May 2023 arXiv papers — page 76

Abdulnasser Hatemi-J, Alan Mustafa

This paper introduces a software component created in Visual Basic for Applications (VBA) that can be applied for creating an optimal portfolio using two different methods. The first method is the seminal approach of Markowitz that is based on finding budget shares via the minimization of the variance of the underlying portfolio. The second method is develop

Kira Maag, Asja Fischer

State-of-the-art deep neural networks have proven to be highly powerful in a broad range of tasks, including semantic image segmentation. However, these networks are vulnerable against adversarial attacks, i.e., non-perceptible perturbations added to the input image causing incorrect predictions, which is hazardous in safety-critical applications like automa

Mengxi Liu, Bo Zhou, Zimin Zhao, Hyeonseok Hong

In this work, we propose an open-source scalable end-to-end RTL framework FieldHAR, for complex human activity recognition (HAR) from heterogeneous sensors using artificial neural networks (ANN) optimized for FPGA or ASIC integration. FieldHAR aims to address the lack of apparatus to transform complex HAR methodologies often limited to offline evaluation to

Stéphane Vujasinović, Sebastian Bullinger, Stefan Becker, Norbert Scherer-Negenborn

We present READMem (Robust Embedding Association for a Diverse Memory), a modular framework for semi-automatic video object segmentation (sVOS) methods designed to handle unconstrained videos. Contemporary sVOS works typically aggregate video frames in an ever-expanding memory, demanding high hardware resources for long-term applications. To mitigate memory

Megha Chakraborty, Khushbu Pahwa, Anku Rani, Shreyas Chatterjee

Combating disinformation is one of the burning societal crises -- about 67% of the American population believes that disinformation produces a lot of uncertainty, and 10% of them knowingly propagate disinformation. Evidence shows that disinformation can manipulate democratic processes and public opinion, causing disruption in the share market, panic and anxi

Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen, K. Joost Batenburg

Background: X-ray imaging is widely used for the non-destructive detection of defects in industrial products on a conveyor belt. In-line detection requires highly accurate, robust, and fast algorithms. Deep Convolutional Neural Networks (DCNNs) satisfy these requirements when a large amount of labeled data is available. To overcome the challenge of collectin

Sihong Chen, Taisong Pan, Zhengcheng Mou, Mingde Du

The sensitivity to deformation plays a key role in determining the applicability of stretchable metamaterials (MMs) to be used for conformal integration or mechanical reconfiguration. Typically, different unit designs are required to achieve the desired sensitivity, but this article proposes a block definition design for stretchable MMs that enables regulati

Minho Heo, Youngwoon Lee, Doohyun Lee, Joseph J. Lim

Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP) have demonstrated impressive performance across various robotic manipulation tasks. However, these approaches have been limited to learning simple behaviors in current real-world manipulation benchmarks, such as pushing or pick-and-place. To enable more complex, long-ho

Vaishali Pal, Andrew Yates, Evangelos Kanoulas, Maarten de Rijke

Recent advances in tabular question answering (QA) with large language models are constrained in their coverage and only answer questions over a single table. However, real-world queries are complex in nature, often over multiple tables in a relational database or web page. Single table questions do not involve common table operations such as set operations,

Leandros Perivolaropoulos

We use the hemisphere comparison method to test the isotropy of the SnIa absolute magnitudes of the Pantheon+ and SH0ES samples in various redshift/distance bins. We compare the identified levels of anisotropy in each bin with Monte-Carlo simulations of corresponding isotropised data to estimate the frequency of such levels of anisotropy in the context of an

Tamás Gábor Csapó, Frigyes Viktor Arthur, Péter Nagy, Ádám Boncz

Previous initial research has already been carried out to propose speech-based BCI using brain signals (e.g. non-invasive EEG and invasive sEEG / ECoG), but there is a lack of combined methods that investigate non-invasive brain, articulation, and speech signals together and analyze the cognitive processes in the brain, the kinematics of the articulatory mov

Yihong Liu, Haotian Ye, Leonie Weissweiler, Renhao Pei

In comparative linguistics, colexification refers to the phenomenon of a lexical form conveying two or more distinct meanings. Existing work on colexification patterns relies on annotated word lists, limiting scalability and usefulness in NLP. In contrast, we identify colexification patterns of more than 2,000 concepts across 1,335 languages directly from an

