May 2023 arXiv papers — page 27

Anton Golubev, Nicolay Rusnachenko, Natalia Loukachevitch

The paper describes the RuSentNE-2023 evaluation devoted to targeted sentiment analysis in Russian news texts. The task is to predict sentiment towards a named entity in a single sentence. The dataset for RuSentNE-2023 evaluation is based on the Russian news corpus RuSentNE having rich sentiment-related annotation. The corpus is annotated with named entities

Ming Shan Hee, Wen-Haw Chong, Roy Ka-Wei Lee

Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classification output. A major reason for the lack of explainable hateful meme methods is the absence of a hateful m

Collin Meese, Hang Chen, Syed Ali Asif, Wanxin Li

Accurate real-time traffic flow prediction can be leveraged to relieve traffic congestion and associated negative impacts. The existing centralized deep learning methodologies have demonstrated high prediction accuracy, but suffer from privacy concerns due to the sensitive nature of transportation data. Moreover, the emerging literature on traffic prediction

Jie Li, Bo Yang, Wenbin Lin

We investigate the hydrostatic equilibrium of white dwarfs within the framework of Rastall-Rainbow gravity, aiming to explore the effects of this modified gravitational theory on their properties. By employing the Chandrasekhar equation of state in conjunction with the modified Tolman-Oppenheimer-Volkoff equation, we derive the mass-radius relations for whit

Chao Chen Ye, W. L. Vleeshouwers, S. Heatley, V. Gritsev

Topological insulators have been studied intensively over the last decades. Earlier research focused on Hermitian Hamiltonians, but recently, peculiar and interesting properties were found by introducing non-Hermiticity. In this work, we apply a quantum geometric approach to various Hermitian and non-Hermitian versions of the Su-Schrieffer-Heeger (SSH) model

Tanishk Nandal, Vaibhav Fulara, Raj Kumar Singh

In the realm of computational fluid dynamics (CFD), accurate prediction of aerodynamic behaviour plays a pivotal role in aerofoil design and optimization. This study proposes a novel approach that synergistically combines autoencoders and Generative Adversarial Networks (GANs) for the purpose of generating CFD results. Our innovative framework harnesses the

Yingze Li, Hongzhi Wang, Xianglong Liu

Innovative learning based structures have recently been proposed to tackle index and cardinality estimation tasks, specifically learned indexes and data driven cardinality estimators. These structures exhibit excellent performance in capturing data distribution, making them promising for integration into AI driven database kernels. However, accurate estimati

Jawad Haidar, Douaa Khalil, Daniel Asmar

Photometric calibration is essential to many computer vision applications. One of its key benefits is enhancing the performance of Visual SLAM, especially when it depends on a direct method for tracking, such as the standard KLT algorithm. Another advantage could be in retrieving the sensor irradiance values from measured intensities, as a pre-processing ste

Ilya Tyuryukanov, Marjan Popov, Jorrit A. Bos, Mart A. M. M. van der Meijden

This paper presents a new formulation for intentional controlled islanding (ICI) of power transmission grids based on mixed-integer linear programming (MILP) DC optimal power flow (OPF) model. We highlight several deficiencies of the most well-known formulation for this problem and propose new enhancements for their improvement. In particular, we propose a n

Yurui Lai, Taiyan Zhang, Rui Fan

Graph Neural Networks (GNNs) have been highly successful for the node classification task. GNNs typically assume graphs are homophilic, i.e. neighboring nodes are likely to belong to the same class. However, a number of real-world graphs are heterophilic, and this leads to much lower classification accuracy using standard GNNs. In this work, we design a nove

Benjamin Bisping, David N. Jansen

We provide the first generalized game characterization of van Glabbeek's linear-time--branching-time spectrum with silent steps. Thereby, one multi-dimensional energy game can be used to characterize and decide a wide array of weak behavioral equivalences between stability-respecting branching bisimilarity and weak trace equivalence in one go. To establish c

Weize Chen, Xu Han, Yankai Lin, Zhiyuan Liu

Parameter-efficient tuning methods (PETs) have achieved promising results in tuning large pre-trained language models (PLMs). By formalizing frozen PLMs and additional tunable parameters as systems and controls respectively, PETs can be theoretically grounded to optimal control and further viewed as optimizing the terminal cost and running cost in the optima

