October 2020 arXiv papers — page 22

Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash

Self-supervised learning aims to learn good representations with unlabeled data. Recent works have shown that larger models benefit more from self-supervised learning than smaller models. As a result, the gap between supervised and self-supervised learning has been greatly reduced for larger models. In this work, instead of designing a new pseudo task for se

Letian Wang, Liting Sun, Masayoshi Tomizuka, Wei Zhan

As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is becoming increasingly important. In order to generate safe and efficient actions, AVs need to not only predict the future behaviors of other traffic participants, but also be aware of the uncertainties associated with such behavi

Xingyong Zhang, Cuiling Liu

In this paper, we investigate the existence of solutions for a class of quasilinear elliptic system \begin{eqnarray*} \begin{cases}{ccc} -\mbox{div}(\phi_1(|\nabla u|)\nabla u)+V_1(x)\phi_1(|u|)u=\lambda F_u(x, u,v), \ \ x\in \mathbb R^N, -\mbox{div}(\phi_2(|\nabla v|)\nabla v)+V_2(x)\phi_2(|v|)v=\lambda F_v(x, u,v), \ \ x\in \mathbb R^N, u\in W^{1,\Phi_1}(\

Cheng Huang, Jing Guo, Kang Zhao, Fan Cui

Here we report a pressure-induced reemergence of superconductivity in recently discovered superconductor K2Mo3As3, which is the first experimental case observed in quasi-one-dimensional superconductors. We find that, after full suppression of the ambient-pressure superconducting (SC-I) state at 8.7 GPa, an intermediary non-superconducting state sets in and p

Yihao Chen, Alexander Lerch

Automatic lyrics generation has received attention from both music and AI communities for years. Early rule-based approaches have~---due to increases in computational power and evolution in data-driven models---~mostly been replaced with deep-learning-based systems. Many existing approaches, however, either rely heavily on prior knowledge in music and lyrics

Subeen Pang, George Barbastathis

Solution of the discretized Lippmann-Schwinger equation in the spatial frequency domain involves the inversion of a linear operator specified by the scattering potential. To regularize this inevitably ill-conditioned problem, we propose a machine learning approach: a recurrent neural network with long short-term memory (LSTM) and with the null space projecti

Xuetao Jiang, Binbin Yong, Soheila Garshasbi, Jun Shen

CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective functions are not thoroughly compatible wit

Yang Qian, Yuanchun Jiang, Yidong Chai, Yezheng Liu

Topic models provide a flexible and principled framework for exploring hidden structure in high-dimensional co-occurrence data and are commonly used natural language processing (NLP) of text. In this paper, we design and implement a Java package, TopicModel4J, which contains 13 kinds of representative algorithms for fitting topic models. The TopicModel4J in

Chuanpeng Hou, Jiansen He, Xingyu Zhu, Ying Wang

By analyzing the magnetosheath measurements from MMS, we obtain the statistical results for the contribution of magnetic reconnection (MR) events at electron scales to the energy dissipation of coherent structures. The Partial Variance of Increments (PVI) method is employed to find coherent structures in the magnetic field data. The current sheet structures

Bryan Chu, Mohammad Farazmand

We develop a data-driven method, based on semi-supervised classification, to predict the asymptotic state of multistable systems when only sparse spatial measurements of the system are feasible. Our method predicts the asymptotic behavior of an observed state by quantifying its proximity to the states in a precomputed library of data. To quantify this proxim

Md Taufeeq Uddin, Shaun Canavan

The quantification of visual affect data (e.g. face images) is essential to build and monitor automated affect modeling systems efficiently. Considering this, this work proposes quantified facial Temporal-expressiveness Dynamics (TED) to quantify the expressiveness of human faces. The proposed algorithm leverages multimodal facial features by incorporating s

Yucheng He, Jing-Xin Liu, F. -Q. Guo, Lei-Lei Yan

We present a scheme to realize multiple-qubit quantum state transfer and quantum logic gate by combining the advantages of Vitanov-style pulses and dressed-state-based shortcut to adiabaticity (STA) in Rydberg atoms. The robustness of the scheme to spontaneous emission can be achieved by reducing the population of Rydberg excited states through the STA techn

Stefany Moreno-Gámez, Alma Dal Co, Simon van Vliet, Martin Ackermann

Nutrient limitation is one of the most common triggers of antibiotic tolerance and persistence. Here, we present two microfluidic setups to study how spatial and temporal variation in nutrient availability lead to increased survival of bacteria to antibiotics. The first setup is designed to mimic the growth dynamics of bacteria in spatially structured popula

