October 2020 arXiv papers — page 82

Jinlu Li, Yanghai Yu, Weipeng Zhu

In this paper, we consider the solution map of the initial value problem to the two-component Camassa-Holm equation on the line. We prove that the solution map of this problem is not uniformly continuous in Sobolev spaces $H^s(\R)\times H^{s-1}(\R)$ for $s>3/2$.

Jonathan Pelliciari, Sangjae Lee, Keith Gilmore, Jiemin Li

Spin excitations of magnetic thin films are the founding element for novel transport concepts in spintronics, magnonics, and magnetic devices in general. While spin dynamics have been extensively studied in bulk materials, their behaviour in mesoscopic films is less known due to experimental limitations. Here, we employ Resonant Inelastic X-Ray Scattering to

Igor Altsybeev, Vitalii Petrov

Precise determination of the moments of multiplicity distributions of identified particles could be challenging due to the misidentification in detectors. The so-called Identity Method allows one to solve this problem. In this contribution, performance of the Identity Method was tested on the A-A events simulated in the conditions of the MPD experiment at NI

Yanliang Zou, Tongliang Deng, Jian Zhang, Chen Chen

The lookup procedure in Linux costs a significant portion of file accessing time as the virtual file system (VFS) traverses the file path components one after another. The lookup procedure becomes more time consuming when applications frequently access files, especially those with small sizes. We propose Stage Lookup, which dynamically caches popular directo

Jun Quan, Shian Zhang, Qian Cao, Zizhong Li

In order to alleviate the shortage of multi-domain data and to capture discourse phenomena for task-oriented dialogue modeling, we propose RiSAWOZ, a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic Annotations. RiSAWOZ contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues, with more than 150K utterances span

Eric Bruneton

We propose a real-time method to render high-quality images of a non-rotating black hole with an accretion disc and background stars. Our method is based on beam tracing, but uses precomputed tables to find the intersections of each curved light beam with the scene in constant time per pixel. It also uses a specific texture filtering scheme to integrate the

Pradipkumar H. Keskar, Priyanka Kumari

We provide some applications of a polynomial criterion for difference sets. These include counting the difference sets with specified parameters in terms of Hilbert functions, in particular a count of bent functions. We also consider the question about the bentness of certain Boolean functions introduced by Carlet when the $\mathcal{C}$-condition introduced

Pavel Fadeev, Chris Timberlake, Tao Wang, Andrea Vinante

A ferromagnetic gyroscope (FG) is a ferromagnet whose angular momentum is dominated by electron spin polarization and that will precess under the action of an external torque, such as that due to a magnetic field. Here we model and analyze FG dynamics and sensitivity, focusing on practical schemes for experimental realization. In the case of a freely floatin

Jiatong Li, Fangda Han, Ricardo Guerrero, Vladimir Pavlovic

Increased awareness of the impact of food consumption on health and lifestyle today has given rise to novel data-driven food analysis systems. Although these systems may recognize the ingredients, a detailed analysis of their amounts in the meal, which is paramount for estimating the correct nutrition, is usually ignored. In this paper, we study the novel an

Xiaofeng Xue

In this paper we are concerned with a generalized $N$-urn Ehrenfest model, where balls keeps independent random walks between $N$ boxes uniformly laid on $[0, 1]$. After a proper scaling of the transition rates function of the aforesaid random walk, we derive the hydrodynamic limit of the model, i.e., the law of large numbers which the empirical measure of t

Andrew Merritt, Chenhui Chu, Yuki Arase

Multimodal neural machine translation (NMT) has become an increasingly important area of research over the years because additional modalities, such as image data, can provide more context to textual data. Furthermore, the viability of training multimodal NMT models without a large parallel corpus continues to be investigated due to low availability of paral

Reza Dehghanizade, Seyed Mohamad Sadegh Modarres Mosadegh

Similar to linear spaces, many examples of quasilinear spaces have a notion of multiplication of the elements. To characterising these examples, in the present paper we generalize the notion of quasilinear spaces and introduce quasi-algebras and normed quasi-algebras with the help of what was done in constructing algebras. After examining some properties of

