January 2022 arXiv papers — page 132

Wolfgang Mack, Julian Wechsler, Emanuël A. P. Habets

The direction-of-arrival (DOA) of sound sources is an essential acoustic parameter used, e.g., for multi-channel speech enhancement or source tracking. Complex acoustic scenarios consisting of sources-of-interest, interfering sources, reverberation, and noise make the estimation of the DOAs corresponding to the sources-of-interest a challenging task. Recentl

P. K. Sahoo, Sanjay Mandal, Simran Arora

In today's scenario, going beyond Einstein's theory of gravity leads us to some more complete and modified gravity theories. One of them is the $f(R,T)$ gravity in which $ R $ is the Ricci scalar, and $ T $ is the trace of the energy-momentum tensor. Using a well-motivated linear $f(R,T)$ gravity model with a single parameter, we studied the strong e

Antoine Bret

In a recent work [Bret, EPL \textbf{135} (2021) 35001], quantum electrodynamic (QED) effects were evaluated for the two-stream instability. It pertains to the growth of perturbations with a wave vector oriented along the flow in a collisionless counter-streaming system. Here, the analysis is extended to every possible orientation of the wave vector. The prev

Asha Sebastian, V. Ravichandran

For a normalised analytic function f defined on the open unit disk in the complex plane, we determine several sufficient conditions for starlikeness in terms of the quotients Q_{ST}:=zf'(z)/f(z), Q_{CV}:=1+zf"(z)/f'(z) and the Schwarzian derivative Q_{SD}:=z^2((f"(z)/f'(z))'-(f"(z)/f'(z))^2/2)$. These conditions were obtained

Oleg Kiselyov, Jeremy Yallop

Let insertion in program generation is producing code with definitions (let-statements). Although definitions precede uses in generated code, during code generation `uses' come first: we might not even know a definition is needed until we encounter a reoccurring expression. Definitions are thus generated `in hindsight', which explains why this proces

Gregory Faletto, Jacob Bien

Stability selection (Meinshausen and Buhlmann, 2010) makes any feature selection method more stable by returning only those features that are consistently selected across many subsamples. We prove (in what is, to our knowledge, the first result of its kind) that for data containing highly correlated proxies for an important latent variable, the lasso typical

Kshitija Taywade, Brent Harrison, Judy Goldsmith

Many past attempts at modeling repeated Cournot games assume that demand is stationary. This does not align with real-world scenarios in which market demands can evolve over a product's lifetime for a myriad of reasons. In this paper, we model repeated Cournot games with non-stationary demand such that firms/agents face separate instances of non-stationa

Rakesh Kumar, Mehdi Alipour, David Black-Schaffer

Exploiting memory level parallelism (MLP) is crucial to hide long memory and last level cache access latencies. While out-of-order (OoO) cores, and techniques building on them, are effective at exploiting MLP, they deliver poor energy efficiency due to their complex and energy-hungry hardware. This work revisits slice-out-of-order (sOoO) cores as an energy e

Hisham A. Kholidy, Mohammad A. Rahman, Andrew Karam, Zahid Akhtar

The 5G network would fuel next-gen, bandwidth-heavy technologies such as automation, IoT, and AI on the factory floor. It will improve efficiency by powering AR overlays in workflows, as well as ensure safer practices and reduce the number of defects through predictive analytics and real-time detection of damage. The Dynamic Spectrum Sharing (DSS) in 5G netw

Pablo Azcue, Xiaoqing Liang, Nora Muler, Virginia R. Young

In this paper, we consider an optimal reinsurance problem to minimize the probability of drawdown for the scaled Cramér-Lundberg risk model when the reinsurance premium is computed according to the mean-variance premium principle. We extend the work of Liang et al. [16] to the case of minimizing the probability of drawdown. By using the comparison method and

Xupeng Jia, Dongmei Li

Deep learning based single-channel speech enhancement tries to train a neural network model for the prediction of clean speech signal. There are a variety of popular network structures for single-channel speech enhancement, such as TCNN, UNet, WaveNet, etc. However, these structures usually contain millions of parameters, which is an obstacle for mobile appl

