July 2023 arXiv papers — page 80

Eric-Jan Wagenmakers, Sandy Zabell, Quentin F. Gronau

After Bayes, the oldest Bayesian account of enumerative induction is given by Laplace's so-called rule of succession: if all $n$ observed instances of a phenomenon to date exhibit a given character, the probability that the next instance of that phenomenon will also exhibit the character is $\frac{n+1}{n+2}$. Laplace's rule however has the apparently counter

Xin-Lei Zhang, Heng Xiao, Solkeun Jee, Guowei He

Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high--fidelity data. However, the interpretability of the learned model is often not fully analyzed, which has been one of the main criticism of neural network-based turbulence modeling. Therefore, it is increasingly demanding to provi

Martin Ryner, Jan Kronqvist, Johan Karlsson

This paper presents a framework for computing the Gromov-Wasserstein problem between two sets of points in low dimensional spaces, where the discrepancy is the squared Euclidean norm. The Gromov-Wasserstein problem is a generalization of the optimal transport problem that finds the assignment between two sets preserving pairwise distances as much as possible

Xin Li, Xuli Tang

Drug research and development are embracing translational research for its potential to increase the number of drugs successfully brought to clinical applications. Using the publicly available PubMed database, we sought to describe the status of drug translational research, the distribution of translational lags for all drugs as well as the collaborations be

Tong Wu

Recently, tensor low-rank representation (TLRR) has become a popular tool for tensor data recovery and clustering, due to its empirical success and theoretical guarantees. However, existing TLRR methods consider Gaussian or gross sparse noise, inevitably leading to performance degradation when the tensor data are contaminated by outliers or sample-specific c

Guy Lachman, Anurag Rao, Uri Shapira, Yuval Yifrach

We introduce a novel concept in topological dynamics, referred to as $k$-divergence, which extends the notion of divergent orbits. Motivated by questions in the theory of inhomogeneous Diophantine approximations, we investigate this notion in the dynamical system given by a certain flow on the space of unimodular lattices in $\mathbb{R}^d$. Our main result i

Jorge Castillejos, Robert Neagu

We investigate $^*$-homomorphisms with nuclear dimension equal to zero. In the framework of classification of $^*$-homo-morphisms, we characterise such maps as those that can be approximately factorised through an AF-algebra. Along the way, we obtain various obstructions for the total invariant of zero-dimensional morphisms and show that in the presence of r

Hao Liu, Xue-Cheng Tai, Raymond Chan

Deep neural network is a powerful tool for many tasks. Understanding why it is so successful and providing a mathematical explanation is an important problem and has been one popular research direction in past years. In the literature of mathematical analysis of deep neural networks, a lot of works is dedicated to establishing representation theories. How to

Xiufeng Huang, Sheng Zhou

To improve the performance of multi-agent reinforcement learning under the constraint of wireless resources, we propose a message importance metric and design an importance-aware scheduling policy to effectively exchange messages. The key insight is spending the precious communication resources on important messages. The message importance depends not only o

Yingjie Niu, Ming Ding, Maoning Ge, Robin Karlsson

Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper presents a novel method to enhance the explainability of Transformer-based image classification models. Our method aims to improve trust in classification resul

Yukinao Akamatsu, Masayuki Asakawa

We discuss how heavy quark dynamics is affected by the critical fluctuations near the QCD critical point at finite temperature and density. We find that the heavy quark momentum diffusion constant scales as $\kappa\propto\xi^{z-3-\eta}$. In the model H scenario, which is widely accepted for the critical dynamics, the exponents are known as $z\simeq 3, \eta\s

Sungwon Park, Sungwon Han, Fangzhao Wu, Sundong Kim

Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. Previous defense mechanisms have focused on the server-side by using careful model aggregation, but this may no

Shrey Mishra, Antoine Gauquier, Pierre Senellart

We address the extraction of mathematical statements and their proofs from scholarly PDF articles as a multimodal classification problem, utilizing text, font features, and bitmap image renderings of PDFs as distinct modalities. We propose a modular sequential multimodal machine learning approach specifically designed for extracting theorem-like environments

