July 2022 arXiv papers — page 123

Andrew Kirby, Takafumi Nishino, Thomas D Dunstan

Turbine wake and farm blockage effects may significantly impact the power produced by large wind farms. In this study, we perform Large-Eddy Simulations (LES) of 50 infinitely large offshore wind farms with different turbine layouts and wind directions. The LES results are combined with the two-scale momentum theory (Nishino & Dunstan 2020, J. Fluid Mech. 89

Lei Song, Xiaopeng Xia, Jinxing Xu

We prove a higher-dimensional Chevalley restriction theorem for orthogonal groups, which was conjectured by Chen and Ng\^{o} for reductive groups. In characteristic $p>2$, we also prove a weaker statement. In characteristic $0$, the theorem implies that the categorical quotient of a commuting scheme by the diagonal adjoint action of the group is integral and

Daniel Niederlöhner, Michael Ulrich, Sascha Braun, Daniel Köhler

This paper presents a method to learn the Cartesian velocity of objects using an object detection network on automotive radar data. The proposed method is self-supervised in terms of generating its own training signal for the velocities. Labels are only required for single-frame, oriented bounding boxes (OBBs). Labels for the Cartesian velocities or contiguo

Quentin Brabant, Lina Maria Rojas-Barahona, Claire Gardent

In human conversations, ellipsis and coreference are commonly occurring linguistic phenomena. Although these phenomena are a mean of making human-machine conversations more fluent and natural, only few dialogue corpora contain explicit indications on which turns contain ellipses and/or coreferences. In this paper we address the task of automatically detectin

Hechao Liu, Yufei Huang

The generalized reciprocal distance matrix $RD_{\alpha}(G)$ was defined as $RD_{\alpha}(G)=\alpha RT(G)+(1-\alpha)RD(G),\quad 0\leq \alpha \leq 1.$ Let $\lambda_{1}(RD_{\alpha}(G))\geq \lambda_{2}(RD_{\alpha}(G))\geq \cdots \geq \lambda_{n}(RD_{\alpha}(G))$ be the eigenvalues of $RD_{\alpha}$ matrix of graphs $G$. Then the $RD_{\alpha}$-spread of graph $G$ c

Manuel Araújo

We give a definition of a coherent adjunction in a $4$-category consisting of a finite list of $k$-morphisms for $k\leq 4$, plus equations beetween $4$-morphisms. We prove that the restriction map from the space of coherent adjunctions in a $4$-category to the space of $1$-morphisms which admit an adjoint is a trivial fibration. We prove that other restricti

Yu-Cheng Qiu, S. -H. Henry Tye

In a general phenomenological model with local supersymmetry, the amount of massive gravitinos produced in early universe tends to violate the known dark matter density bound by many orders of magnitude. In the brane world scenario in Type IIB string theory, we propose a novel way to evade this problem. There, the standard model of strong and electroweak int

Jelena Sedlar, Riste Škrekovski

A graph is locally irregular if the degrees of the end-vertices of every edge are distinct. An edge coloring of a graph G is locally irregular if every color induces a locally irregular subgraph of G. A colorable graph G is any graph which admits a locally irregular edge coloring. The locally irregular chromatic index X'irr(G) of a colorable graph G is the s

Ibrahim Saleh

A mutation loop of a valued quiver $Q$, is a combination of quiver automorphisms (permutations of vertices and valuations) and mutations that sends $Q$ to itself. In this article we study what we called \emph{global mutations loops} which are sequences formed of mutations only that send each quiver $Q'$ in the mutation class $[Q]$ to $\sigma (Q')$ for some p

Kenji Tsutsui, Kazuya Shinjo, Shigetoshi Sota, Takami Tohyama

The photoexcitation of a Mott insulator on a square lattice weakens the intensity of both single- and two-magnon excitations as observed in time-resolved resonant-inelastic X-ray scattering and time-resolved Raman scattering, respectively. However, the spectral changes in the low-energy regions below the magnons have not yet been clearly understood. To uncov

Marcel Hinsche, Marios Ioannou, Alexander Nietner, Jonas Haferkamp

The task of learning a probability distribution from samples is ubiquitous across the natural sciences. The output distributions of local quantum circuits form a particularly interesting class of distributions, of key importance both to quantum advantage proposals and a variety of quantum machine learning algorithms. In this work, we provide an extensive cha

Andrew Chappell, Leigh H. Whitehead

Training deep neural networks using simulations typically requires very large numbers of simulated events. This can be a large computational burden and a limitation in the performance of the deep learning algorithm when insufficient numbers of events can be produced. We investigate the use of transfer learning, where a set of simulated images are used to fin

