December 2024 arXiv papers — page 168

J. Aalbers, D. S. Akerib, A. K. Al Musalhi, F. Alder

We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xeno

Wenzhe Yang, Shixun Huang, Sheng Wang, Zhiyong Peng

Data is undoubtedly becoming a commodity like oil, land, and labor in the 21st century. Although there have been many successful marketplaces for data trading, the existing data marketplaces lack consideration of the case where buyers want to acquire a collection of datasets (instead of one), and the overall spatial coverage and connectivity matter. In this

Zilan Wang, Junfeng Guo, Jiacheng Zhu, Yiming Li

Recent advances in large-scale text-to-image (T2I) diffusion models have enabled a variety of downstream applications, including style customization, subject-driven personalization, and conditional generation. As T2I models require extensive data and computational resources for training, they constitute highly valued intellectual property (IP) for their legi

Ekeabino Momoh, Harsha S. Bhat, Stephen Tait, Muriel Gerbault

Here, we present a new thermomechanical geodynamic, numerical implementation that incorporates Maxwell viscoelastic rheology accounting for temperature-dependent power-law dislocation creep and pressure-sensitive, non-associated Drucker-Prager brittle failure, as well as for volumetric stresses and strains during viscoplastic flow, a departure from the tradi

Yang Ding

This study investigates the transformative impact of artificial intelligence on art research by analysing data from 749 art research projects and 555,982 non art research projects, as well as 23,999 journal articles. We utilized the SciBERT model for text analysis on research funding proposals and the econometric model to evaluate AI impact on the academic p

Pablo Rodriguez-Lopez

We obtain the large distance limit of the Casimir energy between two equal parallel straight single wall carbon nanotubes by the use of the Multiscattering formalism, for low and high temperatures.

Alessio Caminata, Ryann Cartor, Alessio Meneghetti, Rocco Mora

This paper presents enhanced reductions of the bounded-weight and exact-weight Syndrome Decoding Problem (SDP) to a system of quadratic equations. Over $\mathbb{F}_2$, we improve on a previous work and study the degree of regularity of the modeling of the exact weight SDP. Additionally, we introduce a novel technique that transforms SDP instances over $\math

Ye Sun, Lei Shi, Yongxin Tong

Link prediction (LP) is crucial for Knowledge Graphs (KG) completion but commonly suffers from interpretability issues. While several methods have been proposed to explain embedding-based LP models, they are generally limited to local explanations on KG and are deficient in providing human interpretable semantics. Based on real-world observations of the char

Yuan-Heng Wang, Hoshin V. Gupta

Due largely to challenges associated with physical interpretability of machine learning (ML) methods, and because model interpretability is key to credibility in management applications, many scientists and practitioners are hesitant to discard traditional physical-conceptual (PC) modeling approaches despite their poorer predictive performance. Here, we exam

Alberto Marchisio, Emman Sychiuco, Muhammad Kashif, Muhammad Shafique

The rapid advancement in Quantum Computing, particularly through Noisy-Intermediate Scale Quantum (NISQ) devices, has spurred significant interest in Quantum Machine Learning (QML) applications. Despite their potential, fully-quantum algorithms remain impractical due to the limitations of current NISQ devices. Hybrid quantum-classical neural networks (HQNNs)

Jin Wei

Hypersequent calculus G{\L}$\forall$ for first-order {\L}ukasiewicz logic was first introduced by Baaz and Metcalfe, along with a proof of its approximate completeness with respect to standard $[0,1]$-semantics. The completeness result was later pointed out by Gerasimov that it only applies to prenex formulas. In this paper, we will present our proof of appr

Rui Chen, Zehuan Wu, Yichen Liu, Yuxin Guo

The creation of diverse and realistic driving scenarios has become essential to enhance perception and planning capabilities of the autonomous driving system. However, generating long-duration, surround-view consistent driving videos remains a significant challenge. To address this, we present UniMLVG, a unified framework designed to generate extended street

Ao Chen, Wei Chen, Bo Ai, Petar Popovski

The problem of uplink transmissions in massive connectivity is commonly dealt with using schemes for grant-free random access. When a large number of devices transmit almost synchronously, the receiver may not be able to resolve the collision. This could be addressed by assigning dedicated pilots to each user, leading to a contention-free random access (CFRA

