October 2023 arXiv papers — page 182

M. Casolino

Ultra High Energy Cosmic Rays (UHECRs) offer a unique chance to study the universe at energies inaccessible by man-made accelerators. Observations by ground based observatories have clarified several characteristics of these particles, but their origin, nature, and acceleration mechanisms are still unclear, mostly due to their extremely low flux. Space-based

Yaashia Gautam, Marco M. Nicotra

This paper introduces a new framework for analyzing the stability of discrete-time model predictive controllers acting on continuous-time systems. The proposed framework introduces the distinction between discretization time (used to generate the optimal control problem) and sampling time (used to implement the controller). The paper not only shows that thes

Angel Lozano, Sundeep Rangan

This article puts the spotlight on the receiver front-end (RFE), an integral part of any wireless device that information theory typically idealizes into a mere addition of noise. While this idealization was sound in the past, as operating frequencies, bandwidths, and antenna counts rise, a soaring amount of power is required for the RFE to behave accordingl

Christian Winter

We say that a poset $(Q,\le_{Q})$ contains an induced copy of a poset $(P,\le_P)$ if there is an injective function $\phi\colon P\to Q$ such that for every two $X,Y\in P$,\;\;$X\le_P Y$ if and only if $\phi(X)\le_Q \phi(Y)$. We denote the Boolean lattice $(2^{[n]},\subseteq)$ by $Q_n$. Given a fixed $2$-coloring $c$ of a poset $P$, the poset Erd\H{o}s-Hajnal

Zhongfu Guo, Xinsheng Ji, Wei You, Yu Zhao

This paper introduces REDC, a comprehensive strategy for offloading computational tasks within mobile Edge Networks (EN) to Distributed Computing (DC) after Rateless Encoding (RE). Despite the efficiency, reliability, and scalability advantages of distributed computing in ENs, straggler-induced latencies and failures pose significant challenges. Coded distri

Yi Jiang, Shohei Shimizu

While economic theory explains the linkages among the financial markets of different countries, empirical studies mainly verify the linkages through Granger causality, without considering latent variables or instantaneous effects. Their findings are inconsistent regarding the existence of causal linkages among financial markets, which might be attributed to

Martyną Soszynska, Thomas Richter

We present a priori error estimates for a multirate time-stepping scheme for coupled differential equations. The discretization is based on Galerkin methods in time using two different time meshes for two parts of the problem. We aim at surface coupled multiphysics problems like two-phase flows. Special focus is on the handling of the interface coupling to g

Ilan Naiman, N. Benjamin Erichson, Pu Ren, Michael W. Mahoney

Generating realistic time series data is important for many engineering and scientific applications. Existing work tackles this problem using generative adversarial networks (GANs). However, GANs are unstable during training, and they can suffer from mode collapse. While variational autoencoders (VAEs) are known to be more robust to the these issues, they ar

Georges Kariniotakis, Pierre Pinson

The paper introduces a new methodology for assessing on-line the prediction risk of short-term wind power forecasts. The first part of this methodology consists in computing confidence intervals with a confidence level defined by the end-user. The resampling approach is used for this purpose since it permits to avoid a restrictive hypothesis on the distribut

Giulia Romoli

Mini-EUSO (Multiwavelength Imaging New Instrument for the Extreme Universe Space Observatory) is a telescope observing the Earth in the ultraviolet band (290-430 nm) from the Russian Zvezda module of the International Space Station since 2019. The telescope is capable of observing UV emissions of cosmic, atmospheric, and terrestrial origin on different time

Vinicius Sanches, Fabiene Barbosa da Silva

The observation of space seems to have always caused wonder into people's collective consciousness, generating a series of historical myths. More recently specially with the development of better tools alongside the constant refinement of the scientific method Astronomy has consolidated into increasing field of Physics. Yet, representing such field in an acc

Alberto Boscaggin, Walter Dambrosio, Guglielmo Feltrin

We investigate bifurcation of closed orbits with a fixed energy level for a class of nearly integrable Hamiltonian systems with two degrees of freedom. More precisely, we make a joint use of Moser invariant curve theorem and Poincar\'e-Birkhoff fixed point theorem to prove that a periodic non-degenerate invariant torus $\mathcal{T}$ of the unperturbed proble

Chunyan Mu, Jun Pang

In multiagent systems (MASs), agents' observation upon system behaviours may improve the overall team performance, but may also leak sensitive information to an observer. A quantified observability analysis can thus be useful to assist decision-making in MASs by operators seeking to optimise the relationship between performance effectiveness and information

