November 2024 arXiv papers — page 97

Yuhong Chou, Man Yao, Kexin Wang, Yuqi Pan

Various linear complexity models, such as Linear Transformer (LinFormer), State Space Model (SSM), and Linear RNN (LinRNN), have been proposed to replace the conventional softmax attention in Transformer structures. However, the optimal design of these linear models is still an open question. In this work, we attempt to answer this question by finding the be

Zhong-Xi Shen, Wen Zhou, Dong-Ping Xuan, Zhi-Xi Wang

The monogamy of entanglement stands as an indispensable feature within multipartite quantum systems. We study monogamy relations with respect to any partitions for the generalized $W$-class (GW) states based on the unified-($q,s$) entanglement (UE). We provide the monogamy relation based on the squared UE for a reduced density matrix of a qudit GW state, as

Jiangang Chen, Yung-Hong Sun, Kristen Pickett, Barbara King

We developed a shoe-mounted gait monitoring system capable of tracking up to 17 gait parameters, including gait length, step time, stride velocity, and others. The system employs a stereo camera mounted on one shoe to track a marker placed on the opposite shoe, enabling the estimation of spatial gait parameters. Additionally, a Force Sensitive Resistor (FSR)

Peter Skjøtt Thorup, Rasmus Baden Stubkjær, Kim-Khuong Huynh, Pavankumar Ventrapati

Materials with a low thermal conductivity are important for a variety of applications such as thermal barrier coatings and thermoelectrics, and understanding the underlying mechanisms of low heat transport, and relating them to structural features, remains a central goal within material science. Here, we report on the ultra-low thermal conductivity of the qu

Dawei Zhong, Akhil Francis, Ermal Rrapaj

Optimization problems in finance, physics and computer science are typically very hard to tackle in classical computing and quantum computing could help speed up computations and provide efficient methods for tackling large problems. Typically, to treat the problem with a quantum computer, the optimal solution is cast as the ground state of a diagonal Hamilt

M. G. Dainotti, S. Bhardwaj, E. Bissaldi, N. Fraija

Gamma-ray bursts (GRBs) are intense pulses of high-energy emission associated with massive stars' death or compact objects' coalescence. Their multi-wavelength observations help verify the reliability of the standard fireball model. We analyze 14 GRBs observed contemporaneously in gamma-rays by the \textit{Fermi} Large Area Telescope (LAT), in X-rays by the

Suman Pal, Gargi Chaudhuri

In this work, we have studied the medium effects in strange quark matter in the framework of a grand-canonical ensemble using the phenomenological quasi-particle model. This model is studied with proper self-consistent thermodynamical treatment by incorporating chemical potential-dependent quark mass. We have also included the vector interaction in a self-co

Zeel B Patel, Yash Bachwana, Nitish Sharma, Sarath Guttikunda

Nearly 6.7 million lives are lost due to air pollution every year. While policymakers are working on the mitigation strategies, public awareness can help reduce the exposure to air pollution. Air pollution data from government-installed sensors is often publicly available in raw format, but there is a non-trivial barrier for various stakeholders in deriving

Zu-Jian Ying

Squeezing and transition criticality are two main sensitivity resources for quantum metrology (QM), combination of them may yield an upgraded metrology protocol for higher upper bound of measurement precision (MP). We show that such a combination is feasible in light-matter interactions by a realizable asymmetric non-linear quantum Rabi model (QRM). Indeed,

Andrew Rajchert

We explore Mahler numbers originating from functions $f(z)$ that satisfy the functional equation $f(z) = (A(z)f(z^d) + C(z))/B(z)$. A procedure to compute the irrationality exponents of such numbers is developed using continued fractions for formal Laurent series, and the form of all such irrationality exponents is investigated. This serves to extend Dmitry

Dohyun Kim, Amiya K. Pani, Eun-Jae Park

In this paper, C1-conforming element methods are analyzed for the stream function formulation of a single layer non-stationary quasi-geostrophic equation in the ocean circulation model. In its first part, some new regularity results are derived, which show exponential decay property when the wind shear stress is zero or exponentially decaying. Moreover, when

Diksha Prajapati, Chandrima Banerjee

Chirality is pivotal in magnonics, particularly for achieving spin wave non-reciprocity which is critical in advancing spin wave based communication and logic operations. In general, chirality in magnetic systems is realized through the interfacial antisymmetric exchange interaction, namely, the Dzyaloshinskii-Moriya Interaction (DMI), which is an intrinsic

