October 2023 arXiv papers — page 157

Pankhi Kashyap, Kajal Shivgan, Sheetal Patil, Ramana Raja B

Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units (GPUs), various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying

Huang Lin

We first investigate a connected quiver consisting of all dominant maximal weights for an integrable highest weight module in affine type C. This quiver provides an efficient method to obtain all dominant maximal weights. Then, we completely determine the representation type of cyclotomic Khovanov-Lauda-Rouquier algebras of arbitrary level in affine type C,

Marcio Santetti

This paper empirically assesses predictions of Goodwin's model of cyclical growth regarding demand and distributive regimes when integrating the real and financial sectors. In addition, it evaluates how financial and employment shocks affect the labor market and monetary policy variables over six different U.S. business-cycle peaks. It identifies a parsimoni

Philippe Anjolras

In this paper, we consider the Born-Infeld system, arising as a nonlinear model of electromagnetism, and its extension introduced by Brenier [Bre04] the so-called "augmented Born-Infeld system". We show that this system enjoys a non-resonance structure and prove global existence and linear asymptotic behaviour of small (admissible) perturbations of arbitrary

Sean Yiu

In clinical trials of longitudinal continuous outcomes, reference based imputation (RBI) has commonly been applied to handle missing outcome data in settings where the estimand incorporates the effects of intercurrent events, e.g. treatment discontinuation. RBI was originally developed in the multiple imputation framework, however recently conditional mean i

Phillip Schneider, Nils Rehtanz, Kristiina Jokinen, Florian Matthes

Exploratory search is an open-ended information retrieval process that aims at discovering knowledge about a topic or domain rather than searching for a specific answer or piece of information. Conversational interfaces are particularly suitable for supporting exploratory search, allowing users to refine queries and examine search results through interactive

Zhangyin Feng, Xiaocheng Feng, Dezhi Zhao, Maojin Yang

Large language models augmented with task-relevant documents have demonstrated impressive performance on knowledge-intensive tasks. However, regarding how to obtain effective documents, the existing methods are mainly divided into two categories. One is to retrieve from an external knowledge base, and the other is to utilize large language models to generate

Brahim Lemkalli, Saad Bensallam, Muamer Kadic, Sébastien Guenneau

Acoustic metamaterials have gained popularity as promising materials for enhancing noise reduction. Here, we explore the use of metamaterials, based on Helmholtz resonators (HRs), to enhance the performance of standard clay hollow brick. By incorporating HRs in the upper and lower hollows, we transform the standard brick into metaBrick, which is essentially

Nils Hausbrandt, Oliver Bachtler, Stefan Ruzika, Luca E. Schäfer

We introduce the parametric matroid one-interdiction problem. Given a matroid, each element of its ground set is associated with a weight that depends linearly on a real parameter from a given parameter interval. The goal is to find, for each parameter value, one element that, when being removed, maximizes the weight of a minimum weight basis. The complexity

John Chong Min Tan, Mehul Motani

We attempt to solve the Abstraction and Reasoning Corpus (ARC) Challenge using Large Language Models (LLMs) as a system of multiple expert agents. Using the flexibility of LLMs to be prompted to do various novel tasks using zero-shot, few-shot, context-grounded prompting, we explore the feasibility of using LLMs to solve the ARC Challenge. We firstly convert

Theo Charalambous, Yaniv Aspis, Alessandra Russo

Symbolic rule learners generate interpretable solutions, however they require the input to be encoded symbolically. Neuro-symbolic approaches overcome this issue by mapping raw data to latent symbolic concepts using a neural network. Training the neural and symbolic components jointly is difficult, due to slow and unstable learning, hence many existing syste

Biplab Bawali, Jayashree Saha, Alokmay Datta

We present Monte Carlo simulations under constant NVT conditions on a minimal three beads coarse grained implicit solvent model of lipid molecules, with the hydrophilic head represented by one bead and the hydrophobic tail represented by two beads. We consider two lipids, one with the head and tail bead sizes equal and the other with the tail beads smaller t

Matteo Priorelli, Federico Maggiore, Antonella Maselli, Francesco Donnarumma

The way the brain selects and controls actions is still widely debated. Mainstream approaches based on Optimal Control focus on stimulus-response mappings that optimize cost functions. Ideomotor theory and cybernetics propose a different perspective: they suggest that actions are selected and controlled by activating action effects and by continuously matchi

Wang Lu, Hao Yu, Jindong Wang, Damien Teney

When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources. In addition to typical limitations such as data, computation, and communication costs, access to the models is also often limited. This paper endeavors to solve both the challenges of limited resources and personalization. i.e.

