October 2020 arXiv papers — page 55

Dimuthu D. Arachchige, Yue Chen, Ian D. Walker, Isuru S. Godage

We propose a novel tri-fingered soft robotic gripper with decoupled stiffness and shape control capability for performing adaptive grasping with minimum system complexity. The proposed soft fingers adaptively conform to object shapes facilitating the handling of objects of different types, shapes, and sizes. Each soft gripper finger has an inextensible artic

Sekhar Jyoti Baishya

In this paper, we characterize finite group $G$ with unique proper non-abelian element centralizer. This improves \cite[Theorem 1.1]{nab}. Among other results, we have proved that if $C(a)$ is the proper non-abelian element centralizer of $G$ for some $a \in G$, then $\frac{C(a)}{Z(G)}$ is the Fitting subgroup of $\frac{G}{Z(G)}$, $C(a)$ is the Fitting subgr

Xiang Guo, Bo Li, Yuchao Dai, Tongxin Zhang

Novel view synthesis is a challenging problem in computer vision and robotics. Different from the existing works, which need the reference images or 3D models of the scene to generate images under novel views, we propose a novel paradigm to this problem. That is, we synthesize the novel view from only a 6-DoF camera pose directly. Although this setting is th

Saisai Yang, Tusheng Zhang

Consider stochastic differential equations (SDEs) in $\Rd$: $dX_t=dW_t+b(t,X_t)\d t$, where $W$ is a Brownian motion, $b(\cdot, \cdot)$ is a measurable vector field. It is known that if $|b|^2(\cdot, \cdot)=|b|^2(\cdot)$ belongs to the Kato class $\K_{d,2}$, then there is a weak solution to the SDE. In this article we show that if $|b|^2$ belongs to the Kato

Hongyu Ren, Jure Leskovec

One of the fundamental problems in Artificial Intelligence is to perform complex multi-hop logical reasoning over the facts captured by a knowledge graph (KG). This problem is challenging, because KGs can be massive and incomplete. Recent approaches embed KG entities in a low dimensional space and then use these embeddings to find the answer entities. Howeve

Haruhiro Fujita, Masatoshi Itagaki, Kenta Ichikawa, Yew Kwang Hooi

This study evaluates road surface object detection tasks using four Mask R-CNN models as a pre-study of surface deterioration detection of stone-made archaeological objects. The models were pre-trained and fine-tuned by COCO datasets and 15,188 segmented road surface annotation tags. The quality of the models were measured using Average Precisions and Averag

Xiaoxiao Li, Yangsibo Huang, Binghui Peng, Zhao Song

To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data representations, as a way to control the trade-off between data utility and vulnerability to inversion attacks. Our method is motivated by the theoretical insights of data separabi

Yangxia Hu, Maode Ma, Wenhuan Lu, Neal N. Xiong

This work studied embedding positions of digital audio watermarking in wavelet domain, to make beginners understand the nature of watermarking in a short time. Based on the theory of wavelet transform, this paper analyzed statistical distributions of each level after transformation and the features of watermark embedded in different transform levels. Through

Shreya Biswas, Upasak Das, Prasenjit Sarkhel

Higher duration of programs that involve legal protection may entail gradual positive changes in social norms that can be leveraged by potential beneficiaries in their favor. This paper examines the heterogeneous impact of the duration of exposure to gender-neutral reforms in the inheritance law in India on two latent domains of women empowerment: intrinsic,

Jangho Lee, Jaihyun Koh, Sungroh Yoon

Unsupervised representation learning has shown remarkable achievement by reducing the performance gap with supervised feature learning, especially in the image domain. In this study, to extend the technique of unsupervised learning to the speech domain, we propose the Momentum Contrast for VoxCeleb (MoCoVox) as a form of learning mechanism. We pre-trained th

Dennis Fischer, Tim A. Hartmann, Stefan Lendl, Gerhard J. Woeginger

We investigate the so-called recoverable robust assignment problem on balanced bipartite graphs with $2n$ vertices, a mainstream problem in robust optimization: For two given linear cost functions $c_1$ and $c_2$ on the edges and a given integer $k$, the goal is to find two perfect matchings $M_1$ and $M_2$ that minimize the objective value $c_1(M_1)+c_2(M_2

