October 2020 arXiv papers — page 37

Avinash Vicholous Dass, Thomas Georgelin, Frances Westall, Frédéric Foucher

The exclusive presence of $\beta$-D-ribofuranose in nucleic acids is still a conundrum in prebiotic chemistry, given that pyranose species are substantially more stable at equilibrium. However, a precise characterisation of the relative furanose/pyranose fraction at temperatures higher than about 50$^{\,\rm o}$C is still lacking. Here, we employ a combinatio

Akash Srivastava, Yamini Bansal, Yukun Ding, Cole Lincoln Hurwitz

Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors. This approach introduces a trade-off between disentangled representation learning and reconstruction quality since the model does not have enough capacity to learn

Nhat A. Nghiem, Samuel Yen-Chi Chen, Tzu-Chieh Wei

Quantum machine learning is an emerging field that combines machine learning with advances in quantum technologies. Many works have suggested great possibilities of using near-term quantum hardware in supervised learning. Motivated by these developments, we present an embedding-based framework for supervised learning with trainable quantum circuits. We intro

Hideki Kawahara, Kohei Yatabe

We introduce a new member of TSP (Time Stretched Pulse) for acoustic and speech measurement infrastructure, based on a simple all-pass filter and systematic randomization. This new infrastructure fundamentally upgrades our previous measurement procedure, which enables simultaneous measurement of multiple attributes, including non-linear ones without requirin

Pietro d'Avenia, Jarosław Mederski, Alessio Pomponio

We find radial and nonradial solutions to the following nonlocal problem $$-\Delta u +\omega u= \big(I_\alpha\ast F(u)\big)f(u)-\big(I_\beta\ast G(u)\big)g(u) \text{ in } \mathbb{R}^N$$ under general assumptions, in the spirit of Berestycki and Lions, imposed on $f$ and $g$, where $N\geq 3$, $0\leq \beta \leq \alpha<N$, $\omega\geq 0$, $f,g:\mathbb{R}\to \ma

Zuowei Shen, Haizhao Yang, Shijun Zhang

A three-hidden-layer neural network with super approximation power is introduced. This network is built with the floor function ($\lfloor x\rfloor$), the exponential function ($2^x$), the step function ($1_{x\geq 0}$), or their compositions as the activation function in each neuron and hence we call such networks as Floor-Exponential-Step (FLES) networks. Fo

H. Sebastian Scheid

Dielectrons are an excellent probe for the QCD matter created in created in ultra-relativistic heavy-ion collisions, since they are emitted during the whole evolution of the collision and do not interact strongly with the medium. To isolate the QGP signals, measurement of the dielectron production in vacuum and its modifications due to the presence of cold n

Feng Shen, Chenqiang Hua, Xikang Sun, Jie Hu

Exciting phenomena may emerge in non-centrosymmetric two-dimensional (2D) electronic systems when spin-orbit coupling (SOC) interplays dynamically with Coulomb interactions, band topology, and external modulating forces, etc. Here, we report illuminating synergetic effects between SOC and Stark in centrosymmetric few-layer black arsenic (BAs), manifested as

Raffaele Stefano Cattolico, Roberto Capuzzo-Dolcetta

Stellar flybys in star clusters may perturb the evolution of young planetary systems in terms of disk truncation, planetary migration and planetary mass accretion. We investigate the feedback of a young planetary system during a single close stellar encounter in a typical open young stellar cluster. We consider 5 masses for the stellar perturbers: 0.5, 0.8,

Alexandros Haridis

Shape grammars compute over shapes which are defined in the universe $U^*$. Shapes in the universe $U^*$ are analogous to line drawings that can be physically realized in the plane. Any shape is embedded or contained in an arrangement of points and lines in the plane called, respectively, registration marks and construction lines, that satisfy special incide

Jason Yang, Jun Wan

In this paper, we study the $d$-dimensional update-query problem. We provide lower bounds on update and query running times, assuming a long-standing conjecture on min-plus matrix multiplication, as well as algorithms that are close to the lower bounds. Given a $d$-dimensional matrix, an \textit{update} changes each element in a given submatrix from $x$ to $

Saghar Bagheri, Gene Cheung, Antonio Ortega, Fen Wang

Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph spectral signal compression and denoising. Previous graph learning algorithms either i) make some assumptions on connectivity (e.g., graph sparsity), or ii) make simple graph edge assumptions such as positive edges only. In this paper, given an