Jun Wan, Zhihui Lai, Jun Liu, Jie Zhou

Heatmap regression (HR) has become one of the mainstream approaches for face alignment and has obtained promising results under constrained environments. However, when a face image suffers from large pose variations, heavy occlusions and complicated illuminations, the performances of HR methods degrade greatly due to the low resolutions of the generated land

Pengbo Zhao, Zhenshen Qu, Yingjia Bu, Wenming Tan

Fast and precise object detection for high-resolution aerial images has been a challenging task over the years. Due to the sharp variations on object scale, rotation, and aspect ratio, most existing methods are inefficient and imprecise. In this paper, we represent the oriented objects by polar method in polar coordinate and propose PolarDet, a fast and accu

Derek Weber, Mehwish Nasim, Lewis Mitchell, Lucia Falzon

To study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In

Yogiro Hama, Takeshi Kodama, Wei-Liang Qian

In this paper, we give an account of the peripheral-tube model, which has been developed to give an intuitive and dynamical description of the so-called ridge effect in two-particle correlations in high-energy nuclear collisions. Starting from a realistic event-by-event fluctuating hydrodynamical model calculation, we first show the emergence of ridge + shou

Ilya Kuzovkin

The thesis explores the role machine learning methods play in creating intuitive computational models of neural processing. Combined with interpretability techniques, machine learning could replace human modeler and shift the focus of human effort to extracting the knowledge from the ready-made models and articulating that knowledge into intuitive descroptio

N. R. Hutzler, A. Borschevsky, D. Budker, D. DeMille

We discuss how molecule-based searches offer complementary probes to study the violation of fundamental symmetries. These experiments have the potential to probe not only the electron EDM, but also hadronic CPV phenomena. Future experimental developments will offer generic sensitivity to probe flavor neutral sources of both leptonic and hadronic CPV at scale

Hantao Huang, Tao Han, Wei Han, Deep Yap

Visual Question Answering (VQA) is challenging due to the complex cross-modal relations. It has received extensive attention from the research community. From the human perspective, to answer a visual question, one needs to read the question and then refer to the image to generate an answer. This answer will then be checked against the question and image aga

Filipe Pereira, Daniel Selva

The past decade has witnessed a growing interest in lunar exploration missions. The autonomy of lunar surface and in-orbit missions is, however, dependent on accurate and instantaneous navigation services. These services can not be provided by current Global Navigation Satellite Systems (GNSS) whose signals suffer from poor geometry and coverage in the vicin

Shuai Xie, Zunlei Feng, Ying Chen, Songtao Sun

Active learning aims to address the paucity of labeled data by finding the most informative samples. However, when applying to semantic segmentation, existing methods ignore the segmentation difficulty of different semantic areas, which leads to poor performance on those hard semantic areas such as tiny or slender objects. To deal with this problem, we propo

Aaron Hudson, Ali Shojaie

Qualitative interactions occur when a treatment effect or measure of association varies in sign by sub-population. Of particular interest in many biomedical settings are absence/presence qualitative interactions, which occur when an effect is present in one sub-population but absent in another. Absence/presence interactions arise in emerging applications in

W. Cai, Y. Ma, W. Wang, C. -L. Zou

Quantum information is vulnerable to environmental noise and experimental imperfections, hindering the reliability of practical quantum information processors. Therefore, quantum error correction (QEC) that can protect quantum information against noise is vital for universal and scalable quantum computation. Among many different experimental platforms, super

Dan Wang, Tianrui Wang, Ionuţ Florescu

In 2012, SEC mandated all corporate filings for any company doing business in US be entered into the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system. In this work we are investigating ways to analyze the data available through EDGAR database. This may serve portfolio managers (pension funds, mutual funds, insurance, hedge funds) to get auto

Imad El Bouchairi, Jalal Fadili, Abderrahim Elmoataz

In this paper we study continuum limits of the discretized $p$-Laplacian evolution problem on sparse graphs with homogeneous Neumann boundary conditions. This extends the results of [24] to a far more general class of kernels, possibly singular, and graph sequences whose limit are the so-called $L^q$-graphons. More precisely, we derive a bound on the distanc