K. P. Singh, V. Girish, M. Pavana, Jan-Uwe Ness

Two long AstroSat Soft X-ray Telescope observations were taken of the third recorded outburst of the Symbiotic Recurrent Nova, V3890 Sgr. The first observing run, 8.1-9.9 days after the outburst, initially showed a stable intensity level with a hard X-ray spectrum that we attribute to shocks between the nova ejecta and the pre-existing stellar companion. On

Ayush Kumar, Mrinalini Shridhar, Sahithya Swaminathan, Teng Joon Lim

In this work, we present a lightweight IoT botnet detection solution, EDIMA, which is designed to be deployed at the edge gateway installed in home networks and targets early detection of botnets prior to the launch of an attack. EDIMA includes a novel two-stage Machine Learning (ML)-based detector developed specifically for IoT bot detection at the edge gat

Matthias Bolten, Eric de Sturler, Camilla Hahn

Krylov subspace recycling is a powerful tool for solving long series of large, sparse linear systems that change slowly. In PDE constrained shape optimization, these appear naturally, as hundreds or more optimization steps are needed with only small changes in the geometry. In this setting, however, applying Krylov subspace recycling can be difficult. As the

Andy Shih, Stefano Ermon

Inference in discrete graphical models with variational methods is difficult because of the inability to re-parameterize gradients of the Evidence Lower Bound (ELBO). Many sampling-based methods have been proposed for estimating these gradients, but they suffer from high bias or variance. In this paper, we propose a new approach that leverages the tractabili

Alexander Wei, Fred Zhang

We study the problem of improving the performance of online algorithms by incorporating machine-learned predictions. The goal is to design algorithms that are both consistent and robust, meaning that the algorithm performs well when predictions are accurate and maintains worst-case guarantees. Such algorithms have been studied in a recent line of works due t

David Bryant, Raúl Felipe, Mauricio Toledo-Acosta, Paul Tupper

Diversities are a generalization of metric spaces, where instead of the non-negative function being defined on pairs of points, it is defined on arbitrary finite sets of points. Diversities have a well-developed theory. This includes the concept of a diversity tight span that extends the metric tight span in a natural way. Here we explore the generalization

Isaac Elias, Heiga Zen, Jonathan Shen, Yu Zhang

Although neural end-to-end text-to-speech models can synthesize highly natural speech, there is still room for improvements to its efficiency and naturalness. This paper proposes a non-autoregressive neural text-to-speech model augmented with a variational autoencoder-based residual encoder. This model, called \emph{Parallel Tacotron}, is highly parallelizab

Quan Liu, Isabella M. Gaeta, Bryan A. Millis, Matthew J. Tyska

The unsupervised segmentation is an increasingly popular topic in biomedical image analysis. The basic idea is to approach the supervised segmentation task as an unsupervised synthesis problem, where the intensity images can be transferred to the annotation domain using cycle-consistent adversarial learning. The previous studies have shown that the macro-lev

Jun Seo, Young-Hyun Park, Sung-Whan Yoon, Jaekyun Moon

Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has also seen great interest. In this paper, we propose a learnable module for few-shot segmentation, the task-adaptive feature transformer (TAFT). TAFT linearly transforms task-speci

Gurbir Dhillon, Sam Raskin

We prove a localization theorem for affine $W$-algebras in the spirit of Beilinson--Bernstein and Kashiwara--Tanisaki. More precisely, for any non-critical regular weight $λ$, we identify $λ$-monodromic Whittaker $D$-modules on the enhanced affine flag variety with a full subcategory of Category $\mathscr{O}$ for the $W$-algebra. To identify the essential im

Sung Hwan Mun, Woo Hyun Kang, Min Hyun Han, Nam Soo Kim

In this paper, we propose a simple but powerful unsupervised learning method for speaker recognition, namely Contrastive Equilibrium Learning (CEL), which increases the uncertainty on nuisance factors latent in the embeddings by employing the uniformity loss. Also, to preserve speaker discriminability, a contrastive similarity loss function is used together.

