February 2024 arXiv papers — page 100

Zihong Luo, Zheng Tao, Yuxuan Huang, Kexin He

Recent advancements in multi-modal artificial intelligence (AI) have revolutionized the fields of stock market forecasting and heart rate monitoring. Utilizing diverse data sources can substantially improve prediction accuracy. Nonetheless, additional data may not always align with the original dataset. Interpolation methods are commonly utilized for handlin

Vivek Tetarwal, Sandeep Kumar

With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object dete

Eugenio Ressa, Alberto Marchisio, Maurizio Martina, Guido Masera

The Continuous Learning (CL) paradigm consists of continuously evolving the parameters of the Deep Neural Network (DNN) model to progressively learn to perform new tasks without reducing the performance on previous tasks, i.e., avoiding the so-called catastrophic forgetting. However, the DNN parameter update in CL-based autonomous systems is extremely resour

Richard Culpan, Matti Dorsch, Stephan Geier, Ingrid Pelisoli

Context. Stars that are found on the blue horizontal-branch (BHB) have evolved from low-mass stars that have completed their core hydrogen burning main sequence stage and have undergone the helium flash at the end of their red-giant phase. The fact that their luminosity is virtually constant at all effective temperatures also makes them good standard candles

Dennis Linde, Azadeh Moradinezhad Dizgah, Christian Radermacher, Santiago Casas

The power spectrum is the most commonly applied summary statistics to extract cosmological information from the observed three-dimensional distribution of galaxies in spectroscopic surveys. We present CLASS-OneLoop, a new numerical tool, fully integrated into the Boltzmann code CLASS, enabling the calculation of the one-loop power spectrum of biased tracers

Thomas Bunel, Matteo Conforti, Julien Lumeau, Antonin Moreau

We report the generation of broadband frequency combs in fiber Fabry-Perot resonators in the normal dispersion regime enabled by the excitation of switching waves. We theoretically characterise the process by means of a transverse linear stability analysis of the Lugiato-Lefever equation, enabling precise prediction of the switching waves' frequencies. Exper

Aviv Yaish, Svetlana Abramova, Rainer Böhme

We study the effect of public tallies on online elections, in a setting where voting is costly and voters are allowed to strategically time their votes. The strategic importance of choosing \emph{when} to vote arises when votes are public, such as in online event scheduling polls (e.g., Doodle), or in blockchain governance mechanisms. In particular, there is

Pierre Illien, Ramin Golestanian

We consider a binary mixture of chemically active particles, that produce or consume solute molecules, and that interact with each other through the long-range concentrations fields they generate. We analytically calculate the effective phoretic mobility of these particles when the mixture is submitted to a constant, external concentration gradient, at leadi

Shoto Aoki, Hidenori Fukaya, Naoto Kan

In the standard lattice domain-wall fermion formulation, one needs two flat domain-walls where both of the left- and right-handed massless modes appear. In this work we investigate a single domain-wall system with a nontrivial curved background. Specifically we consider a massive fermion on a $3D$ square lattice, whose domain-wall is a $2D$ sphere. In the fr

Shengrui Li, Junzhe Chen, Xueting Han, Jing Bai

The considerable size of Large Language Models (LLMs) presents notable deployment challenges, particularly on resource-constrained hardware. Structured pruning, offers an effective means to compress LLMs, thereby reducing storage costs and enhancing inference speed for more efficient utilization. In this work, we study data-efficient and resource-efficient s

Ali Eshragh, Matthew P. Skerritt, Bruno Salvy, Thomas McCallum

We develop an efficient algorithm to find optimal observation times by maximizing the Fisher information for the birth rate of a partially observable pure birth process involving $n$ observations. Partially observable implies that at each of the $n$ observation time points for counting the number of individuals present in the pure birth process, each individ

N. Buatip, T. Auzelle, P. John, S. Rauwerdink

In this study, detailed analysis of the direct piezo-response of AlN nanowire-based vertically integrated nanogenerators (VINGs) is undertaken as a function of mechanical excitation frequency. We show that the piezo-charge, piezo-voltage, and impedance measured at the same position of the devices can be directly correlated through an equivalent circuit model

