November 2022 arXiv papers — page 62

Guang-Wei Li, Chao Wu, Gui-Ping Zhou, Chen Yang

In the archive of the Ground Wide Angle Camera (GWAC), we found 43 white light flares from 43 stars, among which, three are sympathetic or homologous flares, and one of them also has a quasi-periodic pulsation with a period of $13.0\pm1.5$ minutes. Among these 43 flare stars, there are 19 new active stars and 41 stars that have available TESS and/or K2 light

Anirban Debnath, Nitish Kumar, Upasana Baishya, Nirmal K. Viswanathan

The degenerate case of normal incidence and reflection of an optical beam (both paraxial and non-paraxial) at a plane isotropic dielectric interface, which is azimuthally symmetric in terms of the momentum-spatial variation of Fresnel coefficients but not in terms of the fundamental polarization inhomogeneity of the incident field, requires in-depth analyses

Sijie Wang, Qiyu Kang, Rui She, Wee Peng Tay

Camera relocalization has various applications in autonomous driving. Previous camera pose regression models consider only ideal scenarios where there is little environmental perturbation. To deal with challenging driving environments that may have changing seasons, weather, illumination, and the presence of unstable objects, we propose RobustLoc, which deri

Caucher Birkar

We develop a moduli theory of algebraic varieties and pairs of non-negative Kodaira dimension. We define stable minimal models and construct their projective coarse moduli spaces under certain natural conditions. This can be applied to a wide range of moduli problems in algebraic geometry.

Jiafeng Wang, Zhaoyu Chen, Kaixun Jiang, Dingkang Yang

Deep Neural Networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding human-imperceptible perturbations to the benign inputs. Simultaneously, adversarial examples exhibit transferability across models, enabling practical black-box attacks. However, existing methods are still incapable of achieving the desired transfer attack perform

Nicolas Bédaride, Arnaud Hilion, Martin Lustig

Based on previous work of the authors, to any $S$-adic development of a subshift $X$ a "directive sequence" of commutative diagrams is associated, which consists at every level $n \geq 0$ of the measure cone and the letter frequency cone of the level subshift $X_n$ associated canonically to the given $S$-adic development. The issuing rich picture enables one

Nicolas Bédaride, Arnaud Hilion, Martin Lustig

Every non-erasing monoid morphism $\sigma: \mathcal{A}^* \to \mathcal{B}^*$ induces a {\em measure transfer map} $\sigma_X^{\mathcal{M}}: \mathcal{M}(X) \to \mathcal{M}(\sigma(X))$ between the measure cones $\mathcal{M}(X)$ and $\mathcal{M}(\sigma(X))$, associated to any subshift $X \subset \mathcal{A}^{\mathbb{Z}}$ and its image subshift $\sigma(X) \subset

Jelle J. Schoppink, Jan Krizek, Christophe Moser, David Fernandez Rivas

Bubbles generated with lasers under confinement have been investigated for their potential use as the driving mechanism for liquid micro-jets in various microfluidic devices, such as needle free jet injectors. Here, we report on the study of bubble formation by a continuous-wave (CW) and a pulsed laser inside an open-ended microfluidic capillary. This result

Andreas Nessmann

In this article, a novel method to compute all discrete polyharmonic functions in the quarter plane for models with small steps, zero drift and a finite group is proposed. A similar method is then introduced for continuous polyharmonic functions, and convergence between the discrete and continuous cases is shown.

Tomoyuki Yokouchi, Yuya Ikeda, Takahiro Morimoto, Yuki Shiomi

The concept of Berry curvature is essential for various transport phenomena. However, an effect of the Berry curvature on magnetochiral anisotropy, i.e. nonreciprocal magneto-transport, is still elusive. Here, we report the Berry curvature originates the large magnetochiral anisotropy. In Weyl-semimetal WTe2, we observed the strong enhancement of the magneto

Priyankur Chaudhuri

We show that given any two minimal models of a generalized lc pair, there exist small birational models which are connected by a sequence of symmetric flops. We also present some applications.

