on mainstream languages such as Python and Java, neglecting the Solidity language, the predominant programming language for Ethereum smart contracts. Due to the lack of adequate benchmarks for Solidity, LLMs' ability to generate secure, cost-effective smart

Andrew P. C. Underwood, P. B. Blakie

This paper develops and implements the stochastic projected Gross-Pitaevskii equation for spin-1 Bose gases, addressing key considerations for numerical simulations. As an application of the theory we explore equilibrium phases in a two-dimensional spin-1 gas, where quasi-long-range order emerges via a Berezinskii-Kosterlitz-Thouless transition. Our analysis

Jungsoo Park, Junmo Kang, Gabriel Stanovsky, Alan Ritter

The surge of LLM studies makes synthesizing their findings challenging. Analysis of experimental results from literature can uncover important trends across studies, but the time-consuming nature of manual data extraction limits its use. Our study presents a semi-automated approach for literature analysis that accelerates data extraction using LLMs. It autom

Wen-Zheng Chen, Yang Liu, Yi-Ming Wang, Hong Li

The detection of primordial B-modes, a key probe of cosmic inflation, is increasingly challenged by contamination from weak gravitational lensing B-modes induced by large-scale structure (LSS). We present a delensing pipeline designed to enhance the sensitivity to the inflationary parameter r, minimizing reliance on foreground mitigation during lensing recon

C. Ugwumadu, D. A. Drabold, R. Tutchton

The impact of irradiation on the thermal and electronic properties of materials is a persistent puzzle, particularly defect formation at the atomic and nanoscales. This work examines the nanoscale effects of low-energy irradiation on tungsten (W), focusing on defect-induced modifications to thermal and electronic transport. Using the Site-Projected Thermal C

Miguel Lagos

The non-relativistic interacting electron gas in an external field of positively charged massive cores is dealt with in the scheme of second quantization. Ladder operators that change between stationary states of contiguous energy eigenvalues are derived. The method is particularized to the two-electron Helium atom in order to explain it avoiding too much no

Efstathios Konstantinos Chrontsios Garitsis, Vyron Vellis

We establish a quantitative necessary and sufficient condition for a spiral arc to be a H\"older arc. The class of spiral arcs contains the polynomial spirals studied by Fraser, and the elliptical spirals studied by Burrell-Falconer-Fraser. As an application, we recover the sharp result on the H\"older winding problem for polynomial spirals. Moreover, we pro

Ahmad Bazzi, Marwa Chafii

Incorporating integrated sensing and communication capabilities into forthcoming 6G wireless networks is crucial for achieving seamless synchronization between the digital and physical worlds. The following paper focuses on a scenario where a passive radar (PR) is subject to weak line-of-sight signals of opportunity, emanating from an access point and subseq

Jun-En Ding, Dongsheng Luo, Anna Zilverstand, Kaustubh Kulkarni

Mental disorders are among the most widespread diseases globally. Analyzing functional brain networks through functional magnetic resonance imaging (fMRI) is crucial for understanding mental disorder behaviors. Although existing fMRI-based graph neural networks (GNNs) have demonstrated significant potential in brain network feature extraction, they often fai

Haixin Li, Enci Wang, Cheqiu Lyu, Yangyao Chen

The element abundances of galaxies provide crucial insights into their formation and evolution. Using high-resolution IFU data from the MaNGA survey, we analyze the central spectra (0-0.5 $R_{\rm e}$) of 1,185 quenched galaxies ($z = 0.012-0.15$) to study their element abundances and stellar populations. We employ the full-spectrum fitting code {\tt alf} to

Xuan Yu

Matsui and Takahashi introduce a general spectra construction for triangulated categories in [J. Math. Soc. Japan, 4:2121-2150,2020], which is later used to establish Matsui's theory of triangular geometry. In this paper, we study several topological aspects of this general construction and give criteria for soberness and spectralness of the spectra. Further

Jiacheng Lu, Fa Chen, Yiming Shu, Yukang Wen

We predict high-velocity magnetic domain wall (DW) motion driven by out-of-plane acoustic spin in surface acoustic waves (SAWs). We demonstrate that the SAW propagating at a 30-degree angle relative to the x-axis of a 128-degree Y-LiNbO3 substrate exhibits uniform out-of-plane spin angular momentum. This acoustic spin triggers the DW motion at a velocity exc

Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran, Seyed Abolghasem Mirroshandel

The rise of Large Language Models (LLMs) has boosted the use of Few-Shot Learning (FSL) methods in natural language processing, achieving acceptable performance even when working with limited training data. The goal of FSL is to effectively utilize a small number of annotated samples in the learning process. However, the performance of FSL suffers when unsui

Lior Shamir

JWST provides a view of the Universe never seen before, and specifically fine details of galaxies in deep space. JWST Advanced Deep Extragalactic Survey (JADES) is a deep field survey, providing unprecedentedly detailed view of galaxies in the early Universe. The field is also in relatively close proximity to the Galactic pole. Analysis of spiral galaxies by

Qian Cui, Zhihong He, Shunhong Deng, Liming Peng

We identified blue straggler stars (BSSs) in 53 open clusters utilizing data from Gaia DR3. Most of these clusters are situated in the outer regions of the Galactic disc, encompassing structures such as the warp and the Outer arm. We analyzed their astrometric parameters and determined that 48 of them demonstrate high reliability in radial density profile. F

Zhengmian Hu, Tong Zheng, Vignesh Viswanathan, Ziyi Chen

Large Language Models (LLMs) have become an indispensable part of natural language processing tasks. However, autoregressive sampling has become an efficiency bottleneck. Multi-Draft Speculative Decoding (MDSD) is a recent approach where, when generating each token, a small draft model generates multiple drafts, and the target LLM verifies them in parallel,

Qingpei Guo, Kaiyou Song, Zipeng Feng, Ziping Ma

We present M2-omni, a cutting-edge, open-source omni-MLLM that achieves competitive performance to GPT-4o. M2-omni employs a unified multimodal sequence modeling framework, which empowers Large Language Models(LLMs) to acquire comprehensive cross-modal understanding and generation capabilities. Specifically, M2-omni can process arbitrary combinations of audi

Ziyan Chen, Zhentao Liu, Jianrong Wu, Shensheng Han

Ghost imaging via sparsity constraints (GISC) spectral camera modulates the three-dimensional (3D) hyperspectral image into a two-dimensional (2D) compressive image with speckles in a single shot. It obtains a 3D hyperspectral image (HSI) by reconstruction algorithms. The rapid development of deep learning has provided a new method for 3D HSI reconstruction.

Sara Issaoun, Dominic W. Pesce, María J. Rioja, Richard Dodson

Frequency Phase Transfer (FPT) is a technique designed to increase coherence and sensitivity in radio interferometry by making use of the non-dispersive nature of the troposphere to calibrate high-frequency data using solutions derived at a lower frequency. While the Korean VLBI Network has pioneered the use of simultaneous multi-band systems for routine FPT

Kiranmayee Janardhan, Christy Bobby Thomas

Glioma, the prevalent primary brain tumor, exhibits diverse aggressiveness levels and prognoses. Precise classification of glioma is paramount for treatment planning and predicting prognosis. This study aims to develop an algorithm to fuse the MRI images from T1, T2, T1ce, and fluid-attenuated inversion recovery (FLAIR) sequences to enhance the efficacy of g

Alex Berke, Kent Larson, Sandy Pentland, Dana Calacci

What do pickles and trampolines have in common? In this paper we show that while purchases for these products may seem innocuous, they risk revealing clues about customers' personal attributes - in this case, their race. As online retail and digital purchases become increasingly common, consumer data has become increasingly valuable, raising the risks of pri

Yuqing Wang, Xiao Yang

This study addresses the challenge of resource scheduling optimization in edge-cloud collaborative computing using deep reinforcement learning (DRL). The proposed DRL-based approach improves task processing efficiency, reduces overall processing time, enhances resource utilization, and effectively controls task migrations. Experimental results demonstrate th

Xueqing Peng, Triantafillos Papadopoulos, Efstathia Soufleri, Polydoros Giannouris

Despite Greece's pivotal role in the global economy, large language models (LLMs) remain underexplored for Greek financial context due to the linguistic complexity of Greek and the scarcity of domain-specific datasets. Previous efforts in multilingual financial natural language processing (NLP) have exposed considerable performance disparities, yet no dedica