October 2024 arXiv papers — page 60

Xuesong Liu, Yao Sun, Runze Cheng, Le Xia

Semantic communication (SC) offers promising advancements in data transmission efficiency and reliability by focusing on delivering true meaning rather than solely binary bits of messages. However, privacy concerns in SC might become outstanding. Eavesdroppers equipped with advanced semantic coding models and extensive knowledge could be capable of correctly

Maarten Buyl, Alexander Rogiers, Sander Noels, Guillaume Bied

Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These models have become popular in artificial intelligence (AI) assistants like ChatGPT and already play an influential role in how humans access information. However, the behavior of L

Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen

Unsupervised skill discovery carries the promise that an intelligent agent can learn reusable skills through autonomous, reward-free environment interaction. Existing unsupervised skill discovery methods learn skills by encouraging distinguishable behaviors that cover diverse states. However, in complex environments with many state factors (e.g., household e

Kun Li, Tianhua Zhang, Xixin Wu, Hongyin Luo

Knowledge Graphs (KGs) can serve as reliable knowledge sources for question answering (QA) due to their structured representation of knowledge. Existing research on the utilization of KG for large language models (LLMs) prevalently relies on subgraph retriever or iterative prompting, overlooking the potential synergy of LLMs' step-wise reasoning capabilities

Andrew J. Christlieb, William A. Sands, Stephen R. White

In this paper, we introduce a new family of spatially co-located field solvers for particle-in-cell applications which evolve the potential formulation of Maxwell's equations under the Lorenz gauge. Our recent work introduced the concept of time-consistency, which connects charge conservation to the preservation of the gauge at the semi-discrete level. It wi

Gonzalo E. Constante-Flores, André H. Quisaguano, Antonio J. Conejo, Can Li

This paper presents a parametric quadratic approximation of the AC optimal power flow (AC-OPF) problem for time-sensitive and market-based applications. The parametric approximation preserves the physics-based but simple representation provided by the DC-OPF model and leverages market and physics information encoded in the data-driven demand-dependent parame

Xudong Sun, Zhuo Chen, Jingyang Shi, Yiyu Zhang

Data races are critical issues in multithreaded program, leading to unpredictable, catastrophic and difficult-to-diagnose problems. Despite the extensive in-house testing, data races often escape to deployed software and manifest in production runs. Existing approaches suffer from either prohibitively high runtime overhead or incomplete detection capability.

Sadaf R. Alam, Christopher Woods, Matt Williams, Dave Moore

Scientific workflows have become highly heterogenous, leveraging distributed facilities such as High Performance Computing (HPC), Artificial Intelligence (AI), Machine Learning (ML), scientific instruments (data-driven pipelines) and edge computing. As a result, Identity and Access Management (IAM) and Cybersecurity challenges across the diverse hardware and

Zhengqiang Zhang, Ruihuang Li, Lei Zhang

While image generation with diffusion models has achieved a great success, generating images of higher resolution than the training size remains a challenging task due to the high computational cost. Current methods typically perform the entire sampling process at full resolution and process all frequency components simultaneously, contradicting with the inh

Chenyin Gao, Shu Yang, Mingyang Shan, Wenyu Wendy Ye

Censored survival data are common in clinical trials, but small control groups can pose challenges, particularly in rare diseases or where balanced randomization is impractical. Recent approaches leverage external controls from historical studies or real-world data to strengthen treatment evaluation for survival outcomes. However, using external controls dir

Haotian Wang, Meng Yang, Xinhu Zheng, Gang Hua

Depth completion, inferring dense depth maps from sparse measurements, is crucial for robust 3D perception. Although deep learning based methods have made tremendous progress in this problem, these models cannot generalize well across different scenes that are unobserved in training, posing a fundamental limitation that yet to be overcome. A careful analysis

Songbo Hou, Xiaoqing Kong

For $n \geq 2$, consider $\mathbb{Z}^n$ as a lattice graph. We explore a generalized Chern-Simons equation on $\mathbb{Z}^n$. Employing the method of exhaustion, we prove that there exists a global solution that also qualifies as a topological solution. Our results extend those of Hua et al. [arXiv:2310.13905] and complement the findings of Chao and Hou [J.