Omar W. Abdalla, Aditya Joshi, Rahat Masood, Salil S. Kanhere

Satirical news is real news combined with a humorous comment or exaggerated content, and it often mimics the format and style of real news. However, satirical news is often misunderstood as misinformation, especially by individuals from different cultural and social backgrounds. This research addresses the challenge of distinguishing satire from truthful new

Luciano S. Martinez-Rau, Yuxuan Zhang, Bengt Oelmann, Sebastian Bader

Conveyor belts are crucial in mining operations by enabling the continuous and efficient movement of bulk materials over long distances, which directly impacts productivity. While detecting anomalies in specific conveyor belt components has been widely studied, identifying the root causes of these failures, such as changing production conditions and operator

X. Liu, H. Yu

This paper evaluates the impact of a mandatory shutdown policy of small-capacity coal power plants during China's $11^{th}$ 5-Year Plan period (2006--2010) on under-5 mortality. We collect capacity and location information on 2181 coal power plants that operated during 2000--2010 and compile a unique data set that combines coal power plants, county-level und

Debayani Ghosh, Sahaj Saxena, Navin Kumar

The management of type 1 diabetes has been revolutionized by the artificial pancreas system (APS), which automates insulin delivery based on continuous glucose monitor (CGM). While conventional closed-loop systems rely on CGM data, which leads to higher energy consumption at the sensors and increased data redundancy in the underlying communication network. I

Yan Dolinsky

We study an optimal execution problem in the infinite horizon setup. Our financial market is given by the Black-Scholes model with a linear price impact. The main novelty of the current note is that we study the constrained case where the number of shares and the selling rate are non-negative processes. For this case we give a complete characterization of th

Latesh G. Malik, Rohini Shambharkar, Shivam Morey, Shubhlak Kanpate

In recent years, Cyber attacks have increased in number, and with them, the intensity of the attacks and their potential to damage the user have also increased significantly. In an ever-advancing world, users find it difficult to keep up with the latest developments in technology, which can leave them vulnerable to attacks. To avoid such situations we need t

Fan Yang, Xuzhi-Zhou, Ying Liu, Yi-Xin Sun

Recent observations from the Juno spacecraft during its transit over flux tubes of the Galilean moons have identified sharp enhancements of particle fluxes at discrete energies. These banded structures have been suspected to originate from a bounce resonance between particles and standing Alfven waves generated by the moon-magnetospheric interaction. Here, w

Peyman Nasehpour

In this paper, we prove prime avoidance for ringoids. We also generalize McCoy's and Davis' prime avoidance theorems in the context of semiring theory. Next, we proceed to define and characterize compactly packed semirings and show that a commutative semiring is compactly packed if and only if each prime ideal is the radical of a principal ideal. Finally, we

Anton Alekseev, Gulnara Kabaeva

One of the key tasks in modern applied computational linguistics is constructing word vector representations (word embeddings), which are widely used to address natural language processing tasks such as sentiment analysis, information extraction, and more. To choose an appropriate method for generating these word embeddings, quality assessment techniques are

Nhan Thanh Nguyen, Van-Dinh Nguyen, Hieu V. Nguyen, Hien Quoc Ngo

Integrated sensing and communications (ISAC) is envisioned as a key feature in future wireless communications networks. Its integration with massive multiple-input-multiple-output (MIMO) techniques promises to leverage substantial spatial beamforming gains for both functionalities. In this work, we consider a massive MIMO-ISAC system employing a uniform plan

Mangyu Kong, Jaewon Lee, Seongwon Lee, Euntai Kim

We introduce Dynamic Gaussian Splatting SLAM (DGS-SLAM), the first dynamic SLAM framework built on the foundation of Gaussian Splatting. While recent advancements in dense SLAM have leveraged Gaussian Splatting to enhance scene representation, most approaches assume a static environment, making them vulnerable to photometric and geometric inconsistencies cau

Pranav Rajbhandari, Karthick Dhileep, Sridhar Ravi, Donald Sofge

In prior research, we analyzed the backwards swimming motion of mosquito larvae, parameterized it, and replicated it in a Computational Fluid Dynamics (CFD) model. Since the parameterized swimming motion is copied from observed larvae, it is not necessarily the most efficient locomotion for the model of the swimmer. In this project, we further optimize this

Mengwen Chen, Chenyu Wang, Kunpeng Jia, Xiao-Hui Tian

While thin film lithium niobate (TFLN) is known for efficient signal generation, on-chip signal amplification remains challenging from fully integrated optical communication circuits. Here we demonstrate the continuous-wave-pump optical parametric amplification (OPA) using an x-cut domain-engineered TFLN waveguide, with high gain over the telecom band up to