Iolanda Di Bernardo, James Blyth, Liam Watson, Kaijian Xing

Chalcogen vacancies in transition metal dichalcogenides are widely acknowledged as both donor dopants and as a source of disorder. The electronic structure of sulphur vacancies in MoS2 however is still controversial, with discrepancies in the literature pertaining to the origin of the in-gap features observed via scanning tunneling spectroscopy (STS) on sing

Ye Ouyang, Yaqin Zhang, Xiaozhou Ye, Yunxin Liu

6G is the next-generation intelligent and integrated digital information infrastructure, characterized by ubiquitous interconnection, native intelligence, multi-dimensional perception, global coverage, green and low-carbon, native network security, etc. 6G will realize the transition from serving people and people-things communication to supporting the effic

Qipeng Li, Yuan Zhuang, Yiwen Chen, Jianzhu Huai

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving object

Fernando Montaner, Irene Paniello

We study Jordan 3-graded Lie algebras satisfying 3-graded polynomial identities. Taking advantage of the Tits-Kantor-Koecher construction, we interpret the PI condition in terms of their associated Jordan pairs, which allows us to formulate an analogous of Posner-Rowen Theorem for strongly prime PI Jordan 3-graded Lie algebras. Arbitrary PI Jordan 3-graded L

Xuena Wang, Xueting Li, Zi Yin, Yue Wu

Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and complex reasoning. However, their alignment with human emotions and values, which is critical for real-world applications, has not been systematically evaluated. Here, we assessed

XueGuang Zhang

In the manuscript, a candidate of sub-pc binary black hole (BBH) system is reported in SDSS J1257+2023 through different properties of broad Balmer emission lines. After subtractions of host galaxy contributions, Gaussian functions are applied to measure emission lines in SDSS J1257+2023, leading line width (second moment) 760${\rm km/s}$ of broad H$\beta$ t

Alessio Trebbi, Nolwenn Fougeron, Yohan Payan

Diabetic foot ulcers are triggered by mechanical loadings applied to the surface of the plantar skin. Strain is considered to play a crucial role in relation to ulcer etiology and can be assessed by Finite Element (FE) modelling. A difficulty in the generation of these models is the choice of the soft tissue material properties. In the literature, many studi

Xue-Cheng Tai, Hao Liu, Raymond Chan

For problems in image processing and many other fields, a large class of effective neural networks has encoder-decoder-based architectures. Although these networks have made impressive performances, mathematical explanations of their architectures are still underdeveloped. In this paper, we study the encoder-decoder-based network architecture from the algori

Parada T. P. Hutauruk, Hana Gil, Seung-il Nam, Chang Ho Hyun

In this paper, the implications of the symmetry energy on the hadron and quark phase transitions in the compact star, including the properties of the possible configurations of the quark-hadron hybrid stars, are investigated in the frameworks of the energy-density functional (EDF) models and the flavor SU(2) Nambu--Jona-Lasinio (NJL) model with the help of t

Manon Ryckebusch, Abderrahman Bouhamidi, Pierre-Louis Giscard

Solving non-autonomous systems of ordinary differential equations leads to consider a new product of bivariate distributions called the $\star$~product in the literature. This product, distinct from the convolution product, has recently been used to establish structural results concerning non-autonomous differential systems, yet its formal underpinnings rema

Yingchaojie Feng, Xingbo Wang, Kam Kwai Wong, Sijia Wang

Generative text-to-image models have gained great popularity among the public for their powerful capability to generate high-quality images based on natural language prompts. However, developing effective prompts for desired images can be challenging due to the complexity and ambiguity of natural language. This research proposes PromptMagician, a visual anal

Shun-Yang Lee, Julian Runge, Daniel Yoo, Yakov Bart

Research has investigated the impact of the COVID-19 pandemic on business performance and survival, indicating particularly adverse effects for small and midsize businesses (SMBs). Yet only limited work has examined whether and how online advertising technology may have helped shape these outcomes, particularly for SMBs. The aim of this study is to address t

Elie Cerf

We give in this paper two characterizations of the minimal exponential rate of survival $\theta$ * of the M/M/$\infty$ queue. The first one is a derivation of a known result on the duration of excursions of this process. The second one was conjectured by Mart{\'i}nez and Ycart [8] and is proved using complex analysis by establishing a connection with the fir

