April 2023 arXiv papers — page 38

Ying-Ming Xie, Étienne Lantagne-Hurtubise, Andrea F. Young, Stevan Nadj-Perge

Recent experiments on Bernal bilayer graphene (BLG) deposited on monolayer WSe$_2$ revealed robust, ultra-clean superconductivity coexisting with sizable induced spin-orbit coupling. Here we propose BLG/WSe$_2$ as a platform to engineer gate-defined planar topological Josephson junctions, where the normal and superconducting regions descend from a common mat

Lernik Asserian, Susan E. Luczak, I. G. Rosen

The existence and consistency of a maximum likelihood estimator for the joint probability distribution of random parameters in discrete-time abstract parabolic systems are established by taking a nonparametric approach in the context of a mixed effects statistical model using a Prohorov metric framework on a set of feasible measures. A theoretical convergenc

Lernik Asserian, Suzan E. Luczak, I. G. Rosen

We consider a Prohorov metric-based nonparametric approach to estimating the probability distribution of a random parameter vector in discrete-time abstract parabolic systems. We establish the existence and consistency of a least squares estimator. We develop a finite-dimensional approximation and convergence theory, and obtain numerical results by applying

Xuexue Li, Wenhui Diao, Yongqiang Mao, Peng Gao

Occlusion between objects is one of the overlooked challenges for object detection in UAV images. Due to the variable altitude and angle of UAVs, occlusion in UAV images happens more frequently than that in natural scenes. Compared to occlusion in natural scene images, occlusion in UAV images happens with feature confusion problem and local aggregation chara

Zhengyi Zhou

We use spinal open books to construct contact manifolds with infinitely many different Weinstein fillings in any odd dimension $> 1$, which were previously unknown for dimensions equal to $4n+1$. The argument does not involve understanding factorizations in the symplectic mapping class group.

Zhaonan Wang, Yingpu Deng

The properties of continued fractions whose partial quotients belong to a quadratic number field K are distinct from those of classical continued fractions. Unlike classical continued fractions, it is currently impossible to identify elements with periodic continued fraction expansions, akin to Lagrange's theorem. In this paper, we fix a real quadratic field

Abel C. H. Chen

This study mainly modifies the butterfly key expansion (BKE) mechanism and applies it to the healthcare system. The system mainly includes a Root Certificate Authority (RCA), an Enrollment Certificate Authority (ECA), a Pseudonym Certificate Authority (PCA), a Registration Authority (RA), and End Entities (EEs)(i.e. user devices). Certificates can be issued

Jingyao Wang, Yuxuan Yang, Wenwen Qiang, Changwen Zheng

Meta-learning, also known as ``learning to learn'', enables models to acquire great generalization abilities by learning from various tasks. Recent advancements have made these models applicable across various fields without data constraints, offering new opportunities for general artificial intelligence. However, applying these models can be challenging due

Changhao Zhai, Hui Fu, Jiachen Si, Zhenghua Huang

The origination and generation mechanisms of small magnetic flux ropes (SFRs), which are important structures in solar wind, are not clearly known. In present study, 1993 SFRs immersed in coronal holes, active regions, and quiet Sun solar wind are analyzed and compared. We find that the properties of SFRs immersed in three types of solar wind are signicantly

Shengzeng Huo, Anqing Duan, Lijun Han, Luyin Hu

Classical policy search algorithms for robotics typically require performing extensive explorations, which are time-consuming and expensive to implement with real physical platforms. To facilitate the efficient learning of robot manipulation skills, in this work, we propose a new approach comprised of three modules: (1) learning of general prior knowledge wi

Yutaro Chiyo, Masaaki Mizukami

This paper deals with the fully parabolic chemotaxis-convection model with sensitivity functions for tumor angiogenesis, \begin{align*} \begin{cases} u_t=\Delta u-\nabla \cdot (u\chi_1(v)\nabla v) +\nabla \cdot (u\chi_2(w)\nabla w), &x \in \Omega,\ t>0, \\[1.05mm] v_t=\Delta v+\nabla \cdot (v\xi(w)\nabla w)+\alpha u-\beta v, &x \in \Omega,\ t>0, \\[1.05mm] w

