May 2023 arXiv papers — page 89

Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi

Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its cells and complexity of the interactions. Generative models can provide more rapid sample production, but cur

John Friedlander

We survey and study some aspects of the distribution of primes in very short intervals.

Hanan S. Murayshid, Hafida Benhidour, Said Kerrache

This paper proposes a sequence-to-sequence learning approach for Arabic pronoun resolution, which explores the effectiveness of using advanced natural language processing (NLP) techniques, specifically Bi-LSTM and the BERT pre-trained Language Model, in solving the pronoun resolution problem in Arabic. The proposed approach is evaluated on the AnATAr dataset

Jonas Tallberg, Eva Erman, Markus Furendal, Johannes Geith

Artificial intelligence (AI) represents a technological upheaval with the potential to change human society. Because of its transformative potential, AI is increasingly becoming subject to regulatory initiatives at the global level. Yet, so far, scholarship in political science and international relations has focused more on AI applications than on the emerg

Honghao Gui, Shuofei Qiao, Jintian Zhang, Hongbin Ye

Large language models can perform well on general natural language tasks, but their effectiveness is still suboptimal for information extraction (IE). Recent works indicate that the main reason lies in the lack of extensive data on IE instructions. Note that the existing datasets on IE instructions not only have limited coverage but also involve high constru

Hao Liu, Huimin Ma, Tianyu Hu

The safe and stable operation of power systems is greatly challenged by the high variability and randomness of wind power in large-scale wind-power-integrated grids. Wind power forecasting is an effective solution to tackle this issue, with wind speed forecasting being an essential aspect. In this paper, a Graph-attentive Frequency-enhanced Spatial-Temporal

Sergio B. Juárez, Diego Gonzalez, Daniel Gutiérrez-Ruiz, J. David Vergara

The quantum geometric tensor, composed of the quantum metric tensor and Berry curvature, fully encodes the parameter space geometry of a physical system. We first provide a formulation of the quantum geometrical tensor in the path integral formalism that can handle both the ground and excited states, making it useful to characterize excited state quantum pha

Friedrich Klaus

In this work we consider integrable PDE with higher dimensional Lax pairs. Our main example is a quadratic dNLS equation with a $3 \times 3$ Lax pair. For this equation we show a-priori estimates in Sobolev spaces of negative regularity $H^s(\mathbb{R}), s > -\frac{1}{2}$. We also prove that for general $N \times N$ Lax operators $L$, the transmission coeffi

Jonas Tallberg, Magnus Lundgren, Johannes Geith

As the development and use of artificial intelligence (AI) continues to grow, policymakers are increasingly grappling with the question of how to regulate this technology. The most far-reaching international initiative is the European Union (EU) AI Act, which aims to establish the first comprehensive, binding framework for regulating AI. In this article, we

Heyuan Li, Bo Wang, Yu Cheng, Mohan Kankanhalli

Sensitivity to severe occlusion and large view angles limits the usage scenarios of the existing monocular 3D dense face alignment methods. The state-of-the-art 3DMM-based method, directly regresses the model's coefficients, underutilizing the low-level 2D spatial and semantic information, which can actually offer cues for face shape and orientation. In this

Andrew Mummery

The aperiodic variability ubiquitously observed from accreting black hole X-ray binary systems is generally analysed within the framework of the so-called ``theory of propagating fluctuations''. In this paper we derive the Fourier transforms of the Green's function solutions of the thin disc equations. These solutions suffice to describe all possible solutio

Dariusz Miśkowiec

Basic concepts and terminology of relativistic heavy-ion collision physics are introduced and illustrated by experimental results. Most plots are taken from a recent ALICE overview paper arxiv:2211.04384 [nucl-ex].

