May 2023 arXiv papers — page 37

Jacek Sroka, Jerzy Tyszkiewicz

Prefix aggregation operation (also called scan), and its particular case, prefix summation, is an important parallel primitive and enjoys a lot of attention in the research literature. It is also used in many algorithms as one of the steps. Aggregation over dominated points in $\mathbb{R}^m$ is a multidimensional generalisation of prefix aggregation. It is a

Anna Wróblewska, Bartosz Pieliński, Karolina Seweryn, Sylwia Sysko-Romańczuk

This paper explores the automatic knowledge extraction of formal institutional design - norms, rules, and actors - from international agreements. The focus was to analyze the relationship between the visibility and centrality of actors in the formal institutional design in regulating critical aspects of cultural heritage relations. The developed tool utilize

Xiang Li, Songxiang Liu, Max W. Y. Lam, Zhiyong Wu

Expressive human speech generally abounds with rich and flexible speech prosody variations. The speech prosody predictors in existing expressive speech synthesis methods mostly produce deterministic predictions, which are learned by directly minimizing the norm of prosody prediction error. Its unimodal nature leads to a mismatch with ground truth distributio

Mohammed Thousif, Shridhar Velhal, Suresh Sundaram, Shirin Dora

This paper proposes a novel Decentralized Spike-based Learning (DSL) framework for the discrete Perimeter Defense Problem (d-PDP). A team of defenders is operating on the perimeter to protect the circular territory from radially incoming intruders. At first, the d-PDP is formulated as a spatio-temporal multi-task assignment problem (STMTA). The problem of ST

Ya'acov Peterzil, Anand Pillay, Francoise Point

We continue our earlier study of finite dimensional definable groups in models of the the model companion of an o-minimal L-theory T expanded by a generic derivation as in [F-K]. We generalize Buium's notion of an algebraic D-group to L-definable D-groups, namely (G,s), where G is a L-definable group in a model of T, and s is an L-definable group section int

Aristotelis Ballas, Christos Diou

In search of robust and generalizable machine learning models, Domain Generalization (DG) has gained significant traction during the past few years. The goal in DG is to produce models which continue to perform well when presented with data distributions different from the ones available during training. While deep convolutional neural networks (CNN) have be

Richard Froese, Ira Herbst

We continue the search, begun by Kato, for all pairs of real, bounded, measurable functions $\{f,g\}$ that result in a positive commutator $[if(P),g(Q)]$. We prove a number of partial results including a connection with Loewner's celebrated theorem on matrix monotone functions.

Huaxiaoyue Wang, Gonzalo Gonzalez-Pumariega, Yash Sharma, Sanjiban Choudhury

Language instructions and demonstrations are two natural ways for users to teach robots personalized tasks. Recent progress in Large Language Models (LLMs) has shown impressive performance in translating language instructions into code for robotic tasks. However, translating demonstrations into task code continues to be a challenge due to the length and comp

Friedrich Hübner, Christoph Dauer, Sebastian Eggert, Corinna Kollath

We investigate the transport properties of a Fermi-Hubbard chain with an impurity which is formed by a site with a periodically modulated chemical potential. We determine the momentum resolved transmission through this impurity in dependence of the modulation frequency and strength for a single particle and a pair of fermions. We find that the pair transmiss

Baohao Liao, Yan Meng, Christof Monz

Parameter-efficient fine-tuning (PEFT) of pre-trained language models has recently demonstrated remarkable achievements, effectively matching the performance of full fine-tuning while utilizing significantly fewer trainable parameters, and consequently addressing the storage and communication constraints. Nonetheless, various PEFT methods are limited by thei

Tahira Iqbal, Kuldar Taveter, Tarmo Strenze, Waqar Hussain

Human values are principles that guide human actions and behaviour in personal and social life. Ignoring human values during requirements engineering introduces a negative impact on software uptake and continued use. Embedding human values into software is admittedly challenging; however, early elicitation of stakeholder values increases the chances of their

Royi Rassin, Yoav Goldberg, Reut Tsarfaty

Verbal omissions are complex syntactic phenomena in VP coordination structures. They occur when verbs and (some of) their arguments are omitted from subsequent clauses after being explicitly stated in an initial clause. Recovering these omitted elements is necessary for accurate interpretation of the sentence, and while humans easily and intuitively fill in

Yuxing Long, Binyuan Hui, Caixia Yuan1, Fei Huang

Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario. This paper introduces a new dataset SURE (Multimodal Recommendation Dialog with SUbjective PREference), which contains 12K shopping dialogs in complex store scenes. The data is b

