December 2023 arXiv papers — page 81

Sara Qayyum, Muhammad Moiz Asghar, Muhammad Fouzan Yaseen

The automatic transformation of verbose, natural language descriptions into structured process models remains a challenge of significant complexity - This paper introduces a contemporary solution, where central to our approach, is the use of dependency parsing and Named Entity Recognition (NER) for extracting key elements from textual descriptions. Additiona

Markus Mueller, Kathrin Gruber, Dennis Fok

Score-based generative models, commonly referred to as diffusion models, have proven to be successful at generating text and image data. However, their adaptation to mixed-type tabular data remains underexplored. In this work, we propose CDTD, a Continuous Diffusion model for mixed-type Tabular Data. CDTD is based on a novel combination of score matching and

Marel Alvarado, Luke Gayler, Alex Seals, Tao Wang

The paper explains that post-quantum cryptography is necessary due to the introduction of quantum computing causing certain algorithms to be broken. We analyze the different types of post-quantum cryptography, quantum cryptography and quantum-resistant cryptography, to provide a thorough understanding of the current solutions to the problems and their limita

Pumeng Lyu, Tao Tang, Fenghua Ling, Jing-Jia Luo

Recent studies have shown that deep learning (DL) models can skillfully predict the El Ni\~no-Southern Oscillation (ENSO) forecasts over 1.5 years ahead. However, concerns regarding the reliability of predictions made by DL methods persist, including potential overfitting issues and lack of interpretability. Here, we propose ResoNet, a DL model that combines

Mai Katada

The aim of this paper is to compute the first homology of $\operatorname{IA}_n$ with coefficients in the $\operatorname{Aut}(F_n)$-module $A_2(n)$ of Jacobi diagrams of degree $2$ on $n$-component oriented arcs. We also prove the self-duality of the $\operatorname{Aut}(F_3)$-submodule $A''_2(3)$ of $A_2(3)$ which is generated by a certain element.

Ramon Oliver-Bonafoux, Emmanuel Risler

For nonlinear parabolic gradient systems of the form \[ u_t = -\nabla V(u) + u_{xx} \,, \] where the spatial domain is the whole real line, the state variable $u$ is multidimensional, and the potential function $V$ is coercive at infinity, the following result is proved: for every critical point of $V$ which is not a global minimum point, there exists a trav

Alex Saveau

Accurate branch prediction is a critical part of high performance instruction stream processing. In this paper, I present a hardware implementation of branch prediction for a RV32IM CPU, starting with static decode stage predictions and culminating in the use of BATAGE. In addition, I detail my experience writing the RTL in Hardcaml, a hardware description l

Xiaorui Jiang, Yu Gao, Hengwei Xu, Qi Zhang

Federated Learning (FL) allows several clients to cooperatively train machine learning models without disclosing the raw data. In practical applications, asynchronous FL (AFL) can address the straggler effect compared to synchronous FL. However, Non-IID data and stale models pose significant challenges to AFL, as they can diminish the practicality of the glo

Siddharth Chandak, Vivek S. Borkar

We derive uniform all-time concentration bound of the type 'for all $n \geq n_0$ for some $n_0$' for TD(0) with linear function approximation. We work with online TD learning with samples from a single sample path of the underlying Markov chain. This makes our analysis significantly different from offline TD learning or TD learning with access to independent

Xiaobin Huang, Lei Song, Ke Xue, Chao Qian

Bayesian optimization (BO) is a sample-efficient method and has been widely used for optimizing expensive black-box functions. Recently, there has been a considerable interest in BO literature in optimizing functions that are affected by context variable in the environment, which is uncontrollable by decision makers. In this paper, we focus on the optimizati

Songlin Yang, Wei Wang, Yushi Lan, Xiangyu Fan

Face reenactment is challenging due to the need to establish dense correspondence between various face representations for motion transfer. Recent studies have utilized Neural Radiance Field (NeRF) as fundamental representation, which further enhanced the performance of multi-view face reenactment in photo-realism and 3D consistency. However, establishing de

Khaled Ramadan

The purpose of this article is to investigate the viability of Multi-Carrier Modulation (MCM) systems based on the Fast Walsh Hadamard Transform (FWHT). In addition, a nonlinear Joint Low-Complexity Optimized Zero Forcing Successive Interference Cancellation (JLCOZF-SIC) equalizer is proposed. To that end, general equations for the number of flops of the pro