Zhiqiang Tian, Jin-Yang Li, Tao Ouyang, Chao-Fei Liu

In flat-band materials, the strong Coulomb interaction between electrons can lead to exotic physical phenomena. Recently, $\alpha$-In$_2$Se$_3$ thin films were found to possess ferroelectricity and flat bands. In this work, using first-principles calculations, we find that for the monolayer, there is a Weyl point at $\Gamma$ in the flat band, where the inclu

Aditya Somasundaram, Pushkal Mishra, Ayon Borthakur

We propose a learning algorithm to overcome the limitations of traditional backpropagation in resource-constrained environments: Solo Pass Embedded Learning Algorithm (SPELA). SPELA operates with local loss functions to update weights, significantly saving on resources allocated to the propagation of gradients and storing computational graphs while being suf

Akito Fujii, Osamu Saeki, Daisuke Sakurai

This article proposes to integrate two Reeb graphs with the information of their isosurfaces' inclusion relation. As computing power evolves, there arise numerical data that have small-scale physics inside larger ones -- for example, small clouds in a simulation can be contained inside an atmospheric layer, which is further contained in an enormous hurricane

Hiroyuki Abe, Akinari Koichi, Yusuke Yamada

We compute the one-loop vacuum energy in 10 dimensional (10D) super Yang-Mills theory compactified on $\mathbb{R}^{1,3}\times (\mathbb{T}^2)^3$ in the presence of the Abelian magnetic fluxes. The regularization of infinite Kaluza-Klein (KK) sum is achieved by the use of Barnes $\zeta$-functions, which can be applied to the case with Landau level structure of

Valeriy Shevchenko, Nikita Belousov, Alexey Vasilev, Vladimir Zholobov

In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approach may fail to holistically reflect their effectiveness due to the significant impact of dataset characteristics on algorithm performance. Ad

Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac

This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods delivery. We develop a novel SVRP formulation that accounts for uncertain travel costs and demands, alongside specific customer time windows. An attention-based neural network trained

Dias Azhigulov, Zeqin Lu, James Pond, Lukas Chrostowski

We present a method to model photonic components in Verilog-A by introducing bidirectional signaling through a single port. To achieve this, the concept of power waves and scattering parameters from electromagnetism are employed. As a consequence, one can simultaneously transmit forward and backward propagating waves on a single wire while also capturing rea

Dexun Li, Cong Zhang, Kuicai Dong, Derrick Goh Xin Deik

Deep Reinforcement Learning is widely used for aligning Large Language Models (LLM) with human preference. However, the conventional reward modelling is predominantly dependent on human annotations provided by a select cohort of individuals. Such dependence may unintentionally result in skewed models that reflect the inclinations of these annotators, thereby

Yehonatan Fridman

Consider the following game between a random player R and a deterministic player D. There is a pile of n elements at the beginning. The rules for playing are as follows: In each turn of R, if the pile contains exactly m elements, R removes k elements from the pile, where k is independently identically distributed from {1, . . . , m}. In each turn of D, D rem

Lu Li

In the domain of physics experiments, data fitting is a pivotal technique for extracting insights from both experimental and simulated datasets. This article presents an approximation method designed to estimate the systematic errors prevalent in data analyses. By applying our method to the Nab experiment, we compare our findings with simulation-derived resu

Chun-Hung Liu, Bruce Reed

We introduce peaceful colourings, a variant of $h$-conflict free colourings. We call a colouring with no monochromatic edges $p$-peaceful if for each vertex $v$, there are at most $p$ neighbours of $v$ coloured with a colour appearing on another neighbour of $v$. An $h$-conflict-free colouring of a graph is a (vertex)-colouring with no monochromatic edges so

Chinmay Prakash Swami

Human gait has been shown to provide crucial motion cues for various applications. Recognizing patterns in human gait has been widely adopted in various application areas such as security, virtual reality gaming, medical rehabilitation, and ailment identification. Furthermore, wearable inertial sensors have been widely used for not only recording gait but al

Hongjin Qian, Zheng Liu, Kelong Mao, Yujia Zhou

This paper presents a novel Chunking-Free In-Context (CFIC) retrieval approach, specifically tailored for Retrieval-Augmented Generation (RAG) systems. Traditional RAG systems often struggle with grounding responses using precise evidence text due to the challenges of processing lengthy documents and filtering out irrelevant content. Commonly employed soluti