Marios Christodoulou, Andrea Di Biagio, Richard Howl, Carlo Rovelli

We discuss the physical interpretation of the gravity mediated entanglement effect. We show how to read it in terms of quantum reference systems. We pinpoint the single gravitational degree of freedom mediating the entanglement. We clarify why the distinction between longitudinal and transverse degrees of freedom is irrelevant for the interpretation of the r

Masaki Watabe, Yasuhiro Hirano, Atsuko Iwane, Osamu Matoba

Transport of intensity equation (TIE) exhibits a non-interferometric correlation between intensity and phase variations of intermediate fields (e.g., light and electron) in biological imaging. Previous TIE formulations have generally assumed a free space propagation of monochromatic coherent field functions crossing phase distributions along a longitudinal d

Andreas Maurer

Incomplete U-statistics have been proposed to accelerate computation. They use only a subset of the subsamples required for kernel evaluations by complete U-statistics. This paper gives a finite sample bound in the style of Bernstein's inequality. Applied to complete U-statistics the resulting inequality improves over the bounds of both Hoeffding and Arcones

V. Blondot, C. Arnold, A. Delteil, D. Gérard

We report on the synthesis of hybrid light emitting particles with a diameter ranging between 100 and 500 nm, consisting in a compact semiconductor CdSe/CdS/CdZnS nanocrystal aggregate encapsulated by a controlled nanometric size silica and gold layers. We first characterize the Purcell decay rate enhancement corresponding to the addition of the gold nanoshe

Fei Han, Varghese Mathai

In this paper, we establish graded T-duality for $2d$ $\sigma$-models with $H$-flux after localization. This establishes the most general version of T-duality for Type II String Theory. The graded T-duality map, which we call {\bf graded Hori morphism}, is compatible with the Jacobi property of the graded fields, that was earlier studied in \cite{HM21}. Also

Kosuke Nishida, Kyosuke Nishida, Shuichi Nishioka

Humans can obtain the knowledge of novel visual concepts from language descriptions, and we thus use the few-shot image classification task to investigate whether a machine learning model can have this capability. Our proposed model, LIDE (Learning from Image and DEscription), has a text decoder to generate the descriptions and a text encoder to obtain the t

Wentao Tan, Changxing Ding, Pengfei Wang, Mingming Gong

Domain generalization (DG) for person re-identification (ReID) is a challenging problem, as access to target domain data is not permitted during the training process. Most existing DG ReID methods update the feature extractor and classifier parameters based on the same features. This common practice causes the model to overfit to existing feature styles in t

M. C. Crabb

Let $f: R^{m+1}\to R^{m+2^r}$, where $2^{r-1}\leq m+1 <2^r$, be a continuous map. Improving a recent result of Frick and Harrison, we show that there are $4$ points $x_0,\, x_1,\, y_0,\, y_1$ in $R^m$, which are distinct if $m+1\not=2^{r-1}$, and satisfy $x_0\not=x_1$, $y_0\not=y_1$, $\{ x_0, x_1\} \not=\{ y_0,y_1\}$ if $m+1=2^{r-1}$, such that the vectors $

Lars Jaffke, Paloma T. Lima

We determine the maximum number of edges that a planar graph can have as a function of its maximum degree and matching number.

Shota Hoshinaga, Ikkei Hotta, Hiroshi Yanagihara

Recently in relation to the theory of non-commutative probability, a notion of evolution families $\{\omega_{s,t}\}_{s \le t}$ is generalized that are only continuous in parameters, namely $(s,t) \mapsto \omega_{s,t}$ is continuous with respect to locally uniform convergence on a planar domain. In this article we present various equivalence conditions to the

Qijian Zhang, Junhui Hou, Yue Qian

As two fundamental representation modalities of 3D objects, 3D point clouds and multi-view 2D images record shape information from different domains of geometric structures and visual appearances. In the current deep learning era, remarkable progress in processing such two data modalities has been achieved through respectively customizing compatible 3D and 2

Ruisi He, Bo Ai, Zhangdui Zhong, Mi Yang

To overcome increasing traffic, provide various new services, further ensure safety and security, significantly improve travel comfort, a new communication system for railways is required. Since 2019, public networks have been evolving to the fifth generation communication (5G) worldwide, whereas the main communication system of railway is still based on the