Adam J. Bowman, Cheng Huang, Mark J. Schnitzer, Mark A. Kasevich

The development of voltage-sensitive fluorescent probes suggests fluorescence lifetime as a promising readout for electrical activity in biological systems. Existing approaches fail to achieve the speed and sensitivity required for voltage imaging in neuroscience applications. Here we demonstrate that wide-field electro-optic fluorescence lifetime imaging (E

Xiaokai Hou, Guanyu Zhou, Qingyu Li, Shan Jin

The universality of a quantum neural network refers to its ability to approximate arbitrary functions and is a theoretical guarantee for its effectiveness. A non-universal neural network could fail in completing the machine learning task. One proposal for universality is to encode the quantum data into identical copies of a tensor product, but this will subs

Ana Kostovska, Carola Doerr, Sašo Džeroski, Dragi Kocev

Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. MLC is increasingly gaining interest in different application domains such as text mining, computer vision, and bioinformatics. Several MLC algorithms have been proposed in the literature, resulting in a meta-optimizat

Yongrui Chen, Xinnan Guo, Tongtong Wu, Guilin Qi

Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, whil

Nicolas Jonason, Bob L. T. Sturm

We present work in progress on TimbreCLIP, an audio-text cross modal embedding trained on single instrument notes. We evaluate the models with a cross-modal retrieval task on synth patches. Finally, we demonstrate the application of TimbreCLIP on two tasks: text-driven audio equalization and timbre to image generation.

Snehal Singh Tomar, Maitreya Suin, A. N. Rajagopalan

The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inversion of real images and determination of controllable latent

Man Wai Ho, Lancelot F. James, John W. Lau

Gibbs partitions of the integers generated by stable subordinators of index $\alpha\in(0,1)$ form remarkable classes of random partitions where in principle much is known about their properties, including practically effortless obtainment of otherwise complex asymptotic results potentially relevant to applications in general combinatorial stochastic processe

Takenori Yoshimura, Shinji Takaki, Kazuhiro Nakamura, Keiichiro Oura

This paper integrates a classic mel-cepstral synthesis filter into a modern neural speech synthesis system towards end-to-end controllable speech synthesis. Since the mel-cepstral synthesis filter is explicitly embedded in neural waveform models in the proposed system, both voice characteristics and the pitch of synthesized speech are highly controlled via a

Abinash Sahu, Naga Dileep Varikuti, Vaibhav Madhok

How does quantum chaos lead to rapid scrambling of information as well as systematic errors across a system when one introduces perturbations in the dynamics? What are its consequences for the reliability of quantum simulations and quantum information processing? We employ continuous measurement quantum tomography as a paradigm to study these questions. The

Rongqin Liang, Yuanman Li, Jiantao Zhou, Xia Li

The pedestrian trajectory prediction task is an essential component of intelligent systems. Its applications include but are not limited to autonomous driving, robot navigation, and anomaly detection of monitoring systems. Due to the diversity of motion behaviors and the complex social interactions among pedestrians, accurately forecasting their future traje

Gautam Goel, Naman Agarwal, Karan Singh, Elad Hazan

We consider the fundamental problem of online control of a linear dynamical system from two different viewpoints: regret minimization and competitive analysis. We prove that the optimal competitive policy is well-approximated by a convex parameterized policy class, known as a disturbance-action control (DAC) policies. Using this structural result, we show th

Himanshu Gaur, Urjit A. Yajnik

In this paper, we study the relation between entanglement and global internal symmetries on de Sitter space. We consider two symmetric causally disconnected regions in the hyperbolic chart on de Sitter space. Since entanglement measures characterises correlations, the study of entanglement between the two causally disconnected regions gives information about

Dan-Radu Grigore

We consider the general framework of perturbative quantum field theory for the pure Yang-Mills model developed in [9] and prove that the tree contributions do not give anomalies. We will provide a more general form of this gauge invariance property.

Lang Qin, Rui Yan, Huajin Tang

In recent years, spiking neural networks (SNNs) have been used in reinforcement learning (RL) due to their low power consumption and event-driven features. However, spiking reinforcement learning (SRL), which suffers from fixed coding methods, still faces the problems of high latency and poor versatility. In this paper, we use learnable matrix multiplication

Shangda Wu, Maosong Sun

Benefiting from large-scale datasets and pre-trained models, the field of generative models has recently gained significant momentum. However, most datasets for symbolic music are very small, which potentially limits the performance of data-driven multimodal models. An intuitive solution to this problem is to leverage pre-trained models from other modalities