Eric Risser

This paper presents a light-weight, high-quality texture synthesis algorithm that easily generalizes to other applications such as style transfer and texture mixing. We represent texture features through the deep neural activation vectors within the bottleneck layer of an auto-encoder and frame the texture synthesis problem as optimal transport between the a

Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen

We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal image$\leftrightarrow$text models, and mathematical problem solving. In all cases autoregressive Transformers smoothly improve in performance as model size and compute budgets increase, following a power-law plus constant scali

Da Xu

Tensor regression models, such as CP regression and Tucker regression, have many successful applications in neuroimaging analysis where the covariates are of ultrahigh dimensionality and possess complex spatial structures. The high-dimensional covariate arrays, also known as tensors, can be approximated by low-rank structures and fit into the generalized lin

Luke Rast, Jan Drugowitsch

The adaptation of neural codes to the statistics of their environment is well captured by efficient coding approaches. Here we solve an inverse problem: characterizing the objective and constraint functions that efficient codes appear to be optimal for, on the basis of how they adapt to different stimulus distributions. We formulate a general efficient codin

Miranda Yew, Miroslav D. Filipović, Milorad Stupar, Sean D. Points

We present a new optical sample of three Supernova Remnants and 16 Supernova Remnant (SNR) candidates in the Large Magellanic Cloud(LMC). These objects were originally selected using deep H$\alpha$, [SII] and [OIII] narrow-band imaging. Most of the newly found objects are located in less dense regions, near or around the edges of the LMC's main body. Togethe

Luke Lindemann, Claire Bowern

This paper outlines the creation of three corpora for multilingual comparison and analysis of the Voynich manuscript: a corpus of Voynich texts partitioned by Currier language, scribal hand, and transcription system, a corpus of 294 language samples compiled from Wikipedia, and a corpus of eighteen transcribed historical texts in eight languages. These corpo

Taekyun Kim, Dae San Kim, Lee-Chae Jang, Hyunseok Lee

We introduce degenerate Hermite polynomials as a degenerate version of the ordinary Hermite polynomials. Then, among other things, by using the formula about representing one lambda-Sheffer polynomial in terms of other lambda-Sheffer polynomials we represent the degenerate Hermite polynomials in terms of the higher-order degenerate Bernoulli, Euler, and Frob

Erhan Bayraktar, Thomas Bernhardt

We show that the barrier function in Root's solution to the Skorokhod embedding problem is continuous and finite at every point where the target measure has no atom and its absolutely continuous part is locally bounded away from zero.

Daniel Armstrong, André Jonasson

In this paper, we present a new algorithm that extends RRT* and RT-RRT* for online path planning in complex, dynamic environments. Sampling-based approaches often perform poorly in environments with narrow passages, a feature common to many indoor applications of mobile robots as well as computer games. Our method extends RRT-based sampling methods to enable

Sharan Nayak, Michael W. Otte

Bidirectional motion planning approaches decrease planning time, on average, compared to their unidirectional counterparts. In single-query feasible motion planning, using bidirectional search to find a continuous motion plan requires an edge connection between the forward and reverse search trees. Such a tree-tree connection requires solving a two-point Bou

Zherong Pan, Kris Hauser

We present non-convex maximal dissipation principle (NMDP), a time integration scheme for articulated bodies with simultaneous contacts. Our scheme resolves contact forces via the maximal dissipation principle (MDP). Prior MDP solvers compute contact forces via convex programming by assuming linearized dynamics integrated using the forward multistep scheme.

Naoto Shida

We consider the Kato-Ponce type inequality for bilinear pseudo-differential operators with $S_{0,0}$-type symbols in the scale of Besov spaces. In particular, the borderline whether the boundedness of those operators holds or not is discussed.