Manh-Huong Phan, Vijaysankar Kalappattil, Valery Ortiz Jimenez, Yen Thi Hai Pham

The exchange bias (EB) effect is known as a fundamentally and technologically important magnetic property of a magnetic bilayer film. It is manifested as a horizontal shift in a magnetic hysteresis loop of a film subject to cooling in the presence of a magnetic field. The EB effect in van der Waals (vdW) heterostructures offers a novel approach for tuning th

Phan Van Thien

We prove invariant of the regularity index of fat points under changes of the linear subspace containing the support of the fat points. Then we show that Segre's bound is attained by any set of s non-degenerate equimultiple fat points in $\mathbb P^n$, $s\le n+3$. We also give an example showing that there always exists a set of n+4 non-degenerate equimu

Ming Li

Weakly supervised object localization (WSOL) is a challenging task to localize the object by only category labels. However, there is contradiction between classification and localization because accurate classification network tends to pay attention to discriminative region of objects rather than the entirety. We propose this discrimination is caused by hand

Deyu Kong, Xike Xie, Zhuoxu Zhang

Graph partitioning plays a vital role in distributedlarge-scale web graph analytics, such as pagerank and labelpropagation. The quality and scalability of partitioning strategyhave a strong impact on such communication- and computation-intensive applications, since it drives the communication costand the workload balance among distributed computing nodes.Rec

Constantine J. Roros, Avinash C. Kak

We propose a recurrent neural network-based spatio-temporal framework named maskGRU for the detection and tracking of small objects in videos. While there have been many developments in the area of object tracking in recent years, tracking a small moving object amid other moving objects and actors (such as a ball amid moving players in sports footage) contin

Yuxin Zhang, Jindong Wang, Yiqiang Chen, Han Yu

Unsupervised anomaly detection aims to build models to effectively detect unseen anomalies by only training on the normal data. Although previous reconstruction-based methods have made fruitful progress, their generalization ability is limited due to two critical challenges. First, the training dataset only contains normal patterns, which limits the model ge

Thomas Stanke, H. G. Arce, J. Bally, P. Bergman

The Orion molecular cloud complex harbours the nearest GMCs and site of high-mass star formation. Its YSO populations are thoroughly characterized. The region is therefore a prime target for the study of star formation. Here, we verify the performance of the SuperCAM 64 pixel heterodyne array on APEX. We give a descriptive overview of a set of wide-field CO(

Leonardo Queiroz, Kenneth Lai, Svetlana Yanushkevich, Vlad Shmerko

In this study of the face recognition on masked versus unmasked faces generated using Flickr-Faces-HQ and SpeakingFaces datasets, we report 36.78% degradation of recognition performance caused by the mask-wearing at the time of pandemics, in particular, in border checkpoint scenarios. We have achieved better performance and reduced the degradation to 1.79% u

Ze Liu, Siyu Yi, Jianghu, Dong

The spread of COVID-19 makes it essential to investigate its prevalence. In such investigation research, as far as we know, the widely-used sampling methods didn't use the information sufficiently about the numbers of the previously diagnosed cases, which provides a priori information about the true numbers of infections. This motivates us to develop a n

Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Keyvan Farahani

Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability, and is also time-consuming, considering the fact that only experts are capable of providing annotati

Daizong Liu, Xiaoye Qu, Pan Zhou, Yang Liu

This paper addresses temporal sentence grounding. Previous works typically solve this task by learning frame-level video features and align them with the textual information. A major limitation of these works is that they fail to distinguish ambiguous video frames with subtle appearance differences due to frame-level feature extraction. Recently, a few metho

Nabin Bhatta, Tatsu Takeuchi

In this contribution to Peter Suranyi Festschrift, we study the Halliday-Suranyi perturbation method for calculating the energy eigenvalues of the quartic anharmonic oscillator.

Bejan Sadeghian

Significant work has been placed in the Q&A NLP space to build models that are more robust to adversarial attacks. Two key areas of focus are in generating adversarial data for the purposes of training against these situations or modifying existing architectures to build robustness within. This paper introduces an approach that joins these two ideas together

Daizong Liu, Xiaoye Qu, Xing Di, Yu Cheng

Temporal sentence grounding (TSG) is crucial and fundamental for video understanding. Although the existing methods train well-designed deep networks with a large amount of data, we find that they can easily forget the rarely appeared cases in the training stage due to the off-balance data distribution, which influences the model generalization and leads to