Jesus Zarzar, Sara Rojas, Silvio Giancola, Bernard Ghanem

Recent advances in Neural Radiance Fields (NeRF) boast impressive performances for generative tasks such as novel view synthesis and 3D reconstruction. Methods based on neural radiance fields are able to represent the 3D world implicitly by relying exclusively on posed images. Yet, they have seldom been explored in the realm of discriminative tasks such as 3

Sam Witteveen, Martin Andrews

With the spread of the use of Text2Img diffusion models such as DALL-E 2, Imagen, Mid Journey and Stable Diffusion, one challenge that artists face is selecting the right prompts to achieve the desired artistic output. We present techniques for measuring the effect that specific words and phrases in prompts have, and (in the Appendix) present guidance on the

Haitao Lin, Yufei Huang, Odin Zhang, Siqi Ma

Generating molecules that bind to specific proteins is an important but challenging task in drug discovery. Previous works usually generate atoms in an auto-regressive way, where element types and 3D coordinates of atoms are generated one by one. However, in real-world molecular systems, the interactions among atoms in an entire molecule are global, leading

Jiaozi Wang, Barbara Dietz, Dario Rosa, Giuliano Benenti

We study the dynamical generation of entanglement for a two-body interacting system, starting from a separable coherent state. We show analytically that in the quasiclassical regime the entanglement growth rate can be simply computed by means of the underlying classical dynamics. Furthermore, this rate is given by the Kolmogorov-Sinai entropy, which characte

Junjie Sheng, Lu Wang, Fangkai Yang, Bo Qiao

Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while satisfying the some safety constraints r

Tim Tianyi Yang, Tom Tianze Yang, Andrew Liu, Jie Tang

Under the Autonomous Mobile Clinics (AMCs) initiative, we are developing, open sourcing, and standardizing health AI technologies to enable healthcare access in least developed countries (LDCs). We deem AMCs as the next generation of health care delivery platforms, whereas health AI engines are applications on these platforms, similar to how various applicat

G. E. Anderson, T. D. Russell, H. M. Fausey, A. J. van der Horst

We observed the rapid radio brightening of GRB 210702A with the Australian Telescope Compact Array (ATCA) just 11hr post-burst, tracking early-time radio variability over a 5hr period on ~15min timescales at 9.0, 16.7, and 21.2GHz. A broken power-law fit to the 9.0GHz light curve showed that the 5hr flare peaked at a flux density of 0.4+/-0.1mJy at ~13hr pos

Esraa Khafagy, khaled A. Edris, Osama M. Shalabiea, Anna Bartkiewicz

We introduce a study of the massive star forming region IRAS 18144--1723 using observations of the 6.7 GHz methanol maser line. Such regions are opaque at short wavelengths but can be observed through radio emission lines. In this study we traced the kinematics of the source on milliarcsecond scales using the Multi-Element-Radio-Interferometer-Network (MERLI

Yuting Wang, Jinpeng Wang, Bin Chen, Ziyun Zeng

Self-Supervised Video Hashing (SSVH) models learn to generate short binary representations for videos without ground-truth supervision, facilitating large-scale video retrieval efficiency and attracting increasing research attention. The success of SSVH lies in the understanding of video content and the ability to capture the semantic relation among unlabele

Haibin Zeng, Yueyong Lyu, Jiaming Qi, Shuangquan Zou

Nowadays, with the continuous expansion of application scenarios of robotic arms, there are more and more scenarios where nonspecialist come into contact with robotic arms. However, in terms of robotic arm visual servoing, traditional Position-based Visual Servoing (PBVS) requires a lot of calibration work, which is challenging for the nonspecialist to cope

Jingxiang Sun, Xuan Wang, Lizhen Wang, Xiaoyu Li

3D-aware generative adversarial networks (GANs) synthesize high-fidelity and multi-view-consistent facial images using only collections of single-view 2D imagery. Towards fine-grained control over facial attributes, recent efforts incorporate 3D Morphable Face Model (3DMM) to describe deformation in generative radiance fields either explicitly or implicitly.