Donald W. Kurtz, Gerald Handler, Daniel L. Holdsworth, Margarida S. Cunha

HD 60435 is a well-known rapidly oscillating (roAp) Ap star with a series of alternating even and odd degree modes, making it a prime asteroseismic target. It is also an oblique pulsator with rotational inclination, $i$, and magnetic/pulsation obliquity, $\beta$, such that both magnetic/pulsation poles are viewed over the rotation period, $P_{\rm rot} = 7.67

Daniel Carney, Akira Matsumura

We analyze the framework recently proposed by Oppenheim et al. to model relativistic quantum fields coupled to relativistic, classical, stochastic fields (in particular, as a model of quantum matter coupled to ``classical gravity''). Perhaps surprisingly, we find that we can define and calculate scattering probabilities which are Lorentz-covariant and conser

Zi-Rui Zhong, Xia-Lin Su, Xiang-Ming Hu, Ke-Xuan Chen

Postselected weak measurement has shown significant potential for detecting small physical effects due to its unique weak-value-amplification phenomenon. Previous works suggest that Heisenberg-limit precision can be attained using only the optical coherent states. However, the measurement object is the distribution of postselection, limiting the practical ap

Hezi Zhang, Yiran Xu, Haotian Hu, Keyi Yin

Distributed Quantum Computing (DQC) enables scalability by interconnecting multiple QPUs. Among various DQC implementations, quantum data centers (QDCs), which utilize reconfigurable optical switch networks to link QPUs across different racks, are becoming feasible in the near term. However, the latency of cross-rack communications and dynamic reconfiguratio

Yotaro Kubo, Xingyu Cai, Michiel Bacchiani

This paper proposes a method to effectively perform joint training-and-pruning based on adaptive dropout layers with unit-wise retention probabilities. The proposed method is based on the estimation of a unit-wise retention probability in a dropout layer. A unit that is estimated to have a small retention probability can be considered to be prunable. The ret

Ran Tian, Yilin Wu, Chenfeng Xu, Masayoshi Tomizuka

Visuomotor robot policies, increasingly pre-trained on large-scale datasets, promise significant advancements across robotics domains. However, aligning these policies with end-user preferences remains a challenge, particularly when the preferences are hard to specify. While reinforcement learning from human feedback (RLHF) has become the predominant mechani

Rashi Jain, Yogesh Wadadekar

We report the discovery of Alaknanda, a large ($\sim10$ kpc diameter), massive ($\log(M_\star/M_\odot)\sim10.2$), candidate grand-design spiral galaxy with photometric redshift $z_{phot}\sim4.05$ in the UNCOVER and Medium band, Mega Science surveys with JWST. This is among the highest redshift spiral galaxies discovered with JWST. Our morphological analysis

Peiyan Hu, Rui Wang, Xiang Zheng, Tao Zhang

Simulating and controlling physical systems described by partial differential equations (PDEs) are crucial tasks across science and engineering. Recently, diffusion generative models have emerged as a competitive class of methods for these tasks due to their ability to capture long-term dependencies and model high-dimensional states. However, diffusion model

Chaozheng Wen, Jingwen Tong, Yingdong Hu, Zehong Lin

Wireless channel modeling plays a pivotal role in designing, analyzing, and optimizing wireless communication systems. Nevertheless, developing an effective channel modeling approach has been a long-standing challenge. This issue has been escalated due to denser network deployment, larger antenna arrays, and broader bandwidth in next-generation networks. To

Jian Jin, Yang Shen, Zhenyong Fu, Jian Yang

Customized generation aims to incorporate a novel concept into a pre-trained text-to-image model, enabling new generations of the concept in novel contexts guided by textual prompts. However, customized generation suffers from an inherent trade-off between concept fidelity and editability, i.e., between precisely modeling the concept and faithfully adhering

Jie Zheng, Da-Chun Qiang, Zhi-Qiang You

Recently, the measurements of baryon acoustic oscillations (BAO) by the Dark Energy Spectroscopic Instrument (DESI) indicate a potential deviation from the standard $\Lambda$CDM model. Some studies suggest that the data points from the luminous red galaxies (LRG) survey in DESI BAO data may contribute to this discrepancy. In this work, our main goal is to in

Nima Maghooli, Omid Mahdizadeh, Mohammad Bajelani, S. Ali A. Moosavian

This paper presents a learning-based approach for centralized position control of Tendon Driven Continuum Robots (TDCRs) using Deep Reinforcement Learning (DRL), with a particular focus on the Sim-to-Real transfer of control policies. The proposed control method employs the Modified Transpose Jacobian (MTJ) control strategy, with its parameters optimally tun

