July 2022 arXiv papers — page 147

Hao Wang, Bin Guo, Yating Zeng, Yasan Ding

The intelligent dialogue system, aiming at communicating with humans harmoniously with natural language, is brilliant for promoting the advancement of human-machine interaction in the era of artificial intelligence. With the gradually complex human-computer interaction requirements (e.g., multimodal inputs, time sensitivity), it is difficult for traditional

Zhengchuan Chen, Dapeng Deng, Howard H. Yang, Nikolaos Pappas

We study the average Age of Information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process. Although the double queue parallel transmission is instrumental in reducing AoI, the out of order of data arrivals also imposes a significant challenge to the performance analysis. We consider two settings: the M-M

M. Kobayashi, K. Yumino, T. Ogawa, A. Shoji

We have developed a novel technique for the measurement of the size of avalanche fluctuations of gaseous detectors using a gating device (gating foil) prepared for the time projection chamber in the international linear collider experiment (ILD-TPC). In addition to the gating function, the gating foil is capable of controlling the average fraction of drift e

Aaron Chan, Shaoliang Nie, Liang Tan, Xiaochang Peng

Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior. However, free-text rationales' unconstrained nature makes them prone to hallucination, so it is important to have metrics for free-text rationale quality. Existing free-text rationale metrics measure how consistent the rationale

STAR Collaboration, M. S. Abdallah, B. E. Aboona, J. Adam

Measurements of mass and $\Lambda$ binding energy of $\rm ^4_{\Lambda}H$ and $\rm ^4_{\Lambda}He$ in Au+Au collisions at $\sqrt{s_{_{\rm NN}}}=3$ GeV are presented, with an aim to address the charge symmetry breaking (CSB) problem in hypernuclei systems with atomic number A = 4. The $\Lambda$ binding energies are measured to be $\rm 2.22\pm0.06(stat.) \pm0.1

Dheeraj Kulkarni, Monika Yadav

In this article, we explore a polynomial invariant for Legendrian knots which is a natural extension of Jones polynomial for (topological) knots. To this end, a new type of skein relation is introduced for the front projections of Legendrian knots. Further, we give a categorification of the polynomial invariant for Legendrian knots which is a natural extensi

Xin Tong, Zhaoyang Zhang, Yihan Zhang, Zhaohui Yang

In this paper, we consider the problem of sensing the environment within a wireless cellular framework. Specifically, multiple user equipments (UEs) send sounding signals to one or multiple base stations (BSs) and then a centralized processor retrieves the environmental information from all the channel information obtained at the BS(s). Taking into account t

Xun Gao, Liwei Duan, Pinghua Tang, Junlong Tian

We have obtained the solutions of the multimode quantum Rabi model when all modes have identical frequencies $\omega$, including dark states $|\phi_K\rangle$ with at least $K$ $(K=1,2,3,\ldots)$ photons. Extended to the multiqubit case, they lie close to another dark state $\vert \psi\rangle$ with at most one photon in the spectrum. Taking advantages of such

Daniel Korzekwa, Jaime Lorenzo-Trueba, Thomas Drugman, Bozena Kostek

The research community has long studied computer-assisted pronunciation training (CAPT) methods in non-native speech. Researchers focused on studying various model architectures, such as Bayesian networks and deep learning methods, as well as on the analysis of different representations of the speech signal. Despite significant progress in recent years, exis

Oladapo Fapetu, Segun Michael Ojo, Adekunle Alexander Balogun, Adeoba Adepoju Asaolu

The study examined the relationship between capital market performance and the macroeconomic dynamics in Nigeria, and it utilized secondary data spanning 1993 to 2020. The data was analyzed using vector error correction model (VECM) technology. The result revealed a significant long run relationship between capital market performance and macroeconomic dynami

Xiaoying Pang, Qi Shu, Long Wang, M. B. N. Kouwenhoven

We study the origin of the UV-excess in star clusters by performing N-body simulations of six clusters with N=10k and N=100k (single stars & binary systems) and metallicities of Z=0.01, 0.001, and 0.0001, using PETAR. All models initially have a 50 percent primordial binary fraction. Using GalevNB we convert the simulated data into synthetic spectra and phot

S. K. Maurya, G. Mustafa, M. Govender, Ksh. Newton Singh

In this work we take our cue from the observations of gravitational waves of the GW190814 event which suggests that source of the signals can be ascribed to a compact binary coalescence of a 22.2 to 24.3$ M_{\odot}$ black hole and a compact object endowed with a mass of 2.50 to 2.67$M_{\odot}$. In the current exposition, we are concerned with modeling of the

