January 2022 arXiv papers — page 79

Yongjie Guan, Xueyu Hou, Nan Wu, Bo Han

Mobile headsets should be capable of understanding 3D physical environments to offer a truly immersive experience for augmented/mixed reality (AR/MR). However, their small form-factor and limited computation resources make it extremely challenging to execute in real-time 3D vision algorithms, which are known to be more compute-intensive than their 2D counter

J. R. Dawson, George Hobbs, Yansong Gao, Seyit Camtepe

We demonstrate how radio pulsars can be used as random number generators. Specifically, we focus on publicly verifiable randomness (PVR), in which the same sequence of trusted and verifiable random numbers is obtained by multiple parties. PVR is a critical building block for many processes and algorithms (including cryptography, scientific trials, electoral

Guangyuan Shen, Dehong Gao, Libin Yang, Fang Zhou

Client selection strategies are widely adopted to handle the communication-efficient problem in recent studies of Federated Learning (FL). However, due to the large variance of the selected subset's update, prior selection approaches with a limited sampling ratio cannot perform well on convergence and accuracy in heterogeneous FL. To address this problem, in

Alexandre Lopes, Roberto Souza, Helio Pedrini

RGB-D data is essential for solving many problems in computer vision. Hundreds of public RGB-D datasets containing various scenes, such as indoor, outdoor, aerial, driving, and medical, have been proposed. These datasets are useful for different applications and are fundamental for addressing classic computer vision tasks, such as monocular depth estimation.

Tianya Zhang

Traffic state data, such as speed, volume and travel time collected from ubiquitous traffic monitoring sensors require advanced network level analytics for forecasting and identifying significant traffic patterns. This paper leverages diverse traffic state datasets from the Caltrans Performance Measurement System (PeMS) hosted on the open benchmark and achie

Haonan Wang, Ziwei Wu, Jingrui He

Most fair machine learning methods either highly rely on the sensitive information of the training samples or require a large modification on the target models, which hinders their practical application. To address this issue, we propose a two-stage training algorithm named FAIRIF. It minimizes the loss over the reweighted data set (second stage) where the s

Ersin Daş, Richard M. Murray

In a complex real-time operating environment, external disturbances and uncertainties adversely affect the safety, stability, and performance of dynamical systems. This paper presents a robust stabilizing safety-critical controller synthesis framework with control Lyapunov functions (CLFs) and control barrier functions (CBFs) in the presence of disturbance.

Zhiying Wu, Jieli Liu, Jiajing Wu, Zibin Zheng

Security incidents such as scams and hacks, have become a major threat to the health of the blockchain ecosystem, causing billions of dollars in losses each year for blockchain users. To reveal the real-world entities behind the pseudonymous blockchain account and recover the stolen funds from the massive transaction data, much effort has been devoted to tra

Guanghui Lan, Yan Li, Tuo Zhao

In this paper, we present a new policy gradient (PG) methods, namely the block policy mirror descent (BPMD) method for solving a class of regularized reinforcement learning (RL) problems with (strongly)-convex regularizers. Compared to the traditional PG methods with a batch update rule, which visits and updates the policy for every state, BPMD method has ch

Ryan Sheppard, Cameron Baribeau, Tor Pedersen, Mark Boland

Machine learning has recently been applied and deployed at several light source facilities in the domain of Accelerator Physics. We introduce an approach based on machine learning to produce a fast-executing model that predicts the polarization and energy of the radiated light produced at an insertion device. This paper demonstrates how a machine learning mo

Rogerio Rossi, Kechi Hirama

Analytics corresponds to a relevant and challenging phase of Big Data. The generation of knowledge from extensive data sets (petabyte era) of varying types, occurring at a speed able to serve decision makers, is practiced using multiple areas of knowledge, such as computing, statistics, data mining, among others. In the Big Data domain, Analytics is also con

Fei Liu, Mingen Li, Jingpei Lu, Entong Su

Simulation modeling of robots, objects, and environments is the backbone for all model-based control and learning. It is leveraged broadly across dynamic programming and model-predictive control, as well as data generation for imitation, transfer, and reinforcement learning. In addition to fidelity, key features of models in these control and learning contex

Zhihe Zhao, Xian Shuai, Yang Bai, Neiwen Ling

Achieving efficient execution of machine learning models has attracted significant attention recently. To generate tensor programs efficiently, a key component of DNN compilers is the cost model that can predict the performance of each configuration on specific devices. However, due to the rapid emergence of hardware platforms, it is increasingly labor-inten