Rustem Khasanov, Bin-Bin Ruan, Yun-Qing Shi, Gen-Fu Chen

The first-principles calculations and measurements of the magnetic penetration depths, the upper critical field, and the specific heat were performed for a family of Mo$_5$Si$_{3-x}$P$_x$ superconducotrs. First-principles calculations suggest the presence of a flat band dispersion, which gradually shifts to the Fermi level as a function of phosphorus doping

Yiqi Zhong, Xianming Liu, Deming Zhai, Junjun Jiang

Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series. However, the adversarial robustness of incremental learners has not been widely verified, leaving potential security risks. Specifically, for poisoning-based backdoor attacks, we argue

Pietro Menotti

Exploiting the generalization of the Weierstrass $\wp$ function to genus $2$ given by Komori, we give the exact connection of the related monodromy problem for genus $2$ and the classical weak $n$-point correlation functions. We also provide the Green function of an Helmholtz operator on genus $2$ surfaces and prove the real analyticity of the higher genus G

Edmund Dervakos, Konstantinos Thomas, Giorgos Filandrianos, Giorgos Stamou

Counterfactual explanations have been argued to be one of the most intuitive forms of explanation. They are typically defined as a minimal set of edits on a given data sample that, when applied, changes the output of a model on that sample. However, a minimal set of edits is not always clear and understandable to an end-user, as it could, for instance, const

Muhammad Yasir Muzayan Haq, Abhishta Abhishta, Raffaele Sommese, Mattijs Jonker

We conduct longitudinal and temporal analyses on active DNS measurement data to investigate how the Russia-Ukraine conflict impacted the network infrastructures supporting domain names under ICANN's CZDS new gTLDs. Our findings revealed changes in the physical locations of network infrastructures, utilization of managed DNS services, infrastructure redundanc

Kejie Tang, Weidong Liu, Yichen Zhang, Xi Chen

Stochastic gradient descent with momentum (SGDM) has been widely used in many machine learning and statistical applications. Despite the observed empirical benefits of SGDM over traditional SGD, the theoretical understanding of the role of momentum for different learning rates in the optimization process remains widely open. We analyze the finite-sample conv

Erdal Pekel, Martin Dierolf, Franz Pfeiffer, Tobias Lasser

This work presents methods for the seamless execution of arbitrary spherical trajectories with a seven-degree-of-freedom robotic arm as a sample holder. The sample holder is integrated into an existing X-ray computed tomography setup. We optimized the path planning and robot control algorithms for the seamless execution of spherical trajectories. A precision

Po-Ya Angela Wang, Pin-Er Chen, Hsin-Yu Chou, Yu-Hsiang Tseng

Multimodal corpora have become an essential language resource for language science and grounded natural language processing (NLP) systems due to the growing need to understand and interpret human communication across various channels. In this paper, we first present our efforts in building the first Multimodal Corpus for Languages in Taiwan (MultiMoco). Base

Congmin Liu, Zhuowei Sun, Hongyuan Cao

We consider varying-coefficient models for mixed synchronous and asynchronous longitudinal covariates, where asynchronicity refers to the misalignment of longitudinal measurement times within an individual. We propose three different methods of parameter estimation and inference. The first method is a one-step approach that estimates non-parametric regressio

Anuj Jakhar, Ravi Kalwaniya

Let $n \neq 8$ be a positive integer such that $n+1 \neq 2^u$ for any integer $u\geq 2$. Let $\phi(x)$ belonging to $\mathbb{Z}[x]$ be a monic polynomial which is irreducible modulo all primes less than or equal to $n+1$. Let $a_j(x)$ with $0\leq j\leq n-1$ belonging to $\mathbb{Z}[x]$ be polynomials having degree less than $\deg\phi(x)$. Assume that the con

Yefan Zhou, Yaoqing Yang, Arin Chang, Michael W. Mahoney

Recent work has highlighted the complex influence training hyperparameters, e.g., the number of training epochs, can have on the prunability of machine learning models. Perhaps surprisingly, a systematic approach to predict precisely how adjusting a specific hyperparameter will affect prunability remains elusive. To address this gap, we introduce a phenomeno

Anuj Jakhar

Let $c$ be a fixed integer such that $c \in \{0,2\}.$ Let $n$ be a positive integer such that either $n\geq 2$ or $2n+1 \neq 3^u$ for any integer $u\geq 2$ according as $c = 0$ or not. Let $\phi(x)$ belonging to $\mathbb{Z}[x]$ be a monic polynomial which is irreducible modulo all primes less than $2n+c$. Let $a_i(x)$ with $0\leq i\leq n-1$ belonging to $\ma