Orestis Plevrakis, Elad Hazan

We study the control of an \emph{unknown} linear dynamical system under general convex costs. The objective is minimizing regret vs. the class of disturbance-feedback-controllers, which encompasses all stabilizing linear-dynamical-controllers. In this work, we first consider the case of known cost functions, for which we design the first polynomial-time algo

Tadashi Okazaki

We evaluate half-indices of $\mathcal{N}=(2,2)$ half-BPS boundary conditions in 3d $\mathcal{N}=4$ supersymmetric Abelian gauge theories. We confirm that the Neumann boundary condition is dual to the generic Dirichlet boundary condition for its mirror theory as the half-indices perfectly match with each other. We find that a naive mirror symmetry between the

Adam Clay, Idrissa Ba

Work of Linnell shows that the space of left-orderings of a group is either finite or uncountable, and in the case that the space is finite, the isomorphism type of the group is known---it is what is known as a Tararin group. By defining semiconjugacy of circular orderings in a general setting (that is, for arbitrary circular orderings of groups that may not

Yihong Sun, Adam Kortylewski, Alan Yuille

Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms. The aim is to segment those object boundaries which are occluded and hence invisible. This task is particularly challenging for deep neural networks because data is difficult to obtain and annotate. Therefore, we formulate amodal segmentation

Sevastos Diamantidis, Theodoros P. Horikis, Nikos I. Karachalios

We investigate, by direct numerical simulations, the dynamics of the damped and forced nonlinear Schr\"odinger (NLS) equation in the presence of a time periodic forcing and for certain parametric regimes. It is thus revealed, that the wave-number of a plane-wave initial condition dictates the number of emerged Peregrine type rogue waves at the early stages o

Farah Shahid, Aneela Zameer, Mudasser Afzal, Muhammad Hassan

Smooth power generation from solar stations demand accurate, reliable and efficient forecast of solar energy for optimal integration to cater market demand; however, the implicit instability of solar energy production may cause serious problems for the smooth power generation. We report daily prediction of solar energy by exploiting the strength of machine l

Marta Lazzaretti, Luca Calatroni, Claudio Estatico

We propose a continuous non-convex variational model for Single Molecule Localisation Microscopy (SMLM) super-resolution in order to overcome light diffraction barriers. Namely, we consider a variation of the Continuous Exact $\ell_0$ (CEL0) penalty recently introduced to relax the $\ell_2-\ell_0$ problem where a weighted-$\ell_2$ data fidelity is considered

Jörg Sander, Bob D. de Vos, Ivana Išgum

Although high resolution isotropic 3D medical images are desired in clinical practice, their acquisition is not always feasible. Instead, lower resolution images are upsampled to higher resolution using conventional interpolation methods. Sophisticated learning-based super-resolution approaches are frequently unavailable in clinical setting, because such met

Alon Mamistvalov, Yonina C. Eldar

Wireless ultrasound (US) systems that produce high-quality images can improve current clinical diagnosis capabilities by making the imaging process much more efficient, affordable, and accessible to users. The most common technique for generating B-mode US images is delay and sum (DAS) beamforming, where an appropriate delay is introduced to signals sampled

Hamza Salem, Fabian Stephany

Election prediction has long been an evergreen in political science literature. Traditionally, such efforts included polling aggregates, economic indicators, partisan affiliation, and campaign effects to predict aggregate voting outcomes. With increasing secondary usage of online-generated data in social science, researchers have begun to consult metadata fr

Ce Jin, Jelani Nelson, Kewen Wu

We provide improved upper bounds for the simultaneous sketching complexity of edit distance. Consider two parties, Alice with input $x\in\Sigma^n$ and Bob with input $y\in\Sigma^n$, that share public randomness and are given a promise that the edit distance $\mathsf{ed}(x,y)$ between their two strings is at most some given value $k$. Alice must send a messag

B. Branman

We study the pants complex of surfaces of infinite type. When $S$ is a surface of infinite type, the usual definition of the pants graph $\mathcal{P}(S)$ yields a graph with infinitely many connected-components. In the first part of our paper, we study this disconnected graph. In particular, we show that the extended mapping class group $\mathrm{Mod}(S)$ is

