November 2022 arXiv papers — page 23

Zhuo Qu, Wenlin Dai, Marc G. Genton

Two frameworks for multivariate functional depth based on multivariate depths are introduced in this paper. The first framework is multivariate functional integrated depth, and the second framework involves multivariate functional extremal depth, which is an extension of the extremal depth for univariate functional data. In each framework, global and local m

Andreas Pusch, Udo Römer, Dimitrie Culcer, Nicholas J. Ekins-Daukes

The bulk photovoltaic effect (BPVE) leads to directed photo-currents and photo-voltages in bulk materials. Unlike photo-voltages in p-n junction solar cells that are limited by carrier recombination to values below the bandgap energy of the absorbing material, the BPVE photo-voltages have been shown to greatly exceed the bandgap energy. Therefore the BPVE is

Shin-Jeong Kim, Se-Heon Oh, Jing Wang, Lister Staveley-Smith

We examine the HI gas kinematics of galaxy pairs in two clusters and a group using Australian Square Kilometre Array Pathfinder (ASKAP) WALLABY pilot survey observations. We compare the HI properties of galaxy pair candidates in the Hydra I and Norma clusters, and the NGC 4636 group, with those of non-paired control galaxies selected in the same fields. We p

Halil İbrahim Bayrak, Çağıl Koçyiğit, Daniel Kuhn, Mustafa Çelebi Pınar

We consider the mechanism design problem of a principal allocating a single good to one of several agents without monetary transfers. Each agent desires the good and uses it to create value for the principal. We designate this value as the agent's private type. Even though the principal does not know the agents' types, she can verify them at a cost. The allo

Vladimir Müller, Roland Schnaubelt, Yuri Tomilov

We propose a new approach to the study of (nonlinear) growth and instability for semilinear evolution equations with compact nonlinearities. We show, in particular, that compact nonlinear perturbations of a linear evolution equation can be treated as linear ones as far as the growth of their solutions is concerned. We obtain exponential lower bounds of solut

Tongzhou Wang, Phillip Isola

Asymmetrical distance structures (quasimetrics) are ubiquitous in our lives and are gaining more attention in machine learning applications. Imposing such quasimetric structures in model representations has been shown to improve many tasks, including reinforcement learning (RL) and causal relation learning. In this work, we present four desirable properties

Fei Dai, Yawen Chen, Zhiyi Huang, Haibo Zhang

All-gather collective communication is one of the most important communication primitives in parallel and distributed computation, which plays an essential role in many HPC applications such as distributed Deep Learning (DL) with model and hybrid parallelism. To solve the communication bottleneck of All-gather, optical interconnection network can provide unp

Jiehao Liang, Somdeb Sarkhel, Zhao Song, Chenbo Yin

$k$-means++ is an important algorithm for choosing initial cluster centers for the $k$-means clustering algorithm. In this work, we present a new algorithm that can solve the $k$-means++ problem with nearly optimal running time. Given $n$ data points in $\mathbb{R}^d$, the current state-of-the-art algorithm runs in $\widetilde{O}(k )$ iterations, and each it

Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros, Darko Zibar

An autoencoder is used to optimize bit-to-symbol mappings for geometric constellation shaping. The mappings allow for net rate adaptivity without additional hardware complexity, while achieving up to 300km of transmission distance compared to uniform QAM.

Xiaoke Gao, Xiaoyu Zhao, Ruoyu Huang, Siyuan Ma

With the flourishing development of nanophotonics, Cherenkov radiation pattern can be designed to achieve superior performance in particle detection by fine-tuning the properties of metamaterials such as photonic crystals (PCs) surrounding the swift particle. However, the radiation pattern can be sensitive to the geometry and material properties of PCs, such

Dzung Tri Tran, Khiem Hong Phan

In this paper, we present analytical results for one-loop contributing to the decay processes $H\rightarrow Z \nu_l\bar{\nu}_l$ (for $l = e, \mu, \tau$). The calculations are performed within the Standard Model framework in 't Hooft-Veltman gauge. One-loop form factors are then written in terms of scalar one-loop functions in the standard notations of {\tt L

Wenbin An, Feng Tian, Qinghua Zheng, Wei Ding

Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Without considering differences between known and novel categories, current methods learn about them in a coupled manner, which can hurt model's generalization and discriminative abi

Konstantin Kutzkov

Local graph neighborhood sampling is a fundamental computational problem that is at the heart of algorithms for node representation learning. Several works have presented algorithms for learning discrete node embeddings where graph nodes are represented by discrete features such as attributes of neighborhood nodes. Discrete embeddings offer several advantage

Farhod Shokir

In this paper, using the Majorana method, mathematical modeling of the state of quantum systems with spin number S=j\^h. An expression for the correlation functions of oriented states in the general case j>=0.5 is obtained.