Yun-Zhi Du, Huai-Fan Li, Ren Zhao

Since the black hole and cosmological horizons in the de Sitter (dS) spacetime with the Reissner-Nordstrom (RN) black hole are not independent to each other, which is caused by the gravitational effect, the interplay between two horizons should be considered. Based on this, by introducing the interactive entropy the RN-dS spacetime is analogous to a thermody

Josu C. Aurrekoetxea, Katy Clough, Eugene A. Lim

In numerical relativity simulations with non-trivial matter configurations, one must solve the Hamiltonian and momentum constraints of the ADM formulation for the metric variables in the initial data. We introduce a new scheme based on the standard Conformal Transverse-Traceless (CTT) decomposition, in which instead of solving the Hamiltonian constraint as a

I Made Aswin Nahrendra, Christian Tirtawardhana, Byeongho Yu, Eungchang Mason Lee

Studies that broaden drone applications into complex tasks require a stable control framework. Recently, deep reinforcement learning (RL) algorithms have been exploited in many studies for robot control to accomplish complex tasks. Unfortunately, deep RL algorithms might not be suitable for being deployed directly into a real-world robot platform due to the

Takaaki Ishii, Youka Kaku, Keiju Murata

Superradiance is known as a wave amplification process caused by rotating or charged black holes. We argue that the superradiance of stationary black holes in asymptotically AdS spacetimes can be characterized by the ability of energy extraction. Specifically, we demonstrate that energy can be extracted from Reissner-Nordstr\"{o}m-AdS$_4$ and Kerr-AdS$_4$ un

Zhifeng Wang, Wenxing Yan, Chunyan Zeng, Shi Dong

Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners' current knowledge mastery status and predict their future learning performance. The significant challenge with traditional learning diagnosis methods is the inability to balance diagnostic accuracy and interpretability. Although the existing

Andrea Mannocci, Ornella Irrera, Paolo Manghi

Authorship of scientific articles has profoundly changed from early science until now. If once upon a time a paper was authored by a handful of authors, scientific collaborations are much more prominent on average nowadays. As authorship (and citation) is essentially the primary reward mechanism according to the traditional research evaluation frameworks, it

Jing Guo, Heping Zhang

A graph $G$ of order $n$ is said to be $k$-factor-critical for integers $1\leq k < n$, if the removal of any $k$ vertices results in a graph with a perfect matching. $1$- and $2$-factor-critical graphs are the well-known factor-critical and bicritical graphs, respectively. A $k$-factor-critical graph $G$ is called minimal if for any edge $e\in E(G)$, $G-e$ i

Padraig Davidson, Michael Steininger, André Huhn, Anna Krause

Time series are ubiquitous and therefore inherently hard to analyze and ultimately to label or cluster. With the rise of the Internet of Things (IoT) and its smart devices, data is collected in large amounts any given second. The collected data is rich in information, as one can detect accidents (e.g. cars) in real time, or assess injury/sickness over a give

Valerio Proietti, Makoto Yamashita

We study homological invariants of \'etale groupoids arising from Smale spaces, continuing on our previous work, but going beyond the stably disconnected case by incorporating resolutions in the space direction. We show that the homology groups defined by Putnam are isomorphic to the Crainic-Moerdijk groupoid homology with integer coefficients. We also show

Zhiming Cui, Zhihao Yuan, Jaehyung Ju

Similar to Poisson's effect, mechanical coupling is a directional indirect response by a directional input loading. With the advance in manufacturing techniques of 3D complex geometry, architected materials with unit cells of finite volume rather than a point yield more degrees of freedom and foster exotic mechanical couplings such as axial-shear, axial-rota

Alexander Braverman, Michael Finkelberg, Roman Travkin

This is a companion paper of arXiv:1909.11492 and arXiv:1912.01930. We prove an equivalence relating representations of a degenerate orthosymplectic supergroup with the category of twisted $Sp(2n,{\mathbb C}[\![t]\!])$-equivariant $D$-modules on the so called mirabolic affine Grassmannian of $Sp(2n)$. We also discuss (conjectural) extension of this equivalen

Shanwei Ding, Guanghan Li

In this paper, we study the long-time existence and asymptotic behavior for a class of anisotropic non-homogeneous curvature flows without global forcing terms. By the stationary solutions of such anisotropic flows, we obtain existence results for a class of dual Orlicz Christoffel-Minkowski type problems, which is equivalent to solve the PDE $G(x,u_K,Du_K)F