Olexandr Polishchuk

The main types of negative internal and external influences on complex network systems (NS) and intersystem interactions in monoflow partially overlapped multilayer network systems (MLNS) are analyzed. Among such influences, targeted attacks on real complex systems and their non-targeted damages of various nature are primarily singled out. On the basis of st

Shaily Bhatt, Sunipa Dev, Partha Talukdar, Shachi Dave

Recent research has revealed undesirable biases in NLP data and models. However, these efforts largely focus on social disparities in the West, and are not directly portable to other geo-cultural contexts. In this position paper, we outline a holistic research agenda to re-contextualize NLP fairness research for the Indian context, accounting for Indian soci

Alessandro Tuniz, Boris T. Kuhlmey

Paradoxically, imaging with resolution much below the wavelength $\lambda$ - now common place in the visible spectrum - remains challenging at lower frequencies, where arguably it is needed most due to the large wavelengths used. Techniques to break the diffraction limit in microscopy have led to many breakthroughs across sciences, but remain largely confine

Bocong Chen, Yun Fan, Gaojun Luo

For any finite group $G$, any transitive $G$-set $X$ and any field ${\Bbb F}$, we consider the vector space ${\Bbb F}^X$ of all functions from $X$ to ${\Bbb F}$, which is a $G$-space isomorphic to the permutation ${\Bbb F} G$-module ${\Bbb F} X$. When the group algebra ${\Bbb F} G$ is semisimple and split, we find a specific basis $\widehat X$ of ${\Bbb F}^X

Yichen Huang, Aram W. Harrow

We consider the asymptotic limits where certain parameters in the definitions of the Laguerre and Jacobi ensembles diverge. In these limits, Dette, Imhof, and Nagel proved that up to a linear transformation, the joint probability distributions of the ensembles become more and more concentrated around the zeros of the Laguerre and Jacobi polynomials, respecti

Hao Zhang, Tianyuan Dai, Yu-Wing Tai, Chi-Keung Tang

This paper presents the first significant work on directly predicting 3D face landmarks on neural radiance fields (NeRFs). Our 3D coarse-to-fine Face Landmarks NeRF (FLNeRF) model efficiently samples from a given face NeRF with individual facial features for accurate landmarks detection. Expression augmentation is applied to facial features in a fine scale t

Jihwan Bae, Junwon Seo, Taekyung Kim, Hae-gon Jeon

Traversability estimation for mobile robots in off-road environments requires more than conventional semantic segmentation used in constrained environments like on-road conditions. Recently, approaches to learning a traversability estimation from past driving experiences in a self-supervised manner are arising as they can significantly reduce human labeling

Robert Vakhitov, Rostislav Solonetskiy, Alina Nizyamova

The paper investigates the influence of the magnetic field on the behavior of 180-degree domain boundaries in a uniaxial ferromagnetic film with inhomogeneous magnetoelectric interaction. It is shown that, depending on the magnitude and direction of the field, it is possible to strengthen or weaken the flexomagnetoelectric effect in the sample under study. I

Zhaonan Luo, Wei Luo, Zhaoyang Yin

In this paper, we are concerned with optimal decay rate for the 2-D generalized Oldroyd-B model with only stress tensor diffusion $(-\Delta)^{\beta}\tau$. In the case $\beta=1$, we first establish optimal decay rate in $H^1$ framework and remove the smallness assumption of low frequencies by virtue of the Fourier splitting method and the Littlewood-Paley dec

Michael P. Kinach, Matthew W. Choptuik

We study the dynamics of $U(1)$ gauged Q-balls using fully non-linear numerical evolutions in axisymmetry. Focusing on two models with logarithmic and polynomial scalar field potentials, we numerically evolve perturbed gauged Q-ball configurations in order to assess their stability and determine the fate of unstable configurations. Our simulations suggest th

Prajwal BN, Harsha Yelchuri, Vishwanath Shastry, T. V. Prabhakar

In industrial process automation, sensors (pressure, temperature, etc.), controllers, and actuators (solenoid valves, electro-mechanical relays, circuit breakers, motors, etc.) make sure that production lines are working under the pre-defined conditions. When these systems malfunction or sometimes completely fail, alerts have to be generated in real-time to

Syful Islam, Raula Gaikovina Kula, Christoph Treude, Bodin Chinthanet

The package manager (PM) is crucial to most technology stacks, acting as a broker to ensure that a verified dependency package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of PMs with various features. While our recent study indicates that package management features of PM are related to

Gou Nishida, Takamatsu Takahiro, Noboru Sakamoto

This paper derives the Hamilton-Jacobi-Bellman equation of nonlinear optimal control problems for cost functions with fractional discount rate from the Bellman's principle of optimality. The fractional discount rate is described by Mittag-Leffler function that can be considered as a generalized exponential function.

