October 2020 arXiv papers — page 29

Tô Tat Dat, Protin Frédéric, Nguyen T. T. Hang, Martel Jules

We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number $I(t)$ of infectious individuals at time $t$ in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning based

Jonguk Yang

Consider a polynomial $f$ of degree $d \geq 2$ that has a Siegel disk $\Delta_f$ with a rotation number of bounded type. We prove that there does not exist a hedgehog containing $\Delta_f$. Moreover, if the Julia set $J_f$ of $f$ is connected, then it is locally connected at the Siegel boundary $\partial \Delta_f$.

Kazuma Iwata, Koki Yamada, Yuichi Tanaka

We propose a blind deconvolution method for signals on graphs, with the exact sparseness constraint for the original signal. Graph blind deconvolution is an algorithm for estimating the original signal on a graph from a set of blurred and noisy measurements. Imposing a constraint on the number of nonzero elements is desirable for many different applications.

Kyung-Yong Park, Hyun-Jung Lee, Ki-Seok Kim

Although quantum phase transitions involved with Anderson localization had been investigated for more than a half century, the role of spin polarization in these metal-insulator transitions has not been clearly addressed as a function of both the range of interactions and energy scales. Based on the Anderson-Hartree-Fock study, we reveal that the spin polari

Xiaowei Jia, Beiyu Lin, Jacob Zwart, Jeffrey Sadler

Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This

Akito Daido, Atsuo Shitade, Youichi Yanase

Higher-rank electric/magnetic multipole moments are attracting attention these days as candidate order parameters for exotic material phases. However, quantum-mechanical formulation of those multipole moments is still an ongoing issue. In this paper, we propose a thermodynamic definition of electric quadrupole moments as a measure of symmetry breaking, follo

Stefany G. Medellin-Gonzalez, L. Arturo Urena-Lopez, Alma X. Gonzalez-Morales

Ultra-light bosons as dark matter has become a model of major interest in Cosmology, due to the possible imprint of a distinct signature in the cosmic structure both at the linear and non-linear scales. In this work we show that the equations of motion for density perturbations for this kind of models can be written in terms of a modified gravitational poten

Sudeep Salgia, Sattar Vakili, Qing Zhao

We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space. We propose a Gaussian process-based algorithm and establish its order-optimal regret performance (up to a poly-logarithmic factor). This is the first GP-based algorithm with an order-optimal regret guarantee. The proposed algorithm is rooted in the methodology o

Ryoichi Kase, Ken Nakashima

In this paper, we study the maximal length of maximal green sequences for quivers of type $\widetilde{\mathbf{D}}$ and $\widetilde{\mathbf{E}}$ by using the theory of tilting mutation. We show that the maximal length does not depend on the choice of the orientation, and determine it explicitly. Moreover, we give a program which counts all maximal green seque

You Zhang, Fei Jiang, Zhiyao Duan

Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion. Recently, researchers developed anti-spoofing techniques to improve the reliability of ASV systems against spoofing attacks. H

Ki-Seok Kim

To clarify the mathematical structure of the RG-derived holographic dual field theory, we rewrite the string-theory based conventionally utilized dual holographic effective field theory based on the ADM decomposition of the metric tensor. This comparison leads us to claim that the RG-derived emergent holographic dual field theory takes into account higher-de

Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim

Graph Neural Networks (GNNs) are the predominant technique for learning over graphs. However, there is relatively little understanding of why GNNs are successful in practice and whether they are necessary for good performance. Here, we show that for many standard transductive node classification benchmarks, we can exceed or match the performance of state-of-

Yang Sun, Dingshan Deng, Haibo Yuan

The Chinese Space Station Telescope (CSST) spectroscopic survey plans to deliver high-quality low-resolution ($R > 200$) slitless spectra for hundreds of millions of targets down to a limiting magnitude of about 21 mag, covering a large survey area (17500 deg$^2$) and a wide wavelength range (255-1000 nm by 3 bands GU, GV, and GI). In this work, we use empir

Dongwei Jiang, Wubo Li, Miao Cao, Wei Zou

Self-supervised visual pretraining has shown significant progress recently. Among those methods, SimCLR greatly advanced the state of the art in self-supervised and semi-supervised learning on ImageNet. The input feature representations for speech and visual tasks are both continuous, so it is natural to consider applying similar objective on speech represen