Chong Ye, Xiaowei Mu, Yifan Sun, Libin Fu

Enantiodetection is an important and challenging task across natural science. Nowadays, some chiroptical methods of enantiodetection based on decoherence-free cyclic three-level models of chiral molecules can reach the ultimate limit of the enantioselectivities in the molecular responses. They are thus more efficient than traditional chiroptical methods. How

Zhen-Yu Li, Guo-Liang Yu, Zhi-Gang Wang, Jian-Zhong Gu

In this paper, we investigate the mass spectra of bottom-charm baryons systematically, where the relativistic quark model and the infinitesimally shifted Gaussian basis function method are employed. Our calculation shows that the $\rho$-mode appears lower in energy than the other excited modes. According to this feature, the allowed quantum states are select

Yong Huang, Qinfeng Li, Qiuqi Li, Ruofei Yao

Let $\Omega$ be a bounded Lipshcitz domain in $\mathbb{R}^n$ and we study boundary behaviors of solutions to the Laplacian eigenvalue equation with constant Neumann data. \begin{align} \label{cequation0} \begin{cases} -\Delta u=cu\quad &\mbox{in $\Omega$}\\ \frac{\partial u}{\partial \nu}=-1\quad &\mbox{on $\partial \Omega$}. \end{cases} \end{align}First, by

Princy Randriambololondrantomalala, Sania Asif

In this paper, we study the generalized derivation of a Lie sub-algebra of the Lie algebra of polynomial vector fields on $\mathbb{R}^n$ where $n\geq1$, containing all constant vector fields and the Euler vector field, under some conditions on this Lie sub-algebra.

Samad Khabbazi Oskouei, Stefano Mancini, Milajiguli Rexiti

We investigate the usefulness of side entanglement in discriminating between two generic qubit channels, {\ up to unitary pre- and post-processing,} and determine exact conditions under which it does enhance (as well as conditions under which it does not) the success probability. This is done in a constructive way by first analyzing the problem for channels

Yash Bhalgat, Joao F. Henriques, Andrew Zisserman

Transformers are powerful visual learners, in large part due to their conspicuous lack of manually-specified priors. This flexibility can be problematic in tasks that involve multiple-view geometry, due to the near-infinite possible variations in 3D shapes and viewpoints (requiring flexibility), and the precise nature of projective geometry (obeying rigid la

Eddy Collin, Ilya Golokolenov, Olivier Maillet, Laurent Saminadayar

We report on the theoretical derivation of macroscopic thermal properties (specific heat, thermal conductivity) of an electrically insulating rod connected to two reservoirs, from the linear superposition of its mechanical mode Brownian motions. The calculation is performed for a weak thermal gradient, in the classical limit (high temperature). The developme

Hanung Adi Nugroho, Rizki Nurfauzi, E. Elsa Herdiana Murhandarwati, Purwono Purwono

Indonesia holds the second-highest-ranking country for the highest number of malaria cases in Southeast Asia. A different malaria parasite semantic segmentation technique based on a deep learning approach is an alternative to reduce the limitations of traditional methods. However, the main problem of the semantic segmentation technique is raised since large

Alexandre Didier, Robin C. Jacobs, Jerome Sieber, Kim P. Wabersich

A predictive control barrier function (PCBF) based safety filter is a modular framework to verify safety of a control input by predicting a future trajectory. The approach relies on the solution of two optimization problems, first computing the minimal state constraint violation given the current state in the form of slacks on the constraint, and then comput

Kashu Yamazaki, Khoa Vo, Sang Truong, Bhiksha Raj

Video paragraph captioning aims to generate a multi-sentence description of an untrimmed video with several temporal event locations in coherent storytelling. Following the human perception process, where the scene is effectively understood by decomposing it into visual (e.g. human, animal) and non-visual components (e.g. action, relations) under the mutual

