October 2023 arXiv papers — page 77

Xi Xiong, Lu Liu

With the advancement of data-driven techniques, addressing continuous con-trol challenges has become more efficient. However, the reliance of these methods on historical data introduces the potential for unexpected decisions in novel scenarios. To enhance performance in autonomous driving and collision avoidance, we propose a symbiotic fusion of policy gradi

Xiaoqi Ma, Jin Zhang, Wenchao Zheng

This article investigates a local discontinuous Galerkin (LDG) method for one-dimensional and two-dimensional singularly perturbed reaction-diffusion problems on a Shishkin mesh. During this process, due to the inability of the energy norm to fully capture the behavior of the boundary layers appearing in the solutions, a balanced norm is introduced. By desig

Jaeyoung Choe, Keonwoong Noh, Nayeon Kim, Seyun Ahn

Over the past few years, various domain-specific pretrained language models (PLMs) have been proposed and have outperformed general-domain PLMs in specialized areas such as biomedical, scientific, and clinical domains. In addition, financial PLMs have been studied because of the high economic impact of financial data analysis. However, we found that financia

Johan Thunberg, Florian Bernard

We propose a novel non-negative spherical relaxation for optimization problems over binary matrices with injectivity constraints, which in particular has applications in multi-matching and clustering. We relax respective binary matrix constraints to the (high-dimensional) non-negative sphere. To optimize our relaxed problem, we use a conditional power iterat

Ethan Berger, Hannu-Pekka Komsa

Raman spectroscopy is a powerful and nondestructive method that is widely used to study the vibrational properties of solids or molecules. Simulations of finite-temperature Raman spectra rely on obtaining polarizabilities along molecular dynamics trajectories, which is computationally highly demanding if calculated from first principles. Machine learning for

Nadime Francis, Victor Marsault

We consider the problem of enumerating a regular language $L$ in radix order, or more precisely, the equivalent problem of enumerating all words in $L$ of a given length in lexicographic order. Ackerman and Shallit gave in 2009 the principles of an efficient solution to this problem, but they did not use the enumeration complexity framework for their analysi

Taekoon Lee

A general model is proposed for time-varying coupling constants in field theory, assuming the ultraviolet cutoff is a varying entity in the expanding universe. It is assumed that the cutoff depends on the scale factor of the universe and all bare couplings remain constant. This leads to varying renormalized coupling constants that evolve in proportion to the

Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang

Recent instruction-finetuned large language models (LMs) have achieved notable performances in various tasks, such as question-answering (QA). However, despite their ability to memorize a vast amount of general knowledge across diverse tasks, they might be suboptimal on specific tasks due to their limited capacity to transfer and adapt knowledge to target ta

Ying-Hao Lu, Bor Kae Chang, Yi-Fan Chen

Configuring TiO2 into bicontinuous mesostructures greatly improves its photocatalytic efficiency. This is often ascribed to the expanded surface area. Yet, whether mesostructuring modulates TiO2's electronic structure and how that contributes to the improvement are rarely discussed. Here, we employed spectroscopic and density functional theory approaches to

P. Limelette, M. El Kamily, H. Aramberri, F. Giovannelli

Enabling on-demand control of heat flow is key for the development of next-generation electronic devices, solid-state heat pumps, and thermal logic. However, precise and agile tuning of the relevant microscopic material parameters for adjusting thermal conductivities remains elusive. Here, we study several single crystals of lanthanum aluminate (LaAlO$_{3}$)

Nan Gao, Sam Nolan, Kaixin Ji, Shakila Khan Rumi

Home confinement, a situation experienced by individuals for reasons ranging from medical quarantines, rehabilitation needs, disability accommodations, and remote working, is a common yet impactful aspect of modern life. While essential in various scenarios, confinement within the home environment can profoundly influence mental well-being and digital device

Bowen Hao, Chaoqun Yang, Lei Guo, Junliang Yu

Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of data sparsity and cold start by transferring general knowledge from the source to the target domain. However, existing CDR models suffer limitations in adaptability across various scenarios due to their inherent complexity. To tackle this challenge, recent advancements intr