Ming-Zhu Chen, Ning Wang, Long-Tu Yuan, Xiao-Dong Zhang

The bipartite Turán number of a graph $H$, denoted by $ex(m,n; H)$, is the maximum number of edges in any bipartite graph $G=(X,Y; E)$ with $|X|=m$ and $|Y|=n$ which does not contain $H$ as a subgraph. In this paper, we determined $ex(m,n; F_{\ell})$ for arbitrary $\ell$ and appropriately large $n$ with comparing to $m$ and $\ell$, where $F_\ell$ is a linear

Joshua A. De La Cruz, Qingkun Liu, Bohdan Senyuk, Allister W. Frazier

Many promising approaches for designing interactions of synthetic materials with light involve solid optical monocrystals and nanofabricated photonic crystal structures with spatially periodic variations of refractive index. Although their high costs limit current technological applications, remarkably, such photonic and optically anisotropic materials have

Daniel Ahfock, William J. Astle, Sylvia Richardson

There is an increasing body of work exploring the integration of random projection into algorithms for numerical linear algebra. The primary motivation is to reduce the overall computational cost of processing large datasets. A suitably chosen random projection can be used to embed the original dataset in a lower-dimensional space such that key properties of

Blaise Fleury, Eldho Abraham, Joshua A. De La Cruz, Varun S. Chandrasekar

Improving building energy performance requires the development of new highly insulative materials. An affordable retrofitting solution comprising a thin film could improve the resistance to heat flow in both residential and commercial buildings and reduce overall energy consumption. Here we propose cellulose aerogel films formed from pellicles produced by th

Chuijia Wang

In this article, we study Prasad's conjecture for regular supercuspidal representations based on the machinery developed by Hakim and Murnaghan to study distinguished representations, and the fundamental work of Kaletha on parameterization of regular supercuspidal representations. For regular supercuspidal representations, we give some new interpretation

Kanghee Lee, Junho Park, Seojoo Lee, Soojeong Baek

A temporal boundary refers to a specific time at which the properties of an optical medium are abruptly changed. When light interacts with the temporal boundary, its spectral content can be redistributed due to the breaking of continuous time-translational symmetry of the medium where light resides. In this work, we use this principle to demonstrate, at tera

Didier Ndayikengurukiye, Max Mignotte

The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Herein, we propose a novel and efficient strategy, through a simple model, almos

Carlos A. Bertulani, Angela Bonaccorso

In this brief review we discuss the basic theoretical concepts used in the experimental studies of the most common cases of direct reactions such as (a) elastic scattering, (b) inelastic scattering, (c) Coulomb excitation, (d) transfer reactions and (e) breakup reactions.

Giacomo Paesani, Daniël Paulusma, Paweł Rzążewski

In the Feedback Vertex Set problem, we aim to find a small set $S$ of vertices in a graph intersecting every cycle. The Subset Feedback Vertex Set problem requires $S$ to intersect only those cycles that include a vertex of some specified set $T$. We also consider the Weighted Subset Feedback Vertex Set problem, where each vertex $u$ has weight $w(u)>0$ and

Ludwig Winkler, Klaus-Robert Müller, Huziel E. Sauceda

Molecular dynamics simulations are a cornerstone in science, allowing to investigate from the system's thermodynamics to analyse intricate molecular interactions. In general, to create extended molecular trajectories can be a computationally expensive process, for example, when running $ab-initio$ simulations. Hence, repeating such calculations to either obt

Vitaly V. Kocharovsky, Vladimir V. Kocharovsky, Sergey V. Tarasov

We find a universal analytic formula for a characteristic function (Fourier transform) of a joint probability distribution for the particle occupation numbers in a BEC gas and the Hafnian Master Theorem generalizing the famous Permanent Master Theorem of MacMahon. We suggest an appealing model, a multi-qubit BEC trap formed by a set of qubit potential wells,

Lars Lien Ankile, Kjartan Krange

This paper presents an ensemble forecasting method that shows strong results on the M4 Competition dataset by decreasing feature and model selection assumptions, termed DONUT (DO Not UTilize human beliefs). Our assumption reductions, primarily consisting of auto-generated features and a more diverse model pool for the ensemble, significantly outperform the s