Vladimir Lotoreichik, Thomas Ourmières-Bonafos

We investigate the spectrum of the Dirac operator with infinite mass boundary conditions posed in a tubular neighborhood of a smooth compact hypersurface in $\mathbb{R}^n$ without boundary. We prove that when the tubular neighborhood shrinks to the hypersurface, the asymptotic behavior of the eigenvalues is driven by a Schr\"odinger operator involving electr

Sebastian Arnold, Johanna Ziegel

We introduce isotonic conditional laws (ICL) which extend the classical notion of conditional laws by the additional requirement that there exists an isotonic relationship between the random variable of interest and the conditioning random object. We show existence and uniqueness of ICL building on conditional expectations given $\sigma$-lattices. ICL corres

Yi-Hsien Wu, Leon C. Camenzind, Akito Noiri, Kenta Takeda

Because of their long coherence time and compatibility with industrial foundry processes, electron spin qubits are a promising platform for scalable quantum processors. A full-fledged quantum computer will need quantum error correction, which requires high-fidelity quantum gates. Analyzing and mitigating the gate errors are useful to improve the gate fidelit

Eldad Bettelheim

We show the relationship between the strongly non-linear limit (also termed the dispersionless or the Whitham limit) of the macroscopic fluctuation theory of certain statistical models and the inverse scattering method. We show that in the strongly non-linear limit the inverse scattering problem can be solved using the steepest descent method of the associat

Priya Chakraborty, Sayantari Ghosh

MicroRNAs are extensively known for post-transcriptional gene regulation and pattern formation in the embryonic developmental stage. We explore the origin of these spatio-temporal patterns mathematically, considering three different motifs here. For three scenarios, (1) simple microRNA-based mRNA regulation with a graded response in output, (2) microRNA-base

Guixian Wang, Xiu-Bin Wang, Haie Long, Bo Han

In this paper, we investigate the nonlocal generalized Sasa-Satsuma (ngSS) equation based on an improved Riemann-Hilbert method (RHM). Different from the traditional RHM, the $t$-part of the Lax pair plays a more important role rather than the $x$-part in analyzing the spectral problems. So we start from the $t$-part of the spectral problems. In the process

Connor Lee, Jonathan Gustafsson Frennert, Lu Gan, Matthew Anderson

We present a new method to adapt an RGB-trained water segmentation network to target-domain aerial thermal imagery using online self-supervision by leveraging texture and motion cues as supervisory signals. This new thermal capability enables current autonomous aerial robots operating in near-shore environments to perform tasks such as visual navigation, bat

Hongwei Zheng, Han Li, Bowen Shi, Wenrui Dai

Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence. In this paper, we propose a plug-and-play module named Action Prompt Module (APM) that effectively mines different kinds of action clues for 3D HPE. The

Hanyan Cao, Feng Pan, Yijia Wang, Pan Zhang

We propose a general framework for decoding quantum error-correcting codes with generative modeling. The model utilizes autoregressive neural networks, specifically Transformers, to learn the joint probability of logical operators and syndromes. This training is in an unsupervised way, without the need for labeled training data, and is thus referred to as pr

Milica Tomašević

In this work, we prove the well-posedness and propagation of chaos for a stochastic particle system in mean-field interaction under the assumption that the interacting kernel belongs to a suitable $L_t^q-L_x^p$ space. Contrary to the large deviation principle approach recently proposed in [2], the main ingredient of the proof here are the \textit{Partial Gir

Yuichi Goto, Kou Fujimori

The integer autoregressive (INAR) model is one of the most commonly used models in nonnegative integer-valued time series analysis and is a counterpart to the traditional autoregressive model for continuous-valued time series. To guarantee the integer-valued nature, the binomial thinning operator or more generally the generalized Steutel and van Harn operato

Zhiyu Wu, Jinshi Cui

Facial expression recognition (FER) remains a challenging task due to the ambiguity of expressions. The derived noisy labels significantly harm the performance in real-world scenarios. To address this issue, we present a new FER model named Landmark-Aware Net~(LA-Net), which leverages facial landmarks to mitigate the impact of label noise from two perspectiv

Salma Omer, Montaz Ali

We consider the problem of identifying a maximum clique in a given graph. We have proposed a mathematical model for this problem. The model resembles the matrix decomposition of the adjacency matrix of a given graph. The objective function of the mathematical model includes a weighted $\ell_{1}$-norm of the sparse matrix of the decomposition, which has an ad

