October 2023 arXiv papers — page 68

Conghao Tom Shen, Violet Yao, Yixin Liu

Manga, a widely celebrated Japanese comic art form, is renowned for its diverse narratives and distinct artistic styles. However, the inherently visual and intricate structure of Manga, which comprises images housing multiple panels, poses significant challenges for content retrieval. To address this, we present MaRU (Manga Retrieval and Understanding), a mu

Yingkai Fu, Meng Li, Wenxi Liu, Yuanchen Wang

Event cameras, or dynamic vision sensors, have recently achieved success from fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously capture light intensity changes, event camera has an inherent advantage to capture moving objects in challenging scenarios including objects under low light, high dynamic range, or fast m

Hawi Yohanis Wakjira, D. A. Mosunova, E. S. Postnikova, N. V. Chupina

We study of possible encounters in past epochs of the open star cluster NGC 1977 with host stars. For this purpose, the age of the cluster was determined based on our catalog data. Stars with planetary systems were selected from the NASA Archive. The age of the cluster was determined using the color - absolute magnitude diagram and the isochron system. By ex

Gaurav Sahu, Olga Vechtomova, Dzmitry Bahdanau, Issam H. Laradji

Data augmentation is a widely used technique to address the problem of text classification when there is a limited amount of training data. Recent work often tackles this problem using large language models (LLMs) like GPT3 that can generate new examples given already available ones. In this work, we propose a method to generate more helpful augmented data b

Abhi Saxena, Erfan Abbasgholinejad, Arka Majumdar, Rahul Trivedi

Correlated quantum many-body phenomena in lattice models have been identified as a set of physically interesting problems that cannot be solved classically. Analog quantum simulators, in photonics and microwave superconducting circuits, have emerged as near-term platforms to address these problems. An important ingredient in practical quantum simulation expe

Behrooz Malekolkalami, Taimur Mohammadi

The noncommutativity concept has wide range of applications in physical and mathematical theories. Noncommutativity in the position-time coordinates concerns the microscale structure of space-time. the noncommutativity is an intrinsic property of the space-time and it could be different from usual properties when one encounters the high energy phenomena. on

Yang Song, Prafulla Dhariwal

Consistency models are a nascent family of generative models that can sample high quality data in one step without the need for adversarial training. Current consistency models achieve optimal sample quality by distilling from pre-trained diffusion models and employing learned metrics such as LPIPS. However, distillation limits the quality of consistency mod

Huy Nguyen, Pedram Akbarian, TrungTin Nguyen, Nhat Ho

Mixture-of-experts (MoE) model incorporates the power of multiple submodels via gating functions to achieve greater performance in numerous regression and classification applications. From a theoretical perspective, while there have been previous attempts to comprehend the behavior of that model under the regression settings through the convergence analysis

Anshul Mishra, Prasanta K. Tripathy

In this paper we study black hole and black string solutions in five dimensional N=2 supergravity theories arising from the compactification of M-theory on Calabi-Yau manifolds. In particular, we consider explicit examples of three parameter Calabi-Yau manifolds which are obtained as hypersurfaces in toric varieties. Using the attractor mechanism, we obtain

Himanshu Gaur, Urjit A. Yajnik

We study multi-charged moments and symmetry-resolved R\'enyi entropy of free compact boson for multiple disjoint intervals. The R\'enyi entropy evaluation involves computing the partition function of the theory on Riemann surfaces with genus g>1. This makes R\'enyi entropy sensitive to the local conformal algebra of the theory. The free compact boson possess

Naoaki Okada, Shuji Kijima

This paper is motivated by a question whether it is possible to calculate a chaotic sequence efficiently, e.g., is it possible to get the $n$-th bit of a bit sequence generated by a chaotic map, such as $\beta$-expansion, tent map and logistic map in $o(n)$ time/space? This paper gives an affirmative answer to the question about the space complexity of a ten

Ke Liu, Feng Liu, Haishuai Wang, Ning Ma

$\textit{Implicit neural representations}$ (INRs) aim to learn a $\textit{continuous function}$ (i.e., a neural network) to represent an image, where the input and output of the function are pixel coordinates and RGB/Gray values, respectively. However, images tend to consist of many objects whose colors are not perfectly consistent, resulting in the challeng