Abbas Khalili, Shahram Shahsavari, Mohammad A. Amir Khojastepour, Elza Erkip

Communication in Millimeter wave (mmWave) band relies on narrow beams due to directionality, high path loss, and shadowing. One can use beam alignment (BA) techniques to find and adjust the direction of these narrow beams. In this paper, BA at the base station (BS) is considered, where the BS sends a set of BA packets to scan different angular regions while

Kazue Matsuyama, Jeff Greensite

We explore certain properties of the Hartree-Fock approximation to the ground state of the two-dimensional Hubbard model, emphasizing the fact that in the Hartree approach there is an enormous multiplicity of self-consistent solutions which are nearly degenerate in energy, reminiscent of a spin glass, but which may differ substantially in other bulk properti

Romeo Ortega, Rafael Cisneros, Lei Wang, Arjan van der Schaft

In this note we address the problem of indirect adaptive (regulation or tracking) control of nonlinear, input affine dissipative systems. It is assumed that the supply rate, the storage and the internal dissipation functions may be expressed as nonlinearly parameterized regression equations where the mappings (depending on the unknown parameters) satisfy a m

YeongHyeon Park

Recently, deep learning-based algorithms are widely adopted due to the advantage of being able to establish anomaly detection models without or with minimal domain knowledge of the task. Instead, to train the artificial neural network more stable, it should be better to define the appropriate neural network structure or the loss function. For the training an

Marcos A. van Dam, Sylvain Oberti, Johann Kolb, Jim Lyke

Infrared tip-tilt sensors (IR TTSs) have been deployed on three different laser guide star adaptive optics (AO) systems on three different telescopes. These IR TTS benefit from the high-order loop PSF sharpening in the near infrared, hence they provide a low tip-tilt residual and a good sky coverage. Nevertheless, these IR TTS are challenging and their use i

Qiwei Yu, Yuhai Tu

Nonequilibrium reaction networks (NRNs) underlie most biological functions. Despite their diverse dynamic properties, NRNs share the signature characteristics of persistent probability fluxes and continuous energy dissipation even in the steady state. Dynamics of NRNs can be described at different coarse-grained levels. Our previous work showed that the appa

Ce Ju, Cuntai Guan

Recent progress in geometric deep learning has drawn increasing attention from the machine learning community toward domain adaptation on symmetric positive definite (SPD) manifolds, especially for neuroimaging data that often suffer from distribution shifts across sessions. These data, typically represented as covariance matrices of brain signals, inherentl

Xiuzhen Zhang, Yao Zhang, Dengji Zhao

We study a single task allocation problem where each worker connects to some other workers to form a network and the task requester only connects to some of the workers. The goal is to design an allocation mechanism such that each worker is incentivized to invite her neighbours to join the allocation, although they are competing for the task. Moreover, the p

Yichao Lu

The Science4cast 2021 competition focuses on predicting future edges in an evolving semantic network, where each vertex represents an artificial intelligence concept, and an edge between a pair of vertices denotes that the two concepts have been investigated together in a scientific paper. In this paper, we describe our solution to this competition. We prese

Yunzhi Yao, Shaohan Huang, Li Dong, Furu Wei

Recent days have witnessed a diverse set of knowledge injection models for pre-trained language models (PTMs); however, most previous studies neglect the PTMs' own ability with quantities of implicit knowledge stored in parameters. A recent study has observed knowledge neurons in the Feed Forward Network (FFN), which are responsible for expressing factual kn

Nathaniel Pritchard, Vivak Patel

In large-scale applications including medical imaging, collocation differential equation solvers, and estimation with differential privacy, the underlying linear inverse problem can be reformulated as a streaming problem. In theory, the streaming problem can be effectively solved using memory-efficient, exponentially-converging streaming solvers. In practice

S. Kravitz, H. Chen, R. Gibbons, S. J. Haselschwardt

We have built and operated a crystalline/vapor xenon TPC, with the goal of improving searches for dark matter. The motivation for this instrument is the fact that beta decays from the radon decay chain to the ground state presently limit the state-of-the-art liquid/vapor xenon experiments. In contrast, a crystalline xenon target has the potential to tag and