Yuheng Zha, Yichi Yang, Ruichen Li, Zhiting Hu

Many text generation applications require the generated text to be factually consistent with input information. Automatic evaluation of factual consistency is challenging. Previous work has developed various metrics that often depend on specific functions, such as natural language inference (NLI) or question answering (QA), trained on limited data. Those met

Vipin Michael, Umair Ahmed, Mahmoud Assad, Robert Prosser

The aim of this paper is two-fold: (1) to provide a detailed investigation of the turbulent flow in an inline high-shear rotor stator mixer; (2) to provide a comparison of two different classes of turbulence models and solution methods currently available. The widely used multiple reference frame (MRF) method is contrasted against a more recently developed s

Yvonne Alama Bronsard, Yvain Bruned, Georg Maierhofer, Katharina Schratz

In the present work we introduce a unified framework that allows for the very first systematic construction of symmetric resonance-based integrators to approximate a wide class of nonlinear dispersive equations at low-regularity. The inclusion of symmetries in the construction of resonance-based schemes presents serious challenges and induces a need for a si

Zhihui Zhang, Liang Zhang, Xuan Zhang, Xiaoxu Huang

Grain boundary (GB) segregation of solute atoms plays an important role in the microstructure and macroscopic mechanical properties of materials. The study of GB segregation of solute atoms using computational simulation has become one of the hot spots in recent years. However, most studies mainly focus on ground-state GB structures with the lowest energy, a

James W. Taylor, Xiaochun Meng

When multiple forecasts are available for a probability distribution, forecast combining enables a pragmatic synthesis of the information to extract the wisdom of the crowd. The linear opinion pool has been widely used, whereby the combining is applied to the probabilities of the distributional forecasts. However, it has been argued that this will tend to de

I-Hung Hsu, Zhiyu Xie, Kuan-Hao Huang, Prem Natarajan

Event argument extraction (EAE) identifies event arguments and their specific roles for a given event. Recent advancement in generation-based EAE models has shown great performance and generalizability over classification-based models. However, existing generation-based EAE models mostly focus on problem re-formulation and prompt design, without incorporatin

Stéphane Lhaut, Johan Segers

The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standardized margins. Its statistical recovery is an important step in learning problems involving observations far away from the center. In the common situation that the components of th

Mahasweta Bagchi, Jens Brede, Aline Ramires, Yoichi Ando

Among the family of odd-parity topological superconductors derived from $\mathrm{Bi}_{2}\mathrm{Se}_{3}$, $\mathrm{Cu}_{x}(\mathrm{PbSe})_{5}(\mathrm{Bi}_{2}\mathrm{Se}_{3})_{6}$ (CPSBS) has been elucidated to have gap nodes. Although the nodal gap structure has been established by specific-heat and thermal-conductivity measurements, there has been no direct

Maximilian Wegge, Roman Klinger

The most prominent subtask in emotion analysis is emotion classification; to assign a category to a textual unit, for instance a social media post. Many research questions from the social sciences do, however, not only require the detection of the emotion of an author of a post but to understand who is ascribed an emotion in text. This task is tackled by emo

Evgeny A. Stepanov, Silke Biermann

Spin- or orbital-selective behaviours in correlated electron materials offer rich promise for spintronics or orbitronics phenomena and applications deriving from them. Strong local electronic Coulomb correlations might lead to an orbital-selective Mott state, characterised by the coexistence of localized electrons in some orbitals with itinerant electrons in

Yuki Tanaka, Yutaka Yamaguti

To understand the ability and limitations of convolutional neural networks to generate time series that mimic complex temporal signals, we trained a generative adversarial network consisting of deep convolutional networks to generate chaotic time series and used nonlinear time series analysis to evaluate the generated time series. A numerical measure of dete

Ryosuke Ihara, Kazuyuki Yagasaki

The continuum limit provides a useful tool for analyzing coupled oscillator networks. Recently, Medvedev (Comm. Math. Sci., 17 (2019), no. 4, pp. 883-898) gave a mathematical foundation for such an approach when the networks are defined on single graphs which may be dense or sparse, directed or undirected, and deterministic or random. In this paper, we consi

A. Hetherington, A. Corrochano, R. Abadía-Heredia, E. Lazpita

This article presents an innovative open-source software named ModelFLOWs-app, written in Python, which has been created and tested to generate precise and robust hybrid reduced order models (ROMs) fully data-driven. By integrating modal decomposition and deep learning methods in diverse ways, the software uncovers the fundamental patterns in dynamic systems