Chang Liu, Rui Li, Kaidong Zhang, Xin Luo

Diffusion models have demonstrated impressive abilities in generating photo-realistic and creative images. To offer more controllability for the generation process, existing studies, termed as early-constraint methods in this paper, leverage extra conditions and incorporate them into pre-trained diffusion models. Particularly, some of them adopt condition-sp

Ganesh Chowdary Desina

The study assesses the impact of cloud-based microservices architectures on application performance. Several aspects of performance evaluation are discussed, including response time, throughput, scalability, and reliability. This article examines the advantages and challenges of adopting a cloud-based approach. It explores potential bottlenecks and issues in

Nikodem Tomczak

Adam Smith developed a version of moral philosophy where better decisions are made by interrogating an impartial spectator within us. We discuss the possibility of using an external non-human-based substitute tool that would augment our internal mental processes and play the role of the impartial spectator. Such tool would have more knowledge about the world

Steven D. Bass, Michael Doser

Quantum sensing is a rapidly growing approach to probe fundamental physics and explore new phase space for possible new physics with precision and highly sensitive measurements in our quest to understand the deep structure of matter and its interactions. This field uses properties of quantum mechanics in the detectors to go beyond traditional measurement tec

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

Diffusion models have emerged as the new state-of-the-art family of deep generative models, and their promising potentials for text generation have recently attracted increasing attention. Existing studies mostly adopt a single encoder architecture with partially noising processes for conditional text generation, but its degree of flexibility for conditional

Ryo Nagata, Hiroya Takamura, Naoki Otani, Yoshifumi Kawasaki

In this paper, we propose methods for discovering semantic differences in words appearing in two corpora based on the norms of contextualized word vectors. The key idea is that the coverage of meanings is reflected in the norm of its mean word vector. The proposed methods do not require the assumptions concerning words and corpora for comparison that the pre

Nikolaos S. Papageorgiou, Dušan D. Repovš, Calogero Vetro

We consider a parametric Dirichlet problem driven by the anisotropic $(p,q)$-Laplacian and with a reaction which exhibits the combined effects of a superlinear (convex) term and of a negative sublinear term. Using variational tools and critical groups we show that for all small values of the parameter, the problem has at least three nontrivial smooth solutio

Yuto Miyatake

For Hamiltonian systems with non-canonical structure matrices, a new family of fourth-order energy-preserving integrators is presented. The integrators take a form of a combination of Runge--Kutta methods and continuous-stage Runge--Kutta methods and feature a set of free parameters that offer greater flexibility and efficiency. Specifically, we demonstrate

Leiping Jie, Hui Zhang

As a promptable generic object segmentation model, segment anything model (SAM) has recently attracted significant attention, and also demonstrates its powerful performance. Nevertheless, it still meets its Waterloo when encountering several tasks, e.g., medical image segmentation, camouflaged object detection, etc. In this report, we try SAM on an unexplore

Yivan Zhang, Masashi Sugiyama

Disentangling the explanatory factors in complex data is a promising approach for generalizable and data-efficient representation learning. While a variety of quantitative metrics for learning and evaluating disentangled representations have been proposed, it remains unclear what properties these metrics truly quantify. In this work, we establish algebraic r

Hao Hong, Chen Huang, Chenjun Ma, Jiajie Qi

Optical phase-matching involves establishing a proper phase relationship between the fundamental and generated waves to enable efficient optical parametric processes. It is typically achieved through either birefringence or periodically assigned polarization. Here, we report that twist angle in two-dimensional (2D) materials can generate a nonlinear Berry op

Narges Ramesh, Yasmin Ghodsi, Hamidreza Bolhasani

Machine learning is employed in healthcare to draw approximate conclusions regarding human diseases and mental health problems. Compared to older traditional methods, it can help to analyze data more efficiently and produce better and more dependable results. Millions of people are affected by schizophrenia, which is a chronic mental disorder that can signif

David Vainshtein, Yaakov Sherma, Kiril Solovey, Oren Salzman

In automated warehouses, teams of mobile robots fulfill the packaging process by transferring inventory pods to designated workstations while navigating narrow aisles formed by tightly packed pods. This problem is typically modeled as a Multi-Agent Pickup and Delivery (MAPD) problem, which is then solved by repeatedly planning collision-free paths for agents

Chuying Han, Yasong Feng, Tianyu Wang

Random Search is one of the most widely-used method for Hyperparameter Optimization, and is critical to the success of deep learning models. Despite its astonishing performance, little non-heuristic theory has been developed to describe the underlying working mechanism. This paper gives a theoretical accounting of Random Search. We introduce the concept of \

Chengfeng Dou, Zhi Jin, Wenping Jiao, Haiyan Zhao

The patient-centered medical dialogue systems strive to offer diagnostic interpretation services to users who are less knowledgeable about medical knowledge, through emphasizing the importance of providing responses specific to the patients. It is difficult for the large language models (LLMs) to guarantee the specificity of responses in spite of its promisi