Ying Jin, Zhuoran Zhou, Haoquan Fang, Jenq-Neng Hwang

Medical image understanding requires meticulous examination of fine visual details, with particular regions requiring additional attention. While radiologists build such expertise over years of experience, it is challenging for AI models to learn where to look with limited amounts of training data. This limitation results in unsatisfying robustness in medica

Avinash Paliwal, Xilong Zhou, Andrii Tsarov, Nima Khademi Kalantari

In this paper, we present PanoDreamer, a novel method for producing a coherent 360{\deg} 3D scene from a single input image. Unlike existing methods that generate the scene sequentially, we frame the problem as single-image panorama and depth estimation. Once the coherent panoramic image and its corresponding depth are obtained, the scene can be reconstructe

Qingshan Xu, Jiequan Cui, Xuanyu Yi, Yuxuan Wang

3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner. During training, it relies heavily on the average magnitude of view-space positional gradients to grow Gaussians to reduce rendering loss. However, this average operation smooths the positional gradients from different viewpoints and r

Ge Yan, Kaisen Pan, Ruocheng Wang, Mengfei Ran

Simulating quantum many-body systems represents a fundamental challenge where classical machine learning methods are severely bottlenecked by the exponential curse of dimensionality. Variational Quantum Algorithms (VQAs) offer a native paradigm to tackle this by optimizing parameterized unitary evolutions to find the ground states of problem Hamiltonians. Ho

Anar Dosi

In the paper we investigate the joint spectra of Banach space representations of the quantum q-plane called Banach q-modules. Based on the transversality relation from the topological homology of the trivial modules versus given a left Banach q-module, we introduce the joint (essential) spectra of a Banach q-module. In particular, we have the well defined Ta

Anar Dosi

In the paper we investigate the Banach space representations of Manin's quantum q-plane for |q| is not 1. The Arens-Michael envelope of the quantum plane is extended up to a Frechet algebra presheaf over its spectrum. The obtained ringed space represents the geometry of the quantum plane as a union of two irreducible components being copies of the complex pl

Xudong Bai, Longpan Wang, Yuhua Chen, Xilong Lu

Programmable metasurfaces promise a great potential to construct low-cost phased array systems due to the capability of elaborate modulation over electromagnetic (EM) waves. However, they are in either reflective or transmissive mode, and usually possess a relatively high profile as a result of the external feed source. Besides, it is difficult to conduct mu

Qunhang Fu, Fei Wang, Mengdie Zhu, Han Ding

Wireless sensing has made significant progress in tasks ranging from action recognition, vital sign estimation, pose estimation, etc. After over a decade of work, wireless sensing currently stands at the tipping point transitioning from proof-of-concept systems to the large-scale deployment. We envision a future service scenario where wireless sensing servic

Charles Dietzel, Patrick J. Martin

One key area of research in Human-Robot Interaction is solving the human-robot correspondence problem, which asks how a robot can learn to reproduce a human motion demonstration when the human and robot have different dynamics and kinematic structures. Evaluating these correspondence problem solutions often requires the use of qualitative surveys that can be

S. Sivaprasad Kumar, Surya Giri

We introduce and study a class of starlike functions associated with the non-convex domain \[ \mathcal{S}^*_{nc} = \left\{ f \in \mathcal{A} : \frac{z f'(z)}{f(z)} \prec \frac{1+z}{\cos{z}} =: \varphi_{nc}(z), \;\; z \in \mathbb{D} \right\}. \] Key results include the growth and distortion theorems, initial coefficient bounds, and the sharp estimates for thi

Ilma, M. Rafi Alam, L. Alvarez-Ruso, M. Benitez Galan

New physics beyond the Standard Model (SM) may appear in the form of non-standard neutrino interactions (NSI). We have studied neutral current (anti)neutrino-nucleon scattering in presence of NSI. We obtain that in this scenario, nucleon matrix elements depend not only on the isovector axial nucleon form factor but also on the isoscalar one. For the axial fo

I. A. Karimjanov

In the present paper, we give the classification of a subclass of n-dimensional naturally graded associative algebras with nilindex $n-3$. The subclass has the characteristic sequence $C(\mathcal{A})=(n-3,2,1)$. The result completes the classification of naturally graded associative algebras with nilindex $n-3$ for $n>6$.