Junding Chen, Xinan Zhou

We study three-point correlation functions of scalar operators in conformal field theories with boundaries and interfaces. We focus on two cases where there are one bulk and two boundary operators (B$\partial\partial$), or two bulk and one boundary operators (BB$\partial$). We perform a detailed analysis of the conformal blocks in different OPE channels. In

Franco Flandoli, Dejun Luo

We introduce a stochastic version of Proudman-Taylor model, a 2D-3C fluid approximation of the 3D Navier-Stokes equations, with the small-scale turbulence modeled by a transport-stretching noise. For this model we may rigorously take a scaling limit leading to a deterministic model with additional viscosity on large scales. In certain choice of noises withou

Eleanor A. Dauenhauer, Paul J. Dauenhauer

The game of Gyan Chauper or snakes and ladders exists in many forms throughout history as a board game of varying size, structure, and game elements of snakes and ladders associated with various vices and virtues inscribed within the board. Here, three boards were analyzed via Monte Carlo simulation, including the 1998 Milton Bradley version, the 72-square V

Alexander O. Sushkov

The dark matter puzzle is one of the most important open problems in modern physics. The ultra-light axion is a well-motivated dark matter candidate, conceived to resolve the strong-CP problem of quantum chromodynamics. Numerous precision experiments are searching for the three non-gravitational interactions of axion-like dark matter. Some of the searches ar

Dongmei Wu, Yuying Guan, Xin Xia, Changqing Du

Maintaining both path-tracking accuracy and yaw stability of distributed drive electric vehicles (DDEVs) under various driving conditions presents a significant challenge in the field of vehicle control. To address this limitation, a coordinated control strategy that integrates adaptive model predictive control (AMPC) path-tracking control and direct yaw mom

Fnu Devvrit, Aaron Krim-Yee, Nithish Kumar, Gary MacGillivray

This paper initiates the study of fractional eternal domination in graphs, a natural relaxation of the well-studied eternal domination problem. We study the connections to flows and linear programming in order to obtain results on the complexity of determining the fractional eternal domination number of a graph $G$, which we denote $\gamma_{\,\textit{f}}^{\i

Jun Wu, Xuesong Ye, Chengjie Mou, Weinan Dai

Monitoring the health status of patients in the Intensive Care Unit (ICU) is a critical aspect of providing superior care and treatment. The availability of large-scale electronic health records (EHR) provides machine learning models with an abundance of clinical text and vital sign data, enabling them to make highly accurate predictions. Despite the emergen

Craig Reynolds

Camouflage in nature seems to arise from competition between predator and prey. To survive, predators must find prey, and prey must avoid being found. This work simulates an abstract model of that adversarial relationship. It looks at crypsis through evolving prey camouflage patterns (as color textures) in competition with evolving predator vision. During th

Zhongci Hang, Xiang Li, Dunyan Yan

In this paper, we will study $n$-dimensional Hardy operator and its dual in mixed radial-angular spaces on Heisenberg groups and obtain their sharp bounds by using the rotation method. Furthermore, the sharp bounds of $n$-dimensional weighted Hardy operator and weighted Ces\`{a}ro operator are also obtained.

Fei Huang, Pei Ke, Minlie Huang

Non-AutoRegressive (NAR) text generation models have drawn much attention because of their significantly faster decoding speed and good generation quality in machine translation. However, in a wider range of text generation tasks, existing NAR models lack proper pre-training, making them still far behind the pre-trained autoregressive models. In this paper,

Khoi Minh Nguyen-Duy, Quang Pham, Binh T. Nguyen

Orthogonal parameterization is a compelling solution to the vanishing gradient problem (VGP) in recurrent neural networks (RNNs). With orthogonal parameters and non-saturated activation functions, gradients in such models are constrained to unit norms. On the other hand, although the traditional vanilla RNNs are seen to have higher memory capacity, they suff

Yihan Zhang, Wenhao Jiang, Feng Zheng, Chiu C. Tan

Decentralized minimax optimization has been actively studied in the past few years due to its application in a wide range of machine learning models. However, the current theoretical understanding of its convergence rate is far from satisfactory since existing works only focus on the nonconvex-strongly-concave problem. This motivates us to study decentralize