B. Finociety, J. -F. Donati, P. I. Cristofari, C. Moutou

We report results of a spectropolarimetric monitoring of the young Sun-like star V1298~Tau based on data collected with the near-infrared spectropolarimeter SPIRou at the Canada-France-Hawaii Telescope between late 2019 and early 2023. Using Zeeman-Doppler Imaging and the Time-dependent Imaging of Magnetic Stars methods on circularly polarized spectra, we re

Youxi Wu, Yufei Meng, Yan Li, Lei Guo

Recently, order-preserving pattern (OPP) mining, a new sequential pattern mining method, has been proposed to mine frequent relative orders in a time series. Although frequent relative orders can be used as features to classify a time series, the mined patterns do not reflect the differences between two classes of time series well. To effectively discover th

Aven Le Zhou, Qiufeng Wang, Cheng-Hung Lo, Kaizhu Huang

Shanshui, which means mountain and water, is an East Asian traditional brush painting involving natural landscapes. This paper proposes an interactive and generative system based on a Generative Adversarial Network(GAN), which helps users draw Shanshui easily. We name this system and installation ShanshuiDaDA. ShanshuiDaDA is trained with CycleGAN and wrappe

Jaemoo Choi, Jaewoong Choi, Myungjoo Kang

Optimal Transport (OT) problem aims to find a transport plan that bridges two distributions while minimizing a given cost function. OT theory has been widely utilized in generative modeling. In the beginning, OT distance has been used as a measure for assessing the distance between data and generated distributions. Recently, OT transport map between data and

Manuel Reisenbauer, Henning Rudolph, Livia Egyed, Klaus Hornberger

Non-Hermitian dynamics, as observed in photonic, atomic, electrical, and optomechanical platforms, holds great potential for sensing applications and signal processing. Recently, fully tunable nonreciprocal optical interaction has been demonstrated between levitated nanoparticles. Here, we use this tunability to investigate the collective non-Hermitian dynam

Pei Zhou, Aman Madaan, Srividya Pranavi Potharaju, Aditya Gupta

"Thinking is for Doing." Humans can infer other people's mental states from observations--an ability called Theory-of-Mind (ToM)--and subsequently act pragmatically on those inferences. Existing question answering benchmarks such as ToMi ask models questions to make inferences about beliefs of characters in a story, but do not test whether models can then us

Wei Chen, Huaijin Wang, Weixi Gu, Shuai Wang

Securing operating system (OS) kernel is one central challenge in today's cyber security landscape. The cutting-edge testing technique of OS kernel is software fuzz testing. By mutating the program inputs with random variations for iterations, fuzz testing aims to trigger program crashes and hangs caused by potential bugs that can be abused by the inputs. To

Dorinel Bastide, Stéphane Crépey, Samuel Drapeau, Mekonnen Tadese

For vanilla derivatives that constitute the bulk of investment banks' hedging portfolios, central clearing through central counterparties (CCPs) has become hegemonic. A key mandate of a CCP is to provide an efficient and proper clearing member default resolution procedure. When a clearing member defaults, the CCP can hedge and auction or liquidate its positi

Naveen Gupta, S. Sivananthan, Bharath K. Sriperumbudur

In this paper, we discuss the convergence analysis of the conjugate gradient-based algorithm for the functional linear model in the reproducing kernel Hilbert space framework, utilizing early stopping results in regularization against over-fitting. We establish the convergence rates depending on the regularity condition of the slope function and the decay ra

Osama Ahmad, Omer Abdul Jalil, Usman Nazir, Murtaza Taj

In the realm of applications where data dynamically evolves across spatial and temporal dimensions, Graph Neural Networks (GNNs) are often complemented by sequence modeling architectures, such as RNNs and transformers, to effectively model temporal changes. These hybrid models typically arrange the spatial and temporal learning components in series. A pionee

Nofar Piterman, Tamar Makov, Michael Fire

Online social networks usage has increased significantly in the last decade and continues to grow in popularity. Multiple social platforms use volunteers as a central component. The behavior of volunteers in volunteer-based networks has been studied extensively in recent years. Here, we explore the development of volunteer-based social networks, primarily fo