Matteo Simioni, Carmelo Arcidiacono, Andrea Grazian, Marco Gullieuszik

Observations close to the diffraction limit, with high Strehl ratios from Adaptive Optics (AO)-assisted instruments mounted on ground-based telescopes are a reality and will become even more widespread with the next generation instruments that equip 30 meter-class telescopes. This results in a growing interest in tools and methods to accurately reconstruct t

Ryota Ono, Igor Solovyev, Sergey Artyukhin

Magnetoelectric multiferroics are key materials for next-generation spintronic devices due to their entangled magnetic and ferroelectric properties. Spiral multiferroics possess ferroelectric polarization and are particularly promising for electric control of magnetism and magnetic control of ferroelectricity. In this work, we uncover long-period incommensur

Kunal Rajesh Lahoti, Shivani Hanji, Pratik Kamble, Kavita Vemuri

This study investigates the impact of loss-framing and individual risk attitude on willingness- to purchase insurance products utilizing a game-like interface as choice architecture. The application presents events as experienced in real life. Both financial and emotional loss-framing events are followed by choices to purchase insurance. The participant coho

Motoaki Bamba, Koji Hashimoto, Keiju Murata, Daichi Takeda

According to the AdS/CFT correspondence, certain quantum many-body systems in $d$-dimensions are equivalent to gravitational theories in $(d+1)$-dimensional asymptotically AdS spacetimes. When a massless particle is sent from the AdS boundary to the bulk curved spacetime, it reaches another point of the boundary after a time lag. In the dual quantum system,

Milad Abolpour, MohammadJavad Salehi, Antti Tölli

Coded caching (CC) can substantially enhance network performance by leveraging memory as an additional communication resource. However, the use of CC is challenging in various practical applications due to dynamic user behavior. The existing solutions, based on shared caching, cannot directly handle all scenarios where users freely enter and depart the netwo

Chenkai Sun, Jinning Li, Yi R. Fung, Hou Pong Chan

Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury. To effectively forecast responses, it is essential to develop measures that leverage the social dynamics and contextual informati

Physics Claire Kluber

Trotterization in quantum mechanics is an important theoretical concept in handling the exponential of noncommutative operators. In this communication, we give a mathematical formulation of the Trotter Product Formula, and apply it to basic examples in which the utility of Trotterization is evident. Originally, this article was completed in December 2020 as

Wenhao Yu, Jie Peng, Quecheng Qiu, Hanyu Wang

Robot navigation using deep reinforcement learning (DRL) has shown great potential in improving the performance of mobile robots. Nevertheless, most existing DRL-based navigation methods primarily focus on training a policy that directly commands the robot with low-level controls, like linear and angular velocities, which leads to unstable speeds and unsmoot

Ze Gao, Xiang Li, Changkun Liu, Xian Wang

We present VR PreM+, an innovative VR system designed to enhance web exploration beyond traditional computer screens. Unlike static 2D displays, VR PreM+ leverages 3D environments to create an immersive pre-learning experience. Using keyword-based information retrieval allows users to manage and connect various content sources in a dynamic 3D space, improvin

Neil Glikin, Benjamin A. Stickler, Ryan Tollefsen, Sara Mouradian

The quantum rotor is one of the simplest model systems in quantum mechanics, but only in recent years has theoretical work revealed general fundamental scaling laws for its decoherence. For example, a superposition of orientations decoheres at a rate proportional to the sine squared of the angle between them. Here we observe scaling laws for rotational decoh

Kihyun You, Jawook Gu, Jiyeon Ham, Beomhee Park

A large-scale image-text pair dataset has greatly contributed to the development of vision-language pre-training (VLP) models, which enable zero-shot or few-shot classification without costly annotation. However, in the medical domain, the scarcity of data remains a significant challenge for developing a powerful VLP model. In this paper, we tackle the lack

Ruixiang Tang, Gord Lueck, Rodolfo Quispe, Huseyin A Inan

Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks. However, there is a concern that these models may disclose information in the training data. In this study, we focus on the summarization task and investigate the membership inference (MI) attack: given a sample and black-box access to a mod

Zijie Wang, Md Mosharaf Hossain, Shivam Mathur, Terry Cruz Melo

Yes-no questions expect a yes or no for an answer, but people often skip polar keywords. Instead, they answer with long explanations that must be interpreted. In this paper, we focus on this challenging problem and release new benchmarks in eight languages. We present a distant supervision approach to collect training data. We also demonstrate that direct an