Zhisheng Lin, Yifu Liu, Zhiling Luo, Jinyang Gao

The improvement in translating natural language to structured query language (SQL) can be attributed to the advancements in large language models (LLMs). Open-source LLMs, tailored for specific database dialects such as MySQL, have shown great performance. However, cloud service providers are looking for a unified database manager service (e.g., Cosmos DB fr

Aiqin Yang, Xiangru Tao, Yundi Quan, Peng Zhang

A recent experiment by Shan {\it et al} [arXiv:2304.09011] found that rhenium trioxide ReO$_3$, a simple metal at the ambient pressure, becomes superconducting with a transition temperature as high as 17 K at 30 GPa. In this paper, we analyze the electron-phonon origin of superconductivity in rhombohedral ReO$_3$ in detail. In addition, we also conduct a hig

Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas A. Courtade, Alireza Fallah

Local Differential Privacy (LDP) offers strong privacy guarantees without requiring users to trust external parties. However, LDP applies uniform protection to all data features, including less sensitive ones, which degrades performance of downstream tasks. To overcome this limitation, we propose a Bayesian framework, Bayesian Coordinate Differential Privacy

Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu, Chence Shi

Proteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is crucial for advancing drug discovery. Traditional physics-based simulation methods often struggle with sampling equilibrium conformations and are computationally expensive. Recently, deep generative models have shown promis

Sijia Xia, Michael K. Ng, Xiongjun Zhang

In this paper, we study the problem of low-rank tensor learning, where only a few of training samples are observed and the underlying tensor has a low-rank structure. The existing methods are based on the sum of nuclear norms of unfolding matrices of a tensor, which may be suboptimal. In order to explore the low-rankness of the underlying tensor effectively,

Zhi-fu Chen, Zhe-Geng Chen, Xing-long Peng, Wei-rong Huang

Radio spectral shape of quasars can provide insight into the ages of quasars. We have compiled data for 1804 quasars with $z\lesssim1$ from the Sloan Digital Sky Survey (SDSS). Additionally, these quasars were also mapped by the Low-Frequency Array at 144 MHz and the Very Large Array Sky Survey at 3000 MHz. The radio spectral index, designated as $\alpha^{\r

Huan Cui, Qing Li, Hanling Wang, Yong jiang

We introduce a cutting-edge video compression framework tailored for the age of ubiquitous video data, uniquely designed to serve machine learning applications. Unlike traditional compression methods that prioritize human visual perception, our innovative approach focuses on preserving semantic information critical for deep learning accuracy, while efficient

Huan Cui, Qing Li, Hanling Wang, Yong jiang

Mobile deep vision systems play a vital role in numerous scenarios. However, deep learning applications in mobile vision scenarios face problems such as tight computing resources. With the development of edge computing, the architecture of edge clouds has mitigated some of the issues related to limited computing resources. However, it has introduced increase

Mingbo Hong, Shen Cheng, Haibin Huang, Haoqiang Fan

In this paper, we introduce YOLA, a novel framework for object detection in low-light scenarios. Unlike previous works, we propose to tackle this challenging problem from the perspective of feature learning. Specifically, we propose to learn illumination-invariant features through the Lambertian image formation model. We observe that, under the Lambertian as

Elina Sendonaris, James Williams, Rajveer Nehra, Robert Gray

Integrated photonic quantum information processing (QIP) has advanced rapidly due to progress in various nanophotonic platforms. Single photon detectors have been the subject of intense study due to their ubiquity in QIP systems, yet many state-of-the art detectors operate at cryogenic temperatures under vacuum and suffer from long dead times. We propose and

Kaifeng Jin, Ignavier Ng, Kun Zhang, Biwei Huang

Recent advances in differentiable structure learning have framed the combinatorial problem of learning directed acyclic graphs as a continuous optimization problem. Various aspects, including data standardization, have been studied to identify factors that influence the empirical performance of these methods. In this work, we investigate critical limitations

Ali Izadi, Martin Ester

In this paper, we consider the problem of causal order discovery within the framework of monotonic Structural Causal Models (SCMs), which have gained attention for their potential to enable causal inference and causal discovery from observational data. While existing approaches either assume prior knowledge about the causal order or use complex optimization

Seyed Ali Alavi Bajestan, Mark Pitt, Donald S. Williamson

Carrying conversations in multi-sound environments is one of the more challenging tasks, since the sounds overlap across time and frequency making it difficult to understand a single sound source. One proposed approach to help isolate an attended speech source is through decoding the electroencephalogram (EEG) and identifying the attended audio source using

Zhen Zhou, Jiachen Li, Hongyi Xin, Xiaoyong Pan

Modeling cellular dynamics from single-cell RNA sequencing (scRNA-seq) data is critical for understanding cell development and underlying gene regulatory relationships. Many current methods rely on single-cell velocity to obtain pseudotime, which can lead to inconsistencies between pseudotime and velocity. It is challenging to simultaneously infer cell pseud