Kenan Wood, Runtian Zhou, Haoze Wu, Hammurabi Mendes

Correctness of results from mixed-integer linear programming (MILP) solvers is critical, particularly in the context of applications such as hardware verification, compiler optimization, or machine-assisted theorem proving. To this end, VIPR 1.0 is the first recently proposed general certificate format for answers produced by MILP solvers. We design a schema

Feida Gu, Yanmin Zhou, Zhipeng Wang, Shuo Jiang

Deformable object manipulation (DOM) for robots has a wide range of applications in various fields such as industrial, service and health care sectors. However, compared to manipulation of rigid objects, DOM poses significant challenges for robotic perception, modeling and manipulation, due to the infinite dimensionality of the state space of deformable obje

Lyudong Jin, Ming Tang, Meng Zhang, Hao Wang

Mobile edge computing (MEC) is a promising paradigm for real-time applications with intensive computational needs (e.g., autonomous driving), as it can reduce the processing delay. In this work, we focus on the timeliness of computational-intensive updates, measured by Age-ofInformation (AoI), and study how to jointly optimize the task updating and offloadin

Zhiwei Zha, Jiaan Wang, Zhixu Li, Xiangru Zhu

Multimodal knowledge bases (MMKBs) provide cross-modal aligned knowledge crucial for multimodal tasks. However, the images in existing MMKBs are generally collected for entities in encyclopedia knowledge graphs. Therefore, detailed groundings of visual semantics with linguistic concepts are lacking, which are essential for the visual concept cognition abilit

Yufei Zhang, Ziyang Chen, Dongrui Yu, Jialin Niu

Fiber-delay measurement is one of the key fundamental technologies in numerous fields. Here we propose and experimentally demonstrate a high-precision and concise optical time delay measurement system based on the technique of linear optical sampling, reaching the precision better than 100 fs under averaging. The use of only two optical frequency combs witho

Omar Mohsen

Building on the work of Debord and Skandalis, van Erp and Yuncken introduced a groupoid approach to pseudo-differential operators which has various advantages over the classical approach using H\"ormander's symbolic calculus. In a recent work by Couchet and Yuncken, they showed that for pseudo-differential operators of order $-\dim(M)$ acting on a smooth man

Yann Cado, Christoph Englert, Tanmoy Modak, Mariano Quirós

$R^2$-Higgs inflation stands out as one of the best-fit models of Planck data. Using a covariant formalism for the inflationary dynamics and the production of helical gauge fields, we show that the observed baryon asymmetry of the Universe (BAU) can be obtained when this model is supplemented by a dimension-six CP-violating term $\sim (R/\Lambda^2)\, B_{\mu\

Yixin Cao, Haowei Chen, Shenghua Wang

We are concerned with split graphs and pseudo-split graphs whose complements are isomorphic to themselves. These special subclasses of self-complementary graphs are actually the core of self-complementary graphs. Indeed, we show that all self-complementary graphs with forcibly self-complementary degree sequences are pseudo-split graphs. We also give formulas

Nourhan Hesham, Hesham ElSawy, Jahangir Hossain, Anas Chaaban

With the expected proliferation of delay constrained applications, future communication technologies are pushed towards using short codes. The performance using short codes cannot be inferred through classical channel capacity analysis, which intrinsically assumes long codes and vanishing frame error rate (FER). This paper studies the performance of an uplin

Valerio Capraro, Austin Lentsch, Daron Acemoglu, Selin Akgun

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight h

Youssra Cheriguene, Wael Jaafar, Halim Yanikomeroglu, Chaker Abdelaziz Kerrache

Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent a

A. Porro, G. Colò, T. Duguet, D. Gambacurta

Within the Energy Density Functional (EDF) approach, the use of mean-field wave-functions deliberately breaking (some) symmetries of the underlying Hamiltonian is an efficient and largely utilized way to incorporate static correlations. However, the restoration of broken symmetries is eventually mandatory to recover the corresponding quantum numbers and to a

Nikhil Danny Babu, Girish S. Setlur

The density-density correlation functions (most singular parts) of chiral Luttinger liquids forming the fractional quantum Hall effect (FQHE) edge are systematically derived in presence of a point-contact junction acting as a localised scalar impurity and are shown to be expressible as compact analytical functions with second order poles and involving the ba