Jun-Yi Shen, Yuan-Chuan Zou

Fast radio bursts (FRBs) are millisecond radio signals from cosmological distances. As they propagate, FRBs can interact with ambient photons and initiate a quantum cascade that can limit the electric field strength. This paper examines whether some observed bright and brief FRBs may challenge this limit if the source is not relativistic. The size of a stati

Ruifu Li, Danijela Cabric

Atomic norm minimization is of great interest in various applications of sparse signal processing including super-resolution line-spectral estimation and signal denoising. In practice, atomic norm minimization (ANM) is formulated as semi-definite programming (SDP) that is generally hard to solve. This work introduces a low-complexity solver for a type of ANM

Dehua Tao, Tan Lee, Harold Chui, Sarah Luk

Counseling is carried out as spoken conversation between a therapist and a client. The empathy level expressed by the therapist is considered an important index of the quality of counseling and often assessed by an observer or the client. This research investigates the entrainment of speech prosody in relation to subjectively rated empathy. Experimental resu

S. I. Atwood, S. Hosseinzadeh, V. V. Mkhitaryan, T. H. Tennahewa

We present Floquet theory-based predictions and electrically detected magnetic resonance (EDMR) experiments scrutinizing the nature of two-photon magnetic resonance shifts of charge-carrier spin states in the perdeuterated $\pi$-conjugated polymer poly[2-methoxy-5-(2'-ethylhexyloxy)-1,4-phenylene vinylene] (d-MEH-PPV) under strong magnetic resonant drive con

Wai-Yiu Keung, Wing-Kin Ma

This paper considers the context of multiuser massive MIMO downlink precoding with low-resolution digital-to-analog converters (DACs) at the transmitter. This subject is motivated by the consideration that it is expensive to employ high-resolution DACs for practical massive MIMO implementations. The challenge with using low-resolution DACs is to overcome the

Dehua Tao, Tan Lee, Harold Chui, Sarah Luk

Counseling is usually conducted through spoken conversation between a therapist and a client. The empathy level of therapist is a key indicator of outcomes. Presuming that therapist's empathy expression is shaped by their past behavior and their perception of the client's behavior, we propose a model to estimate the therapist empathy by considering both intr

Cheng Fan, Kazuyuki Sakamoto, Peter Krüger

Bi$_2$Se$_3$ is one of the most promising topological insulators, but it suffers from intrinsic n-doping due to Se-vacancies, which shifts the Fermi level into the bulk conduction band, leading to topologically trivial carriers. Recently it was shown that this Fermi-level shift can be compensated by a locally controlled surface p-doping process, through wate

Junjia Huang, Haofeng Li, Weijun Sun, Xiang Wan

Automatic nuclei detection and classification can produce effective information for disease diagnosis. Most existing methods classify nuclei independently or do not make full use of the semantic similarity between nuclei and their grouping features. In this paper, we propose a novel end-to-end nuclei detection and classification framework based on a grouping

Shunchang Li, Gang Wu

A greedy randomized augmented Kaczmarz (GRAK) method was proposed in [Z.-Z. Bai and W.-T. WU, SIAM J. Sci. Comput., 43 (2021), pp. A3892-A3911] for large and sparse inconsistent linear systems. However, one has to construct two new index sets via computing residual vector with respect to the augmented linear system in each iteration. Thus, the computational

Yantao Liu, Zixuan Li, Xiaolong Jin, Yucan Guo

The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base. Recently, Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. In doing so, a major challenge for LLMs is to overcome the immensity and heterogeneity of knowledge bas

Hejing Zhang, Qiaoxi Zhu, Jian Guan, Haohe Liu

First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target machine types are unseen in training. Existing methods often rely on the availability of normal and abnormal sound data from the target machines. However, due to the lack of anomalous sound data

Zihang Xu, Haifan Gong, Xiang Wan, Haofeng Li

Automatic tissue segmentation of fetal brain images is essential for the quantitative analysis of prenatal neurodevelopment. However, producing voxel-level annotations of fetal brain imaging is time-consuming and expensive. To reduce labeling costs, we propose a practical unsupervised domain adaptation (UDA) setting that adapts the segmentation labels of hig

Hsun-Yu Kuo, Szu-Yu Liu, Chin-Ya Huang, Yu-Chi Chen

We consider the use of a domain proxy assisted private citizen broadband radio service (CBRS) network and propose a Maximum Transmission Continuity (MTC) scheme to transmit Internet of Things (IoT) data reliably. MTC dynamically allocates available CBRS channels to sustain the continuity of data transmission without violating the channel access requirements.