Naresh Garg, Neeraj Misra

We consider component-wise estimation of order restricted location/scale parameters $\theta_1$ and $\theta_2$ ($\theta_1\leq \theta_2$) of a general bivariate distribution under the squared error loss function. To find improvements over the best location/scale equivariant estimators (BLEE/BSEE) of $\theta_1$ and $\theta_2$, we study isotonic regression of su

Richard Dengler

A central and common aspect of renormalizable field theories is scale invariance of the action integral. This note introduces the software tool \emph{Kanon}, which allows to assemble arbitrary action integrals interactively, and to determine their critical dimension and scale invariance. The tool contains more than 60 well-known models with comments and refe

Wenao Ma, Cheng Chen, Shuang Zheng, Jing Qin

Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classification nets usually degrades. Such a label distribution shift problem is common in medical diagnosis since the prevalence of disease vary over location and time. In this paper, we

Kai Jin, Danna Zhang, Canhui Zhang

Sequence partition problems arise in many fields, such as sequential data analysis, information transmission, and parallel computing. In this paper, we study the following partition problem variant: given a sequence of $n$ items $1,\ldots,n$, where each item $i$ is associated with weight $w_i$ and another parameter $s_i$, partition the sequence into several

Arsenii Onuchin

Artificial and natural neural network models are a new toolkit which could be potentially have been used for clarifying of complex brain functions. To attend this goal, such models need to be neurobiologically realistic. However, although neural networks have advanced keenly in recent decades their strict similarity in aspects of brain anatomy and physiology

O. Khorunzhiy

We study a family of tree-type diagrams that arise in studies of the cumulant expansion in discrete Erd\H os-R\'enyi random matrix models. Using a version of the Pr\" ufer code, we obtain an explicit expression for the number of tree-type diagrams assembled from $k$ oriented chains of $q$ edges. Using this modified Pr\"ufer codification, we get an explicit e

Ritika Goel

We initiate a systematic development of $F_N(a, b; t)$, a finite analogue of Fine's function $F(a, b; t)$. Our results are transformations between $F_N(a, b; t)$ and $F_N(aq^{\ell}, bq^{m}; tq^{n})$, where $\ell,m$ and $n$ take the values $0$ or $1$.

Loukas Grafakos, Danqing He, Petr Honzik, Bae Jun Park

We study $m$-linear homogeneous rough singular integral operators $\mathcal{L}_{\Omega}$ associated with integrable functions $\Omega$ on $\mathbb{S}^{mn-1}$ with mean value zero. We prove boundedness for $\mathcal{L}_{\Omega}$ from $L^{p_1}\times \cdots \times L^{p_m}$ to $L^p$ when $1<p_1,\dots, p_m<\infty$ and $1/p=1/p_1+\cdots +1/p_m$ in the largest poss

Shiyuan Hu, Shihan Xiao

The transport capacity of a communication network can be characterized by the transition from a free-flow state to a congested state. Here, we propose a dynamic routing strategy in complex networks based on hierarchical bypass selections. The routing decisions are made by the reinforcement learning agents implemented at selected nodes with high betweenness c

Ruinan Jin, Xiaoxiao Li

Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research. Training generative adversarial neural networks (GAN) usually requires large amounts of training data. Federated learning (FL) provides a way of training a central model using distributed data from different medical

Qiulin Chen, Jan P. Allebach

Image enhancement helps to generate balanced lighting distributions over faces. Our goal is to get an illuminance-balanced enhanced face image from a single view. Traditionally, image enhancement methods ignore the 3D geometry of the face or require a complicated multi-view geometry. Other methods cause color tone shifting or over saturation. Inspired by the

Shahaf Bassan, Yossi Adi, Jeffrey S. Rosenschein

Symbolic music segmentation is the process of dividing symbolic melodies into smaller meaningful groups, such as melodic phrases. We proposed an unsupervised method for segmenting symbolic music. The proposed model is based on an ensemble of temporal prediction error models. During training, each model predicts the next token to identify musical phrase chang

Jiaxiang Liu, Yunhan Xing, Xiaomu Shi, Fu Song

As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical domains. While there are techniques for verifying DNNs with formal guarantees, they are limited in scalability and accuracy. In this paper, we present a novel abstraction-refinement a