Milad Tatar Mamaghani, Xiangyun Zhou, Nan Yang, A. Lee Swindlehurst

In this paper, we propose a secure short-packet communication (SPC) system involving an unmanned aerial vehicle (UAV)-aided relay in the presence of a terrestrial passive eavesdropper. The considered system, which is applicable to various next-generation Internet-of-Things (IoT) networks, exploits a UAV as a mobile relay, facilitating the reliable and secure

Yiyong Liu, Michael Backes, Xiao Zhang

We consider availability data poisoning attacks, where an adversary aims to degrade the overall test accuracy of a machine learning model by crafting small perturbations to its training data. Existing poisoning strategies can achieve the attack goal but assume the victim to employ the same learning method as what the adversary uses to mount the attack. In th

Yushan Qian, Wei-Nan Zhang, Ting Liu

Empathetic dialogue is an indispensable part of building harmonious social relationships and contributes to the development of a helpful AI. Previous approaches are mainly based on fine small-scale language models. With the advent of ChatGPT, the application effect of large language models (LLMs) in this field has attracted great attention. This work empiric

Tesshu Hanaka, Airi Ikeyama, Hirotaka Ono

Fractional hedonic games are coalition formation games where a player's utility is determined by the average value they assign to the members of their coalition. These games are a variation of graph hedonic games, which are a class of coalition formation games that can be succinctly represented. Due to their applicability in network clustering and their rela

Clara N. Breiø, Andreas Kreisel, Mercè Roig, P. J. Hirschfeld

Spontaneous generation of time-reversal symmetry breaking in unconventional superconductors is currently a topic of considerable interest. While chiral superconducting order is often assumed to be the source of such signatures, they can sometimes also arise from nonmagnetic disorder. Here we perform a theoretical study of the impact of dislocations on the su

Gang Guo, Yong-Zhong Qian, Meng-Ru Wu

We explore the possibility that high-energy (HE) neutrinos produced from choked jets can be annihilated with low-energy (LE) neutrinos emitted from the accretion disk around a black hole in binary neutron star mergers and rare core-collapse supernovae. For HE neutrinos produced close to the stellar center ($\lesssim 10^{9}-10^{12}$ cm), we find that the emer

Ronghao Dang, Jiangyan Feng, Haodong Zhang, Chongjian Ge

We propose InstructDET, a data-centric method for referring object detection (ROD) that localizes target objects based on user instructions. While deriving from referring expressions (REC), the instructions we leverage are greatly diversified to encompass common user intentions related to object detection. For one image, we produce tremendous instructions th

Akshaj Kumar Veldanda, Fabian Grob, Shailja Thakur, Hammond Pearce

Large Language Models (LLMs) such as GPT-3.5, Bard, and Claude exhibit applicability across numerous tasks. One domain of interest is their use in algorithmic hiring, specifically in matching resumes with job categories. Yet, this introduces issues of bias on protected attributes like gender, race and maternity status. The seminal work of Bertrand & Mullaina

Artem Nenashev, Mikhail Kurenkov, Andrei Potapov, Iana Zhura

Visual localization is a critical task in mobile robotics, and researchers are continuously developing new approaches to enhance its efficiency. In this article, we propose a novel approach to improve the accuracy of visual localization using Structure from Motion (SfM) techniques. We highlight the limitations of global SfM, which suffers from high latency,

Dominik Hollidt, Clinton Wang, Polina Golland, Marc Pollefeys

We present a novel approach to perform 3D semantic segmentation solely from 2D supervision by leveraging Neural Radiance Fields (NeRFs). By extracting features along a surface point cloud, we achieve a compact representation of the scene which is sample-efficient and conducive to 3D reasoning. Learning this feature space in an unsupervised manner via masked

Mahdi Mozdoor Dashtabi, Mohammad Talebi Khoshmehr, Hamed Nikbakht, Bruno Lopez Rodriguez

Photonic integrated circuits (PICs) are enabling major breakthroughs in a number of areas, including quantum computing, neuromorphic processors, wearable devices, and more. Nevertheless, existing PIC measurement methods lack the spectral precision, speed, and sensitivity required for refining current applications and exploring new frontiers such as point-of-

