December 2023 arXiv papers — page 66

Yulai Cong, Sijia Li

Mixture models serve as one fundamental tool with versatile applications. However, their training techniques, like the popular Expectation Maximization (EM) algorithm, are notoriously sensitive to parameter initialization and often suffer from bad local optima that could be arbitrarily worse than the optimal. To address the long-lasting bad-local-optima chal

Vadim Abzalov, Vlada Pogozhelskaya, Vladimir Kutuev, Semyon Grigorev

We propose GLL-based context-free path querying algorithm which handles queries in Extended Backus-Naur Form (EBNF) using Recursive State Machines (RSM). Utilization of EBNF allows one to combine traditional regular expressions and mutually recursive patterns in constraints natively. The proposed algorithm solves both the reachability-only and the all-paths

P. Gavignet, E. Pincemin, F. Herviou, Y. Loussouarn

We report co-propagation experiments of the quantum channel (at 1310 nm) of a Quantum Key Distribution (QKD) system with Dense Wavelength Division Multiplexing (DWDM) data channels in the 1550 nm range. Two configurations are assessed. The first one is a single span configuration where various lengths of Standard Single Mode Fiber (SSMF) (from 20 to 70 km) a

Xiaomeng Yang, Zhi Qiao, Yu Zhou

Nowadays, scene text recognition has attracted more and more attention due to its diverse applications. Most state-of-the-art methods adopt an encoder-decoder framework with the attention mechanism, autoregressively generating text from left to right. Despite the convincing performance, this sequential decoding strategy constrains the inference speed. Conver

Haowei Du, Quzhe Huang, Chen Li, Chen Zhang

Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge graph (KG), which requires multiple steps of reasoning. Existing retrieval-based approaches solve this task by concentrating on the specific relation at different hops and predicting the intermediate entity within the reasoning path. During the reasoning process o

Jun Wu, Weijie Yuan, Zhiqiang Wei, Jinjin Yan

This paper investigates the bit error rate (BER) minimum pre-coder design for an orthogonal time frequency space (OTFS)-based integrated sensing and communications (ISAC) system, which is considered as a promising technique for enabling future wireless networks. In particular, the BER minimum problem takes into account the maximized available transmission po

Chen Li

One of the key issues in Mandarin Chinese text-to-speech (TTS) systems is polyphone disambiguation when doing grapheme-to-phoneme (G2P) conversion. In this paper, we introduce a novel method to solve the problem as a generation task. Following the trending research of large language models (LLM) and prompt learning, the proposed method consists of three modu

Enric Fuster, Tatiana Fernández, Hermes Carretero, Nicolau Duran-Silva

In the new research and innovation (R&I) paradigm, aimed at a transformation towards more sustainable, inclusive and fair pathways to address societal and environmental challenges, and at generating new patterns of specialisation and new trajectories for socioeconomic development, it is essential to provide monitoring systems and tools to map and understand

Ganesh Bikshandi, Jay Shah

We provide an optimized implementation of the forward pass of FlashAttention-2, a popular memory-aware scaled dot-product attention algorithm, as a custom fused CUDA kernel targeting NVIDIA Hopper architecture and written using the open-source CUTLASS library. In doing so, we explain the challenges and techniques involved in fusing online-softmax with back-t

Corentin Hingrand, Adrien Combes, Nicolas Olivier, Samir Bensaid

The analysis of trail-running performance appears to be complex and cardio-respiratory and muscular factors could have a variable importance depending on the inclination. Our study aims to determine the role of these parameters in performance. 13 subjects with heterogeneous levels participated in the study. They carried out 7 visits including 3 maximal aerob

Kazunori Ando, Hyeonbae Kang, Yoshihisa Miyanishi, Mihai Putinar

Two generic properties of the Neumann--Poincar\'e operator are investigated. We prove that non-zero eigenvalues of the Neumann--Poincar\'e operator on smooth boundaries in three dimensions and higher are generically simple in the sense of Baire category. We also prove that the functions defined by the fundamental solutions to the Laplace operator located at

Mohsen Salimi, Robin V. Nielsen, Henrik B. Pedersen, Aurélien Dantan

We report on the realization of ultrasensitive absolute pressure sensors based on silicon nitride membrane sandwiches. These sandwiches consist in a pair of highly-pretensioned, ultrathin (50 nm), large area (0.25 mm2) films, suspended parallel to each other and forming an ultrashort (500 nm), open cavity. The compression of a gas in this cavity leads to a s