Simone Dussi, Chris H. Rycroft

Understanding how to structure a porous electrode to facilitate fluid, mass, and charge transport is key to enhance the performance of electrochemical devices such as fuel cells, electrolyzers, and redox flow batteries (RFBs). Using a parallel computational framework, direct numerical simulations are carried out on idealized porous electrode microstructures

Gerardo Barrera, Jani Lukkarinen

In this manuscript, we provide a non-asymptotic process level control between the telegraph process and the Brownian motion with suitable diffusivity constant via a Wasserstein distance with quadratic average cost. In addition, we derive non-asymptotic estimates for the corresponding time average $p$-th moments. The proof relies on coupling techniques such a

Francesco Fidaleo

n the present note, which is the first part of a work concerning the study of the set of the symmetric states for Fermi systems, we describe the extension of the De Finetti theorem to the infinite Fermi $C^*$-tensor product of a single (separable) general $\bz^2$-graded $C^*$-algebra.

Abhishek Gupta, Sannidhi Shetty, Raunak Joshi, Ronald Melwin Laban

Prognostication of medical problems using the clinical data by leveraging the Machine Learning techniques with stellar precision is one of the most important real world challenges at the present time. Considering the medical problem of Polycystic Ovary Syndrome also known as PCOS is an emerging problem in women aged from 15 to 49. Diagnosing this disorder by

Peidong Wang, Janni Yuval, Paul A. O'Gorman

Subgrid processes in global climate models are represented by parameterizations which are a major source of uncertainties in simulations of climate. In recent years, it has been suggested that machine-learning (ML) parameterizations based on high-resolution model output data could be superior to traditional parameterizations. Currently, both traditional and

Maria Gillespie, Andrew Reimer-Berg

We give a combinatorial proof of a recent geometric result of Farkas and Lian on linear series on curves with prescribed incidence conditions. The result states that the expected number of degree-$d$ morphisms from a general genus $g$, $n$-marked curve $C$ to $\mathbb{P}^r$, sending the marked points on $C$ to specified general points in $\mathbb{P}^r$, is e

Jakob Fuchs, Tim Laux

In this work, we analyze Merriman, Bence and Osher's thresholding scheme, a time discretization for mean curvature flow. We restrict to the two-phase setting and mean convex initial conditions. In the sense of the minimizing movements interpretation of Esedoglu and Otto we show the time-integrated energy of the approximation to converge to the time-integrate

Virginia X. He, Matt P. Wand

We use Bayesian model selection paradigms, such as group least absolute shrinkage and selection operator priors, to facilitate generalized additive model selection. Our approach allows for the effects of continuous predictors to be categorized as either zero, linear or non-linear. Employment of carefully tailored auxiliary variables results in Gibbsian Marko

Ömer Deniz Akyildiz

We analyze the optimized adaptive importance sampler (OAIS) for performing Monte Carlo integration with general proposals. We leverage a classical result which shows that the bias and the mean-squared error (MSE) of the importance sampling scales with the $\chi^2$-divergence between the target and the proposal and develop a scheme which performs global optim

Seth Pettie, Thatchaphol Saranurak, Longhui Yin

A $k$-vertex connectivity oracle for undirected $G$ is a data structure that, given $u,v\in V(G)$, reports $\min\{k,\kappa(u,v)\}$, where $\kappa(u,v)$ is the pairwise vertex connectivity between $u,v$. There are three main measures of efficiency: construction time, query time, and space. Prior work of Izsak and Nutov shows that a data structure of total siz

Christian Hercher

The Collatz conjecture (or ``Syracuse problem'') considers recursively-defined sequences of positive integers where $n$ is succeeded by $\tfrac{n}{2}$, if $n$ is even, or $\tfrac{3n+1}{2}$, if $n$ is odd. The conjecture states that for all starting values $n$ the sequence eventually reaches the trivial cycle $1, 2, 1, 2, \ldots$ . We are interested in the ex

Jean-Pierre Gazeau, Véronique Hussin, James Moran, Kevin Zelaya

Quantisation with Gaussian type states offers certain advantages over other quantisation schemes, in particular, they can serve to regularise formally discontinuous classical functions leading to well defined quantum operators. In this work we define a squeezed state quantisation in two dimensions using several families of squeezed states for one- and two-mo

Gaiane Panina

We present an elementary approach to local combinatorial formulae for the Euler class of a fiber-oriented triangulated spherical fiber bundle. The approach is based on sections averaging technique and very basic knowledge of simplicial (co)homology theory. Our formulae are close relatives of those by N. Mnëv.