Erica van der Sar, Alessandro Zocca, Sandjai Bhulai

Recent challenges in operating power networks arise from increasing energy demands and unpredictable renewable sources like wind and solar. While reinforcement learning (RL) shows promise in managing these networks, through topological actions like bus and line switching, efficiently handling large action spaces as networks grow is crucial. This paper presen

P. K. Ng, N. Karjanto

This paper explores the impact of active learning in mathematical economics on students' academic performance (assessment scores). An experimental design involving foundation students enrolled in the arts and business and management foundation programmes in a British university located in Malaysia was adopted. The control group underwent the more traditional

Yi Feng, Hu Fu, Qun Hu, Ping Li

Last-iterate convergence has received extensive study in two player zero-sum games starting from bilinear, convex-concave up to settings that satisfy the MVI condition. Typical methods that exhibit last-iterate convergence for the aforementioned games include extra-gradient (EG) and optimistic gradient descent ascent (OGDA). However, all the established last

Gianmarco Bet, Kay Bogerd, Rui M. Castro, Remco van der Hofstad

Motivated by the problem of detecting a change in the evolution of a network, we consider the preferential attachment random graph model with a time-dependent attachment function. Our goal is to detect whether the attachment mechanism changed over time, based on a single snapshot of the network and without directly observable information about the dynamics.

S. Sourav, A. Shukla, R. Dwivedi, K. Singh

Gamma-ray bursts (GRB) are powerful transient events that emit a large output of gamma rays within a few seconds. Studying these short bursts is vital for cosmological research since they originate from sources observed at large redshifts. To effectively carry out these studies, it is crucial to establish a correlation between the observable features of GRBs

Ruiyuan Gao, Kai Chen, Enze Xie, Lanqing Hong

Recent advancements in diffusion models have significantly enhanced the data synthesis with 2D control. Yet, precise 3D control in street view generation, crucial for 3D perception tasks, remains elusive. Specifically, utilizing Bird's-Eye View (BEV) as the primary condition often leads to challenges in geometry control (e.g., height), affecting the represen

Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Jordan Richards, Raphaël Huser

Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where estimation is often a computational bottleneck, neural Bayes estimators in spatial applications have, to date, been restricted to data collected over a regular grid. These estimators

Ike Kunze, Constantin Sander, Klaus Wehrle

Encrypted QUIC traffic complicates network management as traditional transport layer semantics can no longer be used for RTT or packet loss measurements. Addressing this challenge, QUIC includes an optional, carefully designed mechanism: the spin bit. While its capabilities have already been studied in test settings, its real-world usefulness and adoption ar

Vlad Stirbu, Majid Haghparast

The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. The ability of quantum computers to scale computations implies better performance and efficiency for certain algorithmic tasks than current computers provide. However, to gain benefit from such improvement,

Kousuke Nakano, Oto Kohulák, Abhishek Raghav, Michele Casula

TurboGenius is an open-source Python package designed to fully control ab initio quantum Monte Carlo (QMC) jobs using a Python script, which allows one to perform high-throughput calculations combined with TurboRVB [K. Nakano et al. J. Phys. Chem. 152, 204121 (2020)]. This paper provides an overview of the TurboGenius package and showcases several results ob

Weiyu Li, Rui Chen, Xuelin Chen, Ping Tan

It is inherently ambiguous to lift 2D results from pre-trained diffusion models to a 3D world for text-to-3D generation. 2D diffusion models solely learn view-agnostic priors and thus lack 3D knowledge during the lifting, leading to the multi-view inconsistency problem. We find that this problem primarily stems from geometric inconsistency, and avoiding misp

Smitha Subramanian, Chayan Mondal, Venu Kalari

According to the $\Lambda$ cold dark matter model of galaxy formation, the hierarchical assembly process is scale-free and interactions between galaxies in all mass ranges are expected. The effects of interactions between dwarf galaxies on their evolution are not well understood. In this study, we aim to understand the effect of low-mass galaxy interactions

Tianjun Mao, Chenghong Zhang

Spoken language understanding (SLU) typically includes two subtasks: intent detection and slot filling. Currently, it has achieved great success in high-resource languages, but it still remains challenging in low-resource languages due to the scarcity of labeled training data. Hence, there is a growing interest in zero-shot cross-lingual SLU. Despite of the

Hongxin Ding, Peinie Zou, Zhiyuan Wang, Junfeng Zhao

Extracting medical knowledge from healthcare texts enhances downstream tasks like medical knowledge graph construction and clinical decision-making. However, the construction and application of knowledge extraction models lack automation, reusability and unified management, leading to inefficiencies for researchers and high barriers for non-AI experts such a