Andrei V. Frolov

HEALPix by G\'orski et. al. (2005) is de-facto standard for Cosmic Microwave Background (CMB) data storage and analysis, and is widely used in current and upcoming CMB experiments. Almost all the datasets in Legacy Archive for Microwave Background Data Analysis (LAMBDA) use HEALPix as a format of choice. Visualizing the data plays important role in research,

José Miguel Moreno, Narseo Vallina-Rodriguez, Juan Tapiador

The Chromium open-source project has become a fundamental piece of the Web as we know it today, with multiple vendors offering browsers based on its codebase. One of its most popular features is the possibility of altering or enhancing the browser functionality through third-party programs known as browser extensions. Extensions have access to a wide range o

Ritu Gupta, Hariharan V. Chinnasamy, Dipak Sahu, Saravanan Matheshwaran

Bio-spinterfaces present numerous opportunities to study spintronics across the biomolecules attached to (ferro)magnetic electrodes. While it offers various exciting phenomena to investigate, it's simultaneously challenging to make stable bio-spinterfaces, as biomolecules are sensitive to many factors that it encounters during thin-film growth to device fabr

Huaqing He, Li Lin, Zhiyuan Cai, Pujin Cheng

Deep learning-based image segmentation and detection models have largely improved the efficiency of analyzing retinal landmarks such as optic disc (OD), optic cup (OC), and fovea. However, factors including ophthalmic disease-related lesions and low image quality issues may severely complicate automatic OD/OC segmentation and fovea detection. Most existing w

Xiuying Chen, Mingzhe Li, Shen Gao, Xin Cheng

Automatic summarization plays an important role in the exponential document growth on the Web. On content websites such as CNN.com and WikiHow.com, there often exist various kinds of side information along with the main document for attention attraction and easier understanding, such as videos, images, and queries. Such information can be used for better sum

M. Zerbib, V. T. Hoang, J. C. Beugnot, K. P. Huy

We report the observation of Brillouin backscattering in a 50-cm long spiral high-index doped silica chip waveguide and measured a Brillouin frequency shift of 16 GHz which is in very good agreement with theoretical predictions and numerical simulations based on the elastodynamics equation.

Yu Zhao, Yike Wu, Xiangrui Cai, Ying Zhang

Entity Alignment (EA) aims to find the equivalent entities between two Knowledge Graphs (KGs). Existing methods usually encode the triples of entities as embeddings and learn to align the embeddings, which prevents the direct interaction between the original information of the cross-KG entities. Moreover, they encode the relational triples and attribute trip

Yue Chang, Jie Qin

In atomic vapor cells, atoms collide with the inner surface, causing their spin to randomize on the walls. This wall-depolarizing effect is diffusive, and it becomes more pronounced in smaller vapor cells under high temperatures. In this work, we investigate the polarization of optically-pumped alkali-metal atoms in a millimeter-sized cell heated to $% 150 $

Tianci Xue, Ziqi Wang, Zhenhailong Wang, Chi Han

Large language Models (LLMs) have achieved promising performance on arithmetic reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting. However, LLMs face challenges in maintaining factual consistency during reasoning, exhibiting tendencies to condition overlooking, question misinterpretation, and condition hallucination over given pro

Xingyu Bai, Taiqiang Wu, Han Guo, Zhe Zhao

Event Extraction (EE), aiming to identify and classify event triggers and arguments from event mentions, has benefited from pre-trained language models (PLMs). However, existing PLM-based methods ignore the information of trigger/argument fields, which is crucial for understanding event schemas. To this end, we propose a Probabilistic reCoupling model enhanc

Chenchi Zhang, Jun Xiao, Lei Chen, Jian Shao

Prompt tuning has achieved great success in transferring the knowledge from large pretrained vision-language models into downstream tasks, and has dominated the performance on visual grounding (VG). However, almost all existing prompt tuning paradigms suffer from poor interpretability. In this paper, we argue that their poor interpretability is attributed to