Xiaobin Li, Kai Wu, Xiaoyu Zhang, Handing Wang

The core challenge of high-dimensional and expensive black-box optimization (BBO) is how to obtain better performance faster with little function evaluation cost. The essence of the problem is how to design an efficient optimization strategy tailored to the target task. This paper designs a powerful optimization framework to automatically learn the optimizat

Jong Heum Jung, Yong-il Shin

We numerically investigate the dynamics of vortex generation in a two-dimensional, twocomponent Bose-Einstein condensate subjected to an oscillatory magnetic obstacle. The obstacle creates both repulsive and attractive Gaussian potentials for the two symmetric spin-$\uparrow$ and $\downarrow$ components, respectively. We demonstrate that, as the oscillating

Maoshui Lv, Baolin Tan, Ruisheng Zheng, Zhao Wu

Preflare activities contain critical information about the pre-cursors and causes of solar eruptions. Here we investigate the characteristics and origin of a group of broadband pulsations (BBPs) in the decimetric-metric wavelengths, taking place during the preflare stage of the M7.1 flare dated on 2011 September 24. The event was recorded by multiple solar i

Bärbel S. Koribalski, Angie Veronica, Klaus Dolag, Thomas H. Reiprich

We present the serendipitous discovery of (1) a large double radio relic associated with the galaxy cluster PSZ2 G277.93+12.34 and (2) a new odd radio circle, ORC J1027-4422, both found in the same deep MeerKAT 1.3 GHz wide-band radio continuum image. The angular separation of the two arc-shaped cluster relics is ~16 arcmin or ~2.6 Mpc for a cluster redshift

Anchen Sun, Kaiqi Yang

Rip currents pose a significant danger to those who visit beaches, as they can swiftly pull swimmers away from shore. Detecting these currents currently relies on costly equipment and is challenging to implement on a larger scale. The advent of unmanned aerial vehicles (UAVs) and camera technology, however, has made monitoring near-shore regions more accessi

Byoung-moo Ann, Gary A. Steele

It is known that the electromagnetic vacuum is responsible for the Lamb shift, which is a crucial phenomenon in quantum electrodynamics (QED). In circuit QED, the readout or bus resonators that are dispersively coupled can result in a significant Lamb shift of the qubit. However, previous approaches or proposals for controlling the Lamb shift in circuit QED

Hsin-Po Wang, Vlad-Florin Drăgoi

We gather existing methods that are used to compare and rank the BECs synthesized by a polar code constructor, compare them, and propose new methods that compare synthetic BECs faster.

Yusuke Tsunoda, Noritaka Shimizu, Takaharu Otsuka

Neutron-rich Nd and Sm isotopes are known to exhibit shape phase transition as a function of neutron number. Among them, $^{150}$Nd and $^{150}$Sm are important not only because they are transitional nuclei, but also the parent and daughter nuclei of double-beta decay. We performed large-scale shell-model calculations of even-even Nd and Sm isotopes includin

Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen

Drawing on ergodic theory, we introduce a novel training method for machine learning based forecasting methods for chaotic dynamical systems. The training enforces dynamical invariants--such as the Lyapunov exponent spectrum and fractal dimension--in the systems of interest, enabling longer and more stable forecasts when operating with limited data. The tech

Karthick Prasad Gunasekaran, Nikita Jaiman

Occlusions of objects is one of the indispensable problems in Computer vision. While Convolutional Neural Net-works (CNNs) provide various state of the art approaches for regular image classification, they however, prove to be not as effective for the classification of images with partial occlusions. Partial occlusion is scenario where an object is occluded

Yequan Chen, Yingmei Zhu, Renju Lin, Wei Niu

The topological Hall effect (THE) is critical to the exploration of the spin chirality generated by the real-space Berry curvature, which has attracted worldwide attention for its prospective applications in spintronic devices. However, the prominent THE remains elusive at room temperature, which severely restricts the practical integration of chiral spin te

Samir Mondal, K. C. Sivakumar

A real square matrix $A$ of order $n \times n~ (n \geq 3)$ is called an $F_0$-matrix, if it is a $Z$-matrix (off-diagonal entries nonpositive), all of whose principal submatrices of orders at most $n-2$ are $M$-matrices while there is at least one principal submatrix of order $n-1$, which is an $N_0$-matrix. An $M$-matrix is a $Z$-matrix with the property th