Guang Jun Nicholas Ang, Aritejh Kr Goil, Henryk Chan, Jieyi Jeric Lew

In a landscape characterized by heightened connectivity and mobility, coupled with a surge in cardiovascular ailments, the imperative to curtail healthcare expenses through remote monitoring of cardiovascular health has become more pronounced. The accurate detection and classification of cardiac arrhythmias are pivotal for diagnosing individuals with heart i

Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang

Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning has been widely adopted which derives high-quality sentence representations by pulling similar semantics closer and pushing dissimilar ones away. However, these methods fail to ca

Ehsan Sabouni, H. M. Sabbir Ahmad, Christos G. Cassandras, Wenchao Li

We address the problem of merging traffic from two roadways consisting of both Connected Autonomous Vehicles (CAVs) and Human Driven Vehicles (HDVs). Guaranteeing safe merging in such mixed traffic settings is challenging due to the unpredictability of possibly uncooperative HDVs. We develop a hierarchical controller where at each discrete time step first a

I-Hung Hsu, Avik Ray, Shubham Garg, Nanyun Peng

Existing efforts on text synthesis for code-switching mostly require training on code-switched texts in the target language pairs, limiting the deployment of the models to cases lacking code-switched data. In this work, we study the problem of synthesizing code-switched texts for language pairs absent from the training data. We introduce GLOSS, a model built

Oona Rainio, Toshiyuki Sugawa, Matti Vuorinen

Using the definition of uniformly perfect sets in terms of convergent sequences, we apply lower bounds for the Hausdorff content of a uniformly perfect subset $E$ of $\mathbb{R}^n$ to prove new explicit lower bounds for the Hausdorff dimension of $E.$ These results also yield lower bounds for capacity test functions, which we introduce, and enable us to char

Samuël Borza, Kenshiro Tashiro

We initiate the study of synthetic curvature-dimension bounds in sub-Finsler geometry. More specifically, we investigate the measure contraction property $\mathsf{MCP}(K, N)$, and the geodesic dimension on the Heisenberg group equipped with an $\ell^p$-sub-Finsler norm. We show that for $p\in(2,\infty]$, the $\ell^p$-Heisenberg group fails to satisfy any of

Qian-Sheng Zhang, Li Yan, Wu Tao, Jiang Chen

In the overshoot mixing model with an exponentially decreasing diffusion coefficient, the initial value of the diffusion coefficient plays a crucial role. According to the turbulent convective mixing model, the characteristic length of convection in the convection zone differs from that in the overshoot region, resulting in a rapid decrease of the diffusion

Kerstin Weinberg, Laurent Strainier, Sergio Conti, Michael Ortiz

We resort to game theory in order to formulate Data-Driven methods for solid mechanics in which stress and strain players pursue different objectives. The objective of the stress player is to minimize the discrepancy to a material data set, whereas the objective of the strain player is to ensure the admissibility of the mechanical state, in the sense of comp

Rabisankar Samanta, Romain Pierrat, Rémi Carminati, Sushil Mujumdar

We report experimental and theoretical investigations on photon diffusion in a second-order nonlinear disordered medium under conditions of strong nonlinearity. Experimentally, photons at the fundamental wavelength ($\lambda=1064$ nm) are launched into the structure in the form of a cylindrical pellet, and the second-harmonic ($\lambda=532$ nm) photons are t

Luka Santelj

In the forward end-cap of the Belle II spectrometer, particle identification is provided by a proximity focusing RICH detector with an aerogel radiator (ARICH). The ARICH's primary function is to effectively distinguish between pions and kaons in the momentum range of 0.5 GeV/c to about 4 GeV/c, as well as to contribute to identification of low-momentum lept

Sitan Li, Chien Chern Cheah

In most control applications, theoretical analysis of the systems is crucial in ensuring stability or convergence, so as to ensure safe and reliable operations and also to gain a better understanding of the systems for further developments. However, most current deep learning methods are black-box approaches that are more focused on empirical studies. Recent

José Luis Cisneros-Molina, Agustín Romano-Velázquez

A mixed function is a real analytic map $f\colon \mathbb{C}^n \to \mathbb{C}$ in the complex variables $z_1,\dots,z_n$ and their conjugates $\bar{z}_1,\dots,\bar{z}_n$. In this article we define an integer valued index for vector fields $v$ with isolated singularity at $\mathbf{0}$ on real analytic varieties $V_f:=f^{-1}(0)$ defined by mixed functions $f$ wi