Anya Chauhan, Ayush Noori, Zhaozhi Li, Yingnan He

Alzheimer's disease (AD) is a complex, progressive neurodegenerative disorder characterized by extracellular A\b{eta} plaques, neurofibrillary tau tangles, glial activation, and neuronal degeneration, involving multiple cell types and pathways. Current models often overlook the cellular context of these pathways. To address this, we developed a multiscale gr

Sota Kawase, Shuichi Miyazaki

The Seat Arrangement Problem is a problem of finding a desirable seat arrangement for given preferences of agents and a seat graph that represents a configuration of seats. In this paper, we consider decision problems of determining if an envy-free arrangement exists and an exchange-stable arrangement exists, when a seat graph is an $\ell \times m$ grid grap

Matthew Nyaaba

This study examines the transformative potential of Generative AI (GenAI) in teacher education within developing countries, focusing on Ghana, where challenges such as limited pedagogical modeling, performance-based assessments, and practitioner-expertise gaps hinder progress. GenAI has the capacity to address these issues by supporting content knowledge acq

Hao Gu, Moeta Tsukamoto, Yuki Nakamura, Shu Nakaharai

The nanosized boron vacancy ($V_\mathrm{B}^-$) defect spot in hexagonal boron nitride ($h$-BN) is promising for a local magnetic field quantum sensor. One of its advantages is that a helium-ion microscope can make a spot at any location in an $h$-BN flake with nanometer accuracy. In this study, we investigate the properties of the created nanosized $V_\mathr

Nitin Sagar Boyeena, Begari Susheel Kumar

Time series forecasting is crucial in several sectors, such as meteorology, retail, healthcare, and finance. Accurately forecasting future trends and patterns is crucial for strategic planning and making well-informed decisions. In this case, it is crucial to include many forecasting methodologies. The strengths of Auto-regressive Integrated Moving Average (

Yongjin Lee, Hyeon-Mun Jeong, Yurim Jeon, Sanghyun Kim

Multi-modal sensor fusion in Bird's Eye View (BEV) representation has become the leading approach for 3D object detection. However, existing methods often rely on depth estimators or transformer encoders to transform image features into BEV space, which reduces robustness or introduces significant computational overhead. Moreover, the insufficient geometric

Chuyang Xu, Zhongxin Liu, Xiaoxue Ren, Gehao Zhang

Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated promising performance. However, first, these methods are limited in flexibility. They rely on bug-triggering test cases to perform FL and cannot make use of other available bug-rel

Tanjina Sultana Camelia, Faizur Rahman Fahim, Md. Musfique Anwar

Nowadays, the rapid diffusion of fake news poses a significant problem, as it can spread misinformation and confusion. This paper aims to develop an advanced machine learning solution for detecting fake news articles. Leveraging a comprehensive dataset of news articles, including 23,502 fake news articles and 21,417 accurate news articles, we implemented and

Fengjiang Li, Jianyu Ou, Yuanyuan Qu, Guoqiang Wu

Let $(M, g, f)$ be a $5$-dimensional complete noncompact gradient shrinking Ricci soliton with the equation $Ric+\nabla^2f= \lambda g$, where $\text{Ric}$ is the Ricci tensor and $\nabla^2f$ is the Hessian of the potential function $f$. We prove that it is a finite quotient of $\mathbb{R}^2\times \mathbb{S}^3$ if $M$ has constant scalar curvature $R=3 \lambd

Evan M. Carrasco, Matthew Shetrone, Francis Nimmo, Enrico Ramirez-Ruiz

The energy provided in the radioactive decay of thorium (Th) and uranium (U) isotopes, embedded in planetary mantles, sustains geodynamics important for surface habitability such as the generation of a planetary magnetic dynamo. In order to better understand the thermal evolution of nearby exoplanets, stellar photospheric abundances can be used to infer the

Mithilesh Kumar

In a multipartite systems, local operations are conducted by one party and the results are communicated to the other parties. Such models have been studied under the framework of LOCC and SLOCC. In this paper, we study when can an action of one party be simulated by another. We obtain necessary and sufficient conditions for when can a unitary action be simul

Jiawen Li, Qiehe Sun, Renao Yan, Yizhi Wang

With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer diagnosis. Limited by the expensive cost of pixel-level annotation, current research primarily focuses on representation learning with slide-level labels, showing success in various