Angelina Voggenreiter, Sophie Brandt, Fabian Putterer, Andreas Frings

Social media usage has been shown to have both positive and negative consequences for users' mental health. Several studies indicated that peer feedback plays an important role in the relationship between social media use and mental health. In this research, we analyse the impact of receiving online feedback on users' emotional experience, social connectedne

Roberto Rivera, Guido Rocco, Massimiliano Marzo, Enrico Talin

This whitepaper introduces RIV Coin, a cryptocurrency that is fully stabilized by a diversified portfolio of invested reserves that are evaluated by professional independent third parties, and auditable and provable by the protocol. It is born and managed as a decentralized token, minted by a Decentralized Autonomous Organization (DAO). All wealthier Users a

Miguel Aguiar, Amritam Das, Karl H. Johansson

We propose a framework for surrogate modelling of spiking systems. These systems are often described by stiff differential equations with high-amplitude oscillations and multi-timescale dynamics, making surrogate models an attractive tool for system design and simulation. We parameterise the flow function of a spiking system using a recurrent neural network

Xin Lyu, Jun Tao, Peng Wang

In this paper, we investigate the thermodynamic properties of charged AdS Gauss-Bonnet black holes and the associations with the Lyapunov exponent. The chaotic features of the black holes and the isobaric heat capacity characterized by Lyapunov exponent are studied to reveal the stability of black hole phases. With the consideration of both timelike and null

Vikas Singh

Noise is inevitably common in digital images, leading to visual image deterioration. Therefore, a suitable filtering method is required to lessen the noise while preserving the image features (edges, corners, etc.). This paper presents the efficient type-2 fuzzy weighted mean filter with an adaptive threshold to remove the SAP noise. The present filter has t

Weipeng Guan, Peiyu Chen, Huibin Zhao, Yu Wang

Event cameras are bio-inspired, motion-activated sensors that demonstrate substantial potential in handling challenging situations, such as motion blur and high-dynamic range. In this paper, we proposed EVI-SAM to tackle the problem of 6 DoF pose tracking and 3D reconstruction using monocular event camera. A novel event-based hybrid tracking framework is des

Chang-Yan Wang

The development of ultracold atom technology has enabled the precise investigations on quantum dynamics of quantum gases. Recently, inspired by experimental advancement, the $SU(1,1)$ echo, akin to the well-known $SU(2)$ spin echo, has been proposed for single-component Bose-Einstein condensate (BEC). In this paper, we investigate the possibility of quantum

Yukio Hayashi

Message-passing (MP) is a powerful tool for finding an approximate solution in optimization. We generalize it to nonlinear product-sum form, and numerically show the fast convergence for the minimum feedback vertex set and the minimum vertex cover known as NP-hard problems. From the linearity of MP in a logarithmic space, it is derived that an equilibrium so

Haiyuan Feng, Yi Liao, Rong-Jia Yang

With the method of the background field expansion, we investigate the one-loop quantization of the Euclidean nonlocal $f(R)$ model in the de-Sitter universe. We obtain the ghost-free condition (GFC) based on the transformation from the Jordan frame to the Einstein frame and the classical stability condition (CSC) satisfied $f^{(0)}_{RR}-\phi_0F^{(0)}_{RR}<0$

Margret Westerkamp, Jakob Roth, Philipp Frank, Will Handley

Nested sampling provides an estimate of the evidence of a Bayesian inference problem via probing the likelihood as a function of the enclosed prior volume. However, the lack of precise values of the enclosed prior mass of the samples introduces probing noise, which can hamper high-accuracy determinations of the evidence values as estimated from the likelihoo

Joseph Ayoub

In this paper we discuss a general notion of Weil cohomology theories, both in algebraic geometry and in rigid analytic geometry. We allow our Weil cohomology theories to have coefficients in arbitrary commutative ring spectra. Using the theory of motives, we give three equivalent viewpoints on Weil cohomology theories: as a cohomology theory on smooth varie

Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki

Federated learning (FL) achieves collaborative learning without the need for data sharing, thus preventing privacy leakage. To extend FL into a fully decentralized algorithm, researchers have applied distributed optimization algorithms to FL by considering machine learning (ML) tasks as parameter optimization problems. Conversely, the consensus-based multi-h