Heng Jin, Bing Huang

Despite the intensive interest in copper- and nickel-based superconductivity in infinite-layer structures, the physical properties of many other infinite-layer transition-metal oxides remain largely unknown. Here we unveil, by the first-principles calculations, the electronic correlation-driven quantum phase transitions in infinite-layer SrMnO2, where spin a

Mattias T. Johnsson, Daniel Burgarth

The quantum harmonic oscillator is one of the most fundamental objects in physics. We consider the case where it is extended to an arbitrary number modes and includes all possible terms that are bilinear in the annihilation and creation operators, and assume we also have an arbitrary time-dependent drive term that is linear in those operators. Such a Hamilto

Jared Marx-Kuo, Érico Melo Silva

We introduce an Allen-Cahn type functional, $\text{BE}_{\epsilon}$, that defines an energy on separating hypersurfaces, $Y$, of closed Riemannian Manifolds. We establish $\Gamma$-convergence of $\text{BE}_{\epsilon}$ to the area functional, and compute first and second variations of this functional under hypersurface pertrubations. We then compute an explici

Izat B. Baybusinov, Enrico Maria Fenoaltea, Yi-Cheng Zhang

There are two main categories of networks that are investigated in the complexity physics community: monopartite and bipartite networks. In this letter, we report a general finding between these two classes. If a random bipartite network is projected into a monopartite network, under quite general conditions, we obtain a non-random monopartite network with s

Jian Wu, Curtis Struck, Bruce G. Elmegreen, Elena D'Onghia

Previous models have shown that stochastic scattering of stars in a two-dimensional galaxy disc can generate a time-independent surface density distribution that is an exponential divided by radius when a constant inward scattering bias is present. Here we show, using a Markov chain model, that similar profiles result from an outward scattering bias, althoug

Ravi Mistry, Aleksandr Pinzul

We revisit the definition of the temperature of a causal diamond for the case of a free massless scalar field. The stress is given to the intrinsic, direction-dependent character of this definition. Some important limits are also discussed.

Alexis Nikolakopoulos, Alessandro Lovato, Noemi Rocco

Microscopic calculations of neutrino-nucleus scattering cross sections are critical for the success of the neutrino-oscillation program. In addition to retaining nuclear correlations in the initial and final state of the reaction, they are based on consistent nuclear interactions and transition current operators, thereby enabling robust uncertainty quantific

Erik Brynjolfsson, Danielle Li, Lindsey Raymond

We study the staggered introduction of a generative AI-based conversational assistant using data from 5,172 customer support agents. Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15\% on average, with substantial heterogeneity across workers. Less experienced and lower-skilled workers improve both the spee

Xiaoling Chen, Cory Miller, Mithun Goutham, Prasad Dev Hanumalagutti

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a

Phillip Scott, Jaxon Taylor, Larry Beatty, Jim Edlin

We report the discovery of a pair of exoplanets co-orbiting the red dwarf star GJ3470. The larger planet, GJ3470-d, was observed in a 14.9617-days orbit and the smaller planet, GJ3470-e, in a 14.9467-days orbit. GJ3470-d is sub-Jupiter size with a 1.4% depth and a duration of 3 hours, 4 minutes. The smaller planet, GJ3470-e, currently leads the larger planet

Lin Yan, Xin Liang, Hanqi Guo, Bei Wang

Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do not explicitly preserve the topological features in data. When performing post hoc analysis with decompressed data using topological methods, preserving topology in the compression

Antal Jevicki, Xianlong Liu, Junjie Zheng

We discuss the large $N$ expansion in backgrounds of extended states with focus on implementation of Goldstone symmetries and the construction of the associated Hilbert space. The formulation is given in the general framework of collective field theory. Case of translational symmetry is described first, as the basic example. The large $N$ thermofield represe

Jinming Zhao, Yuka Ko, Kosuke Doi, Ryo Fukuda

It remains a question that how simultaneous interpretation (SI) data affects simultaneous machine translation (SiMT). Research has been limited due to the lack of a large-scale training corpus. In this work, we aim to fill in the gap by introducing NAIST-SIC-Aligned, which is an automatically-aligned parallel English-Japanese SI dataset. Starting with a non-