Jiao-Kai Chen, Jia-Qi Xie, Xia Feng, He Song

We attempt to present an unified description of the light meson spectra and the light diquark spectra by applying the Regge trajectory approach. However, we find that the direct application of the linear Regge trajectory formula for the light mesons and baryons fails. To address this issue, we fit the experimental data of light meson spectra and the light di

Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, Linyi Yang

Large language models (LLMs) have shown the ability to produce fluent and cogent content, presenting both productivity opportunities and societal risks. To build trustworthy AI systems, it is imperative to distinguish between machine-generated and human-authored content. The leading zero-shot detector, DetectGPT, showcases commendable performance but is marr

Jiajun He, Zekun Yang, Tomoki Toda

Automatic speech recognition (ASR) systems often encounter difficulties in accurately recognizing rare words, leading to errors that can have a negative impact on downstream tasks such as keyword spotting, intent detection, and text summarization. To address this challenge, we present a novel ASR postprocessing method that focuses on improving the recognitio

Simon Yu, Jie He, Víctor Gutiérrez-Basulto, Jeff Z. Pan

Hierarchical multi-label text classification (HMTC) aims at utilizing a label hierarchy in multi-label classification. Recent approaches to HMTC deal with the problem of imposing an over-constrained premise on the output space by using contrastive learning on generated samples in a semi-supervised manner to bring text and label embeddings closer. However, th

Seokje Lee, Muhammad S. Abbas, Dongha Yoo, Keundong Lee

We report the growth of high-quality GaN epitaxial thin films on graphene-coated c-sapphire substrates using pulsed-mode metalorganic vapor-phase epitaxy, together with the fabrication of freestanding GaN films by simple mechanical exfoliation for transferable light-emitting diodes (LEDs). High-quality GaN films grown on the graphene-coated sapphire substrat

Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye

Text is ubiquitous in our visual world, conveying crucial information, such as in documents, websites, and everyday photographs. In this work, we propose UReader, a first exploration of universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM). By leveraging the shallow text recognition ability of the MLL

Peipei Li, Xing Cui, Yibo Hu, Man Zhang

Point cloud analysis faces computational system overhead, limiting its application on mobile or edge devices. Directly employing small models may result in a significant drop in performance since it is difficult for a small model to adequately capture local structure and global shape information simultaneously, which are essential clues for point cloud analy

Weihua Liu, Lin Li, Chaochao Lin, Said Boumaraf

The rapid advancement of deepfake technologies raises significant concerns about the security of face recognition systems. While existing methods leverage the clues left by deepfake techniques for face forgery detection, malicious users may intentionally manipulate forged faces to obscure the traces of deepfake clues and thereby deceive detection tools. Mean

Wanlong Liu, Shaohuan Cheng, Dingyi Zeng, Hong Qu

Document-level event argument extraction poses new challenges of long input and cross-sentence inference compared to its sentence-level counterpart. However, most prior works focus on capturing the relations between candidate arguments and the event trigger in each event, ignoring two crucial points: a) non-argument contextual clue information; b) the releva

Zi Jing Wang, Ye Zhu, Kai Ming Ting

Trajectory clustering enables the discovery of common patterns in trajectory data. Current methods of trajectory clustering rely on a distance measure between two points in order to measure the dissimilarity between two trajectories. The distance measures employed have two challenges: high computational cost and low fidelity. Independent of the distance meas

Han Miao, Huaxing Peng, Baojun Yan, Shulin Liu

The simulation of the instruments based on the cascade multiplication of electrons has always been an important and challenging subject in no matter high energy physics, astrophysics, radiography and other fields. In this work, a generic simulation toolkit is developed based on Geant4, ROOT and CADMesh toolkits for the electron multipliers based on the emiss

Yong Lu, Zhengmao Qian

In this paper, we consider the homogenization of evolutionary incompressible purely viscous non-Newtonian flows of Carreau-Yasuda type in porous media with small perforation parameter $0< \varepsilon \ll 1$, where the small holes are periodically distributed. Darcy's law is recovered in the homogenization limit. Applying Poincar\'e type inequality in porous

S. Hitarth, George Kenison, Laura Kovács, Anton Varonka

Invariants are key to formal loop verification as they capture loop properties that are valid before and after each loop iteration. Yet, generating invariants is a notorious task already for syntactically restricted classes of loops. Rather than generating invariants for given loops, in this paper we synthesise loops that exhibit a predefined behaviour given