Tan Khang Le, Siu Cheung Hui

Food reviews and recommendations have always been important for online food service websites. However, reviewing and recommending food is not simple as it is likely to be overwhelmed by disparate contexts and meanings. In this paper, we use different deep learning approaches to address the problems of sentiment analysis, automatic review tag generation, and

Justin Sanchez, Christopher Neff, Hamed Tabkhi

Action recognition is a key algorithmic part of emerging on-the-edge smart video surveillance and security systems. Skeleton-based action recognition is an attractive approach which, instead of using RGB pixel data, relies on human pose information to classify appropriate actions. However, existing algorithms often assume ideal conditions that are not repres

Sha Li, Peichen Yang, Rongyang Li, Fadi Farha

Phobia is a widespread mental illness, and severe phobias can seriously impact patients daily lives. One-session Exposure Treatment (OST) has been used to treat phobias in the early days,but it has many disadvantages. As a new way to treat a phobia, virtual reality exposure therapy(VRET) based on serious games is introduced. There have been much researches i

Shuai Ma, Xiaoteng Ma, Li Xia

This paper studies the risk-averse mean-variance optimization in infinite-horizon discounted Markov decision processes (MDPs). The involved variance metric concerns reward variability during the whole process, and future deviations are discounted to their present values. This discounted mean-variance optimization yields a reward function dependent on a disco

S. Deser

I describe the early, from the nineteen sixties, history of attempts at quantizing General Relativity.

Oleg I. Berngardt, Oleg A. Kusonsky, Alexey I. Poddelsky, Alexey V. Oinats

In the paper a method for automatic classification of signals received by EKB and MAGW ISTP SB RAS coherent scatter radars (8-20MHz operating frequency) during 2021 is described. The method is suitable for automatic physical interpretation of the resulting classification of the experimental data in realtime. We called this algorithm Wrapped Classifier with D

LUX Collaboration, D. S. Akerib, S. Alsum, H. M. Araújo

We present the results from combining machine learning with the profile likelihood fit procedure, using data from the Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared with the previous approach, without loss of performance on real data. We establish its flexibility

Elena V. Konstantinova, Son En Gun

The Pancake graphs $P_n, n\geqslant 2$, are Cayley graphs over the symmetric group $\mathrm{Sym}_n$ generated by prefix-reversals. There are six generating sets of prefix-reversals of cardinality three which give connected Cayley graphs over the symmetric group known as cubic Pancake graphs. In this paper we study the girth of the cubic Pancake graphs. It is

Christopher Dilks

Spin asymmetries provide a wide range of insights into nucleon structure and hadronization. Recent measurements of beam spin asymmetries of $\pi^+\pi^-$ dihadrons from Semi-Inclusive Deep Inelastic Scattering (SIDIS) at CLAS12 provide the first empirical evidence of a nonzero $G_1^\perp$, the parton helicity-dependent Dihadron Fragmentation Function (DiFF) e

Y. Suzuki, W. Horiuchi, M. Kimura

Background: In neutron-rich nuclei neighboring 42Si, the quenching of the N = 28 shell gap occurs and is expected to induce the shape coexistence in their excitation spectra. Purpose: We show that different nuclear shapes coexist in N = 28 isotones 40Mg, 42Si, and 44S, and investigate observables to probe it. Method: Antisymmetrized molecular dynamics with G

Wenyan Pan, Zhili Zhou, Miaogen Ling, Xin Geng

The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural Networks (CNNs) to capture the tampering artifacts left in the images to locate the manipulated regions. However, these approaches ignore the feature correlations, i.e., feature i

Zhecan Wang, Noel Codella, Yen-Chun Chen, Luowei Zhou

Contrastive language-image pretraining (CLIP) links vision and language modalities into a unified embedding space, yielding the tremendous potential for vision-language (VL) tasks. While early concurrent works have begun to study this potential on a subset of tasks, important questions remain: 1) What is the benefit of CLIP on unstudied VL tasks? 2) Does CLI

Bing Chen, Si-Qiang Luo, Ke-Wei Wei, Xiang Liu

The dynamical similarity which exists between the $\lambda$-mode excited $bbq$ baryons ($q$ refers to the $u$, $d$, and $s$ quarks) and the $\bar{b}q$ mesons inspires us to carry out a combined study of their spectroscopy. In this work, the masses and strong decays of these low-lying $b\bar{q}$ and $bbq$ states are studied by the same theoretical methods, an

Raja Karmakar, Georges Kaddoum

IEEE 802.11ax uses the concept of primary and secondary channels, leading to the Dynamic Bandwidth Channel Access (DBCA) mechanism. By applying DBCA, a wireless station can select a wider channel bandwidth, such as 40/80/160 MHz, by applying the channel bonding feature. However, during channel bonding, inappropriate bandwidth selection can cause collisions.