Orr Avni, Yuval Dagan

The dispersion of respiratory saliva droplets by indoor wake structures may enhance the transmission of various infectious diseases, as the wake spreads virus-laden droplets across the room. Thus, this study analyses the interaction between vortical wake structures and exhaled multi-component saliva droplets. A self-propelling analytically-described dipolar

Marko Medenjak

In this article we study a set of integrable quantum cellular automata,the quantum hardcore gases (QHCG), with an arbitrary local Hilbert space dimension, and discuss the matrix product ansatz based approach for solving the dynamics of local operators analytically. Subsequently, we focus on the dynamics of operator spreading, in particular on the out-of-time

Christophe Bédard, Ingo Lütkebohle, Michel Dagenais

Testing and debugging have become major obstacles for robot software development, because of high system complexity and dynamic environments. Standard, middleware-based data recording does not provide sufficient information on internal computation and performance bottlenecks. Other existing methods also target very specific problems and thus cannot be used f

Yifan Jiang, Bartlomiej Wronski, Ben Mildenhall, Jonathan T. Barron

Most image denoising networks apply a single set of static convolutional kernels across the entire input image. This is sub-optimal for natural images, as they often consist of heterogeneous visual patterns. Dynamic convolution tries to address this issue by using per-pixel convolution kernels, but this greatly increases computational cost. In this work, we

Etienne Bellin

We are interested in the independence number of large random simply generated trees and related parameters, such as their matching number or the kernel dimension of their adjacency matrix. We express these quantities using a canonical tricolouration, which is a way to colour the vertices of a tree with three colours. As an application we obtain limit theorem

Harold N. Ward

We define a normal graph algebra modeled on algebras used in genetics. Although the algebra does not always determine its graph, it often highlights special features. After developing basic properties of the algebra, we examine those of certain minimal graphs. We then apply the results to the Petersen graph, finding connections between some of its many aspec

Milajiguli Rexiti, Laleh Memarzadeh, Stefano Mancini

The problem of dephasing channel discrimination is addressed for finite-dimensional systems. In particular, the optimization with respect to input states without energy constraint is solved analytically for qubit, qutrit and ququart. Additionally, it is shown that resorting to side entanglement assisted strategy is completely useless in this case.

Linchuan Wei, Alper Atamtürk, Andrés Gómez, Simge Küçükyavuz

We consider the convex quadratic optimization problem with indicator variables and arbitrary constraints on the indicators. We show that a convex hull description of the associated mixed-integer set in an extended space with a quadratic number of additional variables consists of a single positive semidefinite constraint (explicitly stated) and linear constra

Enzo Bonacci

During their pre-university year, six brilliant and motivated students joined the educational project "From Soccerene to Fullerene" (acronym FSTF) proposed by five teachers from the Departments of Physical Education and of Mathematics and Physics at the Scientific High School "G.B. Grassi" in Latina (Italy). The pupils investigated the fuller

Kun Zhang, Jin Wang

Information spreads in time. For example, correlations dissipate when the correlated system locally couples to a third party, such as the environment. This simple but important fact forms the known quantum data-processing inequality. Here we theoretically uncover the quantum fluctuation theorem behind the quantum informational inequality. The fluctuation the

Enea Monzio Compagnoni, Anna Scampicchio, Luca Biggio, Antonio Orvieto

Many finance, physics, and engineering phenomena are modeled by continuous-time dynamical systems driven by highly irregular (stochastic) inputs. A powerful tool to perform time series analysis in this context is rooted in rough path theory and leverages the so-called Signature Transform. This algorithm enjoys strong theoretical guarantees but is hard to sca

Yonghao Long, Jianfeng Cao, Anton Deguet, Russell H. Taylor

Robot-assisted surgery has become progressively more and more popular due to its clinical advantages. In the meanwhile, the artificial intelligence and augmented reality in robotic surgery are developing rapidly and receive lots of attention. However, current methods have not discussed the coherent integration of AI and AR in robotic surgery. In this paper,

Zheng Li, Yue Zhao, Xiyang Hu, Nicola Botta

Outlier detection refers to the identification of data points that deviate from a general data distribution. Existing unsupervised approaches often suffer from high computational cost, complex hyperparameter tuning, and limited interpretability, especially when working with large, high-dimensional datasets. To address these issues, we present a simple yet ef