Hsi-Wei Yen, Pin-Gao Gu

We analyzed the archival data of the continuum emission at six wavelengths from 3 to 0.4 mm and 13CO and C18O (1-0, 2-1, and 3-2) lines in the protoplanetary disk around HD 142527 obtained with the Atacama Large Millimeter/submillimeter Array. We performed fitting to the spectral energy distributions obtained at the six wavelengths with the gray-body slab mo

Khang Manh Huynh

Most recently, in arXiv:1907.05360 [math.AP], we introduced the theory of heatable currents and proved Onsager's conjecture on Riemannian manifolds with boundary, where the weak solution has $B_{3,1}^{\frac{1}{3}}$ spatial regularity. In this sequel, by applying techniques from geometric microlocal analysis to construct the Hodge-Neumann heat kernel, we obta

Shutang You, Yilu Liu

The quality and accuracy of power system models is critical for simulation-based studies, especially for studying actual stability issues in large-scale systems. With the deployment of wide-area monitoring systems (WAMSs), the high-reporting-rate frequency measurement provides a trustworthy ground truth for validating models in frequency response studies. Th

Zhun Deng, Hangfeng He, Weijie J. Su

Algorithmic stability is a key characteristic to ensure the generalization ability of a learning algorithm. Among different notions of stability, \emph{uniform stability} is arguably the most popular one, which yields exponential generalization bounds. However, uniform stability only considers the worst-case loss change (or so-called sensitivity) by removing

Darren Creutz

Every stationary action of a strongly irreducible lattice or commensurator of such a latiice in a general semisimple group, with at least one higher-rank connected factor, either has finite stabilizers almost surely or finite index stabilizers almost surely.

Zhuqi Li, Can Wu, Sigurd Wagner, James C. Sturm

Autonomous vehicles are predicted to dominate the transportation industry in the foreseeable future. Safety is one of the major challenges to the early deployment of self-driving systems. To ensure safety, self-driving vehicles must sense and detect humans, other vehicles, and road infrastructure accurately, robustly, and timely. However, existing sensing te

Priyanka Manchanda, Pankaj Kumar, Pratibha Dev

Platinum diselenide (PtSe$_{2}$) is a recently-discovered extrinsic magnet, with its magnetism attributed to the presence of Pt-vacancies. The host material to these defects itself displays interesting structural and electronic properties, some of which stem from an unusually strong interaction between its layers. To date, it is not clear how the unique intr

Siwon Kim, Jihun Yi, Eunji Kim, Sungroh Yoon

To demystify the "black box" property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an input. Since existing methods replace each token with a predefined value (i.e., zero), the resulting sentence li

Yingshi Chen

Turing machine and decision tree have developed independently for a long time. With the recent development of differentiable models, there is an intersection between them. Neural turing machine(NTM) opens door for the memory network. It use differentiable attention mechanism to read/write external memory bank. Differentiable forest brings differentiable prop

Carlo Baldassi, Enrico M. Malatesta, Matteo Negri, Riccardo Zecchina

We analyze the connection between minimizers with good generalizing properties and high local entropy regions of a threshold-linear classifier in Gaussian mixtures with the mean squared error loss function. We show that there exist configurations that achieve the Bayes-optimal generalization error, even in the case of unbalanced clusters. We explore analytic

David Hanson, Alishba Imran, Abhinandan Vellanki, Sanjeew Kanagaraj

We outline the design and construction of novel robotic arms using machine perception, convolutional neural networks, and symbolic AI for logical control and affordance indexing. We describe our robotic arms built with a humanlike mechanical configuration and aesthetic, with 28 degrees of freedom, touch sensors, and series elastic actuators. The arms were mo

Hung-Ting Chen, Yu-Chieh Chao, Ta-Hsuan Chao, Wei-Yun Ma

Many neural network models nowadays have achieved promising performances in Chit-chat settings. The majority of them rely on an encoder for understanding the post and a decoder for generating the response. Without given assigned semantics, the models lack the fine-grained control over responses as the semantic mapping between posts and responses is hidden on

Ryan Rogers, Adrian Rivera Cardoso, Koray Mancuhan, Akash Kaura

We describe the privatization method used in reporting labor market insights from LinkedIn's Economic Graph, including the differentially private algorithms used to protect member's privacy. The reports show who are the top employers, as well as what are the top jobs and skills in a given country/region and industry. We hope this data will help governments a