Changli Tang, Wenyi Yu, Guangzhi Sun, Xianzhao Chen

Hearing is arguably an essential ability of artificial intelligence (AI) agents in the physical world, which refers to the perception and understanding of general auditory information consisting of at least three types of sounds: speech, audio events, and music. In this paper, we propose SALMONN, a speech audio language music open neural network, built by in

Olivier D. Y. Huet, Ravindra Pethiyagoda, Timothy J. Moroney, Arvind Kumar

We develop a computational model to simulate the immediate post-impact spreading behaviour of surfactant-laden drops that impact a flat and solid surface. The model is built on the InterFoam solver (OpenFOAM software), which uses the volume-of-fluid method to solve the Navier-Stokes equations. In order to incorporate surfactant in the bulk and on the interfa

Yunhao Tong, Fanyi Kong, Lei Zhang, Xinyi Hou

Two-dimensional layered ReX2 (X = Se, S) has attracted researcher's great interest due to its unusual in-plane anisotropic optical and electrical properties and great potential in polarization-sensitive optoelectronic devices, while the clean, energy-saving, and ecological synthesis of highly-crystalline ReSe2 with controlled domains remains challenging yet

Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu

Although pretraining has garnered significant attention and popularity in recent years, its application in graph-based recommender systems is relatively limited. It is challenging to exploit prior knowledge by pretraining in widely used ID-dependent datasets. On one hand, user-item interaction history in one dataset can hardly be transferred to other dataset

Xianzhe Dai, Yukai Sun, Changliang Wang

There has been a lot of interests in Positive Mass Theorems for singular metrics on smooth manifolds. We prove a positive mass theorem for asymptotically flat (AF) spin manifolds with isolated conical singularities or more generally horn singularities. In particular, we allow topological singularities in the space as we do not require the cross sections of t

Azwar Abdulsalam, Joseph G. Makin

Fitting generative models to sequential data typically involves two recursive computations through time, one forward and one backward. The latter could be a computation of the loss gradient (as in backpropagation through time), or an inference algorithm (as in the RTS/Kalman smoother). The backward pass in particular is computationally expensive (since it is

Liping Yi, Han Yu, Gang Wang, Xiaoguang Liu

Federated learning (FL) is an emerging machine learning paradigm in which a central server coordinates multiple participants (clients) collaboratively to train on decentralized data. In practice, FL often faces statistical, system, and model heterogeneities, which inspires the field of Model-Heterogeneous Personalized Federated Learning (MHPFL). With the inc

Seong Hyeon Park, Gahyun Choi, Gyunghun Kim, Jaehyeong Jo

Engineering the admittance of external environments connected to superconducting qubits is essential, as increasing the measurement speed introduces spontaneous emission loss to superconducting qubits, known as Purcell loss. Here, we report a broad bandwidth Purcell filter design within a small footprint, which effectively suppresses Purcell loss without los

Evie Andrew, Travis Monk, André van Schaik

This article introduces the Event based Prediction Suffix Tree (EPST), a biologically inspired, event-based prediction algorithm. The EPST learns a model online based on the statistics of an event based input and can make predictions over multiple overlapping patterns. The EPST uses a representation specific to event based data, defined as a portion of the p

Lukas Anagnostou, Scott Mullane, Paul Norbury

In this paper we study Weil-Petersson volumes of the moduli spaces of conical hyperbolic surfaces. The moduli spaces are parametrised by their cone angles which naturally live inside Hassett's space of stability conditions on nodal curves. Such stability conditions produce weighted pointed stable curves which define compactifications of the moduli space of c

Rui Du, Haocheng Hua, Hailiang Xie, Xianxin Song

With recent advancements, the wireless local area network (WLAN) or wireless fidelity (Wi-Fi) technology has been successfully utilized to realize sensing functionalities such as detection, localization, and recognition. However, the WLANs standards are developed mainly for the purpose of communication, and thus may not be able to meet the stringent requirem

T. Santhosh, P. C. Rout, S. Santra, A. Pal

The nuclear level density is a fundamental quantity in nuclear physics, governing various nuclear reactions and astrophysical processes. In this study, we report on the collective enhancement of nuclear level density and its fade-out with excitation energy in the deformed $^{161}$Dy, obtained through an exclusive measurement of neutron evaporation spectra. T