Vy Vo, Van Nguyen, Trung Le, Quan Hung Tran

Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying explanatory input features, which generally fall into two main categories: attribution and selection. A popular attribution-based approach is to exploit local neighborhoods for learning

Abir Sen, Tapas Kumar Mishra, Ratnakar Dash

In this work, a real-time hand gesture recognition system-based human-computer interface (HCI) is presented. The system consists of six stages: (1) hand detection, (2) gesture segmentation, (3) use of five pre-trained convolutional neural network models (CNN) and vision transformer (ViT), (4) building an interactive human-machine interface (HMI), (5) develop

Yabin Zhang, Jiehong Lin, Chenhang He, Yongwei Chen

Masked auto-encoding is a popular and effective self-supervised learning approach to point cloud learning. However, most of the existing methods reconstruct only the masked points and overlook the local geometry information, which is also important to understand the point cloud data. In this work, we make the first attempt, to the best of our knowledge, to c

Peeyush Singh

In the present study, an interior-exterior penalty discontinuous Galerkin finite element method (DG-FEM) is analysed for solving Elastohydrodynamic lubrication (EHL) line and point contact problems. The existence of discrete penalized solution is examined using Brouwer's fixed point theorem. Furthermore, the uniqueness of solution is proved using Lipschitz c

Lucas Baudin, Rida Laraki

Recent extensions to dynamic games of the well-known fictitious play learning procedure in static games were proved to globally converge to stationary Nash equilibria in two important classes of dynamic games (zero-sum and identical-interest discounted stochastic games). However, those decentralized algorithms need the players to know exactly the model (the

Jae Sung Lee, Joonhyun Yeo

The Lindblad form guarantees complete positivity of a Markovian quantum master equation (QME). However, its microscopic derivation for a quantum system weakly interacting with a thermal bath requires several approximations, which may result in inaccuracies in the QME. Recently, various Lindbladian QMEs were derived without resorting to the secular approximat

Markus R. Mosbech, Celine Boehm, Yvonne Y. Y. Wong

Similarly to warm dark matter which features a cut-off in the matter power spectrum due to free-streaming, many interacting dark matter models predict a suppression of the matter power spectrum on small length scales through collisional damping. Forecasts for 21cm line intensity mapping have shown that an instrument like the SKA will be able to probe a suppr

Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu

We study federated contextual linear bandits, where $M$ agents cooperate with each other to solve a global contextual linear bandit problem with the help of a central server. We consider the asynchronous setting, where all agents work independently and the communication between one agent and the server will not trigger other agents' communication. We propose

Chaoxing Huang, Yurui Qian, Simon Chun Ho Yu, Jian Hou

$T_{1\rho}$ mapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can map $T_{1\rho}$ from a reduced number of $T_{1\rho}$ weighted images, but requires significant amounts of high quality training data. Moreover, existing methods do not provide the confidence level of the $T_{1\rho}

Hideyuki Miyahara, Vwani Roychowdhury

Variational Bayes (VB) inference algorithm is used widely to estimate both the parameters and the unobserved hidden variables in generative statistical models. The algorithm -- inspired by variational methods used in computational physics -- is iterative and can get easily stuck in local minima, even when classical techniques, such as deterministic annealing

Alistair Moffat

A sequence of recent papers has considered the role of measurement scales in information retrieval (IR) experimentation, and presented the argument that (only) uniform-step interval scales should be used, and hence that well-known metrics such as reciprocal rank, expected reciprocal rank, normalized discounted cumulative gain, and average precision, should b

Maya Okawa, Tomoharu Iwata

Opinion formation and propagation are crucial phenomena in social networks and have been extensively studied across several disciplines. Traditionally, theoretical models of opinion dynamics have been proposed to describe the interactions between individuals (i.e., social interaction) and their impact on the evolution of collective opinions. Although these m

Matthew J. Dolan, Frederick J. Hiskens, Raymond R. Volkas

We improve the current upper bound on the axion-photon coupling derived from stellar evolution using the $R_2$ parameter, the ratio of stellar populations on the Asymptotic Giant Branch to Horizontal Branch in Globular Clusters. We compare this with data from simulations using the stellar evolution code MESA which include the effects of axion production. Par

Pavel Dvurechensky, Gabriele Iommazzo, Shimrit Shtern, Mathias Staudigl

We propose a new homotopy-based conditional gradient method for solving convex optimization problems with a large number of simple conic constraints. Instances of this template naturally appear in semidefinite programming problems arising as convex relaxations of combinatorial optimization problems. Our method is a double-loop algorithm in which the conic co