Vaibhav Kumar, Tenzin Singhay Bhotia, Vaibhav Kumar

Non-contextual word embedding models have been shown to inherit human-like stereotypical biases of gender, race and religion from the training corpora. To counter this issue, a large body of research has emerged which aims to mitigate these biases while keeping the syntactic and semantic utility of embeddings intact. This paper describes Fair Embedding Engin

Gianluca Paolini

We prove that every quasi-Hopfian finitely presented structure $A$ has a $d$-$\Sigma_2$ Scott sentence, and that if in addition $A$ is computable and $Aut(A)$ satisfies a natural computable condition, then $A$ has a computable $d$-$\Sigma_2$ Scott sentence. This unifies several known results on Scott sentences of finitely presented structures and it is used

Xin Wang, Yudong Chen, Wenwu Zhu

Curriculum learning (CL) is a training strategy that trains a machine learning model from easier data to harder data, which imitates the meaningful learning order in human curricula. As an easy-to-use plug-in, the CL strategy has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide range of scenario

Brian Skinner

The Appalachian Trail (AT) is a 2193-mile-long hiking trail in the eastern United States. The trail has many bends and turns at different length scales, which gives it a nontrivial fractal dimension. Here I use GPS data from the Appalachian Trail Conservancy to estimate the fractal dimension of the AT. I find that, at length scales between $\sim 20$ m and $\

Zhiqi Bu, Shiyun Xu, Kan Chen

When equipped with efficient optimization algorithms, the over-parameterized neural networks have demonstrated high level of performance even though the loss function is non-convex and non-smooth. While many works have been focusing on understanding the loss dynamics by training neural networks with the gradient descent (GD), in this work, we consider a broa

Nafiseh Ghoroghchian, Stark C. Draper, Roman Genov

Motivated by the emerging area of graph signal processing (GSP), we introduce a novel method to draw inference from spatiotemporal signals. Data acquisition in different locations over time is common in sensor networks, for diverse applications ranging from object tracking in wireless networks to medical uses such as electroencephalography (EEG) signal proce

Benjamin Moon, Harley Eades, Dominic Orchard

Graded type theories are an emerging paradigm for augmenting the reasoning power of types with parameterizable, fine-grained analyses of program properties. There have been many such theories in recent years which equip a type theory with quantitative dataflow tracking, usually via a semiring-like structure which provides analysis on variables (often called

Matthew Hedden, Katherine Raoux

In this article we conjecture a 4-dimensional characterization of tightness: a contact structure is tight if and only if a slice-Bennequin inequality holds for smoothly embedded surfaces in Yx[0,1]. An affirmative answer to our conjecture would imply an analogue of the Milnor conjecture for torus knots: if a fibered link L induces a tight contact structure o

Bernhard Muhlherr, Gianluca Paolini, Saharon Shelah

We lay the foundations of the first-order model theory of Coxeter groups. Firstly, with the exception of the $2$-spherical non-affine case (which we leave open), we characterize the superstable Coxeter groups of finite rank, which we show to be essentially the Coxeter groups of affine type. Secondly, we characterize the Coxeter groups of finite rank which ar

Woojeong Kim, Suhyun Kim, Mincheol Park, Geonseok Jeon

Network pruning is widely used to lighten and accelerate neural network models. Structured network pruning discards the whole neuron or filter, leading to accuracy loss. In this work, we propose a novel concept of neuron merging applicable to both fully connected layers and convolution layers, which compensates for the information loss due to the pruned neur

Carolina Tamborini

An algebraic subvariety Z of A_g is totally geodesic if it is the image via the natural projection map of some totally geodesic submanifold X of the Siegel space. We say that X is the symmetric space uniformizing Z. In this paper we determine which symmetric space uniformizes each of the low genus counterexamples to the Coleman-Oort conjecture obtained study

Steffen Büchholz, Mathias Lemke, Julius Reiss, Jörn Sesterhenn

Monitoring active volcanos is an ongoing and important task helping to understand and predict volcanic eruptions. In recent years, analysing the acoustic properties of eruptions became more relevant. We present an inexpensive, lightweight, portable, easy to use and modular acoustic data acquisition system for field measurements that can record data with up t