Xiang Zhuang, Qiang Zhang, Keyan Ding, Yatao Bian

Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when data for training and testing originate from different environments. To address this issue, we propose a new framework for learning molecular representations that exhibit invariance a

Mijanur Islam, Saurabh Basu

In this paper we investigate the interplay of the Rashba spin-orbit coupling (RSOC) and a topological defect, such as a screw dislocation in an {\alpha}-T3 Aharonov-Bohm quantum ring and scrutinized the effect of an external transverse magnetic field therein. Our study reveals that the energy spectrum follows a parabolic dependence on the Burgers vector asso

Khemraj Shukla, Yeonjong Shin

We present a randomized forward mode gradient (RFG) as an alternative to backpropagation. RFG is a random estimator for the gradient that is constructed based on the directional derivative along a random vector. The forward mode automatic differentiation (AD) provides an efficient computation of RFG. The probability distribution of the random vector determin

Roman Jacome, Edwin Vargas, Kumar Vijay Mishra, Brian M. Sadler

Integrated sensing and communications (ISAC) systems have gained significant interest because of their ability to jointly and efficiently access, utilize, and manage the scarce electromagnetic spectrum. The co-existence approach toward ISAC focuses on the receiver processing of overlaid radar and communications signals coming from independent transmitters. A

Zhen Hao Wong, Ling Yue, Quanming Yao

Graph Neural Networks (GNNs) have shown success in various fields for learning from graph-structured data. This paper investigates the application of ensemble learning techniques to improve the performance and robustness of Graph Neural Networks (GNNs). By training multiple GNN models with diverse initializations or architectures, we create an ensemble model

Hongxiang Gao, Xiangyao Wang, Zhenghua Chen, Min Wu

Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds great potential in advancing human-computer interaction. However, several significant challenges persist in existing research on algorithmic emotion recognition. These challenges include the need for a robust model to effectively learn discriminative node attributes over l

Vina Apriliani, Masato Kimura, Hiroshi Ohtsuka

We present a new method to determine the best constant of the Sobolev-type embedding in one dimension with a norm including a bounded inhomogeneous potential term. This problem is closely connected to the Green function of the Schr\"odinger operator with inhomogeneous potential. A minimization problem of a Rayleigh-type quotient in a Sobolev space gives the

Siddhant Chaudhary, Abhishek Sinha

Contextual bandit algorithms are at the core of many applications, including recommender systems, clinical trials, and optimal portfolio selection. One of the most popular problems studied in the contextual bandit literature is to maximize the sum of the rewards in each round by ensuring a sublinear regret against the best-fixed context-dependent policy. How

Jiangbo Song, Wanqing Li, Ruofan Liu, Xiangwei Zhu

Multi-sensor fusion is an effective way to enhance the positioning performance of autonomous underwater vehicles (AUVs). However, underwater multi-sensor fusion faces challenges such as heterogeneous frequency and dynamic availability of sensors. Traditional filter-based algorithms suffer from low accuracy and robustness when sensors become unavailable. The

Amit Singh

In recent years, end to end steering prediction for autonomous vehicles has become a major area of research. The primary method for achieving end to end steering was to use computer vision models on a live feed of video data. However, to further increase accuracy, many companies have added data from light detection and ranging (LiDAR) and or radar sensors th

Naoto Ohsaka

In this paper, we demonstrate gap amplification for reconfiguration problems. In particular, we prove an explicit factor of PSPACE-hardness of approximation for three popular reconfiguration problems only assuming the Reconfiguration Inapproximability Hypothesis (RIH) due to Ohsaka (STACS 2023). Our main result is that under RIH, Maxmin 2-CSP Reconfiguration

Dayoon Ko, Sangho Lee, Gunhee Kim

As short-form funny videos on social networks are gaining popularity, it becomes demanding for AI models to understand them for better communication with humans. Unfortunately, previous video humor datasets target specific domains, such as speeches or sitcoms, and mostly focus on verbal cues. We curate a user-generated dataset of 10K multimodal funny videos

Luoyao Kang, Haifan Gong, Xiang Wan, Haofeng Li

Deep learning (DL) has been used in the automatic diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) with brain imaging data. However, previous methods have not fully exploited the relation between brain image and clinical information that is widely adopted by experts in practice. To exploit the heterogeneous features from imaging and