Akari Asai, Shayne Longpre, Jungo Kasai, Chia-Hsuan Lee

We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typologically diverse languages. In this task, we adapted two large-scale cross-lingual open-retrieval QA datasets in 14 typologically diverse languages, and newly annotated open-retri

Yu-Ying Yeh, Zhengqin Li, Yannick Hold-Geoffroy, Rui Zhu

Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene, a framework that takes input image(s) of a scene along with approximately aligned CAD geometry (either reconstructed automatically or manually specified) and builds a photorealistic digital twin with high-qualit

Liumeng Xue, Shan Yang, Na Hu, Dan Su

Building a voice conversion system for noisy target speakers, such as users providing noisy samples or Internet found data, is a challenging task since the use of contaminated speech in model training will apparently degrade the conversion performance. In this paper, we leverage the advances of our recently proposed Glow-WaveGAN and propose a noise-independe

Chong Zheng, Shengheng Liu, Yongming Huang, Wei Zhang

Nowadays wireless communication is rapidly reshaping entire industry sectors. In particular, mobile edge computing (MEC) as an enabling technology for industrial Internet of things (IIoT) brings powerful computing/storage infrastructure closer to the mobile terminals and, thereby, significant lowers the response latency. To reap the benefit of proactive cach

Shixin Xu, Robert Eisenberg, Zilong Song, Huaxiong Huang

A mathematical model for chemical reactions in electrolytes is developed using an Energy variational method consistent with classical thermodynamics. Electrostatics and chemical reactions are included in properly defined energetic and dissipative functionals. The energy variation method is generalized to deal with open systems with inputs of charge, mass, an

B. Ananthanarayan, Diganta Das, M. S. A. Alam Khan

We determine the strange quark mass ($m_s$) and quark mixing element $\vert V_{us}\vert $, and their joint determination from the Cabibbo suppressed hadronic $\tau$ decays in various perturbative schemes. Compared to the previous analysis based on the optimal renormalization or the renormalization group summed perturbation theory (RGSPT) scheme, we have impr

Ana-Maria Bucur, Adrian Cosma, Liviu P. Dinu, Paolo Rosso

This work proposes a transformer architecture for user-level classification of gambling addiction and depression that is trainable end-to-end. As opposed to other methods that operate at the post level, we process a set of social media posts from a particular individual, to make use of the interactions between posts and eliminate label noise at the post leve

Md Mamunur Rasid, Masato Kimura, Md Masum Murshed, Erny Rahayu Wijayanti

This study presents a two-step Lagrange-Galerkin scheme for the shallow water equations with a transmission boundary condition (TBC). Firstly, the experimental order of convergence of the scheme is shown to see the second-order accuracy in time. Secondly, the effect of the TBC on a simple domain is discussed; the artificial reflections are kept from the Diri

Jianyi Yang, Shaolei Ren

By integrating domain knowledge with labeled samples, informed machine learning has been emerging to improve the learning performance for a wide range of applications. Nonetheless, rigorous understanding of the role of injected domain knowledge has been under-explored. In this paper, we consider an informed deep neural network (DNN) with over-parameterizatio

Chao Yang, Ru He, Fangquan Lin, Suoyuan Song

Our goal is to build general representation (embedding) for each user and each product item across Alibaba's businesses, including Taobao and Tmall which are among the world's biggest e-commerce websites. The representation of users and items has been playing a critical role in various downstream applications, including recommendation system, search, marketi

Wenli Zhu, Liangqing Luo

The Set-Union Knapsack Problem (SUKP) and Budgeted Maximum Coverage Problem (BMCP) are two closely related variant problems of the popular knapsack problem. Given a set of weighted elements and a set of items with nonnegative values, where each item covers several distinct elements, these two problems both aim to find a subset of items that maximizes an obje

Pedro H. Luz de Araujo, Ana Paula G. S. de Almeida, Fabricio A. Braz, Nilton C. da Silva

The Brazilian Supreme Court receives tens of thousands of cases each semester. Court employees spend thousands of hours to execute the initial analysis and classification of those cases -- which takes effort away from posterior, more complex stages of the case management workflow. In this paper, we explore multimodal classification of documents from Brazil's

Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le

Recent research has shown that rationales, or step-by-step chains of thought, can be used to improve performance in multi-step reasoning tasks. We reconsider rationale-augmented prompting for few-shot in-context learning, where (input -> output) prompts are expanded to (input, rationale -> output) prompts. For rationale-augmented prompting we demonstrate how