Reyna Quita, Yu-Shuo Chen, Hsin-Yi Lee Alex C. Hu, John M. Hong

In this paper, a modified version of conservative Physics-informed Neural Networks (cPINN for short) is provided to construct the weak solutions of Riemann problem for the hyperbolic scalar conservation laws in non-conservative form. To demonstrate the results, we use the model of generalized Buckley-Leverett equation (GBL equation for short) with discontinu

Xiao Wang, Weikang Zhou, Qi Zhang, Jie Zhou

Pretrained language models have achieved remarkable success in various natural language processing tasks. However, pretraining has recently shifted toward larger models and larger data, and this has resulted in significant computational and energy costs. In this paper, we propose Influence Subset Selection (ISS) for language model, which explicitly utilizes

Ashish Sharma, Sudha Rao, Chris Brockett, Akanksha Malhotra

Agency, the capacity to proactively shape events, is central to how humans interact and collaborate. While LLMs are being developed to simulate human behavior and serve as human-like agents, little attention has been given to the Agency that these models should possess in order to proactively manage the direction of interaction and collaboration. In this pap

Jürgen Reuter

Muon colliders offer the possibility to go to very high energies with relatively small circular colliders, energies up to 10 or 14 TeV are envisioned. Due to their very clean collider environment they provide a fantastic tool to search for new physics in the electroweak sector, especially through the production of multiple EW vector and Higgs bosons, and the

Matteo Tacchi, Yingzhao Lian, Colin Jones

While stability analysis is a mainstay for control science, especially computing regions of attraction of equilibrium points, until recently most stability analysis tools always required explicit knowledge of the model or a high-fidelity simulator representing the system at hand. In this work, a new data-driven Lyapunov analysis framework is proposed. Withou

Koya Murakami, Indira Ocampo, Savvas Nesseris, Atsushi J. Nishizawa

The growth-rate $f\sigma_8(z)$ of the large-scale structure of the Universe is an important dynamic probe of gravity that can be used to test for deviations from General Relativity. However, for galaxy surveys to extract this key quantity from cosmological observations, two important assumptions have to be made: i) a fiducial cosmological model, typically ta

Lars Schmarje, Vasco Grossmann, Tim Michels, Jakob Nazarenus

High-quality data is crucial for the success of machine learning, but labeling large datasets is often a time-consuming and costly process. While semi-supervised learning can help mitigate the need for labeled data, label quality remains an open issue due to ambiguity and disagreement among annotators. Thus, we use proposal-guided annotations as one option w

Wanda Kellouai, Jean-Louis Barrat, Patrick Judeinstein, Marie Plazanet

Beyond well-documented confinement and surface effects arising from the large internal surface and severely confining porosity of nanoporous hosts, the transport of nanoconfined fluids remains puzzling by many aspects. With striking examples such as memory, \textit{i.e.} non-viscous, effects, intermittent dynamics and surface barriers, the dynamics of fluids

Jinghan Yang, Linjie Xu, Lequan Yu

When facing an unsatisfactory prediction from a machine learning model, users can be interested in investigating the underlying reasons and exploring the potential for reversing the outcome. We ask: To flip the prediction on a test point $x_t$, how to identify the smallest training subset $\mathcal{S}_t$ that we need to relabel? We propose an efficient algor

E. Garutti, H. Janssen, D. Kreikemeyer-Lorenzo, C. Krieger

We report on the qualification of a piezo-based linear stage for the manipulation of positions of dielectric discs in the booster of the MADMAX axion dark matter search experiment. A first demonstrator of the piezo drives, specifically developed for MADMAX, was tested at room temperature as well as at cryogenic temperatures down to 4.5 K and inside strong ma

Peng Li, Bo Liu

Scheduling problems are often tackled independently, and rarely solved by leveraging the commonalities across problems. Lack of awareness of this inter-task similarity could impede the search efficacy. A quantifiable relationship between scheduling problems is to-date rather unclear, how to leverage it in combinatorial optimization remains largely unknown, a

Belle Collaboration, L. K. Li, A. J. Schwartz, E. Won

We search for $C\!P$ violation using $T$-odd correlations in five $D_{(s)}^{+}$ and $D_{(s)}^{-}$ four-body decays. Our analysis is based on 980 $\rm fb^{-1}$ of data collected by the Belle detector at the KEKB energy-asymmetric $e^+e^-$ collider. Our results for the $T$-odd $C\!P$-violating parameter $a^{T\text{-odd}}_{C\!P}$ are: $a^{T\text{-odd}}_{C\!P}({