Aditya Shankar Pal, Debojyoti Biswas, Joy Mahapatra, Debasis Banerjee

White blood cells (WBCs) play a crucial role in safeguarding the human body against pathogens and foreign substances. Leveraging the abundance of WBC imaging data and the power of deep learning algorithms, automated WBC analysis has the potential for remarkable accuracy. However, the capability of deep learning models to explain their WBC classification rema

Shubo Li, Ran Li, Kai Zhu, Shengdong Lu

We try to understand the trends in the mass density slopes as a function of galaxy properties. We use the results from the best Jeans Anisotropic Modelling (JAM) of the integral-field stellar kinematics for near 6000 galaxies from the MaNGA DynPop project, with stellar masses of $10^{9-12}\ {\rm M_{\odot}}$, including both early-type and late-type galaxies.

Paata Ivanisvili, Ohad Klein, Roman Vershynin

We show that the minimal number of skewed hyperplanes that cover the hypercube $\{0,1\}^{n}$ is at least $\frac{n}{2}+1$, and there are infinitely many $n$'s when the hypercube can be covered with $n-\log_{2}(n)+1$ skewed hyperplanes. The minimal covering problems are closely related to uncertainty principle on the hypercube, where we also obtain an interpol

Xiangru Jian, Yimu Wang

Over recent decades, significant advancements in cross-modal retrieval are mainly driven by breakthroughs in visual and linguistic modeling. However, a recent study shows that multi-modal data representations tend to cluster within a limited convex cone (as representation degeneration problem), which hinders retrieval performance due to the inseparability of

Hassan Noghrei, Mohammad-Reza Sadeghi, Wai Ho Mow

The quality and quantity of data used for training greatly influence the performance and effectiveness of deep learning models. In the context of error correction, it is essential to generate high-quality samples that are neither excessively noisy nor entirely correct but close to the decoding region's decision boundary. To accomplish this objective, this pa

Wontae Kim, Mungon Nam

Black hole complementarity plays a pivotal role in resolving the information loss paradox by treating Hawking radiation as carriers of information, apart from the complicated mechanisms involved in decoding information from this radiation. The thought experiment proposed by Susskind and Thorlacius, as well as the criteria set forth by Hayden and Preskill, pr

Raphael Falque, Cedric Le Gentil, Fouad Sukkar

On the journey to enable robots to interact with the real world where humans, animals, and unpredictable elements are acting as independent agents; it is crucial for robots to have the capability to detect dynamic objects. In this paper, we argue that the detection of dynamic objects can be solved by computing the spatiotemporal normals of a point cloud. In

Susumu Hirose, Naoyuki Monden

Wajnryb proved that the mapping class group of a closed oriented surface is generated by two elements. We proved that the mapping class group is generated by two pseudo-Anosov elements. In particular, if the genus is greater than or equal to nine, we can take the generators to two conjugate pseudo-Anosov elements with arbitrarily large dilatations. Another r

Shui-Sen Zhang, Zi-An Wang, Bo Li, Yuan-Yuan Jiang

Physical phenomena in condensed matter normally arise from the collective effect of all atoms, while selectively addressing a lone atomic sublattice by external stimulus is elusive. The later functionality may, however, benefit various applications, as the response may differ when the external stimulus affects only a specific sublattice rather than the entir

Tomoharu Iwata, Yusuke Tanaka, Naonori Ueda

We propose a neural network-based meta-learning method to efficiently solve partial differential equation (PDE) problems. The proposed method is designed to meta-learn how to solve a wide variety of PDE problems, and uses the knowledge for solving newly given PDE problems. We encode a PDE problem into a problem representation using neural networks, where gov

Mohd. Sayemul Haque, Md. Fahim, Muhammad Ibrahim

Learning-to-rank is an applied domain of supervised machine learning. As feature selection has been found to be effective for improving the accuracy of learning models in general, it is intriguing to investigate this process for learning-to-rank domain. In this study, we investigate the use of a popular meta-heuristic approach called simulated annealing for

Roman Jacome, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello

Hypercomplex signal processing (HSP) provides state-of-the-art tools to handle multidimensional signals by harnessing intrinsic correlation of the signal dimensions through Clifford algebra. Recently, the hypercomplex representation of the phase retrieval (PR) problem, wherein a complex-valued signal is estimated through its intensity-only projections, has a

Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu

Diffusion probabilistic models (DPMs) have exhibited excellent performance for high-fidelity image generation while suffering from inefficient sampling. Recent works accelerate the sampling procedure by proposing fast ODE solvers that leverage the specific ODE form of DPMs. However, they highly rely on specific parameterization during inference (such as nois

Mengjie Zhao, Junya Ono, Zhi Zhong, Chieh-Hsin Lai

Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks. However, there has been limited investigation of and improvement in their language encoder, which is the central component of encoding natural language descriptions of image/audio into vector representations. We extensively evaluate how unsupe

Patrick J. Lawrence, Xia Ning

Due to cancer's complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patients makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods' abili

Jian Sang, Jifeng Lan, Mingyong Zhou, Boning Gao

Reconfigurable intelligent surfaces (RISs) have attracted increasing interest from both academia and industry, thanks to their unique features on controlling electromagnetic (EM) waves. Although theoretical models for RIS-empowered communications have covered a variety of applications, yet, very few papers have investigated the modeling of real propagation c

Le Zhang, Yihong Wu, Fengran Mo, Jian-Yun Nie

Multi-modal open-domain question answering typically requires evidence retrieval from databases across diverse modalities, such as images, tables, passages, etc. Even Large Language Models (LLMs) like GPT-4 fall short in this task. To enable LLMs to tackle the task in a zero-shot manner, we introduce MoqaGPT, a straightforward and flexible framework. Using a

Fausto Ferrari, Nicolò Forcillo

In this paper we provide a different approach to the Alt-Caffarelli-Friedman monotonicity formula, reducing the problem to test the monotone increasing behavior of the mean value of a function involving the norm of the gradient. In particular, we show that our argument holds in the general framework of Carnot groups.

Jiaming Gu, Minchao Jiang, Hongsheng Li, Xiaoyuan Lu

Neural Radiance Fields (NeRF) is a novel implicit 3D reconstruction method that shows immense potential and has been gaining increasing attention. It enables the reconstruction of 3D scenes solely from a set of photographs. However, its real-time rendering capability, especially for interactive real-time rendering of large-scale scenes, still has significant

Pete Rigas

We compute the action-angle variables for a Hamiltonian flow of the inhomogeneous six-vertex model, from a formulation introduced in a 2022 work due to Keating, Reshetikhin, and Sridhar, hence confirming a conjecture of the authors as to whether the Hamiltonian flow is integrable. To demonstrate that such an integrability property of the Hamiltonian holds fr

Xue Zhang, Songming Zhang, Yunlong Liang, Yufeng Chen

Existing syntactically-controlled paraphrase generation (SPG) models perform promisingly with human-annotated or well-chosen syntactic templates. However, the difficulty of obtaining such templates actually hinders the practical application of SPG models. For one thing, the prohibitive cost makes it unfeasible to manually design decent templates for every so

Haoyu Wang, Jialin Liu, Xiaohan Chen, Xinshang Wang

Mixed-integer linear programming (MILP) stands as a notable NP-hard problem pivotal to numerous crucial industrial applications. The development of effective algorithms, the tuning of solvers, and the training of machine learning models for MILP resolution all hinge on access to extensive, diverse, and representative data. Yet compared to the abundant natura

Xu Huang, Jianxun Lian, Hao Wang, Defu Lian

Recommendation systems effectively guide users in locating their desired information within extensive content repositories. Generally, a recommendation model is optimized to enhance accuracy metrics from a user utility standpoint, such as click-through rate or matching relevance. However, a responsible industrial recommendation system must address not only u

Jeremy Lai, Faruk Ahmed, Supriya Vijay, Tiam Jaroensri

Task-specific deep learning models in histopathology offer promising opportunities for improving diagnosis, clinical research, and precision medicine. However, development of such models is often limited by availability of high-quality data. Foundation models in histopathology that learn general representations across a wide range of tissue types, diagnoses,

Kushal Kedia, Prithwish Dan, Atiksh Bhardwaj, Sanjiban Choudhury

Seamless human-robot manipulation in close proximity relies on accurate forecasts of human motion. While there has been significant progress in learning forecast models at scale, when applied to manipulation tasks, these models accrue high errors at critical transition points leading to degradation in downstream planning performance. Our key insight is that