Abhay Sobhanan, Junyoung Park, Jinkyoo Park, Changhyun Kwon

When vehicle routing decisions are intertwined with higher-level decisions, the resulting optimization problems pose significant challenges for computation. Examples are the multi-depot vehicle routing problem (MDVRP), where customers are assigned to depots before delivery, and the capacitated location routing problem (CLRP), where the locations of depots sh

Jianfeng Lin, Yi Xie

Watanabe disproved the 4-dimensional Smale conjecture by constructing topologically trivial $D^{4}$-bundles over spheres and showing that they are smoothly nontrivial using configuration space integrals. In this paper, we define a new version of configuration space integrals that only relies on a formal smooth structure on the $D^{4}$-bundle (i.e., a vector

Cheng Ding, Ran Xiao, Weijia Wang, Elizabeth Holdsworth

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into

Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen

Urban region profiling from web-sourced data is of utmost importance for urban planning and sustainable development. We are witnessing a rising trend of LLMs for various fields, especially dealing with multi-modal data research such as vision-language learning, where the text modality serves as a supplement information for the image. Since textual modality h

Junjia Huang, Haofeng Li, Xiang Wan, Guanbin Li

Multi-class cell nuclei detection is a fundamental prerequisite in the diagnosis of histopathology. It is critical to efficiently locate and identify cells with diverse morphology and distributions in digital pathological images. Most existing methods take complex intermediate representations as learning targets and rely on inflexible post-refinements while

Ding Yu Shao, Bin Yan, Shu-Run Yuan, Cheng Zhang

The anomalous magnetic (MDM) and electric (EDM) dipole moments of the $\tau$ lepton serve as crucial indicators of new physics beyond the Standard Model. Leveraging azimuthal angular asymmetry as a novel tool in ultraperipheral collisions (UPCs), we attain unparalleled precision in the study of these key properties. Driven by the highly linear polarization o

Xiao Wang, Tianze Chen, Qiming Ge, Han Xia

Benefiting from massive corpora and advanced hardware, large language models (LLMs) exhibit remarkable capabilities in language understanding and generation. However, their performance degrades in scenarios where multiple tasks are encountered sequentially, also known as catastrophic forgetting. In this paper, we propose orthogonal low-rank adaptation (O-LoR

Wei Zhu, Xiaoling Wang, Huanran Zheng, Mosha Chen

Biomedical language understanding benchmarks are the driving forces for artificial intelligence applications with large language model (LLM) back-ends. However, most current benchmarks: (a) are limited to English which makes it challenging to replicate many of the successes in English for other languages, or (b) focus on knowledge probing of LLMs and neglect

Jian Li, Greta Tuckute, Evelina Fedorenko, Brian L. Edlow

Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functi

Li Wei, Li Wenjuan, Liu Jie, Wu Lian

Based on a proper hypothesis on the noncommutative Fourier integral operators, we establish in this paper the strong-type $(p,p)$ (with $2\leq p\leq \infty$) estimates for the operator-valued Stein's maximal spherical means.

Sai Teja Somu, Andrzej Kukla, Duc Van Khanh Tran

A positive integer $n$ is said to be a Zumkeller number or an integer-perfect number if the set of its positive divisors can be partitioned into two subsets of equal sums. In this paper, we prove several results regarding Zumkeller numbers. For any positive integer $m$, we prove that there are infinitely many positive integers $n$ for which $n+1,\cdots, n+m$

Tuyen Tran, Kate Figenschou, Phan Tu Vuong

This paper aims to investigate the effectiveness of the recently proposed Boosted Difference of Convex functions Algorithm (BDCA) when applied to clustering with constraints and set clustering with constraints problems. This is the first paper to apply BDCA to a problem with nonlinear constraints. We present the mathematical basis for the BDCA and Difference

Judit Szente, Enrico Landi, Bart van der Holst

In this work we combined AWSoM's non-equilibrium ionization [NEI] calculations from (Szente:2022) with the synthetic spectral computations of SPECTRUM (Szente:2019), to predict non-equilibrium line intensities across the entire domain of the AWSoM 3D global model. We find that the resulting spectra are strongly affected by non-equilibrium effects in the fast

Anru R. Zhang, Ryan P. Bell, Chen An, Runshi Tang

This paper considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study utilized functional MRI (fMRI) and diffusion MRI (dMRI) data collected from 275 individuals, which was then parcellated into 246 regions of interest (ROIs) using the Brainnetome atlas. After data preproc