Mohamed Fkirine, Said Hadd, Abdelaziz Rhandi

In this paper, we delve into the study of evolution equations that exhibit white-noise boundary conditions. Our primary focus is to establish a necessary and sufficient condition for the existence of solutions, by utilizing the concept of admissible observation operators and the Yosida extension for such operators. By employing this criterion, we can derive

Wenjin Qin, Hailin Wang, Feng Zhang, Weijun Ma

Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rank approximation, which suffers from high computational costs when dealing with large-scale tensor data. Mor

Poonam Chauhan, Jaspreet Singh, Ashok Kumar

Highly efficient and sustainable resources of energy are of great demand today to combat with environmental pollution and the energy crisis. In this work, we have examined the novel 2D Janus AsTeX (X = Cl, Br and I) monolayers using first-principles calculations and explore their potential energy conversion applications. We have demonstrated the thermal, ene

Ryo Fujino

Langevin Dynamics has been extensively employed in global non-convex optimization due to the concentration of its stationary distribution around the global minimum of the potential function at low temperatures. In this paper, we propose to utilize a more comprehensive class of stochastic processes, known as reversible diffusion, and apply the Euler-Maruyama

Subong Lim, Wissam Raji

We show a non-vanishing result for the averages of the derivatives of $L$-functions associated with the orthogonal basis of the space of vector-valued cusp forms of weight $k\in \frac12 \mathbb{Z}$ on the full group in the critical strip. We also show the existence of at least one basis element whose $L$-function does not vanish under certain conditions. As

Jingyan Feng, Hui Li, Zheng Sun, Tim Byrnes

We propose a method of generating and detecting entanglement in two spatially separated excitonpolariton Bose-Einstein condensates (BECs) at steady-state. In our scheme we first create a spinor polariton BEC, such that steady-state squeezing is obtained under a one-axis twisting interaction. Then the condensate is split either physically or virtually, which

Suhyeon Lee, Won Jun Kim, Jinho Chang, Jong Chul Ye

Following the impressive development of LLMs, vision-language alignment in LLMs is actively being researched to enable multimodal reasoning and visual IO. This direction of research is particularly relevant to medical imaging because medical image analysis and generation consist of reasoning based on a combination of visual features and prior knowledge. Many

Sifan Fang

Incomplete multi-view clustering is a challenging and non-trivial task to provide effective data analysis for large amounts of unlabeled data in the real world. All incomplete multi-view clustering methods need to address the problem of how to reduce the impact of missing views. To address this issue, we propose diffusion completion to recover the missing vi

Runqi Wang, Xiaoyue Duan, Guoliang Kang, Jianzhuang Liu

Continual learning aims to enable a model to incrementally learn knowledge from sequentially arrived data. Previous works adopt the conventional classification architecture, which consists of a feature extractor and a classifier. The feature extractor is shared across sequentially arrived tasks or classes, but one specific group of weights of the classifier

Guangyan Chen, Meiling Wang, Yi Yang, Kai Yu

Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks. Inspired by the advancements of the GPT, we present PointGPT, a novel approach that extends the concept of GPT to point clouds, addressing the challenges associated with disorder properties

Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao

We study online influence maximization (OIM) under a new model of decreasing cascade (DC). This model is a generalization of the independent cascade (IC) model by considering the common phenomenon of market saturation. In DC, the chance of an influence attempt being successful reduces with previous failures. The effect is neglected by previous OIM works unde

Arne Winterhof

In this survey we summarize properties of pseudorandomness and non-randomness of some number-theoretic sequences and present results on their behaviour under the following measures of pseudorandomness: balance, linear complexity, correlation measure of order $k$, expansion complexity and $2$-adic complexity. The number-theoretic sequences are the Legendre se

Martin Bohnert

We prove area bounds for planar convex bodies in terms of their number of interior integral points and their lattice width data. As an application, we obtain sharp area bounds for rational polygons with a fixed number of interior integral points depending on their denominator. For lattice polygons, we also present an equation for the area based on Noether's

Guobin Shen, Dongcheng Zhao, Yiting Dong, Yang Li

The biological neural network is a vast and diverse structure with high neural heterogeneity. Conventional Artificial Neural Networks (ANNs) primarily focus on modifying the weights of connections through training while modeling neurons as highly homogenized entities and lacking exploration of neural heterogeneity. Only a few studies have addressed neural he