Xikai Wen, Fanghang Yu, Zhigang Gui, Yuqing Zhang

The recent discovery of superconductivity (SC) and charge density wave (CDW) in kagome metals AV3Sb5 (A = K, Rb, Cs) provides an ideal playground for the study of emergent electronic orders. Application of moderate pressure leads to a two-dome-shaped SC phase regime in CsV3Sb5 accompanied by the destabilizing of CDW phase; such unconventional evolution of SC

Masaki J. S. Yang

In this paper, we perform chiral perturbative analysis of an approximate lepton number symmetry associated with a sufficiently light neutrino in the type-I seesaw mechanism. For the Dirac mass matrix $m_{D} = (\bm A \, , \bm B \, , \bm C)$, linearly independent components of $\bm C$ from $\bm A$ and $\bm B$ are treated as symmetry-breaking parameters. A devi

Harvey Cao, Daniel Leykam, Dimitris G. Angelakis

Topological data analysis is a powerful framework for extracting useful topological information from complex datasets. Recent work has shown its application for the dynamical analysis of classical dissipative systems through a topology-preserving embedding method that allows reconstructing dynamical attractors, the topologies of which can be used to identify

Yuzhao Zhang, Yifan Sun

Multi-task learning (MTL) aims to improve the performance of multiple related prediction tasks by leveraging useful information from them. Due to their flexibility and ability to reduce unknown coefficients substantially, the task-clustering-based MTL approaches have attracted considerable attention. Motivated by the idea of semisoft clustering of data, we p

Luis A. Caffarelli, Jean-Michel Roquejoffre, Ignacio Tomasetti

A horizontal $N$-dimensional plane, having a diffusion of its own, exchanges with the lower half space. There, a reaction-diffusion process, modelled by a free boundary problem, takes place. We wish to understand whether, and how, the free boundary meets the plane. The origin of this problem is a two-dimensional reaction diffusion model proposed some time ag

Yingxian Chen, Zhengzhe Liu, Baoheng Zhang, Wilton Fok

Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long videos, we propose a novel glance and focus network to effectively integrate spatial-temporal information for accurate anomaly detection. In addition, we empirically found that exist

Jurriaan Langendorff, Alex Kolmus, Justin Janquart, Chris Van Den Broeck

Due to its speed after training, machine learning is often envisaged as a solution to a manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been given for various applications in gravitational-wave data analysis. In this work, we focus on a challenging problem faced by third-generation detectors: parameter inference for overlapp

Chongbin Zhao, Qirong Jiang

Nyquist criterion-based impedance ratio criteria (IRCs) have been widely applied for inspecting the risk of small-signal instability among converter-based AC power systems. Aided by a comparative study on voltage source converter, including the single-input single-output (SISO) and multiple input multiple output (MIMO) analyses in both the dq and the sequenc

Dhanalakshmi M, Radha V

Air pollution is a vital issue emerging from the uncontrolled utilization of traditional energy sources as far as developing countries are concerned. Hence, ingenious air pollution forecasting methods are indispensable to minimize the risk. To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and controlling air pollutio

Danqing Wang, Zeyu Wen, Fei Ye, Lei Li

Antimicrobial peptides (AMPs) are promising therapeutic approaches against drug-resistant pathogens. Recently, deep generative models are used to discover new AMPs. However, previous studies mainly focus on peptide sequence attributes and do not consider crucial structure information. In this paper, we propose a latent sequence-structure model for designing

Tian-Wei Wu, Yong-Liang Ma

Symmetries play important roles in the understanding of hadron structures and spectroscopy. Motivated by the discovery of the doubly charmed tetraquark $T_{cc}^+(3875)$, we study the ground states of the doubly heavy tetraquarks with the QCD inspired heavy antiquark-diquark symmetry in the constituent quark model. Six ground states of $T_{QQ} (Q=c,b)$ are pr

Jiawei Zhang

This is a follow-up tutorial article of our previous article entitled "Robot Basics: Representation, Rotation and Velocity". For better understanding of the topics covered in this articles, we recommend the readers to first read our previous tutorial article on robot basics. Specifically, in this article, we will cover some more advanced topics on robot kine

Sharvi Endait, Ruturaj Ghatage, DD Kadam

In this modern era of technology with e-commerce developing at a rapid pace, it is very important to understand customer requirements and details from a business conversation. It is very crucial for customer retention and satisfaction. Extracting key insights from these conversations is very important when it comes to developing their product or solving thei