Mohamed Ghattassi, Xiaokai Huo, Nader Masmoudi

This paper is devoted to the diffusive limit of the nonlinear radiative heat transfer system with curved boundary domain (\textit{two dimensional disk}). The solution constructed in \cite{ghattassi2022convergence} by the leading order interior solution and the boundary layer corrections fails here to approximate the solutions in $L^\infty$ sense for the diff

Chaojun Xiao, Zhengyan Zhang, Xu Han, Chi-Min Chan

Large-scale pre-trained models (PTMs) have been widely used in document-oriented NLP tasks, such as question answering. However, the encoding-task coupling requirement results in the repeated encoding of the same documents for different tasks and queries, which is highly computationally inefficient. To this end, we target to decouple document encoding from d

Tian Chen, Kai Du, Zongyuan Huang, Zhen Wu

In this paper, we study an optimal control problem of a mean-field forward-backward stochastic system with random jumps in progressive structure, where both regular and singular controls are considered in our formula. In virtue of the variational technology, the related stochastic maximum principle (SMP) has been obtained, and it is essentially different fro

Ciro Ciliberto, Alessandro Verra, Francesco Zucconi

We prove that the surface $S(X)$ of bitangent lines of a general smooth quartic surface $X$ in $\mP^3$ has unobstructed deformations of dimension $20=h^1(S(X), T_{S(X)})$. In addition, we show that the space of infinitesimal embedded deformations of $X$ injects into the one of $S(X)$. Finally we prove that there is a natural birational map from the 20--dimen

Pintu Bhunia

Several numerical radius inequalities are studied by developing an extension of the Buzano's inequality. It is shown that if $T$ is a bounded linear operator on a complex Hilbert space, then \begin{eqnarray*} w^n(T) &\leq& \frac{1}{2^{n-1}} w(T^n)+ \sum_{k=1}^{n-1} \frac{1}{2^{k}} \left\|T^k \right\| \left\|T \right\|^{n-k}, \end{eqnarray*} for every positiv

Ken Chen, Jia-Hao Lü, Xin Zhu, Hao-Long Zhang

Critical phenomena of quantum systems are useful for enhancement of quantum sensing. However, experimental realizations of criticality enhancement have been confined to very few systems, owing to the stringent requirements, including the thermodynamical or scaling limit, and fine control of interacting quantum susystems or particles. We here propose a simple

Johnnatan Messias, Vabuk Pahari, Balakrishnan Chandrasekaran, Krishna P. Gummadi

Decentralized Autonomous Organizations (DAOs) have emerged as a novel governance mechanism in blockchain ecosystems, particularly within Decentralized Finance (DeFi). By enabling token holders to propose and vote on protocol changes, these systems promise transparent and equitable decision-making without centralized control. In this paper, we present an in-d

LiPing Lu, Qian Xiong, DuanFeng Chu, BingRong Xu

Image dehazing is a typical task in the low-level vision field. Previous studies verified the effectiveness of the large convolutional kernel and attention mechanism in dehazing. However, there are two drawbacks: the multi-scale properties of an image are readily ignored when a large convolutional kernel is introduced, and the standard series connection of a

Zhicheng Guo, Sijie Cheng, Yile Wang, Peng Li

Retrieval-augmented methods have received increasing attention to support downstream tasks by leveraging useful information from external resources. Recent studies mainly focus on exploring retrieval to solve knowledge-intensive (KI) tasks. However, the potential of retrieval for most non-knowledge-intensive (NKI) tasks remains under-explored. There are two

Jiapeng Wang, Chengyu Wang, Xiaodan Wang, Jun Huang

Large-scale pre-trained text-image models with dual-encoder architectures (such as CLIP) are typically adopted for various vision-language applications, including text-image retrieval. However,these models are still less practical on edge devices or for real-time situations, due to the substantial indexing and inference time and the large consumption of comp

Yifan Peng, Yui Sudo, Shakeel Muhammad, Shinji Watanabe

Self-supervised learning (SSL) has achieved notable success in many speech processing tasks, but the large model size and heavy computational cost hinder the deployment. Knowledge distillation trains a small student model to mimic the behavior of a large teacher model. However, the student architecture usually needs to be manually designed and will remain fi

Guan Wang, Yuhao Sun, Sijie Cheng, Sen Song

Recurrent spiking neural networks (RSNNs) hold great potential for advancing artificial general intelligence, as they draw inspiration from the biological nervous system and show promise in modeling complex dynamics. However, the widely-used surrogate gradient-based training methods for RSNNs are inherently inaccurate and unfriendly to neuromorphic hardware.