Jiawei Mao, Yu Yang, Xuesong Yin, Ling Shao

Image restoration models often face the simultaneous interaction of multiple degradations in real-world scenarios. Existing approaches typically handle single or composite degradations based on scene descriptors derived from text or image embeddings. However, due to the varying proportions of different degradations within an image, these scene descriptors ma

Robert R. Nerem, Zhishang Luo, Akbar Rafiey, Yusu Wang

Continuously extending combinatorial optimization objectives is a powerful technique commonly applied to the optimization of set functions. However, few such methods exist for extending functions on permutations, despite the fact that many combinatorial optimization problems, such as the quadratic assignment problem (QAP) and the traveling salesperson proble

Shoucheng Wang, Song He, Li Li

We holographically study the far-from-equilibrium isotropization dynamics of the strongly coupled $\mathcal{N}=4$ supersymmetric Yang-Mills plasma. The dual gravitational background is driven to be out of equilibrium and anisotropic by a time-dependent change in boundary conditions. At late times, the system relaxes and asymptotically approaches a static con

Virgile Troude, Sandro Claudio Lera, Ke Wu, Didier Sornette

Abrupt shifts in ecosystems, brains, markets, and climate are often diagnosed as signs of approaching a tipping point, i.e. a critical bifurcation where stability is lost. Here we reveal a broader and more deceptive mechanism: pseudo-bifurcations. In stochastic non-normal systems, asymmetric interactions produce transient episodes of apparent instability des

Md Nurul Absur, Swastik Brahma, Saptarshi Debroy

Ad-hoc edge deployments to support real-time complex video processing applications such as, multi-view 3D reconstruction often suffer from spatio-temporal system disruptions that greatly impact reconstruction quality. In this poster paper, we present a novel portfolio theory-inspired edge resource management strategy to ensure reliable multi-view 3D reconstr

Yue Zhou, Mengcheng Lan, Xiang Li, Litong Feng

Remote sensing (RS) visual grounding aims to use natural language expression to locate specific objects (in the form of the bounding box or segmentation mask) in RS images, enhancing human interaction with intelligent RS interpretation systems. Early research in this area was primarily based on horizontal bounding boxes (HBBs), but as more diverse RS dataset

Susobhan Mandal

The presence of background classical sources affects a quantum field theory significantly in different ways. Neutrino oscillation is a phenomenon that confirms that neutrinos are massive fermions in nature, a celebrated result in modern physics. Neutrino oscillation plays an important role in many astrophysical observations. However, the interactions between

Ebrahim Farahmand, Shovito Barua Soumma, Nooshin Taheri Chatrudi, Hassan Ghasemzadeh

The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose trajectories and provide automated interventions to prevent devastating chronic complications that arise from poor glucose control. However, forecasting blood glucose levels is ch

Ke Wang, Wanchun Liu, Teng Joon Lim

In this paper, we consider a wireless resource allocation problem in a cyber-physical system (CPS) where the control channel, carrying resource allocation commands, is subjected to denial-of-service (DoS) attacks. We propose a novel concept of collaborative distributed and centralized (CDC) resource allocation to effectively mitigate the impact of these atta

Ying Yang, De Cheng, Chaowei Fang, Yubiao Wang

Unsupervised out-of-distribution (OOD) detection aims to identify out-of-domain data by learning only from unlabeled In-Distribution (ID) training samples, which is crucial for developing a safe real-world machine learning system. Current reconstruction-based methods provide a good alternative approach by measuring the reconstruction error between the input

Mohsen Sombolestan, Quan Nguyen

As legged robots take on roles in industrial and autonomous construction, collaborative loco-manipulation is crucial for handling large and heavy objects that exceed the capabilities of a single robot. However, ensuring the safety of these multi-robot tasks is essential to prevent accidents and guarantee reliable operation. This paper presents a hierarchical

Jia Yu, Yuchen Zhou, Xiao Wang, Xuejian Ma

The unique electronic properties of amorphous indium gallium zinc oxide (a-IGZO) thin films are closely associated with the complex charge dynamics of the materials. Conventional studies of charge transport in a-IGZO usually involve steady-state or transient measurements on field-effect transistors. Here, we employed microwave impedance microscopy to carry o

Jiao Liu, Zhu Sun, Shanshan Feng, Caishun Chen

In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functionality of evolutionary algorithms (EAs), enabling them to tackle optimization problems involving structured language or program code. Although this field is still in its early stages