Zeqiu Wu, Ryu Parish, Hao Cheng, Sewon Min

In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types according to the available knowledge sources. However, most current studies either fail to or artificially incorporate such agent-side initiative. This work presents InSCIt, a dataset

Javad Ansarifar, Faezeh Akhavizadegan, Lizhi Wang

Producing higher-quality crops within shortened breeding cycles ensures global food availability and security, but this improvement intensifies logistical and productivity challenges for seed industries in the year-round breeding process due to the storage limitations. In the 2021 Syngenta crop challenge in analytics, Syngenta raised the problem to design an

Zeyu Xiong, Daizong Liu, Pan Zhou

Spatial-Temporal Video Grounding (STVG) is a challenging task which aims to localize the spatio-temporal tube of the interested object semantically according to a natural language query. Most previous works not only severely rely on the anchor boxes extracted by Faster R-CNN, but also simply regard the video as a series of individual frames, thus lacking the

Miyu Suzuki, Hiroyoshi Tamori

Let $G=\mathrm{GL}_{2n}(\mathbb{R})$ or $G=\mathrm{GL}_n(\mathbb{H})$ and $H=\mathrm{GL}_n(\mathbb{C})$ regarded as a subgroup of $G$. Here, $\mathbb{H}$ is the quaternion division algebra over $\mathbb{R}$. For a character $\chi$ on $\mathbb{C}^\times$, we say that an irreducible smooth admissible moderate growth representation $\pi$ of $G$ is $\chi_H$-dist

Jinwoo Hwang, Minsu Kim, Daeun Kim, Seungho Nam

Modern retrospective analytics systems leverage cascade architecture to mitigate bottleneck for computing deep neural networks (DNNs). However, the existing cascades suffer two limitations: (1) decoding bottleneck is either neglected or circumvented, paying significant compute and storage cost for pre-processing; and (2) the systems are specialized for tempo

J. Mumford

A 2D Fock-state lattice (FSL is constructed from the many-body states of two interacting two-mode quantum gases. By periodically driving the interspecies interactions and pulsing the tunneling between the two modes of each gas, a synthetic gauge field is generated. We derive an effective Hamiltonian in the short pulse limit which resembles the Harper-Hofstad

Yujia Li, Shunbo Lei, Wei Sun, Chenxi Hu

When performing the resilience enhancement for distribution networks, there are two obstacles to reliably model the uncertain contingencies: 1) decision-dependent uncertainty (DDU) due to various line hardening decisions, and 2) distributional ambiguity due to limited outage information during extreme weather events (EWEs). To address these two challenges, t

Zhi Lu, Vrizlynn L. L. Thing

The explanation to an AI model's prediction used to support decision making in cyber security, is of critical importance. It is especially so when the model's incorrect prediction can lead to severe damages or even losses to lives and critical assets. However, most existing AI models lack the ability to provide explanations on their prediction results, despi

Yichen Feng, Ming Min, Jean-Pierre Fouque

The aim of this paper is to study a new methodological framework for systemic risk measures by applying deep learning method as a tool to compute the optimal strategy of capital allocations. Under this new framework, systemic risk measures can be interpreted as the minimal amount of cash that secures the aggregated system by allocating capital to the single

Xiaocheng Tang, Soheil Sadeghi Eshkevari, Haoyu Chen, Weidan Wu

Transformers have enabled breakthroughs in NLP and computer vision, and have recently began to show promising performance in trajectory prediction for Autonomous Vehicle (AV). How to efficiently model the interactive relationships between the ego agent and other road and dynamic objects remains challenging for the standard attention module. In this work we p

Shaoshan Liu, Jianda Wang, Zhendong Wang, Bo Yu

This paper is the first to provide a thorough system design overview along with the fusion methods selection criteria of a real-world cooperative autonomous driving system, named Infrastructure-Augmented Autonomous Driving or IAAD. We present an in-depth introduction of the IAAD hardware and software on both road-side and vehicle-side computing and communica

Jingbang Chen, Li Chen, Yang P. Liu, Richard Peng

In 2013, Cuturi [Cut13] introduced the Sinkhorn algorithm for matrix scaling as a method to compute solutions to regularized optimal transport problems. In this paper, aiming at a better convergence rate for a high accuracy solution, we work on understanding the Sinkhorn algorithm under regularization scheduling, and thus modify it with a mechanism that adap