Huang Jianhui, Qiu Zhenghong, Wang Shujun, Wu Zhen

This paper revisits well-studied dynamic decisions of weakly coupled large-population (LP) systems. Specifically, three types of LP decision problems: mean-field game (MG), mean-field team (MT), and mean-field-type control (MC), are completely analyzed in a general stochastic linear-quadratic setting with controlled-diffusion in state dynamics and indefinite

Xinghao Dong, Koffi Enakoutsa

Morrey Conjecture deals with two properties of functions which are known as quasi-convexity and rank-one convexity. It is well established that every function satisfying the quasi-convexity property also satisfies rank-one convexity. Morrey (1952) conjectured that the reversed implication will not always hold. In 1992, Vladimir Sverak found a counterexample

Florian Mentzel, Micah Barnes, Kevin Kröninger, Michael Lerch

Fast and accurate dose predictions are one of the bottlenecks in treatment planning for microbeam radiation therapy (MRT). In this paper, we propose a machine learning (ML) model based on a 3D U-Net. Our approach predicts separately the large doses of the narrow high intensity synchrotron microbeams and the lower valley doses between them. For this purpose,

Eugene Bilokopytov

We show that for an ideal $H$ in an Archimedean vector lattice $F$ the following conditions are equivalent: $\bullet$ $H$ is a projection band; $\bullet$ Any collection of mutually disjoint vectors in $H$, which is order bounded in $F$, is order bounded in $H$; $\bullet$ $H$ is an infinite meet-distributive element of the lattice $\mathcal{I}_{F}$ of all ide

Zixuan Xu, Penghui Wei, Shaoguo Liu, Weimin Zhang

Advanced recommender systems usually involve multiple domains (such as scenarios or categories) for various marketing strategies, and users interact with them to satisfy diverse demands. The goal of multi-domain recommendation (MDR) is to improve the recommendation performance of all domains simultaneously. Conventional graph neural network based methods usu

Qi Zheng, Chaoyue Wang, Daqing Liu, Dadong Wang

Multi-modal reasoning in visual question answering (VQA) has witnessed rapid progress recently. However, most reasoning models heavily rely on shortcuts learned from training data, which prevents their usage in challenging real-world scenarios. In this paper, we propose a simple but effective cross-modal contrastive learning strategy to get rid of the shortc

Yiyang Huang, Clément L. Canonne

We aim to collect buried lemmas that are useful for proofs. In particular, we try to provide self-contained proofs for those lemmas and categorise them according to their usage.

Congliang Li, Shijie Sun, Xiangyu Song, Huansheng Song

Multiple object detection and pose estimation are vital computer vision tasks. The latter relates to the former as a downstream problem in applications such as robotics and autonomous driving. However, due to the high complexity of both tasks, existing methods generally treat them independently, which is sub-optimal. We propose simultaneous neural modeling o

Ananya Joshi, Aditi Kajale, Janhavi Gadre, Samruddhi Deode

Sentence representation from vanilla BERT models does not work well on sentence similarity tasks. Sentence-BERT models specifically trained on STS or NLI datasets are shown to provide state-of-the-art performance. However, building these models for low-resource languages is not straightforward due to the lack of these specialized datasets. This work focuses

Yiting Wu, Zhaodi Zhang, Zhiyi Xue, Si Liu

The robustness of neural networks is fundamental to the hosting system's reliability and security. Formal verification has been proven to be effective in providing provable robustness guarantees. To improve the verification scalability, over-approximating the non-linear activation functions in neural networks by linear constraints is widely adopted, which tr

Yiqin Wang, Yuanbo Li, Yi Chen, Ziming Yu

Owning abundant bandwidth resource, the Terahertz (0.1-10 THz) band is a promising spectrum to support sixth-generation (6G) and beyond communications. As the foundation of channel study in the spectrum, channel measurement is ongoing in covering representative 6G communication scenarios and promising THz frequency bands. In this paper, a wideband channel me

Sreejith Sreekumar, Ziv Goldfeld, Kengo Kato

$f$-divergences, which quantify discrepancy between probability distributions, are ubiquitous in information theory, machine learning, and statistics. While there are numerous methods for estimating $f$-divergences from data, a limit distribution theory, which quantifies fluctuations of the estimation error, is largely obscure. As limit theorems are pivotal

Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets

Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice. While various fairness metrics have emerged to assess fairness in decision-making processes, a growing focus has been on causality-based fairness concepts due to their capacity to mitigat

Brian Okorn, Chuer Pan, Martial Hebert, David Held

Orientation estimation is the core to a variety of vision and robotics tasks such as camera and object pose estimation. Deep learning has offered a way to develop image-based orientation estimators; however, such estimators often require training on a large labeled dataset, which can be time-intensive to collect. In this work, we explore whether self-supervi

Sarvesh Kumar Singh, Bikram Pratap Banerjee, Simit Raval

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scannin

Yiqin Wang, Yuanbo Li, Yi Chen, Ziming Yu

The Terahertz (0.1-10 THz) band has been envisioned as one of the promising spectrum bands to support ultra-broadband sixth-generation (6G) and beyond communications. In this paper, a wideband channel measurement campaign in a 500- square-meter indoor lobby at 306-321 GHz is presented. The measurement system consists of a vector network analyzer (VNA)-based

Siqiao Xue, Xiaoming Shi, Hongyan Hao, Lintao Ma

Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals. In this paper we aim at modeling latent dynamics of event propagation in graph, where the event sequence propagates in a directed weighted graph whose nodes represent event marks (e.g., event types). Most existing works have only considered encoding sequenti

Zheng Dong, Xiuyuan Cheng, Yao Xie

Point process data are becoming ubiquitous in modern applications, such as social networks, health care, and finance. Despite the powerful expressiveness of the popular recurrent neural network (RNN) models for point process data, they may not successfully capture sophisticated non-stationary dependencies in the data due to their recurrent structures. Anothe

Haibin Zeng, Yueyong Lyu, Jiaming Qi, Shuangquan Zou

The image-based visual servoing without models of system is challenging since it is hard to fetch an accurate estimation of hand-eye relationship via merely visual measurement. Whereas, the accuracy of estimated hand-eye relationship expressed in local linear format with Jacobian matrix is important to whole system's performance. In this article, we proposed

Shitao Tang, Sicong Tang, Andrea Tagliasacchi, Ping Tan

This paper presents an end-to-end neural mapping method for camera localization, dubbed NeuMap, encoding a whole scene into a grid of latent codes, with which a Transformer-based auto-decoder regresses 3D coordinates of query pixels. State-of-the-art feature matching methods require each scene to be stored as a 3D point cloud with per-point features, consumi

Siyi Tang, Jared A. Dunnmon, Liangqiong Qu, Khaled K. Saab

Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in multivariate biosignals is challenging due to (1) long-range temporal dependencies and (2) complex spatial correlations between the electrodes. To address these challenges, we propose

Michelle K Croughan, Ying Ying How, Allan Pennings, Kaye S Morgan

Directional dark-field imaging is an emerging x-ray modality that is sensitive to unresolved anisotropic scattering from sub-pixel sample microstructures. A single-grid imaging set-up can be used to capture dark-field images by looking at changes in a grid pattern projected upon the sample. By creating analytical models for the experiment, we have developed

Andrea Piazzoni, Jim Cherian, Roshan Vijay, Lap-Pui Chau

In this paper, we introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective and efficient integration of V2X solutions within a virtual test environment. We focus our analysis on the occlusion problem in the (onboard) perception of Autonomous Vehicles (AV), which can manifest as misdetection errors on the occluded ob

Zenglin Shi, Jing Jie, Ying Sun, Joo Hwee Lim

In AI, generalization refers to a model's ability to perform well on out-of-distribution data related to the given task, beyond the data it was trained on. For an AI agent to excel, it must also possess the continual learning capability, whereby an agent incrementally learns to perform a sequence of tasks without forgetting the previously acquired knowledge

Tinghan Ye, David Shmoys

A retrospective fleet-sizing problem can be solved via bipartite matching, where a maximum cardinality matching corresponds to the minimum number of vehicles needed to cover all trips. We prove a min-max theorem on this minimum fleet-size problem: the maximum number of pairwise incompatible trips is equal to the minimum fleet size needed.