Tanmay Parekh, Jeffrey Kwan, Jiarui Yu, Sparsh Johri

Social media is often the first place where communities discuss the latest societal trends. Prior works have utilized this platform to extract epidemic-related information (e.g. infections, preventive measures) to provide early warnings for epidemic prediction. However, these works only focused on English posts, while epidemics can occur anywhere in the worl

Li Cai, Yangyu Fan, Shilin Lai

We explain how the unramified Plancherel formula in the relative Langlands program gives a natural way of constructing test vectors which satisfy the tame norm relations of an Euler system. This uniformly recovers many of the known Euler systems, and in the twisted Friedberg--Jacquet setting, we produce a new split anticyclotomic Euler system.

Andrew Lowy, Daogao Liu, Hilal Asi

We study private stochastic convex optimization (SCO) under user-level differential privacy (DP) constraints. In this setting, there are $n$ users (e.g., cell phones), each possessing $m$ data items (e.g., text messages), and we need to protect the privacy of each user's entire collection of data items. Existing algorithms for user-level DP SCO are impractic

Xinyu Wang, Wenbo Zhang, Sarah Rajtmajer

In today's global digital landscape, misinformation transcends linguistic boundaries, posing a significant challenge for moderation systems. Most approaches to misinformation detection are monolingual, focused on high-resource languages, i.e., a handful of world languages that have benefited from substantial research investment. This survey provides a compre

Avinash J. Karamchandani

We introduce an extension to the standard reduction of oscillatory systems to a single phase variable. The standard reduction is often insufficient, particularly when the oscillations have variable amplitude and the magnitude of each oscillatory excursion plays a defining role in the impact of that oscillator on other systems, i.e. on its output. For instanc

Bo Han, Yuheng Jia, Hui Liu, Junhui Hou

Spectral variations pose a common challenge in analyzing hyperspectral images (HSI). To address this, low-rank tensor representation has emerged as a robust strategy, leveraging inherent correlations within HSI data. However, the spatial distribution of ground objects in HSIs is inherently irregular, existing naturally in tensor format, with numerous class-s

Lehan Wang, Haonan Wang, Honglong Yang, Jiaji Mao

Several medical Multimodal Large Languange Models (MLLMs) have been developed to address tasks involving visual images with textual instructions across various medical modalities, achieving impressive results. Most current medical generalist models are region-agnostic, treating the entire image as a holistic representation. However, they struggle to identify

A. Kundu, Jagdish C. Joshi, C. Venter, N. E. Engelbrecht

Recent observations of a few young pulsar wind nebulae (PWNe) have revealed their morphologies in some detail. Given the availability of spatio-spectral-temporal data, we use our multi-zone (1D) leptonic emission code to model the PWNe associated with G29.7-0.3 (Kes 75) and G21.5-0.9 (G21.5) and obtain (by-eye) constraints on additional model parameters comp

Dae Yon Hwang, Bilal Taha, Harshit Pande, Yaroslav Nechaev

Despite the recent advancements in information retrieval (IR), zero-shot IR remains a significant challenge, especially when dealing with new domains, languages, and newly-released use cases that lack historical query traffic from existing users. For such cases, it is common to use query augmentations followed by fine-tuning pre-trained models on the documen

Xiaojing Dong, Yunqing Huang, Tianwen Wang

This paper proposes a virtual element method (VEM) combined with a second-order implicit-explicit scheme based on the scalar auxiliary variable (SAV) method for the incompressible magnetohydrodynamics (MHD) equations. We employ the BDF2 scheme for time discretization and a conservative VEM for spatial discretization, in which the mass conservation in the vel

Guanyu Li

We introduce the \verb|Macaulay2| package \verb|RepHomology| for the computations of representation homology of certain spaces. The main methods implement computing the representation homology of surfaces (with group coefficients, and analogies with algebra and Lie algebra coefficients), and the representation homology of link complements.