Yubin Xiao, Di Wang, Boyang Li, Mingzhao Wang

Neural construction models have shown promising performance for Vehicle Routing Problems (VRPs) by adopting either the Autoregressive (AR) or Non-Autoregressive (NAR) learning approach. While AR models produce high-quality solutions, they generally have a high inference latency due to their sequential generation nature. Conversely, NAR models generate soluti

Dancheng Lu, Zexin Wang

We present a natural and explicit multigraded minimal free resolution for each generalized co-letterplace ideal (see Definition 1.1). Our resolution differs significantly from the ones presented in the works of Ene et al. \cite{EHM} and D'Al{`\i} et al. \cite{DFN}. Additionally, we show that each power of a large class of generalized co-letterplace ideals ca

Basma Kalandar, Ziemowit Dworakowski

The count of people suffering from various levels of hearing loss reached 1.57 billion in 2019. This huge number tends to suffer on many personal and professional levels and strictly needs to be included with the rest of society healthily. This paper presents a proof of concept of an automatic sign language recognition system based on data obtained using a w

Haoyu Ma, Shahin Mahdizadehaghdam, Bichen Wu, Zhipeng Fan

Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However, the computational demands of diffusion-based methods are substantial, often necessitating large-scale paired datasets f

Elliot Glazer

We prove that Global Choice is not conservative over ZC and that ZF $-$ Union does not prove existence of $x \cup y$ for all $x$ and $y$. Each proof is by constructing a pathological model inside a symmetric extension of $L.$

Joan Elias

We characterize the finite codimension sub-K-algebras of K[[t]] as the solutions of a computable finite family of higher differential operators. For this end, we establish a duality between such a sub-algebras and the finite codimension K-vector spaces of K[u], this ring acts on K[[t]] by differentiation.

Jiahuan Zhu, Xutao Zheng, Hua Feng, Ming Zeng

We propose a future mission concept, the MeV Astrophysical Spectroscopic Surveyor (MASS), which is a large area Compton telescope using 3D position sensitive cadmium zinc telluride (CZT) detectors optimized for emission line detection. The payload consists of two layers of CZT detectors in a misaligned chessboard layout, with a total geometric area of 4096 c

Hiroaki W. H. Tahara, Kazufumi Takahashi, Masato Minamitsuji, Hayato Motohashi

It has recently been pointed out that one can construct invertible conformal transformations with a parity-violating conformal factor, which can be employed to generate a novel class of parity-violating ghost-free metric theories from general relativity. We obtain exact solutions for rotating black holes in such theories by performing the conformal transform

Jie Liu, Yijia Cao, Yong Li, Yixiu Guo

Accurately predicting line loss rates is vital for effective line loss management in distribution networks, especially over short-term multi-horizons ranging from one hour to one week. In this study, we propose Attention-GCN-LSTM, a novel method that combines Graph Convolutional Networks (GCN), Long Short-Term Memory (LSTM), and a three-level attention mecha

Hao Chen, Lun Du, Yuxuan Lu, Qiang Fu

Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions. While existing works leverage historical or contextual information, they often disregard a crucial aspect: job seekers' social relationships in professional networks. This paper emphasizes the i

Bruno Korbar, Yongqin Xian, Alessio Tonioni, Andrew Zisserman

In this paper we present a text-conditioned video resampler (TCR) module that uses a pre-trained and frozen visual encoder and large language model (LLM) to process long video sequences for a task. TCR localises relevant visual features from the video given a text condition and provides them to a LLM to generate a text response. Due to its lightweight design

Zishan Guo, Qinran Hu, Tao Qian, Xin Fang

Production cost minimization (PCM) simulation is commonly employed for assessing the operational efficiency, economic viability, and reliability, providing valuable insights for power system planning and operations. However, solving a PCM problem is time-consuming, consisting of numerous binary variables for simulation horizon extending over months and years

Nirmalya Thakur, Yuvraj Nihal Duggal, Zihui Liu

The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this area of research have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aims to address this research gap and makes

Mosam Dabhi, Laszlo A. Jeni, Simon Lucey

The lifting of 3D structure and camera from 2D landmarks is at the cornerstone of the entire discipline of computer vision. Traditional methods have been confined to specific rigid objects, such as those in Perspective-n-Point (PnP) problems, but deep learning has expanded our capability to reconstruct a wide range of object classes (e.g. C3DPO and PAUL) wit