Nancy Aggarwal, George P. Winstone, Mae Teo, Masha Baryakhtar

The Levitated Sensor Detector (LSD) is a compact resonant gravitational-wave (GW) detector based on optically trapped dielectric particles that is under construction. The LSD sensitivity has more favorable frequency scaling at high frequencies compared to laser interferometer detectors such as LIGO. We propose a method to substantially improve the sensitivit

Rahul Chhabra, Kazuo Tsushima

By an effective Lagrangian plus QCD sum-rule approach, we investigate the mass shift of the $J/\psi$ state in medium, in symmetric nuclear matter with zero and finite temperature, and cold strange matter. The in-medium mass of the $J/\psi$ state is evaluated through the intermediate pseudoscalar $D$-meson loop for the $J/\psi$ self-energy. The effect of medi

Mohammed Shayan, Kanad Basu, Ramesh Karri

With transistor scaling reaching its limits, interposer-based integration of dies (chiplets) is gaining traction. Such an interposer-based integration enables finer and tighter interconnect pitch than traditional system-on-packages and offers two key benefits: 1. It reduces design-to-market time by bypassing the time-consuming process of verification and fab

Cem Subakan, Mirco Ravanelli, Samuele Cornell, Mirko Bronzi

Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-to-sequence learning. RNNs, however, are inherently sequential models that do not allow parallelization of their computations. Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism. In thi

A. Pikalev, I. Semenov, M. Pustylnik, C. Räth

We demonstrate experimentally that the void in capacitively-coupled RF complex plasmas can exist in two qualitative different regimes. The "bright" void is characterized by bright plasma emission associated with the void, whereas the "dim" void possesses no detectable emission feature. The transition from the dim to the bright regime occurs with an increase

Carey E. Priebe, Cencheng Shen, Ningyuan Huang, Tianyi Chen

Neural networks have achieved remarkable successes in machine learning tasks. This has recently been extended to graph learning using neural networks. However, there is limited theoretical work in understanding how and when they perform well, especially relative to established statistical learning techniques such as spectral embedding. In this short paper, w

Roberto Livrea, Antonio Iannizzotto

We consider a Dirichlet type problem for a nonlinear, nonlocal equation driven by the degenerate fractional p-Laplacian, whose reaction combines a sublinear term depending on a positive parameter and an asymmetric perturbation (superlinear at positive infinity, at most linear at negative infinity). By means of critical point theory and Morse theory, we prove

Kailai Li, Meng Li, Uwe D. Hanebeck

We present a novel tightly-coupled LiDAR-inertial odometry and mapping scheme for both solid-state and mechanical LiDARs. As frontend, a feature-based lightweight LiDAR odometry provides fast motion estimates for adaptive keyframe selection. As backend, a hierarchical keyframe-based sliding window optimization is performed through marginalization for directl

Nir Regev, Lior Rokach, Asaf Shabtai

Despite continuous investments in data technologies, the latency of querying data still poses a significant challenge. Modern analytic solutions require near real-time responsiveness both to make them interactive and to support automated processing. Current technologies (Hadoop, Spark, Dataflow) scan the dataset to execute queries. They focus on providing a

Danwei Cai, Weiqing Wang, Ming Li

In this paper, we propose an iterative framework for self-supervised speaker representation learning based on a deep neural network (DNN). The framework starts with training a self-supervision speaker embedding network by maximizing agreement between different segments within an utterance via a contrastive loss. Taking advantage of DNN's ability to learn fro

Shuang Guo, Bo Liu, Shen Zhang, Jifeng Guo

In this paper, we extend a famous motion planning approach GPMP2 to multi-robot cases, yielding a novel centralized trajectory generation method for the multi-robot formation. A sparse Gaussian Process model is employed to represent the continuous-time trajectories of all robots as a limited number of states, which improves computational efficiency due to th

S. O. Semenov, N. Yu. Zolotykh

We propose a cut-based algorithm for finding all vertices and all facets of the convex hull of all integer points of a polyhedron defined by a system of linear inequalities. Our algorithm DDM Cuts is based on the Gomory cuts and the dynamic version of the double description method. We describe the computer implementation of the algorithm and present the resu

Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon

Value Iteration Networks (VINs) have emerged as a popular method to incorporate planning algorithms within deep reinforcement learning, enabling performance improvements on tasks requiring long-range reasoning and understanding of environment dynamics. This came with several limitations, however: the model is not incentivised in any way to perform meaningful