Xinran Fang, Chengleyang Lei, Wei Feng, Yunfei Chen

Rapid advancements in field robots have brought a new kind of cyber physical system (CPS)--unmanned robotic system--under the spotlight. In the upcoming sixth-generation (6G) era, these systems hold great potential to replace humans in hazardous tasks. This paper investigates an unmanned robotic system comprising a multi-functional unmanned aerial vehicle (U

Shakeeb Khan, Elie Tamer, Qingsong Yao

A class of simultaneous equation models arise in the many domains where observed binary outcomes are themselves a consequence of the existing choices of of one of the agents in the model. These models are gaining increasing interest in the computer science and machine learning literatures where they refer the potentially endogenous sample selection as the {\

Robert Dilworth, Charan Gudla

This paper explores the application of Positive-Unlabeled (PU) learning for enhanced Distributed Denial-of-Service (DDoS) detection in cloud environments. Utilizing the $\texttt{BCCC-cPacket-Cloud-DDoS-2024}$ dataset, we implement PU learning with four machine learning algorithms: XGBoost, Random Forest, Support Vector Machine, and Na\"{i}ve Bayes. Our resul

Ike Obi, Jenna Butler, Sankeerti Haniyur, Brian Hassan

Software development is a dynamic activity that requires engineers to work effectively with tools, processes, and collaborative teams. As a result, the presence of friction can significantly hinder productivity, increase frustration, and contribute to low morale among developers. By contrast, higher satisfaction levels are positively correlated with higher l

Chen Gong, Zhenzhe Zheng, Fan Wu, Xiaofeng Jia

In modern mobile applications, users frequently encounter various new contexts, necessitating on-device continual learning (CL) to ensure consistent model performance. While existing research predominantly focused on developing lightweight CL frameworks, we identify that data scarcity is a critical bottleneck for on-device CL. In this work, we explore the po

Yasha Neiman

We present a new Lagrangian formulation of General Relativity with cosmological constant, coupled to Yang-Mills gauge theory. The formulation has a manifest color/kinematics-dual structure, both in the choice of fundamental fields and in the way they appear in the Lagrangian. The color/kinematics duality is between gauge and Lorentz generators. The fundament

Xiaojing Dong, Yunqing Huang, Tianwen Wang

In this paper, we propose a conservative nonconforming virtual element method for the full stationary incompressible magnetohydrodynamics model. We leverage the virtual element satisfactory divergence-free property to ensure mass conservation for the velocity field. The condition of the well-posedness of the proposed method, as well as the stability are deri

Xiaojing Dong, Yibing Han, Yunqing Huang

We propose a new stable variational formulation for the quad-div problem in three dimensions and prove its well-posedness. Using this weak form, we develop and analyze the $\boldsymbol{H}(\operatorname{grad-div})$-conforming virtual element method of arbitrary approximation orders on polyhedral meshes. Three families of $\boldsymbol{H}(\operatorname{grad-div

Zi-Rui Wang

The segmentation-free research efforts for addressing handwritten text recognition can be divided into three categories: connectionist temporal classification (CTC), hidden Markov model and encoder-decoder methods. In this paper, inspired by the above three modeling methods, we propose a new recognition network by using a novel three-dimensional (3D) attenti

Peizhen Li, Longbing Cao, Xiao-Ming Wu, Xiaohan Yu

Equipping humanoid robots with the capability to understand emotional states of human interactants and express emotions appropriately according to situations is essential for affective human-robot interaction. However, enabling current vision-aware multimodal emotion recognition models for affective human-robot interaction in the real-world raises embodiment

Bhargav Bhatt, Mel Hochster, Linquan Ma

We introduce the notion of a lim Cohen-Macaulay sequence of modules. We prove the existence of such sequences in positive characteristic, and show that their existence in mixed characteristic implies the long open conjecture about positivity of Serre intersection multiplicities for all regular local rings, as well as a new proof of the existence of big Cohen

Ruoxi Cheng, Yizhong Ding, Shuirong Cao, Shitong Shao

Audio can disclose PII, particularly when combined with related text data. Therefore, it is essential to develop tools to detect privacy leakage in Contrastive Language-Audio Pretraining(CLAP). Existing MIAs need audio as input, risking exposure of voiceprint and requiring costly shadow models. We first propose PRMID, a membership inference detector based pr

Xinran Fang, Chengleyang Lei, Wei Feng, Yunfei Chen

To accommodate the evolving demands of unmanned operations, the future sixth-generation (6G) network will support not only communication links but also sensing-communication-computing-control ($\mathbf{SC}^3$) loops. In each $\mathbf{SC}^3$ cycle, the sensor uploads sensing data to the computing center, and the computing center calculates the control command

Jingqi Chen, Joonho Lee, Wenjie Dou

One key challenge in the study of nonadiabatic dynamics in open quantum systems is to balance computational efficiency and accuracy. Although Ehrenfest dynamics (ED) is computationally efficient and well-suited for large complex systems, ED often yields inaccurate results. To address these limitations, we improve the accuracy of the traditional ED by adding