Sangho Suh, Bryan Min, Srishti Palani, Haijun Xia

People are increasingly turning to large language models (LLMs) for complex information tasks like academic research or planning a move to another city. However, while they often require working in a nonlinear manner -- e.g., to arrange information spatially to organize and make sense of it, current interfaces for interacting with LLMs are generally linear t

Yihong Tang, Bo Wang, Miao Fang, Dongming Zhao

The personalized dialogue explores the consistent relationship between dialogue generation and personality. Existing personalized dialogue agents model persona profiles from three resources: sparse or dense persona descriptions and dialogue histories. However, sparse structured persona attributes are explicit but uninformative, dense persona texts contain ri

Wenxuan Wang, Jing Liu, Xingjian He, Yisi Zhang

Referring image segmentation (RIS) is a fundamental vision-language task that intends to segment a desired object from an image based on a given natural language expression. Due to the essentially distinct data properties between image and text, most of existing methods either introduce complex designs towards fine-grained vision-language alignment or lack r

Jie Huang, Yifan Gao, Zheng Li, Jingfeng Yang

We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e.g., "Digital Cameras", generating a list of complementary concepts, e.g., 1) Camera Lenses 2) Batteries 3) Camera Cases 4) Memory Cards 5) Battery Chargers. CCGen is beneficial for various applications like query suggestion and item recommendation, especially in the

Danny C. Price, Cheuk-Yu Edward Tong, Adrian T. Sutinjo, Nipanjana Patra

Radiometer experiments to detect 21-cm Hydrogen line emission from the Cosmic Dawn and Epoch of Reionization rely upon precise absolute calibration. During calibration, noise generated by amplifiers within the radiometer receiver must be accounted for; however, it is difficult to measure as the noise power varies with source impedance. In this letter, we int

S. V. Astashkin, K. V. Lykov

We study density estimates of an index set $\mathcal{A}$, under which unconditionality (or even a weaker property of the random unconditional divergence) of the corresponding Rademacher fractional chaos $\{r_{j_1}(t)\cdot r_{j_2}(t)\cdot\dots\cdot r_{j_d}(t)\}_{(j_1,j_2,\dots,j_d)\in \mathcal{A}}$ in a symmetric space $X$ implies its equivalence in $X$ to th

Mawson W. Sammons, Adam T. Deller, Marcin Glowacki, Kelly Gourdji

Temporal broadening is a commonly observed property of fast radio bursts (FRBs), associated with turbulent media which cause radiowave scattering. Similarly to dispersion, scattering is an important probe of the media along the line of sight to an FRB source, such as the circum-burst or circum-galactic mediums (CGM). Measurements of characteristic scattering

Yuhua Jiang, Qihan Liu, Xiaoteng Ma, Chenghao Li

Among the great successes of Reinforcement Learning (RL), self-play algorithms play an essential role in solving competitive games. Current self-play algorithms optimize the agent to maximize expected win-rates against its current or historical copies, making it often stuck in the local optimum and its strategy style simple and homogeneous. A possible soluti

Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg

Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non-linear transformations of the features. Despite the current enthusiasm for GAMs, their susceptibility to concurvity - i.e., (possibly non-linear) dependencies between the features

Haram Choi, Cheolwoong Na, Jihyeon Oh, Seungjae Lee

Although many recent works have made advancements in the image restoration (IR) field, they often suffer from an excessive number of parameters. Another issue is that most Transformer-based IR methods focus only on either local or global features, leading to limited receptive fields or deficient parameter issues. To address these problems, we propose a light

Peiling Jiang, Jude Rayan, Steven P. Dow, Haijun Xia

Large language models (LLMs) have recently soared in popularity due to their ease of access and the unprecedented ability to synthesize text responses to diverse user questions. However, LLMs like ChatGPT present significant limitations in supporting complex information tasks due to the insufficient affordances of the text-based medium and linear conversatio

Joseph Sifakis

We discuss the adequacy of tests for intelligent systems and practical problems raised by their implementation. We propose the replacement test as the ability of a system to replace successfully another system performing a task in a given context. We show how it can characterize salient aspects of human intelligence that cannot be taken into account by the T