Amogh Mannekote

Dialogue-based human-AI collaboration can revolutionize collaborative problem-solving, creative exploration, and social support. To realize this goal, the development of automated agents proficient in skills such as negotiating, following instructions, establishing common ground, and progressing shared tasks is essential. This survey begins by reviewing the

Sunder Ali Khowaja, Lewis Nkenyereye, Ghulam Mujtaba, Ik Hyun Lee

With the surge in emerging technologies such as Metaverse, spatial computing, and generative AI, the application of facial style transfer has gained a lot of interest from researchers as well as startups enthusiasts alike. StyleGAN methods have paved the way for transfer-learning strategies that could reduce the dependency on the huge volume of data that is

Qingkui Chen, Yiqin Zhang

Time series prediction plays a crucial role in various industrial fields. In recent years, neural networks with a transformer backbone have achieved remarkable success in many domains, including computer vision and NLP. In time series analysis domain, some studies have suggested that even the simplest MLP networks outperform advanced transformer-based networ

Anastasiya Belyaeva, Justin Cosentino, Farhad Hormozdiari, Krish Eswaran

Foundation large language models (LLMs) have shown an impressive ability to solve tasks across a wide range of fields including health. To effectively solve personalized health tasks, LLMs need the ability to ingest a diversity of data modalities that are relevant to an individual's health status. In this paper, we take a step towards creating multimodal LLM

Ufuk Korkmaz, Deniz Türkpençe

The promising performance increase offered by quantum computing has led to the idea of applying it to neural networks. Studies in this regard can be divided into two main categories: simulating quantum neural networks with the standard quantum circuit model, and implementing them based on hardware. However, the ability to capture the non-linear behavior in n

Gobinda Garai, Bankim C. Mandal

This paper is concerned with the designing, analyzing and implementing linear and nonlinear discretization scheme for the distributed optimal control problem (OCP) with the Cahn-Hilliard (CH) equation as constrained. We propose three difference schemes to approximate and investigate the solution behaviour of the OCP for the CH equation. We present the conver

Oliver Müller, Ethan Crosby

Previous results suggest that there exists a correlation between the size of the bulge of a galaxy and the number of its dwarf galaxy satellites. This was found to be inconsistent with the standard model of cosmology based on comparisons to semi-analytical dark matter-only simulations, where no such correlation was found. In this work, we extend these studie

Daniele Jahier Pagliari, Matteo Risso, Beatrice Alessandra Motetti, Alessio Burrello

Accurate yet efficient Deep Neural Networks (DNNs) are in high demand, especially for applications that require their execution on constrained edge devices. Finding such DNNs in a reasonable time for new applications requires automated optimization pipelines since the huge space of hyper-parameter combinations is impossible to explore extensively by hand. In

Jinwoo Ha, Dongsoo Kim

This study aims to explore user acceptance of Autonomous Vehicle (AV) policies with improved text-mining methods. Recently, South Korean policymakers have viewed Autonomous Driving Car (ADC) and Autonomous Driving Robot (ADR) as next-generation means of transportation that will reduce the cost of transporting passengers and goods. They support the constructi

Shixiong Zhang, Jiao Li, Lu Yang

Image synthesis has attracted emerging research interests in academic and industry communities. Deep learning technologies especially the generative models greatly inspired controllable image synthesis approaches and applications, which aim to generate particular visual contents with latent prompts. In order to further investigate low-level controllable imag

Xuan Mao, Guo-Qing Qin, Hao Zhang, Bo-Yang Wang

Non-Hermitian systems associated with exceptional points (EPs) are expected to demonstrate a giant response enhancement for various sensors. The widely investigated enhancement mechanism based on diverging from an EP should destroy the EP and further limits its applications for multiple sensing scenarios in a time sequence. To break the above limit, here we

Juhi Oudichhya, Keval Gandhi, Ajay Kumar Rai

In the present work, the mass-spectra of the light mesons, the kaons ($u\overline{s}$) and strangeonium ($s\overline{s}$) is systematically studied within the framework of Regge phenomenology. Several relations between Regge slope, intercept, and meson masses are extracted with the assumption of linear Regge trajectories. Using these relations the ground sta