Qiantong Xu, Alexei Baevski, Tatiana Likhomanenko, Paden Tomasello

Self-training and unsupervised pre-training have emerged as effective approaches to improve speech recognition systems using unlabeled data. However, it is not clear whether they learn similar patterns or if they can be effectively combined. In this paper, we show that pseudo-labeling and pre-training with wav2vec 2.0 are complementary in a variety of labele

Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui

Recently, SpineNet has demonstrated promising results on object detection and image classification over ResNet model. However, it is unclear if the improvement adds up when combining scale-permuted backbone with advanced efficient operations and compound scaling. Furthermore, SpineNet is built with a uniform resource distribution over operations. While this

Abhimanyu Dubey, Alex Pentland

The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning. In this paper, we study this in context of the contextual linear bandit: we consider a collection of agents cooperating to solve a common contextual bandit, while ensuring that their communication remains private. For this problem, we

Chengxin Xiao, Jianju Tang, Pei Zhao, Qingjun Tong

When atomically thin van der Waals (vdW) magnet forms long-period moiré pattern with a magnetic substrate, the sensitive dependence of interlayer magnetic coupling on the atomic registries can lead to moiré defined magnetization textures in the two-dimensional (2D) magnets. The recent discovery of 2D magnetic topological insulators such as MnBi2Te4 leads to

Rodrigo Santa Cruz, Leo Lebrat, Pierrick Bourgeat, Clinton Fookes

The study of neurodegenerative diseases relies on the reconstruction and analysis of the brain cortex from magnetic resonance imaging (MRI). Traditional frameworks for this task like FreeSurfer demand lengthy runtimes, while its accelerated variant FastSurfer still relies on a voxel-wise segmentation which is limited by its resolution to capture narrow conti

Ildoo Kim, Younghoon Kim, Sungwoong Kim

Data augmentation has been actively studied for robust neural networks. Most of the recent data augmentation methods focus on augmenting datasets during the training phase. At the testing phase, simple transformations are still widely used for test-time augmentation. This paper proposes a novel instance-level test-time augmentation that efficiently selects s

Jeongmin Chae, Songnam Hong

We study a pool-based sequential active learning (AL), in which one sample is queried at each time from a large pool of unlabeled data according to a selection criterion. For this framework, we propose two selection criteria, named expected-kernel-discrepancy (EKD) and expected-kernel-loss (EKL), by leveraging the particular structure of multiple kernel lear

Kai Han, Zongmai Cao, Shuang Cui, Benwei Wu

We study the problem of maximizing a non-monotone, non-negative submodular function subject to a matroid constraint. The prior best-known deterministic approximation ratio for this problem is $\frac{1}{4}-ε$ under $\mathcal{O}(({n^4}/ε)\log n)$ time complexity. We show that this deterministic ratio can be improved to $\frac{1}{4}$ under $\mathcal{O}(nr)$ tim

Zhiwei Liu, Xiaohan Li, Ziwei Fan, Stephen Guo

The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket. Existing methods solve this problem by assuming the items within the same basket are correlated by one semantic relation, thus optimizing the item embeddings. However, this assumption breaks when there exist multiple intents within a basket. For example,

Diego Mesquita, Amauri H. Souza, Samuel Kaski

Graph pooling is a central component of a myriad of graph neural network (GNN) architectures. As an inheritance from traditional CNNs, most approaches formulate graph pooling as a cluster assignment problem, extending the idea of local patches in regular grids to graphs. Despite the wide adherence to this design choice, no work has rigorously evaluated its i

Jiaxin Wu, Suxin Chen, Changpeng Chen, Yihan Ma

In this study, a newly pillared black phosphorene hybrid nanostructures (PBP) is constructed with its thermal stability as well as thermal conductivity are studied by performing equilibrium molecular dynamics simulation (EMD). The results show that the curvature of the nanotube and the intense thermal vibration of the atoms are responsible for the failure of

K. C. Kharkwal, Roumita Roy, Harish Kumar, A. K. Bera

The 3$d$-5$d$ based double perovskites offer an ideal playground to study the interplay between electron correlation ($U$) and spin-orbit coupling (SOC) effect, showing exotic physics. The Sr$_2$FeIrO$_6$ is an interesting member in this family with ionic distribution of Fe$^{3+}$ (3$d^5$) and Ir$^{5+}$ (5$d^4$) where the later is believed to be nonmagnetic

Sung Hwan Mun, Woo Hyun Kang, Min Hyun Han, Nam Soo Kim

This paper describes our submission to Task 1 of the Short-duration Speaker Verification (SdSV) challenge 2020. Task 1 is a text-dependent speaker verification task, where both the speaker and phrase are required to be verified. The submitted systems were composed of TDNN-based and ResNet-based front-end architectures, in which the frame-level features were

Rohan Abraham, Valentina Shuman, Leonid Portsel, Anatoly Lodygin

Interstitial magnesium acts as a moderately deep double donor in silicon, and is relatively easily introduced by diffusion. Unlike the case of the chalcogen double donors, the binding energies of the even-parity valley-orbit excited states 1sT$_2$ and 1sE have remained elusive. Here we report on temperature dependence absorption measurements focusing on the