Vít Novotný, Kristýna Luger, Michal Štefánik, Tereza Vrabcová

Although pre-trained named entity recognition (NER) models are highly accurate on modern corpora, they underperform on historical texts due to differences in language OCR errors. In this work, we develop a new NER corpus of 3.6M sentences from late medieval charters written mainly in Czech, Latin, and German. We show that we can start with a list of known hi

Qichao Wang, Huan Ma, Wentao Wei, Hangyu Li

The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning. The evolution of DRM architecture has been driven by changes in data f

Lisa Kausch, Sarina Thomas, Holger Kunze, Jan Siad El Barbari

Surgical treatment of complicated knee fractures is guided by real-time imaging using a mobile C-arm. Immediate and continuous control is achieved via 2D anatomy-specific standard views that correspond to a specific C-arm pose relative to the patient positioning, which is currently determined manually, following a trial-and-error approach at the cost of time

Wei Yu, Qing-Cui Bu, He-Xin Liu, Yue Huang

We have performed a spectral-timing analysis on the black hole X-ray binary MAXI J1535--571 during its 2017 outburst, with the aim of exploring the evolution of the inner accretion flow geometry. X-ray reverberation lags are observed in the hard-intermediate state (HIMS) and soft-intermediate state (SIMS) of the outburst. During the HIMS, the characteristic

Lena Patterer, Pavel Ondračka, Dimitri Bogdanovski, Stanislav Mráz

To understand the interfacial bond formation between polycarbonate (PC) and magnetron-sputtered metal nitride thin films, PC | X interfaces (X = AlN, TiN, TiAlN) are comparatively investigated by ab initio simulations as well as X-ray photoelectron spectroscopy. The simulations predict significant differences at the interface, as N and Ti form bonds with all

Mike Steel

The emergence of an autocatalytic network from an available set of elements is a fundamental step in early evolutionary processes, such as the origin of metabolism. Given a set of elements, the reactions between them (chemical or otherwise), and certain elements catalysing certain reactions, a Reflexively Autocatalytic F-generated (RAF) set is a subset $R'$

Jeeho Hyun, Sangyun Kim, Giyoung Jeon, Seung Hwan Kim

Anomaly detection is crucial to the advanced identification of product defects such as incorrect parts, misaligned components, and damages in industrial manufacturing. Due to the rare observations and unknown types of defects, anomaly detection is considered to be challenging in machine learning. To overcome this difficulty, recent approaches utilize the com

Shashwat Mishra, Rishabh Raj, Siddhartha P. Chakrabarty

In this article, we present a novel approach for the construction of an environment-friendly green portfolio using the ESG ratings, and application of the modern portfolio theory to present what we call as the ``green efficient frontier'' (wherein the environmental score is included as a third dimension to the traditional mean-variance framework). Based on t

Hongwei Qu, Yuanchang Li

Exciton binding energy and lifetime are the two most important parameters controlling exciton dynamics, and the general consensus is that the larger the former the larger the latter. However our first-principles study of monolayer ferroelectric TiOCl$_2$ shows that this is not always the case. We find that ferroelectric polarization tends to weaken exciton b

Mohammed Ali Aamir, Paul Jamet Suria, José Antonio Marín Guzmán, Claudia Castillo-Moreno

Although classical thermal machines power industries and modern living, quantum thermal engines have yet to prove their utility. Here, we demonstrate a useful quantum absorption refrigerator formed from superconducting circuits. We use it to cool a transmon qubit to a temperature lower than that achievable with any one available bath, thereby resetting the q

Shota Kikuchi, Tatsuo Kobayashi, Kaito Nasu, Shohei Takada

We study of fermion zero-modes on magnetized $T^6/\mathbb{Z}_N$ orbifolds. In particular, we focus on non-factorizable orbifolds, i.e. $T^6/\mathbb{Z}_7$ and $T^6/\mathbb{Z}_{12}$ corresponding to $SU(7)$ and $E_6$ Lie lattices respectively. The number of degenerated zero-modes corresponds to the generation number of low energy effective theory in four dimen

Yi Loo, Chen Gong, Malika Meghjani

A major challenge for deep reinforcement learning (DRL) agents is to collaborate with novel partners that were not encountered by them during the training phase. This is specifically worsened by an increased variance in action responses when the DRL agents collaborate with human partners due to the lack of consistency in human behaviors. Recent work have sho