Benyou Wang, Xiangbo Wu, Xiaokang Liu, Jianquan Li

Language is the principal tool for human communication, in which humor is one of the most attractive parts. Producing natural language like humans using computers, a.k.a, Natural Language Generation (NLG), has been widely used for dialogue systems, chatbots, machine translation, as well as computer-aid creation e.g., idea generations, scriptwriting. However,

Y. -Z. Cai, A. Pastorello, M. Fraser, X. -F. Wang

We present an observational study of the luminous red nova (LRN) AT\,2021biy in the nearby galaxy NGC\,4631. The field of the object was routinely imaged during the pre-eruptive stage by synoptic surveys, but the transient was detected only at a few epochs from $\sim 231$\,days before maximum brightness. The LRN outburst was monitored with unprecedented cade

Keyu Wen, Zhenshan Tan, Qingrong Cheng, Cheng Chen

Recently, the cross-modal pre-training task has been a hotspot because of its wide application in various down-streaming researches including retrieval, captioning, question answering and so on. However, exiting methods adopt a one-stream pre-training model to explore the united vision-language representation for conducting cross-modal retrieval, which easil

Bharadwaj Manda, Prasad Kendre, Subhrajit Dey, Ramanathan Muthuganapathy

Search and retrieval remains a major research topic in several domains, including computer graphics, computer vision, engineering design, etc. A search engine requires primarily an input search query and a database of items to search from. In engineering, which is the primary context of this paper, the database consists of 3D CAD models, such as washers, pis

Zhihua Zhou, Yongtao Tan, Qian Yang, Achintya Bera

Nanoporous membranes based on two dimensional materials are predicted to provide highly selective gas transport in combination with extreme permeability. Here we investigate membranes made from multilayer graphdiyne, a graphene-like crystal with a larger unit cell. Despite being nearly a hundred of nanometers thick, the membranes allow fast, Knudsen-type per

Arindam Banerjee, Huy Tai Ha

Let $k$ be a field, let $A$ and $B$ be polynomial rings over $k$, and let $S= A \otimes_k B$. Let $I \subseteq A$ and $J \subseteq B$ be monomial ideals. We establish a binomial expansion for rational powers of $I+J \subseteq S$ in terms of those of $I$ and $J$. Particularly, for a positive rational number $u$, we prove that $(I+J)_u = \sum_{0 \le \omega \le

William Merrill, Ashish Sabharwal

Despite their omnipresence in modern NLP, characterizing the computational power of transformer neural nets remains an interesting open question. We prove that transformers whose arithmetic precision is logarithmic in the number of input tokens (and whose feedforward nets are computable using space linear in their input) can be simulated by constant-depth lo

Lei Cai, Yuli Fu, Wanliang Huo, Youjun Xiang

Multi-scale architectures and attention modules have shown effectiveness in many deep learning-based image de-raining methods. However, manually designing and integrating these two components into a neural network requires a bulk of labor and extensive expertise. In this article, a high-performance multi-scale attentive neural architecture search (MANAS) fra

S. Jalalzadeh

We investigate the quantum cosmology of a closed spatially homogeneous and isotropic Friedmann-Lema\^itre-Robertson-Walker (FLRW) minisuperspace model with electromagnetic radiation as matter content. We solve the corresponding Wheeler-DeWitt equation by utilizing Riemann's zeta function regularization method. We demonstrate that the regularized vacuum energ

Zhiyu Huang, Xiaoyu Mo, Chen Lv

This paper proposes a novel deep learning framework for multi-modal motion prediction. The framework consists of three parts: recurrent neural networks to process the target agent's motion process, convolutional neural networks to process the rasterized environment representation, and a distance-based attention mechanism to process the interactions among dif

J. Senthilnath, K. Harikumar, S. Suresh

This paper presents a new Metacognitive Decision Making (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search without communication for detecting stationary targets (fixed/sudden pop-up) and dynamic targets. The UAVs are equipped with

Zhongyuan Zhang, Yi Qian, Yanxiang Zhao, Lin Zhu

Unlike ordinary computer vision tasks that focus more on the semantic content of images, the image manipulation detection task pays more attention to the subtle information of image manipulation. In this paper, the noise image extracted by the improved constrained convolution is used as the input of the model instead of the original image to obtain more subt