Yuga Kanaya, Kenjiro Takazawa

The traveling tournament problem (TTP) is to minimize the total traveling distance of all teams in a double round-robin tournament. In this paper, we focus on TTP-2, in which each team plays at most two consecutive home games and at most two consecutive away games. For the case where the number of teams $n\equiv2$ (mod 4), Zhao and Xiao (2022) presented a $(

Mehdi Neshat, Muktar Ahmed, Hossein Askari, Menasha Thilakaratne

Diagnosing lung inflammation, particularly pneumonia, is of paramount importance for effectively treating and managing the disease. Pneumonia is a common respiratory infection caused by bacteria, viruses, or fungi and can indiscriminately affect people of all ages. As highlighted by the World Health Organization (WHO), this prevalent disease tragically accou

Govind Rajendran, Rishabh Roy, Preyas Hathi, Nadeem Akhtar

The Target Wake Time (TWT) feature, introduced in Wi-Fi 6, was primarily meant as an advanced power save mechanism. However, it has some interesting applications in scheduling and resource allocation. TWT-based resource allocation can be used to improve the user experience for certain applications, e.g., VoIP, IoT, video streaming, etc. In this work, we anal

Bing Cheng, Di Cheng, Kyuho Lee, Martin Mootz

The dynamics of Higgs mode in superconductors, manifested as coherent oscillations of the superconducting order parameter amplitude, provides vital insights into the nature of the superconducting gap structure and symmetry. Here we utilize two-dimensional terahertz coherent spectroscopy to investigate Higgs dynamics of a newly discovered infinite-layer nicke

Seok-Yong Byun, Wonju Lee

This paper presents a novel approach to address the challenges of understanding the prediction process and debugging prediction errors in Vision Transformers (ViT), which have demonstrated superior performance in various computer vision tasks such as image classification and object detection. While several visual explainability techniques, such as CAM, Grad-

Mehmet Batu Bayındırlı, Dilan Nur Demirtaş, Can Kozçaz, Yegor Zenkevich

We introduce an R-matrix formulation of qq-characters and corresponding Frenkel-Reshetikhin deformed W-algebras. The R-matrix featuring in the construction is of Ding-Iohara-Miki (DIM) algebra, while the type of the qq-character is determined by the network of Fock representations corresponding to a web of 5-branes geometrically engineering a quiver gauge th

Bing Cheng, Di Cheng, Kyuho Lee, Liang Luo

The discovery of superconductivity in infinite-layer nickelates establishes a new category of unconventional superconductors that share structural and electronic similarities with cuprates. Despite exciting advances, such as the establishment of a cuprate-like phase diagram and the observation of charge order and short-range antiferromagnetic fluctuation, th

Shuo Li, Wenchao Li, Qiang Sun, Bill Moran

The localisation of fluorophores is an important aspect of the determination of the biological function of cellular systems. Quantum correlation microscopy (QCM) is a promising technique for providing diffraction unlimited emitter localisation that can be used with either confocal or widefield modalities. However, so far, QCM has not been applied to three di

André Beck, Vianney Lebouteiller, Suzanne C. Madden, Aaron Bryant

Context. Multi-wavelength studies of galaxies and galactic nuclei allow us to build a relatively more complete picture of the interstellar medium (ISM), especially in the dusty regions of starburst galaxies. An understanding of the physical processes in nearby galaxies can assist in the study of more distant sources at higher redshifts, which cannot be resol

Seyed Mo Mirvakili, Ehsan Haghighat, Douglas Sim

With their unique combination of characteristics - an energy density almost 100 times that of human muscle, and a power density of 5.3 kW/kg, similar to a jet engine's output - Nylon artificial muscles stand out as particularly apt for robotics applications. However, the necessity of integrating sensors and controllers poses a limitation to their practical u

Bharat Manvi, Easwar Subramanian

We consider a periodic double auction (PDA) setting where buyers of the auction have multiple (but finite) opportunities to procure multiple but fixed units of a commodity. The goal of each buyer participating in such auctions is to reduce their cost of procurement by planning their purchase across multiple rounds of the PDA. Formulating such optimal bidding