Arnaud Brothier

Vaughan Jones discovered unexpected connections between Richard Thompson's group and subfactor theory while attempting to construct conformal field theories (in short CFT). Among other this founded Jones' technology: a powerful new method for constructing actions of fraction groups which had numerous applications in mathematical physics, operator algebras, g

Yiping Yuan, Jing Zhang, Shaunak Chatterjee, Shipeng Yu

Mobile notifications have become a major communication channel for social networking services to keep users informed and engaged. As more mobile applications push notifications to users, they constantly face decisions on what to send, when and how. A lack of research and methodology commonly leads to heuristic decision making. Many notifications arrive at an

Zhi Xu, Hongbo Zhu, Hua Chen, Wei Zhang

This paper studies the problem of constructing polytopic representations of planar regions from depth camera readings. This problem is of great importance for terrain mapping in complicated environment and has great potentials in legged locomotion applications. To address the polytopic planar region characterization problem, we propose a two-stage solution s

A. S. Vokhmintsev, I. A. Petrenyov, R. V. Kamalov, M. S. Karabanalov

ZrO$_2$ nanotubular arrays with intrinsic defects are a promising solid-state basis for the development of devices for detecting, storing, and converting energy. Layers of the self-ordered zirconia nanotubes of 5 $\mu$m length and 30 nm diameter, containing oxygen vacancies and their complexes, have been synthesized by anodic oxidation. The spectrally resolv

Swarna Chatterjee, Majidul Rahaman, Abhirup Datta, Ramij Raja

Abell 1351 is a massive merging cluster that hosts a giant radio halo and a bright radio edge blended in the halo. In this paper, we present the first ever spectral analysis of this cluster using GMRT 610 MHz and VLA 1.4 GHz archival data and discuss the radio edge property. Using \textit{Chandra} data, we report the first tentative detection of shock front

Zhong Wen, Zhenyu Dong, Chenlei Pang, Clemens F. Kaminski

Super-resolution microscopy normally requiring complex and cumbersome optics is not applicable for in situ imaging through a narrow channel. Here, we demonstrate single hair-thin multimode fiber (MMF) endoscope (less than 250 ${\mu}m$) for in vivo light-field nano-imaging, which is called spatial-frequency tracking adaptive beacon light-field encoded nano-en

Nie Lin, Minjie Cai

In this report, we present the technical details of our submission to the 2022 EPIC-Kitchens Unsupervised Domain Adaptation (UDA) Challenge. Existing UDA methods align the global features extracted from the whole video clips across the source and target domains but suffer from the spatial redundancy of feature matching in video recognition. Motivated by the

Huijie Qiao, Jiang-Lun Wu

In this paper, we are concerned with stochastic Volterra equations with singular kernels and H\"older continuous coefficients. We first establish the well-posedness of these equations by utilising the Yamada-Watanabe approach. Then, we aim to characterise the path-independence for additive functionals of these equations. The main challenge here is that the s

Eugene Tan, Débora Corrêa, Thomas Stemler, Michael Small

Dynamical networks are versatile models that can describe a variety of behaviours such as synchronisation and feedback. However, applying these models in real world contexts is difficult as prior information pertaining to the connectivity structure or local dynamics is often unknown and must be inferred from time series observations of network states. Additi

T. Yanagimoto, Y. Miyata

A Bayesian estimator aiming at improving the conditional MLE is proposed by introducing a pair of priors. After explaining the conditional MLE by the posterior mode under a prior, we define a promising estimator by the posterior mean under a corresponding prior. The prior is equivalent to the reference prior in familiar models. Advantages of the present appr

Adhyyan Narang, Omid Sadeghi, Lillian J Ratliff, Maryam Fazel

In the context of online interactive machine learning with combinatorial objectives, we extend purely submodular prior work to more general non-submodular objectives. This includes: (1) those that are additively decomposable into a sum of two terms (a monotone submodular and monotone supermodular term, known as a BP decomposition); and (2) those that are onl

Belle Collaboration, S. Jia, C. P. Shen, C. Z. Yuan

Using $\Upsilon(1S)$, $\Upsilon(2S)$, and $\Upsilon(3S)$ data collected by the Belle detector, we discover a new three-body decay, $\Omega(2012)^-\to\Xi(1530)\bar K\to\Xi\pi\bar K$, with a significance of 5.2~$\sigma$. The mass of the $\Omega(2012)^-$ is $(2012.5\pm0.7\pm0.5)$ MeV and its effective couplings to $\Xi(1530)\bar{K}$ and $\Xi\bar{K}$ are $(39^{+

Hidenori Katsurada, Henry H. Kim

We compute the twisted Koecher-Maass series of the first and second kind of the Ikeda type lift for the exceptional group of type $E_{7,3}$. As an application, we obtain their rationality result.