Kayhan Gultekin, Sarah Burke-Spolaor, Tod R. Lauer, T. Joseph W. Lazio

We use Chandra X-ray observations to look for evidence of a recoiling black hole from the brightest cluster galaxy in Abell 2261 (A2261-BCG). A2261-BCG is a strong candidate for a recoiling black hole because of its large, flat stellar core, revealed by Hubble Space Telescope imaging observations. We took 100-ksec observations with Chandra and combined it wi

Jian Xu, Ali K. Jahromi, Changhuei Yang

Diffusing wave spectroscopy (DWS) is a well-known set of methods to measure the temporal dynamics of dynamic samples. In DWS, dynamic samples scatter the incident coherent light, and the information of the temporal dynamics is encoded in the scattered light. To record and analyze the light signal, there exist two types of methods - temporal sampling methods

Marcos Curi, Jose Zotin

This paper aims at the transient heat transfer analysis on extended surfaces with temperature-dependent thermal conductivity, constant internal heat generation, and five different geometries. The governing equations developed in this work consider the effects of the function that describes the shapes studied. To ensure a more effective thermal analysis, we i

Ru Zhang, Wenxin Sun, Jianyi Liu, Jingwen Li

Advanced Persistent Threat (APT) attack, also known as directed threat attack, refers to the continuous and effective attack activities carried out by an organization on a specific object. They are covert, persistent and targeted, which are difficult to capture by traditional intrusion detection system(IDS). The traffic generated by the APT organization, whi

Shaowen Xu, Fanhao Jia, Shunbo Hu, A. Sundaresan

Density functional theory (DFT) calculations are performed to predict the structural, electronic and magnetic properties of electrically neutral or charged few-atomic-layer (AL) oxides whose parent systems are based on polar perovskite $KTaO_{3}$. Their properties vary greatly with the number of ALs ($n_{AL}$) and the stoichiometric ratio. In the few-AL limi

Jie Du, Yadi Wu

We follow the approach developed by Beilinson-Lusztig-MacPherson and modified by Fu and the first author to investigate a new realization for the i-quantum groups U^j(n) of type B, building on the multiplication formulas discovered in [BKLW,Lem.~3.2]. This allows us to present U^j(n) via a basis and multiplication formulas by generators. We also establish a

Sina Alemohammad, Hossein Babaei, Randall Balestriero, Matt Y. Cheung

High dimensionality poses many challenges to the use of data, from visualization and interpretation, to prediction and storage for historical preservation. Techniques abound to reduce the dimensionality of fixed-length sequences, yet these methods rarely generalize to variable-length sequences. To address this gap, we extend existing methods that rely on the

Philip Bos, Mumtaz Hussain, David Simmons

Let $\psi:\mathbb R_+\to\mathbb R_+$ be a non-increasing function. A real number $x$ is said to be $\psi$-Dirichlet improvable if the system $$|qx-p|< \, \psi(t) \ \ {\text{and}} \ \ |q|<t$$ has a non-trivial integer solution for all large enough $t$. Denote the collection of such points by $D(\psi)$. In this paper, we prove a zero-infinity law valid for all

Changhoon Kim, Yi Ren, Yezhou Yang

Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement. One solution to these threats is model attribution, i.e., the identification of user-end models where the contents under question are generated from. Existing studies showed empirical feasibility of attribution through a central

Jiawei Shao, Haowei Zhang, Yuyi Mao, Jun Zhang

The recent advancements of three-dimensional (3D) data acquisition devices have spurred a new breed of applications that rely on point cloud data processing. However, processing a large volume of point cloud data brings a significant workload on resource-constrained mobile devices, prohibiting from unleashing their full potentials. Built upon the emerging pa

Jiaju Zhang, M. A. Rajabpour

The R\'enyi entropies of quasiparticle excitations in the many-body gapped systems show a remarkable universal picture which can be understood partially by combination of a semiclassical argument with the quantum effect of (in)distinguishability. The universal R\'enyi entropies are independent of the model, the quasiparticle momenta, and the connectedness of

Rory Mitchell, Eibe Frank, Geoffrey Holmes

SHAP (SHapley Additive exPlanation) values provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values. While exact calculation of SHAP values is computationally intractable in general, a recursive polynomial-time algorithm called TreeShap is available for decision tree models. However, despite its polynomial