Sandip Das, Subhamay Pramanik, Sumit Mukherjee, Tatan Ghosh

Graphene oxide (GO) is one of the important functional materials. Large-scale synthesis of it is very challenging. Following a simple cost-effective route, large-scale GO was produced by mechanical (ball) milling, in air, of carbon nanoparticles (CNPs) present in carbon soot in the present study. The thickness of the GO layer was seen to decrease with an inc

Po-Rong Lai, Jhen-Dong Lin, Yi-Te Huang, Yueh-Nan Chen

We propose novel charging protocols for quantum batteries based on quantum superpositions of trajectories. Specifically, we consider that a qubit (the battery) interacts with multiple cavities or a single cavity at various positions, where the cavities act as chargers. Further, we introduce a quantum control prepared in a quantum superposition state, allowin

Lingjiao Chen, Matei Zaharia, James Zou

GPT-3.5 and GPT-4 are the two most widely used large language model (LLM) services. However, when and how these models are updated over time is opaque. Here, we evaluate the March 2023 and June 2023 versions of GPT-3.5 and GPT-4 on several diverse tasks: 1) math problems, 2) sensitive/dangerous questions, 3) opinion surveys, 4) multi-hop knowledge-intensive

Zhi-Song Liu, Zijia Wang, Zhen Jia

Continuous image super-resolution (SR) recently receives a lot of attention from researchers, for its practical and flexible image scaling for various displays. Local implicit image representation is one of the methods that can map the coordinates and 2D features for latent space interpolation. Inspired by Variational AutoEncoder, we propose a Soft-introVAE

Yinghui Li, Haojing Huang, Shirong Ma, Yong Jiang

Recently, the development and progress of Large Language Models (LLMs) have amazed the entire Artificial Intelligence community. Benefiting from their emergent abilities, LLMs have attracted more and more researchers to study their capabilities and performance on various downstream Natural Language Processing (NLP) tasks. While marveling at LLMs' incredible

Jaesung Huh, Max Bain, Andrew Zisserman

This report presents the technical details of our submission on the EGO4D Audio-Visual (AV) Automatic Speech Recognition Challenge 2023 from the OxfordVGG team. We present WhisperX, a system for efficient speech transcription of long-form audio with word-level time alignment, along with two text normalisers which are publicly available. Our final submission

Feng-Ting Liao, Yung-Chieh Chan, Yi-Chang Chen, Chan-Jan Hsu

In this work, we propose a method to create domain-sensitive speech recognition models that utilize textual domain information by conditioning its generation on a given text prompt. This is accomplished by fine-tuning a pre-trained, end-to-end model (Whisper) to learn from demonstrations with prompt examples. We show that this ability can be generalized to d

Heng Li, Haojin Li, Wei Zhao, Huazhu Fu

The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmenta

Jinhong Wang, Yi Cheng, Jintai Chen, Tingting Chen

Ordinal regression refers to classifying object instances into ordinal categories. It has been widely studied in many scenarios, such as medical disease grading, movie rating, etc. Known methods focused only on learning inter-class ordinal relationships, but still incur limitations in distinguishing adjacent categories thus far. In this paper, we propose a s

Ulderico Dardano, Fausto De Mari

It is shown that a gerenalised radical group has no chain of non-pronormal subgroups with the same order type as the set of the real numbers if and only if either the group is minimax or all subgroups are pronormal.

Susu Cui, Cong Dong, Meng Shen, Yuling Liu

Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and benign traffic. Compared with signature-based methods, they have higher scalability and flexibility. However, affected by th

Robert Dumitru, Thorben Moos, Andrew Wabnitz, Yuval Yarom

In recent years a new class of side-channel attacks has emerged. Instead of targeting device emissions during dynamic computation, adversaries now frequently exploit the leakage or response behaviour of integrated circuits in a static state. Members of this class include Static Power Side-Channel Analysis (SCA), Laser Logic State Imaging (LLSI) and Impedance

Tengfei Xue, Yuqian Chen, Chaoyi Zhang, Alexandra J. Golby

Diffusion MRI tractography parcellation classifies streamlines into anatomical fiber tracts to enable quantification and visualization for clinical and scientific applications. Current tractography parcellation methods rely heavily on registration, but registration inaccuracies can affect parcellation and the computational cost of registration is high for la

Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth

A recent line of work has shown a surprising connection between multicalibration, a multi-group fairness notion, and omniprediction, a learning paradigm that provides simultaneous loss minimization guarantees for a large family of loss functions. Prior work studies omniprediction in the batch setting. We initiate the study of omniprediction in the online adv

Takao Komatsu, Jiaxin Mu

For a nonnegative integer $p$, the $p$-numerical semigroup $S_p$ is defined as the set of integers whose nonnegative integral linear combinations of given positive integers $a_1,a_2,\dots,a_\kappa$ with $\gcd(a_1,a_2,\dots,a_\kappa)=1$ are expressed in more than $p$ ways. When $p=0$, $S=S_0$ is the original numerical semigroup. The largest element and the ca

Ryan Burn

Berger et al. (2001) and Ren et al. (2012) derived noninformative priors for Gaussian process models of spatially correlated data using the reference prior approach (Berger, Bernardo, 1991). The priors have good statistical properties and provide a basis for objective Bayesian analysis (Berger, 2006). Using a trust-region algorithm for optimization with exac

Yang Zhao, Tingbo Hou, Yu-Chuan Su, Xuhui Jia. Yandong Li

An authentic face restoration system is becoming increasingly demanding in many computer vision applications, e.g., image enhancement, video communication, and taking portrait. Most of the advanced face restoration models can recover high-quality faces from low-quality ones but usually fail to faithfully generate realistic and high-frequency details that are

Kai Katsumata, Duc Minh Vo, Bei Liu, Hideki Nakayama

The exploration of the latent space in StyleGANs and GAN inversion exemplify impressive real-world image editing, yet the trade-off between reconstruction quality and editing quality remains an open problem. In this study, we revisit StyleGANs' hyperspherical prior $\mathcal{Z}$ and combine it with highly capable latent spaces to build combined spaces that f

Seyed Rohollah Hosseyni, Sanaz Seyedin, Hasan Taheri

Understanding the relationship between different parts of an image is crucial in a variety of applications, including object recognition, scene understanding, and image classification. Despite the fact that Convolutional Neural Networks (CNNs) have demonstrated impressive results in classifying and detecting objects, they lack the capability to extract the r

Léopold Van Brandt, Jean-Charles Delvenne

We propose a novel bound on the mimimum dissipation required in any circumstances to transfer a certain amount of charge through any resistive device. We illustrate it on the task of writing a logical 1 (encoded as a prescribed voltage) into a capacitance, through various linear or nonlinear devices. We show that, even though the celebrated Landauer bound (w

Zhi-Song Liu, Zijia Wang, Zhen Jia

Point clouds acquired from 3D sensors are usually sparse and noisy. Point cloud upsampling is an approach to increase the density of the point cloud so that detailed geometric information can be restored. In this paper, we propose a Dual Back-Projection network for point cloud upsampling (DBPnet). A Dual Back-Projection is formulated in an up-down-up manner

Yuzhe He, Shuang Liang, Xiaofei Rui, Chengying Cai

Accurate and reliable ego-localization is critical for autonomous driving. In this paper, we present EgoVM, an end-to-end localization network that achieves comparable localization accuracy to prior state-of-the-art methods, but uses lightweight vectorized maps instead of heavy point-based maps. To begin with, we extract BEV features from online multi-view i

Wenhan Wang, Yanzhou Li, Anran Li, Jian Zhang

Recently, deep learning models have been widely applied in program understanding tasks, and these models achieve state-of-the-art results on many benchmark datasets. A major challenge of deep learning for program understanding is that the effectiveness of these approaches depends on the quality of their datasets, and these datasets often contain noisy data s

Xinxing Yang, Genke Yang, Jian Chu

Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new targets. However, the performance of previous computational models is limited by the following drawbacks. The learning of drug representation relies only on supervised data, without taki

Yingyu Chen, Ziyuan Yang, Chenyu Shen, Zhiwen Wang

Recently, uncertainty-aware methods have attracted increasing attention in semi-supervised medical image segmentation. However, current methods usually suffer from the drawback that it is difficult to balance the computational cost, estimation accuracy, and theoretical support in a unified framework. To alleviate this problem, we introduce the Dempster-Shafe