Zhihao He, Tin Seng Manfred Ho, Rolf Lortz, Iam Keong Sou

The Bi-Te binary system, characterized by the homologous series of the (Bi2)m(Bi2Te3)n, has always attracted research interest for its layered structures and potential in advanced materials applications. Despite Bi2Te3 has been extensively studied, exploration of other compounds has been constrained by synthesis challenges. This study reports the molecular b

Wentao Wang, Xuanyao Huang, Tianyang Wang, Swalpa Kumar Roy

This paper explores the image synthesis capabilities of GPT-4, a leading multi-modal large language model. We establish a benchmark for evaluating the fidelity of texture features in images generated by GPT-4, comprising manually painted pictures and their AI-generated counterparts. The contributions of this study are threefold: First, we provide an in-depth

Yiqun Liu, Mohsen Abdoli, Thomas Guionnet, Christine Guillemot

The promising improvement in compression efficiency of Versatile Video Coding (VVC) compared to High Efficiency Video Coding (HEVC) comes at the cost of a non-negligible encoder side complexity. The largely increased complexity overhead is a possible obstacle towards its industrial implementation. Many papers have proposed acceleration methods for VVC. Still

Hao Wang

Borda Count Method is an important theory in the field of voting theory. The basic idea and implementation methodology behind the approach is simple and straight forward. Borda Count Method has been used in sports award evaluations and many other scenarios, and therefore is an important aspect of our society. An often ignored ground truth is that online cult

Ziyi Jin, Ziyang Chen, Kai Wu, Dongrui Yu

To meet the requirements of time-frequency networks and enable frequency downloadability for nodes along the link, we demonstrated the extraction of stable frequency signals at nodes using a mode-locked laser under the condition of 100 km laboratory fiber. The node consists of a simple structure that utilizes widely used optoelectronic devices and enables pl

Haibo Li

The singular value decomposition (SVD) of a matrix is a powerful tool for many matrix computation problems. In this paper, we consider generalizing the standard SVD to analyze and compute the regularized solution of linear ill-posed problems that arise from discretizing the first kind Fredholm integral equations. For the commonly used quadrature method for d

Gakusei Sato, Taketo Akama

Automatic Music Transcription (AMT) is a vital technology in the field of music information processing. Despite recent enhancements in performance due to machine learning techniques, current methods typically attain high accuracy in domains where abundant annotated data is available. Addressing domains with low or no resources continues to be an unresolved c

Qirui Ji, Jiangmeng Li, Jie Hu, Rui Wang

Graph contrastive learning is a general learning paradigm excelling at capturing invariant information from diverse perturbations in graphs. Recent works focus on exploring the structural rationale from graphs, thereby increasing the discriminability of the invariant information. However, such methods may incur in the mis-learning of graph models towards the

Sohan Ghodla, J. J. Eldridge

The gravitational wave signature of a binary black hole (BBH) merger is dependent on its component mass and spin. If such black holes originate from rapidly rotating progenitors, the large angular momentum reserve in the star could drive a collapsar-like supernova explosion, hence substantially impacting these characteristics of the black holes in the binary

Andrew Zhao

Fermions are fundamental particles which obey seemingly bizarre quantum-mechanical principles, yet constitute all the ordinary matter that we inhabit. As such, their study is heavily motivated from both fundamental and practical incentives. In this dissertation, we will explore how the tools of quantum information and computation can assist us on both of the

Chaitanya Prasad Sishtla, Immanuel Christopher Jebaraj, Jens Pomoell, Norbert Magyar

The nonlinear evolution of Alfv\'en waves in the solar corona leads to the generation of Alfv\'enic turbulence. This description of the Alfv\'en waves involves parametric instabilities where the parent wave decays into slow mode waves giving rise to density fluctuations. These density fluctuations, in turn, play a crucial role in the modulation of the dynami

Benjamin Noë Bauml

The weak value approximation has been in use for thirty-five years, but it has not as of yet received a truly complete derivation, leaving its mathematical validity in a state of limbo. Herein, I fill this gap, deriving the weak value approximation under the von Neumann and qubit probe models. Not only does this provide a level of mathematical support to the

Mohit Sharma, Amit Deshpande

A general belief in fair classification is that fairness constraints incur a trade-off with accuracy, which biased data may worsen. Contrary to this belief, Blum & Stangl (2019) show that fair classification with equal opportunity constraints even on extremely biased data can recover optimally accurate and fair classifiers on the original data distribution.