Xiaotong Zhao, Mian Li, Bo Wang, Enbin Song

Recently, the decentralized baseband processing (DBP) paradigm and relevant detection methods have been proposed to enable extremely large-scale massive multiple-input multiple-output technology. Under the DBP architecture, base station antennas are divided into several independent clusters, each connected to a local computing fabric. However, current detect

Haibin Wu, Jiawen Kang, Lingwei Meng, Helen Meng

Automatic speaker verification (ASV) plays a critical role in security-sensitive environments. Regrettably, the reliability of ASV has been undermined by the emergence of spoofing attacks, such as replay and synthetic speech, as well as adversarial attacks and the relatively new partially fake speech. While there are several review papers that cover replay a

Attila Joó

Aharoni and Ziv conjectured that if $ M $ and $ N $ are finitary matroids on $ E $, then a certain ``Hall-like'' condition is sufficient to guarantee the existence of an $ M $-independent spanning set of $ N $. We show that their condition ensures that every finite subset of $ E $ is $ N $-spanned by an $ M $-independent set.

Na Li, Zied Bouraoui, Steven Schockaert

Ultra-fine entity typing (UFET) is the task of inferring the semantic types, from a large set of fine-grained candidates, that apply to a given entity mention. This task is especially challenging because we only have a small number of training examples for many of the types, even with distant supervision strategies. State-of-the-art models, therefore, have t

Oliver Lorscheid, Samarpita Ray

In this paper we introduce congruence spaces, which are topological spaces that are canonically attached to monoid schemes and that reflect closed topological properties. This leads to satisfactory topological characterizations of closed morphisms and closed immersions as well as separated and proper morphisms. We study congruence spaces thoroughly and exten

Yachun Li, Jingjing Wang, Yuhui Chen, Di Xie

Iris presentation attack detection (PAD) has achieved great success under intra-domain settings but easily degrades on unseen domains. Conventional domain generalization methods mitigate the gap by learning domain-invariant features. However, they ignore the discriminative information in the domain-specific features. Moreover, we usually face a more realisti

Qifan Yu, Juncheng Li, Wentao Ye, Siliang Tang

Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. In parallel, the problem of data scarcity has brought a growing interest in employing AIGC technology for high-quality data expansion. However, this paradigm requires well-designed prompt engineering that cost-less data expansion and labeli

Chi Han, Jialiang Xu, Manling Li, Yi Fung

Language models (LMs) automatically learn word embeddings during pre-training on language corpora. Although word embeddings are usually interpreted as feature vectors for individual words, their roles in language model generation remain underexplored. In this work, we theoretically and empirically revisit output word embeddings and find that their linear tra

Seyed Reza Seyednejad, Saeedeh Shoarinejad, Mohammad Reza Mozaffari, Faezeh Amini Joneghani

The present study investigates the arrangement of hollow pyramidal cone shells and their interactions with degenerate planar anchoring on the inner and outer surfaces of particles within the nematic host. The shell thickness is in order of the nematic coherence length. The numerical behavior of colloids is determined by minimizing the Landau-de Gennes free e

Nan Li, Mehdi Bennis, Alexandros Iosifidis, Qi Zhang

This paper studies the computational offloading of video action recognition in edge computing. To achieve effective semantic information extraction and compression, following semantic communication we propose a novel spatiotemporal attention-based autoencoder (STAE) architecture, including a frame attention module and a spatial attention module, to evaluate

Zirui Xu, Xiaofeng Lin, Vasileios Tzoumas

We study the problem of multi-agent coordination in unpredictable and partially observable environments, that is, environments whose future evolution is unknown a priori and that can only be partially observed. We are motivated by the future of autonomy that involves multiple robots coordinating actions in dynamic, unstructured, and partially observable envi

Hadi Ghasemi, Tayebe Lal Shateri

In the present paper, we examine the perturbation of continuous frames and Riesz-type frames in Hilbert $C^*$-modules. We extend the Casazza-Christensen general perturbation theorem for Hilbert space frames to continuous frames in Hilbert $C^*$-modules. We obtain a necessary condition under which the perturbation of a Riesz-type frame of Hilbert $C^*$-module