M. N. N. Namboodiri

This article analyzes the algebraic structure of the set of all quantum channels and its subset consisting of quantum channels that have Holevo representation. The regularity of these semigroups under composition of mappings is analyzed. It is also known that these sets are compact convex sets and, therefore, rich in geometry. An attempt is made to identify

Caroline Mauron, Terry Farrelly, Thomas M. Stace

Tensor network codes enable structured construction and manipulation of stabilizer codes out of small seed codes. Here, we apply reinforcement learning to tensor network code geometries and demonstrate how optimal stabilizer codes can be found. Using the projective simulation framework, our reinforcement learning agent consistently finds the best possible co

Jinhui Hu, Xin Chen, Lifeng Zheng, Ling Zhang

This paper studies optimization problems over multi-agent systems, in which all agents cooperatively minimize a global objective function expressed as a sum of local cost functions. Each agent in the systems uses only local computation and communication in the overall process without leaking their private information. Based on the Barzilai-Borwein (BB) metho

Yuxuan Zhou, Zhi-Qi Cheng, Jun-Yan He, Bin Luo

Graph Convolutional Networks (GCNs) have long defined the state-of-the-art in skeleton-based action recognition, leveraging their ability to unravel the complex dynamics of human joint topology through the graph's adjacency matrix. However, an inherent flaw has come to light in these cutting-edge models: they tend to optimize the adjacency matrix jointly wit

Junqiao Zhao, Fenglin Zhang, Yingfeng Cai, Gengxuan Tian

Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame. To improve the robustness of VPR in perceptually aliasing scenarios, sequence-based VPR methods are proposed. These methods are either based on matching between frame sequences or extracting sequence descriptors for direct r

Wanting Cai, Mingjie Cai, Qingguo Li, Qiong Liu

Three-way decision (3WD) is a powerful tool for granular computing to deal with uncertain data, commonly used in information systems, decision-making, and medical care. Three-way decision gets much research in traditional rough set models. However, three-way decision is rarely combined with the currently popular field of machine learning to expand its resear

Grzegorz W. Strzelecki, Katarzyna Nowakowska-Langier, Katarzyna Mulewska, Maciej Zielinski

This paper presents the findings of the synthesis of multicomponent (Al, W, Ni, Ti, Nb) alloy coatings from mosaic targets. For the study, a pulsed magnetron sputtering method was employed under different plasma generation conditions: modulation frequency (10 Hz and 1000 Hz), and power (600 W and 1000 W). The processes achieved two types of alloy coatings, h

Hikaru Asano, Ryo Yonetani, Mai Nishimura, Tadashi Kozuno

Multi-robot navigation is the task of finding trajectories for a team of robotic agents to reach their destinations as quickly as possible without collisions. In this work, we introduce a new problem: fair-delay multi-robot navigation, which aims not only to enable such efficient, safe travels but also to equalize the travel delays among agents in terms of a

Akshay Sreekumar, Adhithyan Sakthivelu, Rimvydas Baltaduonis, Lynne Kiesling

This paper presents a limit order book (LOB) market mechanism design for transactive energy systems. The proposed design is planned for deployment in New Hampshire and Maine under a US Department of Energy Connected Communities project. The new LOB mechanism is intended to replace or work in conjunction with the conventional transactive energy double auction

Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann

Maximum mean discrepancy (MMD) flows suffer from high computational costs in large scale computations. In this paper, we show that MMD flows with Riesz kernels $K(x,y) = - \|x-y\|^r$, $r \in (0,2)$ have exceptional properties which allow their efficient computation. We prove that the MMD of Riesz kernels, which is also known as energy distance, coincides wit

Anupiya Nugaliyadde

Breakthroughs in deep learning and memory networks have made major advances in natural language understanding. Language is sequential and information carried through the sequence can be captured through memory networks. Learning the sequence is one of the key aspects in learning the language. However, memory networks are not capable of holding infinitely lon

Iok Tong Lei, Ziyu Zhu, Han Yu, Yige Yao

Prompting a language model (LM) is an increasingly important research topic for better utilization of large language models (LLMs). While simple prompting is effective for single-step questions, it fails to activate the correct knowledge path for multi-step reasoning tasks consistently. The few-shot Chain of Thought (CoT), serves as an advanced prompting str