Qiao Zhang, Yingke Ji, Siqi Hu, Zhiwen Li

We report a high-responsive hot-electron photodetector based on the integration of an Au-MoS$_2$ junction with a silicon nitride microring resonator (MRR) for detecting telecom-band light. The coupling of the evanescent field of the silicon nitride MRR with the Au-MoS$_2$ Schottky junction region enhances the hot-electron injection efficiency. The device exh

Anil S, Sudharani R, Suresh N

The study focuses on the Impact of Employment Benefit Cots on the Profitability of Companies listed in the National Stock Exchange. The study has considered the Amount spent on Employment Benefit Cots as an Independent variable and Profit after tax, Total Assets, Return on Equity, and Return on Asset and Debt equity Ration as the Dependent variable. The pres

Sarvesh Patil, Samuel C. Alvares, Pragna Mannam, Oliver Kroemer

This paper presents the DeltaZ robot, a centimeter-scale, low-cost, delta-style robot that allows for a broad range of capabilities and robust functionalities. Current technologies allow DeltaZ to be 3D-printed from soft and rigid materials so that it is easy to assemble and maintain, and lowers the barriers to utilize. Functionality of the robot stems from

P. Aishwarya, Sudharani R, Suresh N

Current research helps in understanding both positive and negative impacts of capital structure on profits of Indian automobile companies by using variables like Return on Capital Employed, Return on Long Term Funds, Return on Net Worth, Gross Profit Margin, and Operating Profit, and Return on Asset. The study hypothesized that RoCE, RoLT, and RoNW have a po

Jin Liu, Chongfeng Fan, Fengyu Zhou, Huijuan Xu

The knowledge graph (KG) stores a large amount of structural knowledge, while it is not easy for direct human understanding. Knowledge graph-to-text (KG-to-text) generation aims to generate easy-to-understand sentences from the KG, and at the same time, maintains semantic consistency between generated sentences and the KG. Existing KG-to-text generation meth

Guangliang Gao, Weichao Liang, Ming Yuan, Hanwei Qian

The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and ne

Yuliy Baryshnikov, Stephen Melczer, Robin Pemantle

Let F be the quotient of an analytic function with a product of linear functions. Working in the framework of analytic combinatorics in several variables, we compute asymptotic formulae for the Taylor coefficients of F using multivariate residues and saddle-point approximations. Because the singular set of F is the union of hyperplanes, we are able to make e

Nandini E. S, Sudharani R, Suresh N

The study focuses on the Impact of Environmental Accounting on the Profitability of Companies listed on the Bombay Stock Exchange. The study has considered the Amount spent on Environmental protection as an Independent variable and Return on Capital Employed, Return on Assets, Return on Net worth/equity, Net Profit Margin, and Dividend per Share as the Depen

Suresh N, Pooja M

Determination of the correct mix of dividend and retained earnings and its effect on profitability has been a subject of controversy in financial management literature. This paper seeks to contribute to the ongoing debate by examining the relationship between dividend payout policy and the financial performance of 60 firms listed on the National Stock Exchan

Yuan-Ze Yang, Tian-Xiang Zhu, Zhi-Peng Li, Xiao-Dong Zeng

Optically addressable spins in two-dimensional hexagonal boron nitride (hBN) attract widespread attention for their potential advantage in on-chip quantum devices, such as quantum sensors and quantum network. A variety of spin defects have been found in hBN, but no convenient and deterministic generation methods have been reported for other defects except ne

Yanwei Jia, Xun Yu Zhou

We study the continuous-time counterpart of Q-learning for reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation introduced by Wang et al. (2020). As the conventional (big) Q-function collapses in continuous time, we consider its first-order approximation and coin the term ``(little) q-function". This function i

Yang Zhao, Yan Song

Temporal action segmentation in videos has drawn much attention recently. Timestamp supervision is a cost-effective way for this task. To obtain more information to optimize the model, the existing method generated pseudo frame-wise labels iteratively based on the output of a segmentation model and the timestamp annotations. However, this practice may introd

Targyn A. Nauryz, Adriana C. Briozzo

In this article we study a mathematical model of the heat transfer in semi infinite material with a variable cross section, when the radial component of the temperature gradient can be neglected in comparison with the axial component is considered. In particular, the temperature distribution in liquid and solid phases of such kind of body can be modelled by

Benjamin Carleton, Michael C. Chavrimootoo, Lane A. Hemaspaandra, David E. Narváez