Zahra Atashgahi, Mykola Pechenizkiy, Raymond Veldhuis, Decebal Constantin Mocanu

Efficient time series forecasting has become critical for real-world applications, particularly with deep neural networks (DNNs). Efficiency in DNNs can be achieved through sparse connectivity and reducing the model size. However, finding the sparsity level automatically during training remains challenging due to the heterogeneity in the loss-sparsity tradeo

Yue Xu, Yong-Lu Li, Kaitong Cui, Ziyu Wang

Data-efficient learning has garnered significant attention, especially given the current trend of large multi-modal models. Recently, dataset distillation has become an effective approach by synthesizing data samples that are essential for network training. However, it remains to be explored which samples are essential for the dataset distillation process it

Saar Beck, Ronen Weiss, Nir Barnea

We analyze the asymptotic behaviour of the coupled cluster many-body wave-function in the limit of highly excited two- and three-particles states. We find that in this limit the different coupled cluster amplitudes exhibit a recurring behaviour, factorizing into a common asymptotic two- or three-body term. These asymptotic terms depend on the potential and i

Kim Hoang Tran, Anh Duy Le Dinh, Tien Phat Nguyen, Thinh Phan

Despite recent significant progress, Multi-Object Tracking (MOT) faces limitations such as reliance on prior knowledge and predefined categories and struggles with unseen objects. To address these issues, Generic Multiple Object Tracking (GMOT) has emerged as an alternative approach, requiring less prior information. However, current GMOT methods often rely

Utku Ayvaz, Chih-Hong Cheng, Hao Shen

While autonomous vehicles (AVs) may perform remarkably well in generic real-life cases, their irrational action in some unforeseen cases leads to critical safety concerns. This paper introduces the concept of collaborative reinforcement learning (RL) to generate challenging test cases for AV planning and decision-making module. One of the critical challenges

S. A. Simmons, J. C. Pillay, K. V. Kheruntsyan

We continue the study of dispersive quantum shock waves in a one-dimensional Bose gas beyond the mean-field approximation. In a recent work by Simmons et al. [Phys. Rev. Let. 125, 180401 (2020)], the oscillatory shock wave train developing in this system from an initial localized density bump on a uniform background was interpreted as a result of quantum mec

Kareem T. Elgindy, Hareth M. Refat

This study is concerned with the numerical solution of a class of infinite-horizon linear regulation problems with state equality constraints and output feedback control. We propose two numerical methods to convert the optimal control problem into nonlinear programming problems (NLPs) using collocations in a semi-infinite domain based on rational Gegenbauer

Su Yao, S. Komossa

As one of the drivers of feedback in active galactic nuclei (AGNs), the jets launched from supermassive black holes (SMBHs) are important for understanding the co-evolution of SMBHs and their host galaxies. However, the formation of AGN jets is far from clear. The discovery of gamma-ray narrow-line Seyfert 1 (NLS1) galaxies during the past two decades has pr

Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar

We consider solving equality-constrained nonlinear, nonconvex optimization problems. This class of problems appears widely in a variety of applications in machine learning and engineering, ranging from constrained deep neural networks, to optimal control, to PDE-constrained optimization. We develop an adaptive inexact Newton method for this problem class. In

Kyle Hsu, Will Dorrell, James C. R. Whittington, Jiajun Wu

In disentangled representation learning, a model is asked to tease apart a dataset's underlying sources of variation and represent them independently of one another. Since the model is provided with no ground truth information about these sources, inductive biases take a paramount role in enabling disentanglement. In this work, we construct an inductive bias

Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han

Label-noise learning (LNL) aims to increase the model's generalization given training data with noisy labels. To facilitate practical LNL algorithms, researchers have proposed different label noise types, ranging from class-conditional to instance-dependent noises. In this paper, we introduce a novel label noise type called BadLabel, which can significantly

Jin Sun, Xiaoshuang Shi, Zhiyuan Wang, Kaidi Xu

Modeling in Computer Vision has evolved to MLPs. Vision MLPs naturally lack local modeling capability, to which the simplest treatment is combined with convolutional layers. Convolution, famous for its sliding window scheme, also suffers from this scheme of redundancy and lower parallel computation. In this paper, we seek to dispense with the windowing schem