Yuhang Li, Yuecai Han

In this paper, we investigate the optimal control problem for systems driven by mixed fractional Brownian motion (including a fractional Brownian motion with Hurst parameter $H>1/2$ and the standard Brownian motion). By using Malliavin calculus and introducing a disturbance control region, we obtain a modified maximum principle. Through martingale representa

Nicole F. Bell, Giorgio Busoni, Sandra Robles, Michael Virgato

The capture of dark matter, and its subsequent annihilation, can heat old, isolated neutron stars. In order for kinetic heating to be achieved, the captured dark matter must undergo sufficient scattering to deposit its kinetic energy in the star. We find that this energy deposit typically occurs quickly, for most of the relevant parameter space. In order for

Soaad Hossain, Syed Ishtiaque Ahmed

Given the success of ChatGPT, LaMDA and other large language models (LLMs), there has been an increase in development and usage of LLMs within the technology sector and other sectors. While the level in which LLMs has not reached a level where it has surpassed human intelligence, there will be a time when it will. Such LLMs can be referred to as advanced LLM

Yuwei Cao, Hao Peng, Zhengtao Yu, Philip S. Yu

As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance. However, GNN-based methods can miss useful message correlations. Moreover, they require manual labeling for training and predetermining

Unggi Lee, Sungjun Yoon, Joon Seo Yun, Kyoungsoo Park

This paper presents novel techniques for enhancing the performance of knowledge tracing (KT) models by focusing on the crucial factor of question and concept difficulty level. Despite the acknowledged significance of difficulty, previous KT research has yet to exploit its potential for model optimization and has struggled to predict difficulty from unseen da

Parvez Mahbub, Mohammad Masudur Rahman

Software defects consume 40% of the total budget in software development and cost the global economy billions of dollars every year. Unfortunately, despite the use of many software quality assurance (SQA) practices in software development (e.g., code review, continuous integration), defects may still exist in the official release of a software product. There

Xu Fang, Xiaolei Li, Lihua Xie

This paper investigates the localization problem of a network in 2-D and 3-D spaces given the positions of anchor nodes in a global frame and inter-node relative measurements in local coordinate frames. It is assumed that the local frames of different nodes have different unknown orientations. First, an angle-displacement rigidity theory is developed, which

Vivian L. Tang, Piero Madau, Elisa Bortolas, Eric W. Peng

Intermediate mass black holes (IMBHs) may be the link between stellar mass holes and the supermassive variety in the nuclei of galaxies, and globular clusters (GCs) may be one of the most promising environments for their formation. Here we carry out a pilot study of the observability of tidal disruption events (TDEs) from 10^3 Msun < M_BH < 10^5 Msun IMBHs e

Yongjin Shin, Kenneth R. Poeppelmeier, James M. Rondinelli

Ordered oxygen vacancies (OOVs) in perovskites can exhibit long-range order and may be used to direct materials properties through modifications in electronic structures and broken symmetries. Based on the various vacancy patterns observed in previously known compounds, we explore the ordering principles of OOVs in oxygen-deficient perovskite oxides with $AB

Nirmalya Thakur, Shuqi Cui, Kesha A. Patel, Isabella Hall

The World Health Organization added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimoda

Aastha Pant, Simone V. Spiegler, Rashina Hoda, Jeremy Yoon

The importance of teaching software ethics to software engineering (SE) students is more critical now than ever before as software related ethical issues continue to impact society at an alarming rate. Traditional classroom methods, vignettes, role-play games, and quizzes have been employed over the years to teach SE students about software ethics. Recognisi

U. T. Ahmed, A. M. Hopkins, J. Ware, Y. A. Gordon

We demonstrate the importance of radio selection in probing heavily obscured galaxy populations. We combine Evolutionary Map of the Universe (EMU) Early Science data in the Galaxy and Mass Assembly (GAMA) G23 field with the GAMA data, providing optical photometry and spectral line measurements, together with Wide-field Infrared Survey Explorer (WISE) infrare

Ziqian Zeng, Yihuai Hong, Hongliang Dai, Huiping Zhuang

Early Exiting is one of the most popular methods to achieve efficient inference. Current early exiting methods adopt the (weighted) sum of the cross entropy loss of all internal classifiers during training, imposing all these classifiers to predict all instances correctly. However, during inference, as long as one internal classifier predicts an instance cor