Durgarao Kamireddy, Sreekanth Karanam, Arup Nandy

While doing electromagnetic analysis using FEM (Finite element method), if we can implement the underlying symmetric nature of the problem, there will be significant reduction in the computational cost. Symmetric nature of the problem can be identified from the given physical loading and boundary conditions of the problem. But for electromagnetic analysis in

Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz

Selecting the right set of hyperparameters is crucial in time series forecasting. The classical temporal cross-validation framework for hyperparameter optimization (HPO) often leads to poor test performance because of a possible mismatch between validation and test periods. To address this test-validation mismatch, we propose a novel technique, H-Pro to driv

Jongpil Yun, Se Kwon Kim

The existing proposals for superfluid-like spin transport have been based on easy-plane magnets where the U(1) spin-rotational symmetry is spontaneously broken in equilibrium, and this has been limiting material choices for realizing superfluid-like spin transport to restricted class of magnets. In this work, we lift this limitation by showing that superflui

Olexandr Polishchuk

On the basis of structural model of intersystem interactions, the main local and global structural characteristics of nodes of the multilayer network (MLN) are determined. The notions of weighted and binary aggregate-networks of MLN are introduced and integral indicators of the importance of nodes of the multilayer network are determined. The effectiveness o

Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov

Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement. Their success hinges on the fact that the underlying physical phenomena are continuous. For inherently discrete and categorical data such as language, various diffusion-inspired alternatives have been

Bingyuan Liu, Jérôme Rony, Adrian Galdran, Jose Dolz

Recent studies have revealed that, beyond conventional accuracy, calibration should also be considered for training modern deep neural networks. To address miscalibration during learning, some methods have explored different penalty functions as part of the learning objective, alongside a standard classification loss, with a hyper-parameter controlling the r

Wenlin Dai, Xingwei Tong, Tiejun Tong

Difference-based methods have been attracting increasing attention in nonparametric regression, in particular for estimating the residual variance.To implement the estimation, one needs to choose an appropriate difference sequence, mainly between {\em the optimal difference sequence} and {\em the ordinary difference sequence}. The difference sequence selecti

Tobias Boltz, Miriam Brosi, Bastian Haerer, Patrik Schönfeldt

The self-interaction of short electron bunches with their own radiation field can have a significant impact on the longitudinal beam dynamics in a storage ring. While higher bunch currents increase the power of the emitted CSR which can be provided to dedicated experiments, it simultaneously amplifies the strength of the self-interaction. Eventually, this le

Zhenbing Gong, Ji Li, Brett D. Wick

In this paper, we establish the Schatten class and endpoint weak Schatten class estimates for the commutator of Riesz transforms on weighted $L^2$ spaces. It provides a weighted version for the estimate of the quantised derivative introduced by Alain Connes and studied recently by Lord--McDonald--Sukochev--Zanin.

Kyungjun Lee, Hong Li, Muhammad Rizky Wellyanto, Yu Jiang Tham

A central challenge of social computing research is to enable people to communicate expressively with each other remotely. Augmented reality has great promise for expressive communication since it enables communication beyond texts and photos and towards immersive experiences rendered in recipients' physical environments. Little research, however, has explor

Chien-Wen Chuang, Seigo Souma, Ayumi Moriya, Kosuke Nakayama

We performed angle-resolved photoemission spectroscopy with micro-focused beam on a topological line-nodal compound CaSb2 which undergoes a superconducting transition at the onset Tc~1.8 K, to clarify the Fermi-surface topology relevant to the occurrence of superconductivity. We found that a three-dimensional hole pocket at the G point is commonly seen for t

Peiqi Yin, Xiao Yan, Jinjing Zhou, Qiang Fu

While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed. For instance, using the widely adopted node-wise approach, model evaluation can account for up to 94% of the time in the end-to-end training process due to neighbor explosion, which means that a node accesses its mult

Yoonhyuk Choi, Jiho Choi, Taewook Ko, Chong-Kwon Kim

Graph neural networks (GNNs) are commonly used in semi-supervised settings. Previous research has primarily focused on finding appropriate graph filters (e.g. aggregation methods) to perform well on both homophilic and heterophilic graphs. While these methods are effective, they can still suffer from the sparsity of node features, where the initial data cont