M. Koussour, Avik De

In the past few years, $f(Q)$ theories have drawn a lot of research attention in replacing Einstein's theory of gravity successfully. The current study examines the novel cosmological possibilities emerging from two specific classes of $f(Q)$ models using the parametrization form of the equation of state (EoS) parameter as $\omega \left( z\right) =-\frac{1}{

Vinicius Trentin, Chenxu Ma, Jorge Villagra, Zaid Al-Ars

Motion prediction is a key factor towards the full deployment of autonomous vehicles. It is fundamental in order to assure safety while navigating through highly interactive complex scenarios. In this work, the framework IAMP (Interaction- Aware Motion Prediction), producing multi-modal probabilistic outputs from the integration of a Dynamic Bayesian Network

Ghina Al-Atat, Andrea Fresa, Adarsh Prasad Behera, Vishnu Narayanan Moothedath

Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL) inference at the edge of the network. Toward this end, there is a significant research effort in developing tinyML models - Deep Learning (DL) models with reduced computation and memory storage requirements

Nermin Samet, Oriane Siméoni, Gilles Puy, Georgy Ponimatkin

We propose SeedAL, a method to seed active learning for efficient annotation of 3D point clouds for semantic segmentation. Active Learning (AL) iteratively selects relevant data fractions to annotate within a given budget, but requires a first fraction of the dataset (a 'seed') to be already annotated to estimate the benefit of annotating other data fraction

Matteo Barbone, Mustafa Gündoğan, Dhiren M. Kara, Benjamin Pingault

We propose an academic publishing system where research papers are stored in a network of data centres owned by university libraries and research institutions, and are interfaced with the academic community through a website. In our system, the editor is replaced by an initial adjusted community-wide evaluation, the standard peer-review is accompanied by a p

Andrew B. Kahng, Ravi Varadarajan, Zhiang Wang

In a typical RTL to GDSII flow, floorplanning or macro placement is a critical step in achieving decent quality of results (QoR). Moreover, in today's physical synthesis flows (e.g., Synopsys Fusion Compiler or Cadence Genus iSpatial), a floorplan .def with macro and IO pin placements is typically needed as an input to the front-end physical synthesis. Recen

Yasuhiro Utsumi, Dimitry Golubev, Ferdinand Peper

We analyze the thermodynamic cost of a logically reversible Brownian Turing machine operating in the first-passage time protocol based on the stochastic thermodynamics of resetting. In this framework, the thermodynamic cost of computation is the reset entropy production, which is interpreted as the information reduction by a resetter external to the computer

Hiba F. Fayoumi, Hee Sik Kim

Right feeble groups are defined as groupoids $(X,*)$ such that (i) $x, y\in X$ implies the existence of $a, b \in X$ such that $a*x = y$ and $b*y = x$. Furthermore, (ii) if $x, y, z \in X$ then there is an element $w\in X$ such that $x*(y*z) = w*z$. These groupoids have a "remnant" group structure, which includes many other groupoids. In this paper, we inves

Oleg I. Berngardt

The paper discusses the use of the Absolute activation function in classification neural networks. An examples are shown of using this activation function in simple and more complex problems. Using as a baseline LeNet-5 network for solving the MNIST problem, the efficiency of Absolute activation function is shown in comparison with the use of Tanh, ReLU and

Scott Brown, Mohammad Khajenejad, Sze Zheng Yong, Sonia MartInez

In this paper, we consider the computation of controlled invariant sets (CIS) of discrete-time nonlinear control affine systems. We propose an iterative refinement procedure based on polytopic inclusion functions, which is able to approximate the maximal controlled invariant set to within a guaranteed precision. In particular, this procedure allows us to gua

Andrea D'Amico, Giacomo Borraccini, Vittorio Curri

The continuously increasing IP data traffic demand, with geometrical growth rate exceeding 26%, requires a large transmission capacity increment from the fiber optical infrastructure. As the deploy of new fiber cables requires extensive investments, the development of multi-band amplifiers and transceivers, already available as prototypes, is progressively c

Nirabhra Mandal, Mohammad Khajenejad, Sonia Martinez

In this paper, we study the control properties of a new class of stochastic ensemble systems that consists of families of random variables. These random variables provide an increasingly good approximation of an unknown discrete, linear-time invariant (DLTI) system, and can be obtained by a standard, data-driven procedure. Our first result relates the reacha