Shiyao Ding, Takayuki Ito

Finding an agreement among diverse opinions is a challenging topic in multiagent systems. Recently, large language models (LLMs) have shown great potential in addressing this challenge due to their remarkable capabilities in comprehending human opinions and generating human-like text. However, they typically rely on extensive human-annotated data. In this pa

A. K. Wabeto, K. N. Nigussa, L. D. Deja

In this study, we have employed a DFT+U calculation using quantum-espresso (QE) code to investigate the structural, electronic, optical, and magnetic properties of LiFePO$\rm_{4}$ cathode material for Li-ion batteries. Crystals of LiFePO$\rm_{4}$ and related materials have recently received a lot of attention due to their very promising use as cathodes in re

Jingjing Zheng, Wenzhe Wang, Xiaoqin Zhang, Xianta Jiang

This study aims to solve the over-reliance on the rank estimation strategy in the standard tensor factorization-based tensor recovery and the problem of a large computational cost in the standard t-SVD-based tensor recovery. To this end, we proposes a new tensor norm with a dual low-rank constraint, which utilizes the low-rank prior and rank information at t

Adel Nikfarjam, Ralf Rothenberger, Frank Neumann, Tobias Friedrich

Computing diverse solutions for a given problem, in particular evolutionary diversity optimisation (EDO), is a hot research topic in the evolutionary computation community. This paper studies the Boolean satisfiability problem (SAT) in the context of EDO. SAT is of great importance in computer science and differs from the other problems studied in EDO litera

T. Peter Rakitzis, Michail E. Koutrakis, George E. Katsoprinakis

In quantum mechanics, spatial wavefunctions describe distributions of a particle's position or momentum, but not of angular momentum $j$. In contrast, here we show that a spatial wavefunction, $j_m (\phi,\theta,\chi)=~e^{i m \phi} \delta (\theta - \theta_m) ~e^{i(j+1/2)\chi}$, which treats $j$ in the $|jm>$ state as a three-dimensional entity, is an asymptot

Wanqiao Xu, Shi Dong, Dilip Arumugam, Benjamin Van Roy

A centerpiece of the ever-popular reinforcement learning from human feedback (RLHF) approach to fine-tuning autoregressive language models is the explicit training of a reward model to emulate human feedback, distinct from the language model itself. This reward model is then coupled with policy-gradient methods to dramatically improve the alignment between l

Li Wang, Fang-Bao Tian, John Young

An immersed boundary method for the fluid--structure--thermal interaction in rarefied gas flow is presented. In this method, the slip model is incorporated with the penalty immersed boundary method to address the velocity and temperature jump conditions at the fluid--structure interface in rarefied gas flow within slip regime. In this method, the compressibl

Dawei Wu, Ji-chong Yang, Yu Shi

Exact conditions for antiUnruh effect in (1+1)-dimensional spacetime are obtained. For detectors with Gaussian switching functions, the analytic results are similar to previous ones, indicating that antiUnruh effect occurs when the energy gap matches the characteristic time scale. However, this conclusion does not hold for detectors with square wave switchin

Shiwei Jin, Zhen Wang, Lei Wang, Ning Bi

Learning-based gaze estimation methods require large amounts of training data with accurate gaze annotations. Facing such demanding requirements of gaze data collection and annotation, several image synthesis methods were proposed, which successfully redirected gaze directions precisely given the assigned conditions. However, these methods focused on changin

Muhammad Abdullah Jamal, Omid Mohareri

There has been a growing interest in using deep learning models for processing long surgical videos, in order to automatically detect clinical/operational activities and extract metrics that can enable workflow efficiency tools and applications. However, training such models require vast amounts of labeled data which is costly and not scalable. Recently, sel

Xiaohui Ni, An-Bao Xu

Matrix decomposition is a very important mathematical tool in numerical linear algebra for data processing. In this paper, we introduce a new randomized matrix decomposition algorithm, which is called randomized approximate SVD based on Qatar Riyal decomposition (RCSVD-QR). Our method utilize random sampling and the OR decomposition to address a serious bott

Yiduo Guo, Yaobo Liang, Dongyan Zhao, Bing Liu

Existing research has shown that a multilingual pre-trained language model fine-tuned with one (source) language also performs well on downstream tasks for non-source languages, even though no fine-tuning is done on these languages. However, there is a clear gap between the performance of the source language and that of the non-source languages. This paper a