Jesse Haviland, Peter Corke

The elementary transform sequence (ETS) provides a universal method of describing the kinematics of any serial-link manipulator. The ETS notation is intuitive and easy to understand, while avoiding the complexity and limitations of Denvit-Hartenberg frame assignment. In this paper, we describe a systematic method for computing the manipulator Jacobian and He

Kartik Lakhotia, Rajgopal Kannan, Viktor Prasanna, Cesar A. F. De Rose

Tip decomposition is a crucial kernel for mining dense subgraphs in bipartite networks, with applications in spam detection, analysis of affiliation networks etc. It creates a hierarchy of vertex-induced subgraphs with varying densities determined by the participation of vertices in butterflies (2,2-bicliques). To build the hierarchy, existing algorithms ite

Xiao Hu, Shouzhuo Sun, Shweta Patwa, Debmalya Panigrahi

We investigate the computational complexity of minimizing the source side-effect in order to remove a given number of tuples from the output of a conjunctive query. This is a variant of the well-studied {\em deletion propagation} problem, the difference being that we are interested in removing the smallest subset of input tuples to remove a given number of o

Kayla Makela, Tim Ophelders, Michelle Quigley, Elizabeth Munch

Tree ring widths are an important source of climatic and historical data, but measuring these widths typically requires extensive manual work. Computer vision techniques provide promising directions towards the automation of tree ring detection, but most automated methods still require a substantial amount of user interaction to obtain high accuracy. We perf

Jeffrey M. Shainline

To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and electronics for computation are complementary and interdependent. Using light for communication enables high f

Anar Amgalan, Lilianne R. Mujica-Parodi, Steven S. Skiena

Physical or geographic location proves to be an important feature in many data science models, because many diverse natural and social phenomenon have a spatial component. Spatial autocorrelation measures the extent to which locally adjacent observations of the same phenomenon are correlated. Although statistics like Moran's $I$ and Geary's $C$ are w

Germán Barquero, Carles Fernández, Isabelle Hupont

Most current multi-object trackers focus on short-term tracking, and are based on deep and complex systems that do not operate in real-time, often making them impractical for video-surveillance. In this paper, we present a long-term multi-face tracking architecture conceived for working in crowded contexts, particularly unconstrained in terms of movement and

N. Kent, N. Reynolds, D. Raftrey, I. T. G. Campbell

Topological solitons have been studied for decades in classical field theories, and have started recently to impact condensed matter physics. Among those solitons, magnetic skyrmions are two-dimensional particle-like objects with a continuous winding of the magnetization, and magnetic Hopfions are three-dimensional topological solitons that can be formed fro

Ian W. McKeague, Xin Zhang

We consider the problem of testing for the presence of linear relationships between large sets of random variables based on a post-selection inference approach to canonical correlation analysis. The challenge is to adjust for the selection of subsets of variables having linear combinations with maximal sample correlation. To this end, we construct a stabiliz

Alex Arnell, Richard Chen, Evelyn Choi, Miroslav Marinov

The Banzhaf and Shapley-Shubik power indices were first introduced to measure the power of voters in a weighted voting system. Given a weighted voting system, the fixed point of such a system is found by continually reassigning each voter's weight with its power index until the system can no longer be changed by the operation. We characterize all fixed p

Sarah Williamson

In this thesis we classify modules over a Witt-type Lie algebra and superalgebra such that when considered as modules of $\mathcal{U}(\mathfrak{h})$ they are free of rank 1. We provide sufficient conditions for simplicity, and compute the action of an automorphism on the module. Lastly, we apply the weighting functor and show that the resulting modules are i

erhat Ozgur Catak, Samed Sivaslioglu, Kevser Sahinbas

In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. With the rapid developments of deep learning techniques, it is critical to take the security concern into account for the application of the algorithms. While machine learning offers significant advantages in terms o

Alonso Marco, Dominik Baumann, Majid Khadiv, Philipp Hennig

In the past decade, numerous machine learning algorithms have been shown to successfully learn optimal policies to control real robotic systems. However, it is common to encounter failing behaviors as the learning loop progresses. Specifically, in robot applications where failing is undesired but not catastrophic, many algorithms struggle with leveraging dat

Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko, Judy Hoffman

Generalizing deep neural networks to new target domains is critical to their real-world utility. In practice, it may be feasible to get some target data labeled, but to be cost-effective it is desirable to select a maximally-informative subset via active learning (AL). We study the problem of AL under a domain shift, called Active Domain Adaptation (Active D

Sanchita Paul, Sayan Gupta, Shamik Ghosh

Catch digraphs was introduced by Hiroshi Maehara in 1984 as an analog of intersection graphs where a family of pointed sets represents a digraph. After that Prisner continued his research particularly on interval catch digraphs by characterizing them diasteroidal triple free. It has numerous applications in the field of real world problems like network techn

Manas Gaur, Keyur Faldu, Amit Sheth

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domai

Federico Pernici, Matteo Bruni, Claudio Baecchi, Francesco Turchini

In class-incremental learning, a learning agent faces a stream of data with the goal of learning new classes while not forgetting previous ones. Neural networks are known to suffer under this setting, as they forget previously acquired knowledge. To address this problem, effective methods exploit past data stored in an episodic memory while expanding the fin

Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen

Large scale deep learning provides a tremendous opportunity to improve the quality of content recommendation systems by employing both wider and deeper models, but this comes at great infrastructural cost and carbon footprint in modern data centers. Pruning is an effective technique that reduces both memory and compute demand for model inference. However, pr

MicroBooNE collaboration, P. Abratenko, M. Alrashed, R. An

We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of $e^-$, $γ$, $μ^-$, $π^\pm$, and protons in a single liquid argon time projection chamber (LArTPC) readout plane. The network extends the single particle identifica

Vidit Saxena, Hugo Tullberg, Joakim Jaldén

Wireless links adapt the data transmission parameters to the dynamic channel state -- this is called link adaptation. Classical link adaptation relies on tuning parameters that are challenging to configure for optimal link performance. Recently, reinforcement learning has been proposed to automate link adaptation, where the transmission parameters are modele

Hanwen Zhang, Owen D. Miller

Coupled mode theory (CMT) is a powerful framework for decomposing interactions between electromagnetic waves and scattering bodies into resonances and their couplings with power-carrying channels. It has widespread use in few-resonance, weakly coupled resonator systems across nanophotonics, but cannot be applied to the complex scatterers of emerging importan

Emanuele Fiandrini, Nicola Tomassetti, Bruna Bertucci, Federico Donnini

Galactic cosmic rays (CRs) inside the heliosphere are affected by solar modulation. To investigate this phenomenon and its underlying physical mechanisms, we have performed a data-driven analysis of the temporal dependence of the CR proton flux over the solar cycle. The modulation effect was modeled by means of stochastic simulations of cosmic particles in t

Sami Atailia, Abdelkader Dehici, Najeh Redjel

We prove that a closed convex subset $C$ of a real Hilbert space $X$ has the fixed point property for $(c)$-mappings if and only if $C$ is bounded. Some convergence results about the iterations are obtained.

Emily Barnard, Eric J. Hanson

Let $\Lambda$ be a finite-dimensional associative algebra over a field. A semibrick pair is a finite set of $\Lambda$-modules for which certain Hom- and Ext-sets vanish. A semibrick pair is completable if it can be enlarged so that a generating condition is satisfied. We prove that if $\Lambda$ is $\tau$-tilting finite with at most 3 simple modules, then the

Carlos A. Loza, Laura L. Colgin

We propose a generative model for single-channel EEG that incorporates the constraints experts actively enforce during visual scoring. The framework takes the form of a dynamic Bayesian network with depth in both the latent variables and the observation likelihoods-while the hidden variables control the durations, state transitions, and robustness, the obser

Andrea Apicella, Salvatore Giugliano, Francesco Isgrò, Roberto Prevete

This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence (XAI), to construct explanations for the behaviour of Machine Learning (ML) models in terms of middle-level features. One can isolate two different ways to provide explanations in the context of XAI: low and middle-level explanations. Middle-level explanations