Mohammad Ikbal Hossain, Tanzina Sultana, Waheda Zabeen, Alexander Fosu Sarpong

Transformational outsourcing represents a strategic shift from traditional cost-focused outsourcing to a more profound and collaborative approach. It involves partnering with service providers to accomplish routine tasks and drive substantial organizational change and innovation. The report discusses the significance of pursuing transformational outsourcing

Szymon Ruciński

This study explores the potential of fine-tuning foundational English Large Language Models (LLMs) for generating Polish text. The first step involves Language Adaptive Pre-training (LAPT) on a high-quality dataset of 3.11 GB, consisting of 276 million Polish tokens. The LAPT is followed by additional fine-tuning aimed at solving nine KLEJ challenges. Our tr

Niklas Pfister, Peter Bühlmann

We define extrapolation as any type of statistical inference on a conditional function (e.g., a conditional expectation or conditional quantile) evaluated outside of the support of the conditioning variable. This type of extrapolation occurs in many data analysis applications and can invalidate the resulting conclusions if not taken into account. While extra

Gobinda Ghosh, Sachin Pathak

The rapid progression in wireless communication technologies, especially in multicarrier code-division multiple access (MC-CDMA), there is a need of advanced code construction methods. Traditional approaches, mainly based on generalized Boolean functions, have limitations in code length versatility. This paper introduces a novel approach to constructing comp

Hongyang Du, Guangyuan Liu, Yijing Lin, Dusit Niyato

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation

Linfeng Jiang, Yuan Zhu

Single-cell RNA sequencing (scRNA-seq) determines RNA expression at single-cell resolution. It provides a powerful tool for studying immunity, regulation, and other life activities of cells. However, due to the limitations of the sequencing technique, the scRNA-seq data are represented with sparsity, whichcontains missing gene values, i.e., zero values, call

Sangil Han, Kyoowon Kim, Sungkyu Jung

The singular value decomposition (SVD) is a crucial tool in machine learning and statistical data analysis. However, it is highly susceptible to outliers in the data matrix. Existing robust SVD algorithms often sacrifice speed for robustness or fail in the presence of only a few outliers. This study introduces an efficient algorithm, called Spherically Norma

Peng Xu

Let $G$ be the unramified unitary group $U(2, 1)(E/F)$ defined over a non-archimedean local field $F$ of residue characteristic $p\neq 2$. In this note, we prove the universal supersingular quotients of $G$ are not irreducible in general.

Ting Qing, Shupeng Li, Huashan Yang, Lihan Wang

High-resolution optical spectrometers are crucial in revealing intricate characteristics of signals, determining laser frequencies, measuring physical constants, identifying substances, and advancing biosensing applications. Conventional spectrometers, however, often grapple with inherent trade-offs among spectral resolution, wavelength range, and accuracy.

Rameen Atique, Hafiza Arshi Saeed, Bushra Anwar, Tehreem Rana

The term bacteriophage means killer or eater of bacteria. They were initially discovered by F.W. Twort and later on, Felix d'Herelle unveiled them to the world in 1910. Phage therapy has arisen as a favorable option to conventional antibiotics by reducing the multinational problem of increasing antibacterial resistance. These virulent viruses particularly pr

Sungho Han, Moon-Jin Kang, Jeongho Kim, Hobin Lee

We consider the so-called Naiver-Stokes-Korteweg(NSK) equations for the dynamics of compressible barotropic viscous fluids with internal capillarity. We handle the time-asymptotic stability in 1D of the viscous-dispersive shock wave that is a traveling wave solution to NSK as a viscous-dispersive counterpart of a Riemann shock. More precisely, we prove that

Anqi Wang, Zhizhuo Yin, Yulu Hu, Yuanyuan Mao

Large language models (LLMs) are increasingly integrated into creative coding, yet how users reflect, and how different co-creation conditions influence reflective behavior, remains underexplored. This study investigates situated, moment-to-moment reflection in creative coding under two prompting strategies: the entire task invocation (T1) and decomposed sub

Jiong Li, Yi-Cheng Wang, Li-Wei Duan, Qing-Hu Chen

The $\mathcal{PT}$-symmetric non-Hermitian quantum Rabi model (QRM) with imaginary coupling is solved using the Bogoliubov operators approach. A transcendental function responsible for the exact solutions is derived, with its zeros yielding the regular spectrum. We find two types of intersections: One is the exceptional point (EP), which is widely studied in

