March 2023 arXiv papers — page 121

Hao Wang

Recommender systems serves as an important technical asset in many modern companies. With the increasing demand for higher precision of the technology, more and more research and investment has been allocated to the field. One important sub-field of recommender systems that has been stagnating is context-aware recommender systems. Due to the difficulty of co

Antonello Pellecchia, Michele Bianco, Rui De Oliveira, Francesco Fallavollita

In triple-GEM detectors, the segmentation of GEM foils in electrically independent sectors allows reducing the probability of discharge damage to the detector and improving the detector rate capability; however, a segmented foil presents thin dead regions in the separation between two sectors and the segmentation pattern has to be manually aligned with the G

Hao Wang

Topology is the foundation for many industrial applications ranging from CAD to simulation analysis. Computational topology mostly focuses on structured data such as mesh, however unstructured dataset such as point set remains a virgin land for topology scientists. The significance of point-based topology can never be overemphasized, especially in the area o

Tianqing Zhou, Yanyan Fu, Dong Qin, Xuefang Nie

Ultra-dense networks are widely regarded as a promising solution to explosively growing applications of Internet-of-Things (IoT) mobile devices (IMDs). However, complicated and severe interferences need to be tackled properly in such networks. To this end, both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are utilized at first.

Barbara Drossel

It is often claimed that the fundamental laws of physics are deterministic and time-symmetric and that therefore our experience of the passage of time is an illusion. This paper will critically discuss these claims and show that they are based on the misconception that the laws of physics are an exact and complete description of nature. I will argue that all

Minh Le

The focus of this paper is on solutions to a two-dimensional Keller-Segel system containing sub-logistic sources. We show that the presence of sub-logistic terms is adequate to prevent blow-up phenomena even in strongly degenerate Keller-Segel systems. Our proof relies on several techniques, including parabolic regularity theory in Orlicz spaces, variational

Supriya Karmakar, Priyanka Shukla

The linear stability analysis of a plane Poiseuille flow in a channel with anisotropic and inhomogeneous porous layers is performed. The effect of anisotropy and inhomogeneous permeability on the stability characteristics is addressed in detail. The stability characteristics of the anisotropy parameter (the ratio of permeability in the streamwise and the tra

Yizhuo Wang, Yutong Wang, Yuhong Cao, Guillaume Sartoretti

This work focuses on the persistent monitoring problem, where a set of targets moving based on an unknown model must be monitored by an autonomous mobile robot with a limited sensing range. To keep each target's position estimate as accurate as possible, the robot needs to adaptively plan its path to (re-)visit all the targets and update its belief from meas

Antonio Orvieto, Samuel L Smith, Albert Gu, Anushan Fernando

Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are hard to optimize and slow to train. Deep state-space models (SSMs) have recently been shown to perform remarkably well on long sequence modeling tasks, and have the added benefits of fast parallelizable training and RNN-like fast inference. However, while SSMs are superficially s

Gao-xiang Deng, Wei Shao, Yu Liu, Zheng Cui

The possibility of utilizing quantum effects to enhance the performance of quantum heat engines has been an active topic of research, but how to enhance the performance by optimizing the engine parameters needs to be further studied. In this study, the temperature difference and dissipation modes affecting the performance of a three-level quantum heat engine

Kesen Zhao, Lixin Zou, Xiangyu Zhao, Maolin Wang

Improving user retention with reinforcement learning~(RL) has attracted increasing attention due to its significant importance in boosting user engagement. However, training the RL policy from scratch without hurting users' experience is unavoidable due to the requirement of trial-and-error searches. Furthermore, the offline methods, which aim to optimize th

Yizhak Ben-Shabat, Oren Shrout, Stephen Gould

We propose a novel method for 3D point cloud action recognition. Understanding human actions in RGB videos has been widely studied in recent years, however, its 3D point cloud counterpart remains under-explored. This is mostly due to the inherent limitation of the point cloud data modality -- lack of structure, permutation invariance, and varying number of p