K. Yue, J. T. Zhang, X. L. Tu, C. J. Shao

The very first in-ring reaction experiment at the HIRFL-CSR heavy-ion storage ring, namely proton elastic scattering on stable $^{58}$Ni nuclei, is presented. The circulating $^{58}$Ni$^{19+}$ ions with an energy of 95 MeV/u were interacting repeatedly with an internal hydrogen gas target in the CSRe experimental ring. Low energy proton recoils from the elas

Bhupinder Kaur, Mathieu Dugré, Aiman Hanna, Tristan Glatard

Software containers greatly facilitate the deployment and reproducibility of scientific data analyses in various platforms. However, container images often contain outdated or unnecessary software packages, which increases the number of security vulnerabilities in the images, widens the attack surface in the container host, and creates substantial security r

Yongjie Shi, Chengjie Yu

In this paper, we obtain a comparison of Steklov eigenvalues and Laplacian eigenvalues on graphs and discuss its rigidity. As applications of the comparison of eigenvalues, we obtain Lichnerowicz-type estimates and some combinatorial estimates for Steklov eigenvalues on graphs.

CMS Collaboration

Angular distributions of the decay B$^+$ $\to$ K$^*$(892)$^+\mu^+\mu^-$ are studied using events collected with the CMS detector in $\sqrt{s} =$ 8 TeV proton-proton collisions at the LHC, corresponding to an integrated luminosity of 20.0 fb$^{-1}$. The forward-backward asymmetry of the muons and the longitudinal polarization of the K$^*$(892)$^+$ meson are d

Carlo Russo, Sidong Liu, Antonio Di Ieva

Pre-processing and Data Augmentation play an important role in Deep Convolutional Neural Networks (DCNN). Whereby several methods aim for standardization and augmentation of the dataset, we here propose a novel method aimed to feed DCNN with spherical space transformed input data that could better facilitate feature learning compared to standard Cartesian sp

Yongjie Shi, Chengjie Yu

In this paper, we obtain a Lichnerowicz-type estimate for the first Steklov eigenvalues on graphs and discuss its rigidity.

Ali Frotanpour, Justin Woods, Barry Farmer, Amrit P. Kaphle

We present the dynamic response of a connected Kagome artificial spin ice with emphasis on the effect of the vertex magnetization configuration on the mode characteristics. We use broadband ferromagnetic resonance (FMR) spectroscopy and micromagnetic simulations to identify and characterize resonant modes. We find the mode frequencies of elongated, single-do

Nicolas Charon, Thomas Pierron

In this paper, we revisit the notion of length measures associated to planar closed curves. These are a special case of area measures of hypersurfaces which were introduced early on in the field of convex geometry. The length measure of a curve is a measure on the circle $\mathbb{S}^1$ that intuitively represents the length of the portion of curve which tang

Zeeshan Ahmad, Victor Venturi, Hasnain Hafiz, Venkatasubramanian Viswanathan

The existence of passivating layers at the interfaces is a major factor enabling modern lithium-ion (Li-ion) batteries. Their properties determine the cycle life, performance, and safety of batteries. A special case is the solid electrolyte interphase (SEI), a heterogeneous multi-component film formed due to the instability and subsequent decomposition of th

A. Wofford, A. Vidal-García, A. Feltre, J. Chevallard

Among the nearest most metal-poor starburst-dwarf galaxies known, SBS 0335-052E is the most luminous in integrated nebular He II {\lambda}4686 emission. This makes it a unique target to test spectral synthesis models and spectral interpretation tools of the kind that will be used to interpret future rest-frame UV observations of primeval galaxies. Previous a

Cat P. Le, Mohammadreza Soltani, Robert Ravier, Vahid Tarokh

The design of handcrafted neural networks requires a lot of time and resources. Recent techniques in Neural Architecture Search (NAS) have proven to be competitive or better than traditional handcrafted design, although they require domain knowledge and have generally used limited search spaces. In this paper, we propose a novel framework for neural architec

Yueyang Zheng, Jingtao Shi

This paper is concerned with a linear-quadratic partially observed Stackelberg stochastic differential game with correlated state and observation noises, where the diffusion coefficient does not contain the control variable and the control set is not necessarily convex. Both the leader and the follower have their own observation equations, and the informatio