Pau Ferrer-Cid, Jose M. Barcelo-Ordinas, Jorge Garcia-Vidal

Air pollution monitoring platforms play a very important role in preventing and mitigating the effects of pollution. Recent advances in the field of graph signal processing have made it possible to describe and analyze air pollution monitoring networks using graphs. One of the main applications is the reconstruction of the measured signal in a graph using a

Yongchao Zhang

We summarize the potential charged lepton flavor violation (LFV) from neutrino mass relevant models, for instance the seesaw mechanisms. In particular, we study, in a model-dependent way, the LFV signals at the high-energy hadron and lepton colliders originating from the beyond standard model (BSM) neutral scalar $H$, doubly charged scalar $H^{\pm\pm}$, heav

Piotr Sowinski, Katarzyna Wasielewska-Michniewska, Maria Ganzha, Marcin Paprzycki

The vast body of scientific publications presents an increasing challenge of finding those that are relevant to a given research question, and making informed decisions on their basis. This becomes extremely difficult without the use of automated tools. Here, one possible area for improvement is automatic classification of publication abstracts according to

Ouriel Gotesdyner, Bnaya Gross, Dana Vaknin Ben Porath, Shlomo Havlin

Many realistic systems such as infrastructures are characterized by spatial structure and anisotropic alignment. Here we propose and study a model for dealing with such characteristics by introducing a parameter that controls the strength of the anisotropy in the spatial network. This parameter is added to an existing isotropic model used to describe network

Ze-Cheng Zou, Yong-Feng Huang

Primordial black holes of planetary masses captured by compact stars are widely studied to constrain their composition fraction of dark matter. Such a capture may lead to an inspiral process and be detected through gravitational wave signals. In this Letter, we study the post-capture inspiral process by considering two different kinds of compact stars, i.e.,

Xiao-Lan Zong, Hao-Hao Yin, Wei Song, Zhuo-Liang Cao

Unlike classical correlation, quantum entanglement cannot be freely shared among many parties. This restricted shareability of entanglement among multi-party systems is known as monogamy of entanglement, which is one of the most fundamental properties of entanglement. Here, we summarize recent theoretical progress in the field of monogamy of entanglement. We

Wen-Long Ma, Shu-Shen Li, Liang Jiang

Path-independent (PI) quantum control has recently been proposed to integrate quantum error correction and quantum control [Phys. Rev. Lett. 125, 110503 (2020)], achieving fault-tolerant quantum gates against ancilla errors. Here we reveal the underlying algebraic structure of PI quantum control. The PI Hamiltonians and propagators turn out to lie in an alge

Francesco Calavalle, Manuel Suárez-Rodríguez, Beatriz Martín-García, Annika Johansson

Chiral materials are the ideal playground for exploring the relation between symmetry, relativistic effects, and electronic transport. For instance, chiral organic molecules have been intensively studied to electrically generate spin-polarized currents in the last decade, but their poor electronic conductivity limits their potential for applications. Convers

Chung Pang Mok

In this paper we present, using the arithmetic of elliptic curves over finite fields, an algorithm for the efficient generation of a sequence of uniform pseudorandom vectors in high dimensions, that simulates a sample of a sequence of i.i.d. random variables, with values in the hypercube $[0,1]^d$ with uniform distribution. As an application, we obtain, in t

Felipe C. Minuzzi, Leandro Farina

We present a framework for forecasting significant wave height on the Southwestern Atlantic Ocean using the long short-term memory algorithm (LSTM), trained with the ERA5 database available through Copernicus Climate Data Store (CDS) implemented by ECMWF (European Center for Medium Range Forecast) and also with buoy data. The predictions are made for seven d

Samuel Ackerman, Guy Barash, Eitan Farchi, Orna Raz

The crafting of machine learning (ML) based systems requires statistical control throughout its life cycle. Careful quantification of business requirements and identification of key factors that impact the business requirements reduces the risk of a project failure. The quantification of business requirements results in the definition of random variables rep

Hao Sun, Taiyi Wang

Although it is well known that exploration plays a key role in Reinforcement Learning (RL), prevailing exploration strategies for continuous control tasks in RL are mainly based on naive isotropic Gaussian noise regardless of the causality relationship between action space and the task and consider all dimensions of actions equally important. In this work, w