Chengxu Zhuang, Evelina Fedorenko, Jacob Andreas

Modern neural language models (LMs) are powerful tools for modeling human sentence production and comprehension, and their internal representations are remarkably well-aligned with representations of language in the human brain. But to achieve these results, LMs must be trained in distinctly un-human-like ways - requiring orders of magnitude more language da

Zhihe Zhang, Hao Wei, Hongtao Nie

Simultaneous localization and mapping, as a fundamental task in computer vision, has gained higher demands for performance in recent years due to the rapid development of autonomous driving and unmanned aerial vehicles. Traditional SLAM algorithms highly rely on basic geometry features such as points and lines, which are susceptible to environment. Conversel

Sipeng Zheng, Jiazheng Liu, Yicheng Feng, Zongqing Lu

Recent studies have presented compelling evidence that large language models (LLMs) can equip embodied agents with the self-driven capability to interact with the world, which marks an initial step toward versatile robotics. However, these efforts tend to overlook the visual richness of open worlds, rendering the entire interactive process akin to "a blindfo

Jiayi Li, Matthew Motoki, Baosen Zhang

A central challenge in using price signals to coordinate the electricity consumption of a group of users is the operator's lack of knowledge of the users due to privacy concerns. In this paper, we develop a two-time-scale incentive mechanism that alternately updates between the users and a system operator. As long as the users can optimize their own consumpt

Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Mingdai Yang

The field of Recommender Systems (RecSys) has been extensively studied to enhance accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of accuracy frequently engenders diminished diversity, culminating in the well-recognized "echo chamber" phenomenon. Diversified RecSys has emerged as a countermeasure, placing diversity

Mohannad Alkhraijah, Rachel Harris, Samuel Litchfield, David Huggins

This paper investigates the vulnerability of the Alternating Direction Method of Multipliers (ADMM) algorithm to shared data manipulation, with a focus on solving optimal power flow (OPF) problems. Deliberate data manipulation may cause the ADMM algorithm to converge to suboptimal solutions. We derive a sufficient condition for detecting data manipulation ba

Jiangshan Wang, Zheng Peng

We introduce two new stochastic conjugate frameworks for a class of nonconvex and possibly also nonsmooth optimization problems. These frameworks are built upon Stochastic Recursive Gradient Algorithm (SARAH) and we thus refer to them as Acc-Prox-CG-SARAH and Acc-Prox-CG-SARAH-RS, respectively. They are efficiently accelerated, easy to implement, tune free a

Guangqi Xie, Xin Li, Xiaohan Pan, Zhibo Chen

Remote medical diagnosis has emerged as a critical and indispensable technique in practical medical systems, where medical data are required to be efficiently compressed and transmitted for diagnosis by either professional doctors or intelligent diagnosis devices. In this process, a large amount of redundant content irrelevant to the diagnosis is subjected t

Eunkyu Oh, Taehun Kim

Session-based recommendations which predict the next action by understanding a user's interaction behavior with items within a relatively short ongoing session have recently gained increasing popularity. Previous research has focused on capturing the dynamics of sequential dependencies from complicated item transitions in a session by means of recurrent neur

Yuxiao Qu, Jinmeng Rao, Song Gao, Qianheng Zhang

Understanding and measuring the resilience of food supply networks is a global imperative to tackle increasing food insecurity. However, the complexity of these networks, with their multidimensional interactions and decisions, presents significant challenges. This paper proposes FLEE-GNN, a novel Federated Learning System for Edge-Enhanced Graph Neural Netwo

Zefang Liu, John Buford

Anomaly detection in command shell sessions is a critical aspect of computer security. Recent advances in deep learning and natural language processing, particularly transformer-based models, have shown great promise for addressing complex security challenges. In this paper, we implement a comprehensive approach to detect anomalies in Unix shell sessions usi

Robert Meadows, Y. Xue, Nicholas Allbritton, G. P. Zhang

Laser-driven electron transport across a sample has garnered enormous attentions over several decades, because it potentially allows one to control spin transports in spintronics. But light is a transverse electromagnetic wave, how an electron acquires a longitudinal velocity has been very puzzling. In this paper, we show a general mechanism is working. It i