Liang Kong, Hao Zheng

We continue our study of the categories of quantum liquids started in a previous work. We combine local quantum symmetries with topological skeletons into a single mathematical theory of topological nets and defect nets. In particular, we introduce the notion of a topological net, which is motivated from and generalizes that of a conformal net, and the notio

Brian Dincau, Connor Tang, Emilie Dressaire, Alban Sauret

Clogging is a common obstacle encountered during the transport of suspensions and represents a significant energy and material cost across applications, including water purification, irrigation, biopharmaceutical processing, and aquifer recharge. Pulsatile pressure-driven flows can help mitigate clogging when compared to steady flows. Here, we study experime

Mengyi Tang, Kumbit Hwang, Sung Ha Kang

We propose a new deterministic methodology to predict RNA sequence and protein folding. Is stem enough for structure prediction? The main idea is to consider all possible stem formation in the given sequence. With the stem loop energy and the strength of stem, we explore how to deterministically utilize stem information for RNA sequence and protein folding s

Kaihong Wang, Kumar Akash, Teruhisa Misu

Unpaired video-to-video translation aims to translate videos between a source and a target domain without the need of paired training data, making it more feasible for real applications. Unfortunately, the translated videos generally suffer from temporal and semantic inconsistency. To address this, many existing works adopt spatiotemporal consistency constra

Ruofei Guan, Jana Kopfová, Dmitrii Rachinskii

In recent years, classical epidemic models, which assume stationary behavior of individuals, have been extended to include an adaptive heterogeneous response of the population to the current state of the epidemic. However, it is widely accepted that human behavior can exhibit history-dependence as a consequence of learned experiences. This history-dependence

Zhengnan Xie, Alice Saebom Kwak, Enfa George, Laura W. Dozal

Space situational awareness typically makes use of physical measurements from radar, telescopes, and other assets to monitor satellites and other spacecraft for operational, navigational, and defense purposes. In this work we explore using textual input for the space situational awareness task. We construct a corpus of 48.5k news articles spanning all known

Sergio Sainz-Palacios

Shared Automated Vehicles (SAVs) Fleets companies are starting pilot projects nationwide. In 2020 in Fairfax Virginia it was announced the first Shared Autonomous Vehicle Fleet pilot project in Virginia. SAVs promise to improve quality of life. However, SAVs will also induce some negative externalities by generating excessive vehicle miles traveled (VMT), wh

Daniel Davies, Michael Dine, Benjamin V. Lehmann

Lattice gauge theory simulations are our principal probe of the masses of the light quarks. Results from such computations are the primary evidence against the $m_u=0$ solution to the strong CP problem. The large-$N$ approximation offers an independent approach to light quarks. We extend existing literature, noting that one can determine the parameters of th

Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, Luca Bertinetto

Training state-of-the-art vision models has become prohibitively expensive for researchers and practitioners. For the sake of accessibility and resource reuse, it is important to focus on adapting these models to a variety of downstream scenarios. An interesting and practical paradigm is online test-time adaptation, according to which training data is inacce

Golsa Mirbagheri

With conventional narrowband filters, the center-wavelength of the narrow transmission band undergoes large shifts as the angle of incident light changes. In this project, we designed and experimentally verified a type of hyperbolic metamaterial Bragg stack that eliminates, or vastly reduces, this angle-of-incidence dependence (i.e., dispersion) of the trans

Péter Bereczky, Xiaohong Chen, Dániel Horpácsi, Lucas Peña

Matching logic is a formalism for specifying, and reasoning about, mathematical structures, using patterns and pattern matching. Growing in popularity, it has been used to define many logical systems such as separation logic with recursive definitions and linear temporal logic. In addition, it serves as the logical foundation of the K semantic framework, whi

Volodymyr Perederiy

This paper proposes a simple technical approach for the analytical derivation of Point-in-Time PD (probability of default) forecasts, with minimal data requirements. The inputs required are the current and future Through-the-Cycle PDs of the obligors, their last known default rates, and a measurement of the systematic dependence of the obligors. Technically,