Hugo P. Maia, Silvio C. Ferreira, Marcelo L. Martins

Far beyond its relevance for commercial and political marketings, opinion formation and decision making processes are central for representative democracy, government functioning, and state organization. In the present report, a stochastic agent-based model is investigated. The model assumes that bounded confidence and homophily mechanisms drive both opinion

Jakub Takáč

We explore boundedness properties of kernel integral operators acting on rearrangement-invariant (r.i.) spaces. In particular, for a given r.i. space $X$ we characterize its optimal range partner, that is, the smallest r.i. space $Y$ such that the operator is bounded from $X$ to $Y$. We apply the general results to Lorentz spaces to illustrate their strength

Péter Biró, Avinatan Hassidim, Assaf Romm, Ran I. Shorrer

We study stable allocations in college admissions markets where students can attend the same college under different financial terms. The deferred acceptance algorithm identifies a stable allocation where funding is allocated based on merit. While merit-based stable allocations assign the same students to college, non-merit-based stable allocations may diffe

Elyes Balti

In this work, we present a framework analysis of full-duplex (FD) systems for Millimeter Wave (mmWave) analog architecture. Given that FD systems can double the ergodic capacity, such systems experience large losses caused by the loopback self-interference (SI). In addition, systems with analog architecture also suffer from other forms of losses mainly incur

S. Shajidul Haque, Bret Underwood

Exponential growth in the out-of-time-order correlator (OTOC) is an important potential signature of quantum chaos. The OTOC is quite simple to calculate for squeezed states, whose applications are frequently found in quantum optics and cosmology. We find that the OTOC for a generic highly squeezed quantum state is exponentially large, suggesting that highly

Qiuyun Zhu, Yves Atchade

Canonical correlation analysis (CCA) is a popular statistical technique for exploring relationships between datasets. In recent years, the estimation of sparse canonical vectors has emerged as an important but challenging variant of the CCA problem, with widespread applications. Unfortunately, existing rate-optimal estimators for sparse canonical vectors hav

Pietro Corvaja, Francesco Zucconi

Let X be a smooth quartic surface not containing lines, defined over a number field K. We prove that there are only finitely many bitangents to X which are defined over K. This result can be interpreted as saying that a certain surface, having vanishing irregularity, contains only finitely many rational points. In our proof, we use the geometry of lines of t

Doried Ghader

The rich and unconventional physics in layered 2D magnets can open new avenues for topological magnonics and magnon valleytronics. In particular, two-dimensional (2D) bilayer quantum magnets are gaining increasing attention due to their intriguing stacking-dependent magnetism, controllable ground states, and topological excitations induced by magnetic spin-o

Do Duc Thai, Duc-Viet Vu

We characterize the class of probability measures on a compact Kahler manifold such that the associated Monge-Ampère equation has a solution of finite pluricomplex energy. Our results are also valid in the big cohomology class setting.

Gangshan Jing, He Bai, Jemin George, Aranya Chakrabortty

Individual agents in a multi-agent system (MAS) may have decoupled open-loop dynamics, but a cooperative control objective usually results in coupled closed-loop dynamics thereby making the control design computationally expensive. The computation time becomes even higher when a learning strategy such as reinforcement learning (RL) needs to be applied to dea

Sonja Hossbach, Mathias Lemke, Julius Reiss

The stable operation of gas networks is an important optimization target. While for this task commonly finite volume methods are used, we introduce a new finite difference approach. With a summation by part formulation for the spatial discretization, we get well-defined fluxes between the pipes. This allows a simple and explicit formulation of the coupling c

Anna M. Brandenberger, Luc Devroye, Tommy Reddad

The Horton-Strahler number of a tree is a measure of its branching complexity; it is also known in the literature as the register function. We show that for critical Galton-Watson trees with finite variance conditioned to be of size $n$, the Horton-Strahler number grows as $\frac{1}{2}\log_2 n$ in probability. We further define some generalizations of this n

F. Richard Guo, Emilija Perković

In observational studies, when a total causal effect of interest is not identified, the set of all possible effects can be reported instead. This typically occurs when the underlying causal DAG is only known up to a Markov equivalence class, or a refinement thereof due to background knowledge. As such, the class of possible causal DAGs is represented by a ma