Zhao Yang, Jiaqi Wang, Yansong Tang, Kai Chen

Referring image segmentation segments an image from a language expression. With the aim of producing high-quality masks, existing methods often adopt iterative learning approaches that rely on RNNs or stacked attention layers to refine vision-language features. Despite their complexity, RNN-based methods are subject to specific encoder choices, while attenti

Babur M. Mirza

Reaction network of fluorine-18, oxygen-15 and nitrogen-15 is considered for its temperature dependent energy output. The main reactions for generation and annihilation of oxygen and fluorine are coupled in the reaction equations while nitrogen is produced as a decay product. We find that the governing set of equations for F18(p, alpha)O15 process in the pha

Wenhan Yang, Baharan Mirzasoleiman

Graph contrastive learning (CL) methods learn node representations in a self-supervised manner by maximizing the similarity between the augmented node representations obtained via a GNN-based encoder. However, CL methods perform poorly on graphs with heterophily, where connected nodes tend to belong to different classes. In this work, we address this problem

Leonardo Pedro

We show that the unitary operator on a separable Hilbert space is a parametrization of any conditional probability measure in a standard measure space. We propose unitary inference, a generalization of Bayesian inference. We study implications for classical statistical mechanics.

Dong-Hee Paek, Seung-Hyun Kong, Kevin Tirta Wijaya

Recent works have shown the superior robustness of four-dimensional (4D) Radar-based three-dimensional (3D) object detection in adverse weather conditions. However, processing 4D Radar data remains a challenge due to the large data size, which require substantial amount of memory for computing and storage. In previous work, an online density reduction is per

Pengcheng Guo, He Wang, Bingshen Mu, Ao Zhang

This paper describes our NPU-ASLP system for the Audio-Visual Diarization and Recognition (AVDR) task in the Multi-modal Information based Speech Processing (MISP) 2022 Challenge. Specifically, the weighted prediction error (WPE) and guided source separation (GSS) techniques are used to reduce reverberation and generate clean signals for each single speaker

Yu-Jia An, Sheng-Chen Bai, Lin Cheng, Xiao-Guang Li

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), se

Ivan A. Troyan, Dmitrii V. Semenok, Anna G. Ivanova, Andrey V. Sadakov

We studied chemical interaction of Sn with H$_2$ by X-ray diffraction methods at pressures of 180-210 GPa. A previously unknown tetrahydride SnH$_4$ with a cubic structure (${fcc}$) exhibiting superconducting properties below ${T}$$_C$ = 72 K was obtained; the formation of a high molecular ${C2/m}$-SnH$_{14}$ superhydride and several lower hydrides, ${fcc}$

Zhen Wang, Jun Xiao, Yueting Zhuang, Fei Gao

Controllable Image Captioning (CIC) -- generating natural language descriptions about images under the guidance of given control signals -- is one of the most promising directions towards next-generation captioning systems. Till now, various kinds of control signals for CIC have been proposed, ranging from content-related control to structure-related control

Muyang Li, Zijian Zhang, Xiangyu Zhao, Wanyu Wang

Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length b

Adam Dominiak, Matthew Kovach, Gerelt Tserenjigmid

We introduce and characterize inertial updating of beliefs. Under inertial updating, a decision maker (DM) chooses a belief that minimizes the subjective distance between their prior belief and the set of beliefs consistent with the observed event. Importantly, by varying the subjective notion of distance, inertial updating provides a unifying framework that

Minjae Lee, Kyeongsu Kang, Hyeonwoo Yu

With the advent of Neural Radiance Field (NeRF), representing 3D scenes through multiple observations has shown remarkable improvements in performance. Since this cutting-edge technique is able to obtain high-resolution renderings by interpolating dense 3D environments, various approaches have been proposed to apply NeRF for the spatial understanding of robo

Chanda G Kamra, Indra Deep Mastan, Debayan Gupta

CLIPStyler demonstrated image style transfer with realistic textures using only the style text description (instead of requiring a reference style image). However, the ground semantics of objects in style transfer output is lost due to style spillover on salient and background objects (content mismatch) or over-stylization. To solve this, we propose Semantic