[HHM20] discovered, for 7 pairs (C,D) of seemingly distinct standard electoral control types, that C and D are identical: For each input I and each election system, I is a Yes instance of both C and D, or of neither. Surprisingly this had gone undetected, even as the field was score-carding how many std. control types election systems were resistant to; vari

Fernanda Sánchez-Puig, Rogelio Lozano-Aranda, Dante Pérez-Méndez, Ewan Colman

Statistical linguistics has advanced considerably in recent decades as data has become available. This has allowed researchers to study how statistical properties of languages change over time. In this work, we use data from Twitter to explore English and Spanish considering the rank diversity at different scales: temporal (from 3 to 96 hour intervals), spat

Ziwen Han, Evgeniya Gorobets, Pan Chen

Biological neurons are more powerful than artificial perceptrons, in part due to complex dendritic input computations. Inspired to empower the perceptron with biologically inspired features, we explore the effect of adding and tuning input branching factors along with input dropout. This allows for parameter efficient non-linear input architectures to be dis

David R. Stoutemyer

A strict integer Laurent polynomial in a variable $x$ is 0 or a sum of one or more terms having integer coefficients times $x$ raised to a negative integer exponent. Equations that can be transformed to certain such polynomials times $\exp(-x)=\mathit{constant}$ are exactly solvable by inverses of modified spherical Bessel functions of the second kind $k_{n}

Theresa Breiner, Swaroop Ramaswamy, Ehsan Variani, Shefali Garg

Personalization of speech models on mobile devices (on-device personalization) is an active area of research, but more often than not, mobile devices have more text-only data than paired audio-text data. We explore training a personalized language model on text-only data, used during inference to improve speech recognition performance for that user. We exper

Feng Xue, Weizhong Yan

Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned representations enable anomaly detection as the normality model is trained to capture certain key underlying data regularities under normal circumstances. In practical settings, particularly industrial time series anomal

Andrey Gogolev, Yi Shi

Let $f\colon\mathbb{T}^d\to\mathbb{T}^d$ be an Anosov diffeomorphism whose linearization $A\in{\rm GL}(d,\mathbb{Z})$ is irreducible. Assume that $f$ is also absolutely partially hyperbolic where a weak stable subbundle is considered as the center subbundle. We show that if the strong stable and unstable subbundles are jointly integrable, then $f$ is dynamic

Bin Chen, Nan Li, Siwei Liu

We obtain a partial parallelism of the complex structure on K\"ahler Finsler manifolds. As applications, we prove Synge-Tsukamoto theorem and Bonnet-Myers theorem for positively curved K\"ahler Finsler manifolds. Moreover, we generalize a comparison theorem due to Ni-Zheng by introducing the notion of orthogonal Ricci curvature to K\"ahler Finsler geometry.

C. Y. Jiang, Y. X. Yang, Y. X. Gao, Z. T. Wan

We report results of magnetization, specific-heat and muon-spin relaxation measurements on single crystals of disorder-free Yb$^{3+}$ triangular lattice Yb(BaBO$_3$)$_3$. The magnetization experiments show anisotropic magnetic properties with Curie-Weiss temperatures $\theta_{\perp}=-1.40$~K ($H \perp c$) and $\theta_{\parallel}=-1.16$~K ($H \parallel c$) de

Tom Hutchcroft

We study the growth and isoperimetry of infinite clusters in slightly supercritical Bernoulli bond percolation on transitive nonamenable graphs under the $L^2$ boundedness condition ($p_c<p_{2\to 2}$). Surprisingly, we find that the volume growth of infinite clusters is always purely exponential (that is, the subexponential corrections to growth are bounded)

Riccardo Durastanti, Lorenzo Giacomelli

In the regime of lubrication approximation, we look at spreading phenomena under the action of singular potentials of the form $P(h)\approx h^{1-m}$ as $h\to 0^+$ with $m>1$, modeling repulsion between the liquid-gas interface and the substrate. We assume zero slippage at the contact line. Based on formal analysis arguments, we report that for any $m>1$ and

Toshinori Oaku

We compute Bernstein-Sato polynomials of some pairs of topologically equivalent plane curve singularities. Some pairs have the same Tjurina number but distinct Bernstein-Sato polynomials, which implies that they are not analytically equivalent.