Douglas T. Pfeffer, Michael T. Jury

We establish versions of Szegő's distance formula and Widom's theorem on invertibility of (a family of) Toeplitz operators in a class of finite codimension subalgebras of uniform algebras, obtained by imposing a finite number of linear constraints. Each such algebra is naturally represented on a family of reproducing kernel Hilbert spaces, which play

J. M. Yao, A. Belley, R. Wirth, T. Miyagi

We report ab initio benchmark calculations of nuclear matrix elements (NMEs) for neutrinoless double-beta ($0νββ$) decays in light nuclei with mass number ranging from $A=6$ to $A=22$. We use the transition operator derived from light-Majorana neutrino exchange and evaluate the NME with three different methods: two variants of in-medium similarity renormaliz

Juan Alcaraz Maestre

We review in some detail the QCD corrections to the measurement of the forward-backward charge asymmetry of heavy quarks in the $\mathrm{e^+e^-\rightarrow Q\overline{Q}(g)}$ process at the Z pole. We show that the size of these corrections can be reduced by an order of magnitude by using simple cuts on jet acollinearity. Such a reduction is expected to lead

Ziye Ma, Somayeh Sojoudi

Although neural networks have been applied to several systems in recent years, they still cannot be used in safety-critical systems due to the lack of efficient techniques to certify their robustness. A number of techniques based on convex optimization have been proposed in the literature to study the robustness of neural networks, and the semidefinite progr

Arnab Dhani

The post-merger signal in binary black hole merger is described by linear, black-hole perturbation theory. Historically, this has been modeled using the dominant positive-frequency (corotating) fundamental mode. Recently, there has been a renewed effort in modeling the post-merger waveform using higher, positive-frequency overtones in an attempt to achieve g

Claudia Pérez-D'Arpino, Can Liu, Patrick Goebel, Roberto Martín-Martín

Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments, such as buildings and homes. While research on social navigation has focused mainly on the scalability with the number of pedestrians in open spaces, typical indoor environments present the additional challenge of constrained spaces such as corr

Jonathan Sterling, Robert Harper

The theory of program modules is of interest to language designers not only for its practical importance to programming, but also because it lies at the nexus of three fundamental concerns in language design: the phase distinction, computational effects, and type abstraction. We contribute a fresh "synthetic" take on program modules that treats modul

Xin Ye, Yezhou Yang

Despite the significant success at enabling robots with autonomous behaviors makes deep reinforcement learning a promising approach for robotic object search task, the deep reinforcement learning approach severely suffers from the nature sparse reward setting of the task. To tackle this challenge, we present a novel policy learning paradigm for the object se

Shi Wang

We show there exists a closed locally symmetric manifold $M$ modeled on $SL_n(\mathbb R)/SO(n)$, and a non-trivial homology class in degree $dim(M)-rank(M)$ represented by a totally geodesic submanifold that contains a circle factor. As a result, the comparison map $c^k:H_b^k(M,\mathbb R)\rightarrow H^k(M,\mathbb R)$ is not surjective in degree $k=dim(M)-ran

Rishi Bhandia, Bing Cheng, Tobias Brown-Heft, Shouvik Chatterjee

The $\mathrm{Co_2}$ family of ferromagnetic Heusler alloys have attracted interest due to their fully spin-polarized nature, making them ideal for applications in spintronic devices. More recently, the existence of room temperature time-reversal-breaking Weyl nodes near the Fermi level was predicted and confirmed in these systems. As a result of the presence

Sarah Brandsen, Kevin D. Stubbs, Henry D. Pfister

Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems, including some problems in quantum information theory. In this work, we apply RLNN to quantum hypothesis testing and determine the optimal measurement strategy for distinguishing between multiple quantum states $\{ ρ_{j} \}$ while minimizing the erro

Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng

Learning dexterous manipulation in high-dimensional state-action spaces is an important open challenge with exploration presenting a major bottleneck. Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail to effectively utilize combinations of h

Vivek Iyer, Arvind Agarwal, Harshit Kumar

Ontology Alignment is an important research problem that finds application in various fields such as data integration, data transfer, data preparation etc. State-of-the-art (SOTA) architectures in Ontology Alignment typically use naive domain-dependent approaches with handcrafted rules and manually assigned values, making them unscalable and inefficient. Dee