Weihua Liu, Youyuan Xue, Chaochao Lin, Said Boumaraf

The automated generation of radiology diagnostic reports helps radiologists make timely and accurate diagnostic decisions while also enhancing clinical diagnostic efficiency. However, the significant imbalance in the distribution of data between normal and abnormal samples (including visual and textual biases) poses significant challenges for a data-driven t

Yiquan Zhou, Meng Chen, Yi Lei, Jihua Zhu

This paper presents the T02 team's system for the Singing Voice Conversion Challenge 2023 (SVCC2023). Our system entails a VITS-based SVC model, incorporating three modules: a feature extractor, a voice converter, and a post-processor. Specifically, the feature extractor provides F0 contours and extracts speaker-independent linguistic content from the input

Marjan Petreski, Jaakko Pehkonen

The objective of the paper is to understand if the minimum wage plays a role for the labor share of manufacturing workers in North Macedonia. We decompose labor share movements on those along a share-capital curve, shifts of this locus, and deviations from it. We use the capital-output ratio, total factor productivity and prices of inputs to capture these fa

Wanlong Liu, Dingyi Zeng, Li Zhou, Yichen Xiao

Document-level event argument extraction is a crucial yet challenging task within the field of information extraction. Current mainstream approaches primarily focus on the information interaction between event triggers and their arguments, facing two limitations: insufficient context interaction and the ignorance of event correlations. Here, we introduce a n

Nayoung Choi

Contextual word embeddings obtained from pre-trained language model (PLM) have proven effective for various natural language processing tasks at the word level. However, interpreting the hidden aspects within embeddings, such as syntax and semantics, remains challenging. Disentangled representation learning has emerged as a promising approach, which separate

Marjan Petreski

In this paper we simulate the poverty effect of the Covid-19 pandemic in North Macedonia and we analyze the income-saving power of three key government measures: the employment-retention scheme, the relaxed Guaranteed Minimum Income support, and one-off cash allowances. In this attempt, the counterfactual scenarios are simulated by using MK-MOD, the Macedoni

Insu Baek, Seungyun Han, Suik Cheon, Hyun-Woo Lee

Nonlinear spintronics combines nonlinear dynamics with spintronics, opening up new possibilities beyond linear responses. A recent theoretical work [Xiao et al., Phys. Rev. Lett. 130, 166302 (2023)] predicts the nonlinear generation of spin density [nonlinear spin Edelstein effect (NSEE)] in centrosymmetric metals based on symmetry analysis combined with fir

John M. Campbell

A Ramanujan-type series satisfies $$ \frac{1}{\pi} = \sum_{n=0}^{\infty} \frac{\left( \frac{1}{2} \right)_{n} \left( \frac{1}{s} \right)_{n} \left(1 - \frac{1}{s} \right)_{n} }{ \left( 1 \right)_{n}^{3} } z^{n} (a + b n), $$ where $s \in \{ 2, 3, 4, 6 \}$, and where $a$, $b$, and $z$ are real algebraic numbers. The level $3$ case whereby $s = 3$ has been con

Protopapas Eleftherios

In Mathematics is common to make a mistake and therefore a false conclusion arises. In each case it is important to recognize the mistake in order to avoid a similar one in the future. Geometric figures provide decisive help in order to have a strict mathematical proof, but also can easily lead to wrong conclusions without a mathematical proof. In this paper

Dieter Bothe, Mathis Fricke, Kohei Soga

The linear transport equation allows to advect level-set functions to represent moving sharp interfaces in multiphase flows as zero level-sets. A recent development in computational fluid dynamics is to modify the linear transport equation by introducing a nonlinear term to preserve certain geometrical features of the level-set function, where the zero level

Marjan Petreski

The objective of this paper is to estimate the expected effects of the pandemic of Covid-19 for child poverty in North Macedonia. We rely on MK-MOD Tax & Benefit Microsimulation Model for North Macedonia based on the Survey on Income and Living Conditions 2019. The simulation takes into account the development of income, as per the observed developments in t

Yixin Chen, Shuai Zhang, Boran Han, Jiaya Jia

In-context learning (ICL) involves reasoning from given contextual examples. As more modalities comes, this procedure is becoming more challenging as the interleaved input modalities convolutes the understanding process. This is exemplified by the observation that multimodal models often struggle to effectively extrapolate from contextual examples to perform