Jianfeng He, Julian Salazar, Kaisheng Yao, Haoqi Li

End-to-end (E2E) spoken language understanding (SLU) is constrained by the cost of collecting speech-semantics pairs, especially when label domains change. Hence, we explore \textit{zero-shot} E2E SLU, which learns E2E SLU without speech-semantics pairs, instead using only speech-text and text-semantics pairs. Previous work achieved zero-shot by pseudolabeli

Mayeul Arminjon

According to a scalar theory of gravity with a preferred frame, electromagnetism in the presence of a gravitational field implies that there is an additional energy tensor, which might contribute to dark matter. The expression of this tensor is determined by a mere scalar $p$, that depends on the EM field and (for a weak field) on the Newtonian gravitational

Zhilei Hu, Zixuan Li, Xiaolong Jin, Long Bai

Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually expressed by implicit associations between events. Existing methods usually capture such associations by directly modeling the texts with pre-trained language models, which underest

Yutaro Ono, Ryohei Tsuruta, Tomohiro Nobeyama, Kazuki Matsui

Four-nitrogen-containing 5,6,13,14-Tetraazapentacene (BTANC) has attracted attention as a new n-type organic semiconductor with a rigid crystalline phase due to intermolecular CH-N hydrogen bonding. However, in the thin film transistor of BTANC, poor carrier transport properties and low stability in the ambient condition have been reported so far; thus furth

Mykhailo Osypchuk

In the paper, the transition probability density of isotropic $\alpha$-stable stochastic process in a finite dimensional Euclidean space is considered. The results of applying pseudo differential operators with respect spatial variables to this function are estimated from the both side: above and below. Operators in the consideration are defined by the symbo

Yuqian Zhang, Abhishek Chakrabortty, Jelena Bradic

In modern large-scale observational studies, data collection constraints often result in partially labeled datasets, posing challenges for reliable causal inference, especially due to potential labeling bias and relatively small size of the labeled data. This paper introduces a decaying missing-at-random (decaying MAR) framework and associated approaches for

Pengcheng Jiang, Cao Xiao, Adam Cross, Jimeng Sun

Clinical predictive models often rely on patients' electronic health records (EHR), but integrating medical knowledge to enhance predictions and decision-making is challenging. This is because personalized predictions require personalized knowledge graphs (KGs), which are difficult to generate from patient EHR data. To address this, we propose \textsc{GraphC

Sohei Horibe, Hiroki Shimizu, Koujiro Hoshi, Takahiko Makiuchi

Parametric oscillation occurs when the resonance frequency of an oscillator is periodically modulated. Owing to time-reversal symmetry breaking in magnets, nonreciprocal magnons can be parametrically excited when spatial-inversion symmetry breaking is provided. This means that magnons with opposite propagation directions have different amplitudes. Here we de

Wen Lai, Alexandra Chronopoulou, Alexander Fraser

Despite advances in multilingual neural machine translation (MNMT), we argue that there are still two major challenges in this area: data imbalance and representation degeneration. The data imbalance problem refers to the imbalance in the amount of parallel corpora for all language pairs, especially for long-tail languages (i.e., very low-resource languages)

Hanxing Ding, Liang Pang, Zihao Wei, Huawei Shen

Multi-aspect controllable text generation aims to generate fluent sentences that possess multiple desired attributes simultaneously. Traditional methods either combine many operators in the decoding stage, often with costly iteration or search in the discrete text space, or train separate controllers for each aspect, resulting in a degeneration of text quali

Hritvik Taneja, Jason Kim, Jie Jeff Xu, Stephan van Schaik

The drive to create thinner, lighter, and more energy efficient devices has resulted in modern SoCs being forced to balance a delicate tradeoff between power consumption, heat dissipation, and execution speed (i.e., frequency). While beneficial, these DVFS mechanisms have also resulted in software-visible hybrid side-channels, which use software to probe ana

Prabhat Santi, Kamakhya Mishra, Sibabrata Mohanty

Although it will be a while before a practical quantum computer is available, there is no need to hold off. Methods and algorithms are being developed to demonstrate the feasibility of running machine learning (ML) pipelines in QC (Quantum Computing). There is a lot of ongoing work on general QML (Quantum Machine Learning) algorithms and applications. Howeve

Liang Chen, Hongru Wang, Yang Deng, Wai-Chung Kwan

Generating persona consistent dialogue response is important for developing an intelligent conversational agent. Recent works typically fine-tune large-scale pre-trained models on this task by concatenating persona texts and dialogue history as a single input sequence to generate the target response. While simple and effective, our analysis shows that this p

David Maltese, Chokri Ogabi

In this article, we study some anisotropic singular perturbations for a class of linear elliptic problems. A uniform estimates for conforming $Q_1$ finite element method are derived, and some other results of convergence and regularity for the continuous problem are proved.