Hua Shen, Tongshuang Wu

A surge of advances in language models (LMs) has led to significant interest in using LMs to build co-writing systems, in which humans and LMs interactively contribute to a shared writing artifact. However, there is a lack of studies assessing co-writing systems in interactive settings. We propose a human-centered evaluation framework, Parachute, for interac

Kan Chen, Jeffrey Zhang, Bingkai Wang, Dylan S. Small

We consider identification and inference for the average treatment effect and heterogeneous treatment effect conditional on observable covariates in the presence of unmeasured confounding. Since point identification of these treatment effects is not achievable without strong assumptions, we obtain bounds on these treatment effects by leveraging differential

Anjali. J. Kaithakkal, J. M. Borrero, A. Pastor Yabar, J. de la Cruz Rodríguez

The focus of this investigation is to quantify the conversion of magnetic to thermal energy initiated by a quiet Sun cancellation event and to explore the resulting dynamics from the interaction of the opposite polarity magnetic features. We used imaging spectroscopy in the H$\alpha$ line, along with spectropolarimetry in the \ion{Fe}{I} 6173~{\AA} and \ion{

Zhijie Xiao, Zhicheng Dong, Hao Xiang

In recent years, self-supervised learning has attracted widespread academic debate and addressed many of the key issues of computer vision. The present research focus is on how to construct a good agent task that allows for improved network learning of advanced semantic information on images so that model reasoning is accelerated during pre-training of the c

Ren Wang, Haoliang Sun, Yuling Ma, Xiaoming Xi

Existing multi-view representation learning methods typically follow a specific-to-uniform pipeline, extracting latent features from each view and then fusing or aligning them to obtain the unified object representation. However, the manually pre-specify fusion functions and view-private redundant information mixed in features potentially degrade the quality

Maria Emilia De Rossi, Volker Bromm

By implementing a model of primordial dust emission, we predict dust-continuum fluxes for massive galaxy sources similar to those recently detected by JWST at $z \gtrsim 7$. Current upper flux limits, obtained with ALMA for some of these sources, can constrain gas metallicity and dust fraction of the first galaxies. Encouragingly, if assuming expected proper

Xinwei Cai, Zhen Li, Xin Bian

We model a slip boundary condition at fluid-solid interface of an arbitrary geometry in smoothed particle hydrodynamics and smoothed dissipative particle dynamics simulations. Under an assumption of linear profile of the tangential velocity at quasi-steady state near the interface, an arbitrary slip length $b$ can be specified and correspondingly, an artific

Zhe Wang, Shilong Wu, Hang Chen, Mao-Kui He

The Multi-modal Information based Speech Processing (MISP) challenge aims to extend the application of signal processing technology in specific scenarios by promoting the research into wake-up words, speaker diarization, speech recognition, and other technologies. The MISP2022 challenge has two tracks: 1) audio-visual speaker diarization (AVSD), aiming to so

G. G. L. Nashed, Shin'ichi Nojiri

The Chern-Simons (CS) gravity is a modified theory of Einstein's general relativity (GR). The CS theory arises from the low energy limit of string theory which involves anomaly correction to the Einstein-Hilbert action. The CS term is given by the product of the Pontryagin density with a scalar field. In this study, we derive a charged slowly rotating black

Aleksis Vuoksenmaa

The discrete nonlinear Schr\"odinger equation on \(\Z^d\), \(d \geq 1\) is an example of a dispersive nonlinear wave system. Being a Hamiltonian system that conserves also the \(\ell^2(\Z^d)\)-norm, the well-posedness of the corresponding Cauchy problem follows for square-summable initial data. In this paper, we prove that the well-posedness continues to hol

Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan

Distributed deep neural network training necessitates efficient GPU collective communications, which are inherently susceptible to deadlocks. GPU collective deadlocks arise easily in distributed deep learning applications when multiple collectives circularly wait for each other. GPU collective deadlocks pose a significant challenge to the correct functioning

Yujiro Kawamata

We make some remarks on deformations over non-commutative base. We describe the base algebra of versal deformations using $T^1$ and $T^2$.