Bozorgmehr Vaziri, Frahad Rahmati

This paper explores a previously uncharted automaton group generated by a 5-state automaton $(\Pi, A)$ acts by self-similarity on the regular rooted tree $A^{*}$ over a 2-letter alphabet set $A$. Group $G$ has been subjected to several observations that reveal the self-similar $G$-action possesses several notable characteristics: it is a weak branch, contrac

Marcel Nutz, Kevin Webster, Long Zhao

We study how to unwind stochastic order flow with minimal transaction costs. Stochastic order flow arises, e.g., in the central risk book (CRB), a centralized trading desk that aggregates order flows within a financial institution. The desk can warehouse in-flow orders, ideally netting them against subsequent opposite orders (internalization), or route them

Abdul Aziz, Nihad Karim Chowdhury, Muhammad Ashad Kabir, Abu Nowshed Chy

Desire is a set of human aspirations and wishes that comprise verbal and cognitive aspects that drive human feelings and behaviors, distinguishing humans from other animals. Understanding human desire has the potential to be one of the most fascinating and challenging research domains. It is tightly coupled with sentiment analysis and emotion recognition tas

Yihui He, Fang Han

This paper reexamines Abadie and Imbens (2016)'s work on propensity score matching for average treatment effect estimation. We explore the asymptotic behavior of these estimators when the number of nearest neighbors, $M$, grows with the sample size. It is shown, hardly surprising but technically nontrivial, that the modified estimators can improve upon the o

Pedro H. G. Lugão, Renato Portugal

This work examines the time complexity of quantum search algorithms on combinatorial $t$-designs with multiple marked elements using the continuous-time quantum walk. Through a detailed exploration of $t$-designs and their incidence matrices, we identify a subset of bipartite graphs that are conducive to success compared to random-walk-based search algorithm

Masayuki Ohzeki

The Ising model is the simplest to describe many-body effects in classical statistical mechanics. Duality analysis leads to a critical point under several assumptions. The Ising model itself has $Z(2)$ symmetry. The basis of the duality analysis is a nontrivial relationship between low and high-temperature expansions. However, the discrete Fourier transforma

Mike Huisman, Thomas M. Moerland, Aske Plaat, Jan N. van Rijn

Deep learning requires large amounts of data to learn new tasks well, limiting its applicability to domains where such data is available. Meta-learning overcomes this limitation by learning how to learn. In 2001, Hochreiter et al. showed that an LSTM trained with backpropagation across different tasks is capable of meta-learning. Despite promising results of

Matthew P Hamilton, Caroline X Gao, Glen Wiesner, Kate M Filia

We are developing an economic model to explore multiple topics in Australian youth mental health policy. We want that model to be readily transferable to other jurisdictions. We developed a software framework for authoring transparent, reusable and updatable Computational Health Economic Models (CHEMs) (the software files that implement health economic model

Nate Haris, Kendree Chen, Ann Song, Benjamin Pou

Currently, Application Programming Interfaces (APIs) are becoming increasingly popular to facilitate data transfer in a variety of mobile applications. These APIs often process sensitive user information through their endpoints, which are potentially exploitable due to developer misimplementation. In this paper, a custom, modular endpoint vulnerability detec

Danica Kosanović, Peter Teichner

Given a $d$-dimensional manifold $M$ and a knotted sphere $s\colon\mathbb{S}^{k-1}\hookrightarrow\partial M$ with $1\leq k\leq d$, for which there exists a framed dual sphere $G\colon\mathbb{S}^{d-k}\hookrightarrow\partial M$, we show that the space of neat embeddings $\mathbb{D}^k\hookrightarrow M$ with boundary $s$ can be delooped by the space of neatly em

Michael D. McManus, Kristopher G. Klein, Davin Larson, Stuart D. Bale

Ion scale wave events or "wave storms" in the solar wind are characterised by enhancements in magnetic field fluctuations as well as coherent magnetic field polarisation signatures at or around the local ion cyclotron frequencies. In this paper we study in detail one such wave event from Parker Solar Probe's (PSP) fourth encounter, consisting of an initial p