Franck Djeumou, Cyrus Neary, Eric Goubault, Sylvie Putot

Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown continuous-time dynamical systems from data. However, every forward evaluation of a NODE requires numerical integration of the neural network used to capture the system dynamics, mak

Anna C. Childs, Jason H. Steffen

We present a fragmentation module and a composition tracking code for the $n$-body code REBOUND. Our fragmentation code utilises previous semi-analytic models and follows an implementation method similar to fragmentation for the $n$-body code MERCURY. In our $n$-body simulations with fragmentation, we decrease the collision and planet formation timescales by

Satoshi Kamo, Yiqiang Sheng

Network intelligence is a discipline that builds on the capabilities of network systems to act intelligently by the usage of network resources for delivering high-quality services in a changing environment. Wide area network intelligence is a class of network intelligence in wide area network which covers the core and the edge of Internet. In this paper, we

Satoshi Kamo, Yiqiang Sheng

In this paper, we propose a novel architecture for a deep learning system, named k-degree layer-wise network, to realize efficient geo-distributed computing between Cloud and Internet of Things (IoT). The geo-distributed computing extends Cloud to the geographical verge of the network in the neighbor of IoT. The basic ideas of the proposal include a k-degree

Payman Eskandari, V. Kumar Murty

The family of all mixed Hodge structures on a given rational vector space $M_\mathbb{Q}$ with a fixed weight filtration $W_\cdot$ and a fixed associated graded Hodge structure $Gr^WM$ is naturally in a one to one correspondence with a complex affine space. We study the unipotent radical of the very general Mumford-Tate group of the family. We do this by usin

Hristos Tyralis, Georgia Papacharalampous, Sina Khatami

Predictions of hydrological models should be probabilistic in nature. Our aim is to introduce a method that estimates directly the uncertainty of hydrological simulations using expectiles, thus complementing previous quantile-based direct approaches as well as generalizing mean-based approaches. Expectiles are new risk measures in hydrology. Compared to quan

Jack R. Dale, James D. Sartor, R. Cameron Dennis, Eric I. Corwin

A spatial distribution is hyperuniform if it has local density fluctuations that vanish in the limit of long length scales. Hyperuniformity is a well known property of both crystals and quasicrystals. Of recent interest, however, is disordered hyperuniformity: the presence of hyperuniform scaling without long-range configurational order. Jammed granular pack

Thanh Hai Nguyen, Matthew Bundas, Tran Cao Son, Marcello Balduccini

This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition, various problems related to concerns in a CPS can be precisely formalized and implemented using Answer Set Programming (ASP). These include pr

David Ardia, Keven Bluteau, Thien Duy Tran

We explore the realized alpha-performance heterogeneity in green and brown stocks' universes using the peer performance ratios of Ardia and Boudt (2018). Focusing on S&P 500 index firms over 2014-2020 and defining peer groups in terms of firms' greenhouse gas emission levels, we find that, on average, about 20% of the stocks differentiate themselves from the

Payman Eskandari, V. Kumar Murty

Let $\mathbf{T}$ be a neutral Tannakian category over a field of characteristic zero with unit object $\mathbf{1}$, and equipped with a filtration $W_\cdot$ similar to the weight filtration on mixed motives. Let $M$ be an object of $\mathbf{T}$, and $\underline{\mathfrak{u}}(M)\subset W_{-1}\underline{Hom}(M,M)$ the Lie algebra of the kernel of the natural s

Zhen Chao, Sheng Gui, Benzhuo Lu, Dexuan Xie

A finite element solution of an ion channel dielectric continuum model such as Poisson-Boltzmann equation (PBE) and a system of Poisson-Nernst-Planck equations (PNP) requires tetrahedral meshes for an ion channel protein region, a membrane region, and an ionic solvent region as well as an interface fitted irregular tetrahedral mesh of a simulation box domain

Nishan Khatri, Agnibh Dasgupta, Yucong Shen, Xin Zhong

Incorporating geometric transformations that reflect the relative position changes between an observer and an object into computer vision and deep learning models has attracted much attention in recent years. However, the existing proposals mainly focus on the affine transformation that is insufficient to reflect such geometric position changes. Furthermore,

Mateus F. B. Granha, André L. M. Vilela, Chao Wang, Kenric P. Nelson

We investigate the financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize the individuals in a financial market by their trading strategy, namely noise traders and fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavi

MicroBooNE collaboration, P. Abratenko, R. An, J. Anthony

In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is

Ethan Patrick White

We obtain a substantially improved lower bound for the minimum overlap problem asked by Erd\H{o}s. Our approach uses elementary Fourier analysis to translate the problem to a convex optimization program.