Weidong Liu, Jiyuan Tu, Xi Chen, Yichen Zhang

Reinforcement learning has emerged as one of the prominent topics attracting attention in modern statistical learning, with policy evaluation being a key component. Unlike the traditional machine learning literature on this topic, our work emphasizes statistical inference for the model parameters and value functions of reinforcement learning algorithms. Whil

Jae-Gyun Baak, Uwe R. Fischer

We investigate performing classical and quantum metrology and parameter estimation by using interacting trapped bosons, which we theoretically treat by a self-consistent many-body approach of the multiconfigurational Hartree type. Focusing on a tilted double-well geometry, we compare a self-consistently determined and monitored two-mode truncation, with dyna

Yinan Huang, William Lu, Joshua Robinson, Yu Yang

Designing effective positional encodings for graphs is key to building powerful graph transformers and enhancing message-passing graph neural networks. Although widespread, using Laplacian eigenvectors as positional encodings faces two fundamental challenges: (1) \emph{Non-uniqueness}: there are many different eigendecompositions of the same Laplacian, and (

Grigory Bednik, Vladyslav Kozii

We study the second-order optical response of Weyl semimetals in the presence of a magnetic field. We consider an idealized model of a perfectly linear Weyl node and use the Kubo formula at zero temperature to calculate the intrinsic contribution to photocurrent and second harmonic generation conductivity components. We obtain exact analytical expressions ap

Tetsuya Ito

A non-trivial element of a group is a generalized torsion element if some products of its conjugates is the identity. The minimum number of such conjugates is called a generalized torsion order. We provide several restrictions for generalized torsion orders by using $G$-invariant norm and Alexander polynomials.

Chao Huang, Zhao Kang, Hong Wu

Image anomaly detection and localization perform not only image-level anomaly classification but also locate pixel-level anomaly regions. Recently, it has received much research attention due to its wide application in various fields. This paper proposes ProtoAD, a prototype-based neural network for image anomaly detection and localization. First, the patch

Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu

Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously. A recent development known as task arithmetic has revealed that several models, each fine-tuned for distinct tasks, can be directly merged into a single model to execute MTL without necessitating a retraining process using the initial training data. Nevertheless, this

Haoyuan Zhang, Shuya Ke, Wenqi Liu, Yongwen Zhang

The scientific process of earthquake forecasting involves estimating the probability and intensity of earthquakes in a specific area within a certain timeframe, based on seismic activity laws and observational data. Epidemic-Type Aftershock Sequence (ETAS) models, which rely on seismic empirical laws, is one of the most commonly used methods for earthquake f

Zhenwei Niu, Lyes Saad Saoud, Irfan Hussain

Ensuring safety is paramount in the field of collaborative robotics to mitigate the risks of human injury and environmental damage. Apart from collision avoidance, it is crucial for robots to rapidly detect and respond to unexpected collisions. While several learning-based collision detection methods have been introduced as alternatives to purely model-based

Sayantan Chowdhury, Ben Liang, Ali Tizghadam, Ilijc Albanese

Knowledge distillation (KD) improves the performance of a low-complexity student model with the help of a more powerful teacher. The teacher in KD is a black-box model, imparting knowledge to the student only through its predictions. This limits the amount of transferred knowledge. In this work, we introduce a novel Knowledge Explaining Distillation (KED) fr

Anton A. Stoorvogel, Ali Saberi, Zhenwei Liu

This paper provides necessary and sufficient conditions for the existence of solutions to the state synchronization problem of homogeneous multi-agent systems (MAS) via scale-free linear dynamic non-collaborative protocol for both continuous- and discrete-time. These conditions guarantee for which class of MAS, one can achieve scale-free state synchronizatio

Bence Mark Halpern, Wen-Chin Huang, Lester Phillip Violeta, R. J. J. H. van Son

In healthy-to-pathological voice conversion (H2P-VC), healthy speech is converted into pathological while preserving the identity. The paper improves on previous two-stage approach to H2P-VC where (1) speech is created first with the appropriate severity, (2) then the speaker identity of the voice is converted while preserving the severity of the voice. Spec

Zejun Li, Ye Wang, Mengfei Du, Qingwen Liu

Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs). Benefiting from the strong language backbones and efficient cross-modal alignment strategies, LVLMs exhibit surprising capabilities to perceive visual signals and perform visually grounded reasoning. However, the capabilities of LVLMs have not been com

Zihan Chen, Jingyi Sun, Rong Liu, Feng Mai

Although pervasive spread of misinformation on social media platforms has become a pressing challenge, existing platform interventions have shown limited success in curbing its dissemination. In this study, we propose a stance-aware graph neural network (stance-aware GNN) that leverages users' stances to proactively predict misinformation spread. As differen