Sizhu Lu, Peng Ding

Causal inference with observational studies often suffers from unmeasured confounding, yielding biased estimators based on the unconfoundedness assumption. Sensitivity analysis assesses how the causal conclusions change with respect to different degrees of unmeasured confounding. Most existing sensitivity analysis methods work well for specific types of stat

Binh T. Bui

To economically produce from very low permeability shale formations, hydraulic fracturing stimulation is typically used to improve their conductivity. This process deforms and breaks the rock, hence requires the geomechanics data and calculation. The development of unconventional reservoirs requires large geomechanical data, and geomechanics has involved in

R. P. Woodard, B. Yesilyurt

Nonlinear sigma models on de Sitter background have proved a useful prototype for quantum gravity in summing the large logarithms which arise from loop corrections. We consider a model whose evolution is described, at leading logarithm order, by the trace of the coincident, doubly differentiated scalar propagator. An analytic approximation for this quantity

Jun-Gi Jang, Jeongyoung Lee, Yong-chan Park, U Kang

How can we efficiently and accurately analyze an irregular tensor in a dual-way streaming setting where the sizes of two dimensions of the tensor increase over time? What types of anomalies are there in the dual-way streaming setting? An irregular tensor is a collection of matrices whose column lengths are the same while their row lengths are different. In a

Debarshi Majumder, Binod Sreenivasan, Gaurav Maurya

The dipole-multipole transition in rapidly rotating dynamos is investigated through the analysis of forced magnetohydrodynamic waves in an unstably stratified fluid. The focus of this study is on the inertia-free limit applicable to planetary cores, where the Rossby number is small not only on the core depth but also on the length scale of columnar convectio

Jan Thorbecke, Mohammed Almobarak, Johno van IJsseldijk, Joeri Brackenhoff

The Marchenko algorithm can suppress the disturbing effects of internal multiples that are present in seismic reflection data. To achieve this, a set of coupled equations with four unknowns is solved. These coupled equations are separated into a set of two equations with two unknowns using a time window. The two unknown focusing functions can be resolved by

Takuro Mochizuki

The moduli space of stable Higgs bundles of degree $0$ is equipped with the hyperk\"ahler metric, called the Hitchin metric. On the locus where the spectral curves are smooth, there is the hyperk\"ahler metric called the semi-flat metric, associated with the algebraic integrable systems with the Hitchin section. We prove the exponentially rapid decay of the

Tapas Sahoo, Gautam Gangopadhyay

A path integral ground state approach has been used to estimate the ground-state energy and structural properties of hydrogen fluoride molecules pinned to a one-dimensional lattice. In the simulations, the molecules are assumed to be rigid, and only the continuous rotational degrees of freedom are considered. The constituents of a many-body system interact t

Tianqi Chen, Mingyuan Zhou

Learning to denoise has emerged as a prominent paradigm to design state-of-the-art deep generative models for natural images. How to use it to model the distributions of both continuous real-valued data and categorical data has been well studied in recently proposed diffusion models. However, it is found in this paper to have limited ability in modeling some

Edoardo D'Amico, Aonghus Lawlor, Neil Hurley

The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF). While it has been demonstrated that the graph convolution operation is connected to a filtering operation on the graph spectral domain, the theoretical rationale for why this leads to higher

Manas K Patra

Given a unitary operator $U$ acting on a composite quantum system what is the entangling capacity of $U$? This question is investigated using a geometric approach. The entangling capacity, defined via metrics on the unitary groups, leads to a \emph{minimax} problem. The dual, a \emph{maximin} problem, is investigated in parallel and yields some familiar enta

Christian Fronsdal

A dynamical model for Landau's original approach to superfluid Helium is presented, with two velocities but only one mass density. Second sound is an adiabatic perturbation that involves the temperature and the roton, aka the notoph. The action incorporates all the conservation laws, including the equation of continuity. With only 4 canonical variables it ha

Badih Ghazi, Ravi Kumar, Pasin Manurangsi

We obtain a new protocol for binary counting in the $\varepsilon$-shuffle-DP model with error $O(1/\varepsilon)$ and expected communication $\tilde{O}\left(\frac{\log n}{\varepsilon}\right)$ messages per user. Previous protocols incur either an error of $O(1/\varepsilon^{1.5})$ with $O_\varepsilon(\log{n})$ messages per user (Ghazi et al., ITC 2020) or an er