Brian Zhou, Jason Geder, Alisha Sharma, Julian Lee

The last few decades have led to the rise of research focused on propulsion and control systems for bio-inspired unmanned underwater vehicles (UUVs), which provide more maneuverable alternatives to traditional UUVs in underwater missions. Propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for esse

Justin Lyle

Let $R$ be a commutative Noetherian local ring. We study tensor products involving a finitely generated $R$-module $M$ through the natural action of its endomorphism ring. In particular, we study torsion properties of self tensor products in the case where $\operatorname{End}_R(M)$ has an $R^*$-algebra structure, and prove that if $M$ is indecomposable, then

Jinyang Liu, Sheng Di, Kai Zhao, Xin Liang

With the ever-increasing execution scale of high performance computing (HPC) applications, vast amounts of data are being produced by scientific research every day. Error-bounded lossy compression has been considered a very promising solution to address the big-data issue for scientific applications because it can significantly reduce the data volume with lo

Arka Adhikari, Amir Dembo

Consider the matrix $A_{\mathcal{G}}$ chosen uniformly at random from the finite set of all $N$-dimensional matrices of zero main-diagonal and binary entries, having each row and column of $A_{\mathcal{G}}$ sum to $d$. That is, the adjacency matrix for the uniformly random $d$-regular simple digraph $\mathcal{G}$. Fixing $d \ge 3$, it has long been conjectur

Donu Arapura, Laurentiu Maxim, Botong Wang

We propose several Hodge theoretic analogues of the conjectures of Hopf and Singer, and prove them in some special cases.

M. Fakharany, Faten Alamri, Mohamed Abd Allah El-Hadidy

Finding hidden/lost targets in a broad region costs strenuous effort and takes a long time. From a practical view, it is convenient to analyze the available data to exclude some parts of the search region. This paper discusses the coordinated search technique of a one-dimensional problem with a search region consisting of several mutual intervals. In other w

Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao

We study the batched best arm identification (BBAI) problem, where the learner's goal is to identify the best arm while switching the policy as less as possible. In particular, we aim to find the best arm with probability $1-\delta$ for some small constant $\delta>0$ while minimizing both the sample complexity (total number of arm pulls) and the batch comple

Alessandro Jacoud Peixoto, Wenderson G. Serrantola, Fernando Lizarralde

This paper proposes a sliding mode controller with smooth control effort for a class of nonlinear plants. The proposed controller is created by allowing some constant parameters of the earlier smooth sliding control (SSC) to vary as a function of the output tracking error, improving the control chattering alleviation in practical implementations. Furthermore

Zeraoulia Rafik, Alvaro Humberto Salas

This study delves into the realm of chaotic dynamics derived from Dirichlet L-functions, drawing inspiration from Yitang Zhang's groundbreaking work on Landau-Siegel zeros. The dynamic behavior reveals profound chaos, corroborated by the calculated Lyapunov exponents and entropy, attesting to the system's inherent unpredictability. Furthermore, we establish

Yuxiang Liu, Jie Huang, Kevin Chen-Chuan Chang

We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. The task aims to generate questions from an entity perspective. To solve ECQG, we propose a coherent PLM-based framework GenCONE with two novel modules: content focusing and que

Rostand A. K. Fezeu, Timothy J. Salo, Amy Zhang, Zhi-Li Zhang

We examine in detail the provisioning process used by many common, consumer-grade Internet of Things (IoT) devices. We find that this provisioning process involves the IoT device, the vendor's cloud-based server, and a vendor-provided mobile app. In order to better understand this process, we develop two toolkits. IoT-Dissect I enables us to decrypt and exam

Alban Petit, Caio Corro, François Yvon

In many Natural Language Processing applications, neural networks have been found to fail to generalize on out-of-distribution examples. In particular, several recent semantic parsing datasets have put forward important limitations of neural networks in cases where compositional generalization is required. In this work, we extend a neural graph-based semanti

Francisco J. Herranz, Angel Ballesteros, Giulia Gubitosi, Ivan Gutierrez-Sagredo

The aim of this contribution is twofold. First, we show that when two (or more) different quantum groups share the same noncommutative spacetime, such an 'ambiguity' can be resolved by considering together their corresponding noncommutative spaces of geodesics. In any case, the latter play a mathematical/physical role by themselves and, in some cases, they c