Frederic Mentink-Vigier, Vinayak Rane, Thierry Dubroca, Krishnendu Kundu

We propose a new, more efficient, and potentially cost effective, solid-state nuclear spin hyperpolarization method combining the Cross Effect mechanism and electron spin optical hyperpolarization in rotating solids. We first demonstrate optical hyperpolarization in the solid state at low temperature and low field, and then investigate its field dependence t

Zhi Chai, Kai-Kit Wong, Kin-Fai Tong, Yu Chen

Fluid antenna system promises to obtain enormous diversity in the small space of a mobile device by switching the position of the radiating element to the most desirable position from a large number of prescribed locations of the given space. Previous researches have revealed the promising performance of fluid antenna systems if the position with the maximum

Davood Karimi, Ali Gholipour

Diffusion tensor imaging (DTI) is a widely used method for studying brain white matter development and degeneration. However, standard DTI estimation methods depend on a large number of high-quality measurements. This would require long scan times and can be particularly difficult to achieve with certain patient populations such as neonates. Here, we propose

Sai Koneru, Danni Liu, Jan Niehues

While Active Learning (AL) techniques are explored in Neural Machine Translation (NMT), only a few works focus on tackling low annotation budgets where a limited number of sentences can get translated. Such situations are especially challenging and can occur for endangered languages with few human annotators or having cost constraints to label large amounts

Juyeon Won, Soyeun Kim, Martin Gutierrez-Amigo, Simon Bettler

Previous band structure calculations predicted Ag3AuSe2 to be a semiconductor with a band gap of approximately 1 eV. Here, we report single crystal growth of Ag3AuSe2 and its transport and optical properties. Single crystals of Ag3AuSe2 were synthesized by slow-cooling from the melt, and grain sizes were confirmed to be greater than 2 mm using electron backs

Aditya M. Medhi, Abhishek D. Patange, Sujit S. Pardeshi, R. Jegadeeshwaran

The movement for open-design focuses on the creation of machines, physical systems, and products using design information shared publicly. It consists of the development of systems incorporating open-source hardware and software which can be easily/freely customized and implemented. Generally, this movement is adopted through the Internet and usually execute

Xinye Chen, Stefan Güttel

Symbolic representations are a useful tool for the dimension reduction of temporal data, allowing for the efficient storage of and information retrieval from time series. They can also enhance the training of machine learning algorithms on time series data through noise reduction and reduced sensitivity to hyperparameters. The adaptive Brownian bridge-based

J. J. Cobos-Martínez, G. N. Zeminiani, K. Tsushima

$\Upsilon$ and $\eta_b$ nuclear bound state energies are calculated for various nuclei neglecting any possible effects of the widths. Essential input for the calculations, namely the medium-modified $B$ and $B^{*}$ meson masses, as well as the density distributions in nuclei, are calculated within the quark-meson coupling (QMC) model. The attractive potentia

Alexander Grigor'yan, Philipp Sürig

We investigate heat kernel estimates of the form $p_{t}(x, x)\geq c_{x}t^{-\alpha},$ for large enough $t,$ where $\alpha$ and $c_{x}$ are positive reals and $c_{x}$ may depend on $x,$ on manifolds having at least one end.

Anna Kamińska, Mariusz Żyluk

We provide necessary and sufficient conditions for the space of smooth functions with compact supports $C^\infty_C(\Omega)$ to be dense in Musielak-Orlicz spaces $L^\Phi(\Omega)$ where $\Omega$ is an open subset of $\mathbb{R}^d$. In particular we prove that if $\Phi$ satisfies condition $\Delta_2$, the closure of $C^\infty_C(\Omega)\cap L^\Phi(\Omega)$ is e

Gamal Mograby, Radhakrishnan Balu, Kasso A. Okoudjou, Alexander Teplyaev

We study the spectral theory of a class of piecewise centrosymmetric Jacobi operators defined on an associated family of substitution graphs. Given a finite centrosymmetric matrix viewed as a weight matrix on a finite directed path graph and a probabilistic Laplacian viewed as a weight matrix on a locally finite strongly connected graph, we construct a new g