Canberk Ekmekci, Mujdat Cetin

Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction methods. Although deep unrolling methods achieve state-of-the-art performance for imaging problems and allow the incorporation of the observation model into the reconstruction process,

Konstantinos Dovelos, Stylianos D. Assimonis, Hien Quoc Ngo, Michail Matthaiou

Terahertz (THz) communication is widely deemed the next frontier of wireless networks owing to the abundant spectrum resources in the THz band. Whilst THz signals suffer from severe propagation losses, a massive antenna array can be deployed at the base station (BS) to mitigate those losses through beamforming. Nevertheless, a very large number of antennas i

Andreas Mantziris, Tommi Markkanen, Arttu Rajantie

Based on the current experimental data, the Standard Model predicts that the current vacuum state of the Universe is metastable, leading to a non-zero rate of vacuum decay through nucleation of bubbles of true vacuum. Our existence implies that there cannot have been any such bubble nucleation events anywhere in our whole past lightcone. We consider a minima

David M. Williams, Qingguo Hong

The purpose of this paper is to construct a new class of discrete generalized Korn's inequalities for piecewise H2 vector fields in three-dimensional space. The resulting Korn's inequalities are different from the standard Korn's inequalities, as they involve the trace-free symmetric gradient operator, in place of the usual symmetric gradient operator. It is

Maximilian Kaufmann, Yiren Zhao, Ilia Shumailov, Robert Mullins

Neural networks are susceptible to adversarial examples-small input perturbations that cause models to fail. Adversarial training is one of the solutions that stops adversarial examples; models are exposed to attacks during training and learn to be resilient to them. Yet, such a procedure is currently expensive-it takes a long time to produce and train model

Julen Balzategui, Luka Eciolaza

In industry, Deep Neural Networks have shown high defect detection rates surpassing other more traditional manual feature engineering based proposals. This has been achieved mainly through supervised training where a great amount of data is required in order to learn good classification models. However, such amount of data is sometimes hard to obtain in indu

Charles Khazoom, Daniel Gonzalez-Diaz, Yanran Ding, Sangbae Kim

This work combines control barrier functions (CBFs) with a whole-body controller to enable self-collision avoidance for the MIT Humanoid. Existing reactive controllers for self-collision avoidance cannot guarantee collision-free trajectories as they do not leverage the robot's full dynamics, thus compromising kinematic feasibility. In comparison, the propose

Robert Wolfe, Aylin Caliskan

Three state-of-the-art language-and-image AI models, CLIP, SLIP, and BLIP, are evaluated for evidence of a bias previously observed in social and experimental psychology: equating American identity with being White. Embedding association tests (EATs) using standardized images of self-identified Asian, Black, Latina/o, and White individuals from the Chicago F

Zahari Kassabov, Emanuele R. Nocera, Michael Wilson

We show how an inaccurate determination of experimental uncertainty correlations in high-precision LHC measurements may undermine the reliability of the associated $\chi^2$. We formulate the problem rigorously, and devise a regularisation procedure that increases the stability of the $\chi^2$ by altering the covariance matrix of the measurement as little as

Hyunwoong Chang, Changwoo J. Lee, Zhao Tang Luo, Huiyan Sang

The multiple-try Metropolis (MTM) algorithm is an extension of the Metropolis-Hastings (MH) algorithm by selecting the proposed state among multiple trials according to some weight function. Although MTM has gained great popularity owing to its faster empirical convergence and mixing than the standard MH algorithm, its theoretical mixing property is rarely s

Perez Ogayo, Graham Neubig, Alan W Black

Modern speech synthesis techniques can produce natural-sounding speech given sufficient high-quality data and compute resources. However, such data is not readily available for many languages. This paper focuses on speech synthesis for low-resourced African languages, from corpus creation to sharing and deploying the Text-to-Speech (TTS) systems. We first cr

Jun Yang, James D Whitfield

The construction of a better exchange-correlation potential in time-dependent density functional theory (TDDFT) can improve the accuracy of TDDFT calculations and provide more accurate predictions of the properties of many-electron systems. Here, we propose a machine learning method to develop the energy functional and the Kohn-Sham potential of a time-depen

Mohammad Alidoosti, Davoud Nasr Esfahani, Reza Asgari

The coupling of electrons and phonons is governed wisely by the symmetry properties of the crystal structures. In particular, for two-dimensional (2D) systems, it has been suggested that the electrons do not couple to phonons with pure out-of-plane distortion, as long as there is a $\sigma_h$ symmetry. We show that such a statement is correct when constituen