Huabin Diao, Gongyan Li, Shaoyun Xu, Yuexing Hao

At present, the quantification methods of neural network models are mainly divided into post-training quantization (PTQ) and quantization aware training (QAT). Post-training quantization only need a small part of the data to complete the quantification process, but the performance of its quantitative model is not as good as the quantization aware training. T

Bharat Chandra P, Mayuresh Sarpotdar, Binukumar G. Nair, Richa Rai

We present here a low-cost Raspberry Pi (RPi)-based star sensor StarberrySense using commercial-off-the-shelf (COTS) components, developed and built for applications in small satellites and CubeSat-based missions. A star sensor is one of the essential instruments onboard a satellite for attitude determination. However, most commercially available star sensor

Akira Matsui, Emilio Ferrara

To extract essential information from complex data, computer scientists have been developing machine learning models that learn low-dimensional representation mode. From such advances in machine learning research, not only computer scientists but also social scientists have benefited and advanced their research because human behavior or social phenomena lies

Viti Chandra, Michael McGuigan

The quantum mechanics of Rotating, Charged, de Sitter and String Theory black holes are of recent interest because of their peculiar thermodynamic properties, as well the mysterious nature of their microstates. A full quantum treatment of the operators involved in this systems could yield valuable information into their nature, similar to how quantum treatme

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee

Bayesian optimization (BO) has become a popular strategy for global optimization of many expensive real-world functions. Contrary to a common belief that BO is suited to optimizing black-box functions, it actually requires domain knowledge on characteristics of those functions to deploy BO successfully. Such domain knowledge often manifests in Gaussian proce

Ali Mottaghi, Aidean Sharghi, Serena Yeung, Omid Mohareri

Automatic surgical activity recognition enables more intelligent surgical devices and a more efficient workflow. Integration of such technology in new operating rooms has the potential to improve care delivery to patients and decrease costs. Recent works have achieved a promising performance on surgical activity recognition; however, the lack of generalizabi

Xinyi Luo, Andreas Waechter

We propose a new method for linear second-order cone programs. It is based on the sequential quadratic programming framework for nonlinear programming. In contrast to interior point methods, it can capitalize on the warm-start capabilities of active-set quadratic programming subproblem solvers and achieve a local quadratic rate of convergence. In order to ov

Ukcheol Shin, Kyunghyun Lee, In So Kweon

In this paper, we propose a multi-objective camera ISP framework that utilizes Deep Reinforcement Learning (DRL) and camera ISP toolbox that consist of network-based and conventional ISP tools. The proposed DRL-based camera ISP framework iteratively selects a proper tool from the toolbox and applies it to the image to maximize a given vision task-specific re

Anwesh Ray

Let $\ell$ and $p$ be (not necessarily distinct) prime numbers and $F$ be a global function field of characteristic $\ell$ with field of constants $\kappa$. Assume that there exists a prime $P_\infty$ of $F$ which has degree $1$, and let $\mathcal{O}_F$ be the subring of $F$ consisting of functions with no poles away from $P_\infty$. Let $f(X)$ be a polynomi

Kengo Miyamoto, Qi Wang

In this paper, we report on the $\tau$-tilting finiteness of some classes of finite-dimensional algebras over an algebraically closed field, including symmetric algebras of polynomial growth, $0$-Hecke algebras and $0$-Schur algebras. Consequently, we find that derived equivalence preserves the $\tau$-tilting finiteness over symmetric algebras of polynomial

Kaiming Kuang, Li Zhang, Jingyu Li, Hongwei Li

3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer, which facilitates preservation of pulmonary function and helps ensure low recurrence rates. However, automatic reconstruction of pulmonary segments remains unexplored in the era of deep learning. In this paper, we investigate what makes for automati

John Gallagher, Chun-Kit Lai, Eric Weber

A pattern is called universal in another collection of sets, when every set in the collection contains some linear and translated copy of the original pattern. Paul Erd\H{o}s proposed a conjecture that no infinite set is universal in the collection of sets with positive measure. This paper explores an analogous problem in the topological setting. Instead of