Huaqin Zhao, Jiaxi Li, Yi Pan, Shizhe Liang

Fine-tuning large language models (LLMs) poses significant memory challenges, as the back-propagation process demands extensive resources, especially with growing model sizes. Recent work, MeZO, addresses this issue using a zeroth-order (ZO) optimization method, which reduces memory consumption by matching the usage to the inference phase. However, MeZO expe

Xinbo Shi, Yijie Peng, Bruno Tuffin

This paper addresses the challenge of improving finite sample performance in Ranking and Selection by developing a Bahadur-Rao type expansion for the Probability of Correct Selection (PCS). While traditional large deviations approximations captures PCS behavior in the asymptotic regime, they can lack precision in finite sample settings. Our approach enhances

Justine Fasquel, Vladimir Kovalchuk, Shigenori Nakatsuka

In this article, the Virasoro-type reduction and the corresponding inverse reductions are established for W-algebras associated with classical Lie type and nilpotent orbits of height two. Moreover, these results are lifted to the universal objects by analyzing the Virasoro-type reduction of the vertex algebra $\mathcal{W}^{\mathfrak{sp}}_{\infty}$.

Qi Wang, Jinjia Zhou

In previous studies on knowledge distillation, the significance of logit distillation has frequently been overlooked. To revitalize logit distillation, we present a novel perspective by reconsidering its computation based on the semantic properties of logits and exploring how to utilize it more efficiently. Logits often contain a substantial amount of high-l

Nikhil P Ghanathe, Steven J E Wilton

TinyML models often operate in remote, dynamic environments without cloud connectivity, making them prone to failures. Ensuring reliability in such scenarios requires not only detecting model failures but also identifying their root causes. However, transient failures, privacy concerns, and the safety-critical nature of many applications-where systems cannot

Chaoming Song

The Gutzwiller trace formula establishes a profound connection between the quantum spectrum and classical periodic orbits. However, its application is limited by its reliance on the semiclassical saddle point approximation. In this work, we explore the full quantum version of the trace formula using the Lefschetz thimble method by incorporating complexified

Jinnan Chen, Chen Li, Gim Hee Lee

We introduce DiHuR, a novel Diffusion-guided model for generalizable Human 3D Reconstruction and view synthesis from sparse, minimally overlapping images. While existing generalizable human radiance fields excel at novel view synthesis, they often struggle with comprehensive 3D reconstruction. Similarly, directly optimizing implicit Signed Distance Function

Shingo Tagami, Takayuki Myo, Masanobu Yahiro

Foster {\it et al.} measured total neutron cross sections $\sigma_{\rm T}$ of n+$^{208}$Pb scattering at $14.137$MeV. Carlson {\it et al.} measured $\sigma_{\rm R}$ for $p$+$^{48}$Ca scattering in $23 \text{--} 48$MeV. Tanaka {\it et al.} measured $\sigma_{\rm I}$ for $^{42\text{--}51}$Ca + $^{12}$C scattering at 280MeV/u. Bagchi {\it et al.} measured the ch

Morgan Bryant

Given two Fra\"iss\'e-like classes with generic limits, we ask whether we can merge the two classes into one class with a generic limit. We study the properties of these merges and their generics, as well as their connections to structural Ramsey theory and the Hrushovski property (EPPA).

Vladimir I. Kruglov

We present the derivation of generic equations describing the long gravity waves in incompressible fluid with decaying effect. We show that in this theory the only restriction to the surface deviation is connected with the stability condition for the waves. Derivation of these generic equations is based on Euler equations for inviscid incompressible fluid an

Shingo Haruna, Koki Doi, Takuji Nomura, Hirono Kaneyasu

In this study, we examined the temperature dependence of the spin-lattice relaxation using an f-d-p model, which is an effective model of UTe2. Solving the linearized Eliashberg equation in the f-d-p model based on third-order perturbation theory, we obtain a point-node-like s-wave pairing state. Our result shows that the Hebel-Slichter peak in the point-nod

Steve Oney, Yue Shen, Fei Wu, Young Suh Hong

Large Language Models (LLMs) have shown the potential to be valuable teaching tools, with the potential of giving every student a personalized tutor. However, one challenge with using LLMs to learn new concepts is that when learning a topic in an unfamiliar domain, it can be difficult to know what questions to ask. Further, language models do not always enco