Mohamed Sorour

High demand for painters is required nowadays and foreseen in the near future for both developed and developing countries. To satisfy such demand, this paper presents the detailed computer aided design (CAD) model of a fully functional wall painting robot for interior finishes. The RoboPainter is capable of performing full scale wall-ceil painting in additio

Yuhang Chen, Chong Han, Emil Björnson

Ultra-massive multiple-input multiple-output (UM-MIMO) is the enabler of Terahertz (THz) communications in next-generation wireless networks. In THz UM-MIMO systems, a new paradigm of cross-field communications spanning from near-field to far-field is emerging, since the near-field range expands with higher frequencies and larger array apertures. Precise bea

Inga Strümke, Helge Langseth

Diffusion models are loosely modelled based on non-equilibrium thermodynamics, where \textit{diffusion} refers to particles flowing from high-concentration regions towards low-concentration regions. In statistics, the meaning is quite similar, namely the process of transforming a complex distribution $p_{\text{complex}}$ on $\mathbb{R}^d$ to a simple distrib

Jiachuan Cao, Buyang Li, Yanping Lin, Fangyan Yao

A high-frequency recovered fully discrete low-regularity integrator is constructed to approximate rough and possibly discontinuous solutions of the semilinear wave equation. The proposed method, with high-frequency recovery techniques, can capture the discontinuities of the solutions correctly without spurious oscillations and approximate rough and discontin

Zuhair Al-Johar

Here it is shown that standard set theory can be interpreted in a theory about order. The ordering here is about non-extensional flat classes, i.e. classes that are not elements of classes. So, stipulating a nearly well order over all those classes coupled together with indexing that order by elements of those classes, thereby having those elements serve as

Jicheng Liu, Chenao He, Weijie Huang, Zhihao Zhen

We observe a considerable hysteresis effect of low-field microwave absorption (LFMA) in copper-substituted lead apatite. By continuously rotating samples under external magnetic field, this effect is diminished which can not be renewed by a strong magnetic field but will be spontaneously recovered after two days, indicating its glassy features and excluding

ChuXin Wang, Wenfei Yang, Tianzhu Zhang

Semi-supervised 3D object detection from point cloud aims to train a detector with a small number of labeled data and a large number of unlabeled data. The core of existing methods lies in how to select high-quality pseudo-labels using the designed quality evaluation criterion. However, these methods treat each pseudo bounding box as a whole and assign equal

Yaxin Feng, Yuan Lan, Luchan Zhang, Yang Xiang

The task of lane detection involves identifying the boundaries of driving areas in real-time. Recognizing lanes with variable and complex geometric structures remains a challenge. In this paper, we explore a novel and flexible way of implicit lanes representation named \textit{Elastic Lane map (ELM)}, and introduce an efficient physics-informed end-to-end la

Lebin Yu, Yunbo Qiu, Quanming Yao, Yuan Shen

Communication in multi-agent reinforcement learning (MARL) has been proven to effectively promote cooperation among agents recently. Since communication in real-world scenarios is vulnerable to noises and adversarial attacks, it is crucial to develop robust communicative MARL technique. However, existing research in this domain has predominantly focused on p

Yijun Li, Cheuk Hang Leung, Xiangqian Sun, Chaoqun Wang

Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales. To understand the causal impact of credit lines on spending, previous studies have employed causal estimators, based on direct regression (DR), inverse propensity weighting (IPW), and double machine

Weichao Lin, Liang Chen, Wei Xiong, Kang Ran

With the rapid progress in virtual reality (VR) technology, the scope of VR applications has greatly expanded across various domains. However, the superiority of VR training over traditional methods and its impact on learning efficacy are still uncertain. To investigate whether VR training is more effective than traditional methods, we designed virtual train

Jun Sun, Xinxin Zhang, Shoukang Han, Yu-ping Ruan

Multimodal learning is susceptible to modality missing, which poses a major obstacle for its practical applications and, thus, invigorates increasing research interest. In this paper, we investigate two challenging problems: 1) when modality missing exists in the training data, how to exploit the incomplete samples while guaranteeing that they are properly s

Huy Hoang, Tien Mai, Pradeep Varakantham

A popular framework for enforcing safe actions in Reinforcement Learning (RL) is Constrained RL, where trajectory based constraints on expected cost (or other cost measures) are employed to enforce safety and more importantly these constraints are enforced while maximizing expected reward. Most recent approaches for solving Constrained RL convert the traject