Shreejit Jadhav, Nikhil Mukund, Bhooshan Gadre, Sanjit Mitra

We present a novel Machine Learning (ML) based strategy to search for binary black hole (BBH) mergers in data from ground-based gravitational wave (GW) observatories. This is the first ML-based search that not only recovers all the compact binary coalescences (CBCs) in the first GW transients catalog (GWTC-1), but also makes a clean detection of GW151216 by

Sarah E. Gerard, Jacob Herrmann, Yi Xin, Kevin T. Martin

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labele

Osman Asif Malik, Stephen Becker

We propose a sampling-based method for computing the tensor ring (TR) decomposition of a data tensor. The method uses leverage score sampled alternating least squares to fit the TR cores in an iterative fashion. By taking advantage of the special structure of TR tensors, we can efficiently estimate the leverage scores and attain a method which has complexity

Chuanrong Li, Lin Shengshuo, Leo Z. Liu, Xinyi Wu

Although large-scale pretrained language models, such as BERT and RoBERTa, have achieved superhuman performance on in-distribution test sets, their performance suffers on out-of-distribution test sets (e.g., on contrast sets). Building contrast sets often re-quires human-expert annotation, which is expensive and hard to create on a large scale. In this work,

Jason Bell, Dragos Ghioca, Rahim Moosa

The isotrivial Mordell-Lang theorem of Moosa and Scanlon describes the set $X\cap\Gamma$ when $X$ is a subvariety of a semiabelian variety $G$ over a finite field $\mathbb{F}_q$ and $\Gamma$ is a finitely generated subgroup of $G$ that is invariant under the $q$-power Frobenius endomorphism $F$. That description is here made effective, and extended to arbitr

Martin Černý, Jan Bok, David Hartman, Milan Hladík

Incomplete cooperative games generalise the classical model of cooperative games by omitting the values of some of the coalitions. This allows to incorporate uncertainty into the model and study the underlying games as well as possible payoff distribution based only on the partial information. In this paper we perform a systematic study of incomplete games,

Jesper Nederlof, Karol Węgrzycki

We present an $\mathcal{O}^\star(2^{0.5n})$ time and $\mathcal{O}^\star(2^{0.249999n})$ space randomized algorithm for solving worst-case Subset Sum instances with $n$ integers. This is the first improvement over the long-standing $\mathcal{O}^\star(2^{n/2})$ time and $\mathcal{O}^\star(2^{n/4})$ space algorithm due to Schroeppel and Shamir (FOCS 1979). We b

S. Zúñiga-Fernández, A. Bayo, P. Elliott, C. Zamora

We seek to update the spectroscopy binary fraction of the SACY (Search for Associations Containing Young stars) sample taking in consideration all possible biases in our identification of binary candidates, such as activity and rotation. Using high-resolution spectroscopic observations we have produced $\sim$1300 cross-correlation functions (CCFs) to disenta

Mahdie Karbasi, Stefan Bleeck, Dorothea Kolossa

The estimation of speech intelligibility is still far from being a solved problem. Especially one aspect is problematic: most of the standard models require a clean reference signal in order to estimate intelligibility. This is an issue of some significance, as a reference signal is often unavailable in practice. In this work, therefore a non-intrusive speec

Ian McInerney, Eric C. Kerrigan, George A. Constantinides

First-order optimization solvers, such as the Fast Gradient Method, are increasingly being used to solve Model Predictive Control problems in resource-constrained environments. Unfortunately, the convergence rate of these solvers is significantly affected by the conditioning of the problem data, with ill-conditioned problems requiring a large number of itera

Jenna Samuel, Andrew Wetzel, Sierra Chapman, Erik Tollerud

We examine the prevalence, longevity, and causes of planes of satellite dwarf galaxies, as observed in the Local Group. We use 14 Milky Way/Andromeda-(MW/M31) mass host galaxies from the FIRE-2 simulations. We select the 14 most massive satellites by stellar mass within 300 kpc of each host and correct for incompleteness from the foreground galactic disc whe