Peiyue Shen, Xianliang Zhou, Jiajun Chen, Aolin Deng

ABC-stacked trilayer graphene has exhibited a variety of correlated phenomena owing to its relatively flat bands and gate-tunable bandgap. However, convenient methods are still lacking for identifying ABC graphene with nanometer-scale resolution. Here we demonstrate that the scanning near-field optical microscope (SNOM) working in ambient conditions can prov

E. Subramanian, M. Mithun Karthik, G Prem Krishna, D. Vaisnav Prasath

This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model selection, training, evaluation, and deployment. High-quality data from multiple sources, including weather data, solar ir

Benjamin A. Burton, Alexander He

We often rely on censuses of triangulations to guide our intuition in $3$-manifold topology. However, this can lead to misplaced faith in conjectures if the smallest counterexamples are too large to appear in our census. Since the number of triangulations increases super-exponentially with size, there is no way to expand a census beyond relatively small tria

Stuart Bartlett, Michael L Wong

We survey some of the philosophical challenges and pitfalls within origins research. Several of these challenges exhibit circularities, paradoxes, or anthropic biases. We present origins approaches in terms of three broad categories: unlikely (life's origin was a chance event), construction (life's origin was a stepwise series of synthesis and assembly proce

Yuni Iwamasa

In this note, we provide two characterizations of the set of integer points in an integral bisubmodular polyhedron. Our characterizations do not require the assumption that a given set satisfies the hole-freeness, i.e., the set of integer points in its convex hull coincides with the original set. One is a natural multiset generalization of the exchange axiom

Zhiguo Ding, Octavia A. Dobre, Pingzhi Fan, H. Vincent Poor

The aim of this letter is to develop a new design of cognitive radio inspired non-orthogonal multiple access (CR-NOMA), which ensures that multiple new users can be supported without causing disruption to the legacy network. Analytical results are developed to characterize the statistical properties of the number of supported new users. The developed CRNOMA

Siddharth Singh, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari

Mixture-of-Experts (MoE) is a neural network architecture that adds sparsely activated expert blocks to a base model, increasing the number of parameters without impacting computational costs. However, current distributed deep learning frameworks are limited in their ability to train high-quality MoE models with large base models. In this work, we present De

Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick

One can typically form a local robustness metric for a particular problem quite directly, for Markov chain Monte Carlo applications as well as optimization problems such as variational Bayes. However, we argue that simply forming a local robustness metric is not enough: the hard work is showing that it is useful. Computability, interpretability, and the abil

Feng-Lei Fan, Hang-Cheng Dong, Zhongming Wu, Lecheng Ruan

Inspired by neuronal diversity in the biological neural system, a plethora of studies proposed to design novel types of artificial neurons and introduce neuronal diversity into artificial neural networks. Recently proposed quadratic neuron, which replaces the inner-product operation in conventional neurons with a quadratic one, have achieved great success in

Ji Zhang, Lianli Gao, Xu Luo, Hengtao Shen

Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge of the test task, rely only on few-labeled support samples. Previous approaches generally focus on developing advanced algorithms to achieve the goal, while neglecting the inherent problems of the given support samples. In fact,

Jiawei Huang, Akito Iizuka, Hajime Tanaka, Taku Komura

The variance reduction speed of physically-based rendering is heavily affected by the adopted importance sampling technique. In this paper we propose a novel online framework to learn the spatial-varying density model with a single small neural network using stochastic ray samples. To achieve this task, we propose a novel closed-form density model called the

Jian Xu, Meiling Yang, Wenbo Ding, Shao-Lun Huang

The label distribution skew induced data heterogeniety has been shown to be a significant obstacle that limits the model performance in federated learning, which is particularly developed for collaborative model training over decentralized data sources while preserving user privacy. This challenge could be more serious when the participating clients are in u

Sayada Sonia Akter, Mohammad Shahriar Rahman

Cloud computing is a web-based utility model that is becoming popular every day with the emergence of 4th Industrial Revolution, therefore, cybercrimes that affect web-based systems are also relevant to cloud computing. In order to conduct a forensic investigation into a cyber-attack, it is necessary to identify and locate the source of the attack as soon as