Kiyoto Yoshino

In 2018, Sz\"{o}ll\H{o}si and \"{O}sterg\r{a}rd used a computer to enumerate sets of equiangular lines with common angle $\arccos(1/3)$ in dimension $7$. They observed that the numbers $\omega(n)$ of sets of $n$ equiangular lines with common angle $\arccos(1/3)$ in dimension $7$ are almost symmetric around $n=14$. In this paper, we prove without a computer t

N. Hyvönen, A. Jääskeläinen, R. Maity, A. Vavilov

This work considers the optimization of electrode positions in head imaging by electrical impedance tomography. The study is motivated by maximizing the sensitivity of electrode measurements to conductivity changes when monitoring the condition of a stroke patient, which justifies adopting a linearized version of the complete electrode model as the forward m

Jingwen Han, Yun Wang, Chunjing Xie

In this paper, the Liouville-type theorems for the steady Navier-Stokes system are investigated. First, we prove that any bounded smooth helically symmetric solution in $\mathbb{R}^3$ must be a constant vector. Second, for steady Navier-Stokes system in a slab supplemented with Navier boundary conditions, we prove that any bounded smooth solution must be zer

Yaoxun Xu, Hangting Chen, Jianwei Yu, Qiaochu Huang

Speech emotions are crucial in human communication and are extensively used in fields like speech synthesis and natural language understanding. Most prior studies, such as speech emotion recognition, have categorized speech emotions into a fixed set of classes. Yet, emotions expressed in human speech are often complex, and categorizing them into predefined g

Yuting Ma, Yuanzhi Yao, Xiaohua Xu

Federated learning (FL) has attracted growing attention since it allows for privacy-preserving collaborative training on decentralized clients without explicitly uploading sensitive data to the central server. However, recent works have revealed that it still has the risk of exposing private data to adversaries. In this paper, we conduct reconstruction attac

Yue Li, Xu Cheng, Lingna Wang, Xingyu Zhao

Squeezing a quantum state along a specific direction has long been recognized as a crucial technique for enhancing the precision of quantum metrology by reducing parameter uncertainty. However, practical quantum metrology often involves the simultaneous estimation of multiple parameters, necessitating the use of high-quality squeezed states along multiple or

Takefumi Nosaka

Given a 3-cocycle $\psi$ in the cohomology of a finite group $G$, we can define the Dijkgraaf-Witten invariant of closed 3-manifolds. In this paper, we focus on the case where $\psi$ is a 3-cocycle canonically obtained from the second Chern class of a representation of $G$, and show some procedures for computing the invariant, and clarify its topological int

Sumin Huang, Sergey Kitaev, Artem Pyatkin

Word-representable graphs, which are the same as semi-transitively orientable graphs, generalize several fundamental classes of graphs. In this paper we propose a novel approach to study word-representability of graphs using a technique of homomorphisms. As a proof of concept, we apply our method to show word-representability of the simplified graph of overl

Wenjie Pei, Tongqi Xia, Qizhong Tan, Jiandong Tian

Typical methods for visual prompt tuning follow the sequential modeling paradigm originating from NLP, learning a sequence of unordered parameterized tokens as visual prompts, which are then prefixed to the flattened image representation for model adaptation. While such a sequential prompting paradigm has exhibited great promise, it presents two potential li

Yukio Ohsawa, Tomohide Maekawa, Hiroki Yamaguchi, Hiro Yoshida

Feature concepts and data leaves have been invented using datasets to foster creative thoughts for creating well-being in daily life. The idea, simply put, is to attach selected and collected data leaves that are summaries of event flows to be discovered from corresponding datasets, on the target feature concept representing the well-being aimed. A graph of

Yihuai Zhang, Ruiguo Zhong, Huan Yu

This paper introduces a novel approach to PDE boundary control design using neural operators to alleviate stop-and-go instabilities in congested traffic flow. Our framework leverages neural operators to design control strategies for traffic flow systems. The traffic dynamics are described by the Aw-Rascle-Zhang (ARZ) model, which comprises a set of second-or

Yuan Liao, Vasilis Friderikos, Halim Yanikomeroglu

Robotic aerial base stations (RABSs) that are able to anchor at tall urban landforms are expected to bring further flexibility to millimeter-wave (mmWave) multi-hop backhaul networks in highly dense urban environments. In this paper, a swarm of RABSs are deployed to construct a dynamic mmWave backhaul network according to the traffic spatial distribution, an