Jingyi Xiang, Holly Dinkel

MultiDLO is a real-time algorithm for estimating the shapes of multiple, intertwining deformable linear objects (DLOs) from RGB-D image sequences. Unlike prior methods that track only a single DLO, MultiDLO simultaneously handles several objects. It uses the geodesic distance in the Global-Local Topology Preservation algorithm to define both inter-object ide

Sander R. Dahmen, Joey M. van Langen

In this paper we consider elliptic divisibility sequences generated by a point on an elliptic curve over $\mathbb{Q}$ with $j$-invariant $1728$ given by an integral short Weierstrass equation. For several different such elliptic divisibility sequences, we determine explicitly a finite set of primes such that for all primes $l$ outside this set, the elliptic

Shengyao Zhuang, Bing Liu, Bevan Koopman, Guido Zuccon

In the field of information retrieval, Query Likelihood Models (QLMs) rank documents based on the probability of generating the query given the content of a document. Recently, advanced large language models (LLMs) have emerged as effective QLMs, showcasing promising ranking capabilities. This paper focuses on investigating the genuine zero-shot ranking effe

Yunpeng Xue, Yongling Zhao, Shuo-Jun Mei, Yuan Chao

The study of turbulent boundary layer flow holds significant importance in urban climate research, particularly concerning numerical simulation studies where it serves as a crucial inflow boundary condition. However, understanding the turbulent boundary layer's influence on flow and heat features within canyon and canopy flow remains incomplete. To address t

Roberto Bochicchio, Boris Maulén

The physical regions (domains or basins) within the molecular structure are open systems that exchange charge between them and consequently house a fractional number of electrons (net charge). The natural framework describing the quantum states for these domains is the density matrix (DM) in its grand-canonical version which corresponds to a convex expansion

Patrick Rehill, Nicholas Biddle

Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, i.e., there is no globally interpretable way to understand how a model makes estimates. This is a clear problem in policy evaluati

João V. P. e Silva

The article focuses on a class of second countable groups assembled from profinite and discrete by elementary operations. We focus on a rank associated with these groups that measure their complexity, the decomposition rank. A collection of groups acting on $\aleph_0$-regular trees is defined and used for the first construction of a group with decomposition

João V. P. e Silva

We focus on working on incidence rings, a class of (possibly infinite) matrix rings indexed by ordered sets. Some general properties about them are given, including how they are always the inverse limit of finite matrix rings, giving a natural way to define their topology. We then show that such construction can be translated as a functor from the category o

Hiroshi Nagaoka

The Fisher metric on a manifold of probability distributions is usually treated as a metric on the tangent bundle. In this paper, we focus on the metric on the cotangent bundle induced from the Fisher metric with calling it the Fisher co-metric. We show that the Fisher co-metric can be defined directly without going through the Fisher metric by establishing

Loc X. Nguyen, Huy Q. Le, Ye Lin Tun, Pyae Sone Aung

Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication systems are built upon advanced deep learning models whose training performance heavily relies on data availability. Existing studies often make unrealistic assumptions of a readil

Qiqi Hou, Feng Liu

This paper investigates super resolution to reduce the number of pixels to render and thus speed up Monte Carlo rendering algorithms. While great progress has been made to super resolution technologies, it is essentially an ill-posed problem and cannot recover high-frequency details in renderings. To address this problem, we exploit high-resolution auxiliary

Luc Paoli, Pavan K. Inguva, Andrew J. Haslam, Pierre J. Walker

Computational elements in thermodynamics have become increasingly important in contemporary chemical-engineering research and practice. However, traditional thermodynamics instruction provides little exposure to computational thermodynamics, leaving students ill-equipped to engage with the state-of-the-art deployed in industry and academia. The recent rise o

S. Park, L. Y. Gorelik, S. I. Kulinich, H. C. Park

We propose a nanomechanical device which can actuate and probe a flying qubit that can be used to facilitate quantum information transfer over a long distance. The flying qubit is formed by a movable Cooper pair box (CPB) consisting of a superconducting dot and a bulk superconductor which are entangled by removing the Coulomb blockade of Cooper pair tunnelin

Tianyu Liu, Somabha Mukherjee

In this paper, we study two well known methods of Ising structure learning, namely the pseudolikelihood approach and the interaction screening approach, in the context of tensor recovery in $k$-spin Ising models. We show that both these approaches, with proper regularization, retrieve the underlying hypernetwork structure using a sample size logarithmic in t