Huiting Liu, Avinesh P. V. S., Siddharth Patwardhan, Peter Grasch

In this paper, we study the "stability" of machine learning (ML) models within the context of larger, complex NLP systems with continuous training data updates. For this study, we propose a methodology for the assessment of model stability (which we refer to as jitter under various experimental conditions. We find that model design choices, including network

Florent Benaych-Georges

This is a short proof of Ledoit-P\'ech\'e's RIE formula for covariance matrices. The proof is based on the Stein formula, which gives a very simple way to derive the result. One of the advantages of this approach is that it shows that the only really needed hypothesis, for the machinery to work, is that the mean of the eigenvalues of the true covariance matr

Ahmad Darwiche

Let $\{\xi(k), k \in \mathbb{Z} \}$ be a stationary sequence of random variables and let $\{S_n, n \in \mathbb{N}_+ \}$ be a transient random walk in the domain of attraction of a stable law. In the previous work \cite{Nicolas_Ahmad}, under conditions of type $D(u_n)$ and $D'(u_n)$ we provided a limit theorem for the maximum of the first $n$ terms of the seq

Çağın Ararat, Barış Bilir, Elisa Mastrogiacomo

Convexity and quasiconvexity are two properties that capture the concept of diversification for risk measures. Between the two, there is natural quasiconvexity, an old but not so well-known property weaker than convexity but stronger than quasiconvexity. A detailed discussion on natural quasiconvexity is still missing and this paper aims to fill this gap in

Jayakrishnan M. P. Nair, Debsuvra Mukhopadhyay, Girish S. Agarwal

We characterize some of the distinctive hallmarks of magnon-magnon interaction mediated by the intracavity field of a microwave cavity, along with their testable ramifications. In general, we foreground two widely dissimilar parameter domains that bring forth the contrasting possibilities of level splitting and level crossing. The former is observed in the r

F. L. Carneiro, S. C. Ulhoa, J. W. Maluf, J. F. da Rocha-Neto

In this article, we consider the Alcubierre spacetime, such a spacetime describes a ``bubble'' that propagates with arbitrary global velocity. This setting allows movement at a speed greater than that of light. There are some known problems with this metric, e.g., the source's negative energy and the violation of the source's energy conservation when the bub

Ziwei Zhu, Changxi Zheng

Semi-analytical methods, such as rigorous coupled wave analysis, have been pivotal for numerical analysis of photonic structures. In comparison to other methods, they offer much faster computation, especially for structures with constant cross-sectional shapes (such as metasurface units). However, when the cross-sectional shape varies even mildly (such as a

F. A. Gómez Albarracín, H. D. Rosales

Recently, there has been an increased interest in the application of machine learning (ML) techniques to a variety of problems in condensed matter physics. In this regard, of particular significance is the characterization of simple and complex phases of matter. Here, we use a ML approach to construct the full phase diagram of a well known spin model combini

Morteza A. Sharif, M. Shahnazary, M. Pourezzat

We develop a model for simulating the heat transfer phenomena within a biological soft material using the Ikeda chaotic map. Our approach is implemented by sampling the optical intensity via the Ikeda map to investigate the influence on the heat distribution over the tissue. Our method has many potential advantages including the possibility of investigating

Dimitrios C. Gklezakos, Rajesh P. N. Rao

We introduce Active Predictive Coding Networks (APCNs), a new class of neural networks that solve a major problem posed by Hinton and others in the fields of artificial intelligence and brain modeling: how can neural networks learn intrinsic reference frames for objects and parse visual scenes into part-whole hierarchies by dynamically allocating nodes in a

László Zsidó

G. K. Pedersen and M. Takesaki have proved in 1973 that if $\varphi$ is a faithful, semi-finite, normal weight on a von Neumann algebra $M\;\!$, and $\psi$ is a $\sigma^{\varphi}$-invariant, semi-finite, normal weight on $M\;\!$, equal to $\varphi$ on the positive part of a weak${}^*$-dense $\sigma^{\varphi}$-invariant $*$-subalgebra of $\mathfrak{M}_{\varph

A. Koulas-Simos, J. Buchgeister, M. Drechsler, T. Zhang

Metallic nanocavity lasers provide important technological advancement towards even smaller integrable light sources. They give access to widely unexplored lasing physics in which the distinction between different operational regimes, like those of thermal or a coherent light emission, becomes increasingly challenging upon approaching a device with a near-pe