Qixuan Jin, Walter Gerych, Marzyeh Ghassemi

Spurious features associated with class labels can lead image classifiers to rely on shortcuts that don't generalize well to new domains. This is especially problematic in medical settings, where biased models fail when applied to different hospitals or systems. In such cases, data-driven methods to reduce spurious correlations are preferred, as clinicians c

Jinhong Lin, Cheng-En Wu, Huanran Li, Jifan Zhang

Masked Image Modeling (MIM) has emerged as a powerful self-supervised learning paradigm for visual representation learning, enabling models to acquire rich visual representations by predicting masked portions of images from their visible regions. While this approach has shown promising results, we hypothesize that its effectiveness may be limited by optimiza

Haoxu Huang, Cem M. Deniz, Kyunghyun Cho, Sumit Chopra

Chest X-ray imaging is a widely accessible and non-invasive diagnostic tool for detecting thoracic abnormalities. While numerous AI models assist radiologists in interpreting these images, most overlook patients' historical data. To bridge this gap, we introduce Temporal MIMIC dataset, which integrates five years of patient history, including radiographic sc

Kun Li, Shichao Zhuang, Yue Zhang, Minghui Xu

Large Language Models (LLMs) excel in diverse tasks such as text generation, data analysis, and software development, making them indispensable across domains like education, business, and creative industries. However, the rapid proliferation of LLMs (with over 560 companies developing or deploying them as of 2024) has raised concerns about their originality

Yi Liu, Qiuping Jiang, Xinyi Wang, Ting Luo

Underwater image enhancement (UIE) is a highly challenging task due to the complexity of underwater environment and the diversity of underwater image degradation. Due to the application of deep learning, current UIE methods have made significant progress. Most of the existing deep learning-based UIE methods follow a single-stage network which cannot effectiv

Yixiang Chen, Xinyu Zhang, Jinran Wang, Xurong Xie

The Structured Dialogue System, referred to as SuDoSys, is an innovative Large Language Model (LLM)-based chatbot designed to provide psychological counseling. SuDoSys leverages the World Health Organization (WHO)'s Problem Management Plus (PM+) guidelines to deliver stage-aware multi-turn dialogues. Existing methods for employing an LLM in multi-turn psycho

Feng Zhao, Benchang Yang, Chunhai Li, Chuan Zhang

Due to insufficient scalability, the existing consortium chain cannot meet the requirements of low latency, high throughput, and high security when applied to Internet of Vehicles (IoV) data sharing. Therefore, we propose a two-layer consensus algorithm based on the master-slave consortium chain - Weighted Raft and Byzantine Fault Tolerance (WRBFT). The intr

Huan Kang, Hui Li, Tianyang Xu, Xiao-Jun Wu

Euclidean representation learning methods have achieved promising results in image fusion tasks, which can be attributed to their clear advantages in handling with linear space. However, data collected from a realistic scene usually has a non-Euclidean structure, evaluating the consistency of latent representations from paired views using Euclidean distance

Grace Sng, Yanming Zhang, Klaus Mueller

The increasing use of large language models (LLMs) in causal discovery as a substitute for human domain experts highlights the need for optimal model selection. This paper presents the first hallucination survey of popular LLMs for causal discovery. We show that hallucinations exist when using LLMs in causal discovery so the choice of LLM is important. We pr

Mónica Clapp, Angela Pistoia, Alberto Saldaña

We consider a nonautonomous semilinear elliptic problem where the power nonlinearity is multiplied by a discontinuous coefficient that equals one inside a bounded open set $\Omega$ and it equals minus one in its complement. In the slightly subcritical regime, we prove the existence of concentrating positive and nodal solutions. Moreover, depending on the geo

Huafeng Li, Jiaqi Fang, Yafei Zhang, Yu Liu

Infrared and visible (IR-VIS) image fusion has gained significant attention for its broad application value. However, existing methods often neglect the complementary role of infrared image in restoring visible image features under hazy conditions. To address this, we propose a joint learning framework that utilizes infrared image for the restoration and fus

Gibeom Son, Songky Moon, Seunghoon Oh, Junseo Ha

Frequency transduction, which converts photons from one energy level to another, provides a way to bridge different quantum devices. The frequency transduction has been studied across various systems and frequency ranges, depending on the applications. In particular, infrared photons are ideal for long-distance communication, but their detection efficiency i

Zhangchi Zhu, Wei Zhang

In this paper, we analyze the feature-based knowledge distillation for recommendation from the frequency perspective. By defining knowledge as different frequency components of the features, we theoretically demonstrate that regular feature-based knowledge distillation is equivalent to equally minimizing losses on all knowledge and further analyze how this e