Xavier D'Haultfoeuille, Christophe Gaillac, Arnaud Maurel

We study linear regressions in a context where the outcome of interest and some of the covariates are observed in two different datasets that cannot be matched. Traditional approaches obtain point identification by relying, often implicitly, on exclusion restrictions. We show that without such restrictions, coefficients of interest can still be partially ide

Priyesh Kumar Tripathi, Indranil Chattopadhyay, Raj Kishor Joshi

We investigate accretion onto an isolated black hole from uniform winds. If the winds are directed towards the black hole, then the accretion process can be well described by the classical Bondi-Hoyle Lyttleton or BHL accretion. If the wind is not directed towards the black hole and flows past it, then a smaller fraction of the flow can be attracted by the b

Yibin Wang, Zhiyu Tan, Junyan Wang, Xiaomeng Yang

Recent advances in text-to-video (T2V) generative models have shown impressive capabilities. However, these models are still inadequate in aligning synthesized videos with human preferences (e.g., accurately reflecting text descriptions), which is particularly difficult to address, as human preferences are subjective and challenging to formalize as objective

Lihao Zhang, Xiaoyu Wang, Qi Li, Haibo Xie

Atomically thin materials with coupled magnetic and electric polarization are critical for developing energy-efficient and high-density spintronic devices, yet they remain scarce due to often conflicting requirements of stabilizing both magnetic and electric orders. The recent discovery of the magnetoelectric effect in the 2D stripy antiferromagnet CrOCl hig

Zeynel A. Samak, Philip Clatworthy, Majid Mirmehdi

Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medical reports and other sensor information,

Evgeny Korotyaev

We consider time periodic Hamiltonian on periodic graphs and estimate the number of its quasi-energy eigenvalues on the finite interval.

Hao-Cheng Thong, Fang-Zhou Yao, Xian-Xian Cai, Ze Xu

The functionality of ferroelectrics is often constrained by their Curie temperature, above which depolarization occurs. Lithium (Li) is the only experimentally known substitute that can increase the Curie temperature in ferroelectric niobate-based perovskites, yet the mechanism remains unresolved. Here, the unique phenomenon in Li-substituted KNbO3 is invest

Alexey Vylegzhanin, Dylan J. Brown, Sergey Abdrakhmanov, Sile Nic Chormaic

We present a magnetic trapping scheme for cold 87Rb atoms based on light-induced fictitious magnetic fields generated by the evanescent field of an optical nanofiber (ONF) integrated with an optical tweezers. We calculate and compare the trapping potentials for both Gaussian and Laguerre-Gaussian modes of the tweezers beam, combined with a quasi-linearly pol

Nikhil Bharti, Nguyen Van Thin

In this paper, we study the concepts of normal functions and $\varphi$-normal functions in the framework of planar harmonic mappings. We establish the harmonic mapping counterpart of the well-known Zalcman-Pang lemma and as a consequence, we prove that a harmonic mapping whose spherical derivative is bounded away from zero is normal. Furthermore, we introduc

Qinfeng Zhu, Sihui Li, Zuozhen Cao, Yao Shen

Ex-vivo MRI offers invaluable insights into the complexity of the human brain, enabling high-resolution anatomical delineation and integration with histopathology, and thus, contributes to both basic and clinical studies on normal and pathological brains. However, ex-vivo MRI is challenging in sample preparation, acquisition, and data analysis, and existing

Jiachen Bai, Jun-Qing Xia, Gong-Bo Zhao

In this work, we perform a detailed analysis to constrain the Hu-Sawicki \(f(R)\) gravity model, using cosmic shear data from three prominent Stage-III weak lensing surveys: DES-Y3, KiDS-1000, and HSC-Y3. To accurately model the nonlinear matter clustering in the analysis of cosmic shear signals, we employ \texttt{FREmu}, a recently developed power spectrum

Jayanie Bogahawatte, Sachith Seneviratne, Maneesha Perera, Saman Halgamuge

Adapting Large Language Models (LLMs) trained on discrete text data, to forecast continuous time series signals is challenging. While finetuning the LLMs enables such adaptation, effectively integrating both textual and time series information in the prompt is critical. Current LLM-based time series forecasting methods combine the two modalities through simp

Wenzhe Yang, Sheng Wang, Shixun Huang, Hao Liu

There has been increased interest in data search as a means to find relevant datasets or data points in data lakes and repositories. Although approaches have been proposed to support spatial dataset search and data point search, they consider the two types of searches independently. To enable search operations ranging from the coarse-grained dataset level to