Huiling Jiang, Qing Li, Yong Jiang, Gengbiao Shen

Machine learning (ML) has seen a significant surge and uptake across many diverse applications. The high flexibility, adaptability and computing capabilities it provides extends traditional approaches used in multiple fields including network operation and management. Numerous surveys have explored ML in the context of networking, such as traffic engineering

Adeli Hutton

The flipped classroom technique has recently been a focus of attention for many math instructors and pedagogical researchers. Although research on the subject has greatly increased in recent years, it is still debated whether the flipped classroom technique can significantly increase the overall success of students in undergraduate math courses. While there

Yaser Faghan, Nancirose Piazza, Vahid Behzadan, Ali Fathi

Deep Reinforcement Learning (DRL) has become an appealing solution to algorithmic trading such as high frequency trading of stocks and cyptocurrencies. However, DRL have been shown to be susceptible to adversarial attacks. It follows that algorithmic trading DRL agents may also be compromised by such adversarial techniques, leading to policy manipulation. In

Sheng-Chieh Lin, Jheng-Hong Yang, Jimmy Lin

We present an approach to ranking with dense representations that applies knowledge distillation to improve the recently proposed late-interaction ColBERT model. Specifically, we distill the knowledge from ColBERT's expressive MaxSim operator for computing relevance scores into a simple dot product, thus enabling single-step ANN search. Our key insight i

Wanli Li, Tieyun Qian

Manual annotation of the labeled data for relation extraction is time-consuming and labor-intensive. Semi-supervised methods can offer helping hands for this problem and have aroused great research interests. Existing work focuses on mapping the unlabeled samples to the classes to augment the labeled dataset. However, it is hard to find an overall good mappi

Caroline Davis, Jasmine Powell, Rebecca R. Winarski, Jonguk Yang

Recent work of Dylan Thurston gives a condition for when a post-critically finite branched self-cover of the sphere is equivalent to a rational map. We apply D. Thurston's positive criterion for rationality to give a new proof of a theorem of Rees, Shishikura, and Tan about the mateability of quadratic polynomials when one polynomial is in the main molec

Guy Blanc, Jane Lange, Li-Yang Tan

We consider the problem of designing query strategies for priced information, introduced by Charikar et al. In this problem the algorithm designer is given a function $f : \{0,1\}^n \to \{-1,1\}$ and a price associated with each of the $n$ coordinates. The goal is to design a query strategy for determining $f$'s value on unknown inputs for minimum cost.

Jamison R. Ebert, Vamsi K. Amalladinne, Jean-Francois Chamberland, Krishna R. Narayanan

This article seeks to advance coded compressed sensing (CCS) as a practical scheme for unsourced random access. The original CCS algorithm features a concatenated structure where an inner code is tasked with support recovery, and an outer tree code conducts message disambiguation. Recently, a link between CCS and sparse regression codes was established, lead

Meng Jia, Matthew Kyan

Implicit function based surface reconstruction has been studied for a long time to recover 3D shapes from point clouds sampled from surfaces. Recently, Signed Distance Functions (SDFs) and Occupany Functions are adopted in learning-based shape reconstruction methods as implicit 3D shape representation. This paper proposes a novel method for learning occupanc

Bo Fu, William Smith, Denise Rizzo, Matthew Castanier

For robot swarms operating on complex missions in an uncertain environment, it is important that the decision-making algorithm considers both heterogeneity and uncertainty. This paper presents a stochastic programming framework for the vehicle routing problem with stochastic travel energy costs and heterogeneous vehicles and tasks. We represent the heterogen

Devendra Singh Sachan, Lingfei Wu, Mrinmaya Sachan, William Hamilton

Prior work on automated question generation has almost exclusively focused on generating simple questions whose answers can be extracted from a single document. However, there is an increasing interest in developing systems that are capable of more complex multi-hop question generation, where answering the questions requires reasoning over multiple documents

Chao Zhang, Yuefang Wu, Xunchuan Liu, Sheng-li Qin

Sixty five Planck Galactic cold clumps (PGCCs) from the first quadrant (IQuad) and thirty nine of PGCCs from the Anti-Center direction region (ACent) were observed in $^{12}$CO, $^{13}$CO and C$^{18}$O J=1-0 lines using the PMO 13.7-m telescope. All the targets were detected with all the three lines, except for 12 IQuad and 8 ACent PGCCs without C$^{18}$O de