Wenxiao Wang, Wei Chen, Yicong Luo, Yongliu Long

Transformer based large language models have achieved tremendous success. However, the significant memory and computational costs incurred during the inference process make it challenging to deploy large models on resource-constrained devices. In this paper, we investigate compression and efficient inference methods for large language models from an algorith

Jiahao Wang, Hong Peng, Shengchao Chen, Sufen Ren

Retinal optical coherence tomography (OCT) images provide crucial insights into the health of the posterior ocular segment. Therefore, the advancement of automated image analysis methods is imperative to equip clinicians and researchers with quantitative data, thereby facilitating informed decision-making. The application of deep learning (DL)-based approach

Hang Yuan, Saizhuo Wang, Jian Guo

Recently, we introduced a new paradigm for alpha mining in the realm of quantitative investment, developing a new interactive alpha mining system framework, Alpha-GPT. This system is centered on iterative Human-AI interaction based on large language models, introducing a Human-in-the-Loop approach to alpha discovery. In this paper, we present the next-genera

Xinyue Liu, Zihe Song, Weike Fang, Wei Yang

Web end-to-end (e2e) testing evaluates the workflow of a web application. It simulates real-world user scenarios to ensure the application flows behave as expected. However, web e2e tests are notorious for being flaky, i.e., the tests can produce inconsistent results despite no changes to the code. One common type of flakiness is caused by nondeterministic e

Alexander Mayer, Dominik Wied, Victor Troster

We propose a new framework for assessing Granger causality in quantiles in unstable environments, for a fixed quantile or over a continuum of quantile levels. Our proposed test statistics are consistent against fixed alternatives, they have nontrivial power against local alternatives, and they are pivotal in certain important special cases. In addition, we s

Saqib Abbas Baba, Arpan Chattopadhyay

This paper addresses the problem of detecting false data injection (FDI) attacks in a distributed network without a fusion center, represented by a connected graph among multiple agent nodes. Each agent node is equipped with a sensor, and uses a Kalman consensus information filter (KCIF) to track a discrete time global process with linear dynamics and additi

Zhihao Fan, Jialong Tang, Wei Chen, Siyuan Wang

Artificial intelligence has significantly advanced healthcare, particularly through large language models (LLMs) that excel in medical question answering benchmarks. However, their real-world clinical application remains limited due to the complexities of doctor-patient interactions. To address this, we introduce \textbf{AI Hospital}, a multi-agent framework

P. C. Mahato, Suprotim Saha, Bikash Das, Subhadeep Datta

We investigate thickness-dependent transformation from a paramagnetic to ferromagnetic phase in Cr$_{2}$Ge$_{2}$Te$_{6}$ (CGT) in bulk and few-layer flake forms. 2D Ising-like critical transition in bulk CGT occurs at $T_{c}$ = 67 K with out-of-plane magnetic anisotropy. Few-layer CGT on hBN/SiO$_{2}$/Si substrate displays the same $T_{c}$ but also exhibits

Won-Kwang Park

In this study, we consider the application of orthogonality sampling method (OSM) with single and multiple sources for a fast identification of small objects in limited-aperture inverse scattering problem. We first apply the OSM with single source and show that the indicator function with single source can be expressed by the Bessel function of order zero of

Alexander Wettig, Aatmik Gupta, Saumya Malik, Danqi Chen

Selecting high-quality pre-training data is important for creating capable language models, but existing methods rely on simple heuristics. We introduce QuRating, a method for selecting pre-training data that can capture human intuitions about data quality. In this paper, we investigate four qualities - writing style, required expertise, facts & trivia, and

Eftekhar Hossain, Omar Sharif, Mohammed Moshiul Hoque, Sarah M. Preum

Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal representation that effectively captures the interplay between visual and textual features through intermediate fusion is critical. Conventional fusion techniques are unable to attend to

Giancarlo Maero, Francesca Ferrero, Massimiliano Romé

An electron plasma can be confined for a theoretically infinite time in a Penning-Malmberg trap, a linear, azimuthally-symmetric magneto-electrostatic device where upon suitable conditions (high magnetization) the transverse dynamics of the plasma column is isomorphic to the one displayed by a two-dimensional ideal fluid. Fluid dynamics can thus be reproduce