Taehong Jang, Joonmo Ahn, Sojung Lucia Kim

In Korean ancient documents, there is no spacing or punctuation, and they are written in classical Chinese characters. This makes it challenging for modern individuals and translation models to accurately interpret and translate them. While China has models predicting punctuation and spacing, applying them directly to Korean texts is problematic due to data

Alperen Enes Bayar, Ufuk Uyan, Elif Toprak, Cao Yuheng

Urban environments are characterized by complex structures and diverse features, making accurate segmentation of point cloud data a challenging task. This paper presents a comprehensive study on the application of RandLA-Net, a state-of-the-art neural network architecture, for the 3D segmentation of large-scale point cloud data in urban areas. The study focu

Wei Xiong, Cheng-Yong Zhang, Peng-Cheng Li

The Einstein-Maxwell-scalar (EMS) theory with a quartic coupling function features three branches of fundamental black hole (BH) solutions, labeled as cold, hot, and bald black holes. The static bald black holes (the Reissner-Nordstr\"om BH) exhibit an intriguing nonlinear instability beyond the spontaneous scalarization. We study the rotating scalarized bla

Sergei O. Ivanov

For a real $r\geq 0,$ we consider the notion of $r$-homotopy equivalence in the category quasimetric spaces, which includes metric spaces and directed graphs. We show that for a finite quasimetric space $X$ there is a unique (up to isometry) $r$-homotopy equivalent quasimetric space of the minimal possible cardinality. It is called the $r$-minimal model of $

Ziran Liu, Tianqi Wu

We propose a discrete approach for approximating solutions to the prescribed Gaussian curvature problem in two-dimensional manifolds, based on the notion of discrete conformality. Our approach provides an efficient numerical method to compute the solution by minimizing a convex functional.

Linda Cook

This is my PhD thesis which was defended in May 2021. We call an induced cycle of length at least four a hole. The parity of a hole is the parity of its length. Forbidding holes of certain types in a graph has deep structural implications. In 2006, Chudnovksy, Seymour, Robertson, and Thomas famously proved that a graph is perfect if and only if it does not c

Weixi Song, Zuchao Li, Lefei Zhang, Hai Zhao

With the prevalence of pre-training-fine-tuning paradigm, how to efficiently adapt the pre-trained model to the downstream tasks has been an intriguing issue. Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed for low-cost adaptation. Although PEFT has demonstrated effectiveness and been widely applied, the underlying principles are still uncl

Karabo Mosala, Thuso Mathaha, Mukesh Kumar, Bruce Mellado

We present growing excesses consistent with a 95 GeV scalar. We provide a comprehensive analysis of the Two Higgs Doublet Model and an additional singlet (2HDM+S) at future $e^{+} e^{-}$ collider. In particular, we provide a precise mass reconstruction measurement for the scalar, $m_{S}$, using the recoil mass method through $e^{+} e^{-} \to Z S$ where $Z \t

Yanqi Ge, Qiang Nie, Ye Huang, Yong Liu

One of the ultimate goals of representation learning is to achieve compactness within a class and well-separability between classes. Many outstanding metric-based and prototype-based methods following the Expectation-Maximization paradigm, have been proposed for this objective. However, they inevitably introduce biases into the learning process, particularly

Qiang Su, Shaofeng Wu, Zhixiong Niu, Ran Shu

SmartNICs are touted as an attractive substrate for network application offloading, offering benefits in programmability, host resource saving, and energy efficiency. The current usage restricts offloading to local hosts and confines SmartNIC ownership to individual application teams, resulting in poor resource efficiency and scalability. This paper presents

Zizhong Li, Haopeng Zhang, Jiawei Zhang

The proliferation of fake news has emerged as a critical issue in recent years, requiring significant efforts to detect it. However, the existing fake news detection datasets are sourced from human journalists, which are likely to have inherent bias limitations due to the highly subjective nature of this task. In this paper, we revisit the existing fake news

Bella Cochran, Anish Gupta, Colin Holzman-Klima, Wanchaloem Wunkaew

Systems of pinned billiard balls serve as simplified models of collisions, where all particles remain fixed in their positions while their (pseudo-)velocities evolve in accordance with the laws of conservation of energy and momentum. For some families of ball configurations, Athreya, Burdzy, and Duarte have established the maximum upper bound for the number