Zining Zhang, Bingsheng He, Zhenjie Zhang

To efficiently perform inference with neural networks, the underlying tensor programs require sufficient tuning efforts before being deployed into production environments. Usually, enormous tensor program candidates need to be sufficiently explored to find the one with the best performance. This is necessary to make the neural network products meet the high

Anastasiia Zalogina, Pavel Tonkaev, Aditya Tripathi, Hoo-Cheol Lee

Multiphoton processes of absorption photoluminescence have enabled a wide range of applications including three-dimensional microfabrication, data storage, and biological imaging. While the applications of two-photon and three-photon absorption and luminescence have matured considerably, higher-order photoluminescence processes remain more challenging to stu

Sergei Manzhos, Manabu Ihara

Kernel based methods including Gaussian process regression (GPR) and generally kernel ridge regression (KRR) have been finding increasing use in computational chemistry, including the fitting of potential energy surfaces and density functionals in high-dimensional feature spaces. Kernels of the Matern family such as Gaussian-like kernels (basis functions) ar

Ruilin Zhang, Zhongyi Wang, Tianyi Wu, Yuhang Cai

First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. S

Huanchen Bao, Xuhua He

We show that the totally nonnegative part of the twisted product of flag varieties of a Kac-Moody group admits a cellular decomposition, and the closure of each cell is a topological manifold with boundary. We also establish explicit parameterizations of each totally positive cell. In the special cases of double flag varieties and braid varieties, we show th

Huaibo Huang, Xiaoqiang Zhou, Jie Cao, Ran He

Vision transformer has achieved impressive performance for many vision tasks. However, it may suffer from high redundancy in capturing local features for shallow layers. Local self-attention or early-stage convolutions are thus utilized, which sacrifice the capacity to capture long-range dependency. A challenge then arises: can we access efficient and effect

Joshua Pickard, Can Chen, Rahmy Salman, Cooper Stansbury

Recent advances in biological technologies, such as multi-way chromosome conformation capture (3C), require development of methods for analysis of multi-way interactions. Hypergraphs are mathematically tractable objects that can be utilized to precisely represent and analyze multi-way interactions. Here we present the Hypergraph Analysis Toolbox (HAT), a sof

Likai Wang, Xiangqun Zhang, Ruize Han, Jialin Yang

Person re-identification (Re-ID) is a classical computer vision task and has achieved great progress so far. Recently, long-term Re-ID with clothes-changing has attracted increasing attention. However, existing methods mainly focus on image-based setting, where richer temporal information is overlooked. In this paper, we focus on the relatively new yet pract

Sunyo Moon, Hyungkee Yoo

For some positive integer $k$, if the finite cyclic group $\mathbb{Z}_k$ can act freely on a graph $G$, then we say that $G$ is $k$-symmetric. In 1985, Faria showed that the multiplicity of Laplacian eigenvalue 1 is greater than or equal to the difference between the number of pendant vertices and the number of quasi-pendant vertices. But if a graph has a pe

Minh Le

We consider classical solutions to the chemotaxis system with logistic source $f(u) := au-\mu u^2$ under nonlinear Neumann boundary condition $\frac{\partial u}{ \partial \nu } = |u|^{p}$ with $p>1$ in a smooth convex bounded domain $\Omega \subset \mathbb{R}^n$ where $n \geq 2$. This paper aims to show that if $p<\frac{3}{2}$, and $\mu >0$, $n=2$, or $\mu$

S. M. Kuzenko, E. S. N. Raptakis, G. Tartaglino-Mazzucchelli

We provide a unified description of the three covariant superspace approaches to ${\cal N}=2$ conformal supergravity in four dimensions: (i) conformal superspace; (ii) $\mathsf{U}(2)$ superspace; and (iii) $\mathsf{SU}(2)$ superspace. Each of them can be used to formulate general supergravity-matter systems, although conformal superspace has the largest stru

Hui Liu, Xiangyu Zhu, Xiaoxue Zhang, Yongquan Liao

Recent advances in iterative wavefront shaping (WFS) techniques have made it possible to manipulate the light focusing and transport in scattering media. To improve the optimization performance, various optimization algorithms and improved strategies have been utilized. Here, a novel guided mutation (GM) strategy is proposed to improve optimization efficienc

Sijie Cheng, Zhiyong Wu, Jiangjie Chen, Zhixing Li

While large pre-trained language models (PLM) have shown their great skills at solving discriminative tasks, a significant gap remains when compared with humans for explanation-related tasks. Among them, explaining the reason why a statement is wrong (e.g., against commonsense) is incredibly challenging. The major difficulty is finding the conflict point, wh