Zhaopeng Hao, Zhiqiang Cai, Zhongqiang Zhang

We study the numerical evaluation of the integral fractional Laplacian and its application in solving fractional diffusion equations. We derive a pseudo-spectral formula for the integral fractional Laplacian operator based on fractional order-dependent, generalized multi-quadratic radial basis functions (RBFs) to address efficient computation of the hyper-si

Xiaohe Dong, Kuokuo Zhang, Caiming Sun, Jun Zhang

The breakthroughs of communication distance and data rate have been eagerly anticipated by scientists in the area of underwater wireless optical communication (UWOC), which is seriously limited by the obvious aquatic attenuation in underwater channel. High-power laser source and ultra-sensitive photodetector are straightforward to extend the UWOC distance. H

Jinbo Wang, Ruijin Wang, Fengli Zhang

Federated learning (FL) is vulnerable to model poisoning attacks due to its distributed nature. The current defenses start from all user gradients (model updates) in each communication round and solve for the optimal aggregation gradients (horizontal solution). This horizontal solution will completely fail when facing large-scale (>50%) model poisoning attac

Sayaka Kochiyama, Haneesh Kesari

Intriguing physical phenomena observed in natural materials have inspired the development of several engineering materials with dramatically improved performance. Marine sponge glass fibers, for instance, have attracted interest in recent decades. We tested the glass fibers in tension and observed that the strength of these fibers scales inversely with their

Xinran Li, Matt Pyle, Bernard Sadoulet

The characteristic energy of a relic dark matter interaction with a detector scales strongly with the putative dark matter mass. Consequently, experimental search sensitivity at the lightest masses will always come from interactions whose size is similar to noise fluctuations and low energy backgrounds in the detector. In this paper, we correctly calculate t

Juan A. Rodriguez, Nicholas Botzer, David Vazquez, Christopher Pal

In today's digitally driven world, dialogue systems play a pivotal role in enhancing user interactions, from customer service to virtual assistants. In these dialogues, it is important to identify user's goals automatically to resolve their needs promptly. This has necessitated the integration of models that perform Intent Detection. However, users' intents

Hyeonhoon Lee, Hanseul Kim, Kyungmin Cho, Hyung-Chul Lee

The National Strategic Technology Research Institute (NSTRI) Data Platform operated by Seoul National University Hospital (SNUH) addresses the challenge of accessing Korean healthcare data for international research. This platform provides secure access to pseudonymized Korean healthcare data while integrating international datasets, enabling the development

Jinqiang Long, Yanqi Dai, Guoxing Yang, Hongpeng Lin

As the research of Multimodal Large Language Models (MLLMs) becomes popular, an advancing MLLM model is typically required to handle various textual and visual tasks (e.g., VQA, Detection, OCR, and ChartQA) simultaneously for real-world applications. However, due to the significant differences in representation and distribution among data from various tasks,

Berat Kurar-Barakat, Nachum Dershowitz

The discovery of the Dead Sea Scrolls over 60 years ago is widely regarded as one of the greatest archaeological breakthroughs in modern history. Recent study of the scrolls presents ongoing computational challenges, including determining the provenance of fragments, clustering fragments based on their degree of similarity, and pairing fragments that origina

Liangwei Zeng, Boris A. Malomed, Dumitru Mihalache, Jingzhen Li

We produce families of two-dimensional gap solitons (GSs) maintained by moir\'{e} lattices (MLs) composed of linear and nonlinear sublattices, with the defocusing sign of the nonlinearity. Depending on the angle between the sublattices, the ML may be quasiperiodic or periodic, composed of mutually incommensurate or commensurate sublattices, respectively (in

Yuxuan Hu, Ke Wang, Xiaokang Zhang, Fanjin Zhang

Speculative decoding (SD) has been demonstrated as an effective technique for lossless LLM inference acceleration. Retrieval-based SD methods, one kind of model-free method, have yielded promising speedup, but they often rely on incomplete retrieval resources, inefficient retrieval methods, and are constrained to certain domains. This paper presents a novel

Ye Cheng, Minghui Xu, Yue Zhang, Kun Li

IoT platforms, particularly smart home platforms providing significant convenience to people's lives such as Apple HomeKit and Samsung SmartThings, allow users to create automation rules through trigger-action programming. However, some users may lack the necessary knowledge to formulate automation rules, thus preventing them from fully benefiting from the c