Zachary Lee, Xueying Yu

In this note, we generalize the nonlinearity-recovery result in [7] for classical cubic nonlinear Schr\"odinger equations to higher-order Schr\"odinger equations with a more general nonlinearity. More precisely, we consider a spatially-localized nonlinear higher-order Schr\"odinger equation and recover the spatially-localized coefficient by the solutions wit

S. Li, I. Ostrovskiy, Z. Li, L. Yang

Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based o

Chollakorn Nimpattanavong, Ibrahim Khan, Thai Van Nguyen, Ruck Thawonmas

This paper presents a new communication interface for the DareFightingICE platform, a Java-based fighting game focused on implementing AI for controlling a non-player character. The interface uses an open-source remote procedure call, gRPC to improve the efficiency of data transfer between the game and the AI, reducing the time spent on receiving information

Dipti Rani Hazra, Md. Shah Naoaj, Mohammed Mahinur Alam, Abdul Kader

Inspired by the recent debate on the macroeconomic implications of the new bank regulatory standards known as Basel III, we tried to find out in this study that the impact of Basel III liquidity and capital requirements in Bangladesh proposed by Basel Committee on Banking Supervision (BCBS, 2010a). A small set of macro variables, using a sample of 22 private

Jagbeer Singh, Ritika Kanojia, Rishika Singh, Rishita Bansal

The majority of human deaths and injuries are caused by traffic accidents. A million people worldwide die each year due to traffic accident injuries, consistent with the World Health Organization. Drivers who do not receive enough sleep, rest, or who feel weary may fall asleep behind the wheel, endangering both themselves and other road users. The research o

Jagbeer Singh, Yash Goel, Shubhi Jain, Shiva Yadav

The purpose of this paper is to enhance the performance of the virtual assistant. So, what exactly is a virtual assistant. Application software, often called virtual assistants, also known as AI assistants or digital assistants, is software that understands natural language voice commands and can perform tasks on your behalf. What does a virtual assistant do

Kyeongsu Kang, Minjae Lee, Hyeonwoo Yu

Global place recognition and 3D relocalization are one of the most important components in the loop closing detection for 3D LiDAR Simultaneous Localization and Mapping (SLAM). In order to find the accurate global 6-DoF transform by feature matching approach, various end-to-end architectures have been proposed. However, existing methods do not consider the f

Jiayi Zhao, Denizalp Goktas, Amy Greenwald

A Fisher market is an economic model of buyer and seller interactions in which each buyer's utility depends only on the bundle of goods she obtains. Many people's interests, however, are affected by their social interactions with others. In this paper, we introduce a generalization of Fisher markets, namely influence Fisher markets, which captures the impact

Jagbeer Singh, Utkarsh Rastogi, Yash Goel, Brijesh Gupta

This research study focuses primarily on Block-Chain-based voting systems, which facilitate participation in and administration of voting for voters, candidates, and officials. Because we used Block-Chain in the backend, which enables everyone to trace vote fraud, our system is incredibly safe. This paper approach any unique identification the Aadhar Card nu

Tuong Do, Binh X. Nguyen, Hien Nguyen, Erman Tjiputra

Federated learning has been widely applied in autonomous driving since it enables training a learning model among vehicles without sharing users' data. However, data from autonomous vehicles usually suffer from the non-independent-and-identically-distributed (non-IID) problem, which may cause negative effects on the convergence of the learning process. In th

Hao Chen, Yunlong Yu, Yonghan Dong, Zheming Lu

Few-Shot Segmentation (FSS) is challenging for limited support images and large intra-class appearance discrepancies. Most existing approaches focus on extracting high-level representations of the same layers for support-query correlations, neglecting the shift issue between different layers and scales, due to the huge difference between support and query sa

Jian Li, Zhu-Cai Yin, Qing-Xv Li, Jia-Ji Zhu

The ferromagnetic and antiferromagnetic nanostructure are crucial for fundamental spintronics devices, motivated by its potential application in spintronics, we theoretically investigate the electronic structure of the ferromagnetic and antiferromagnetic phases of the cylindrical intrinsically magnetic topological insulator $\mathrm{MnBi_{2}Te_{4}}$ quantum