Matthieu Calvez

In this paper we show the statement in the title. To any Garside group of finite type, Wiest and the author associated a hyperbolic graph called the \emph{additional length graph} and they used it to show that central quotients of Artin-Tits groups of spherical type are acylindrically hyperbolic. In general, a euclidean Artin-Tits group is not \emph{a priori

Mikhail Khenner

Assuming a vacancy-mediated diffusion, a continuum model for electromigration-driven transport of an embedded atom cluster across a surface terrace of a phase-separating A$_x$B$_{1-x}$ surface alloy, such as fcc AgPt(111), is presented. Computations show that the electron wind carries the cluster over hundreds of lattice spacings and in the set direction, wh

Anup Bhattacharya, Arijit Bishnu, Gopinath Mishra, Anannya Upasana

We study a graph coloring problem that is otherwise easy but becomes quite non-trivial in the one-pass streaming model. In contrast to previous graph coloring problems in streaming that try to find an assignment of colors to vertices, our main work is on estimating the number of conflicting or monochromatic edges given a coloring function that is streaming a

Rajesh Chaunsali, Haitao Xu, Jinkyu Yang, Panayotis G. Kevrekidis

We examine the role of strong nonlinearity on the topologically-robust edge state in a one-dimensional system. We consider a chain inspired from the Su-Schrieffer-Heeger model, but with a finite-frequency edge state and the dynamics governed by second-order differential equations. We introduce a cubic onsite-nonlinearity and study this nonlinear effect on th

D. J. G. Pearce, S. Gat, G. Livne, A. Bernheim-Groswasser

Active matter is characterized by its ability to induce motion by self-generated stress. In the case of a solid, such motion can lead to shape transformations. The stress-generating components can be anisotropic endowing the material with mesoscopic orientational order. It is currently unknown how the specific postions and orientations of these active consti

A. Nindos, S. Patsourakos, A. Vourlidas, P. C. Liewer

Aims: Our goal is to develop methodologies to seamlessly track transient solar wind flows viewed by coronagraphs or heliospheric imagers from rapidly varying viewpoints. Methods: We constructed maps of intensity versus time and elongation (J-maps) from Parker Solar Probe (PSP) Wide-field Imager (WISPR) observations during the fourth encounter of PSP. From th

S. Tahmasebi, M. Longobardi, M. R. Kazemi, M. Alizadeh

In this paper, we consider the information content of maximum ranked set sampling procedure with unequal samples (MRSSU) in terms of Tsallis entropy which is a nonadditive generalization of Shannon entropy. We obtain several results of Tsallis entropy including bounds, monotonic properties, stochastic orders, and sharp bounds under some assumptions. We also

Alexandra Dorofeeva, Victor Korolev, Alexander Zeifman

In applied probability, the normal approximation is often used for the distribution of data with assumed additive structure. This tradition is based on the central limit theorem for sums of (independent) random variables. However, it is practically impossible to check the conditions providing the validity of the central limit theorem when the observed sample

Lijun Zhu, Robert A. Buhrman

After one decade of the intensive theoretical and experimental explorations, whether interfacial spin-orbit coupling (ISOC) at metallic magnetic interfaces can effectively generate a spin current has remained in dispute. Here, utilizing the Ti/FeCoB bilayers that are unique for the negligible bulk spin Hall effect and the strong tunable ISOC, we establish th

Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov

In this paper, we propose a fully differentiable pipeline for estimating accurate dense correspondences between 3D point clouds. The proposed pipeline is an extension and a generalization of the functional maps framework. However, instead of using the Laplace-Beltrami eigenfunctions as done in virtually all previous works in this domain, we demonstrate that

Jeovanny de Jesus Muentes Acevedo

M. Gromov introduced the mean dimension for a continuous map in the late 1990's, which is an invariant under topological conjugacy. On the other hand, the notion of metric mean dimension for a dynamical system was introduced by Lindenstrauss and Weiss in 2000 and this refines the topological entropy for dynamical systems with infinite topological entropy. In

Marino Echavarria, Max Everett, Shinyu Huang, Liza Jacoby

Given a lattice polygon $P$ with $g$ interior lattice points, we associate to it the moduli space of tropical curves of genus $g$ with Newton polygon $P$. We completely classify the possible dimensions such a moduli space can have. For non-hyperelliptic polygons the dimension must be between $g$ and $2g+1$, and can take on any integer value in this range, wi

Shiqi Ma

We give some details about the stationary phase lemma. We first prove a special case where the high order terms are derived explicitly. Based on that, we prove a more general case by using Morse lemma.