Honglei Zhuang, Zhen Qin, Kai Hui, Junru Wu

Zero-shot text rankers powered by recent LLMs achieve remarkable ranking performance by simply prompting. Existing prompts for pointwise LLM rankers mostly ask the model to choose from binary relevance labels like "Yes" and "No". However, the lack of intermediate relevance label options may cause the LLM to provide noisy or biased answers for documents that

Mallory E. Gaspard, Alexander Vladimirsky

When traveling through a graph with an accessible deterministic path to a target, is it ever preferable to resort to stochastic node-to-node transitions instead? And if so, what are the conditions guaranteeing that such a stochastic optimal routing policy can be computed efficiently? We aim to answer these questions here by defining a class of Opportunistica

Saurav K. Aryal, Howard Prioleau, Surakshya Aryal

Sentiment analysis is a fundamental and valuable task in NLP. However, due to limitations in data and technological availability, research into sentiment analysis of African languages has been fragmented and lacking. With the recent release of the AfriSenti-SemEval Shared Task 12, hosted as a part of The 17th International Workshop on Semantic Evaluation, an

Zechen Xiong, Suyu Luohong, Jeong Hun Lee, Hod Lipson

Sinusoidal undulation has long been considered the most successful swimming pattern for fish and bionic aquatic robots [1]. However, a swimming pattern generated by the hair clip mechanism (HCM, part iii, Figure 1A) [2]~[5] may challenge this knowledge. HCM is an in-plane prestressed bi-stable mechanism that stores elastic energy and releases the stored ener

Imane Moumene, Angela Bonaccorso

In this work we extend a previously derived $n$- $^9$Be optical potential up to 500 MeV and apply it to the system $n$- $^{12}$C, finding excellent results for the energy dependence of the total cross sections. Results obtained with a standard optical model calculation are compared to those from the eikonal formalism in order to asses the accuracy of the lat

Paschal C. Amusuo, Kyle A. Robinson, Tanmay Singla, Huiyun Peng

Third-party libraries like Log4j accelerate software application development but introduce substantial risk. Vulnerabilities in these libraries have led to Software Supply Chain (SSC) attacks that compromised resources within the host system. These attacks benefit from current application permissions approaches: thirdparty libraries are implicitly trusted in

Zakariya Laouar, Qi Heng Ho, Rayan Mazouz, Tyler Becker

In this paper, we present a controller framework that synthesizes control policies for Jump Markov Linear Systems subject to stochastic mode switches and imperfect mode estimation. Our approach builds on safe and robust methods for Model Predictive Control (MPC), but in contrast to existing approaches that either optimize without regard to feasibility or uti

Thorsten Hertl

We construct non-trivial elements in the homotopy groups of the observer moduli space of positive sectional curvature metrics on $\mathbb{C}P^n$ and non-trivial elements in the homotopy groups of the observer moduli space of positive scalar curvature metrics on $\mathbb{C}P^2 \sharp M^4$.

Josef Rukavicka

Let $c>1$ be a real constant. We say that a language $L$ is $c$-\emph{constantly growing} if for every word $u\in L$ there is a word $v\in L$ with $\vert u\vert<\vert v\vert\leq c+\vert u\vert$. We say that a language $L$ is $c$-\emph{geometrically growing} if for every word $u\in L$ there is a word $v\in L$ with $\vert u\vert<\vert v\vert\leq c\vert u\vert$

Ellen Vitercik, Manolis Zampetakis, David Zhang

We introduce a novel noisy sorting model motivated by the Just Noticeable Difference (JND) model from experimental psychology. The goal of our model is to capture the low quality of the data that are collected from crowdsourcing environments. Compared to other celebrated models of noisy sorting, our model does not rely on precise data-generation assumptions

Trevor J. Steiner, Maximilian Shen, Joshua E. Castro, John E. Bowers

Using an aluminum gallium arsenide microring resonator, we demonstrate a bright quantum optical microcomb with $>300$ nm bandwidth and more than 20 sets of time-energy entangled modes, enabling spectral demultiplexing with simple, off-the-shelf commercial telecom components. We report high-rate continuous entanglement distribution for two sets of entangled-p

Malcolm Garbutt, Ilhaam Ismail, Calvineo Juries, Raeez Adams

This paper reports on students' readiness for using Metaverse for education in a university in a developing country facing infrastructure and poverty challenges. Covid-19 forced many universities to adopt a hybrid approach to teaching and supervision. While online meeting technologies have become commonplace, there is a lack of the connectedness of face-to-f