Tejas Kalelkar, Ramya Nair

Two-sided incompressible surfaces in Seifert fiber spaces with isolated singular fibers are well-understood. Frohman and Rannard have shown that one-sided incompressible surfaces in Seifert fiber spaces which have isolated singular fibers are either pseudo-horizontal or psuedo-vertical. We extend their result to characterise essential surfaces in Seifert fib

Gregor Rauw, Yaël Nazé, Eric Gosset

The optical spectrum of WR 138 exhibits emission lines typical of a WN6o star and absorption lines from a rapidly-rotating OB star. Using a large set of spectroscopic data, we establish a new orbital solution of the WN6o star based on the radial velocities of highly-ionized nitrogen lines. We show that the WN6o star moves on a 4.3 yr orbit with a comparative

Baihua Shi, Yang Wang, Danqi Li, Wenlong Cai

Different with conventional reconfigurable intelligent surface (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) can reflect and transmit the signals to the receiver. In this paper, to serve more ground users and increase the deployment flexibility, we investigate an unmanned aerial vehicle equipped with a STAR-RIS (STAR-RIS-UAV) aided wireless

Polina Tsvilodub, Michael Franke

Evaluating grounded neural language model performance with respect to pragmatic qualities like the trade off between truthfulness, contrastivity and overinformativity of generated utterances remains a challenge in absence of data collected from humans. To enable such evaluation, we present a novel open source image-text dataset "Annotated 3D Shapes" (A3DS) c

Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang, Hao-En Li

Neural-network quantum states (NQS) employ artificial neural networks to encode many-body wave functions in second quantization through variational Monte Carlo (VMC). They have recently been applied to accurately describe electronic wave functions of molecules and have shown the challenges in efficiency comparing with traditional quantum chemistry methods. H

Marco Braun, Alessandro Cennamo, Markus Schoeler, Kevin Kollek

For autonomous driving, radar sensors provide superior reliability regardless of weather conditions as well as a significantly high detection range. State-of-the-art algorithms for environment perception based on radar scans build up on deep neural network architectures that can be costly in terms of memory and computation. By processing radar scans as point

Shoutao Guo, Shaolei Zhang, Yang Feng

Simultaneous machine translation (SiMT) starts to output translation while reading the source sentence and needs a precise policy to decide when to output the generated translation. Therefore, the policy determines the number of source tokens read during the translation of each target token. However, it is difficult to learn a precise translation policy to a

M. Malinowski, D. T. C. Allcock, C. J. Ballance

One of the most formidable challenges of scaling up quantum computers is that of control signal delivery. Today's small-scale quantum computers typically connect each qubit to one or more separate external signal sources. This approach is not scalable due to the I/O limitations of the qubit chip, necessitating the integration of control electronics. However,

Victor Falgas-Ravry, Klas Markström, Eero Räty

Let $\mathbf{G}:=(G_1, G_2, G_3)$ be a triple of graphs on a common vertex set $V$ of size $n$. A rainbow triangle in $\mathbf{G}$ is a triple of edges $(e_1, e_2, e_3)$ with $e_i\in G_i$ for each $i$ and $\{e_1, e_2, e_3\}$ forming a triangle in $V$. In this paper we consider the following question: what triples of minimum degree conditions $(\delta(G_1), \

Sang-Shin Baak, Satadal Datta, Uwe R. Fischer

In an effort to invariantly characterize the conformal curvature structure of analogue spacetimes built from a nonrelativistic fluid background, we determine the Petrov type of a variety of laboratory geometries. Starting from the simplest examples, we increase the complexity of the background, and thereby determine how the laboratory fluid symmetry affects

Kun Li, Fan Zhang, Wei Guo

Malware detection models based on deep learning have been widely used, but recent research shows that deep learning models are vulnerable to adversarial attacks. Adversarial attacks are to deceive the deep learning model by generating adversarial samples. When adversarial attacks are performed on the malware detection model, the attacker will generate advers