Pu Zhang, Christos Tserkezis, N. Asger Mortensen

Plasmonic gap structures are among the few configurations capable of generating extreme light confinement, finding applications in surface-enhanced spectroscopy, ultrasensitive detection, photocatalysis and more. Their plasmonic response undergoes a dramatic, quantum effect-driven transition as the gap size approaches zero. Modal analysis can reveal insights

Silpa K., Sreedevi E. P., P. G. Sankaran

In this work, we present two defective regression models for the analysis of interval-censored competing risk data in the presence of cured individuals, viz., defective Gompertz and defective inverse Gaussian regression models. The proposed models enable us to estimate the cure fraction directly from the model. Simultaneously, we estimate the regression para

Songcheng Du, Yang Zou, Zixu Wang, Xingyuan Li

Fusion-based hyperspectral image super-resolution aims to fuse low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) to reconstruct high spatial and high spectral resolution images. Current methods typically apply direct fusion from the two modalities without effective supervision, leading to an incomplete perceptio

Shinya Kudo

Let $q$ be a Pisot or Salem number. Let $f_j(x)$ $(j=1,2,\dots)$ be integer-valued polynomials of degree $\ge2$ with positive leading coefficients, and let $\{a_j (n)\}_{n\ge1}$ $(j=1,2,\dots)$ be sequences of algebraic integers in the field $\mathbb{Q}(q)$ with suitable growth conditions. In this paper, we investigate linear independence over $\mathbb{Q}(q)

Yue-Yue Tian, Hui-fen Zhang, Bo-Yuan Ning, Xi-Jing Ning

The high-temperature and high-pressure equations of states (EOSs) of rhenium up to 3000 K and 900 GPa are predicted by a recently developed method in the framework of statistical ensemble theory with \textit{ab initio} computational precision. The predicted isothermal EOSs are generally consistent with semi-empirical calculations below 150 GPa and 3000 K. Es

Suhan Guo, Furao Shen, Ni Li

Understanding the effects of quarantine policies in populations with underlying social networks is crucial for public health, yet most causal inference methods fail here due to their assumption of independent individuals. We introduce DeepNetTMLE, a deep-learning-enhanced Targeted Maximum Likelihood Estimation (TMLE) method designed to estimate time-sensitiv

Haizhou Yang, Jiyang Zhang, Ismael Z. Assi, Brahmajee K. Nallamothu

Coronary Microvascular Dysfunction (CMD) is characterized by impaired vasodilation and can lead to insufficient blood flow to the myocardium during stress or exertion, affecting millions of people globally. Despite their diagnostic value, invasive, wire-based diagnosis techniques of CMD, such as index of microcirculatory resistance (IMR) and coronary flow re

Fanchen Wu, Zheng Chen

This paper is concerned with the minimum-time path-planning problem for a Dubins airplane under the influence of steady wind. The path-planning problem, by transforming into the air-relative frame, is equivalent to finding the minimum-time control strategy for a Dubins airplane to intercept a moving target. In the air-relative frame, by applying Pontryagin's

Soheila Sadeghi

The growing integration of Artificial Intelligence (AI) into Human Resources (HR) processes has transformed the way organizations manage recruitment, performance evaluation, and employee engagement. While AI offers numerous advantages, such as improved efficiency, reduced bias, and hyper-personalization, it raises significant concerns about employee well-bei

Masaaki Harada

For lengths $36$, $48$ and $60$, we construct new ternary near-extremal self-dual codes with weight enumerators for which no ternary near-extremal self-dual codes were previously known to exist.

Yixin Liu, Shaoling Liang, Kunlun Wang, Wen Chen

This paper proposes a distributed massive multiple input multiple-output (DM-MIMO) aided multi-tier vehicular edge computing (VEC) system. In particular, each vehicle terminal (VT) offloads its computational task to the roadside unit (RSU) by orthogonal frequency division multiple access (OFDMA), which can be computed locally at the RSU and offloaded to the

Mahfuzul Haque, Abu Saleh Musa Miah, Debashish Gupta, Md. Maruf Al Hossain Prince

Heart disease is a leading cause of premature death worldwide, particularly among middle-aged and older adults, with men experiencing a higher prevalence. According to the World Health Organization (WHO), non-communicable diseases, including heart disease, account for 25\% (17.9 million) of global deaths, with over 43,204 annual fatalities in Bangladesh. How