Robert Reynolds

Generating functions and functional equations of Dickson polynomials of the first and second kind are derived and continued analytically. These formulae are expressed in terms of the incomplete gamma function over complex variables of the parameters involved. Special cases are evaluated in terms of composite incomplete gamma functions and mathematical consta

Yifan Ma, Wentao Yu, Xianghao Yu, Jun Zhang

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) needs to be sent back to the base station (BS) by the users, which causes prohibitive feedback overhead. In this paper, we propose a lightweight and flexible deep learning-based CSI feedback approach by capitalizing on deep eq

Alena Kopaničáková

Solving large-scale nonlinear minimization problems is computationally demanding. Nonlinear multilevel minimization (NMM) methods explore the structure of the underlying minimization problem to solve such problems in a computationally efficient and scalable manner. The efficiency of the NMM methods relies on the quality of the coarse-level models. Traditiona

D. Nour, K. Sriram

Soft X-ray emission (0.5--2.0 keV) plays a pivotal role in regulating the optical and UV emission in the AGNs. We collected a sample of 1811 AGNs from the SDSS database and obtained various parameters of Balmer lines, optical continuum, MgII line \& UV continuum and studied their dependencies on soft X-ray luminosity. Based on the linear regression analysis,

Xian Zhong, Zipeng Li, Shuqin Chen, Kui Jiang

Video captioning aims to generate natural language sentences that describe the given video accurately. Existing methods obtain favorable generation by exploring richer visual representations in encode phase or improving the decoding ability. However, the long-tailed problem hinders these attempts at low-frequency tokens, which rarely occur but carry critical

Ji Won Yoon, Beom Jun Woo, Sunghwan Ahn, Hyeonseung Lee

Recently, the advance in deep learning has brought a considerable improvement in the end-to-end speech recognition field, simplifying the traditional pipeline while producing promising results. Among the end-to-end models, the connectionist temporal classification (CTC)-based model has attracted research interest due to its non-autoregressive nature. However

Ruihao Liu, Li Cai, Teng Xu, Jiahao Liu

The anomalous Nernst effect (ANE) is one of the most intriguing thermoelectric phenomena which has attracted growing interest both for its underlying physics and potential applications. Typically, a large ANE response is observed in magnets with pronounced magnetizations or nontrivial Berry curvature. Here, we report a significant ANE signal in compensated f

Gary P. T. Choi, Lucy Liu, L. Mahadevan

Controlling the connectivity and rigidity of kirigami, i.e. the process of cutting paper to deploy it into an articulated system, is critical in the manifestations of kirigami in art, science and technology, as it provides the resulting metamaterial with a range of mechanical and geometric properties. Here we combine deterministic and stochastic approaches f

Puheng Li, James Zou, Linjun Zhang

Algorithmic fairness plays an increasingly critical role in machine learning research. Several group fairness notions and algorithms have been proposed. However, the fairness guarantee of existing fair classification methods mainly depends on specific data distributional assumptions, often requiring large sample sizes, and fairness could be violated when the

Ivaxi Sheth, Aamer Abdul Rahman, Mohammad Havaei, Samira Ebrahimi Kahou

Humans have perfected the art of learning from multiple modalities through sensory organs. Despite their impressive predictive performance on a single modality, neural networks cannot reach human level accuracy with respect to multiple modalities. This is a particularly challenging task due to variations in the structure of respective modalities. Conditional

Song Wei, Yao Xie

We present a computationally efficient online kernel Cumulative Sum (CUSUM) method for change-point detection that utilizes the maximum over a set of kernel statistics to account for the unknown change-point location. Our approach exhibits increased sensitivity to small changes compared to existing kernel-based change-point detection methods, including Scan-

Xinjiang Wang, Zeyu Liu, Yu Hu, Wei Xi

We introduce a lightweight network to improve descriptors of keypoints within the same image. The network takes the original descriptors and the geometric properties of keypoints as the input, and uses an MLP-based self-boosting stage and a Transformer-based cross-boosting stage to enhance the descriptors. The boosted descriptors can be either real-valued or

Ayush Raina, Jonathan Cagan, Christopher McComb

Building an AI agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. However, learning over prior data limits us only to solve problems that have been solved before and biases data-driven learning towards existing sol

Burai Murayama, Masato Kobayashi, Masamitsu Aoki, Suguru Ishibashi

A reaction route map (RRM) constructed using the GRRM program is a collection of elementary reaction pathways, each of which comprises two equilibrium (EQ) geometries and one transition state (TS) geometry connected by an intrinsic reaction coordinate (IRC). An RRM can be mathematically represented by a graph with weights assigned to both vertices, correspon