Gelareh Mohammadi, Patrik Vuilleumier

Emotions are an inseparable part of human nature affecting our behavior in response to the outside world. Although most empirical studies have been dominated by two theoretical models including discrete categories of emotion and dichotomous dimensions, results from neuroscience approaches suggest a multi-processes mechanism underpinning emotional experience

Colin Samplawski, Jannik Wolff, Tassilo Klein, Moin Nabi

The task of zero-shot learning (ZSL) requires correctly predicting the label of samples from classes which were unseen at training time. This is achieved by leveraging side information about class labels, such as label attributes or word embeddings. Recently, attention has shifted to the more realistic task of generalized ZSL (GZSL) where test sets consist o

Zhiyan Ding, Qin Li, Jianfeng Lu, Stephen J. Wright

The Underdamped Langevin Monte Carlo (ULMC) is a popular Markov chain Monte Carlo sampling method. It requires the computation of the full gradient of the log-density at each iteration, an expensive operation if the dimension of the problem is high. We propose a sampling method called Random Coordinate ULMC (RC-ULMC), which selects a single coordinate at eac

Gang-Xuan Lin, Shih-Wei Hu, Chun-Shien Lu

In this paper, we reformulate the non-convex $\ell_q$-norm minimization problem with $q\in(0,1)$ into a 2-step problem, which consists of one convex and one non-convex subproblems, and propose a novel iterative algorithm called QISTA ($\ell_q$-ISTA) to solve the $\left(\ell_q\right)$-problem. By taking advantage of deep learning in accelerating optimization

Rithesh Kumar, Kundan Kumar, Vicki Anand, Yoshua Bengio

In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling). Audio upsampling is an important problem since productionizing generative speech technology requires operating at high sampling rates. Such applications use audio at a resolution of 44.1 kHz or 48 kHz, whereas current speech synthesis methods

Seungjai Lee

Let $G$ be a finitely generate nilpotent class-2 torsion-free group. We study how the zeta function enumerating normal subgroups of G varies on residue classes. In particular, we show that for small such $G$ of Hirsch length less than or equal to 7, the normal zeta functions are generically always rational functions on residue classes. We then show that ther

Aaron Baier-Reinio, Hans De Sterck

We use neural ordinary differential equations to formulate a variant of the Transformer that is depth-adaptive in the sense that an input-dependent number of time steps is taken by the ordinary differential equation solver. Our goal in proposing the N-ODE Transformer is to investigate whether its depth-adaptivity may aid in overcoming some specific known the

Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma

Over-parametrization is an important technique in training neural networks. In both theory and practice, training a larger network allows the optimization algorithm to avoid bad local optimal solutions. In this paper we study a closely related tensor decomposition problem: given an $l$-th order tensor in $(R^d)^{\otimes l}$ of rank $r$ (where $r\ll d$), can

Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti, Yaser P. Fallah

Situational awareness as a necessity in the connected and autonomous vehicles (CAV) domain is the subject of a significant number of researches in recent years. The driver's safety is directly dependent on the robustness, reliability, and scalability of such systems. Cooperative mechanisms have provided a solution to improve situational awareness by util

Lijun Zhu, Zhiyong Chen, David J. Hill, Shengli Du

The paper proposes a novel event-triggered control scheme for nonlinear systems based on the input-delay method. Specifically, the closed-loop system is associated with a pair of auxiliary input and output. The auxiliary output is defined as the derivative of the continuous-time input function, while the auxiliary input is defined as the input disturbance ca

Pranshu Pant, Amir Barati Farimani

Within the domain of Computational Fluid Dynamics, Direct Numerical Simulation (DNS) is used to obtain highly accurate numerical solutions for fluid flows. However, this approach for numerically solving the Navier-Stokes equations is extremely computationally expensive mostly due to the requirement of greatly refined grids. Large Eddy Simulation (LES) presen

Fenghe Hu, Yansha Deng, A. Hamid Aghvami

With the stringent requirement of receiving video from unmanned aerial vehicle (UAV) from anywhere in the stadium of sports events and the significant-high per-cell throughput for video transmission to virtual reality (VR) users, a promising solution is a cell-free multi-group broadcast (CF-MB) network with cooperative reception and broadcast access points (