Qihang Ai, Jianwu Zhou, Haiyun Jiang, Lemao Liu

Graph data is ubiquitous in the physical world, and it has always been a challenge to efficiently model graph structures using a unified paradigm for the understanding and reasoning on various graphs. Moreover, in the era of large language models, integrating complex graph information into text sequences has become exceptionally difficult, which hinders the

Wei Chen, Gang Zhao, Xiaojin Zhang, Xiang Bai

Automatic psychological counseling requires mass of professional knowledge that can be found in online counseling forums. Motivated by this, we propose K-ESConv, a novel prompt learning based knowledge injection method for emotional support dialogue system, transferring forum knowledge to response generation. We evaluate our model on an emotional support dat

Nicolas Hubert, Heiko Paulheim, Armelle Brun, Davy Monticolo

Knowledge graph embedding models (KGEMs) developed for link prediction learn vector representations for entities in a knowledge graph, known as embeddings. A common tacit assumption is the KGE entity similarity assumption, which states that these KGEMs retain the graph's structure within their embedding space, \textit{i.e.}, position similar entities within

Apoorv Jain

Bollywood is one of the largest film-producing industries with a large worldwide audience. In this paper, we will try to find the most important stars in the era of 1990 to 2014, as well as try to use social network analysis methods and metrics to analyze the role of blood connections in getting opportunities in the industry. We created the actor relationshi

Jingying Zeng, Jaewon Yang, Waleed Malik, Xiao Yan

While generative AI excels in content generation, it does not always increase user engagement. This can be attributed to two main factors. First, generative AI generates content without incorporating explicit or implicit feedback about user interactions. Even if the generated content seems to be more informative or well-written, it does not necessarily lead

Yusuf Hakan Kalayci, David Kempe, Vikram Kher

We introduce a novel definition for a small set R of k points being "representative" of a larger set in a metric space. Given a set V (e.g., documents or voters) to represent, and a set C of possible representatives, our criterion requires that for any subset S comprising a theta fraction of V, the average distance of S to their best theta*k points in R shou

Vivas Bagwe, Rishabh Duhan, Bhagyashree Chalke, Jayesh Parmar

The most common allotrope of tungsten, {\alpha}-W, has a superconducting transition at a temperature of ~11 mK. However, two other forms of tungsten have been reported to have superconducting transitions in the temperature range Tc ~ 2-5 K when synthesized as thin films: Crystalline \b{eta}-W and amorphous W (a-W). In this work we carry out a systematic stud

Yaron Hadad, Ido Kaminer, Avshalom Elitzur, Eliahu Cohen

This paper revisits the geometric foundations of electromagnetic theory, by studying Faraday's concept of field lines. We introduce "covariant electromagnetic field lines," a novel construct that extends traditional field line concepts to a covariant framework. Our work includes the derivation of a closed-form formula for the field line curvature in proximit

Kumar Shubham, Pranav Sastry, Prathosh AP

Programmatic Weak Supervision (PWS) and generative models serve as crucial tools that enable researchers to maximize the utility of existing datasets without resorting to laborious data gathering and manual annotation processes. PWS uses various weak supervision techniques to estimate the underlying class labels of data, while generative models primarily con

Yuntao Gui, Xiao Yan, Peiqi Yin, Han Yang

Transformer-based large language models (e.g., BERT and GPT) achieve great success, and fine-tuning, which tunes a pre-trained model on a task-specific dataset, is the standard practice to utilize these models for downstream tasks. However, Transformer fine-tuning has long running time and high memory consumption due to the large size of the models. We propo

Antoni Kijowski, Qing Liu, Ye Zhang, Xiaodan Zhou

This paper is concerned with a PDE approach to horizontally quasiconvex (h-quasiconvex) functions in the Heisenberg group based on a nonlinear second order elliptic operator. We discuss sufficient conditions and necessary conditions for upper semicontinuous, h-quasiconvex functions in terms of the viscosity subsolution to the associated elliptic equation. Si

Ron Shnapp, Markus Holzner

Buoyancy-driven bubbly flows play pivotal roles in various scenarios, such as the oxygenation and mixing in the upper ocean and the reaction kinetics in chemical and bio-reactors. This work focuses on the convective flow induced by the localized release of large air bubbles ($D_b=3.7$ mm, $\mathrm{Re}_b=950$) in a water tank, exploring the resulting flow and