Junheng Li, Junchao Ma, Omar Kolt, Manas Shah

Despite their remarkable advancement in locomotion and manipulation, humanoid robots remain challenged by a lack of synchronized loco-manipulation control, hindering their full dynamic potential. In this work, we introduce a versatile and effective approach to controlling and generalizing dynamic locomotion and loco-manipulation on humanoid robots via a Forc

Hui Wu, Yi Gan, Feng Yuan, Jing Ma

Transformer based Large Language Models (LLMs) have been widely used in many fields, and the efficiency of LLM inference becomes hot topic in real applications. However, LLMs are usually complicatedly designed in model structure with massive operations and perform inference in the auto-regressive mode, making it a challenging task to design a system with hig

Kaiyi Zhang, Yang Chen, Ximing Yang, Weizhong Zhang

Ideal part editing should guarantee the diversity of edited parts, the fidelity to the remaining parts, and the quality of the results. However, previous methods do not disentangle each part completely, which means the edited parts will affect the others, resulting in poor diversity and fidelity. In addition, some methods lack constraints between parts, whic

Youn-Yeol Yu, Jeongwhan Choi, Woojin Cho, Kookjin Lee

Recently, many mesh-based graph neural network (GNN) models have been proposed for modeling complex high-dimensional physical systems. Remarkable achievements have been made in significantly reducing the solving time compared to traditional numerical solvers. These methods are typically designed to i) reduce the computational cost in solving physical dynamic

Yuman Gao, Jialin Ji, Qianhao Wang, Rui Jin

Perching on the moving platforms is a promising solution to enhance the endurance and operational range of quadrotors, which could benefit the efficiency of a variety of air-ground cooperative tasks. To ensure robust perching, tracking with a steady relative state and reliable perception is a prerequisite. This paper presents an adaptive dynamic tracking and

Weiyu Ma, Qirui Mi, Yongcheng Zeng, Xue Yan

StarCraft II is a challenging benchmark for AI agents due to the necessity of both precise micro level operations and strategic macro awareness. Previous works, such as Alphastar and SCC, achieve impressive performance on tackling StarCraft II , however, still exhibit deficiencies in long term strategic planning and strategy interpretability. Emerging large

Ke Di, Xi Wang, Huarong Xia, Yinxue Zhao

We consider a two-dimensional opto-magnomechanical (OMM) system including two optical cavity modes, a magnon mode, a phonon mode, and a collection of two-level atoms. In this study, we demonstrate the methodology for generating stationary entanglement between two-level atoms and magnons, which are implemented using two optical cavities inside the setup. Addi

Di Wu, Yuling Jiao, Li Shen, Haizhao Yang

Deep reinforcement learning (RL) has shown remarkable success in specific offline decision-making scenarios, yet its theoretical guarantees are still under development. Existing works on offline RL theory primarily emphasize a few trivial settings, such as linear MDP or general function approximation with strong assumptions and independent data, which lack g

Karthikeyan Natesan Ramamurthy, Aldo Guzmán-Sáenz, Mustafa Hajij

Due to their ability to model meaningful higher order relations among a set of entities, higher order network models have emerged recently as a powerful alternative for graph-based network models which are only capable of modeling binary relationships. Message passing paradigm is still dominantly used to learn representations even for higher order network mo

Luyao Guo, Luqing Wang, Xinli Shi, Jinde Cao

Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these methods, nodes perform several iterations of local computations between the communication rounds. Nevertheless, this capability is effective only when the network is sufficiently well-connected and the loss f

Kazumi Okuyama, Takeshi Tachibana

We study the refined R\'{e}nyi negativity in the matrix model of Jackiw-Teitelboim (JT) gravity. We first consider the JT gravity with dynamical branes, which serves as a toy model of the evaporating black hole. By including the backreaction of branes, we find that the refined R\'{e}nyi negativity monotonically decreases at late time of the evaporation. Next

Kihong Pak, Barry Davids, Yong Kyun Kim

Recently, the TRIUMF Storage Ring (TRISR), a storage ring for the existing Isotope Separator and Accelerator-I (ISAC-I) radioactive ion beam facility at TRIUMF, was proposed. It may be possible to directly measure neutron-induced radiative capture reactions in inverse kinematics by combining the ring with a high-flux neutron generator as the neutron target.