Antonio Castellanos, Galit B. Yom-Tov, Yair Goldberg

In the quest to improve services, companies offer customers the opportunity to interact with agents through contact centers, where the communication is mainly text-based. This has become one of the favorite channels of communication with companies in recent years. However, contact centers face operational challenges, since the measurement of common proxies f

Shahabedin Sagheb, Soham Gandhi, Dylan P. Losey

We often assume that robots which collaborate with humans should behave in ways that are transparent (e.g., legible, explainable). These transparent robots intentionally choose actions that convey their internal state to nearby humans: for instance, a transparent robot might exaggerate its trajectory to indicate its goal. But while transparent behavior seems

Debasis Ganguly, Emine Yilmaz

Due to the massive size of test collections, a standard practice in IR evaluation is to construct a 'pool' of candidate relevant documents comprised of the top-k documents retrieved by a wide range of different retrieval systems - a process called depth-k pooling. A standard practice is to set the depth (k) to a constant value for each query constituting the

Berthy T. Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang

Priors are essential for reconstructing images from noisy and/or incomplete measurements. The choice of the prior determines both the quality and uncertainty of recovered images. We propose turning score-based diffusion models into principled image priors ("score-based priors") for analyzing a posterior of images given measurements. Previously, probabilistic

Zhijun Liu, Yiwei Guo, Kai Yu

In this work, we present DiffVoice, a novel text-to-speech model based on latent diffusion. We propose to first encode speech signals into a phoneme-rate latent representation with a variational autoencoder enhanced by adversarial training, and then jointly model the duration and the latent representation with a diffusion model. Subjective evaluations on LJS

Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin

Missing values are a fundamental problem in data science. Many datasets have missing values that must be properly handled because the way missing values are treated can have large impact on the resulting machine learning model. In medical applications, the consequences may affect healthcare decisions. There are many methods in the literature for dealing with

Wanting Li, Shuo Wang, Yongcai Wang, Yu Shao

For robust visual-inertial SLAM in perceptually-challenging indoor environments,recent studies exploit line features to extract descriptive information about scene structure to deal with the degeneracy of point features. But existing point-line-based SLAM methods mainly use Pl\"ucker matrix or orthogonal representation to represent a line, which needs to cal

Ashirbad Padhan, Rajashri Parida, Sayan Lahiri, Mrinal Kanti Giri

Bosons in periodic potentials with very strong local interactions, known as the constrained bosons often exhibit interesting physical behavior. We investigate the ground state properties of a two-leg Bose-Hubbard ladder by imposing three-body constraint in one leg and hardcore constraint in the other. By using the cluster-mean-field theory approximation and

Amartya Goswami

The purpose of this note is a wide generalization of the topological results of various classes of ideals of rings, semirings, and modules, endowed with Zariski topologies, to strongly irreducible ideals (endowed with Zariski topologies) of monoids, called terminal spaces. We show that terminal spaces are $T_0$, quasi-compact, and every nonempty irreducible

Yongbo Yu, Fuxun Yu, Mingjia Zhang, Di Wang

As deep learning continues to advance and is applied to increasingly complex scenarios, the demand for concurrent deployment of multiple neural network models has arisen. This demand, commonly referred to as multi-tenant computing, is becoming more and more important. However, even the most mature GPU-based computing systems struggle to adequately address th

Zhiyu Qu, Yulia Gryaditskaya, Ke Li, Kaiyue Pang

This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a ``human-centred'' data form, represents a natural interface to study explainability. We focus on cultivating sketch-specific explainability designs. This starts by identifying strokes as a unique building blo

Hoang M. Le, Brian Price, Scott Cohen, Michael S. Brown

Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit within the small-gamut standard RGB (sRGB) color space, representing only 30% of visible colors. Recovering the l

Chirantan Chowdhury

This article is first in a series of papers where we reprove the statements in constructing the Enhanced Operation Map and the abstract six-functor formalism developed by Liu-Zheng. In this paper, we prove a theorem regarding constructing functors between simplicial sets using the category of simplices. We shall reprove the statement using the language of ma

Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni

In this paper, we study the stochastic linear bandit problem under the additional requirements of differential privacy, robustness and batched observations. In particular, we assume an adversary randomly chooses a constant fraction of the observed rewards in each batch, replacing them with arbitrary numbers. We present differentially private and robust varia

Suril Mehta, Nipun Kwatra, Mohit Jain, Daniel McDuff

The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels

Sariah Mghames, Luca Castri, Marc Hanheide, Nicola Bellotto

Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for human motion prediction (NeuroSyM), whi

Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes, Min Chi

In deductive domains, three metacognitive knowledge types in ascending order are declarative, procedural, and conditional learning. This work leverages Deep Reinforcement Learning (DRL) in providing adaptive metacognitive interventions to bridge the gap between the three knowledge types and prepare students for future learning across Intelligent Tutoring Sys

Rahat Masum

Smart grid is the modern two way mechanism combining the power grid, control center, smart metering facility, energy routing and customer demand response services. The system being complicated, security vulnerabilities are paramount for the sound operation and process continuation. Since smart grid connects with the end user to the energy providers, these tw

Aleksandr Beznosikov, David Dobre, Gauthier Gidel

The Frank-Wolfe (FW) method is a popular approach for solving optimization problems with structured constraints that arise in machine learning applications. In recent years, stochastic versions of FW have gained popularity, motivated by large datasets for which the computation of the full gradient is prohibitively expensive. In this paper, we present two new

Zoltan Nagy, Davison E. Soper

We explore the features of interpolating gauge for QCD. This gauge, defined by Doust and by Baulieu and Zwanziger, interpolates between Feynman gauge or Lorenz gauge and Coulomb gauge. We argue that it could be useful for defining the splitting functions for a parton shower beyond order $\as$ or for defining the infrared subtraction terms for higher order pe

Lihua Lei, Roshni Sahoo, Stefan Wager

Practitioners often use data from a randomized controlled trial to learn a treatment assignment policy that can be deployed on a target population. A recurring concern in doing so is that, even if the randomized trial was well-executed (i.e., internal validity holds), the study participants may not represent a random sample of the target population (i.e., ex

Mehdi Rafiei, Alexandros Iosifidis

Using a discriminative representation obtained by supervised deep learning methods showed promising results on diverse Content-Based Image Retrieval (CBIR) problems. However, existing methods exploiting labels during training try to discriminate all available classes, which is not ideal in cases where the retrieval problem focuses on a class of interest. In

Subhrangshu Adhikary, Sonam Chaturvedi, Sudhir Kumar Chaturvedi, Saikat Banerjee

The COVID-19 pandemic is considered as the most alarming global health calamity of this century. COVID-19 has been confirmed to be mutated from coronavirus family. As stated by the records of The World Health Organization (WHO at April 18 2020), the present epidemic of COVID-19, has influenced more than 2,164,111 persons and killed more than 146,198 folks in

Xiaozhe Yin, Masoud Fallah-Shorshani, Rob McConnell, Scott Fruin

As the availability, size and complexity of data have increased in recent years, machine learning (ML) techniques have become popular for modeling. Predictions resulting from applying ML models are often used for inference, decision-making, and downstream applications. A crucial yet often overlooked aspect of ML is uncertainty quantification, which can signi

Subhrangshu Adhikary, Sudhir Kumar Chaturvedi, Saikat Banerjee, Sourav Basu

Marine chlorophyll which is present within phytoplankton are the basis of photosynthesis and they have a high significance in sustaining ecological balance as they highly contribute toward global primary productivity and comes under the food chain of many marine organisms. Imbalance in the concentrations of phytoplankton can disrupt the ecological balance. T

Yashika Dhote, Anil Kumar, Lohit Kayal, Partha Sarathi Goswami

We show numerically that large amplitude, \textit{shape deformations}, imposed on a spherical-cap, incompressible, sessile gas bubble pinned on a rigid wall can produce a sharp, wall-directed jet. For such a bubble filled with a permanent gas, the temporal spectrum for surface-tension driven, linearised perturbations has been studied recently in \citet{ding2

Shuai Li, Lars Davidson, Shia-Hui Peng

A fluid flow through a perforated plate is a common problem in a wide variety of practical applications in thermal, mechanical, chemical, civil, nuclear, ocean and aerospace engineering. In this paper, we proposed a novel fluid flow model for the pressure loss through plates with circular perforations in both laminar and turbulent flows. The design of this m