Bruno Premoselli, Jérôme Vétois

On a smooth, closed Riemannian manifold $\left(M,g\right)$ of dimension $n\ge3$, we consider the stationary Schr\"odinger equation $\Delta_gu+h_0u=\left|u\right|^{2^*-2}u$, where $\Delta_g:=-\text{div}_g\nabla$, $h_0\in C^1\left(M\right)$ and $2^* :=\frac{2n}{n-2}$. We prove that, up to perturbations of the potential function $h_0$ in $C^1\left(M\right)$, th

Christopher Chamberland, Earl T. Campbell

Lattice surgery is a measurement-based technique for performing fault-tolerant quantum computation in two dimensions. When using the surface code, the most general lattice surgery operations require lattice irregularities called twist defects. However, implementing twist-based lattice surgery may require additional resources, such as extra device connectivit

Alexander Spiegelman, Neil Giridharan, Alberto Sonnino, Lefteris Kokoris-Kogias

We present Bullshark, the first directed acyclic graph (DAG) based asynchronous Byzantine Atomic Broadcast protocol that is optimized for the common synchronous case. Like previous DAG-based BFT protocols, Bullshark requires no extra communication to achieve consensus on top of building the DAG. That is, parties can totally order the vertices of the DAG by i

Jorge Ortega, Omar Santos, Liliam Rodríguez, Sabine Mondié

The solution to the infinite horizon optimal control problem for linear distributed time-delay systems is presented. The proposal is based on the use of the Cauchy solution for distributed time-delay systems. In contrast with previous results (for punctual time delay systems), the form of the functional and its properties are formally justified. An important

John Ridley, Huseyin Coskun, David Joseph Tan, Nassir Navab

The video action segmentation task is regularly explored under weaker forms of supervision, such as transcript supervision, where a list of actions is easier to obtain than dense frame-wise labels. In this formulation, the task presents various challenges for sequence modeling approaches due to the emphasis on action transition points, long sequence lengths,

Simon Apers, Yuval Efron, Paweł Gawrychowski, Troy Lee

We study the complexity of determining the edge connectivity of a simple graph with cut queries. We show that (i) there is a bounded-error randomized algorithm that computes edge connectivity with $O(n)$ cut queries, and (ii) there is a bounded-error quantum algorithm that computes edge connectivity with $\~O(\sqrt{n})$ cut queries. We prove these results us

Moran Barenboim, Vadim Indelman

Reasoning about uncertainty is vital in many real-life autonomous systems. However, current state-of-the-art planning algorithms cannot either reason about uncertainty explicitly, or do so with a high computational burden. Here, we focus on making informed decisions efficiently, using reward functions that explicitly deal with uncertainty. We formulate an ap

Paul Goldsmith-Pinkham, Karen Jiang, Zirui Song, Jacob Wallace

We propose a method for reporting how program evaluations reduce gaps between groups, such as the gender or Black-white gap. We first show that the reduction in disparities between groups can be written as the difference in conditional average treatment effects (CATE) for each group. Then, using a Kitagawa-Oaxaca-Blinder-style decomposition, we highlight how

Mathieu Baudet, Alberto Sonnino, Mahimna Kelkar, George Danezis

We introduce Zef, the first Byzantine-Fault Tolerant (BFT) protocol to support payments in anonymous digital coins at arbitrary scale. Zef follows the communication and security model of FastPay: both protocols are asynchronous, low-latency, linearly-scalable, and powered by partially-trusted sharded authorities. Zef further introduces opaque coins represent

T. Adkins, A. A. Schekochihin, P. G. Ivanov, C. M. Roach

Electromagnetic (EM) instabilities and turbulence driven by the electron-temperature gradient are considered in a local slab model of a tokamak-like plasma. The model describes perturbations at scales both larger and smaller than the flux-freezing scale $d_e$, and so captures both electrostatic and EM regimes of turbulence. The well-known electrostatic insta

Nan Miles Xi, Dalong Patrick Huang

The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial in drug safety assessment. In this study, we discuss machine learning approaches in the prediction of drug-induced TdP risks using preclinical data. Specifically, the random forest model was trained on the dataset generated by the rabbit ventricular wedge assay. The model prediction pe