Safoura S. Mirkhalaf, Daniel Benedicto Orenes, Morgan W. Mitchell, Emilia Witkowska

We theoretically investigate estimation of the control parameter in a ferromagnetic Bose-Einstein condensate near second order quantum phase transitions. We quantify sensitivity by quantum and classical Fisher information and using the error-propagation formula. For these different metrics, we find the same, beyond-standard-quantum-limit (SQL) scaling with a

Ahsan Mahmood, Junier Oliva, Martin Styner

We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales. A score is defined to be the gradient of the log density with respect to the input data. Our methodology is completely unsupervised and follows a straight forward training scheme. First, we train a deep network to es

A. Lyaghfouri

We show that bounded solutions of the quasilinear elliptic equation $\Delta_{p(x)} u=g+div(\textbf{F})$ are locally H\"{o}lder continuous provided that the functions $g$ and $\textbf{F}$ are in suitable Lebesgue spaces.

Jingsong Wang, Tom Ko, Zhen Xu, Xiawei Guo

The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks. These tasks, which cover a large variety of domains, will be shown to the automated system in a random order. Each time when the tasks are switched, the information of the new task will be hinted wi

Julen Urain, Michelle Ginesi, Davide Tateo, Jan Peters

We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics. Our approach extends the Normalizing Flows framework to learn stable Stochastic Differential Equations. We prove the Lyapunov stability for a class of Stochastic Differential Equations and we propose a learning algorithm to

Mokanarangan Thayaparan, Marco Valentino, André Freitas

We propose a novel approach for answering and explaining multiple-choice science questions by reasoning on grounding and abstract inference chains. This paper frames question answering as an abductive reasoning problem, constructing plausible explanations for each choice and then selecting the candidate with the best explanation as the final answer. Our syst

W. J. Huang, H. G. Wang

On the basis of the PSRPOPPY software package, we developed an evolution model of population synthesis for isolated radio pulsars by incorporating the fan beam model, an alternative radio emission beam model to the widely used conal beam model. With proper choice of related models and parameters, we succesfully reproduced the observational distributions of G

Yosef Nir

The ATLAS and CMS experiments have made three major discoveries: The discovery of an elementary spin-zero particle, the discovery of the mechanism that makes the weak interactions short-range, and the discovery of the mechanism that gives the third generation fermions their masses. I explain how this progress in our understanding of the basic laws of Nature

Xiaoyu Huang, Edward Jones, Siru Zhang, Shouyu Xie

this paper presents a detailed methodology of a Spiking Neural Network (SNN) based low-power design for radioisotope identification. A low power cost of 72 mW has been achieved on FPGA with the inference accuracy of 100% at 10 cm test distance and 97% at 25 cm. The design verification and chip validation methods are presented. It also discusses SNN simulatio

Mengying Wang, Mohammad Khazaei, Yoshiyuki Kawazoe, Yunye Liang

MXenes, a family of two-dimensional transition metal carbides and nitrides, have various tunable physical and chemical properties. Their diverse prospective applications in electronics and energy storage devices have triggered great interests in science and technology. MXenes can be functionalized by different surface terminations. Some O and F functionalize

Radu Ignat, Felix Otto, Tobias Ried, Pavlos Tsatsoulis

The magnetization ripple is a microstructure formed in thin ferromagnetic films. It can be described by minimizers of a nonconvex energy functional leading to a nonlocal and nonlinear elliptic SPDE in two dimensions driven by white noise, which is singular. We address the universal character of the magnetization ripple using variational methods based on $\Ga

Xu-Xu Yang, Hang Zhou, Tian-Peng Tang, Ning Liu

Searching for the top squark (stop) is a key task to test the naturalness of SUSY. Different from stop pair production, single stop production relies on its electroweak properties and can provide some unique signatures. Following the single production process $pp \to \tilde t_1 \tilde{\chi}^-_1 \to t \tilde{\chi}^0_1 \tilde{\chi}^-_1$, the top quark has two