Donghoon Jang

Let a $k$-dimensional torus $T^k$ act on a $2n$-dimensional compact connected almost complex manifold $M$ with isolated fixed points. As for circle actions, we show that there exists a (directed labeled) multigraph that encodes weights at the fixed points of $M$. This includes the notion of a GKM graph as a special case that weights at each fixed point are p

Javad T. Firouzjaee, Pouriya Khaliliyan

Oil companies are among the largest companies in the world whose economic indicators in the global stock market have a great impact on the world economy\cite{ec00} and market due to their relation to gold\cite{ec01}, crude oil\cite{ec02}, and the dollar\cite{ec03}. This study investigates the impact of correlated features on the interpretability of Long Shor

David S Berman, Gabriel Cardoso

These notes present an introduction to the method of geometric quantization. We discuss the main theorems in a style suitable for a theoretical physicist with an eye towards the physical motivation and the interpretation of the geometric construction as providing a solution to Dirac's axioms of quantization. We provide in detail the examples of free relativi

Davinder Singh, Seogjoo J. Jang, Changbong Hyeon

Electromagnetically induced slow-light medium is a promising system for quantum memory devices, but controlling its noise level remains a major challenge to overcome. This work considers the simplest model for such medium, comprised of three-level $\Lambda$-systems interacting with bosonic bath, and provides a new fundamental trade-off relation in light-matt

Nicolas Eschenbaum, Filip Mellgren, Philipp Zahn

This paper develops a formal framework to assess policies of learning algorithms in economic games. We investigate whether reinforcement-learning agents with collusive pricing policies can successfully extrapolate collusive behavior from training to the market. We find that in testing environments collusion consistently breaks down. Instead, we observe stati

Han Cai, Moshe Schwartz

We prove a new lower bound on the field size of locally repairable codes (LRCs). Additionally, we construct maximally recoverable (MR) codes which are cyclic. While a known construction for MR codes has the same parameters, it produces non-cyclic codes. Furthermore, we prove both necessary conditions and sufficient conditions that specify when the known non-

Sayed H. Kadhem, Aristidis K. Nikoloulopoulos

Factor copula models for item response data are more interpretable and fit better than (truncated) vine copula models when dependence can be explained through latent variables, but are not robust to violations of conditional independence. To circumvent these issues, truncated vines and factor copula models for item response data are joined to define a combin

Andrea Ebner, Markus Haltmeier

Compressed sensing (CS) is a powerful tool for reducing the amount of data to be collected while maintaining high spatial resolution. Such techniques work well in practice and at the same time are supported by solid theory. Standard CS results assume measurements to be made directly on the targeted signal. In many practical applications, however, CS informat

Junfei Shi, Haiyan Jin

As a representation learning method, nearest regularized subspace(NRS) algorithm is an effective tool to obtain both accuracy and speed for PolSAR image classification. However, existing NRS methods use the polarimetric feature vector but the PolSAR original covariance matrix(known as Hermitian positive definite(HPD)matrix) as the input. Without considering

Paolo Lipparini

An algebraization of the notion of topology has been proposed more than seventy years ago in a classical paper by McKinsey and Tarski. However, in McKinsey and Tarski's setting the model theoretical notion of homomorphism does not correspond to the notion of continuity. We notice that the two notions correspond if instead we consider a preorder relation $ \s

Kilian Wenker

Employees face decisions every day - in the absence of supervision. The outcome of these decisions can be influenced by digital workplace design through the power of persuasive technology. This paper provides a structured literature review based on recent research on persuasive technology in the workplace. It examines the design and use of persuasive systems

Adam Nowak, Luz Roncal, Tomasz Z. Szarek

We find sharp conditions for the maximal operator associated with generalized spherical mean Radon transform on radial functions $M^{\a,\b}_t$ to be bounded on power weighted Lebesgue spaces. Moreover, we also obtain the corresponding endpoint results in terms of optimal power weighted weak and restricted weak type estimates.

Shingo Sugiyama, Ade Irma Suriajaya

In this paper, we compute the one-level density of low-lying zeros of Dirichlet $L$-functions in a family weighted by special values of Dirichlet $L$-functions at a fixed $s \in [1/2, 1)$. We verify both Fazzari's conjecture and the first author's conjecture on the weighted one-level density for our family of $L$-functions.