Anna Hasenfratz, Ethan T. Neil, Yigal Shamir, Benjamin Svetitsky

We use lattice simulations and the continuous renormalization-group method, based on the gradient flow, to study a candidate theory of composite Higgs and a partially composite top. The model is an SU(4) gauge theory with four Dirac fermions in each of the fundamental and two-index antisymmetric representations. We find that the theory has an infrared fixed

Othmane Islah

This article is about the proof of the celebrated KAM theorem as sketched out in \cite{KOL} Kolmogorov's original presentation to the ICM. The proof presented here has been detailed as an effort to clarify if Kolmogorov's argument can be made rigorous. This is a legitimate question because the published proof \cite{ARN} a few years later by his then student,

Henri Cohen

We give an extensive list of parametrized families of polynomial continued fractions of smallest possible degrees for $\pi^2$ and $\zeta(3)$, and mention similar results for other constants.

Tony Z. Zhao, Vikash Kumar, Sergey Levine, Chelsea Finn

Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback. Performing these tasks typically requires high-end robots, accurate sensors, or careful calibration, which can be expensive and difficult to

Wenbo Chen, Mathieu Tanneau, Pascal Van Hentenryck

The paper proposes a novel End-to-End Learning and Repair (E2ELR) architecture for training optimization proxies for economic dispatch problems. E2ELR combines deep neural networks with closed-form, differentiable repair layers, thereby integrating learning and feasibility in an end-to-end fashion. E2ELR is also trained with self-supervised learning, removin

Krishnendu K. S

With the tremendous advancements in face recognition technology, face modality has been widely recognized as a significant biometric identifier in establishing a person's identity rather than any other biometric trait like fingerprints that require contact sensors. However, due to inter-class similarities and intra-class variations, face recognition systems

Calford Otieno

Zinc Oxide is a thoroughly studied wide-bandgap semiconductor possessing excellent optical and electronic properties at room temperature. The renewed interest in this material has been generated by doping with various impurities in order to further enhance versatile optoelectronic responses for practical applications. Specifically, Aluminum-doped Zinc Oxide

Udayan Mandal, Amelia Regan, Louis Martin Rousseau, Julian Yarkony

In this research we consider the problem of accelerating the convergence of column generation (CG) for the weighted set cover formulation of the capacitated vehicle routing problem with time windows (CVRPTW). We adapt two new techniques, Local Area (LA) routes and Graph Master (GM) to these problems. LA-routes rely on pre-computing all lowest cost elementary

Zhenwei Tang, Griffin Floto, Armin Toroghi, Shichao Pei

Users may demand recommendations with highly personalized requirements involving logical operations, e.g., the intersection of two requirements, where such requirements naturally form structured logical queries on knowledge graphs (KGs). To date, existing recommender systems lack the capability to tackle users' complex logical requirements. In this work, we

Zhichao Xu, Daniel Cohen

Query-focused summarization (QFS) aims to provide a summary of a document that satisfies information need of a given query and is useful in various IR applications, such as abstractive snippet generation. Current QFS approaches typically involve injecting additional information, e.g. query-answer relevance or fine-grained token-level interaction between a qu

Sheikh Riasat, Bhabani Prasad Mandal

All possible theories of quantum gravity suggest the existence of a minimal length. As a consequence, the usual Heisenberg Uncertainty Principle (HUP) is replaced by a more general uncertainty principle known as the Generalised Uncertainty Principle(GUP). The dynamics of all quantum mechanical system gets modified due to GUP. In this work, we consider both E

Alka Choudhary

Path planning is a classic problem for autonomous robots. To ensure safe and efficient point-to-point navigation an appropriate algorithm should be chosen keeping the robot's dimensions and its classification in mind. Autonomous robots use path-planning algorithms to safely navigate a dynamic, dense, and unknown environment. A few metrics for path planning a

Subhrangshu Adhikary

Steganographic methods have been in the limelight of research and development for concealing secret data within a cover media without being noticed through general visualization. The Least Significant Bits (LSBs) of 8-bit color code for the RGB image arises the possibility of replacing the last two bits with the bits of the encrypted message. Several procedu