Keqin Feng, Lingfei Jin, Chaoping Xing, Chen Yuan

A pure quantum state of $n$ parties associated with the Hilbert space $\CC^{d_1}\otimes \CC^{d_2}\otimes\cdots\otimes \CC^{d_n}$ is called $k$-uniform if all the reductions to $k$-parties are maximally mixed. The $n$ partite system is called homogenous if the local dimension $d_1=d_2=\cdots=d_n$, while it is called heterogeneous if the local dimension are no

Jae-woong Lee, Seongmin Park, Mincheol Yoon, Jongwuk Lee

Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield recommendation lists with popularity bias. Previous studies have utilized inverse propensity weighting (IPW) or causal inference to mitigate this problem. However, they solely employ pointwise or pairwise loss functions and negle

Yunlong Liang, Fandong Meng, Jiaan Wang, Jinan Xu

Many-to-many multimodal summarization (M$^3$S) task aims to generate summaries in any language with document inputs in any language and the corresponding image sequence, which essentially comprises multimodal monolingual summarization (MMS) and multimodal cross-lingual summarization (MXLS) tasks. Although much work has been devoted to either MMS or MXLS and

Chi Han, Ziqi Wang, Han Zhao, Heng Ji

Large language models (LLMs) have initiated a paradigm shift in transfer learning. In contrast to the classic pretraining-then-finetuning procedure, in order to use LLMs for downstream prediction tasks, one only needs to provide a few demonstrations, known as in-context examples, without adding more or updating existing model parameters. This in-context lear

Adam Bednorz

Single particle detection is described in a limited way by simple models of measurements in quantum field theory. We show that a general approach, using Kraus operators in spacetime constructed from natural combinations of fields, leads to an efficient model of a single particle detector. The model is free from any auxiliary objects as it is defined solely w

Qing-Hong Cao, Kun Cheng, Changlong Xu

We extend the framework of analyzing the 2HDM in its orbit space to study the one-loop effective potential before and after electroweak symmetry breaking. In this framework, we present a comprehensive analysis of global symmetries of the one-loop thermal effective potential in the 2HDM, demonstrating when the global symmetries of the tree-level 2HDM potentia

Yiting Chen, Tracy Xiao Liu, You Shan, Songfa Zhong

As large language models (LLMs) like GPT become increasingly prevalent, it is essential that we assess their capabilities beyond language processing. This paper examines the economic rationality of GPT by instructing it to make budgetary decisions in four domains: risk, time, social, and food preferences. We measure economic rationality by assessing the cons

Xiangyue Cui, Hejin Yan, Xuefei Yan, Kun Zhou

As a prototype of the Weyl superconductor, layered molybdenum telluride (MoTe2) encompasses two semimetallic phases (1T_prime and Td) which differentiate from each other via a slight tilting of the out-of-plane lattice. Both phases are subjected to serious phase mixing which complicates the analysis of its origin of superconductivity. Herein, we explore the

Shuang Li, Xuming Hu, Aiwei Liu, Yawen Yang

Cross-lingual natural language inference is a fundamental problem in cross-lingual language understanding. Many recent works have used prompt learning to address the lack of annotated parallel corpora in XNLI. However, these methods adopt discrete prompting by simply translating the templates to the target language and need external expert knowledge to desig

Nourhan Hesham, Anas Chaaban, Hesham ElSawy, Jahangir Hossain

Some emerging 5G and beyond use-cases impose stringent latency constraints, which necessitates a paradigm shift towards finite blocklength performance analysis. In contrast to Shannon capacity-achieving codes, the codeword length in the finite blocklength regime (FBR) is a critical design parameter that imposes an intricate tradeoff between delay, reliabilit

Hao Wang, Hirofumi Shimizu, Daisuke Kawahara

Recent studies in natural language processing (NLP) have focused on modern languages and achieved state-of-the-art results in many tasks. Meanwhile, little attention has been paid to ancient texts and related tasks. Classical Chinese first came to Japan approximately 2,000 years ago. It was gradually adapted to a Japanese form called Kanbun-Kundoku (Kanbun)

Fritz Colonius, Alexandre J. Santana

Affine flows on vector bundles with chain transitive base flow are lifted to linear flows and the decomposition into exponentially separated subbundles provided by Selgrade's theorem is determined. The results are illustrated by an application to affine control systems with bounded control range.