Moyukh Laha, Dibbendu Roy, Sourav Dutta, Goutam Das

Extended Reality (XR) is one of the most important 5G/6G media applications that will fundamentally transform human interactions. However, ensuring low latency, high data rate, and reliability to support XR services poses significant challenges. This letter presents a novel AI-assisted service provisioning scheme that leverages predicted frames for processin

Hiroyuki Sakai, Hideaki Iiduka

This paper presents modified memoryless quasi-Newton methods based on the spectral-scaling Broyden family on Riemannian manifolds. The method involves adding one parameter to the search direction of the memoryless self-scaling Broyden family on the manifold. Moreover, it uses a general map instead of vector transport. This idea has already been proposed with

Seungho Baek, Hyerin Im, Jiseung Ryu, Juhyeong Park

Text-to-image generation model is able to generate images across a diverse range of subjects and styles based on a single prompt. Recent works have proposed a variety of interaction methods that help users understand the capabilities of models and utilize them. However, how to support users to efficiently explore the model's capability and to create effectiv

Meng Wei, Long Chen, Wei Ji, Xiaoyu Yue

Video Visual Relation Detection (VidVRD) aims to detect visual relationship triplets in videos using spatial bounding boxes and temporal boundaries. Existing VidVRD methods can be broadly categorized into bottom-up and top-down paradigms, depending on their approach to classifying relations. Bottom-up methods follow a clip-based approach where they classify

Makoto Araya, Masaaki Harada, Vladimir D. Tonchev

A classification of Hadamard matrices of order $2p+2$ with an automorphism of order $p$ is given for $p=29$ and $31$. The ternary self-dual codes spanned by the newly found Hadamard matrices of order $60$ with an automorphism of order $29$ are computed, as well as the binary doubly even self-dual codes of length $120$ with generator matrices defined by relat

Shibo Yao, Dantong Yu, Ioannis Koutis

Neural networks have achieved remarkable performance in various application domains. Nevertheless, a large number of weights in pre-trained deep neural networks prohibit them from being deployed on smartphones and embedded systems. It is highly desirable to obtain lightweight versions of neural networks for inference in edge devices. Many cost-effective appr

G. C. Bourantas, B. F. Zwick, D. S. Lampropoulos, V. C. Loukopoulos

We present an efficient and accurate immersed boundary (IB) finite element (FE) method for internal flow problems with complex geometries (e.g., blood flow in the vascular system). In this study, we use a voxelized flow domain (discretized with hexahedral and tetrahedral elements) instead of a box domain, which is frequently used in IB methods. The proposed

Ethan Shen, Scotty Singh, Bhavesh Kumar

Multi-modal tasks involving vision and language in deep learning continue to rise in popularity and are leading to the development of newer models that can generalize beyond the extent of their training data. The current models lack temporal generalization which enables models to adapt to changes in future data. This paper discusses a viable approach to crea

Hui-Min Li, Jin-Min Liang, Zhi-Xi Wang, Shao-Ming Fei

Quantum approximate optimization algorithm (QAOA) is a promising variational quantum algorithm for combinatorial optimization problems. However, the implementation of QAOA is limited due to the requirement that the problems be mapped to Ising Hamiltonians and the nonconvex optimization landscapes. Although the Ising Hamiltonians for many NP hard problems hav

Quanquan C. Liu, Yiduo Ke, Samir Khuller

In this paper, we give new auction algorithms for maximum weighted bipartite matching (MWM) and maximum cardinality bipartite $b$-matching (MCbM). Our algorithms run in $O\left(\log n/\varepsilon^8\right)$ and $O\left(\log n/\varepsilon^2\right)$ rounds, respectively, in the blackboard distributed setting. We show that our MWM algorithm can be implemented in

Hadi Hadizadeh, Ivan V. Bajić

Video coding has traditionally been developed to support services such as video streaming, videoconferencing, digital TV, and so on. The main intent was to enable human viewing of the encoded content. However, with the advances in deep neural networks (DNNs), encoded video is increasingly being used for automatic video analytics performed by machines. In app

Ankit Bhojak

In this note, we show that the $L\log L$ hypothesis is the strongest size condition on a homogeneous rough function on the sphere which ensures the weak type $(1,1)$ boundedness of the corresponding singular integral $T_\Omega$, provided $T_\Omega$ is bounded in $L^2$.