Massoud Amini, Mahdi Moosazadeh

Cuntz algebra $\mathcal O_2$ is the universal $C^*$-algebra generated by two isometries $s_1, s_2$ satisfying $s_1s_1^*+s_2s_2^*=1$. This is separable, simple, infinite $C^*$-algebra containing a copy of any nuclear $C^*$-algebra. The $C^*$-algebra $\mathcal O_2$ plays a central role in the modern theory of $C^*$-algebras and appears in many substantial stat

H. S. Tan, Kuancheng Wang, Rafe Mcbeth

In this work, we study various hybrid models of entropy-based and representativeness sampling techniques in the context of active learning in medical segmentation, in particular examining the role of UMAP (Uniform Manifold Approximation and Projection) as a technique for capturing representativeness. Although UMAP has been shown viable as a general purpose d

Mehmet Aktas, Emina Soljanin

Distributed systems store data objects redundantly to balance the data access load over multiple nodes. Load balancing performance depends mainly on 1) the level of storage redundancy and 2) the assignment of data objects to storage nodes. We analyze the performance implications of these design choices by considering four practical storage schemes that we re

Mingbin Xu, Alex Jin, Sicheng Wang, Mu Su

With increasingly more powerful compute capabilities and resources in today's devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the cloud to devices to better protect user privacy. However, it is still challenging to implement on-device ASR on resource-constrained devices, such as smartphones, smart wearables, a

Yayue Deng, Jinlong Xue, Yukang Jia, Qifei Li

Conversational speech synthesis (CSS) incorporates historical dialogue as supplementary information with the aim of generating speech that has dialogue-appropriate prosody. While previous methods have already delved into enhancing context comprehension, context representation still lacks effective representation capabilities and context-sensitive discriminab

Laura Baldelli, David Krejcirik

The Dirichlet p-Laplacian in tubes of arbitrary cross-section along infinite curves in Euclidean spaces of arbitrary dimension is investigated. First, it is shown that the gap between the lowest point of the generalised spectrum and the essential spectrum is positive whenever the cross-section is circular and the tube is asymptotically straight, untwisted an

Jiacheng Li, Yongxiang Zhao, Chunxi Li, Zonghui Li

As required by Industry 4.0, companies will move towards flexible and individual manufacturing. To succeed in this transition, convergence of 5G and time-sensitive networks (TSN) is the most promising technology and has thus attracted considerable interest from industry and standardization groups. However, the delay and jitter of end-to-end (e2e) transmissio

Peiyuan Gong, Jiaxin Mao

Recently, natural language generation (NLG) evaluation has shifted from a single-aspect to a multi-aspect paradigm, allowing for a more accurate assessment. Large language models (LLMs) achieve superior performance on various NLG evaluation tasks. However, current work often employs the LLM to independently evaluate different aspects, which largely ignores t

Hidefumi Takahashi, Tomohiro Sasaki, Akitoshi Nakano, Kazuto Akiba

Given the rarity of metallic systems that exhibit ferroelectric-like transitions, it is apparently challenging to find a system that simultaneously possesses superconductivity and ferroelectric-like structural instability. Here, we report the observation of superconductivity at 2.4 K in a layered semimetal SrAuBi characterized by strong spin-orbit coupling (

Palak Patel, Sarika Maitra Bhattacharyya

Pinning particles at the equilibrium configuration of the liquid is expected not to affect the structure and any property that depends on the structure while slowing down the dynamics. This leads to a breakdown of the structure dynamics correlation. Here, we calculate two structural quantities, the pair excess entropy, $S_2$, and the mean field caging potent

D. Ram, J. Singh, M. K. Hooda, K. Singh

We report the physical properties of GdAuGe single crystals, which were grown using Bi flux. The powder x-ray diffraction data shows that the compound crystallizes in hexagonal NdPtSb-type structure (space group P63mc). Magnetization measurements performed for field configuration H||c and H||ab show that GdAuGe orders antiferromagnetically at the Neel temper

Mikhail Tuzhilin, Dong Zhang

This book collects the lectures about graph theory and its applications which were given to students of mathematical departments of Moscow State University and Peking University. Graph theory is a very wide field with a lot of applications in almost every scientific area: in many branches of mathematics, computer science, physics, chemistry, biology and also

Aodong Chen, Fei Xu, Li Han, Yuan Dong

GPUs have become the \emph{defacto} hardware devices for accelerating Deep Neural Network (DNN) inference workloads. However, the conventional \emph{sequential execution mode of DNN operators} in mainstream deep learning frameworks cannot fully utilize GPU resources, even with the operator fusion enabled, due to the increasing complexity of model structures