Seraphina Goldfarb-Tarrant, Eddie Ungless, Esma Balkir, Su Lin Blodgett

Bias research in NLP seeks to analyse models for social biases, thus helping NLP practitioners uncover, measure, and mitigate social harms. We analyse the body of work that uses prompts and templates to assess bias in language models. We draw on a measurement modelling framework to create a taxonomy of attributes that capture what a bias test aims to measure

Mishal Assif P K, William Kennedy, Iraj Saniee

In this paper, we address the problem of fair sharing of the total value of a crowd-sourced network system between major participants (founders) and minor participants (crowd) using cooperative game theory. Shapley allocation is regarded as a fair way for computing the shares of all participants in a cooperative game when the values of all possible coalition

J. Li, Z. Duan, S. Li, X. Yu

In this paper,an Enhanced Self-Attention (ESA) mechanism has been put forward for robust feature extraction.The proposed ESA is integrated with the recursive gated convolution and self-attention mechanism.In particular, the former is used to capture multi-order feature interaction and the latter is for global feature extraction.In addition, the location of i

Satoshi Tsuchimi

In this paper, we give fundamental solutions of some $q$-difference equations satisfied by the universal mock theta functions and the higher level Appell functions. As an application, we provide an alternative proof of the representation formulas of the universal mock theta functions and the higher level Appell functions using Zwegers' $\mu$-function.

Xingxian Liu, Yajing Xu

Query-focused meeting summarization(QFMS) aims to generate a specific summary for the given query according to the meeting transcripts. Due to the conflict between long meetings and limited input size, previous works mainly adopt extract-then-summarize methods, which use extractors to simulate binary labels or ROUGE scores to extract utterances related to th

Shouyong Jiang, Yong Wang, Yaru Hu, Qingyang Zhang

Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments. Such problems pose various challenges to evolutionary algorithms, which have popularly been used to solve complex optimisation problems, due to their dynamic nature and resource restrictions in changing environments.

Matteo Biagiola, Paolo Tonella

Deep Reinforcement Learning (DRL) has received a lot of attention from the research community in recent years. As the technology moves away from game playing to practical contexts, such as autonomous vehicles and robotics, it is crucial to evaluate the quality of DRL agents. In this paper, we propose a search-based approach to test such agents. Our approach,

Ajay Dev, Simon P. Driver, Martin Meyer, Sambit Roychowdhury

We determine the atomic hydrogen (HI) to halo mass relation (HIHM) using Arecibo Legacy Fast ALFA survey HI data at the location of optically selected groups from the Galaxy and Mass Assembly (GAMA) survey. We make direct HI detections for 37 GAMA groups. Using HI group spectral stacking of 345 groups, we study the group HI content as function of halo mass a

Zihan Wang, Tianle Wang, Dheeraj Mekala, Jingbo Shang

Etremely Weakly Supervised Text Classification (XWS-TC) refers to text classification based on minimal high-level human guidance, such as a few label-indicative seed words or classification instructions. There are two mainstream approaches for XWS-TC, however, never being rigorously compared: (1) training classifiers based on pseudo-labels generated by (soft

Pavel Exner

We consider Schr\"odinger operators in $L^2(\mathrm{R}^\nu),\, \nu=2,3$, with the interaction in the form on an array of potential wells, each on them having rotational symmetry, arranged along a curve $\Gamma$. We prove that if $\Gamma$ is a bend or deformation of a line, being straight outside a compact, and the wells have the same arcwise distances, such

Wanlun Ma, Yiliao Song, Minhui Xue, Sheng Wen

AI-powered programming language generation (PLG) models have gained increasing attention due to their ability to generate source code of programs in a few seconds with a plain program description. Despite their remarkable performance, many concerns are raised over the potential risks of their development and deployment, such as legal issues of copyright infr

Xiaofan Wang, Li Zeng, Weiqing Zhang, Xueming Yang

External seeded free-electron lasers (FELs) are compelling tools for generating fully coherent EUV and soft X-ray radiations. Echo-enabled harmonic generation (EEHG), the most typical representative of external seeded FELs, has witnessed a remarkable growth of fully coherent FELs in the last decade, continuously evolving towards higher harmonic conversions a

Yan-Li Zhou, Xiao-Die Yu, Chun-Wang Wu, Xie-Qian Li

We investigate speeding up of relaxation of Markovian open quantum systems with the Liouvillian exceptional point (LEP), where the slowest decay mode degenerate with a faster decay mode. The degeneracy significantly increases the gap of the Liouvillian operator, which determines the timescale of such systems in converging to stationarity, and hence accelerat

Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu

Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning. In this paper, we present Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that decomposes complex claims into simpler sub-tasks that can be solved using a shared library of specialized functions. We first leve