Haoyang Liu, Yijiang Li, Jinglin Jian, Yuxuan Cheng

Machine learning has emerged as a powerful tool for scientific discovery, enabling researchers to extract meaningful insights from complex datasets. For instance, it has facilitated the identification of disease-predictive genes from gene expression data, significantly advancing healthcare. However, the traditional process for analyzing such datasets demands

Zibo Wang, Yifei Zhu, Dan Wang, Zhu Han

The emerging Web 3.0 paradigm aims to decentralize existing web services, enabling desirable properties such as transparency, incentives, and privacy preservation. However, current Web 3.0 applications supported by blockchain infrastructure still cannot support complex data analytics tasks in a scalable and privacy-preserving way. This paper introduces the e

Ruiqi Chen, Giacomo Vedovati, Todd Braver, ShiNung Ching

Dynamical system models such as Recurrent Neural Networks (RNNs) have become increasingly popular as hypothesis-generating tools in scientific research. Evaluating the dynamics in such networks is key to understanding their learned generative mechanisms. However, comparison of learned dynamics across models is challenging due to their inherent nonlinearity a

Drishti Goel, Fiza Husain, Aditya Singh, Supriyo Ghosh

Incident management for large cloud services is a complex and tedious process and requires significant amount of manual efforts from on-call engineers (OCEs). OCEs typically leverage data from different stages of the software development lifecycle [SDLC] (e.g., codes, configuration, monitor data, service properties, service dependencies, trouble-shooting doc

Paulo Garcia

Ensuring artificial intelligence behaves in such a way that is aligned with human values is commonly referred to as the alignment challenge. Prior work has shown that rational agents, behaving in such a way that maximizes a utility function, will inevitably behave in such a way that is not aligned with human values, especially as their level of intelligence

Hanyu Duan, Yi Yang, Kar Yan Tam

Large Language Models (LLMs) can make up answers that are not real, and this is known as hallucination. This research aims to see if, how, and to what extent LLMs are aware of hallucination. More specifically, we check whether and how an LLM reacts differently in its hidden states when it answers a question right versus when it hallucinates. To do this, we i

Yury E. Geints, Ekaterina K. Panina

Microdispersed photocatalysts based on titanium dioxide (TiO2) in the form of hollow core-shell microspheres (microcapsules) with mesoporous structure are widely demanded in modern critical technologies related to the catalysis of various chemicals, solving environmental problems, and obtaining cheap fuel. To date, a number of experimental works are known, s

Jianming Xian

Deep convolutional neural networks (CNNs) based approaches have achieved great performance in video matting. Many of these methods can produce accurate alpha estimation for the target body but typically yield fuzzy or incorrect target edges. This is usually caused by the following reasons: 1) The current methods always treat the target body and edge indiscri

Akshay Sakharam Rane

In this paper, we shall consider the notion of bicomplex inner product and define bicomplex Hilbert space. We shall define $L^{2}[a,b]$ where the functions take bicomplex values. We shall prove the Theorem for a bounded self adjoint operator on a bicomplex Hilbert space which is not compact.

Ruichen Li, Chuwei Wang, Haotian Ye, Di He

Solving partial differential equations (PDEs) efficiently is essential for analyzing complex physical systems. Recent advancements in leveraging deep learning for solving PDE have shown significant promise. However, machine learning methods, such as Physics-Informed Neural Networks (PINN), face challenges in handling high-order derivatives of neural network-

Tailin Zhou, Jiadong Yu, Jun Zhang, Danny H. K. Tsang

This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system. We first introduce a quality of experience (QoE) metric to measure user experience, which considers the MEC system's latency, user attention levels, and preferred resolutions. Then, a QoE maximizati

Ashfak Md Shibli, Mir Mehedi A. Pritom, Maanak Gupta

SMS phishing, also known as "smishing", is a growing threat that tricks users into disclosing private information or clicking into URLs with malicious content through fraudulent mobile text messages. In recent past, we have also observed a rapid advancement of conversational generative AI chatbot services (e.g., OpenAI's ChatGPT, Google's BARD), which are po

Kuang-Huei Lee, Xinyun Chen, Hiroki Furuta, John Canny

Current Large Language Models (LLMs) are not only limited to some maximum context length, but also are not able to robustly consume long inputs. To address these limitations, we propose ReadAgent, an LLM agent system that increases effective context length up to 20x in our experiments. Inspired by how humans interactively read long documents, we implement Re