Runzhen Xue, Hao Wu, Mingyu Yan, Ziheng Xiao

Design Space Exploration (DSE) is essential to modern CPU design, yet current frameworks struggle to scale and generalize in high-dimensional architectural spaces. As the dimensionality of design spaces continues to grow, existing DSE frameworks face three fundamental challenges: (1) reduced accuracy and poor scalability of surrogate models in large design s

Ishani Agarwal, Miroslav Saraivanov, Marek Perkowski

This paper extends the decomposition from the group theory based methods of Sasao and Saraivanov to design binary input multivalued output quantum cascades realized with optical NOT, SWAP, and Fredkin Gates. We present this method for 3, 5, and 7 valued outputs, but in general it can be used for odd prime valued outputs. The method can be extended to realize

Enric Espel Sanchez

Time perception is crucial for a coherent human experience. As life progresses, our perception of the passage of time becomes increasingly non-uniform, often feeling as though it accelerates with age. While various causes for this phenomenon have been theorized, a comprehensive mathematical and theoretical framework remains underexplored. This study aims to

Kareem O. Tawfik, Mohammad M. R. Khan, Ankita Patro, Miriam R. Smetak

Hypothesis: Pre-operative cochlear implant (CI) electrode array (EL) insertion plans created by automated image analysis methods can improve positioning of slim pre-curved EL. Background: This study represents the first evaluation of a system for patient-customized EL insertion planning for a slim pre-curved EL. Methods: Twenty-one temporal bone specimens we

Takayuki Kubo

This Brief Note explores the relationship between residual resistivity ratio (RRR) and the maximum surface magnetic field in superconducting bulk niobium (Nb) cavities. Data from the 1980s to 2020s, covering RRR values from 30 to 500, are compared with theoretical performance limits, including the lower critical field (Bc1), superheating field (Bsh), and the

Peiwen Jiang, Chao-Kai Wen, Shi Jin, Jun Zhang

Semantic communication, augmented by knowledge bases (KBs), offers substantial reductions in transmission overhead and resilience to errors. However, existing methods predominantly rely on end-to-end training to construct KBs, often failing to fully capitalize on the rich information available at communication devices. Motivated by the growing convergence of

Vishakha Lall, Yisi Liu

OpenAI's Whisper Automated Speech Recognition model excels in generalizing across diverse datasets and domains. However, this broad adaptability can lead to diminished performance in tasks requiring recognition of specific vocabularies. Addressing this challenge typically involves fine-tuning the model, which demands extensive labeled audio data that is ofte

Shanchao Liang, Nan Jiang, Shangshu Qian, Lin Tan

Web development involves turning UI designs into functional webpages, which can be difficult for both beginners and experienced developers due to the complexity of HTML's hierarchical structures and styles. While Large Language Models (LLMs) have shown promise in generating source code, two major challenges persist in UI-to-HTML code generation: (1) effectiv

Dan Wei, Zhijuan He, Yunqing Huang, An-Chang Shi

X-shaped liquid crystalline molecules (XLCMs) are obtained by tethering two flexible end A-blocks and two flexible side B-blocks to a rigid backbone (R). A rich array of ordered structures can be formed from XLCMs, driven by the competition between the interactions between the chemically distinct blocks and the molecular connectivity. Here, we report a theor

Gurbir Dhillon, Joakim Færgeman

For a reductive group $G$, we introduce a notion of singular support for cocomplete dualizable DG-categories equipped with a strong $G$-action. This is done by considering the singular support of the sheaves of matrix coefficients arising from the action. We focus particularly on dualizable $G$-categories whose singular support lies in the nilpotent cone of

Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin

Factuality evaluation aims to detect factual errors produced by language models (LMs) and hence guide the development of more factual models. Towards this goal, we train a factuality evaluator, FenCE, that provides LM generators with claim-level factuality feedback. We conduct data augmentation on a combination of public judgment datasets to train FenCE to (

Guillermo Federico Umbricht, Diana Rubio, Domingo Alberto Tarzia

This article presents a theoretical analysis of a one-dimensional heat transfer problem in two layers involving diffusion, advection, internal heat generation or loss linearly dependent on temperature in each layer, and heat generation due to external sources. Additionally, the thermal resistance at the interface between the materials is considered. The situ