The Cherenkov Telescope Array Consortium

A deep survey of the Large Magellanic Cloud at ~0.1-100TeV photon energies with the Cherenkov Telescope Array is planned. We assess the detection prospects based on a model for the emission of the galaxy, comprising the four known TeV emitters, mock populations of sources, and interstellar emission on galactic scales. We also assess the detectability of 30 D

Kazuya Horibe, Keisuke Suzuki, Takato Horii, Hiroshi Ishiguro

A firm cognitive subject or ``individual'' is presupposed for the emergence of mind. However, with the development of recent information technology, the ``individual'' has become more dispersed in society and the cognitive subject has become increasingly unstable and adaptive, necessitating an update in our understanding of the ``individual''. Autopoiesis se

Momir Stankovic, He Ting, Rafal Madonski

In this paper, we uncover a new connection between standard PI/PID controllers and active disturbance rejection control (ADRC), from which we establish formal conditions of equivalence between the two control schemes. Using the equivalence, we devise a step-by-step procedure of transitioning from PI/PID to error-based ADRC. We also show how to go from 1DOF t

Kartik Ahuja, David Lopez-Paz

In-context learning, a capability that enables a model to learn from input examples on the fly without necessitating weight updates, is a defining characteristic of large language models. In this work, we follow the setting proposed in (Garg et al., 2022) to better understand the generality and limitations of in-context learning from the lens of the simple y

Cornelia Gruber, Patrick Oliver Schenk, Malte Schierholz, Frauke Kreuter

Supervised machine learning and predictive models have achieved an impressive standard today, enabling us to answer questions that were inconceivable a few years ago. Besides these successes, it becomes clear, that beyond pure prediction, which is the primary strength of most supervised machine learning algorithms, the quantification of uncertainty is releva

Matti Pekkanen, Francesco Verdoja, Ville Kyrki

Accurate maps are a prerequisite for virtually all mobile robot tasks. Most state-of-the-art maps assume a static world; therefore, dynamic objects are filtered out of the measurements. However, this division ignores movable but non-moving -- i.e., semi-static -- objects, which are usually recorded in the map and treated as static objects, violating the stat

Yixin Wan, Kuan-Hao Huang, Kai-Wei Chang

Syntactically controlled paraphrase generation requires language models to generate paraphrases for sentences according to specific syntactic structures. Existing fine-tuning methods for this task are costly as all the parameters of the model need to be updated during the training process. Inspired by recent studies on parameter-efficient learning, we propos

Timothée Schmude, Laura Koesten, Torsten Möller, Sebastian Tschiatschek

We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences. We shortly introduce the importance of explaining ADM systems, give a brief overview of approaches drawing from other disciplines to improve explanations, and present the results of our qualitative task-b

Seongyeon Park, Bohyung Kim, Tae-hyun Oh

Recently, zero-shot TTS and VC methods have gained attention due to their practicality of being able to generate voices even unseen during training. Among these methods, zero-shot modifications of the VITS model have shown superior performance, while having useful properties inherited from VITS. However, the performance of VITS and VITS-based zero-shot model

Yonghui Wang, Wengang Zhou, Yunyao Mao, Houqiang Li

Segment anything model (SAM) has achieved great success in the field of natural image segmentation. Nevertheless, SAM tends to consider shadows as background and therefore does not perform segmentation on them. In this paper, we propose ShadowSAM, a simple yet effective framework for fine-tuning SAM to detect shadows. Besides, by combining it with long short

Vishal Vivek Saley, Rocktim Jyoti Das, Dinesh Raghu, Mausam

Task-oriented dialog (TOD) agents often ground their responses on external knowledge bases (KBs). These KBs can be dynamic and may be updated frequently. Existing approaches for learning TOD agents assume the KB snapshot contemporary to each individual dialog is available during training. However, in real-world scenarios, only the latest KB snapshot is avail

Axi Niu, Kang Zhang, Trung X. Pham, Pei Wang

Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods. However, degradation-estimation-based methods may be inaccurate in estimating the degradation, making them less applicable to real-world LR images. On the other hand, blind-based methods are often limite

Bang Liu, Li-Hua Zhang, Zong-Kai Liu, Zi-An Deng

Microwave sensing has important applications in areas such as data communication and remote sensing, so it has received much attention from international academia, industry, and governments. Atomic wireless sensing uses the strong response of the large electric dipole moment of a Rydberg atom to an external field to achieve precise measurement of a radio fre