Arthur Bit-Monnot, Malik Ghallab, Félix Ingrand, David E. Smith

Temporal planning offers numerous advantages when based on an expressive representation. Timelines have been known to provide the required expressiveness but at the cost of search efficiency. We propose here a temporal planner, called FAPE, which supports many of the expressive temporal features of the ANML modeling language without loosing efficiency. FAPE'

Said Jawad Saidi, Aniss Maghsoudlou, Damien Foucard, Georgios Smaragdakis

Many network operations, ranging from attack investigation and mitigation to traffic management, require answering network-wide flow queries in seconds. Although flow records are collected at each router, using available traffic capture utilities, querying the resulting datasets from hundreds of routers across sites and over time, remains a significant chall

Jaehuyn Ahn

In this paper, I present churn prediction techniques that have been released so far. Churn prediction is used in the fields of Internet services, games, insurance, and management. However, since it has been used intensively to increase the predictability of various industry/academic fields, there is a big difference in its definition and utilization. In this

Julian Lienen, Eyke Hüllermeier, Ralph Ewerth, Nils Nommensen

In many real-world applications, the relative depth of objects in an image is crucial for scene understanding. Recent approaches mainly tackle the problem of depth prediction in monocular images by treating the problem as a regression task. Yet, being interested in an order relation in the first place, ranking methods suggest themselves as a natural alternat

Danny Stoll, Jörg K. H. Franke, Diane Wagner, Simon Selg

After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO? This question poses a challenging problem, as developer adjustments can change which hyperparameter settings perform well, or even the hyperparameter search space itself. While many appr

Yunfu Song, Huahuan Zheng, Zhijian Ou

A class of recent semi-supervised learning (SSL) methods heavily rely on domain-specific data augmentations. In contrast, generative SSL methods involve unsupervised learning based on generative models by either joint-training or pre-training, and are more appealing from the perspective of being domain-agnostic, since they do not inherently require data augm

Meytal Eran-Jona, Yosef Nir

The absence of women among academic staff in physics is in the focus of our research. To explore the causes of this gender imbalance, we conducted a nationwide representative survey among Ph.D. students and interviews with Ph.D. students and postdoctoral fellows. Studying both context factors and agency, we reveal the multiple and hidden ways in which gender

Ayush Bhardwaj, Sakshee Pimpale, Saurabh Kumar, Biplab Banerjee

Real-world scenarios pose several challenges to deep learning based computer vision techniques despite their tremendous success in research. Deeper models provide better performance, but are challenging to deploy and knowledge distillation allows us to train smaller models with minimal loss in performance. The model also has to deal with open set samples fro

Taher I. Mayassi, Mohammad N. Abdulrahim

We consider the irreducible representations each of dimension 2 of the necklace braid group $\mathcal{NB}_n$ ($n=2,3,4$). We then consider the tensor product of the representations of $\mathcal{NB}_n$ ($n=2,3,4$) and determine necessary and sufficient condition under which the constructed representations are irreducible. Finally, we determine conditions unde

Aleksandr Beznosikov, Valentin Samokhin, Alexander Gasnikov

This paper focuses on the distributed optimization of stochastic saddle point problems. The first part of the paper is devoted to lower bounds for the centralized and decentralized distributed methods for smooth (strongly) convex-(strongly) concave saddle point problems, as well as the near-optimal algorithms by which these bounds are achieved. Next, we pres

Mohammad Ali

Health Monitoring and Management System (HMMS) is an emerging technology for decades. Researchers are working on this field to track health conditions for different users. Researchers emphasize tracking health conditions from an early stage to the human body. Therefore, different research works have been conducted to establish HMMS in schools. Researchers pr

Jing Xu, Fangwei Zhong, Yizhou Wang

Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs). This problem is challenging as the targets usually move randomly but the coverage range of sensors is limited in angle and distance. Thus, it is required to coordinate sensors to get ideal target coverage with low power c

Antonio Bazco-Nogueras, Petros Elia

One of the famous problems in communications was the so-called "PN" problem in the Broadcast Channel, which refers to the setting where a fixed set of users provide perfect Channel State Information (CSI) to a multi-antenna transmitter, whereas the remaining users only provide finite-precision CSI or no CSI. The Degrees-of-Freedom (DoF) of that setting were

Liyang Liu, Simon Fryc, Lan Wu, Thanh Vu

In this letter, we present an interactive probabilistic mapping framework for a mobile manipulator picking objects from a pile. The aim is to map the scene, actively decide where to go next and which object to pick, make changes to the scene by picking the chosen object, and then map these changes alongside. The proposed framework uses a novel dynamic Gaussi