Youlong Ding, Xueyang Wu, Hao Wang, Weike Pan

The Transformer has emerged as a versatile and effective architecture with broad applications. However, it still remains an open problem how to efficiently train a Transformer model of high utility with differential privacy guarantees. In this paper, we identify two key challenges in learning differentially private Transformers, i.e., heavy computation overh

Xianji Piao, Sunkyu Yu, Namkyoo Park

Programmable photonic circuits (PPCs) have garnered substantial interest in achieving deep learning accelerations and universal quantum computations. Although photonic computation using PPCs offers critical advantages, including ultrafast operation, energy-efficient matrix calculation and room-temperature quantum states, its poor scalability impedes the inte

Zhiwei Jia, Pradyumna Narayana, Arjun R. Akula, Garima Pruthi

Image ad understanding is a crucial task with wide real-world applications. Although highly challenging with the involvement of diverse atypical scenes, real-world entities, and reasoning over scene-texts, how to interpret image ads is relatively under-explored, especially in the era of foundational vision-language models (VLMs) featuring impressive generali

Ana Carolina da Cruz, Camila P. E. de Souza

Change-point models deal with ordered data sequences. Their primary goal is to infer the locations where an aspect of the data sequence changes. In this paper, we propose and implement a nonparametric Bayesian model for clustering observations based on their constant-wise change-point profiles via Gibbs sampler. Our model incorporates a Dirichlet Process on

Jieqiu Shao, Mantas Naris, John Hauser, Marco M. Nicotra

The Quantum Projection Operator-Based NewtonMethod for Trajectory Optimization (Q-PRONTO) is a numerical method for solving quantum optimal control problems. This paper significantly improves prior versions of the quantum projection operator by introducing a regulator that stabilizes the solution estimate at every iteration. This modification is shown to not

Yuhan Hou, Jack Ji, Yi Zhu, Thomas Dell

Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease (PD). This work develops flexible wearable sensors that can detect FoG and alert patients and companions to help prevent falls. FoG is detected on the sensors using a deep learning (DL) model with multi-modal sensory inputs collected from distributed wireless sensors. Two types of wirele

Boris Houska

This paper is about operator-theoretic methods for solving nonlinear stochastic optimal control problems to global optimality. These methods leverage on the convex duality between optimally controlled diffusion processes and Hamilton-Jacobi-Bellman (HJB) equations for nonlinear systems in an ergodic Hilbert-Sobolev space. In detail, a generalized Bakry-Emery

Fei Wang, James Y. Huang, Tianyi Yan, Wenxuan Zhou

Natural language understanding (NLU) models often suffer from unintended dataset biases. Among bias mitigation methods, ensemble-based debiasing methods, especially product-of-experts (PoE), have stood out for their impressive empirical success. However, previous ensemble-based debiasing methods typically apply debiasing on top-level logits without directly

Xiaoyang Hu, Shane Storks, Richard L. Lewis, Joyce Chai

Analogical reasoning is a fundamental capacity of human cognition that allows us to reason abstractly about novel situations by relating them to past experiences. While it is thought to be essential for robust reasoning in AI systems, conventional approaches require significant training and/or hard-coding of domain knowledge to be applied to benchmark tasks.

Kang Xu, Chenjia Bai, Xiaoteng Ma, Dong Wang

Generalizing policies across different domains with dynamics mismatch poses a significant challenge in reinforcement learning. For example, a robot learns the policy in a simulator, but when it is deployed in the real world, the dynamics of the environment may be different. Given the source and target domain with dynamics mismatch, we consider the online dyn

Chuong Huynh, Yuqian Zhou, Zhe Lin, Connelly Barnes

In photo editing, it is common practice to remove visual distractions to improve the overall image quality and highlight the primary subject. However, manually selecting and removing these small and dense distracting regions can be a laborious and time-consuming task. In this paper, we propose an interactive distractor selection method that is optimized to a

Kang Xu, Chenjia Bai, Shuang Qiu, Haoran He

Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence. In reinforcement learning, rationally reusing the policies acquired from other tasks or human experts is critical for tackling problems that are difficult to learn from scratch. In this work, we present a framework called Selective Myopic bEhavior Control~(SMEC), whic