Sanskriti Das

We report the first detection of non-thermal broadening of OVII lines in the warm-hot $\approx 10^6$ K circumgalactic medium (CGM) of the Milky Way. We use $z$=0 absorption of OVII K$\alpha$, OVII K$\beta$, and OVIII K$\alpha$ lines in archival grating data of $b>$15$^\circ$ quasar sightlines from $Chandra$ and $XMM$-$Newton$. Non-thermal line broadening is

Xinran Chen, Sufeng Duan, Gongshen Liu

Being one of the IR-NAT (Iterative-refinemennt-based NAT) frameworks, the Conditional Masked Language Model (CMLM) adopts the mask-predict paradigm to re-predict the masked low-confidence tokens. However, CMLM suffers from the data distribution discrepancy between training and inference, where the observed tokens are generated differently in the two cases. I

Shaojie Zhang, Yinghui Wang, Bin Nan, Wei Li

To address the issue of feature descriptors being ineffective in representing grayscale feature information when images undergo high affine transformations, leading to a rapid decline in feature matching accuracy, this paper proposes a region feature descriptor based on simulating affine transformations using classification. The proposed method initially cat

Chengshuai Shi, Kun Yang, Zihan Chen, Jundong Li

The remarkable instruction-following capability of large language models (LLMs) has sparked a growing interest in automatically finding good prompts, i.e., prompt optimization. Most existing works follow the scheme of selecting from a pre-generated pool of candidate prompts. However, these designs mainly focus on the generation strategy, while limited attent

Stephen Hausler, David Hall, Sutharsan Mahendren, Peyman Moghadam

Neural fields, coordinate-based neural networks, have recently gained popularity for implicitly representing a scene. In contrast to classical methods that are based on explicit representations such as point clouds, neural fields provide a continuous scene representation able to represent 3D geometry and appearance in a way which is compact and ideal for rob

Tao Lin, Yiling Chen

In classic principal-agent problems such as Stackelberg games, contract design, and Bayesian persuasion, the agent best responds to the principal's committed strategy. We study repeated generalized principal-agent problems under the assumption that the principal does not have commitment power and the agent uses algorithms to learn to respond to the principal

Jiahe Huang, Yifei Zhu

Low latency and high synchronization among users are critical for emerging multi-user virtual interaction applications. However, the existing ground-based cloud solutions are naturally limited by the complex ground topology and fiber speeds, making it difficult to pace with the requirement of multi-user virtual interaction. The growth of low earth orbit (LEO

Yinan Fang

Recent proposals for building scalable quantum computational devices in semi-conductor based spin qubits introduce shared control lines in order to reduce the overhead of qubits controls. In principle, noises from the shared controls could introduce correlated errors to multi-qubit gates, and identifying them will be helpful for achieving higher gate fidelit

Ali Panahpour, Jussi Kelavuori, Mikko Huttunen

Epsilon-near-zero (ENZ) photonics presents a powerful platform for integrated photonic systems, enabling a range of novel and extraordinary functionalities. However, the practical implementation of ENZ-based systems is often constrained by high material losses and severe impedance mismatch, limiting the efficient interaction of light with ENZ media. To overc

Sungguk Cha, Jusung Lee, Younghyun Lee, Cheoljong Yang

"When did the emperor Napoleon invented iPhone?" Such hallucination-inducing question is well known challenge in generative language modeling. In this study, we present an innovative concept of visual hallucination, referred to as "I Know (IK)" hallucination, to address scenarios where "I Don't Know" is the desired response. To effectively tackle this issue,

Ece Gumusel, Kyrie Zhixuan Zhou, Madelyn Rose Sanfilippo

This study presents a unique framework that applies and extends Solove (2006)'s taxonomy to address privacy concerns in interactions with text-based AI chatbots. As chatbot prevalence grows, concerns about user privacy have heightened. While existing literature highlights design elements compromising privacy, a comprehensive framework is lacking. Through sem

Yu Liu, Zibo Wang, Yifei Zhu, Chen Chen

Federated learning (FL) has emerged as a prevalent distributed machine learning scheme that enables collaborative model training without aggregating raw data. Cloud service providers further embrace Federated Learning as a Service (FLaaS), allowing data analysts to execute their FL training pipelines over differentially-protected data. Due to the intrinsic p