Minh Nguyen, Nancy F. Chen

NLP models excel on tasks with clean inputs, but are less accurate with noisy inputs. In particular, character-level noise such as human-written typos and adversarially-engineered realistic-looking misspellings often appears in text and can easily trip up NLP models. Prior solutions to address character-level noise often alter the content of the inputs (low

Alphonse C. Sterling, Ronald L. Moore, Navdeep K. Panesar

Solar quiet- and coronal-hole region coronal jets frequently clearly originate from erupting minifilaments, but active-region jets often lack an obvious erupting-minifilament source. We observe a coronal-jet-productive active region (AR), AR 12824, over 2021 May 22 0 -- 8 UT, primarily using Solar Dynamics Observatory (SDO) Atmospheric Imaging Array (AIA) EU

Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton, Fartash Faghri

Contrastive language image pretraining (CLIP) is a standard method for training vision-language models. While CLIP is scalable, promptable, and robust to distribution shifts on image classification tasks, it lacks object localization capabilities. This paper studies the following question: Can we augment CLIP training with task-specific vision models from mo

Yanpeng Zhao, Ivan Titov

There has been a significant surge of interest in visually grounded grammar induction in recent times. While a variety of models have been developed for the task and have demonstrated impressive performance, they have not been evaluated on text domains that are different from the training domain, so it is unclear if the improvements brought by visual groundi

Luca Moscadelli, André Oliva, Gabriele Surcis, Alberto Sanna

Our goal is to measure and model the magnetic field distribution in the disk wind of the young stellar object (YSO) IRAS 21078+5211. We performed sensitive global Very Long Baseline Interferometry observations of the polarized emission of the 22 GHz water masers tracing individual streamlines of the magnetohydrodynamic (MHD) disk wind in IRAS 21078+5211. Our

Minh Nguyen, Gia H. Ngo, Mert R. Sabuncu

Given sufficient pairs of resting-state and task-evoked fMRI scans from subjects, it is possible to train ML models to predict subject-specific task-evoked activity using resting-state functional MRI (rsfMRI) scans. However, while rsfMRI scans are relatively easy to collect, obtaining sufficient task fMRI scans is much harder as it involves more complex expe

Natalia Gorobey, Alexander Lukyanenko, A. V. Goltsev

An alternative formulation of the no-boundary initial state of the universe in the Euclidean quantum theory of gravity is proposed. Unlike the no-boundary Hartle-Hawking wave function, in which time appears together with macroscopic space-time in the semiclassical approximation, in the proposed formalism time is present from the very beginning on an equal fo

Manuel Faysse, Gautier Viaud, Céline Hudelot, Pierre Colombo

Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the zero-shot capabilities of Large Language Models (LLMs), but in doing so induces new evaluation metric requirements. We show LLM-based metrics to be well adapted to these requirements, and leverage them to conduct an investigation of task-specialization strategies, quantifying the trade

Pere Masjuan, Alejandro Miranda, Pablo Roig

Windows in Euclidean time have become a standard tool for comparing lattice QCD and data-driven computations of the hadronic vacuum polarization (HVP) contribution to the muon $g-2$. Here we review our results, obtained using isospin-rotated $\tau^-\to\pi^-\pi^0\nu_\tau$ data instead of $e^+e^-\to\pi^+\pi^-$ measurements, and compare them to other approaches

Panagiota Fatourou

This paper summarizes state-of-the-art results on data series processing with the emphasis on parallel and distributed data series indexes that exploit the computational power of modern computing platforms. The paper comprises a summary of the tutorial the author delivered at the 15th International Conference on Management of Digital EcoSystems (MEDES'23).

Tristan Hubsch, Djordje Minic, Konstantin Nikolic, Sinisa Pajevic

We explore the concept of emergent quantum-like theory in complex adaptive systems, and examine in particular the concrete example of such an emergent (or "mock") quantum theory in the Lotka-Volterra system. In general, we investigate the possibility of implementing the mathematical formalism of quantum mechanics on classical systems, and what would be the c

Mohammad Ansari

In this article, first we show that the Fr\'echet space $H(\Bbb D)$ cannot support strongly supercyclic weighted composition operators. Then we compute the constant $\epsilon$ for weighted backward shifts on $\ell^p$ ($1\le p<\infty$) and $c_0$. This constant is used to find strongly hypercyclic scalar multiples of non-invertible strongly supercyclic Banach