Iwnetim I. Abate, C. Das Pemmaraju, Se Young Kim, Kuan H. Hsu

Stabilizing high-valent redox couples and exotic electronic states necessitate an understanding of the stabilization mechanism. In oxides, whether they are being considered for energy storage or computing, highly oxidized oxide-anion species rehybridize to form short covalent bonds and are related to significant local structural distortions. In intercalation

Yao Wei, Shunping Ji

Road surface extraction from remote sensing images using deep learning methods has achieved good performance, while most of the existing methods are based on fully supervised learning, which requires a large amount of training data with laborious per-pixel annotation. In this paper, we propose a scribble-based weakly supervised road surface extraction method

Seongbin Kim, Gyuwan Kim, Seongjin Shin, Sangmin Lee

End-to-end approaches open a new way for more accurate and efficient spoken language understanding (SLU) systems by alleviating the drawbacks of traditional pipeline systems. Previous works exploit textual information for an SLU model via pre-training with automatic speech recognition or fine-tuning with knowledge distillation. To utilize textual information

Akhtar Munir, Barry C. Sanders

We aim to create deterministic collisions between orbiting bodies by applying a time-dependent external force to one or both bodies, whether the bodies are mutually repulsive, as in the two- or multi-electron atomic case or mutually attractive, as in the planetary-orbit case. Specifically, we have devised a mathematical framework for causing deterministic co

Y. Efe Erginbas, Stefan Vlaski, Ali H. Sayed

This paper presents an adaptive combination strategy for distributed learning over diffusion networks. Since learning relies on the collaborative processing of the stochastic information at the dispersed agents, the overall performance can be improved by designing combination policies that adjust the weights according to the quality of the data. Such policie

Yujeong Choi, Yunseong Kim, Minsoo Rhu

In cloud ML inference systems, batching is an essential technique to increase throughput which helps optimize total-cost-of-ownership. Prior graph batching combines the individual DNN graphs into a single one, allowing multiple inputs to be concurrently executed in parallel. We observe that the coarse-grained graph batching becomes suboptimal in effectively

Vijay Shankar A

This work identifies the orientation of galaxies in the Galaxy zoo data set. The images are first identified by the number of principal components required to represent 99 percent of the variance of the image. K means clustering is used to separate the galaxies on the basis of their central brightness along with outlier separation. Spectral clustering is the

H. E. S. S. Collaboration, H. Abdalla, R. Adam, F. Aharonian

The unidentified very-high-energy (VHE; E $>$ 0.1 TeV) $\gamma$-ray source, HESS J1826$-$130, was discovered with the High Energy Stereoscopic System (HESS) in the Galactic plane. The analysis of 215 h of HESS data has revealed a steady $\gamma$-ray flux from HESS J1826$-$130, which appears extended with a half-width of 0.21$^{\circ}$ $\pm$ 0.02$^{\circ}_{\t

Youngeun Kwon, Yunjae Lee, Minsoo Rhu

Personalized recommendations are one of the most widely deployed machine learning (ML) workload serviced from cloud datacenters. As such, architectural solutions for high-performance recommendation inference have recently been the target of several prior literatures. Unfortunately, little have been explored and understood regarding the training side of this

Uri Abend, Anatoly Khina

We address the recently suggested problem of causal lossless coding of a randomly arriving source samples. We construct variable-to-fixed coding schemes and show that they outperform the previously considered fixed-to-variable schemes when traffic is high both in terms of delay and Age of Information by appealing to tools from queueing theory. We supplement

Leandro Candido, Pedro L. Kaufmann

We investigate the problem of classifying the Banach spaces $\mathrm{Lip}_0(C(K))$ for Hausdorff compacta $K$. In particular, sufficient conditions are established for a space $\mathrm{Lip}_0(C(K))$ to be isomorphic to $\mathrm{Lip}_0(c_0(\varGamma))$ for some uncountable set $\varGamma$.

Jonghyeon Lee, Taewon Kang

The commercialization of transistors capable of both switching and amplification in 1960 resulted in the development of second-generation computers, which resulted in the miniaturization and lightening while accelerating the reduction and development of production costs. However, the self-resistance and the resistance used in conjunction with semiconductors,