Ziqi Cai, Kaiwen Jiang, Shu-Yu Chen, Yu-Kun Lai

Synthesizing realistic videos of talking faces under custom lighting conditions and viewing angles benefits various downstream applications like video conferencing. However, most existing relighting methods are either time-consuming or unable to adjust the viewpoints. In this paper, we present the first real-time 3D-aware method for relighting in-the-wild vi

Chenlan Wang, Gaojian Huang, Yue Luo

This study explored how lifestyle, personal background, and family history contribute to the risk of developing Alcohol Use Disorder (AUD). Survey data from the All of Us Program was utilized to extract information on AUD status, lifestyle, personal background, and family history for 6,016 participants. Key determinants of AUD were identified using decision

Mahdi Saki, Rasool Keshavarz, Daniel Franklin, Mehran Abolhasan

This review explores recent advancements in data fusion techniques and Transformer-based remote sensing applications in precision agriculture. Using a systematic, data-driven approach, we analyze research trends from 1994 to 2024, identifying key developments in data fusion, remote sensing, and AI-driven agricultural monitoring. While traditional machine lea

Jong-Ik Park, Srinivasa Pranav, José M. F. Moura, Carlee Joe-Wong

Foundation models are now a major focus of leading technology organizations due to their ability to generalize across diverse tasks. Existing approaches for adapting foundation models to new applications often rely on Federated Learning (FL) and disclose the foundation model weights to clients when using it to initialize the global model. While these methods

Sudhanshu Agrawal, Wonseok Jeon, Mingu Lee

Speculative decoding is a powerful technique that attempts to circumvent the autoregressive constraint of modern Large Language Models (LLMs). The aim of speculative decoding techniques is to improve the average inference time of a large, target model without sacrificing its accuracy, by using a more efficient draft model to propose draft tokens which are th

Megan Roda

Let $X$ be a compact complex surface. Consider a finitely supported probability measure $\mu$ on $\text{Aut}(X)$ such that $\Gamma_{\mu} = \langle \text{Supp}(\mu)\rangle<\text{Aut}(X)$ is non-elementary. We do not assume that $\Gamma_{\mu}$ contains any parabolic elements. In this paper, we study and classify hyperbolic, ergodic $\mu$-stationary probability

M. Szurek, H. Cheng, Z. Pang, Y. Zhang

We investigate the electronic shot noise produced by nanowires of $\beta$-Ta, an archetypal ``bad" metal with resistivity near the Ioffe-Regel localization limit. The Fano factor characterizing the shot noise exhibits a strong dependence on temperature and is suppressed compared to the expectations for quasiparticle diffusion, but hopping transport is ruled

Alaleh Aminzadeh, Andrew M. Kingston, Lindon Roberts, David M. Paganin

Scanning objects with a more tightly focused beam (for example of photons or electrons) can provide higher-resolution images. However the stronger localisation of energy deposition can damage tissues in organic samples or may rearrange the chemical structure or physical properties of inorganic materials. Scanning an object with a broad beam can deliver an eq

Masanao Ozawa

A difficulty in quantum logic is the well-known arbitrariness in choosing a binary operation for conditional among three principal candidates called the Sasaki, the contrapositive Sasaki, and the relevance conditional, mainly chosen from syntactical grounds. A fundamental problem remains to clarify their semantical differences manifest in operational concept

Takahiro Uemura, Taiki Yoda, Yuto Moritake, Shutaro Otsuka

Exceptional points (EPs) in the propagation states give rise to the emergence of intriguing properties with the divergence of the group velocity. However, there have been no experimental reports due to the necessity of maintaining high levels of fabrication precision and the requisite high group velocity contrast. In our study, we propose a design of photoni

Lei Hu, Wenwen Li, Yunqiang Zhu

Geospatial Knowledge Graphs (GeoKGs) model geoentities (e.g., places and natural features) and spatial relationships in an interconnected manner, providing strong knowledge support for geographic applications, including data retrieval, question-answering, and spatial reasoning. However, existing methods for mining and reasoning from GeoKGs, such as popular k

Fengchen Liu, Jordan Jung, Wei Feinstein, Jeff DAmbrogia

This paper introduces a novel approach to enhancing closed-domain Question Answering (QA) systems, focusing on the specific needs of the Lawrence Berkeley National Laboratory (LBL) Science Information Technology (ScienceIT) domain. Utilizing a rich dataset derived from the ScienceIT documentation, our study embarks on a detailed comparison of two fine-tuned