Zhihang Liu, Chao Zheng

We reveal the continuous phase transition in anyonic-PT symmetric systems, contrasting with the discontinuous phase transition corresponding to the discrete (anti-) PT symmetry. The continuous phase transition originates from the continuity of anyonic-PT symmetry. We find there are three information-dynamics patterns for anyonic-PT symmetric systems: damped

Shubhang Shekhar Dvivedi, Vyshnav Vijay, Sai Leela Rahul Pujari, Shoumik Lodh

This paper presents a comprehensive comparative analysis of Large Language Models (LLMs) for generation of code documentation. Code documentation is an essential part of the software writing process. The paper evaluates models such as GPT-3.5, GPT-4, Bard, Llama2, and Starchat on various parameters like Accuracy, Completeness, Relevance, Understandability, R

Ryo Horiuchi

In this note, we investigate a mixture of combinatorial spectra and stratified simplicial sets, which would be thought of as a model of the spectrum objects of $(\infty, \infty)$-categories.

Guanghui Zhao, Jintian Lin, Botao Fu, Renhong Gao

Photonic integrated Raman microlasers, particularly discrete multi-color lasers which are crucial for extending the emission wavelength range of chip-scale laser sources to much shorter wavelength, are highly in demand for various spectroscopy, microscopy analysis, and biological detection. However, integrated multi-color Raman microlasers have yet to be dem

Jiarui Yang, Songpengcheng Xia, Yifan Song, Qi Wu

Human body reconstruction with Millimeter Wave (mmWave) radar point clouds has gained significant interest due to its ability to work in adverse environments and its capacity to mitigate privacy concerns associated with traditional camera-based solutions. Despite pioneering efforts in this field, two challenges persist. Firstly, raw point clouds contain mass

Muhammad Talha, Besma Smida, Md Atiqul Islam, George C. Alexandropoulos

In this paper, we propose a multiple input multiple output (MIMO) Full-Duplex Integrated Sensing and Communication System consisting of multiple targets, a single downlink, and a single uplink user. We employed signal-to-interference plus noise ratio (SINR) as the performance metric for radar, downlink, and uplink communication. We use a communication-centri

Yujie Qin, Mustafa A. Kishk, Ahmed Elzanaty, Luca Chiaraviglio

With the development of fifth-generation (5G) networks, the number of user equipments (UE) increases dramatically. However, the potential health risks from electromagnetic fields (EMF) tend to be a public concern. Generally, EMF exposure-related analysis mainly considers the passive exposure from base stations (BSs) and active exposure that results from the

Yang Sui, Minning Zhu, Lingyi Huang, Chung-Tse Michael Wu

Radio Frequency Neural Networks (RFNNs) have demonstrated advantages in realizing intelligent applications across various domains. However, as the model size of deep neural networks rapidly increases, implementing large-scale RFNN in practice requires an extensive number of RF interferometers and consumes a substantial amount of energy. To address this chall

Chenguang Liu, Jianjun Chen, Yunfei Chen, Ryan Payton

Perception of the driving environment is critical for collision avoidance and route planning to ensure driving safety. Cooperative perception has been widely studied as an effective approach to addressing the shortcomings of single-vehicle perception. However, the practical limitations of vehicle-to-vehicle (V2V) communications have not been adequately inves

Apurba Das

The notion of Lie $H$-pseudoalgebra is a higher-dimensional analogue of Lie conformal algebras. In this paper, we classify the equivalence classes of non-abelian extensions of a Lie $H$-pseudoalgebra $L$ by another Lie $H$-pseudoalgebra $M$ in terms of the non-abelian cohomology group $H^2_{nab} (L, M)$. We also show that the group $H^2_{nab} (L, M)$ can be

Lucas Johns, Santiago Rodriguez

Neutrinos in core-collapse supernovae and neutron-star mergers are susceptible to flavor instabilities of three kinds: slow, fast, and collisional. Prior work has established mappings of the first two onto abstract mechanical systems in flavor space, respectively named the slow and fast flavor pendula. Here we introduce and analyze the flavor pendulum associ

Dajiang Suo, Vindula Jayawardana, Cathy Wu

An emerging public health application of connected and automated vehicle (CAV) technologies is to reduce response times of emergency medical service (EMS) by indirectly coordinating traffic. Therefore, in this work we study the CAV-assisted corridor clearance for EMS vehicles from a short term deployment perspective. Existing research on this topic often ove