Omer Madmon, Idan Pipano, Itamar Reinman, Moshe Tennenholtz

We study a game-theoretic information retrieval model in which strategic publishers aim to maximize their chances of being ranked first by the search engine while maintaining the integrity of their original documents. We show that the commonly used Probability Ranking Principle (PRP) ranking scheme results in an unstable environment where games often fail to

Itai Arieli, Omer Madmon, Moshe Tennenholtz

In this paper, we introduce a two-stage Bayesian persuasion model in which a third-party platform controls the information available to the sender about users' preferences. We aim to characterize the optimal information disclosure policy of the platform, which maximizes average user utility, under the assumption that the sender also follows its own optimal p

The Pierre Auger Collaboration, A. Abdul Halim, P. Abreu, M. Aglietta

The combined fit of the measured energy spectrum and shower maximum depth distributions of ultra-high-energy cosmic rays is known to constrain the parameters of astrophysical models with homogeneous source distributions. Studies of the distribution of the cosmic-ray arrival directions show a better agreement with models in which a fraction of the flux is non

Rahul Vishwakarma, Ravi Monani, Amin Rezaei, Hossein Sayadi

The Global Wearable market is anticipated to rise at a considerable rate in the next coming years and communication is a fundamental block in any wearable device. In communication, encryption methods are being used with the aid of microcontrollers or software implementations, which are power-consuming and incorporate complex hardware implementation. Internet

Benjamin Walker, Felix Krones, Ivan Kiskin, Guy Parsons

This study presents our team PathToMyHeart's contribution to the George B. Moody PhysioNet Challenge 2022. Two models are implemented. The first model is a Dual Bayesian ResNet (DBRes), where each patient's recording is segmented into overlapping log mel spectrograms. These undergo two binary classifications: present versus unknown or absent, and unknown ver

Dehua Tao, Tan Lee, Harold Chui, Sarah Luk

Counseling is an activity of conversational speaking between a therapist and a client. Therapist empathy is an essential indicator of counseling quality and assessed subjectively by considering the entire conversation. This paper proposes to encode long counseling conversation using a hierarchical attention network. Conversations with extreme values of empat

Moein Khalighi, Faïçal Ndaïrou, Leo Lahti

This research presents an advanced fractional-order compartmental model designed to delve into the complexities of COVID-19 transmission dynamics, specifically accounting for the influence of environmental pathogens on disease spread. By enhancing the classical compartmental framework, our model distinctively incorporates the effects of order derivatives and

Vítězslav Kala, Mikuláš Zindulka

We study partitions of complex numbers as sums of non-negative powers of a fixed algebraic number $\beta$. We prove that if $\beta$ is real quadratic, then the number of partitions is always finite if and only if some conjugate of $\beta$ is larger than 1. Further, we show that for $\beta$ satisfying a certain condition, the partition function attains all no

In-Ug Yoon, Tae-Min Choi, Young-Min Kim, Jong-Hwan Kim

Few-shot class-incremental learning (FSCIL) presents the primary challenge of balancing underfitting to a new session's task and forgetting the tasks from previous sessions. To address this challenge, we develop a simple yet powerful learning scheme that integrates effective methods for each core component of the FSCIL network, including the feature extracto

Yongjia Wang, Qingfeng Li

Our knowledge of the properties of dense nuclear matter is usually obtained indirectly via nuclear experiments, astrophysical observations, and nuclear theory calculations. Advancing our understanding of the nuclear equation of state (EOS, which is one of the most important properties and of central interest in nuclear physics) has relied on various data pro

Qi Chen, Yutong Xie, Biao Wu, Xiaomin Chen

Automating radiology report generation can ease the reporting workload for radiologists. However, existing works focus mainly on the chest area due to the limited availability of public datasets for other regions. Besides, they often rely on naive data-driven approaches, e.g., a basic encoder-decoder framework with captioning loss, which limits their ability

Xing Lili, Bao Wendi, Li Weiguo

In this paper, several row and column orthogonal projection methods are proposed for solving matrix equation $AXB=C$, where the matrix $A$ and $B$ are full rank or rank deficient and equation is consistent or not. These methods are iterative methods without matrix multiplication. It is theoretically proved these methods converge to the solution or least-squa

Zhihui Xie, Zichuan Lin, Deheng Ye, Qiang Fu

Recent research in offline reinforcement learning (RL) has demonstrated that return-conditioned supervised learning is a powerful paradigm for decision-making problems. While promising, return conditioning is limited to training data labeled with rewards and therefore faces challenges in learning from unsupervised data. In this work, we aim to utilize genera