Yulong Wang, Tong Sun, Shenghong Li, Xin Yuan

Adversarial attacks and defenses in machine learning and deep neural network have been gaining significant attention due to the rapidly growing applications of deep learning in the Internet and relevant scenarios. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus o

Hodaka Yamaji

Maximal clique enumeration appears in various real-world networks, such as social networks and protein-protein interaction networks for different applications. For general graph inputs, the number of maximal cliques can be up to $3^{|V|/3}$. However, many previous works suggest that the number is much smaller than that on real-world networks, and polynomial-

Mark Shattuck

An avoidance pattern where the letters within an occurrence of which are required to be adjacent is referred to as a subword. In this paper, we enumerate members of the set NC_n of non-crossing partitions of length n according to the number of occurrences of several infinite families of subword patterns each containing repeated letters. As a consequence of o

R. C. Brewster, C. M. Mynhardt, L. E. Teshima

The independent domination number $i(G)$ of a graph $G$ is the minimum cardinality of a maximal independent set of $G$, also called an $i(G)$-set. The $i$-graph of $G$, denoted $\mathcal{I}(G)$, is the graph whose vertices correspond to the $i(G)$-sets, and where two $i(G)$-sets are adjacent if and only if they differ by two adjacent vertices. We show that n

Samir Mitha, Seungho Choe, Pejman Jahbedar Maralani, Alan R. Moody

We propose a novel architecture called MLP-SRGAN, which is a single-dimension Super Resolution Generative Adversarial Network (SRGAN) that utilizes Multi-Layer Perceptron Mixers (MLP-Mixers) along with convolutional layers to upsample in the slice direction. MLP-SRGAN is trained and validated using high resolution (HR) FLAIR MRI from the MSSEG2 challenge dat

Hermie Monterde

We formalize the notion of a sedentary vertex and present a relaxation of the concept of a sedentary family of graphs introduced by Godsil [Linear Algebra Appl. 614:356-375, 2021]. We provide sufficient conditions for a given vertex in a graph to exhibit sedentariness. We also show that a vertex with at least two twins (vertices that share the same neighbour

Shuangfei Zhai, Tatiana Likhomanenko, Etai Littwin, Dan Busbridge

Training stability is of great importance to Transformers. In this work, we investigate the training dynamics of Transformers by examining the evolution of the attention layers. In particular, we track the attention entropy for each attention head during the course of training, which is a proxy for model sharpness. We identify a common pattern across differe

Daizhan Cheng, Xiao Zhang, Zhengping Ji

The semi-tensor product (STP) of matrices is extended to the STP of hypermatrices. Some basic properties of the STP of matrices are extended to the STP of hypermatrices. The hyperdeterminant of hypersquares is introduced. Some algebraic and geometric structures of matrices are extended to hypermatrices. Then the compound hypermatrix is proposed. The STP of h

Sebastian Grabowski

This work looks at the relationship between the index of hydrogen deficiency (IHD) and the enthalpy of combustion of "sugar propellant," as well as the performance of carbohydrates with similar IHD values. The study used eight different carbohydrate sources as fuels in the propellant, combined with potassium nitrate as an oxidizer in a 35:65 ratio. The IHD o

Yanwen Zhang, Huiwen Wang, Jichang Zhao

Newly arriving nodes in dynamics networks would gradually make the node embedding space drifted and the retraining of node embedding and downstream models indispensable. An exact threshold size of these new nodes, below which the node embedding space will be predicatively maintained, however, is rarely considered in either theory or experiment. From the view

Chang Liao

Entities whose changes will significantly affect others in a networked system are called shakers. In recent years, some models have been proposed to detect such shaker from evolving entities. However, limited work has focused on shaker detection in very short term, which has many real-world applications. For example, in financial market, it can enable both i

Tadahiro Kin, Hamid Basiri, Eduardo Cortina Gil, Andrea Giammanco

We have proposed a novel application for cosmic-ray muography, called Magic-$\mu$, which is short for Magnetic field Imaging by Cosmic-ray Muons. The general goal of Magic-$\mu$ is to detect the presence of a magnetic field or magnetic flux density whose three-dimensional distribution is unknown. Depending on the application, Magic-$\mu$ can have three detec