Zhong-Yu Li, Bo-Wen Yin, Yongxiang Liu, Li Liu

Incorporating heterogeneous representations from different architectures has facilitated various vision tasks, e.g., some hybrid networks combine transformers and convolutions. However, complementarity between such heterogeneous architectures has not been well exploited in self-supervised learning. Thus, we propose Heterogeneous Self-Supervised Learning (HSS

Meng Wei, Xiaoyu Yue, Wenwei Zhang, Shu Kong

Segmenting and recognizing diverse object parts is a crucial ability in applications spanning various computer vision and robotic tasks. While significant progress has been made in object-level Open-Vocabulary Semantic Segmentation (OVSS), i.e., segmenting objects with arbitrary text, the corresponding part-level research poses additional challenges. Firstly

Christoph Lamm

We determine the prime strongly positive amphicheiral knots up to 16 crossings and show that a large fraction of them admit knot diagrams with a double symmetry (rotational symmetry for strongly positive amphicheirality and an additional mirror symmetry for the ribbon property). The remaining knots are presented as `almost doubly symmetric' diagrams, defined

Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To f

Ashwani Pandey, Rumen Bachev, Bozena Czerny, Paul J. Wiita

Aims. We aim to investigate the extreme variability properties of the TeV blazar S4 0954+65 using optical photometric and polarisation observations carried out between 2017 and 2023 using three ground-based telescopes. Methods. We examined an extensive dataset comprised of 138 intraday (observing duration shorter than a day) light curves (LCs) of S4 0954+65

Xianjun Yang, Kexun Zhang, Haifeng Chen, Linda Petzold

This work proposes a training-free approach for the detection of LLMs-generated codes, mitigating the risks associated with their indiscriminate usage. To the best of our knowledge, our research is the first to investigate zero-shot detection techniques applied to code generated by advanced black-box LLMs like ChatGPT. Firstly, we find that existing training

Miroslav Popovic, Marko Popovic, Ivan Kastelan, Miodrag Djukic

At present many distributed and decentralized frameworks for federated learning algorithms are already available. However, development of such a framework targeting smart Internet of Things in edge systems is still an open challenge. A solution to that challenge named Python Testbed for Federated Learning Algorithms (PTB-FLA) appeared recently. This solution

Subhra Mudli, Subhanka Mal, Anushree Dey, Bimalendu Deb

We theoretically show that when two largely separated trapped atoms interact with a trapped ion via Rydberg excitation of the atoms, the ion-mediated interaction between the atoms exceeds the direct atom-atom interaction by several orders of magnitude. Since the motion of the atoms is much slower than the motion of the ion, we resort to Born-Oppenheimer appr

Qingyun Wan, Chi-Ming Che

To understand the photophysics of molecular aggregates, exciton model of J- and H-aggregate has been extensively utilized. However, it lacks consideration of crystal symmetry. Although discrete molecules may lack symmetry, their aggregates can exhibit a high degree of symmetry. Herein, we utilized group theory to study the optical properties of centrosymmetr

Abdulrahman Soliman, Amr Mohamed, Elias Yaacoub, Nikhil V. Navkar

Telemedicine applications have recently received substantial potential and interest, especially after the COVID-19 pandemic. Remote experience will help people get their complex surgery done or transfer knowledge to local surgeons, without the need to travel abroad. Even with breakthrough improvements in internet speeds, the delay in video streaming is still

Brahim Lemkalli, Zine El Abiddine Fellah, Sébastien Guenneau, Khalid Lamzoud

Over the last few decades, both heat and broadband sound reduction have become increasingly significant as a result of concerns about the environment and noise pollution. In order to address this challenge, we provide a finite element analysis study of an acoustic metamaterial panel consisting of a unit cell made of two Helmholtz Resonators with a guide in b

Aimee J. Ross, James Chappell, Johannes J. van de Wetering, James Cowley

We demonstrate resonant excitation of a plasma wave by a train of short laser pulses guided in a pre-formed plasma channel, for parameters relevant to a plasma-modulated plasma accelerator (P-MoPA). We show experimentally that a train of $N \approx 10$ short pulses, of total energy $\sim 1$ J, can be guided through $110$ mm long plasma channels with on-axis