Carlos Vargas Venegas, Daning Huang

This paper presents a novel physics-infused reduced-order modeling (PIROM) methodology for efficient and accurate modeling of non-linear dynamical systems. The PIROM consists of a physics-based analytical component that represents the known physical processes, and a data-driven dynamical component that represents the unknown physical processes. The PIROM is

Jamie Tucker-Foltz, Richard Zeckhauser

We study the classic divide-and-choose method for equitably allocating divisible goods between two players who are rational, self-interested Bayesian agents. The players have additive values for the goods. The prior distributions on those values are common knowledge. We consider both the cases of independent values and values that are correlated across playe

Seongjun Yang, Hyeonji Hwang, Daeyoung Kim, Radhika Dua

Federated learning (FL) is an active area of research. One of the most suitable areas for adopting FL is the medical domain, where patient privacy must be respected. Previous research, however, does not provide a practical guide to applying FL in the medical domain. We propose empirical benchmarks and experimental settings for three representative medical da

Wei Sun, Lili Qiu

Depth estimation enables a wide variety of 3D applications, such as robotics, autonomous driving, and virtual reality. Despite significant work in this area, it remains open how to enable accurate, low-cost, high-resolution, and large-range depth estimation. Inspired by the flash-to-bang phenomenon (i.e. hearing the thunder after seeing the lightning), this

Yue Cao, XiaoJiang Zhou, Peihao Huang, Yao Xiao

Real-word search and recommender systems usually adopt a multi-stage ranking architecture, including matching, pre-ranking, ranking, and re-ranking. Previous works mainly focus on the ranking stage while very few focus on the pre-ranking stage. In this paper, we focus on the information transfer from ranking to pre-ranking stage. We propose a new Contrastive

Junyan He, Shashank Kushwaha, Charul Chadha, Seid Koric

This paper explores the possibilities of applying physics-informed neural networks (PINNs) in topology optimization (TO) by introducing a fully self-supervised TO framework that is based on PINNs. This framework solves the forward elasticity problem by the deep energy method (DEM). Instead of training a separate neural network to update the density distribut

Jingwei Ling, Jeremy Staffa, Heming Wang, Boqiang Shen

High coherence visible and near-visible laser sources are centrally important to the operation of advanced position/navigation/timing systems as well as classical/quantum sensing systems. However, the complexity and size of these bench-top lasers is an impediment to their transitioning beyond the laboratory. Here, a system-on-a-chip that emits high-coherence

R. K. Daniels, J. B. Mallinson, Z. E. Heywood, P. J. Bones

Networks of nanowires are currently being explored for a range of applications in brain-like (or neuromorphic) computing, and especially in reservoir computing (RC). Fabrication of real-world computing devices requires that the nanowires are deposited sequentially, leading to stacking of the wires on top of each other. However, most simulations of computatio

Koji Nakano, Daisuke Takafuji, Yasuaki Ito, Takashi Yazane

Quadratic Unconstrained Binary Optimization (QUBO) is a combinatorial optimization to find an optimal binary solution vector that minimizes the energy value defined by a quadratic formula of binary variables in the vector. As many NP-hard problems can be reduced to QUBO problems, considerable research has gone into developing QUBO solvers running on various

Grant Cairns, Yuri Nikolayevsky

One of Pierre Molino's principal mathematical achievements was his theory of Riemannian foliations. One of his last papers, published in 2001, showed that his theory could be extended to a large class of non-integrable distributions. The key example here is that of a \emph{respectful decomposition} of a Lie algebra $\mathfrak{g}$; this is vector space decomp

Xue Jiang, Xiulian Peng, Huaying Xue, Yuan Zhang

Bitrate scalability is a desirable feature for audio coding in real-time communications. Existing neural audio codecs usually enforce a specific bitrate during training, so different models need to be trained for each target bitrate, which increases the memory footprint at the sender and the receiver side and transcoding is often needed to support multiple r

Jiangchao Yao, Feng Wang, Xichen Ding, Shaohu Chen

On-device machine learning enables the lightweight deployment of recommendation models in local clients, which reduces the burden of the cloud-based recommenders and simultaneously incorporates more real-time user features. Nevertheless, the cloud-based recommendation in the industry is still very important considering its powerful model capacity and the eff

Zhan Chen, Hong Liu, Tianyu Guo, Zhengyan Chen

Self-supervised skeleton-based action recognition with contrastive learning has attracted much attention. Recent literature shows that data augmentation and large sets of contrastive pairs are crucial in learning such representations. In this paper, we found that directly extending contrastive pairs based on normal augmentations brings limited returns in ter