Robin Walters, Jinxi Li, Rose Yu

Trajectory prediction is a critical part of many AI applications, for example, the safe operation of autonomous vehicles. However, current methods are prone to making inconsistent and physically unrealistic predictions. We leverage insights from fluid dynamics to overcome this limitation by considering internal symmetry in real-world trajectories. We propose

Matías Toro, David Darais, Chike Abuah, Joe Near

Language support for differentially-private programming is both crucial and delicate. While elaborate program logics can be very expressive, type-system based approaches using linear types tend to be more lightweight and amenable to automatic checking and inference, and in particular in the presence of higher-order programming. Since the seminal design of Fu

Leo Huang, Andrew Graven, David Bindel

A fundamental problem on graph-structured data is that of quantifying similarity between graphs. Graph kernels are an established technique for such tasks; in particular, those based on random walks and return probabilities have proven to be effective in wide-ranging applications, from bioinformatics to social networks to computer vision. However, random wal

Shutang You

This paper studies the frequency response using PV. Multiple control strategies are considered and simulated in the high PV ERCOT model, including inertia control, synthetic governor control, and AGC control. The impact of different parameters in PV inertia control and their correlation and impact on frequency response are analyzed. The simulation results sh

Yun Tang, Juan Pino, Changhan Wang, Xutai Ma

Attention-based sequence-to-sequence modeling provides a powerful and elegant solution for applications that need to map one sequence to a different sequence. Its success heavily relies on the availability of large amounts of training data. This presents a challenge for speech applications where labelled speech data is very expensive to obtain, such as autom

Minju Lee, Hee Oh

Let $G$ be a connected semisimple real algebraic group and $\Gamma$ a Zariski dense Anosov subgroup of $G$ with respect to a minimal parabolic subgroup $P$. Let $N$ be the maximal horospherical subgroup of $G$ given by the unipotent radical of $P$. We describe the $N$-ergodic decompositions of all Burger-Roblin measures as well as the $A$-ergodic decompositi

Muhammad Abdul-Mageed, Chiyu Zhang, Houda Bouamor, Nizar Habash

We present the results and findings of the First Nuanced Arabic Dialect Identification Shared Task (NADI). This Shared Task includes two subtasks: country-level dialect identification (Subtask 1) and province-level sub-dialect identification (Subtask 2). The data for the shared task covers a total of 100 provinces from 21 Arab countries and are collected fro

Deepan Muthirayan, Pramod P. Khargonekar

In this paper we provide provable regret guarantees for an online meta-learning receding horizon control algorithm in an iterative control setting. We consider the setting where, in each iteration the system to be controlled is a linear deterministic system that is different and unknown, the cost for the controller in an iteration is a general additive cost

Dominic Dall'Osto, Tobias Fischer, Michael Milford

Fully autonomous mobile robots have a multitude of potential applications, but guaranteeing robust navigation performance remains an open research problem. For many tasks such as repeated infrastructure inspection, item delivery, or inventory transport, a route repeating capability can be sufficient and offers potential practical advantages over a full navig

Erika L. Wagoner, Eduardo Rozo, Han Aung, Daisuke Nagai

The line-of-sight velocity dispersion profile of galaxy clusters exhibits a "kink" corresponding to the spatial extent of orbiting galaxies. Because the spatial extent of a cluster is correlated with the amplitude of the velocity dispersion profile, we can utilise this feature as a gravity-calibrated standard ruler. Specifically, the amplitude of the velocit

Stephanie McCoy, Nándor Sieben

We study a game where two players take turns selecting points of a convex geometry until the convex closure of the jointly selected points contains all the points of a given winning set. The winner of the game is the last player able to move. We develop a structure theory for these games and use it to determine the nim number for several classes of convex ge

Nobuyoshi Ohta

We revisit the general procedure of gauge fixings and ghosts based on BRST invariance principle. It is shown that when this is applied to the higher-derivative gauge fixings, it gives the correct structure of gauge fixings and ghosts including "third ghost", previously derived at one-loop level. This procedure is solely based on the symmetry principle and is

D. R. Reese, G. M. Mirouh, F. Espinosa Lara, M. Rieutord

Recent numerical and theoretical considerations have shown that low-degree acoustic modes in rapidly rotating stars follow an asymptotic formula and recent observations of pulsations in rapidly rotating delta Scuti stars seem to match these expectations. However, a key question is whether strong gradients or discontinuities can adversely affect this pattern