M. Breuhaus, J. Hahn, C. Romoli, B. Reville

It is generally held that >100 TeV emission from astrophysical objects unambiguously demonstrates the presence of PeV protons or nuclei, due to the unavoidable Klein-Nishina suppression of inverse Compton emission from electrons. However, in the presence of inverse Compton dominated cooling, hard high-energy electron spectra are possible. We show that the en

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

Detection performances on bounding box and segmentation mask outputs of Mask R-CNN models are evaluated. There are significant differences in detection performances of bounding boxes and segmentation masks, where the former is constantly superior to the latter. Harmonic values of precisions and recalls of linear cracks, joints, fillings, and shadows are sign

Benjamin J. Lynch

This report presents a three-dimensional (3D) numerical magnetohydrodynamics (MHD) model of the white-light coronagraph observational phenomena known as coronal inflows and in/out pairs. Coronal inflows in the LASCO/C2 field of view (approximately 2-6 Rs) were thought to arise from the dynamic and intermittent release of solar wind plasma associated with the

Yacine Ali-Haïmoud, Tristan L. Smith, Chiara M. F. Mingarelli

Within the next several years pulsar timing arrays (PTAs) are positioned to detect the stochastic gravitational-wave background (GWB) likely produced by the collection of inspiralling super-massive black holes binaries, and potentially constrain some exotic physics. So far most of the pulsar timing data analysis has focused on the monopole of the GWB, assumi

Tony Z. Zhao, Anusha Nagabandi, Kate Rakelly, Chelsea Finn

Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training tasks. However, the onerous data requirements of meta-training compounded with the challenge of learning from sensory inputs such as images have made meta-RL challenging to apply to

Pengcheng Guo, Florian Boyer, Xuankai Chang, Tomoki Hayashi

In this study, we present recent developments on ESPnet: End-to-End Speech Processing toolkit, which mainly involves a recently proposed architecture called Conformer, Convolution-augmented Transformer. This paper shows the results for a wide range of end-to-end speech processing applications, such as automatic speech recognition (ASR), speech translations (

Kwan Chuen Chan, Nico Hamaus

We investigate the application of volume statistics to probe the distribution of underdense regions in the large-scale structure of the Universe. This statistic measures the distortion of Eulerian volume elements relative to Lagrangian ones and can be built from tracer particles using tessellation methods. We apply Voronoi and Delaunay tessellation to study

Gang Wang, Qunxi Dong, Jianfeng Wu, Yi Su

Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia

Ziyu Xu, Aaditya Ramdas

We derive new algorithms for online multiple testing that provably control false discovery exceedance (FDX) while achieving orders of magnitude more power than previous methods. This statistical advance is enabled by the development of new algorithmic ideas: earlier algorithms are more "static" while our new ones allow for the dynamical adjustment of testing

Farzaneh Khoshnevisan, Min Chi

Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. While most of prior work has mainly focused on developing effective disease progression models using EHRs collected from an individual medical system, relatively little work has investigated building robust yet generalizable diagnosis m

Kohdai Kuroiwa, Yuya O. Nakagawa

The variational quantum eigensolver (VQE) is a promising algorithm to compute eigenstates and eigenenergies of a given quantum system that can be performed on a near-term quantum computer. Obtaining eigenstates and eigenenergies in a specific symmetry sector of the system is often necessary for practical applications of the VQE in various fields ranging from

Nayem Sk, Manas Chakrabortty, Abhik Kumar Sanyal

F(R) theory of gravity is claimed to admit a host of conserved currents under the imposition of Noether symmetry following various techniques. However, for a constrained system such as gravity, Noether symmetry is not on-shell. As a result, the symmetries do not necessarily satisfy the field equations in general, constraints in particular, unless the generat

Elfarouk Harb, Ho Shan Lam

In this paper, we study the problem of fair clustering on the $k-$center objective. In fair clustering, the input is $N$ points, each belonging to at least one of $l$ protected groups, e.g. male, female, Asian, Hispanic. The objective is to cluster the $N$ points into $k$ clusters to minimize a classical clustering objective function. However, there is an ad

Cheuk-Yin Wong

We study the stability of a hypothetical QED neutron, which consists of a color-singlet system of two $d$ quarks and a $u$ quark interacting with the QED interaction. As a quark cannot be isolated, the intrinsic motion of the three quarks in the lowest-energy state may lie predominantly in 1+1 dimensions, as in a $d$-$u$-$d$ open string. The attractive $d$-$