Kangcheng Liu

To overcome the data-hungry challenge, we have proposed a semi-supervised contrastive learning framework for the task of class-imbalanced semantic segmentation. First and foremost, to make the model operate in a semi-supervised manner, we proposed the confidence-level-based contrastive learning to achieve instance discrimination in an explicit manner, and ma

Chen Chen, Hongyao Tang, Yi Ma, Chao Wang

Pessimism is of great importance in offline reinforcement learning (RL). One broad category of offline RL algorithms fulfills pessimism by explicit or implicit behavior regularization. However, most of them only consider policy divergence as behavior regularization, ignoring the effect of how the offline state distribution differs with that of the learning p

Yunpeng Bai, Yanbo Fan, Xuan Wang, Yong Zhang

High-fidelity facial avatar reconstruction from a monocular video is a significant research problem in computer graphics and computer vision. Recently, Neural Radiance Field (NeRF) has shown impressive novel view rendering results and has been considered for facial avatar reconstruction. However, the complex facial dynamics and missing 3D information in mono

Jaehoan Kim, Hoyoung Park, Junyong Park

Linear discriminant analysis (LDA) is a typical method for classification problems with large dimensions and small samples. There are various types of LDA methods that are based on the different types of estimators for the covariance matrices and mean vectors. In this paper, we consider shrinkage methods based on a non-parametric approach. For the precision

Jiten C Kalita, Pankaj Kumar

This study is concerned with the simulation of a complex fluid flow problem involving flow past a wedge mounted on a wall for channel Reynolds numbers $Re_c=1560$, $6621$ and $6873$ in uniform and accelerated flow medium. The transient Navier-Stokes (N-S) equations governing the flow has been discretized using a recently developed second order spatially and

Anish Ghoshal, Gaetano Lambiase, Supratik Pal, Arnab Paul

We study slow roll single field inflationary scenario and the production of non-thermal fermionic dark matter, together with standard model Higgs, during reheating. For the inflationary scenario, we have considered two models of polynomial potential - one is symmetric about the origin and another one is not. We fix the coefficients of the potential from the

Devon Ulrich, Ruth Fong

Many visualization techniques have been created to explain the behavior of computer vision models, but they largely consist of static diagrams that convey limited information. Interactive visualizations allow users to more easily interpret a model's behavior, but most are not easily reusable for new models. We introduce Visual Feature Search, a novel interac

Stefan Stojanov, Anh Thai, Zixuan Huang, James M. Rehg

A hallmark of the deep learning era for computer vision is the successful use of large-scale labeled datasets to train feature representations for tasks ranging from object recognition and semantic segmentation to optical flow estimation and novel view synthesis of 3D scenes. In this work, we aim to learn dense discriminative object representations for low-s

Xixi Hu, Ziyang Chen, Andrew Owens

We present a method for simultaneously localizing multiple sound sources within a visual scene. This task requires a model to both group a sound mixture into individual sources, and to associate them with a visual signal. Our method jointly solves both tasks at once, using a formulation inspired by the contrastive random walk of Jabri et al. We create a grap

Xue Yu, Yifan Sun, Haijun Zhou

High-dimensional linear regression model is the most popular statistical model for high-dimensional data, but it is quite a challenging task to achieve a sparse set of regression coefficients. In this paper, we propose a simple heuristic algorithm to construct sparse high-dimensional linear regression models, which is adapted from the shortest solution-guide

Ivan Kostov

Basso-Dixon integrals evaluate rectangular fishnets -- Feynman graphs with massless scalar propagators which form a $m\times n$ rectangular grid -- which arise in certain one-trace four-point correlators in the `fishnet' limit of $\mathcal{N}=4$ SYM. Recently, Basso {\it et al} explored the thermodynamical limit $m\to\infty$ with fixed aspect ratio $n/m$ of

Enneng Yang, Junwei Pan, Ximei Wang, Haibin Yu

Multi-task learning (MTL) models have demonstrated impressive results in computer vision, natural language processing, and recommender systems. Even though many approaches have been proposed, how well these approaches balance different tasks on each parameter still remains unclear. In this paper, we propose to measure the task dominance degree of a parameter