Shelby Cox, Mark Curiel

Let $\mathbf{v}_1,\ldots,\mathbf{v}_m$ be points in a metric space with distance $d$, and let $w_1,\ldots,w_m$ be positive real weights. The weighted Fermat-Weber points are those points $\mathbf{x}$ which minimize $\sum w_i d(\mathbf{v}_i, \mathbf{x})$. We extend a result of Com\u{a}neci and Joswig, that the set of unweighted Fermat-Weber points agrees with

Oscar Mañas, Benno Krojer, Aishwarya Agrawal

8 years after the visual question answering (VQA) task was proposed, accuracy remains the primary metric for automatic evaluation. VQA Accuracy has been effective so far in the IID evaluation setting. However, our community is undergoing a shift towards open-ended generative models and OOD evaluation. In this new paradigm, the existing VQA Accuracy metric is

Qing Han, Marta Lewicka

We provide an introduction to the old-standing problem of isometric immersions. We combine a historical account of its multifaceted advances, which have fascinated geometers and analysts alike, with some of the applications in the mathematical physics and mathematical materials science, old and new.

Yang Yi, Zonghan Li

Robotic technology has been widely used in nowadays society, which has made great progress in various fields such as agriculture, manufacturing and entertainment. In this paper, we focus on the topic of drumming robots in entertainment. To this end, we introduce an improving drumming robot that can automatically complete music transcription based on the popu

Wen Wang, Wanli Ni, Hui Tian, Naofal Al-Dhahir

Although reconfigurable intelligent surfaces (RISs) can improve the performance of wireless networks by smartly reconfiguring the radio environment, existing passive RISs face two key challenges, i.e., double-fading attenuation and dependence on grid/battery. To address these challenges, this paper proposes a new RIS architecture, called multi-functional RIS

Hassan Jameel Asghar, Zhigang Lu, Zhongrui Zhao, Dali Kaafar

Two parties wish to collaborate on their datasets. However, before they reveal their datasets to each other, the parties want to have the guarantee that the collaboration would be fruitful. We look at this problem from the point of view of machine learning, where one party is promised an improvement on its prediction model by incorporating data from the othe

Wen Wang, Wanli Ni, Hui Tian, Yonina C. Eldar

In this paper, we propose and study a multi-functional reconfigurable intelligent surface (MF-RIS) architecture. In contrast to conventional single-functional RIS (SF-RIS) that only reflects signals, the proposed MF-RIS simultaneously supports multiple functions with one surface, including reflection, refraction, amplification, and energy harvesting of wirel

Haotian Zhang, Shijian Gao, Xiang Cheng, Liuqing Yang

The future of vehicular communication networks relies on mmWave massive multi-input-multi-output antenna arrays for intensive data transfer and massive vehicle access. However, reliable vehicle-to-infrastructure links require exact alignment between the narrow beams, which traditionally involves excessive signaling overhead. To address this issue, we propose

Yongwen Zhang, Maor Elbaz, Shlomo Havlin, Yosef Ashkenazy

Mainshocks are often followed by increased earthquake activity (aftershocks). According to the Omori-Utsu law, the rate of aftershocks decays as a power law over time. While aftershocks typically occur in the vicinity of the mainshock, previous studies have suggested that mainshocks can also trigger earthquakes in remote locations. Here we examine the earthq

Jingheng Zheng, Wanli Ni, Hui Tian, Deniz Gunduz

Under the organization of the base station (BS), wireless federated learning (FL) enables collaborative model training among multiple devices. However, the BS is merely responsible for aggregating local updates during the training process, which incurs a waste of the computational resource at the BS. To tackle this issue, we propose a semi-federated learning

Erez Michaely, Smadar Naoz

The inner $500\rm pc$ in the galactic center is dense with stars and a dynamically hot environment. Here, we focus on wide binaries as a source of tidally or collisional interactions. These binaries were previously ignored as sources of binary interaction because they are too wide to have a close pericenter passage, or they will quickly become unbound (ioniz

Zahra Kadkhodaie, Florentin Guth, Eero P. Simoncelli, Stéphane Mallat

Deep neural networks (DNNs) trained for image denoising are able to generate high-quality samples with score-based reverse diffusion algorithms. These impressive capabilities seem to imply an escape from the curse of dimensionality, but recent reports of memorization of the training set raise the question of whether these networks are learning the "true" con