Muhammad Ahmad

Hyperspectral Image Classification (HSIC) is a difficult task due to high inter and intra-class similarity and variability, nested regions, and overlapping. 2D Convolutional Neural Networks (CNN) emerged as a viable network whereas, 3D CNNs are a better alternative due to accurate classification. However, 3D CNNs are highly computationally complex due to the

Zhaoheng Zheng, Haidong Zhu, Ram Nevatia

In this paper, we study the problem of Compositional Zero-Shot Learning (CZSL), which is to recognize novel attribute-object combinations with pre-existing concepts. Recent researchers focus on applying large-scale Vision-Language Pre-trained (VLP) models like CLIP with strong generalization ability. However, these methods treat the pre-trained model as a bl

Bonan Kou, Muhao Chen, Tianyi Zhang

Software developers often resort to Stack Overflow (SO) to fill their programming needs. Given the abundance of relevant posts, navigating them and comparing different solutions is tedious and time-consuming. Recent work has proposed to automatically summarize SO posts to concise text to facilitate the navigation of SO posts. However, these techniques rely o

Yuma Katsuki, Naoki Ishikawa

In this paper, we propose an optimal but low-complexity optimization method for nonsquare differential massive MIMO. While a discrete nonlinear optimization is required for the conventional nonsquare differential coding, we newly modify it to perform a low-complexity continuous linear optimization. This novel method exhibits immediate convergence as compared

Prit Pritam Paikaray, Sanghamitra Beuria, Nigam Chandra Parida

In this paper, a computational method is developed to find an approximate solution of the stochastic Volterra-Fredholm integral equation using the Walsh function approximation and its operational matrix. Moreover, convergence and error analysis of the method is carried out to strengthen the validity of the method. Furthermore, the method is numerically compa

Xuan Zhang, Liang Zhang, Zhihui Zhang, Xiaoxu Huang

First-principles calculations were carried out to study the segregation behavior of Mg, and Cu and their effect on the energy and mechanical properties of different Al grain boundaries (GBs). Four symmetrical tilt GBs were selected for study, namely {\Sigma}5[001](210) GB, {\Sigma}5[001](310) GB, {\Sigma}9[110](221) GB, and {\Sigma}11[110](332) GB. The resul

Anton V. Baranikov, Egor Khaidarov, Emmanuel Lassalle, Damien Eschimese

Metalenses, in order to compete with conventional bulk optics in commercial imaging systems, often require large field of view (FOV) and broadband operation simultaneously. However, strong chromatic and coma aberrations present in common metalens designs have so far limited their widespread use. Stacking of metalenses as one of the possible solutions increas

Yongqi Li, Nan Yang, Liang Wang, Furu Wei

Instead of simply matching a query to pre-existing passages, generative retrieval generates identifier strings of passages as the retrieval target. At a cost, the identifier must be distinctive enough to represent a passage. Current approaches use either a numeric ID or a text piece (such as a title or substrings) as the identifier. However, these identifier

Robert J. Chapman, Samuel Häusler, Giovanni Finco, Fabian Kaufmann

Quantum computers comprise elementary logic gates that initialize, control and measure delicate quantum states. One of the most important gates is the controlled-NOT, which is widely used to prepare two-qubit entangled states. The controlled-NOT gate for single photon qubits is normally realized as a six-mode network of individual beamsplitters. This archite

Shuntaro Murai, Naoki Sato, Zensho Yoshida

The Clebsch representation of a velocity field represents an effective tool for the analysis of physical properties of fluid flows. Indeed, a suitable choice of Clebsch potentials can be used to extract structural features that would otherwise be hidden within the complexity of fluid patterns and their evolution. In this work, we report the solution of the t

K Ashok Kumar, Nirjan Biswas

Let $B, B'\subset \mathbb{R}^d$ with $d\geq 2$ be two balls such that $B'\subset \subset B$ and the position of $B'$ is varied within $B$. For $p\in (1, \infty ),$ $s\in (0,1)$, and $q \in [1, p^*_s)$ with $p^*_s=\frac{dp}{d-sp}$ if $sp < d$ and $p^*_s=\infty $ if $sp \geq d$, let $\lambda ^s_{p,q}(B\setminus \overline{B'})$ be the first $q$-eigenvalue of th

Mohammad Pedramfar, Christopher John Quinn, Vaneet Aggarwal

This paper presents a unified approach for maximizing continuous DR-submodular functions that encompasses a range of settings and oracle access types. Our approach includes a Frank-Wolfe type offline algorithm for both monotone and non-monotone functions, with different restrictions on the general convex set. We consider settings where the oracle provides ac