Christopher W. Curtis, D. Jay Alford-Lago, Erik Bollt, Andrew Tuma

While the acquisition of time series has become more straightforward, developing dynamical models from time series is still a challenging and evolving problem domain. Within the last several years, to address this problem, there has been a merging of machine learning tools with what is called the dynamic mode decomposition (DMD). This general approach has be

Sepehr Assadi, Nirmit Joshi, Milind Prabhu, Vihan Shah

Estimating quantiles, like the median or percentiles, is a fundamental task in data mining and data science. A (streaming) quantile summary is a data structure that can process a set S of n elements in a streaming fashion and at the end, for any phi in (0,1], return a phi-quantile of S up to an eps error, i.e., return a phi'-quantile with phi'=phi +- eps. We

Weijun Fang, Fang-Wei Fu, Bin Chen, Shu-Tao Xia

Locally repairable codes (LRCs) have emerged as an important coding scheme in distributed storage systems (DSSs) with relatively low repair cost by accessing fewer non-failure nodes. Theoretical bounds and optimal constructions of LRCs have been widely investigated. Optimal LRCs via cyclic and constacyclic codes provide significant benefit of elegant algebra

Jesús Carrillo-Pacheco

In this article a family of recursive and self-similar matrices is constructed. It is shown that the Pl\"ucker matrix of the Isotropic Grassmannian variety is a direct sum of this class of matrices.

Ratnadira Widyasari, Zhou Yang, Ferdian Thung, Sheng Qin Sim

Machine learning (ML) has gained much attention and been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstac

Yueming Lyu, Tianwei Lin, Fu Li, Dongliang He

Text-driven image manipulation remains challenging in training or inference flexibility. Conditional generative models depend heavily on expensive annotated training data. Meanwhile, recent frameworks, which leverage pre-trained vision-language models, are limited by either per text-prompt optimization or inference-time hyper-parameters tuning. In this work,

Chang Liao

Recent years have witnessed a growing number of researches on community characterization. In contrast to the large body of researches on the categorical measures (rise or decline) for evaluating the community development, we propose to estimate the community development strength (to which degree the rise or decline is). More specifically, given already known

Alvin Jian Jia Tan, Chun Yong Chong, Aldeida Aleti

Software remodularization through clustering is a common practice to improve internal software quality. However, the true benefit of software clustering is only realized if developers follow through with the recommended refactoring suggestions, which can be complex and time-consuming. Simply producing clustering results is not enough to realize the benefits

ADMX Collaboration, T. Nitta, T. Braine, N. Du

We report the first result of a direct search for a Cosmic ${\it axion}$ Background (C$a$B) - a relativistic background of axions that is not dark matter - performed with the axion haloscope, the Axion Dark Matter eXperiment (ADMX). Conventional haloscope analyses search for a signal with a narrow bandwidth, as predicted for dark matter, whereas the C$a$B wi

Pierre Barral, Michael Cantara, Li Du, William Lunden

Magnetic atoms in a thin layer have repulsive interactions when their magnetic moments are aligned perpendicular to the layer. We show experimentally and theoretically how this can suppress dipolar relaxation, the dominant loss process in spin mixtures of highly magnetic atoms. Using dysprosium, we observe an order of magnitude extension of the lifetime, and

Ryan Feng, Ashish Hooda, Neal Mangaokar, Kassem Fawaz

Recent work has proposed stateful defense models (SDMs) as a compelling strategy to defend against a black-box attacker who only has query access to the model, as is common for online machine learning platforms. Such stateful defenses aim to defend against black-box attacks by tracking the query history and detecting and rejecting queries that are "similar"

Fulong Dong, Qinzhi Xia, Jie Liu

We employ the two-band density-matrix equations and time-dependent density functional theory to calculate high-order harmonic generation (HOHG) in graphene under a femtosecond laser irradiation. Our investigation uncovers a striking harmonic enhancement structure (HES) within a specific energy range of the HOHG spectrum. In this regime, we find the convergen