Ashwani Pandey, Pankaj Kushwaha, Paul J. Wiita, Raj Prince

Transition blazars exhibit a shift from one subclass to the next during different flux states. It is therefore crucial to study them to understand the underlying physics of blazars. We probe the origin of the multi-wavelength emission from the transition blazar B2 1308+326 using 14-year-long gamma-ray light curve from Fermi and the quasi-simultaneous data fr

Tharindu Kumarage, Paras Sheth, Raha Moraffah, Joshua Garland

In recent years, there has been a rapid proliferation of AI-generated text, primarily driven by the release of powerful pre-trained language models (PLMs). To address the issue of misuse associated with AI-generated text, various high-performing detectors have been developed, including the OpenAI detector and the Stanford DetectGPT. In our study, we ask how

Federico Ghimenti, Misaki Ozawa, Giulio Biroli, Gilles Tarjus

We investigate through numerical simulations how a two-dimensional crystal yields and flows under an applied shear. We focus over a range that allows us to both address the response in the limit of an infinitesimal shear rate and describe the phase behavior of the system at a finite shear rate. In doing so, we carefully discuss the role of the topological de

Qinglun Li, Miao Zhang, Nan Yin, Quanjun Yin

To address the communication burden and privacy concerns associated with the centralized server in Federated Learning (FL), Decentralized Federated Learning (DFL) has emerged, which discards the server with a peer-to-peer (P2P) communication framework. However, most existing DFL algorithms are based on symmetric topologies, such as ring and grid topologies,

Jun Gao, Huan Zhao, Yice Zhang, Wei Wang

Information Extraction (IE) is an essential task in Natural Language Processing. Traditional methods have relied on coarse-grained extraction with simple instructions. However, with the emergence of Large Language Models (LLMs), there is a need to adapt IE techniques to leverage the capabilities of these models. This paper introduces a fine-grained IE benchm

Zechen Liu, Wei Song, Yuhan Wang

With the popularization of different kinds of smart terminals and the development of autonomous driving technology, more and more services based on spatio-temporal data have emerged in our lives, such as online taxi services, traffic flow prediction, and tracking virus propagation. However, the privacy concerns of spatio-temporal data greatly limit the use o

Philippe Da Costa, Karim Bennaceur, Jacques Meyer, Wouter Ryssens

In the framework of nuclear energy density functional (EDF) methods, many nuclear phenomena can be related to the deformation of intrinsic states. Their accurate modeling relies on the correct description of the change of nuclear binding energy with deformation. The two most important contributions to the deformation energy have their origin in shell effects

Ashwani Pandey, Bozena Czerny, Swayamtrupta Panda, Raj Prince

Context. Dust in active galactic nuclei is clearly present right outside the broad-line region (BLR) in the form of a dusty molecular torus. However, some models of the BLR predict that dust may also exist within the BLR. Aims. We study the reprocessing of radiation by the BLR with the aim of observing how the presence of dust affects the reprocessed continu

Bai Xue

This paper tackles the problem of generating safe exit controllers for continuous-time systems described by stochastic differential equations (SDEs). The primary aim is to develop controllers that maximize the lower bounds of the exit probability that the system escapes from a safe but uncomfortable set within a specified time frame and guide it towards a co

Ken Anjyo, Yutaro Kabata

In this paper, we consider the orthogonal projection of a surface in $\mathbb{R}^3$ for a given view direction. We then introduce and investigate several invariants of the families of the plane curves that locally configure the projection image of the surface. Using the invariants, we also show an extension of the d'Ocagne formula that associates a local beh

Sili Huang, Yanchao Sun, Jifeng Hu, Siyuan Guo

In visual-based Reinforcement Learning (RL), agents often struggle to generalize well to environmental variations in the state space that were not observed during training. The variations can arise in both task-irrelevant features, such as background noise, and task-relevant features, such as robot configurations, that are related to the optimal decisions. T

Longfei Fang, Huiqiu Lin

Let ${\rm ex}(n,F)$ and ${\rm spex}(n,F)$ be the maximum size and maximum spectral radius of an $F$-free graph of order $n$, respectively. The value ${\rm spex}(n,F)$ is called the spectral extremal value of $F$. Nikiforov [J. Graph Theory 62 (2009) 362--368] gave the spectral Stability Lemma, which implies that for every $\varepsilon>0$, sufficiently large

Zhihao Ge, Dandan Xu

In this paper, we propose a multiphysics finite element method for a quasi-static thermo-poroelasticity model with a nonlinear convective transport term. To design some stable numerical methods and reveal the multi-physical processes of deformation, diffusion and heat, we introduce three new variables to reformulate the original model into a fluid coupled pr