Lucas Burns, Tatsuya Daniel, Stephon Alexander, Justin Dressel

Both acoustics and electromagnetism represent measurable fields in terms of dynamical potential fields. Electromagnetic force-fields form a spacetime bivector that is represented by a dynamical energy-momentum 4-vector potential field. Acoustic pressure and velocity fields form an energy-momentum density 4-vector field that is represented by a dynamical acti

Sajjad Mohammadi

Let $m$ and $n$ be two positive integers such that $m < n$. Let $Q_{n-m+1}$ be the symplectic quasi-projective space of rank $n-m+1$. In this article, we will study the order of the Samelson product $S^{4m-1}\wedge Q_{n-m+1}\rightarrow Sp(n)$.

Mina Poozesh, Yousef Zamani

Let $G$ be a finite insoluble group with soluble radical $ R(G)$. The solubility graph $\Gamma_{\rm S}(G)$ of $G$ is a simple graph whose vertices are the elements of $G\setminus R(G) $ and two distinct vertices $x$ and $y$ are adjacent if and only if they generate a soluble subgroup of $G$. In this paper, we investigate the several properties of the solubil

Dongrak Choi, Woojung Bae, Jun Yan, Sangwook Kang

We propose a set of goodness-of-fit tests for the semiparametric accelerated failure time (AFT) model, including an omnibus test, a link function test, and a functional form test. This set of tests is derived from a multi-parameter cumulative sum process shown to follow asymptotically a zero-mean Gaussian process. Its evaluation is based on the asymptoticall

Hiroki Ouchi, Hiroyuki Shindo, Shoko Wakamiya, Yuki Matsuda

We have constructed NAIST Academic Travelogue Dataset (ATD) and released it free of charge for academic research. This dataset is a Japanese text dataset with a total of over 31 million words, comprising 4,672 Japanese domestic travelogues and 9,607 overseas travelogues. Before providing our dataset, there was a scarcity of widely available travelogue data f

Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang

Multi-modality image fusion is a technique that combines information from different sensors or modalities, enabling the fused image to retain complementary features from each modality, such as functional highlights and texture details. However, effective training of such fusion models is challenging due to the scarcity of ground truth fusion data. To tackle

Chaoqun Liu, Wenxuan Zhang, Guizhen Chen, Xiaobao Wu

Existing solutions to zero-shot text classification either conduct prompting with pre-trained language models, which is sensitive to the choices of templates, or rely on large-scale annotated data of relevant tasks for meta-tuning. In this work, we propose a new paradigm based on self-supervised learning to solve zero-shot text classification tasks by tuning

Shintaro Yoshiura, Teppei Minoda, Tomo Takahashi

The 21cm global signal is an important probe to reveal the properties of the first astrophysical objects and the processes of the structure formation from which one can constrain astrophysical and cosmological parameters. To extract the information of such parameters, one needs to efficiently evaluate the 21cm global signal for statistical analysis. First we

Ye Tian, Zhengshuo Li

When large-scale uncertain centralized and distributed renewable energy sources are connected to a power system, separate dispatching of the transmission power system (TPS) and the active distribution network (ADN) will lower the network security and frequency security of the system. To address these problems, this paper proposes a coordinated frequency-cons

Runqi Wang, Hao Zheng, Xiaoyue Duan, Jianzhuang Liu

Pre-trained vision-language models have inspired much research on few-shot learning. However, with only a few training images, there exist two crucial problems: (1) the visual feature distributions are easily distracted by class-irrelevant information in images, and (2) the alignment between the visual and language feature distributions is difficult. To deal

Kaiqi Fu, Shaojun Gao, Shuju Shi, Xiaohai Tian

Speech fluency/disfluency can be evaluated by analyzing a range of phonetic and prosodic features. Deep neural networks are commonly trained to map fluency-related features into the human scores. However, the effectiveness of deep learning-based models is constrained by the limited amount of labeled training samples. To address this, we introduce a self-supe

Achintha Wijesinghe, Songyang Zhang, Zhi Ding

Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation owing to its strong potential in capturing underlying data statistics while preserving data privacy. However, in cases of practical data heterogeneity among FL clients, existing FL frameworks still exhibit deficiency in capturing the overall feature properties o