Maria Bertozzi, Tara S. Holm, Emily Maw, Dusa McDuff

We consider the embedding capacity functions $c_{H_b}(z)$ for symplectic embeddings of ellipsoids of eccentricity $z$ into the family of nontrivial rational Hirzebruch surfaces $H_b$ with symplectic form parametrized by $b\in [0,1)$. This function was known to have an infinite staircase in the monotone cases ($b= 0$ and $ b= 1/3$). It is also known that for

Peter West, Ximing Lu, Ari Holtzman, Chandra Bhagavatula

Publicly available, large pretrained LanguageModels (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks that break the unidirectional assumption, such as paraphrasing or text-infilling, necessitating task-specific supervision. In this paper, we present Ref

Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang, Hong Yao

Quantum architecture search (QAS) is the process of automating architecture engineering of quantum circuits. It has been desired to construct a powerful and general QAS platform which can significantly accelerate current efforts to identify quantum advantages of error-prone and depth-limited quantum circuits in the NISQ era. Hereby, we propose a general fram

Tom Melia, Sridip Pal

We establish formulae for the asymptotic growth (with respect to the scaling dimension) of the number of operators in effective field theory, or equivalently the number of $S$-matrix elements, in arbitrary spacetime dimensions and with generic field content. This we achieve by generalising a theorem due to Meinardus and applying it to Hilbert series -- parti

Zvi Bern, Julio Parra-Martinez, Radu Roiban, Eric Sawyer

We present the two-body Hamiltonian and associated eikonal phase, to leading post-Minkowskian order, for infinitely many tidal deformations described by operators with arbitrary powers of the curvature tensor. Scattering amplitudes in momentum and position space provide systematic complementary approaches. For the tidal operators quadratic in curvature, whic

Matteo Bonetti, Elisa Bortolas, Alessandro Lupi, Massimo Dotti

Galaxies are self-gravitating structures composed by several components encompassing spherical, axial and triaxial symmetry. Although real systems feature heterogeneous components whose properties are intimately connected, semi-analytical approaches often exploit the linearity of the Poisson's equation to represent the potential and mass distribution of

Gideon Maschler, Robert Ream

We show that integrability of an almost complex structure in complex dimension $m$ is equivalent, in the presence of an almost hermitian metric, to $m(m-1)$ equations involving what we call shear operators. Inspired by this, we give an ansatz for Kähler metrics in dimension $m>1$, for which at most $m-1$ of these shear equations are non-trivial. The equation

Haoran Liao, Ian Convy, William J. Huggins, K. Birgitta Whaley

State-of-the-art classical neural networks are observed to be vulnerable to small crafted adversarial perturbations. A more severe vulnerability has been noted for quantum machine learning (QML) models classifying Haar-random pure states. This stems from the concentration of measure phenomenon, a property of the metric space when sampled probabilistically, a

Kiana Ehsani, Daniel Gordon, Thomas Nguyen, Roozbeh Mottaghi

Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on visual data such as images or videos. In this paper, we explore a novel approach, where we use human interaction and attention cues to investigate whether we can l

Pablo Lemos, Niall Jeffrey, Lorne Whiteway, Ofer Lahav

We use Density Estimation Likelihood-Free Inference, $Λ$ Cold Dark Matter simulations of $\sim 2M$ galaxy pairs, and data from Gaia and the Hubble Space Telescope to infer the sum of the masses of the Milky Way and Andromeda (M31) galaxies, the two main components of the Local Group. This method overcomes most of the approximations of the traditional timing

Kerrek Stinson

The Cahn-Hilliard reaction model, a nonlinear, evolutionary PDE, was introduced to model phase separation in lithium-ion batteries. Using Butler-Volmer kinetics for electrochemical consistency, this model allows lithium-ions to enter the domain via a nonlinear Robin-type boundary condition $\partial_\nu \mu = R(c,\mu)$ for the chemical potential $\mu$, with

Xingjian Li, Di Hu, Xuhong Li, Haoyi Xiong

Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples. To obtain better generalization, using the starting point as the reference (SPAR), either through weights or features, has been successfully applied to transfer learning as a regularizer. How