Dylain Airey, Steve Jackson, Bill Mance

A Cantor series expansion for a real number $x$ with respect to a basic sequence $Q=(q_1,q_2,\dots)$, where $q_i \geq 2$, is a representation of the form $x=a_0 + \sum_{i=1}^\infty \frac{a_i}{q_1q_2\cdots q_i}$ where $0 \leq a_i<q_i$. These generalize ordinary base $b$ expansions where $q_i=b$. Ki and Linton showed that for ordinary base $b$ expansions the s

Rufus Boyack, Frank Marsiglio

The one-dimensional hydrogen atom is an intriguing quantum mechanics problem that exhibits several properties which have been continually debated. In particular, there has been variance as to whether or not even-parity solutions exist, and specifically whether or not the ground state is an even-parity state with infinite negative energy. We study a "regulari

Ignace Aristide Minlend

We study overdetermined problems for fully nonlinear elliptic equations in subdomains $\O$ of the Euclidean sphere $\mathbb{S}^{N}$ and the hyperbolic space $\mathbb{H}^{N}$. We prove, the existence of a classical solution to the underlined equation forces $\O$ to be a geodesic ball in the ambient space. Our result extends to fully nonlinear equations, a sim

Can Gokler

We show that the Schr\"{o}dinger equation for the quantum harmonic oscillator can be derived as an approximation to the Newtonian mechanics of a classical harmonic oscillator subject to a random force for time intervals $O( m / \hbar)$, when $\hbar / m \ll 1$. Conversely, every solution to the Schr\"{o}dinger equation, including all the superposition states,

Khyathi Raghavi Chandu, Ruo-Ping Dong, Alan Black

Generating long form narratives such as stories and procedures from multiple modalities has been a long standing dream for artificial intelligence. In this regard, there is often crucial subtext that is derived from the surrounding contexts. The general seq2seq training methods render the models shorthanded while attempting to bridge the gap between these ne

Jayanta Mandi, Tias Guns

Solving optimization problems is the key to decision making in many real-life analytics applications. However, the coefficients of the optimization problems are often uncertain and dependent on external factors, such as future demand or energy or stock prices. Machine learning (ML) models, especially neural networks, are increasingly being used to estimate t

Sahand Sadeghi, Samuel Allison, Blake Betsill, Suyi Li

Via numerical simulation and experimental assessment, this study examines the use of origami folding to develop robotic jumping mechanisms with tailored nonlinear stiffness to improve dynamic performance. Specifically, we use Tachi-Miura Polyhedron (TMP) bellow origami -- which exhibits a nonlinear "strain-softening" force-displacement curve -- as a jumping

Layne Hall, Andy Hammerlindl

We explicitly construct a dynamically incoherent partially hyperbolic endomorphisms of $\mathbb{T}^2$ in the homotopy class of any linear expanding map with integer eigenvalues. These examples exhibit branching of centre curves along countably many circles, and thus exhibit a form of coherence that has not been observed for invertible systems.

Ryan R. Ferguson, Luca Dellantonio, Karl Jansen, Abdulrahim Al Balushi

Variational quantum eigensolvers (VQEs) combine classical optimization with efficient cost function evaluations on quantum computers. We propose a new approach to VQEs using the principles of measurement-based quantum computation. This strategy uses entagled resource states and local measurements. We present two measurement-based VQE schemes. The first intro

Marek Biskup, Stephan Gufler, Oren Louidor

We consider the Discrete Gaussian Free Field (DGFF) in domains $D_N\subseteq\mathbb Z^2$ arising, via scaling by $N$, from nice domains $D\subseteq\mathbb R^2$. We study the statistics of the values order $\sqrt{\log N}$ below the absolute maximum. Encoded as a point process on $D\times\mathbb R$, the scaled spatial distribution of these near-extremal level

Julian Chibane, Aymen Mir, Gerard Pons-Moll

In this work we target a learnable output representation that allows continuous, high resolution outputs of arbitrary shape. Recent works represent 3D surfaces implicitly with a Neural Network, thereby breaking previous barriers in resolution, and ability to represent diverse topologies. However, neural implicit representations are limited to closed surfaces