Kun Huang, Shi Pu, Angelia Nedić

In this paper, we introduce an accelerated distributed stochastic gradient method with momentum for solving the distributed optimization problem, where a group of $n$ agents collaboratively minimize the average of the local objective functions over a connected network. The method, termed ``Distributed Stochastic Momentum Tracking (DSMT)'', is a single-loop a

Benoît Collins, Thierry Giordano, Ryosuke Sato

We revisit the quantum de Finetti theorem. We state and prove a couple of variants thereof. In parallel, we introduce an operator version of the Martin boundary on quantum groups and prove generalizations of Biane's theoresm. Our proof of the de Finetti theorem is new in the sense that it is based on an analogy with the theory of operator valued Martin bound

Tao Yang, Cuiling Lan, Yan Lu, Nanning zheng

Disentangled representation learning strives to extract the intrinsic factors within observed data. Factorizing these representations in an unsupervised manner is notably challenging and usually requires tailored loss functions or specific structural designs. In this paper, we introduce a new perspective and framework, demonstrating that diffusion models wit

Zhichun Guo, Tong Zhao, Yozen Liu, Kaiwen Dong

Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show that GNNs struggle to produce good results on low-degree nodes despite their overall strong performance. In practical applications of LP, like recommendation systems, improving perfor

Pranshav Gajjar, Azuka Chiejina, Vijay K. Shah

Deep learning offers a promising solution to improve spectrum access techniques by utilizing data-driven approaches to manage and share limited spectrum resources for emerging applications. For several of these applications, the sensitive wireless data (such as spectrograms) are stored in a shared database or multistakeholder cloud environment and are theref

Kyle Marino, Pengmiao Zhang, Viktor Prasanna

Vision Transformers (ViTs) have emerged as a state-of-the-art solution for object classification tasks. However, their computational demands and high parameter count make them unsuitable for real-time inference, prompting the need for efficient hardware implementations. Existing hardware accelerators for ViTs suffer from frequent off-chip memory access, rest

Stanislav Shabala, Patrick Yates-Jones, Larissa Jerrim, Ross Turner

Odd Radio Circles (ORCs) are a class of low surface brightness, circular objects approximately one arcminute in diameter. ORCs were recently discovered in the Australian Square Kilometre Array Pathfinder (ASKAP) data, and subsequently confirmed with follow-up observations on other instruments, yet their origins remain uncertain. In this paper, we suggest tha

Michael Kapralov, Mikhail Makarov, Christian Sohler

Locality-sensitive hashing~[Indyk,Motwani'98] is a classical data structure for approximate nearest neighbor search. It allows, after a close to linear time preprocessing of the input dataset, to find an approximately nearest neighbor of any fixed query in sublinear time in the dataset size. The resulting data structure is randomized and succeeds with high p

Hiroyuki Nakamura, Hiroto Ohta, Ryuya Kobayashi, Takeshi Waki

The La-Co co-substituted magnetoplumbite-type (M-type) ferrites $A$Fe$_{12}$O$_{19}$ ($A$ = Ca, Sr and Ba, ion sizes Ca$^{2+}$ $<$ Sr$^{2+}$ $<$ Ba$^{2+}$) with Co compositions around 0.2 have been subjected to $^{59}$Co-NMR. The results show that Co occupies the 4f$_1$, 2a and 12k sites, and that the smaller the $A$ ion, the more Co tends to occupy the 4f$_

Xiangyu Gao, Yuwei Jin, Minghao Guo, Henry Chen

Compiling a given quantum algorithm into a target hardware architecture is a challenging optimization problem. The compiler must take into consideration the coupling graph of physical qubits and the gate operation dependencies. The existing noise in hardware architectures requires the compilation to use as few running cycles as possible. Existing approaches

Dionne Ibarra, Gabriel Montoya-Vega

Historically originated as a sub-field of topology, knot theory is an active area of mathematical investigation that has strong connections with a diverse set of scientific fields such as algebra, biology, and statistical mechanics. A popular and important concept in linear algebra, Gram determinants enjoy a connection with the mathematical theory of knots.