Yu-Ji Shi, Ye Xing, Zhi-Peng Xing

Using the recently developed $B$-Mesogenesis scenario, we studied the semi-inclusive decays of $B$ meson into a dark anti-baryon $\psi$ plus any possible states $X$ containing $u/c$ and $d/s$ quarks with unit baryon number. The two types of effective Lagrangians proposed by the scenario are both considered in the study. The semi-inclusive decay branching fra

Sekhar Jyoti Baishya

In this paper, among other results, we give some sufficient conditions for every non-abelian subgroup of a group to be isoclinic with the group itself. It is also seen that under certain conditions, two groups have same number of element centralizers implies they are isoclinic. We prove that if $G$ is any group having $4, 5, 7$ or $9$ element centralizers an

Wenwen Zhang, Hao Zhang

The Vernier effect has seen extensive application in optical structures, serving to augment the free spectral range (FSR). A substantial FSR is vital in a myriad of applications including multiplexers, enabling a broad, clear band comparable to the C-band to accommodate a maximum number of channels. Nevertheless, a large FSR often conflicts with bending loss

Md Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu, Mahsa Azizi

In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts using Natural Language Processing (NLP) techniques, it would be possible to quickly determine which calls are most relevant f

Diogo Gomes, Julian Gutierrez, Mathieu Laurière

We propose a machine learning method to solve a mean-field game price formation model with common noise. This involves determining the price of a commodity traded among rational agents subject to a market clearing condition imposed by random supply, which presents additional challenges compared to the deterministic counterpart. Our approach uses a dual recur

Liqun Qi, Chunfeng Cui

Dual complex matrices have found applications in brain science. There are two different definitions of the dual complex number multiplication. One is noncommutative. Another is commutative. In this paper, we use the commutative definition. This definition is used in the research related with brain science. Under this definition, eigenvalues of dual complex m

Milton Javier Cardenas Mendez, Armando Mauro Vasquez Corro

In this work we generalize the surfaces studied in [8], we define the generalization of Ribaucour-type surfaces (in short, GRT-surfaces). We obtain present a representation for GRT-surfaces with prescribed Gauss map which depends on two holomorphic functions and a real function l. We give explicit examples of GRT-surfaces. Also, we use this representation to

Ashadul Halder, Shashank Shekhar Pandey, A. S. Majumdar

We formulate a model of spacetime with inhomogeneous matter distribution in multiple domains. In the context of the backreaction framework using Buchert's averaging procedure, we evaluate the effect of backreaction due to the inhomogeneities on the late time global evolution of the Universe. Examining the future evolution of this universe, we find that it ca

Lei Bill Wang

A large degree of overidentification causes severe bias in TSLS. A conventional heuristic rule used to motivate new estimators in this context is approximate bias. This paper formalizes the definition of approximate bias and expands the applicability of approximate bias to various classes of estimators that bridge OLS, TSLS, and Jackknife IV estimators (JIVE

Rahul Arun, Tim Colonius

We simulate the head-on collision between vortex rings with circulation Reynolds numbers of 4000 using an adaptive, multiresolution solver based on the lattice Green's function. The simulation fidelity is established with integral metrics representing symmetries and discretization errors. Using the velocity gradient tensor and structural features of local st

Edesiri Bridget Nkemnole, Victor Adoghe

Due to population increase and import constraints, maize, a key cereal crop in Africa, is experiencing a boom in demand. Given this, the study's focus is on determining how maize output in Nigeria interacts with various climatic factors, particularly rainfall and temperature. The Hidden Markov Model (HMM) and the Long Short-Term Memory neural network (LSTM)

Nguyen Mau Nam, Gary Sandine, Nguyen Nang Thieu, Nguyen Dong Yen

In this paper, we present a novel concept of the Fenchel conjugate for set-valued mappings and investigate its properties in finite and infinite dimensions. After establishing the fundamental properties of the Fenchel conjugate for set-valued mappings, we derive its main calculus rules in various settings. Our approach is geometric and draws inspiration from

Syed Asjad, Aniket Gupta, Hanumant Singh

This report describes our approach for the EGO4D 2023 Visual Query 2D Localization Challenge. Our method aims to reduce the number of False Positives (FP) that occur because of high similarity between the visual crop and the proposed bounding boxes from the baseline's Region Proposal Network (RPN). Our method uses a transformer to determine similarity in hig

Liyang Qiu, Haidong Yuan, Saijun Wu

Precise control of hyperfine matterwaves via Raman excitations is instrumental to a class of atom-based quantum technology. We investigate the Raman spinor control technique for alkaline atoms in an intermediate regime of single-photon detuning where a choice can be made to balance the Raman excitation power efficiency with the control speed, excited-state a