Haowei Lin, Yuntian Gu

Detecting out-of-distribution (OOD) instances is crucial for NLP models in practical applications. Although numerous OOD detection methods exist, most of them are empirical. Backed by theoretical analysis, this paper advocates for the measurement of the "OOD-ness" of a test case $\boldsymbol{x}$ through the likelihood ratio between out-distribution $\mathcal

Wei Li, Ruifeng Bian, Wenyi Zhao, Weijin Xu

Semi-supervised medical image segmentation (SSMIS) has witnessed substantial advancements by leveraging limited labeled data and abundant unlabeled data. Nevertheless, existing state-of-the-art (SOTA) methods encounter challenges in accurately predicting labels for the unlabeled data, giving rise to disruptive noise during training and susceptibility to erro

Wei Yu, Jack Hau Yung Lo, Mazen Yousef Kanj

Foam characterization is essential in many applications of foams, such as cleaning, food processing, cosmetics, and oil production, due to these applications diversified requirements. The standard characterization method, the foam column test, cannot provide sufficient information for in-depth studies. Hence, there have been many studies that incorporated di

Andrea Bevilacqua, Jerzy Kowalski-Glikman, Wojciech Wislicki

Quantum gravity phenomenology has been historically regarded as a difficult endeavour, due to the apparent scarcity of phenomena involving the required scales of length (Planck length $l_P$) and energy (Planck energy $E_P$). It was realized, however, that one can look for cumulative effects of a quantum theory of gravity at energies $E/E_P \ll 1$ if even tin

Cheng Zhang, Jianyi Cheng, Ilia Shumailov, George A. Constantinides

The inference of Large language models (LLMs) requires immense computation and memory resources. To curtail these costs, quantisation has merged as a promising solution, but existing LLM quantisation mainly focuses on 8-bit. In this work, we explore the statistical and learning properties of the LLM layer and attribute the bottleneck of LLM quantisation to n

Hemant Yadav, Erica Cooper, Junichi Yamagishi, Sunayana Sitaram

This paper introduces a novel objective function for quality mean opinion score (MOS) prediction of unseen speech synthesis systems. The proposed function measures the similarity of relative positions of predicted MOS values, in a mini-batch, rather than the actual MOS values. That is the partial rank similarity is measured (PRS) rather than the individual M

Ana L. C. Bazzan, Anderson R. Tavares, André G. Pereira, Cláudio R. Jung

The thought-provoking analogy between AI and electricity, made by computer scientist and entrepreneur Andrew Ng, summarizes the deep transformation that recent advances in Artificial Intelligence (AI) have triggered in the world. This chapter presents an overview of the ever-evolving landscape of AI, written in Portuguese. With no intent to exhaust the subje

Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian

Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting model performance. To alleviate the dilemma, we raise a fundamental question: \textit{Is it possible to shar

Yong Zhou, Yuanming Shi, Haibo Zhou, Jingjing Wang

The explosive growth of smart devices (e.g., mobile phones, vehicles, drones) with sensing, communication, and computation capabilities gives rise to an unprecedented amount of data. The generated massive data together with the rapid advancement of machine learning (ML) techniques spark a variety of intelligent applications. To distill intelligence for suppo

Zhiyuan Zhai, Xiaojun Yuan, Xin Wang

Decentralized federated learning (DFL), inherited from distributed optimization, is an emerging paradigm to leverage the explosively growing data from wireless devices in a fully distributed manner.DFL enables joint training of machine learning model under device to device (D2D) communication fashion without the coordination of a parameter server. However, t

Chengcheng Han, Xiaowei Du, Che Zhang, Yixin Lian

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, it is ineffective or even detrimental when applied to reasoning tasks in Smaller Language Models (SLMs) with less than 10 billion parameters. To address this limitation, we introduc

Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li

Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents. Previous research has demonstrated that contextual information is crucial for developing an effective ASE model. However, we observe that merely concatenating sentences in a contextual window does not fully utilize contextual information and can someti

Xusheng Zhu, Wen Chen, Qingqing Wu, Jun Li

In this paper, we investigate a practical structure of reconfigurable intelligent surface (RIS)-based double spatial scattering modulation (DSSM) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. A suboptimal detector is proposed, in which the beam direction is first demodulated according to the received beam strength, and then the