Boshi Tang, Zhiyong Wu, Xixin Wu, Qiaochu Huang

Graph neural networks (GNNs) have exhibited impressive performance in modeling graph data as exemplified in various applications. Recently, the GNN calibration problem has attracted increasing attention, especially in cost-sensitive scenarios. Previous work has gained empirical insights on the issue, and devised effective approaches for it, but theoretical s

Tianyu Li, Yi Peng, Yucheng Wang, Haiping Hu

Hyperbolic lattices, formed by tessellating the hyperbolic plane with regular polygons, exhibit a diverse range of exotic physical phenomena beyond conventional Euclidean lattices. Here, we investigate the impact of disorder on hyperbolic lattices and reveal that the Anderson localization occurs at strong disorder strength, accompanied by the presence of mob

Jiarong Guo, Xiaogang Xu, Hengshuang Zhao

3D-aware Generative Adversarial Networks (3D-GANs) currently exhibit artifacts in their 3D geometrical modeling, such as mesh imperfections and holes. These shortcomings are primarily attributed to the limited availability of annotated 3D data, leading to a constrained "valid latent area" for satisfactory modeling. To address this, we present a Self-Supervis

Ting Guo, Xianhua Tang

In this paper, we study the existence and qualitative properties of positive solutions to a Choquard-type equation with Hardy potential. We develop a nonlocal version of concentration-compactness principle involving the Hardy potential to study the existence and the asymptotic behavior of positive solutions by transforming the original problem into a new non

Xuan He, Yi Ding, Kui Cai, Guanghui Song

In this paper, we consider the outer channel for DNA-based data storage. When transmitting over the outer channel, each DNA string is treated as a unit/symbol that would be either correctly received, or erased, or corrupted by uniformly distributed random symbol substitution errors, and all strings are randomly shuffled with each other. We first derive the c

Amine Jaouadi, Etienne Mangaud, Michèle Desouter-Lecomte

In this study, we reexamine a recent optimal control simulation targeting the preparation of a superposition of two excited electronic states in the UV range in a complex molecular system. We revisit this control from the perspective of reinforcement learning, offering an efficient alternative to conventional quantum control methods. The two excited states a

Zheng Wei Lim, Ekaterina Vylomova, Charles Kemp, Trevor Cohn

Human translators linger on some words and phrases more than others, and predicting this variation is a step towards explaining the underlying cognitive processes. Using data from the CRITT Translation Process Research Database, we evaluate the extent to which surprisal and attentional features derived from a Neural Machine Translation (NMT) model account fo

Xu Fang, Lihua Xie

This paper addresses the formation maneuver control problem of leader-follower multi-agent systems with high-order integrator dynamics. A distributed output feedback formation maneuver controller is proposed to achieve desired maneuvers so that the scale, orientation, translation, and shape of formation can be manipulated continuously, where the followers do

Xinshun Wang, Qiongjie Cui, Chen Chen, Mengyuan Liu

The past few years has witnessed the dominance of Graph Convolutional Networks (GCNs) over human motion prediction.Various styles of graph convolutions have been proposed, with each one meticulously designed and incorporated into a carefully-crafted network architecture. This paper breaks the limits of existing knowledge by proposing Universal Graph Convolut

Yusaku Nishimura

It is known that the average hitting times of simple random walks from any vertex to any other vertex in distance-regular graphs are determined by their intersection array. In this paper, we introduce a new graph classification called $f$-equitable, utilizing both the equitable partition and the function $f$, which represents a generalization of distance-reg

Fan Zhang

In this short note, we draw attention to the possibility that, under favorable conditions, the final parsec problem could be alleviated by the presence of a moderate population of intermediate mass black holes in the centers of merged galaxies.