Dawei Li, Hengyuan Zhang, Yanran Li, Shiping Yang

In this work, we tackle the scenario of understanding characters in scripts, which aims to learn the characters' personalities and identities from their utterances. We begin by analyzing several challenges in this scenario, and then propose a multi-level contrastive learning framework to capture characters' global information in a fine-grained manner. To val

Weiye Zhao, Feihan Li, Yifan Sun, Rui Chen

In recent years, trust region on-policy reinforcement learning has achieved impressive results in addressing complex control tasks and gaming scenarios. However, contemporary state-of-the-art algorithms within this category primarily emphasize improvement in expected performance, lacking the ability to control over the worst-case performance outcomes. To add

Jae Yong Lee, Sungmin Kang, Juyeon Yoon, Shin Yoo

Large Language Models (LLMs) have demonstrated strong natural language processing and code synthesis capabilities, which has led to their rapid adoption in software engineering applications. However, details about LLM training data are often not made public, which has caused concern as to whether existing bug benchmarks are included. In lieu of the training

Hellina Hailu Nigatu, Atnafu Lambebo Tonja, Jugal Kalita

Multilingual Language Models offer a way to incorporate multiple languages in one model and utilize cross-language transfer learning to improve performance for different Natural Language Processing (NLP) tasks. Despite progress in multilingual models, not all languages are supported as well, particularly in low-resource settings. In this work, we investigate

Luolin Xiong, Yang Tang, Chensheng Liu, Shuai Mao

Energy storage systems (ESS) are pivotal component in the energy market, serving as both energy suppliers and consumers. ESS operators can reap benefits from energy arbitrage by optimizing operations of storage equipment. To further enhance ESS flexibility within the energy market and improve renewable energy utilization, a heterogeneous photovoltaic-ESS (PV

Yuchen Zhuang, Xiang Chen, Tong Yu, Saayan Mitra

Large language models (LLMs) have demonstrated powerful decision-making and planning capabilities in solving complicated real-world problems. LLM-based autonomous agents can interact with diverse tools (e.g., functional APIs) and generate solution plans that execute a series of API function calls in a step-by-step manner. The multitude of candidate API funct

Rahman S M Wahidur, Ishmam Tashdeed, Manjit Kaur, Heung-No-Lee

Blockchain technology has revolutionized the financial landscape, with cryptocurrencies gaining widespread adoption for their decentralized and transparent nature. As the sentiment expressed on social media platforms can significantly influence cryptocurrency discussions and market movements, sentiment analysis has emerged as a crucial tool for understanding

Arijit Sehanobish, Krzysztof Choromanski, Yunfan Zhao, Avinava Dubey

We introduce the concept of scalable neural network kernels (SNNKs), the replacements of regular feedforward layers (FFLs), capable of approximating the latter, but with favorable computational properties. SNNKs effectively disentangle the inputs from the parameters of the neural network in the FFL, only to connect them in the final computation via the dot-p

Kate Highnam, Zach Hanif, Ellie Van Vogt, Sonali Parbhoo

Intrusion research frequently collects data on attack techniques currently employed and their potential symptoms. This includes deploying honeypots, logging events from existing devices, employing a red team for a sample attack campaign, or simulating system activity. However, these observational studies do not clearly discern the cause-and-effect relationsh

Margarita Colberg, Jeremy Schofield

A Markov state model is a powerful tool that can be used to track the evolution of populations of configurations in an atomistic representation of a protein. For a coarse-grained linear chain model with discontinuous interactions, the transition rates among states that appear in the Markov model when the monomer dynamics is diffusive can be determined by com

Akio Tomiya, Yuki Nagai

Machine learning, deep learning, has been accelerating computational physics, which has been used to simulate systems on a lattice. Equivariance is essential to simulate a physical system because it imposes a strong induction bias for the probability distribution described by a machine learning model. This reduces the risk of erroneous extrapolation that dev

Matias G. Delgadino, M. Vaughan

We show that nonlocal seminorms are strictly decreasing under the continuous Steiner rearrangement. This implies that all solutions to nonlocal equations which arise as critical points of nonlocal energies are radially symmetric and decreasing. Moreover, we show uniqueness of solutions by exploiting the convexity of the energies under a tailored interpolatio