I-Chao Shen, Fu-Yin Cherng, Takeo Igarashi, Wen-Chieh Lin

Interface icons are prevalent in various digital applications. Due to limited time and budgets, many designers rely on informal evaluation, which often results in poor usability icons. In this paper, we propose a unique human-in-the-loop framework that allows our target users, i.e., novice and professional UI designers, to improve the usability of interface

Ziang Song, Tianle Cai, Jason D. Lee, Weijie J. Su

The extraordinary capabilities of large language models (LLMs) such as ChatGPT and GPT-4 are in part unleashed by aligning them with reward models that are trained on human preferences, which are often represented as rankings of responses to prompts. In this paper, we document the phenomenon of \textit{reward collapse}, an empirical observation where the pre

Quzhe Huang, Yutong Hu, Shengqi Zhu, Yansong Feng

Event temporal relation extraction~(ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formulation ignores the meaning of relations and wipes out their intrinsic dependency. After examining the relation definitions in various ETRE tasks, we observe that all relations can be

Shan-Chang Tang, Yu Shi

We study the two-dimensional motion of a magnetic skyrmion driven by a ratchetlike polarized electric current that is periodic in both space and time. Some general cases are considered, in each of which,in the low temperature and adiabatic limit, regardless of the details of the driving current, the time and statistical average velocity along any direction i

Parosh Aziz Abdulla, Mohamed Faouzi Atig, Adwait Godbole, Shankaranarayanan Krishna

We consider the verification of omega-regular linear temporal properties of concurrent programs running under weak memory semantics. We observe that in particular, these properties may enforce liveness clauses, whose verification in this context is seldom studied. The challenge lies in precluding demonic nondeterminism arising due to scheduling, as well as d

Anya Katsevich

In Bayesian inference, a widespread technique to compute integrals against a high-dimensional posterior is to use a Gaussian proxy to the posterior known as the Laplace approximation. We address the question of accuracy of the approximation in terms of TV distance, in the regime in which dimension $d$ grows with sample size $n$. Multiple prior works have sho

Catie LeDesma, Kendall Mehling, Jieqiu Shao, John Drew Wilson

Performing interferometry in an optical lattice formed by standing waves of light offers potential advantages over its free-space equivalents since the atoms can be confined and manipulated by the optical potential. We demonstrate such an interferometer in a one dimensional lattice and show the ability to control the atoms by imaging and reconstructing the w

Caspar Oesterheld, Johannes Treutlein, Emery Cooper, Rubi Hudson

Proper scoring rules incentivize experts to accurately report beliefs, assuming predictions cannot influence outcomes. We relax this assumption and investigate incentives when predictions are performative, i.e., when they can influence the outcome of the prediction, such as when making public predictions about the stock market. We say a prediction is a fixed

Justin Lidard, Oswin So, Yanxia Zhang, Jonathan DeCastro

Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictors do not account for the interactive nature of multi-agent predictions, they may miss these important interaction outcomes. In this paper, we propose NashFormer, a framework for tr

Jiranan Kerdboon, Xiaowen Zhu

In this paper, we prove pure point spectrum for a large class of Schr\"odinger operators over circle maps with conditions on the rotation number going beyond the Diophantine. More specifically, we develop the scheme to obtain pure point spectrum for Schr\"odinger operators with monotone bi-Lipschitz potentials over orientation-preserving circle homeomorphism

Alex Crane, Brian Lavallee, Blair D. Sullivan, Nate Veldt

A recent trend in data mining has explored (hyper)graph clustering algorithms for data with categorical relationship types. Such algorithms have applications in the analysis of social, co-authorship, and protein interaction networks, to name a few. Many such applications naturally have some overlap between clusters, a nuance which is missing from current com

David T. Nguyen

We heuristically study the shifted convolution $\sum_{n\le X} \tau_k(n) \tau_\ell(n+h)$ using a normalized version of Ramanujan-Fourier expansions for $\tau_k(n)$ and verify they produce the expected answer.

Inigo Incer, Albert Benveniste, Richard M. Murray, Alberto Sangiovanni-Vincentelli

Deriving system-level specifications from component specifications usually involves the elimination of variables that are not part of the interface of the top-level system. This paper presents algorithms for eliminating variables from formulas by computing refinements or relaxations of these formulas in a context. We discuss a connection between this problem