Christopher Dobronyi, Christian Gouriéroux

We introduce two models of non-parametric random utility for demand systems: the stochastic absolute risk aversion (SARA) model, and the stochastic safety-first (SSF) model. In each model, individual-level heterogeneity is characterized by a distribution $\pi\in\Pi$ of taste parameters, and heterogeneity across consumers is introduced using a distribution $F

Yunhai Han, Fei Liu, Michael C. Yip

The control and task automation of robotic surgical system is very challenging, especially in soft tissue manipulation, due to the unpredictable deformations. Thus, an accurate simulator of soft tissues with the ability of interacting with robot manipulators is necessary. In this work, we propose a novel 2D simulation framework for tool-tissue interaction. T

Tommaso Taddei, Lei Zhang

We present a general approach for the treatment of parameterized geometries in projection-based model order reduction. During the offline stage, given (i) a family of parameterized domains $\{ \Omega_{\mu}: \mu \in \mathcal{P} \} \subset \mathbb{R}^D$ where $\mu \in \mathcal{P} \subset \mathbb{R}^P$ denotes a vector of parameters, (ii) a parameterized mappin

Yujie Zhao, Xiaoming Huo

In optimization, it is known that when the objective functions are strictly convex and well-conditioned, gradient-based approaches can be extremely effective, e.g., achieving the exponential rate of convergence. On the other hand, the existing Lasso-type estimator in general cannot achieve the optimal rate due to the undesirable behavior of the absolute func

Jason W. Rocks, Pankaj Mehta

The bias-variance trade-off is a central concept in supervised learning. In classical statistics, increasing the complexity of a model (e.g., number of parameters) reduces bias but also increases variance. Until recently, it was commonly believed that optimal performance is achieved at intermediate model complexities which strike a balance between bias and v

Aubin Arroyo, Gerardo González Robert

L\"uroth series, like regular continued fractions, provide an interesting identification of real numbers with infinite sequences of integers. These sequences give deep arithmetic and measure-theoretic properties of subsets of numbers according to their growth. Although different, regular continued fractions and L\"uroth series share several properties. In th

Yufang Hou

Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS). However, these systems heavily depend on many hand-crafted linguistic features. In this paper, we propose a simple discourse context-aware BERT model for fine-grained IS classification. On the ISNotes corpus (Mar

Jaime M. Moya, Alannah M. Hallas, Vaideesh Loganathan, C. -L. Huang

New phases of matter emerge at the edge of magnetic instabilities. In local moment systems, such as heavy fermions, the magnetism can be destabilized by pressure, chemical doping, and, rarely, by magnetic field, towards a zero-temperature transition at a quantum critical point (QCP). Even more rare are instances of QCPs induced by pressure or doping in itine

Tair Akhmejanov, Balázs Elek

Let $\delta=(\delta_1,\ldots,\delta_n)$ be a string of letters $h$ and $v$. We define a Young tableau to be $\delta$-semistandard if the entries are weakly increasing along rows and columns, and the entries $i$ form a horizontal strip if $\delta_i=h$ and a vertical strip if $\delta_i=v$. We define $\delta$-promotion on such tableaux via a modified jeu-de-taq

Dalton N. Oliveira, Erico L. Rempel, Roman Chertovskih, Bidya B. Karak

The presence of chaotic transients in a nonlinear dynamo is investigated through numerical simulations of the 3D magnetohydrodynamic equations. By using the kinetic helicity of the flow as a control parameter, a hysteretic blowout bifurcation is conjectured to be responsible for the transition to dynamo, leading to a sudden increase in the magnetic energy of

Mike Wu, Jonathan Nafziger, Anthony Scodary, Andrew Maas

We introduce HarperValleyBank, a free, public domain spoken dialog corpus. The data simulate simple consumer banking interactions, containing about 23 hours of audio from 1,446 human-human conversations between 59 unique speakers. We selected intents and utterance templates to allow realistic variation while controlling overall task complexity and limiting v

Agostino Capponi, Zhaoyu Zhang

We propose a novel approach to infer investors' risk preferences from their portfolio choices, and then use the implied risk preferences to measure the efficiency of investment portfolios. We analyze a dataset spanning a period of six years, consisting of end of month stock trading records, along with investors' demographic information and self-assessed fina