Divyanshu Singh, Shiroman Prakash

We show that one can implement the Deutsch-Josza algorithm, one of the first and simplest quantum algorithms, in a fault-tolerant manner using the smallest quantum error-detecting code -- the $[[4,2,2]]$ code -- without any ancillae. We implemented the algorithm on a trapped-ion quantum computer with and without fault-tolerant encoding and compared the resul

M. Miyamoto, M. Horio, K. Moriya, A. Takahashi

The development of the protect-annealing method has extended the superconductivity of the electron-doped cuprate Pr$_{1.3-x}$La$_{0.7}$Ce$_{x}$CuO$_{4}$ (PLCCO) into lower Ce concentrations, while the superconducting volume fraction decreases with underdoping. Employing angle-resolved photoemission spectroscopy with a micro-focused beam, we investigated the

Fardad Dadboud, Hamid Azad, Varun Mehta, Miodrag Bolic

Dependable visual drone detection is crucial for the secure integration of drones into the airspace. However, drone detection accuracy is significantly affected by domain shifts due to environmental changes, varied points of view, and background shifts. To address these challenges, we present the DrIFT dataset, specifically developed for visual drone detecti

Yanyu Chen, Ganhong Huang

Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily due to hardware heterogeneity, inference framework limitations, and workload complexities.Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily due to hardware heterogeneity, inference

Kaiyan Zhao, Tsuguchika Tabaru, Kenichi Kobayashi, Takumi Honda

Although recent quantized Large Language Models (LLMs), such as BitNet, have paved the way for significant reduction in memory usage during deployment with binary or ternary weights, training these models still demands substantial memory footprints. This is partly because high-precision (i.e., unquantized) weights required for straight-through estimation mus

Yitian Zhang, Huseyin Coskun, Xu Ma, Huan Wang

Vision Transformers (ViT) is known for its scalability. In this work, we target to scale down a ViT to fit in an environment with dynamic-changing resource constraints. We observe that smaller ViTs are intrinsically the sub-networks of a larger ViT with different widths. Thus, we propose a general framework, named Scala, to enable a single network to represe

Chunyang Liao, Deanna Needell, Hayden Schaeffer, Alexander Xue

Designing privacy-preserving machine learning algorithms has received great attention in recent years, especially in the setting when the data contains sensitive information. Differential privacy (DP) is a widely used mechanism for data analysis with privacy guarantees. In this paper, we produce a differentially private random feature model. Random features,

Yuangang Li, Jiaqi Li, Zhuo Xiao, Tiankai Yang

Anomaly detection (AD) is an important machine learning task with applications in fraud detection, content moderation, and user behavior analysis. However, AD is relatively understudied in a natural language processing (NLP) context, limiting its effectiveness in detecting harmful content, phishing attempts, and spam reviews. We introduce NLP-ADBench, the mo

Brian Ye, Zhuonan Hao, Priya Shah, Mohammad Khalid Jawed

While its biological significance is well-documented, its application in soft robotics, particularly for the transport of fragile and irregularly shaped objects, remains underexplored. This study presents a modular soft robotic actuator system that addresses these challenges through a scalable, adaptable, and repairable framework, offering a cost-effective s

Zijian Zhao, Zhijie Cai, Tingwei Chen, Xiaoyang Li

Wireless sensing has recently found widespread applications in diverse environments, including homes, offices, and public spaces. By analyzing patterns in channel state information (CSI), it is possible to infer human actions for tasks such as person identification, gesture recognition, and fall detection. However, CSI is highly sensitive to environmental ch

Aditi Singh, Nirmal Prakashbhai Patel, Abul Ehtesham, Saket Kumar

Large Language Models (LLMs) have transformed numerous domains by providing advanced capabilities in natural language understanding, generation, and reasoning. Despite their groundbreaking applications across industries such as research, healthcare, and creative media, their rapid adoption raises critical concerns regarding sustainability. This survey paper

Lin-Feng Mei, Wang-Ji Yan

Clustering based on vibration responses, such as transmissibility functions (TFs), is promising in structural anomaly detection. However, most existing methods struggle to determine the optimal cluster number, handle high-dimensional streaming data, and rely heavily on manually engineered features due to their shallow structures. To address these issues, thi