Hong Xie, Le-Wei He, Xiu-Min Lin

The method of adiabatic elimination has been widely adopted in quantum optics in the past several decades. In the study of cavity-based light-matter interactions, the bad-cavity limit is often encountered, where the damping rate of the cavity is much larger than the interaction strength. The fast-damped cavity will quickly relax to a quasi-stationary state,

Emanuele Contini, Marilena Spavone, Rossella Ragusa, Enrichetta Iodice

We present theoretical predictions and extrapolations from observed data of the stellar halos surrounding central group/cluster galaxies and the transition radius between them and the intracluster or diffuse light. Leveraging the state-of-the-art semi-analytic model of galaxy formation, {\small FEGA} (\citealt{contini2024c}), applied to two dark matter-only

Navin Sridhar, Bart Ripperda, Lorenzo Sironi, Jordy Davelaar

Using two-dimensional general relativistic resistive magnetohydrodynamic simulations, we investigate the properties of the sheath separating the black hole jet from the surrounding medium. We find that the electromagnetic power flowing through the jet sheath is comparable to the overall accretion power of the black hole. The sheath is an important site of en

Ayesha Siddiqua, Atib Mohammad Oni, Abu Saleh Musa Miah, Jungpil Shin

Post-traumatic stress disorder (PTSD) is a significant mental health challenge that affects individuals exposed to traumatic events. Early detection and effective intervention for PTSD are crucial, as it can lead to long-term psychological distress if untreated. Accurate detection of PTSD is essential for timely and targeted mental health interventions, espe

Ya-Xiong Wang, Hai-Jiang Tian, Yin-Long Yang, Tao Zhong

The leptonic decay of $D^+$-meson has attracted significant interest due to its unique characteristics. In this paper, we carry out an investigation into the $D^+$-meson leptonic decays $D^+\to \ell^+\nu_{\ell}$ with $\ell=(e,\mu,\tau)$ by employing the QCD sum rules approach. In which the $D^+$-meson decay constant $f_{D^+}$ is an important input parameter

Marisa Kirisame, Tiezhi Wang, Pavel Panchekha

Latency is a major concern for web rendering engines like those in Chrome, Safari, and Firefox. These engines reduce latency by using an incremental layout algorithm to redraw the page when the user interacts with it. In such an algorithm, elements that change frame-to-frame are marked dirty, and only those elements are processed to draw the next frame, dram

Ziyuan Guo, Yue Sun, Yeming Xu, Liping Zhang

In this paper, a novel distributed optimization framework has been proposed. The key idea is to convert optimization problems into optimal control problems where the objective of each agent is to design the current control input minimizing the original objective function of itself and updated size for the future time instant. Compared with the existing distr

Jingyuan Li, Trung Le, Chaofei Fan, Mingfei Chen

Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature

Eric L. Melin, Adam J. Torek, Nasir U. Eisty, Casey Kennington

Context: Large Language Models (LLMs) like GPT-5 and LLaMA-405b exhibit advanced code generation abilities, but their deployment demands substantial computation resources and energy. Quantization can reduce memory footprint and hardware requirements, yet may degrade code quality. Objective: This study investigates code generation performance of smaller LLMs,

Cassidy R. Nelson, Joseph L. Gabbard

As technology advances, conceptualizations of effective strategies for teaching and learning shift. Due in part to their facilitation of unique affordances for learning, mobile devices, augmented reality, and games are all becoming more prominent elements in learning environments. In this work, we examine mobile augmented reality serious games (MARSGs) as th

Toryn Q. Klassen, Parand A. Alamdari, Sheila A. McIlraith

If an AI system makes decisions over time, how should we evaluate how aligned it is with a group of stakeholders (who may have conflicting values and preferences)? In this position paper, we advocate for consideration of temporal aspects including stakeholders' changing levels of satisfaction and their possibly temporally extended preferences. We suggest how

Dominik Köppl, Jannik Olbrich

Generalizations of plain strings have been proposed as a compact way to represent a collection of nearly identical sequences or to express uncertainty at specific text positions by enumerating all possibilities. While a plain string stores a character at each of its positions, generalizations consider a set of characters (indeterminate strings), a set of str

Huy Tran, Yikun Bai, Ashkan Shahbazi, John R. Hershey

The practical applications of Wasserstein distances (WDs) are constrained by their sample and computational complexities. Sliced-Wasserstein distances (SWDs) provide a workaround by projecting distributions onto one-dimensional subspaces, leveraging the more efficient, closed-form WDs for one-dimensional distributions. However, in high dimensions, most rando