Claus Kadelka, David Murrugarra

Biological networks such as gene regulatory networks possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structural and dynamical features that evolution has incorporated in biological networks. A recent meta-analysis of published, expert-curated Boolean biological network models has reveale

Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang

Even if a model is not globally sparse, it is possible for decisions made from that model to be accurately and faithfully described by a small number of features. For instance, an application for a large loan might be denied to someone because they have no credit history, which overwhelms any evidence towards their creditworthiness. In this work, we introduc

Zhiyuan Lin, Wange Song, Li-Wei Wang, Haoran Xin

Non-Hermitian physics has greatly enriched our understanding of nonequilibrium phenomena and uncovered novel effects such as the non-Hermitian skin effect (NHSE) that has profoundly revolutionized the field. NHSE is typically predicted in systems with nonreciprocal couplings which, however, are difficult to realize in experiments. Without nonreciprocal coupl

Jian-Zhuang Wu, Lian-E Lu, Xin-Yu Zhao, Yong-Hong Ma

In this paper, the higher energy levels of the transmon qubit are taken into consideration to investigate the continuous variable entanglement generation between the transmon qubit and the single-mode cavity. Based on the framework of cavity quantum electrodynamics, we show the entanglement generation depends on the the driving field intensity, coupling stre

G. Maharanaa, J. K. Sahooa, Nestor Thome

This paper introduces new classes of generalized inverses for square matrices named GD1, and the dual, called 1GD inverse. In addition, we discuss a few characterizations and representations of these inverses. The explicit expressions of these inverses have been established via core-nilpotent decomposition. Further, we introduce a binary relation for GD1 inv

Yo Joong Choe, Aaditya Ramdas

In sequential anytime-valid inference, any admissible procedure must be based on e-processes: generalizations of test martingales that quantify the accumulated evidence against a composite null hypothesis at any stopping time. This paper proposes a method for combining e-processes constructed in different filtrations but for the same null. Although e-process

Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian

We study a three-layer data market comprising users (data owners), platforms, and a data buyer. Each user benefits from platform services in exchange for data, incurring privacy loss when their data, albeit noisily, is shared with the buyer. The user chooses platforms to share data with, while platforms decide on data noise levels and pricing before selling

Junhong Liang

Current Grammar Error Correction (GEC) initiatives tend to focus on major languages, with less attention given to low-resource languages like Esperanto. In this article, we begin to bridge this gap by first conducting a comprehensive frequency analysis using the Eo-GP dataset, created explicitly for this purpose. We then introduce the Eo-GEC dataset, derived

Yinglun Xu, Rohan Gumaste, Gagandeep Singh

We study the problem of universal black-boxed reward poisoning attacks against general offline reinforcement learning with deep neural networks. We consider a black-box threat model where the attacker is entirely oblivious to the learning algorithm, and its budget is limited by constraining the amount of corruption at each data point and the total perturbati

Yuxuan Gu, Yi Jin, Ben Wang, Zhixiang Wei

In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios.Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features de

Leon Lufkin, Yihong Wu, Jiaming Xu

This paper studies the problem of shuffled linear regression, where the correspondence between predictors and responses in a linear model is obfuscated by a latent permutation. Specifically, we consider the model $y = \Pi_* X \beta_* + w$, where $X$ is an $n \times d$ standard Gaussian design matrix, $w$ is Gaussian noise with entrywise variance $\sigma^2$,

Mohamed-Ali Belabbas, Xudong Chen

A graphon satisfies the $H$-property if graphs sampled from it contain a Hamiltonian decomposition almost surely, which in turn implies that the corresponding network topologies are, e.g., structurally stable and structurally ensemble controllable. In recent papers, we have exhibited a set of conditions that is essentially necessary and sufficient for the $H

Donald Marolf, Daiming Zhang

Euclidean path integrals for UV-completions of $d$-dimensional bulk quantum gravity were studied in [1] by assuming that they satisfy axioms of finiteness, reality, continuity, reflection-positivity, and factorization. Sectors ${\cal H}_{\cal B}$ of the resulting Hilbert space were defined for any $(d-2)$-dimensional surface ${\cal B}$, where ${\cal B}$ may

Dheeraj R. Pasham, Eric R. Coughlin, Michal Zajacek, Itai Linial

Quasi-periodic Eruptions (QPEs) represent a novel class of extragalactic X-ray transients that are known to repeat at roughly regular intervals of a few hours to days. Their underlying physical mechanism is a topic of heated debate, with most models proposing that they originate either from instabilities within the inner accretion flow or from orbiting objec