Junno Yun, Mehmet Akçakaya

Infrared (IR) imaging is commonly used in various scenarios, including autonomous driving, fire safety and defense applications. Thus, semantic segmentation of such images is of great interest. However, this task faces several challenges, including data scarcity, differing contrast and input channel number compared to natural images, and emergence of classes

Ishan Patwardhan, Sunil Mane, Nidhi Patel

This research provides a critical analysis regarding the way blockchain is being implemented in the financial industry, highlighting its vital role in promoting green finance, guaranteeing compliance with regulations, improving supply chain finance, boosting decentralized finance (DeFi), and strengthening the Internet of Things (IoT). It discusses how blockc

Ryuichiro Kitano, Joe Sato, Sho Sugama

We study the possibility of measuring T (time reversal) violation in a future long baseline neutrino oscillation experiment. By assuming a neutrino factory as a staging scenario of a muon collider at the J-PARC site, we find that the $\nu_e \to \nu_\mu$ oscillation probabilities can be measured with good accuracy at the Hyper-Kamiokande detector. By comparin

Asara Senaratne, Peter Christen, Pouya Omran, Graham Williams

Anomalies such as redundant, inconsistent, contradictory, and deficient values in a Knowledge Graph (KG) are unavoidable, as these graphs are often curated manually, or extracted using machine learning and natural language processing techniques. Therefore, anomaly detection is a task that can enhance the quality of KGs. In this paper, we propose SEKA (SEekin

Kunika Agarwal, Sahil Gopalkrishna Naik, Ananya Chakraborty, Samrat Sen

The zero-error capacity of a noisy classical channel quantifies its ability to transmit information with absolute certainty, i.e., without any error. Unlike Shannon's standard channel capacity, which remains unaffected by pre-shared correlations, zero-error capacity can be enhanced through nonlocal correlations. In this work, we investigate zero-error commun

ChangMin Ye, Yonguk Sim, Youngchae Kim, SeongMin Jin

Transformer-based large language models are a memory-bound model whose operation is based on a large amount of data that are marginally reused. Thus, the data movement between a host and accelerator likely dictates the total wall-clock time. Layer normalization is one of the key workloads in the transformer model, following each of multi-head attention and f

Yu-Wei Fan

We prove that the space of Bridgeland stability conditions, when equipped with the canonical metric, is not a length space in general. This resolves a question posed by Kikuta in the negative. Furthermore, we introduce two modified metrics on the stability spaces, which may exhibit better metric properties.

Xueluan Gong, Bowei Tian, Meng Xue, Shuike Li

Vision transformers have achieved impressive performance in various vision-related tasks, but their vulnerability to backdoor attacks is under-explored. A handful of existing works focus on dirty-label attacks with wrongly-labeled poisoned training samples, which may fail if a benign model trainer corrects the labels. In this paper, we propose Megatron, an e

SuMin Oh, WanSoo Kim, HyunJin Kim

Efficient exploration remains one of the longstanding problems of deep reinforcement learning. Instead of depending solely on extrinsic rewards from the environments, existing methods use intrinsic rewards to enhance exploration. However, we demonstrate that these methods are vulnerable to Noisy TV and stochasticity. To tackle this problem, we propose Tempor

Zhaojun Ding, Zhengliang Liu, Hanqi Jiang, Yizhu Gao

Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource languages due to limited training data and difficulty in understanding cultural nuances. Research is now focusing on multil

Xiaoyu Zhang, Di Wang, Guodong Li, Defeng Sun

Low-rank tensor models are widely used in statistics. However, most existing methods rely heavily on the assumption that data follows a sub-Gaussian distribution. To address the challenges associated with heavy-tailed distributions encountered in real-world applications, we propose a novel robust estimation procedure based on truncated gradient descent for g

Maxime Cherrey, Nicolas F. Bouché, Johannes Zabl, Ilane Schroetter

The circumgalactic medium (CGM) is a key component needed to understand the physical processes governing the flows of gas around galaxies. Quantifying its evolution and its dependence on galaxy properties is particularly important for our understanding of accretion and feedback mechanisms. We select a volume-selected sample of 66 {\it isolated} star-forming

Yi-Jun Chang, Yanyu Chen, Gopinath Mishra

We consider the problem of constructing distributed overlay networks, where nodes in a reconfigurable system can create or sever connections with nodes whose identifiers they know. Initially, each node knows only its own and its neighbors' identifiers, forming a local channel, while the evolving structure is termed the global channel. The goal is to reconfig