Yoshiyuki Y. Yamaguchi, Julien Barré

In a Vlasov equation, the destabilization of a homogeneous stationary state is typically described by a continuous bifurcation characterized by strong resonances between the unstable mode and the continuous spectrum. However, when the reference stationary state has a flat top, it is known that resonances drastically weaken, and the bifurcation becomes discon

Zhen Tian, Ting Bai, Zibin Zhang, Zhiyuan Xu

With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely increases, which limits the use of high-order interaction models in real industrial applications. Some recent knowledge distillation based methods transfer knowledge from complex teach

Yue Yang, Artemis Panagopoulou, Shenghao Zhou, Daniel Jin

Concept Bottleneck Models (CBM) are inherently interpretable models that factor model decisions into human-readable concepts. They allow people to easily understand why a model is failing, a critical feature for high-stakes applications. CBMs require manually specified concepts and often under-perform their black box counterparts, preventing their broad adop

Mingzhong Cai, Yiqun Liu, Yong Liu, Cheng Peng

Two nonzero recursively enumerable (r.e.) degrees $\mathbf{a}$ and $\mathbf{b}$ form a strong minimal pair if $\mathbf{a} \wedge \mathbf{b}=\mathbf{0}$ and $\mathbf{b}\vee \mathbf{x}\geq \mathbf{a}$ for any nonzero r.e. degree $\mathbf{x}\leq \mathbf{a}$. We prove that there is no strong minimal pair in the r.e. degrees. Our construction goes beyond the usua

Ankit Chakraborty, Georg May

We present an anisotropic $hp-$mesh adaptation strategy using a continuous mesh model for discontinuous Petrov-Galerkin (DPG) finite element schemes with optimal test functions, extending our previous work on $h-$adaptation. The proposed strategy utilizes the inbuilt residual-based error estimator of the DPG discretization to compute both the polynomial dist

Jean-François Delmas, Dylan Dronnier, Pierre-André Zitt

We consider the simple epidemiological SIS model for a general heterogeneous population introduced by Lajmanovich and Yorke (1976) in finite dimension, and its infinite dimensional generalization we introduced in previous works. In this model the basic reproducing number $R_0$ is given by the spectral radius of an integral operator. If $R_0>1$, then there ex

Likai Wang, Ruize Han, Wei Feng, Song Wang

Gait recognition is an important AI task, which has been progressed rapidly with the development of deep learning. However, existing learning based gait recognition methods mainly focus on the single domain, especially the constrained laboratory environment. In this paper, we study a new problem of unsupervised domain adaptive gait recognition (UDA-GR), that

Wei Wei, Daheng Li, Peng Wang, Yiming Li

Grasping with anthropomorphic robotic hands involves much more hand-object interactions compared to parallel-jaw grippers. Modeling hand-object interactions is essential to the study of multi-finger hand dextrous manipulation. This work presents DVGG, an efficient grasp generation network that takes single-view observation as input and predicts high-quality

Jiho Jang, Chaerin Kong, Donghyeon Jeon, Seonhoon Kim

Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations. In this paper, we explore the bold hypothesis that an image and its caption can be simply regarded as two different views of the underlying mutual information, and train a model to learn a unified vision-language representation space th

Shengkun Tang, Yaqing Wang, Zhenglun Kong, Tianchi Zhang

Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with increasing model size, resulting in slow inference speed and increased cost for severing. While some certain predictions benefit from the full complexity of the large-scale model, not a

Belle Collaboration, Y. Ma, J. Yelton, K. Tanida

Using the data sample of 980 fb$^{-1}$ collected with the Belle detector operating at the KEKB asymmetric-energy $e^+e^-$ collider, we present the results of an investigation of the $\Lambda\pi^+$ and $\Lambda\pi^-$ invariant mass distributions looking for substructure in the decay $\Lambda_c^+\rightarrow\Lambda\pi^+\pi^+\pi^-$. We find a significant signal

Guo-Peng Li, Da-Bin Lin, Yong Yuan

Star clusters (SCs) and active galactic nuclei (AGNs) are promising sites for the occurrence of hierarchical black hole (BH) mergers. We use simple models to compare hierarchical BH mergers in two of the dynamical formation channels. We find that the primary mass distribution of hierarchical mergers in AGNs is higher than that in SCs, with the peaks of $\sim