Soroush Abbasi Koohpayegani, KL Navaneet, Parsa Nooralinejad, Soheil Kolouri

Fine-tuning Large Language Models (LLMs) and storing them for each downstream task or domain is impractical because of the massive model size (e.g., 350GB in GPT-3). Current literature, such as LoRA, showcases the potential of low-rank modifications to the original weights of an LLM, enabling efficient adaptation and storage for task-specific models. These m

Haotian Liu, Zhiqing Wei, Fengyun Li, Yuewei Lin

As a promising key technology of 6th generation (6G) mobile communication system, integrated sensing and communication (ISAC) technology aims to make full use of spectrum resources to enable the functional integration of communication and sensing. The ISAC-enabled mobile communication system regularly operate in non-continuous spectrum bands due to crowded l

Zhipeng Wang, Nanqing Dong, Jiahao Sun, William Knottenbelt

Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator. FL can be a scalable machine learning solution in big data scenarios. Traditional FL relies on the trust assumption of the central aggregator, which forms cohorts of clients

Zaixi Zhang, Zepu Lu, Zhongkai Hao, Marinka Zitnik

The design of \emph{de novo} functional proteins that bind specific ligand molecules is paramount in therapeutics and bio-engineering. A critical yet formidable task in this endeavor is the design of the protein pocket, which is the cavity region of the protein where the ligand binds. Current methods are plagued by inefficient generation, inadequate context

Sherry Gong

We study families of metrics on the cobordisms that underlie the differential maps in Bloom's monopole Floer spectral sequence, a spectral sequence for links in $S^3$ whose $E^2$ is the Khovanov homology of the link, and which abuts to the monopole Floer homology of the double branched cover of the link. The higher differentials in the spectral sequence coun

Jagdeep Singh, Vaidy Sivaraman, Thomas Zaslavsky

A class $\mathcal{G}$ of graphs is called hereditary if it is closed under taking induced subgraphs. We denote by $\mathcal{G}^\mathrm{apex}$ the class of graphs $G$ that contain a vertex $v$ such that $G-v$ is in $\mathcal{G}$. We prove that if a hereditary class $\mathcal{G}$ has finitely many forbidden induced subgraphs, then so does $\mathcal{G}^\mathrm{

Jianyang Ren, Wanli Ni, Hui Tian, Gaofeng Nie

As the number of sensors becomes massive in Internet of Things (IoT) networks, the amount of data is humongous. To process data in real-time while protecting user privacy, federated learning (FL) has been regarded as an enabling technique to push edge intelligence into IoT networks with massive devices. However, FL latency increases dramatically due to the i

Jared Lichtarge, Ehsan Amid, Shankar Kumar, Tien-Ju Yang

Federated Averaging, and many federated learning algorithm variants which build upon it, have a limitation: all clients must share the same model architecture. This results in unused modeling capacity on many clients, which limits model performance. To address this issue, we propose a method that involves training a small model on the entire pool and a large

Sebastian Barschkis

Boundary conditions (BCs) are a key component in every Physics-Informed Neural Network (PINN). By defining the solution to partial differential equations (PDEs) along domain boundaries, BCs constrain the underlying boundary value problem (BVP) that a PINN tries to approximate. Without them, unique PDE solutions may not exist and finding approximations with P

S. Amano, Y. Aritomo, Y. Miyamoto, S. Ishizaki

The mass angle distribution shows a strong correlation between mass and angle when quasifission events are dominant. Therefore, as long as quasifission events are dominant, the mass angle distribution is characterized in that a diagonal correlation appears. This diagonal correlation could not be reproduced in our previous model that is before introducing $f_

Zhenqiao Song, Yunlong Zhao, Yufei Song, Wenxian Shi

Designing novel proteins with desired functions is crucial in biology and chemistry. However, most existing work focus on protein sequence design, leaving protein sequence and structure co-design underexplored. In this paper, we propose GeoPro, a method to design protein backbone structure and sequence jointly. Our motivation is that protein sequence and its

J. Vanterler C. Sousa, M. Aurora P. Pulido, V. Govindaraj, E. Capelas de Oliveira

In this present paper, we first obtained some estimates involving parts of $\varepsilon$-regular mild solutions of the fractional integro-differential equation. In this sense, through these preliminary results, we investigate the main results of this paper, i.e., the existence, regularity and continuous dependence of $\varepsilon$-regular mild solutions for

Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, David González G., Osvaldo Gonsa

Generalized quadrature spatial modulation (GQSM) schemes are known to achieve high energy- and spectral- efficiencies by modulating information both in transmitted symbols and in coded combinatorial activations of subsets of multiple transmit antennas. A challenge of the approach is, however, the decoding complexity which scales with the efficiency of the sc

KL Navaneet, Soroush Abbasi Koohpayegani, Essam Sleiman, Hamed Pirsiavash

Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches adaptively scale the compute based on the input instance. We show that such models can be vulnerable to a universal adversarial patch attack, wh

J. Vanterler da C. Sousa, Karla B. Lima, Leandro S. Tavares

In this present paper, we investigate a new class of singular double phase $p$-Laplacian equation problems with a $\psi$-Hilfer fractional operator combined from a parametric term. Motivated by the fibering method using the Nehari manifold, we discuss the existence of at least two weak solutions to such problems when the parameter is small enough. Before att

Kaidong Wang, Qianxin Yi, Yao Wang, Xiuwu Liao

We consider the problem of tensor completion with graphs serving as side information to represent interrelationships among variables. Existing approaches suffer from several limitations: (1) they are often task-specific and lack generality or systematic formulation; (2) they typically treat graphs as static structures, ignoring their inherent dynamism in ten

Peiwen Yang, Weisong Wen

Unmanned aerial vehicles (UAV) showed great potential in improving the efficiency of parcel delivery applications in the coming smart cities era. Unfortunately, the trustworthy positioning and control algorithms of the UAV are significantly challenged in complex urban areas. For example, the ubiquitous global navigation satellite system (GNSS) positioning ca

J. Vanterler da C. Sousa, D. S. Oliveira, Ravi P. Agarwal

This paper is divided in two parts. In the first part, we prove coercivity results and minimization of the Euler energy functional. In the second part, we focus on the existence and multiplicity of a positive solution of fractional Dirichlet problem involving the $\gamma(\xi)$-Laplacian equation with non-negative weight functions in $\mathcal{H}^{\alpha,\bet

Zhiwei Xu, Yutong Wang, Spencer Frei, Gal Vardi

Neural networks trained by gradient descent (GD) have exhibited a number of surprising generalization behaviors. First, they can achieve a perfect fit to noisy training data and still generalize near-optimally, showing that overfitting can sometimes be benign. Second, they can undergo a period of classical, harmful overfitting -- achieving a perfect fit to t

Srilekha Mamidala

The prevalence of dementia has increased over time as global life expectancy improves and populations age. An individual's risk of developing dementia is influenced by various genetic, lifestyle, and environmental factors, among others. Predicting dementia risk may enable individuals to employ mitigation strategies or lifestyle changes to delay dementia onse

Danrui Qi, Jinglin Peng, Yongjun He, Jiannan Wang

Classical machine learning models, such as linear models and tree-based models, are widely used in industry. These models are sensitive to data distribution, thus feature preprocessing, which transforms features from one distribution to another, is a crucial step to ensure good model quality. Manually constructing a feature preprocessing pipeline is challeng

Akito Daido, Youichi Yanase

A general principle of condensed matter physics prohibits the electric current in equilibrium. This prevents a zero-resistance state realized solely under a finite electric current, namely unidirectional superconductivity. In this paper, we propose a setup to realize the unidirectional superconductivity as a nonequilibrium steady state. We focus on the in-pl

Ying Zhai, Rui Yuan, Huan Su

This paper investigates the Nash equilibrium seeking problems for networked games with intermittent communication, where each player is capable of communicating with other players intermittently over a strongly connected and directed graph. Noticing that the players are not directly and continuously available for the actions of other players, this paper prop

J. Vanterler da C. Sousa, Mbarki Lamine, Leandro S. Tavares

The purpose of this paper is devoted to \textcolor{red}{discussing} the existence of solutions for a generalized fractional telegraph equation involving a class of $\psi$-Hilfer fractional with $p(x)$-Laplacian differential equation.

Daewon Seo, Sung Hoon Lim, Yongjune Kim

In many sensor network applications, a fusion center often has additional valuable information, such as context data, which cannot be obtained directly from the sensors. Motivated by this, we study a generalized CEO problem where a CEO has access to context information. The main contribution of this work is twofold. Firstly, we characterize the asymptoticall