Yirui Zheng, Juntai Shen

Galactic bars can form via the internal bar instability or external tidal perturbations by other galaxies. We systematically compare the properties of bars formed through the two mechanisms with a series of controlled $N$-body simulations that form bars through internal or external mechanisms. We create three disk galaxy models with different dynamical ``hot

Lei Fan, Junjie Huang, Donglin Di, Anyang Su

For anomaly detection (AD), early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource management. Recent efforts have shifted toward training a single model capable of handling multiple classes. However, directly extending early AD methods to multi-class settings often res

Kaustubh D. Dhole

Using LLMs as rerankers requires experimenting with various hyperparameters, such as prompt formats, model choice, and reformulation strategies. We introduce PyTerrier-GenRank, a PyTerrier plugin to facilitate seamless reranking experiments with LLMs, supporting popular ranking strategies like pointwise and listwise prompting. We validate our plugin through

Bowei Tian, Ziyao Wang, Shwai He, Wanghao Ye

The challenge of balancing fairness and predictive accuracy in machine learning models, especially when sensitive attributes such as race, gender, or age are considered, has motivated substantial research in recent years. Counterfactual fairness ensures that predictions remain consistent across counterfactual variations of sensitive attributes, which is a cr

Shadab Ahamed, Eldad Haber

Inverse problems, which involve estimating parameters from incomplete or noisy observations, arise in various fields such as medical imaging, geophysics, and signal processing. These problems are often ill-posed, requiring regularization techniques to stabilize the solution. In this work, we employ Flow Matching (FM), a generative framework that integrates a

Shuge Ouyang, Yunxuan Tang, Benjamin Osafo Agyare

Low-rank matrix factorization is a powerful tool for understanding the structure of 2-way data, and is usually accomplished by minimizing a sum of squares criterion. Expectile analysis generalizes squared-error loss by introducing asymmetry, allowing tail behavior to be elicited. Here we present a framework for low-rank expectile analysis of a data matrix th

Xiyu Pan, Neda Mohammadi, John E. Taylor

Accurate short-term streamflow and flood forecasting are critical for mitigating river flood impacts, especially given the increasing climate variability. Machine learning-based streamflow forecasting relies on large streamflow datasets derived from rating curves. Uncertainties in rating curve modeling could introduce errors to the streamflow data and affect

Andrei Stan

In this paper, we determine an exact solution to the governing equations in spherical coordinates for an inviscid, incompressible fluid. This solution describes a steady, purely azimuthal equatorial flow with an associated free surface. Using functional analytic techniques, we demonstrate that if a free surface is known beforehand, the variations in pressure

Mewael Isiet, Yunhuan Xiao, Jerry I. Dadap, Ziliang Ye

Spall failure in materials occurs when tensile waves, propagating through a material, interact leading to failure once the generated hydrostatic stress exceeds the material's strength. In this study, we introduce a novel two-pulse laser approach to induce spall failure by simultaneously illuminating both free surfaces of micro- and nanoscale-thick Ni foils u

Xiaotian Xu, Pei Sun, Xin Zhang, Junpeng Cao

We study the Izergin-Korepin Gaudin models with both periodic and open integrable boundary conditions, which describe quantum systems exhibiting novel long-range interactions. Using the Bethe ansatz approach, we derive the eigenvalues of the Gaudin operators and the corresponding Bethe ansatz equations.

Tohid Zarei, Michelle Emery, Dimitrios Saredakis, Gun A. Lee

Effectively using immersive multi-user environments for digital applications (via virtual, augmented and mixed reality technologies) beckons the future of healthcare delivery in the metaverse. We aimed to evaluate the feasibility and effectiveness of these environments used in health applications, while identifying their design features. We systematically se

Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, Insup Lee

Building generalist agents that can rapidly adapt to new environments is a key challenge for deploying AI in the digital and real worlds. Is scaling current agent architectures the most effective way to build generalist agents? We propose a novel approach to pre-train relatively small policies on relatively small datasets and adapt them to unseen environment

Matt MacDermott, James Fox, Francesco Belardinelli, Tom Everitt

We define maximum entropy goal-directedness (MEG), a formal measure of goal-directedness in causal models and Markov decision processes, and give algorithms for computing it. Measuring goal-directedness is important, as it is a critical element of many concerns about harm from AI. It is also of philosophical interest, as goal-directedness is a key aspect of