Pengxin Zeng, Mouxing Yang, Yiding Lu, Changqing Zhang

Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approaches heavily rely on paired samples to realign or impute defective ones, but such preconditions cannot always be satisfied i

Daniel Alpay, Antonino De Martino, Kamal Diki, Mihaela Vajiac

In this paper we extend the concept of tensor product to the bicomplex case and use it to prove the bicomplex counterpart of the classical Choi theorem in the theory of complex matrices and operators. The concept of hyperbolic tensor product is also discussed, and we link these results to the theory of quantum channels in the bicomplex and hyperbolic case, a

Debarpan Bhattacharya, Neeraj Kumar Sharma, Debottam Dutta, Srikanth Raj Chetupalli

This paper presents the Coswara dataset, a dataset containing diverse set of respiratory sounds and rich meta-data, recorded between April-2020 and February-2022 from 2635 individuals (1819 SARS-CoV-2 negative, 674 positive, and 142 recovered subjects). The respiratory sounds contained nine sound categories associated with variants of breathing, cough and sp

Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan

Previous studies have shown that large language models (LLMs) like GPTs store massive factual knowledge in their parameters. However, the stored knowledge could be false or out-dated. Traditional knowledge editing methods refine LLMs via fine-tuning on texts containing specific knowledge. However, with the increasing scales of LLMs, these gradient-based appr

Aman Saggu, Lennart Ante

The introduction of OpenAI's large language model, ChatGPT, catalyzed investor attention towards artificial intelligence (AI) technologies, including AI-related crypto assets not directly related to ChatGPT. Utilizing the synthetic difference-in-difference methodology, we identify significant 'ChatGPT effects' with returns of AI-related crypto assets experie

Chi Han, Qizheng He, Charles Yu, Xinya Du

Probabilistic logical rule learning has shown great strength in logical rule mining and knowledge graph completion. It learns logical rules to predict missing edges by reasoning on existing edges in the knowledge graph. However, previous efforts have largely been limited to only modeling chain-like Horn clauses such as $R_1(x,z)\land R_2(z,y)\Rightarrow H(x,

Zhuang Li, Lizhen Qu, Philip R. Cohen, Raj V. Tumuluri

Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem. Prior works propose to utilize the translations by either humans or machines to alleviate such issues. However, human translations are expensive, while machine translatio

Ali Kazemi Arani, Triet Huynh Minh Le, Mansooreh Zahedi, Muhammad Ali Babar

Background: Machine Learning (ML) methods are being increasingly used for automating different activities, e.g., Test Case Prioritization (TCP), of Continuous Integration (CI). However, ML models need frequent retraining as a result of changes in the CI environment, more commonly known as data drift. Also, continuously retraining ML models consume a lot of t

Nemanja Stefan Perović, Le-Nam Tran, Marco Di Renzo, Mark F. Flanagan

The electromagnetic (EM) features of reconfigurable intelligent surfaces (RISs) fundamentally determine their operating principles and performance. Motivated by these considerations, we study a single-input single-output (SISO) system in the presence of an RIS, which is characterized by a circuit-based EM-consistent model. Specifically, we model the RIS as a

Renshuai Liu, Chengyang Li, Haitao Cao, Yinglin Zheng

Although remarkable progress has been made in recent years, current multi-exposure image fusion (MEF) research is still bounded by the lack of real ground truth, objective evaluation function, and robust fusion strategy. In this paper, we study the MEF problem from a new perspective. We don't utilize any synthesized ground truth, design any loss function, or

Jia-Chen Gu, Chao-Hong Tan, Caiyuan Chu, Zhen-Hua Ling

Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods rely heavily on the necessary addressee labels and can only be applied to an ideal setting where each utterance must be tagged with an addressee label. To study the scarcity of addr

Kento Sugiura, Yoshiharu Ishikawa

The z-order curve is a space-filling curve and is now attracting the interest of developers because of its simple and useful features. In the case of key-value stores, because the z-order curve achieves multi-dimensional range queries in one-dimensional z-ordered space, its use has been proposed for both academic and industrial purposes. However, z-ordered r

Zhujun Zhang

We consider the computational complexity of Hearthstone which is a popular online CCG (collectible card game). We reduce a PSPACE-complete problem, the partition game, to perfect information Hearthstone in which there is no hidden information or random elements. In the reduction, each turn in Hearthstone is used to simulate one choice in the partition game.