Lily Whitler, Daniel P. Stark, Ryan Endsley, Zuyi Chen

Several studies have detected Lyman-alpha (Ly$\alpha$) from bright ($M_\mathrm{UV}\lesssim-21.5$) galaxies during the early stages of reionization despite the significantly neutral intergalactic medium. To explain these detections, it has been suggested that $z>7$ Ly$\alpha$ emitters (LAEs) inhabit physical Mpc (pMpc)-scale ionized regions powered by overden

Koji Hashimoto, Keiju Murata, Norihiro Tanahashi, Ryota Watanabe

Recently, Krylov complexity was proposed as a measure of complexity and chaoticity of quantum systems. We consider the stadium billiard as a typical example of the quantum mechanical system obtained by quantizing a classically chaotic system, and numerically evaluate Krylov complexity for operators and states. Despite no exponential growth of the Krylov comp

Naoki Sato, Michio Yamada

The Generalized Hasegawa-Mima (GHM) equation, which generalizes the standard Hasegawa-Mima (HM) equation, is a nonlinear equation describing the evolution of drift wave turbulence in curved magnetic fields. The GHM equation can be obtained from a drift wave turbulence ordering that does not involve ordering conditions on spatial derivatives of the magnetic f

Masahito Hasegawa, Jean-Simon Pacaud Lemay

A Hopf monad, in the sense of Brugui\`eres, Lack, and Virelizier, is a special kind of monad that can be defined for any monoidal category. In this note, we study Hopf monads in the case of a category with finite biproducts, seen as a symmetric monoidal category. We show that for biproducts, a Hopf monad is precisely characterized as a monad equipped with an

Carlo Orrieri, Luca Scarpa

We prove refined space-time regularity for the classical stochastic Allen-Cahn equation with logarithmic potential. This allows to establish a random separation property, i.e. that the trajectories of the solution are strictly separated from the potential barriers. The present contribution extends the results obtained in Bertacco et al. (2022), where separat

Yixin Wan, Yuan Zhou, Xiulian Peng, Kai-Wei Chang

Noise suppression (NS) models have been widely applied to enhance speech quality. Recently, Deep Learning-Based NS, which we denote as Deep Noise Suppression (DNS), became the mainstream NS method due to its excelling performance over traditional ones. However, DNS models face 2 major challenges for supporting the real-world applications. First, high-perform

Heejin Do, Yunsu Kim, Gary Geunbae Lee

With rapid technological growth, automatic pronunciation assessment has transitioned toward systems that evaluate pronunciation in various aspects, such as fluency and stress. However, despite the highly imbalanced score labels within each aspect, existing studies have rarely tackled the data imbalance problem. In this paper, we suggest a novel loss function

Xuming Hu, Aiwei Liu, Zeqi Tan, Xin Zhang

Relation extraction (RE) tasks show promising performance in extracting relations from two entities mentioned in sentences, given sufficient annotations available during training. Such annotations would be labor-intensive to obtain in practice. Existing work adopts data augmentation techniques to generate pseudo-annotated sentences beyond limited annotations

Dong Liu, Yufeng Pei, Limeng Xia, Kaiming Zhao

In this paper, we classify simple smooth modules over the superconformal current algebra $\frak g$. More precisely, we first classify simple smooth modules over the Heisenberg-Clifford algebra, and then prove that any simple smooth $\frak g$-module is a tensor product of such modules for the super Virasoro algebra and the Heisenberg-Clifford algebra, or an i

Tianxiao Zhang, Andrés M. Bur, Shannon Kraft, Hannah Kavookjian

Flexible laryngoscopy is commonly performed by otolaryngologists to detect laryngeal diseases and to recognize potentially malignant lesions. Recently, researchers have introduced machine learning techniques to facilitate automated diagnosis using laryngeal images and achieved promising results. Diagnostic performance can be improved when patients' demograph

Naoki Sato, Michio Yamada

Recently, a generalized Hasegawa-Mima (gHM) equation describing drift wave turbulence in curved magnetic fields has been derived in [N. Sato and M. Yamada, J. Plasma Phys. (2022), vol. 88, 905880319] for an ion-electron plasma modeled as a two-fluid system. In this work, we show that a mathematically equivalent GHM equation can be obtained within the kinetic

A. V. Kurilin

Motions of a material point along a set of parabolas are studied, taking into account the forces of Coulomb friction. The obtained results are compared with similar motions along the cycloid. The analysis is carried out using numerical calculations in the Mathcad program.