Jihyeug Jang, Jang Soo Kim, Jianping Pan, Joseph Pappe

Refined canonical stable Grothendieck polynomials were introduced by Hwang, Jang, Kim, Song, and Song. There exist two combinatorial models for these polynomials: one using hook-valued tableaux and the other using pairs of a semistandard Young tableau and (what we call) an exquisite tableau. An uncrowding algorithm on hook-valued tableaux was introduced by P

Y. Xing, R. Namba, K. Imamura, K. Ishihara

The layered honeycomb magnet $\alpha$-RuCl$_3$ has emerged as a promising candidate for realizing a Kitaev quantum spin liquid. Previous studies have reported oscillation-like anomalies in the longitudinal thermal conductivity and half-integer quantized thermal Hall conductivity above the antiferromagnetic critical field $H_c$, generating significant interes

Vandana Ramakrishnan, Kyoung-Soo Lee, Nicole Firestone, Eric Gawiser

The One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey is carrying out a systematic search for protoclusters during Cosmic Noon, using Ly$\alpha$-emitting galaxies (LAEs) as tracers. Once completed, ODIN aims to identify hundreds of protoclusters at redshifts of 2.4, 3.1, and 4.5 across seven extragalactic fields, covering a total area of up to 9

Dong-Guw Lee, Jeongyun Kim, Younggun Cho, Ayoung Kim

Thermal Infrared (TIR) imaging provides robust perception for navigating in challenging outdoor environments but faces issues with poor texture and low image contrast due to its 14/16-bit format. Conventional methods utilize various tone-mapping methods to enhance contrast and photometric consistency of TIR images, however, the choice of tone-mapping is larg

Cui-Xian Guo, Luhong Su, Yongliang Wang, Li Li

Anderson localization and non-Hermitian skin effect are two paradigmatic wave localization phenomena, resulting from wave interference and the intrinsic non-Hermitian point gap, respectively. In this study, we unveil a novel localization phenomenon associated with long-range asymmetric coupling, termed scale-tailored localization, where the number of induced

Ufuk Beyaztas, Han Lin Shang, Abhijit Mandal

A function-on-function regression model with quadratic and interaction effects of the covariates provides a more flexible model. Despite several attempts to estimate the model's parameters, almost all existing estimation strategies are non-robust against outliers. Outliers in the quadratic and interaction effects may deteriorate the model structure more seve

Qian Fang, Liming Wang, Kai Chang, Hongxin Yang

Two-dimensional (2D) magnetic semiconductors are a key focus in developing next-generation information storage technologies. MXenes, as emerging 2D early transition metal carbides and nitrides, offer versatile compositions and tunable chemical structures. Incorporating lanthanide metals, with their unique role of 4f-electrons in engineering physical properti

Junyi Ye, Jingyi Gu, Xinyun Zhao, Wenpeng Yin

The mathematical capabilities of AI systems are complex and multifaceted. Most existing research has predominantly focused on the correctness of AI-generated solutions to mathematical problems. In this work, we argue that beyond producing correct answers, AI systems should also be capable of, or assist humans in, developing novel solutions to mathematical ch

Ranjan Sapkota, Manoj Karkee

This study conducted a comprehensive performance evaluation on YOLO11 (or YOLOv11) and YOLOv8, the latest in the "You Only Look Once" (YOLO) series, focusing on their instance segmentation capabilities for immature green apples in orchard environments. YOLO11n-seg achieved the highest mask precision across all categories with a notable score of 0.831, highli

Nguyen Anh Dao, Anh Xuan Do, Nguyen Lam, Guozhen Lu

We establish a new family of the critical higher order Sobolev interpolation inequalities for radial functions as well as for non-radial functions. These Sobolev interpolation inequalities are sharp in the sense that they use the optimal quadratic forms of the sharp Hardy-Rellich inequalities and cover the Sobolev critical exponents. Our results extend those

Jenna Butler, Jina Suh, Sankeerti Haniyur, Constance Hadley

Generative AI coding tools are relatively new, and their impact on developers extends beyond traditional coding metrics, influencing beliefs about work and developers' roles in the workplace. This study aims to illuminate developers' preexisting beliefs about generative AI tools, their self perceptions, and how regular use of these tools may alter these beli

Zhongqiang Ren, Bunyod Suvonov, Guofei Chen, Botao He

This paper investigates Path planning Among Movable Obstacles (PAMO), which seeks a minimum cost collision-free path among static obstacles from start to goal while allowing the robot to push away movable obstacles (i.e., objects) along its path when needed. To develop planners that are complete and optimal for PAMO, the planner has to search a giant state s