Subhajit Kar, Roy Barkan

Fronts and near-inertial waves are energetic motions in the upper ocean that can interact and provide a route for kinetic energy (KE) dissipation of balanced oceanic flows. A quasilinear model is developed to study the KE exchanges between a two-dimensional geostrophically-balanced front undergoing strain-induced semigeostrophic frontogenesis and internal wa

Peter J. Forrester

A number of random matrix ensembles permitting exact determination of their eigenvalue and eigenvector statistics maintain this property under a rank $1$ perturbation. Considered in this review are the additive rank $1$ perturbation of the Hermitian Gaussian ensembles, the multiplicative rank $1$ perturbation of the Wishart ensembles, and rank $1$ perturbati

Jireh Jam, Connah Kendrick, Vincent Drouard, Kevin Walker

Image inpainting is a key technique in image processing task to predict the missing regions and generate realistic images. Given the advancement of existing generative inpainting models with feature extraction, propagation and reconstruction capabilities, there is lack of high-quality feature extraction and transfer mechanisms in deeper layers to tackle pers

Ying Hu, Shanjian Tang, Zuo Quan Xu

In this paper, we consider optimal control of stochastic differential equations subject to an expected path constraint. The stochastic maximum principle is given for a general optimal stochastic control in terms of constrained FBSDEs. In particular, the compensated process in our adjoint equation is deterministic, which seems to be new in the literature. For

V A U De Alwis, A P K De Silva, S D G S P Gunawardane, Young-Ho Lee

This paper presents a Computational Fluid Dynamics (CFD) analysis of a wave energy device called the Hull Reservoir Wave Energy Converter (HRWEC). The device consists of a floating hull and a flap connected to the shaft of power take-off system (PTO), which is integral to the hull structure. It is unique due to its ability to convert wave energy by utilizing

Aamir Miyajiwala, Arnav Ladkat, Samiksha Jagadale, Raviraj Joshi

Text classification is a fundamental Natural Language Processing task that has a wide variety of applications, where deep learning approaches have produced state-of-the-art results. While these models have been heavily criticized for their black-box nature, their robustness to slight perturbations in input text has been a matter of concern. In this work, we

Zefan Yang, Di Lin, Dong Ni, Yi Wang

Automatic segmentation of abdominal organs in computed tomography (CT) images can support radiation therapy and image-guided surgery workflows. Developing of such automatic solutions remains challenging mainly owing to complex organ interactions and blurry boundaries in CT images. To address these issues, we focus on effective spatial context modeling and ex

S. M. Troshin, N. E. Tyurin

We analyse the two possible options of the energy dependency of the elastic and inelastic overlap functions. These correspond to saturation of the black disk limit (BEL effect) and to the unitarity saturation (REL effect) at $s\to\infty$. Relation of the REL effect to increase of the ratio $\sigma_{el}(s)/\sigma_{tot}(s)$ and emergence of black ring picture

Adel Elmahdy, Michelle Kleckler, Soheil Mohajer

The information-theoretic secure exact-repair regenerating codes for distributed storage systems (DSSs) with parameters $(n,k=d,d,\ell)$ are studied in this paper. We consider distributed storage systems with $n$ nodes, in which the original data can be recovered from any subset of $k=d$ nodes, and the content of any node can be retrieved from those of any $

Zhongqiang Hu, Liang Fu, Luqiao Liu

Realization of novel topological phases in magnonic band structures represents a new opportunity for the development of spintronics and magnonics with low power consumption. In this work, we show that in antiparallelly aligned magnetic multilayers, the long-range, chiral dipolar interaction generates bulk bands with non-zero Chern integers and magnonic surfa

Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar

Diffusion probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable latent space, and are slow at generation. On the other hand, standard Variational Autoencoders (VAEs) typically have access to a low-dimensional latent space but exhibit poor sample qu

Yang Han

For a higher hereditary algebra, we calculate its upper (lower) Serre dimension, the entropy and polynomial entropy of Serre functor, and the Hochschild (co)homology entropy of Serre quasi-functor. These invariants are given by its Calabi-Yau dimension for a higher representation-finite algebra, and by its global dimension and the spectral radius and polynom