Yury E. Geints

A photonic molecule (PM) is a miniature diffractive optical structure composed of resonance microcavities called atoms (e.g., cylinders or spheres) supporting a set of high-quality eigenmodes. All atoms in a PM are coupled by the electromagnetic fields of eigenmodes, which form collective supermodes of the whole PM. We consider a particular type of mirror-sy

Tal Golan, Wenxuan Guo, Heiko H. Schütt, Nikolaus Kriegeskorte

Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitatively distinct neural network models often predict similar representational geometries of typical stimulus sets. We propose a Bayesian experimental design approach to synthesizing stim

Zhen Zhang, Yang Sun, Renata M. Wentzcovitch

FeO is a crucial phase of the Earth's core, and its thermodynamic properties are essential to developing more accurate core models. It is also a notorious correlated insulator in the NaCl-type (B1) phase at ambient conditions. It undergoes two polymorphic transitions at 300 K before it becomes metallic in the NiAs-type (B8) structure at $\sim$100 GPa. Althou

Yifan Sun, Ziyi Liu, Wu Wang

Classical functional linear regression models the relationship between a scalar response and a functional covariate, where the coefficient function is assumed to be identical for all subjects. In this paper, the classical model is extended to allow heterogeneous coefficient functions across different subgroups of subjects. The greatest challenge is that the

Prakirt Raj Jhunjhunwala, Martin Zubeldia, Siva Theja Maguluri

We consider a load balancing system comprised of a fixed number of single server queues, operating under the well-known Join-the-Shortest Queue policy, and where jobs/customers are impatient and abandon if they do not receive service after some (random) amount of time. In this setting, we characterize the centered and appropriately scaled steady state queue

Eduardo Velasco Stock, Roberto da Silva

In this work, we propose a simple stochastic agent-based model to describe the revenue dynamics of a nightclub venue based on the relationship between profit and spatial occupation. The system consists of an underlying square lattice (nightclub's dance floor) where every attendee (agent) is allowed to move to its first neighboring cells. Each guess has a cha

Abhishek Balakrishna, Animikh Biswas

In this paper we present two results: (1) A data assimilation algorithm for the 3D Navier-Stokes equation (3D NSE) using nodal data, and, as a consequence (2) a novel regularity criterion for the 3D NSE based on finitely many observations of the velocity. The data assimilation algorithm we employ utilizes nudging, a method based on a Newtonian relaxation sch

Aryan Kalluvila, Neha Koonjoo, Danyal Bhutto, Marcio Rockenbach

Low-field (LF) MRI scanners have the power to revolutionize medical imaging by providing a portable and cheaper alternative to high-field MRI scanners. However, such scanners are usually significantly noisier and lower quality than their high-field counterparts. The aim of this paper is to improve the SNR and overall image quality of low-field MRI scans to i

Jaeho Choi, Yura Kim, Kwang-Ho Kim, Sung-Hwa Jung

The precipitation nowcasting methods have been elaborated over the centuries because rain has a crucial impact on human life. Not only quantitative precipitation forecast (QPF) models and convolutional long short-term memory (ConvLSTM), but also various sophisticated methods such as the latest MetNet-2 are emerging. In this paper, we propose a paired complem

Yixuan Wang, Wengang Zhou, Jianmin Bao, Weilun Wang

In this work, we are dedicated to text-guided image generation and propose a novel framework, i.e., CLIP2GAN, by leveraging CLIP model and StyleGAN. The key idea of our CLIP2GAN is to bridge the output feature embedding space of CLIP and the input latent space of StyleGAN, which is realized by introducing a mapping network. In the training stage, we encode a

Yuanyuan Shi, Zongyi Li, Huan Yu, Drew Steeves

State estimation is important for a variety of tasks, from forecasting to substituting for unmeasured states in feedback controllers. Performing real-time state estimation for PDEs using provably and rapidly converging observers, such as those based on PDE backstepping, is computationally expensive and in many cases prohibitive. We propose a framework for ac

Huixin Ma, Kai Wu, Handing Wang, Jing Liu

Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change in the network occurs; namely, the difference between adjacent snapshots is slight. However, great change exists in the real w

Jin Ren, Guohui Song, Lucia Tabacu, Yuesheng Xu

Electrocardiogram (EMG) signals play a significant role in decoding muscle contraction information for robotic hand prosthesis controllers. Widely applied decoders require large amount of EMG signals sensors, resulting in complicated calculations and unsatisfactory predictions. By the biomechanical process of single degree-of-freedom human hand movements, on