Grigoriy Blekherman, Kevin Shu

We consider two seemingly unrelated questions: the relationship between nonnegative polynomials and sums of squares on real varieties, and sparse semidefinite programming. This connection is natural when a real variety $X$ is defined by a quadratic square-free monomial ideal. In this case nonnegative polynomials and sums of squares on $X$ are also natural ob

Shutang You

This paper introduces a frequency response characteristic (FRC) curve and its application in high renewable power systems. In addition, the paper presents a method for fast frequency response assessment and frequency nadir prediction without performing dynamic simulations using detailed models. The proposed FRC curve and fast frequency response assessment me

Vincent Liu, Yi Yang, John D. Joannopoulos, Marin Soljačić

We theoretically introduce and study a three-dimensional Hofstadter model with linearly varying non-Abelian gauge potentials along all three dimensions. The model can be interpreted as spin-orbit coupling among a trio of Hofstadter butterfly pairs since each Cartesian surface ($xy$, $yz$, or $zx$) of the model reduces to a two-dimensional non-Abelian Hofstad

Ying Mao, Yuqi Fu, Wenjia Zheng, Long Cheng

In the past decade, we have witnessed a dramatically increasing volume of data collected from varied sources. The explosion of data has transformed the world as more information is available for collection and analysis than ever before. To maximize the utilization, various machine and deep learning models have been developed, e.g. CNN [1] and RNN [2], to stu

Shutang You, Hongyu Li, Shengyuan Liu, Kaiqi Sun

The power system frequency is important for the system overall stability. However, there does not exist a single measurement point of the system frequency due to the distributed nature of the system inertia and the small inconsistency of different generator rotor electrical speeds in one synchronized system. This paper proposed a new approach to calculate th

Jie Amy Yang, Jianyu Huang, Jongsoo Park, Ping Tak Peter Tang

In recommendation systems, practitioners observed that increase in the number of embedding tables and their sizes often leads to significant improvement in model performances. Given this and the business importance of these models to major internet companies, embedding tables for personalization tasks have grown to terabyte scale and continue to grow at a si

Wenxuan Zhou, Kevin Huang, Tengyu Ma, Jing Huang

Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible relations. In this paper, we propose two novel techniques, adaptive thresholding and localized context poolin

Britton Jeter, Avery E. Broderick

The Event Horizon Telescope mass estimate for M87* is consistent with the stellar dynamics mass estimate, and inconsistent with the gas dynamics mass estimates by up to $2\sigma$. We have previously explored a new gas dynamics model that incorporated sub-Keplerian gas velocities that could in principle explain the discrepancy in the stellar and gas dynamics

Xuemeng Zhang, Shutang You, Yong Liu, Yilu Liu

Solar photovoltaic (PV) generation is growing rapidly around the world. However, PV generation, based on inverter, is fundamentally different from conventional synchronous generators. It is of vital importance to understand the impacts of increased penetration of PV generation on power system dynamic performance. This paper investigates frequency response of

Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Wong

Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code. Current ML compilers rely on heuristics based algorithms to solve these optimization problems one at a time. However, this approach is not only hard to maintain but often leads to sub-optimal solutions especially for new

Izzet Coskun, Eric Riedl

We introduce and classify 1-clustered families of linear spaces in the Grassmannian $\mathbb{G}(k-1,n)$ and give applications to Lang-type conjectures. Let $X \subset \mathbb{P}^n$ be a very general hypersurface of degree $d$. Let $Z_L$ be the locus of points contained in a line of $X$. Let $Z_2$ be the locus of points on $X$ that are swept out by lines that

Tony Wang, DeaGyu Kim, Yifan Shi, Zhijian Hao

The recent advancements in nanoscale 3D printing and microfabrication techniques have reinvigorated research on microrobots. However, precise motion control of the microrobots on biological environments using compact actuation setups remains challenging to date. This work presents a novel control mechanism and contact design that enables bidirectional steeri

Mengyao Liu, Jonathan C. Tan, Joshua Marvil, Shuo Kong

To study the early phases of massive star formation, we present ALMA observations of SiO(5-4) emission and VLA observations of 6 cm continuum emission towards 32 Infrared Dark Cloud (IRDC) clumps, spatially resolved down to $\lesssim 0.05$ pc. Out of the 32 clumps, we detect SiO emission in 20 clumps, and in 11 of them the SiO emission is relatively strong a

Rajarshi Saha, Stefano Rini, Milind Rao, Andrea Goldsmith

In decentralized optimization, multiple nodes in a network collaborate to minimize the sum of their local loss functions. The information exchange between nodes required for this task, is often limited by network connectivity. We consider a setting in which communication between nodes is hindered by both (i) a finite rate-constraint on the signal transmitted