Elias J. R. Freitas, Miri Weiss Cohen, Frederico G. Guimarães, Luciano C. A. Pimenta

This research presents an online path planner for Unmanned Aerial Vehicles (UAVs) that can handle dynamic obstacles and UAV motion constraints, including maximum curvature and desired orientations. Our proposed planner uses a NURBS path representation and a Differential Evolution algorithm, incorporating concepts from the Velocity Obstacle approach in a cons

Talaya Farasat, JongWon Kim, Joachim Posegga

This paper introduces a Testbed designed for generating network traffic, leveraging the capabilities of containers, Kubernetes, and eBPF/XDP technologies. Our Testbed serves as an advanced platform for producing network traffic for machine learning based network experiments. By utilizing this Testbed, we offer small malicious network traffic dataset publical

Shixian Geng, Ginshi Shimojima, Chi-Lan Yang, Zefan Sramek

Psychotherapy, such as cognitive-behavioral therapy (CBT), is effective in treating various mental disorders. Technology-facilitated mental health therapy improves client engagement through methods like digitization or gamification. However, these innovations largely cater to individual therapy, ignoring the potential of group therapy-a treatment for multipl

Max Hirsch, Franziska Weber, Yukun Yue

The goal of this work is to rigorously study the zero inertia limit for the Q-tensor model of liquid crystals. Though present in the original derivation of the Ericksen-Leslie equations for nematic liquid crystals, the inertia term of the model is often neglected in analysis and applications. We show wellposedness of the model including inertia and then show

Takanobu Hara

This is a progress report on study of uniformly elliptic Poisson-type equations on domains with capacity density conditions (CDC domains). We give a brief summary of known facts of CDC domains, including Hardy's inequality, and review a previous work of existence of globally H\"{o}lder continuous solutions. Additionally, we apply the result to homogenization

Samuel Aeschbach, Rui Mata, Dirk U. Wulff

Accurately capturing individual differences in semantic networks is fundamental to advancing our mechanistic understanding of semantic memory. Past empirical attempts to construct individual-level semantic networks from behavioral paradigms may be limited by data constraints. To assess these limitations and propose improved designs for the measurement of ind

Kim Sung-Bin, Oh Hyun-Bin, JungMok Lee, Arda Senocak

Following the success of Large Language Models (LLMs), expanding their boundaries to new modalities represents a significant paradigm shift in multimodal understanding. Human perception is inherently multimodal, relying not only on text but also on auditory and visual cues for a complete understanding of the world. In recognition of this fact, audio-visual L

Mehrad Soltani, Luis Rueda

The task of spatial clustering of transcriptomics data is of paramount importance. It enables the classification of tissue samples into diverse subpopulations of cells, which, in turn, facilitates the analysis of the biological functions of clusters, tissue reconstruction, and cell-cell interactions. Many approaches leverage gene expressions, spatial locatio

H. Ishikawa

Quantum theory, despite its remarkable success, struggles to represent certain experimental data, particularly those involving integer functions and deterministic relations between quantum jumps. We address this limitation by proposing a hidden variable theory compatible with non-relativistic quantum electrodynamics (QED). Our approach introduces logical var

R N Henriksen

We extract the square root of the Minkowski metric using Dirac/Clifford matrices. The resulting $4\times 4$ operator $d{\bf S}$ that represents the square root, can be used to transform four vectors between relatively moving observers. This effects the usual Lorentz transformation. In addition it acts on a Dirac bi-spinor. The operator is essentially a Hamil

Sarik Dhungel, Gaurav Duggal, Dara Ron, Nishith Tripathi

The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in t

Myeonghoon Ryu, Hongseok Oh, Suji Lee, Han Park

We present Unified Microphone Conversion, a unified generative framework designed to bolster sound event classification (SEC) systems against device variability. While our prior CycleGAN-based methods effectively simulate device characteristics, they require separate models for each device pair, limiting scalability. Our approach overcomes this constraint by

Wenhao Liang, Liangwei Zheng, Wei Zhang, Weitong Chen

Confidence calibration matters wherever a classifier&#39;s probabilities, not just its labels, are consumed downstream. We study Focal Calibration Loss (FCL), which adds a squared probability-error (multiclass Brier) anchor to the focal objective, $\mathcal{L}{\mathrm{FCL}}^{γ,λ} = \mathcal{L}{\mathrm{focal}}^γ + λ|\hat{p}(x) - e_y|_2^2$. Our analysis separa