Avinandan Bose, Mihaela Curmei, Daniel L. Jiang, Jamie Morgenstern

This paper investigates ML systems serving a group of users, with multiple models/services, each aimed at specializing to a sub-group of users. We consider settings where upon deploying a set of services, users choose the one minimizing their personal losses and the learner iteratively learns by interacting with diverse users. Prior research shows that the o

Wenjun Lin, Jiahao Qian, Wenwen Liu, Lang Wu

The exponential growth of collected, processed, and shared data has given rise to concerns about individuals' privacy. Consequently, various laws and regulations have been established to oversee how organizations handle and safeguard data. One such method is Statistical Disclosure Control, which aims to minimize the risk of exposing confidential information

Masataka Kakoi, Takashi Oi, Yujiro Ohshita, Mitsuharu Yashima

We report a $^{139}$La-NMR study of polycrystalline samples of multi($n$)-layered nickelates, La$_3$Ni$_2$O$_{7-\delta}$ ($n=2$) and La$_4$Ni$_3$O$_{10-\delta}$ ($n=3$), at ambient pressure. Measurements of the nuclear magnetic resonance (NMR) spectra and nuclear spin relaxation rate ($1/T_1$) indicate the emergence of a density wave order with a gap below $

Jiaqi Liu, Xiao Qi, Peng Hang, Jian Sun

The integration of Autonomous Vehicles (AVs) into existing human-driven traffic systems poses considerable challenges, especially within environments where human and machine interactions are frequent and complex, such as at unsignalized intersections. To deal with these challenges, we introduce a novel framework predicated on dynamic and socially-aware decis

Xu-Hui Zhou, Jiequn Han, Muhammad I. Zafar, Eric M. Wolf

Recently, the use of neural networks to accelerate the solving of partial differential equations (PDEs) has gained significant traction in both academia and industry. However, employing neural networks as standalone surrogate models raises concerns about solution reliability, especially in precision-critical scientific tasks. This study introduces a novel "s

Chaojian Li, Bichen Wu, Peter Vajda, Yingyan Celine Lin

Neural Radiance Field (NeRF) has emerged as a leading technique for novel view synthesis, owing to its impressive photorealistic reconstruction and rendering capability. Nevertheless, achieving real-time NeRF rendering in large-scale scenes has presented challenges, often leading to the adoption of either intricate baked mesh representations with a substanti

Ben DalFavero, Rahul Sarkar, Jeremiah Rowland, Daan Camps

We introduce a notion of commutativity between operators on a tensor product space, nominally Pauli strings on qubits, that interpolates between qubit-wise commutativity and (full) commutativity. We apply this notion, which we call $k$-commutativity, to measuring expectation values of observables in quantum circuits and show a reduction in the number measure

Saviz Mowlavi, Mouhacine Benosman

Designing estimation algorithms for systems governed by partial differential equations (PDEs) such as fluid flows is challenging due to the high-dimensional and oftentimes nonlinear nature of the dynamics, as well as their dependence on unobserved physical parameters. In this paper, we propose two different lightweight and effective methodologies for real-ti

Andras Farago

We prove the unexpected result that almost uniform sampling of independent sets in graphs is possible via a probabilistic polynomial time algorithm. Note that our sampling algorithm (if correct) has extremely surprising consequences; the most important one being no less than the unlikely collapse NP=RP.

Junkai Xu, Liang Peng, Haoran Cheng, Linxuan Xia

Multi-camera perception tasks have gained significant attention in the field of autonomous driving. However, existing frameworks based on Lift-Splat-Shoot (LSS) in the multi-camera setting cannot produce suitable dense 3D features due to the projection nature and uncontrollable densification process. To resolve this problem, we propose to regulate intermedia

Zihao Xuan, Yuxuan Yang, Wei Xuan, Zijia Su

In this paper, we further explore the potential of analog in-memory computing (AiMC) and introduce an innovative artificial intelligence (AI) accelerator architecture named YOCO, featuring three key proposals: (1) YOCO proposes a novel 8-bit in-situ multiply arithmetic (IMA) achieving 123.8 TOPS/W energy-efficiency and 34.9 TOPS throughput through efficient

Yang Jiao, Kai Yang, Tiancheng Wu, Chengtao Jian

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to the

Hisato Komatsu

In recent years, simulations of pedestrians using the multi-agent reinforcement learning (MARL) have been studied. This study considered the roads on a grid-world environment, and implemented pedestrians as MARL agents using an echo-state network and the least squares policy iteration method. Under this environment, the ability of these agents to learn to mo

Talal Ahmed Chowdhury, Kareem Ezzat, Shaaban Khalil, Ernest Ma

The addition of a Higgs quadruplet to the standard model (SM) of quarks and leptons would shift the $W$ boson mass upward. It could also facilitate the production of dark matter through the conventional thermal freeze-out scenario via Yukawa interaction with the Higgs quadruplet or freeze-in production from the decay of SM Higgs. We investigate the same-sign