Jun-Jie Wan, Yu-Xiao Liu

This study investigates the localization of spinor fields in braneworld models in six or higher dimensions. We study the relationship between fermions in spacetimes of $2n+2$ and $2n$ dimensions. In conformally flat extra-dimensional spacetime, fermions cannot be localized through minimal coupling with gravity. To achieve the localization of spinor fields, w

Avinash Ajit Nargund, Misha Sra

3D human motion prediction is a research area of high significance and a challenge in computer vision. It is useful for the design of many applications including robotics and autonomous driving. Traditionally, autogregressive models have been used to predict human motion. However, these models have high computation needs and error accumulation that make it d

Tomás Notenson, Ignacio García-Mata, Augusto J. Roncaglia, Diego A. Wisniacki

The out-of-time ordered correlator (OTOC) is a measure of scrambling of quantum information. Scrambling is intuitively considered to be a significant feature of chaotic systems and thus the OTOC is widely used as a measure of chaos. For short times exponential growth is related to the classical Lyapunov exponent, sometimes known as butterfly effect. At long

Jonathan Cervantes, Mike Krebs

Given a set $S$ of positive integers, the integer distance graph for $S$ has the set of integers as its vertex set, where two vertices are adjacent if and only if the absolute value of their difference lies in $S$. In 2002, Zhu completely determined the chromatic number of integer distance graphs when $S$ has cardinality $3$. Integer distance graphs can be d

Lixiang Yang, Robert L Lowe

This paper reports a theoretical and numerical framework to model nonlinear waves in elastic-plastic solids. Formulated in the Eulerian frame, the governing equations employed include the continuity equation, the momentum equation, and an elastic-plastic constitutive relation. The complete governing equations are a set of first-order, fully coupled partial d

Zuobai Zhang, Chuanrui Wang, Minghao Xu, Vijil Chenthamarakshan

Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge. In contrast, structure-based

Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Martin Weigert

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named Co

Myeongjun Erik Jang, Thomas Lukasiewicz

ChatGPT has gained a huge popularity since its introduction. Its positive aspects have been reported through many media platforms, and some analyses even showed that ChatGPT achieved a decent grade in professional exams, adding extra support to the claim that AI can now assist and even replace humans in industrial fields. Others, however, doubt its reliabili

Jonathan Cervantes, Mike Krebs

A connected Cayley graph on an abelian group with a finite generating set $S$ can be represented by its Heuberger matrix, i.e., an integer matrix whose columns generate the group of relations between members of $S$. In a previous article, the authors laid the foundation for the use of Heuberger matrices to study chromatic numbers of abelian Cayley graphs. We

Kevin Carter-Fenk, Martin Head-Gordon

Second-order M{\o}ller-Plesset perturbation theory (MP2) often breaks down catastrophically in small-gap systems, leaving much to be desired in its performance for myriad chemical applications such as noncovalent interactions, thermochemistry, and dative bonding in transition metal complexes. This divergence problem has reignited interest in Brillouin-Wigner

V. K. Oikonomou

This study focuses on the static neutron star perspective for two types of cosmological inflationary attractor theories, namely the induced inflationary attractors and the quadratic inflationary attractors. The two cosmological models can be discriminated cosmologically, since one of the two does not provide a viable inflationary phenomenology, thus in this

Conor K. Corbin, Rob Maclay, Aakash Acharya, Sreedevi Mony

Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant. Healthcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable and reliable models that integrate with clinical workflow. Such governance frameworks require an accompanying te

Balamurali Murugesan, Sukesh Adiga, Bingyuan Liu, Hervé Lombaert

Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain. While recent literature on calibrating deep segmentation networks has led to significant progress, their uncertainty is usually modeled by leveraging the information of individual pixels, which disregards the loc

Jonathan Cervantes, Mike Krebs

A cubelike graph is a Cayley graph on the product $\mathbb{Z}_2\times\cdots\times\mathbb{Z}_2$ of the integers modulo $2$ with itself finitely many times. In 1992, Payan proved that no cubelike graph can have chromatic number $3$. The authors of the present paper previously developed a general matrix method for studying chromatic numbers of Cayley graphs on