Xiaowo Xu, Xiaoling Zhang, Tianwen Zhang, Zhenyu Yang

Moving target shadows among video synthetic aperture radar (Video-SAR) images are always interfered by low scattering backgrounds and cluttered noises, causing poor detec-tion-tracking accuracy. Thus, a shadow-background-noise 3D spatial decomposition (SBN-3D-SD) model is proposed to enhance shadows for higher detection-tracking accuracy. It leverages the sp

Luke W. Bertels, Harper R. Grimsley, Sophia E. Economou, Edwin Barnes

Because quantum simulation of molecular systems is expected to provide the strongest advantage over classical computing methods for systems exhibiting strong electron correlation, it is critical that the performance of VQEs be assessed for strongly correlated systems. For classical simulation, strong correlation often results in symmetry-breaking of the Hart

Matthew Connor, Othon Michail

We study a model of programmable matter systems consisting of $n$ devices lying on a 2-dimensional square grid, which are able to perform the minimal mechanical operation of rotating around each other. The goal is to transform an initial shape A into a target shape B. We are interested in characterising the class of shapes which can be transformed into each

Johnson Kuan, Jonas Mueller

We study simple methods for out-of-distribution (OOD) image detection that are compatible with any already trained classifier, relying on only its predictions or learned representations. Evaluating the OOD detection performance of various methods when utilized with ResNet-50 and Swin Transformer models, we find methods that solely consider the model's predic

Debabrata Mahapatra, Chaosheng Dong, Yetian Chen, Deqiang Meng

Learning to Rank (LTR) technique is ubiquitous in the Information Retrieval system nowadays, especially in the Search Ranking application. The query-item relevance labels typically used to train the ranking model are often noisy measurements of human behavior, e.g., product rating for product search. The coarse measurements make the ground truth ranking non-

Yoshihiro Kawasaki, Shunta Koga, Masahiro N. Machida

We determine the time evolution of the dust particle size distribution during the collapse of a cloud core, accounting for both dust coagulation and dust fragmentation, to investigate the influence of dust growth on non-ideal magnetohydrodynamic effects.The density evolution of the collapsing core is given by a one-zone model. We assume two types of dust mod

Rogerio T. Cavalcanti, Kelvin dos S. Alves, Julio M. Hoff da Silva

The horizon structure and thermodynamics of hairy spherically symmetric black holes generated by the gravitational decoupling method are carefully investigated. The temperature and heat capacity of the black hole is determined, as well as how the hairy parameters affect the thermodynamics. It allows the analysis of the thermal stability and the possible exis

Thejan Rajapakshe, Rajib Rana, Sara Khalifa, Bjorn W. Schuller

Computers can understand and then engage with people in an emotionally intelligent way thanks to speech-emotion recognition (SER). However, the performance of SER in cross-corpus and real-world live data feed scenarios can be significantly improved. The inability to adapt an existing model to a new domain is one of the shortcomings of SER methods. To address

Ming Chen, Jie Han, Guanghui Wang, Donglei Yang

Let $H$ be an $h$-vertex graph. The vertex arboricity $ar(H)$ of $H$ is the least integer $r$ such that $V(H)$ can be partitioned into $r$ parts and each part induces a forest in $H$. We show that for sufficiently large $n\in h\mathbb{N}$, every $n$-vertex graph $G$ with $\delta(G)\geq \max\left\{\left(1-\frac{2}{f(H)}+o(1)\right)n, \left(\frac{1}{2}+o(1)\ri

Cleon S. Barroso

For a bounded closed convex set $K$, in this note, we study the FPP for $\alpha$-H\"older nonexpansive maps, i.e. mappings $T\colon K\to K$ for which $\|T x -Ty\| \leq\| x - y\|^\alpha$ for all $x, y\in K$, $\alpha\in (0,1)$. First, we note that only finite-dimensional spaces have the H\"older-FPP. Moreover, the unit ball $B_X$ of any infinite-dimensional sp

Yiqin Zhao, Sheng Wei, Tian Guo

Many augmented reality (AR) applications rely on omnidirectional environment lighting to render photorealistic virtual objects. When the virtual objects consist of reflective materials, such as a metallic sphere, the required lighting information to render such objects can consist of privacy-sensitive information that is outside the current camera view. In t

Ali Kaya

It is shown that membrane and fivebrane of D=11 supergravity theory can support nongauge, linearized spin-3/2 superhairs. Supercharges associated with these fields are calculated. We also generalize the solutions to some overlapping cases and discuss possible implications of their existence.