Naëmi Leo, Matteo Pancaldi, Sabri Koraltan, Pedro Villalba González

Collective dynamics in lithographically-defined artificial spin ices offer profound insights into emergent correlations and phase transitions of geometrically-frustrated Ising spin systems. Their temporal and spatial evolution are often simulated using kinetic Monte Carlo simulations, which rely on the precise knowledge of the switching barriers to obtain pr

Huy Vu, Gene Cheung, Yonina C. Eldar

While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely on training data, lack interpretability, and require tuning of a large parameter set. In this paper, we combine classical graph signal filtering with deep feature learning into a co

Wolfgang Kratsch, Fabian Koenig, Maximilian Roeglinger

Process mining is one of the most active research streams in business process management. In recent years, numerous methods have been proposed for analyzing structured process data. Yet, in many cases, it is only the digitized parts of processes that are directly captured from process-aware information systems, and manual activities often result in blind spo

Euclid Collaboration, M. Knabenhans, J. Stadel, D. Potter

We present a new, updated version of the EuclidEmulator (called EuclidEmulator2), a fast and accurate predictor for the nonlinear correction of the matter power spectrum. Percent-level accurate emulation is now supported in the eight-dimensional parameter space of $w_0w_a$CDM$+\sum m_\nu$models between redshift $z=0$ and $z=3$ for spatial scales within the r

Kent B. Vashaw

The Balmer spectrum of a monoidal triangulated category is an important geometric construction which is closely related to the problem of classifying thick tensor ideals. We prove that the forgetful functor from the Drinfeld center of a finite tensor category ${\mathbf{C}}$ to ${\mathbf{C}}$ extends to a monoidal triangulated functor between their correspond

Qiang Huang, Thomas Hain

Anomalous audio in speech recordings is often caused by speaker voice distortion, external noise, or even electric interferences. These obstacles have become a serious problem in some fields, such as high-quality music mixing and speech processing. In this paper, a novel approach using a temporal convolutional attention network (TCAN) is proposed to tackle t

Liyan Xie, Yao Xie

We present a new non-parametric statistic, called the weighed $\ell_2$ divergence, based on empirical distributions for sequential change detection. We start by constructing the weighed $\ell_2$ divergence as a fundamental building block for two-sample tests and change detection. The proposed statistic is proved to attain the optimal sample complexity in the

Ofer Busani, Timo Seppäläinen

We show that nontrivial bi-infinite polymer Gibbs measures do not exist in typical environments in the inverse-gamma (or log-gamma) directed polymer model on the planar square lattice. The precise technical result is that, except for measures supported on straight-line paths, such Gibbs measures do not exist in almost every environment when the weights are i

Maria Kalweit, Gabriel Kalweit, Moritz Werling, Joschka Boedecker

Challenging problems of deep reinforcement learning systems with regard to the application on real systems are their adaptivity to changing environments and their efficiency w.r.t. computational resources and data. In the application of learning lane-change behavior for autonomous driving, agents have to deal with a varying number of surrounding vehicles. Fu

Wenzhao Chen

We show there exists a topologically slice knot $K$ such that the knots $\{M^n(K)\}_{n=0}^\infty$ obtained by iterated satellite operations by the Mazur pattern span an infinite-rank summand of the smooth knot concordance group. This answers a question raised by Feller-Park-Ray.

Sanjeevi Krishnan, Crichton Ogle

Inverse categories are categories in which every morphism x has a unique pseudo-inverse y in the sense that xyx=x and yxy=y. Persistence modules from topological data analysis and similarly decomposable category representations factor through inverse categories. This paper gives a numerical condition, decidable when the indexing category is finite, character

Alexander Varchenko

We consider the KZ differential equations over $\mathbb C$ in the case, when the hypergeometric solutions are one-dimensional integrals. We also consider the same differential equations over a finite field $\mathbb F_p$. We study the polynomial solutions of these differential equations over $\mathbb F_p$, constructed in a previous work joint with V.\,Schecht

Bastien Lapierre, Per Moosavi

We present a new geometric approach to Floquet many-body systems described by inhomogeneous conformal field theory in 1+1 dimensions. It is based on an exact correspondence with dynamical systems on the circle that we establish and use to prove existence of (non)heating phases characterized by the (absence) presence of fixed or higher-periodic points of coor