Abdullah Al-Ateqi, Mauro Fernandes Pereira

In this study, we address the limitations of previous solutions for modeling high-order harmonics in semiconductor superlattices (SSLs). Earlier research proposed a step function ansatz that effectively modeled high-order even and odd harmonics but introduced numerical noise. An upgrade using a logistic function addressed the noise problem but eliminated hig

Feng Liu, Jie Yang, Kun-peng Xu, Cang-long Pu

The influence of opinion leaders impacts different aspects of social finance. How to analyse the utility of opinion leaders' influence in realizing assets on the blockchain and adopt a compliant regulatory scheme is worth exploring and pondering. Taking Musk's call on social media to buy Dogecoin as an example, this paper uses an event study to empirically i

Ercong Nie, Helmut Schmid, Hinrich Schütze

Pretrained multilingual encoder models can directly perform zero-shot multilingual tasks or linguistic probing by reformulating the input examples into cloze-style prompts. This is accomplished by predicting the probabilities of the label words at the masked token position, without requiring any updates to the model parameters. However, the performance of th

Haru Kanno, Takahiro Okabe, Erika Ushikoshi

We consider the Helmholtz-Weyl decomposition on a time dependent bounded domain $\Omega(t)$ in $\mathbb{R}^3$. Especially, we investigate the domain dependence of each component in the decomposition, namely, the harmonic vector fields (i.e., $\mathrm{div}$ and $\mathrm{rot}$ free vectors), vector potentials, and scalar potentials equipped with suitable bound

Jiaqi Luo, Yuedong Quan, Shixin Xu

Dealing with label noise in tabular classification tasks poses a persistent challenge in machine learning. While robust boosting methods have shown promise in binary classification, their effectiveness in complex, multi-class scenarios is often limited. Additionally, issues like imbalanced datasets, missing values, and computational inefficiencies further co

Chaoxu Pang, Yixuan Cao, Qiang Ding, Ping Luo

Large language models (LLMs) can perform a new task by merely conditioning on task instructions and a few input-output examples, without optimizing any parameters. This is called In-Context Learning (ICL). In-context Information Extraction (IE) has recently garnered attention in the research community. However, the performance of In-context IE generally lags

Haichen Fan, Jun-ichi Nakashima, D. Engels, Yong Zhang

Water fountains (WFs) are thought to be objects in the morphological evolution of the circumstellar envelopes of low- and intermediate-mass evolved stars, transitioning from spherically symmetric to asymmetric shapes. We used databases of circumstellar 1612 MHz OH and 22.235 GHz H$_2$O maser sources to search for new WF candidates using the criterion of a la

Amit Moryossef

This demo paper presents sign.mt, an open-source application pioneering real-time multilingual bi-directional translation between spoken and signed languages. Harnessing state-of-the-art open-source models, this tool aims to address the communication divide between the hearing and the deaf, facilitating seamless translation in both spoken-to-signed and signe

Gerald Woo, Chenghao Liu, Akshat Kumar, Doyen Sahoo

Time series has been left behind in the era of pre-training and transfer learning. While research in the fields of natural language processing and computer vision are enjoying progressively larger datasets to train massive models, the most popular time series datasets consist of only tens of thousands of time steps, limiting our ability to study the effectiv

Bo Yue, Shibei Xue, Yu Pan, Min Jiang

With the rapid advancement of quantum computing, Quantum Approximate Optimization Algorithm (QAOA) is considered as a promising candidate to demonstrate quantum supremacy, which exponentially solves a class of Quadratic Unconstrained Binary Optimization (QUBO) problems. However, limited qubit availability and restricted coherence time challenge QAOA to solve

Jiahao Hu

For a compact smooth manifold with corners (or finite CW-complex) $X$, we can prescribe a finite set of spin or spin$^h$ manifolds (possibly with boundary) mapping into it so that every real vector bundle over $X$ is determined, up to stable equivalence, by the Dirac indices of the real vector bundle when pulled-back onto those prescribed spin or spin$^h$ ma

Tingkai Liu, Yunzhe Tao, Haogeng Liu, Qihang Fan

We present a novel human annotated dataset for evaluating the ability for visual-language models to generate both short and long descriptions for real-world video clips, termed DeVAn (Dense Video Annotation). The dataset contains 8.5K YouTube video clips of 20-60 seconds in duration and covers a wide range of topics and interests. Each video clip is independ