Paris Giampouras, René Vidal, Athanasios Rontogiannis, Benjamin Haeffele

The Schatten-$p$ quasi-norm with $p\in(0,1)$ has recently gained considerable attention in various low-rank matrix estimation problems offering significant benefits over relevant convex heuristics such as the nuclear norm. However, due to the nonconvexity of the Schatten-$p$ quasi-norm, minimization suffers from two major drawbacks: 1) the lack of theoretica

Ofer Kfir, Valerio Di Giulio, F. Javier García de Abajo, Claus Ropers

We investigate theoretically the quantum-coherence properties of the cathodoluminescence (CL) emission produced by a temporally modulated electron beam. Specifically, we consider the quantum-optical correlations of CL from electrons that are previously shaped by a laser field. The main prediction here is the presence of phase correlations between the emitted

Zesen Qian, G. A. Kavvos, Lars Birkedal

We introduce coexponentials, a new set of modalities for Classical Linear Logic. As duals to exponentials, the coexponentials codify a distributed form of the structural rules of weakening and contraction. This makes them a suitable logical device for encapsulating the pattern of a server receiving requests from an arbitrary number of clients on a single cha

Silvia Ghilezan, Jovanka Pantović, Ivan Prokić, Alceste Scalas

This paper presents the first formalisation of the precise subtyping relation for asynchronous multiparty sessions. We show that our subtyping relation is sound (i.e., guarantees safe process replacement) and also complete: any extension of the relation is unsound. To achieve our results, we develop a novel session decomposition technique, from full session

Aya Abdelsalam Ismail, Mohamed Gunady, Héctor Corrada Bravo, Soheil Feizi

Saliency methods are used extensively to highlight the importance of input features in model predictions. These methods are mostly used in vision and language tasks, and their applications to time series data is relatively unexplored. In this paper, we set out to extensively compare the performance of various saliency-based interpretability methods across di

Kaushik Sampath, Jason D. Geder, Ravi Ramamurti, Marius D. Pruessner

We show how phasing between tandem bioinspired fins flapping at high-stroke amplitudes modulates rear fin thrust production and wake characteristics. Load cell thrust measurements show that the rear fin generates 25% more thrust than the front fin when it lags the latter by a quarter cycle, and performs 8% worse when it leads the front fin by the same amount

Erfan Esmaeili

In this thesis, we study the asymptotic structure of $p$-form theories on flat space. $p$-form theories are generalizations of Maxwell's theory of electrodynamics in which the gauge potential is a higher-rank differential form. As in the Maxwell theory, there is a choice of boundary conditions with an infinite-dimensional asymptotic symmetry group. For highe

Caleb Miller, Michael D. Schneider, Jem N. Corcoran, Jason Bernstein

We present a Bayesian data fusion method to approximate a posterior distribution from an ensemble of particle estimates that only have access to subsets of the data. Our approach relies on approximate probabilistic inference of model parameters through Monte Carlo methods, followed by an update and resample scheme related to multiple importance sampling to c

Chien-Sheng Wu, Steven Hoi, Caiming Xiong

Existing dialogue state tracking (DST) models require plenty of labeled data. However, collecting high-quality labels is costly, especially when the number of domains increases. In this paper, we address a practical DST problem that is rarely discussed, i.e., learning efficiently with limited labeled data. We present and investigate two self-supervised objec

A. E. Lovell, T. Kawano, S. Okumura, I. Stetcu

The Hauser-Feshbach fission fragment decay model, $\mathtt{HF^3D}$, which calculates the statistical decay of fission fragments, has been expanded to include multi-chance fission, up to neutron incident energies of 20 MeV. The deterministic decay takes as input pre-scission quantities - fission probabilities and the average energy causing fission - and post-

Rahul Singh

We study the Steinberg variety associated to matrix Schubert varieties, and develop a Robinson-Schensted type correspondence, $\tau\leftrightarrow(\Lambda,\mathsf Q,\mathsf P)$. Here $\tau$ is a partial permutation of size $p\times q$, $\Lambda$ an admissible signed Young diagram of size $p+q$, and $\mathsf P$ (resp. $\mathsf Q$) a standard Young tableau of

H. S. Tang, L. Li, M. Grossberg, Y. J. Liu

As further progress in the accurate and efficient computation of coupled partial differential equations (PDEs) becomes increasingly difficult, it has become highly desired to develop new methods for such computation. In deviation from conventional approaches, this short communication paper explores a computational paradigm that couples numerical solutions of