Md Firoz Ali, Sanjit Pal

Let $\mathcal{A}$ denote the class of analytic functions $f$ in the unit disk $\mathbb{D}=\{z\in\mathbb{C}:|z|<1\}$ normalized by $f(0)=0$, $f'(0)=1$. For $-\pi/2<\alpha<\pi/2$, let $\mathcal{S}_{\alpha}$ be the subclass of $\mathcal{A}$ consisting of functions $f$ that satisfy the relation ${\rm Re\,} \{e^{i\alpha}(1+zf''(z)/f'(z))\}>0$ for $z\in\mathbb{D}$

Marie Chion, Arthur Leroy

Differential analysis is a routine procedure in the statistical analysis toolbox across many applied fields, including quantitative proteomics, the main illustration of the present paper. The state-of-the-art limma approach uses a hierarchical formulation with moderated-variance estimators for each analyte directly injected into the t-statistic. While standa

Giovanni E. Cacciamani, Michael B. Eppler, Conner Ganjavi, Asli Pekan

The swift progress and ubiquitous adoption of Generative AI (GAI), Generative Pre-trained Transformers (GPTs), and large language models (LLMs) like ChatGPT, have spurred queries about their ethical application, use, and disclosure in scholarly research and scientific productions. A few publishers and journals have recently created their own sets of rules; h

E. S. Yorgancioglu, Q. C. Bu, A. Santangelo, L. Tao

4U 1543--47 is one of a handful of known black hole candidates located in the Milky Way Galaxy, and has undergone a very bright outburst in 2021, reaching a total of $\sim$9 Crab, as observed by the Monitor of All-sky Image (MAXI), and exceeding twice its Eddington luminosity. The unprecedented bright outburst of 4U 1543--47 provides a unique opportunity to

Francesco Bonsante, Michael Wolf

We prove that the space of circle packings consistent with a given triangulation on a surface of genus at least two is projectively rigid, so that a packing on a complex projective surface is not deformable within that complex projective structure. More broadly, we show that the space of circle packings is a submanifold within the space of complex projective

Shyoya Tanaka, Nobuya Nishimura, Futoshi Minato, Yoshihiro Aritomo

Background: Precise understanding of nuclear fission is crucial for experimental and theoretical nuclear physics, astrophysics, and industrial applications; however, the complete physical mechanics is unresolved due to the complexities. Purpose: In this study, we present a new method to describe the dynamical-fission process and following prompt-neutron emis

Monika Henzinger, Jalaj Upadhyay, Sarvagya Upadhyay

We study the problem of maintaining a differentially private decaying sum under continual observation. We give a unifying framework and an efficient algorithm for this problem for \emph{any sufficiently smooth} function. Our algorithm is the first differentially private algorithm that does not have a multiplicative error for polynomially-decaying weights. Ou

Clément L. Canonne, Samuel B. Hopkins, Jerry Li, Allen Liu

We consider the question of Gaussian mean testing, a fundamental task in high-dimensional distribution testing and signal processing, subject to adversarial corruptions of the samples. We focus on the relative power of different adversaries, and show that, in contrast to the common wisdom in robust statistics, there exists a strict separation between adaptiv

Zhen Wen, Yihan Liu, Siwei Tan, Jieyi Chen

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have

Anton Bobrov, James Traina

Recent highly cited research uses time-series evidence to argue the decline in interest rates led to a large rise in economic profits and markups. We show the size of these estimates is sensitive to the sample start date: The rise in markups from 1984 to 2019 is 14% larger than from 1980 to 2019, a difference amounting to a $3000 change in income per worker

Shota Takeishi

In subgroup analysis, testing the existence of a subgroup with a differential treatment effect serves as protection against spurious subgroup discovery. Despite its importance, this hypothesis testing possesses a complicated nature: parameter characterizing subgroup classification is not identified under the null hypothesis of no subgroup. Due to this irregu

Kazuharu Bamba, M. Z. Bhatti, Z. Yousaf, Z. Shoukat

Anisotropic spherically symmetric solutions within the framework of the Brans-Dicke theory are uncovered through a unique gravitational decoupling approach involving a minimal geometric transformation. This transformation effectively divides the Einstein field equations into two separate systems, resulting in the alteration of the radial metric component. Th