Xiaojuan Li

In this paper, we obtain the existence and uniqueness theorem of $L^{p}$-solution for coupled forward-backward stochastic differential equations driven by G-Brownian motion (G-FBSDEs) with arbitrary $T$ under weakly coupling condition. Specially, the result for $p\in(1,2)$ is completely different from the one for $p\geq2$. Furthermore, by considering the dua

Subhadip Sau, John W. Moffat

We investigate the shadow cast by a regular black hole in scalar-tensor-vector mOdified gravity theory. This black hole differs from a Schwarzschild-Kerr black hole by the dimensionless parameter $\beta$. The size of the shadow depends on this parameter. Increasing the value of the parameter $\beta$ shrinks the shadow. A critical value of the parameter $\bet

Xirong Li, Aozhu Chen, Ziyue Wang, Fan Hu

We summarize our TRECVID 2022 Ad-hoc Video Search (AVS) experiments. Our solution is built with two new techniques, namely Lightweight Attentional Feature Fusion (LAFF) for combining diverse visual / textual features and Bidirectional Negation Learning (BNL) for addressing queries that contain negation cues. In particular, LAFF performs feature fusion at bot

Zhonghua Liao, Qi Lü

In this paper, we obtain the exact controllability for a refined stochastic wave equation with three controls by establishing a novel Carleman estimate for a backward hyperbolic-like operator. Compared with the known result, the novelty of this paper is twofold: (1) Our model contains the effects in the drift terms when we put controls directly in the diffus

Yusen Sun, Liangyou Li, Qun Liu, Dit-Yan Yeung

Although lyrics generation has achieved significant progress in recent years, it has limited practical applications because the generated lyrics cannot be performed without composing compatible melodies. In this work, we bridge this practical gap by proposing a song rewriting system which rewrites the lyrics of an existing song such that the generated lyrics

Zongcheng Liu, Jiangshuai Huang, Changyun Wen, Jing Zhou

A novel control design framework is proposed for a class of non-Lipschitz nonlinear systems with quantized states, meanwhile prescribed transient performance and lower control design complexity could be guaranteed. Firstly, different from all existing control methods for systems with state quantization, global stability of strict-feedback nonlinear systems i

Tetsuro Sakaguchi, Hiroto Nishijima, Yositake Takane

We study a non-Hermitian Kitaev chain model that contains three sources of non-Hermiticity: a constant imaginary potential, asymmetry between hopping amplitudes $t_{\rm R}$ and $t_{\rm L}$ in the right and left directions, and imbalance in pair potentials $\Delta_{\rm c}$ and $\Delta_{\rm a}$ for pair creation and annihilation, respectively. We show that bul

Whiyoung Jung, Myungsik Cho, Jongeui Park, Youngchul Sung

Constrained reinforcement learning (RL) is an area of RL whose objective is to find an optimal policy that maximizes expected cumulative return while satisfying a given constraint. Most of the previous constrained RL works consider expected cumulative sum cost as the constraint. However, optimization with this constraint cannot guarantee a target probability

Thomas McDermott, Morteza Moradi, Antoni Mikos-Nuszkiewicz, Magdalena Stobińska

Loophole-free Bell tests are essential if one wishes to perform device-independent quantum key distribution (QKD), since any loophole could be used by a potential adversary to undermine the security of the protocol. Crucial work by Eberhard demonstrated that weakly entangled two-qubit states have a far greater resistance to the detection loophole than maxima

Thomas Creutzig, Andrew R. Linshaw, Bailin Song

The question of when a vertex algebra is a quantization of the arc space of its associated scheme has recently received a lot of attention in both the mathematics and physics literature. This property was first studied by Tomoyuki Arakawa and Anne Moreau [Lectures on $\mathcal{W}$-algebras, Australian Representation Theory Workshop 2016, University of Melbou

Daisuke Shiraishi, Satomi Watanabe

Let $\mathcal{U}$ be the uniform spanning tree on $\mathbb{Z}^{3}$. We show the occurrence of log-logarithmic fluctuations around the leading order for the volume of intrinsic balls in $\mathcal{U}$. As an application, we obtain similar fluctuations for the quenched heat kernel of the simple random walk on $\mathcal{U}$.