Erik Daxberger, Eric Nalisnick, James Urquhart Allingham, Javier Antorán

The Bayesian paradigm has the potential to solve core issues of deep neural networks such as poor calibration and data inefficiency. Alas, scaling Bayesian inference to large weight spaces often requires restrictive approximations. In this work, we show that it suffices to perform inference over a small subset of model weights in order to obtain accurate pre

Vikram Rathee, Daniel L. Blair, Jeffrey S. Urbach

We report direct measurements of spatially resolved surface stresses of a dense suspension during large amplitude oscillatory shear (LAOS) in the discontinuous shear thickening regime using boundary stress microscopy. Consistent with previous studies, bulk rheology shows a dramatic increase in the complex viscosity above a frequency-dependent critical strain

Jonathan Ponader, Sandip Kundu, Yan Solihin

The increased use of Convolutional Neural Networks (CNN) in mission critical systems has increased the need for robust and resilient networks in the face of both naturally occurring faults as well as security attacks. The lack of robustness and resiliency can lead to unreliable inference results. Current methods that address CNN robustness require hardware m

Michael Burr, Suddhasattwa Das, Christian Wolf, Yun Yang

We investigate the computability (in the sense of computable analysis) of the topological pressure $P_{\rm top}(\phi)$ on compact shift spaces $X$ for continuous potentials $\phi:X\to {\mathbb R}$. This question has recently been studied for subshifts of finite type (SFTs) and their factors (Sofic shifts). We develop a framework to address the computability

Kookjin Lee, Eric J. Parish

This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs. This extension allows NODEs to learn multiple dynamics specified by the input parameter instances. Our extension is inspired by the concept of parameterized ordinary differential equations, which are widely inve

Pedro Quaresma, Walther Neuper, João Marcos

The 9th International Workshop on Theorem-Proving Components for Educational Software (ThEdu'20) was scheduled to happen on June 29 as a satellite of the IJCAR-FSCD 2020 joint meeting, in Paris. The COVID-19 pandemic came by surprise, though, and the main conference was virtualised. Fearing that an online meeting would not allow our community to fully reprod

Maciej Besta, Marc Fischer, Tal Ben-Nun, Dimitri Stanojevic

Developing high-performance and energy-efficient algorithms for maximum matchings is becoming increasingly important in social network analysis, computational sciences, scheduling, and others. In this work, we propose the first maximum matching algorithm designed for FPGAs; it is energy-efficient and has provable guarantees on accuracy, performance, and stor

Amel Derras-Chouk, Eugene M. Chudnovsky

We study the impact of an exchange-reducing defect on a skyrmion for a thin film of finite thickness. Attraction of the skyrmion to a defect is demonstrated in a lattice model by computing the micromagnetic energy accounting for the exchange, Dzyaloshinskii-Moriya interaction, magnetic anisotropy, and dipole-dipole coupling. The spiraling dynamics of the sky

Chi-Fang Chen, Kohtaro Kato, Fernando G. S. L. Brandão

We study whether one can write a Matrix Product Density Operator (MPDO) as the Gibbs state of a quasi-local parent Hamiltonian. We conjecture this is the case for generic MPDO and give supporting evidences. To investigate the locality of the parent Hamiltonian, we take the approach of checking whether the quantum conditional mutual information decays exponen

Thomas Creutzig, Yasuaki Hikida

We propose new strong/weak dualities in two dimensional conformal field theories by generalizing the Fateev-Zamolodchikov-Zamolodchikov (FZZ-)duality between Witten's cigar model described by the $\mathfrak{sl}(2)/\mathfrak{u}(1)$ coset and sine-Liouville theory. In a previous work, a proof of the FZZ-duality was provided by applying the reduction method fro

Arash Tavakoli, Mehdi Fatemi, Petar Kormushev

Action-value estimation is a critical component of many reinforcement learning (RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens of representation learning, good representations of both state and action can facilitate action-value estimation. While adva

Monika Soraisam, Sarah DeSantis, Chien-Hsiu Lee, Thomas Matheson

The ongoing Zwicky Transient Facility (ZTF) survey is generating a massive alert rate from a variety of optical transients and variable stars, which are being filtered down to subsets meeting user-specified criteria by broker systems such as ANTARES. In a beta implementation of the algorithm of Soraisam et al. (2020) on ANTARES, we flagged AT 2020iko from th

Amir Pouran Ben Veyseh, Franck Dernoncourt, Quan Hung Tran, Thien Huu Nguyen

Acronyms are the short forms of phrases that facilitate conveying lengthy sentences in documents and serve as one of the mainstays of writing. Due to their importance, identifying acronyms and corresponding phrases (i.e., acronym identification (AI)) and finding the correct meaning of each acronym (i.e., acronym disambiguation (AD)) are crucial for text unde

Felipe A. Franco

Generalizing the involution length of the complex hyperbolic plane, we obtain that the $\alpha$-length of $\mathrm{PU}(2,1)$ is $4$, that is, every element of $\mathrm{PU}(2,1)$ can be decomposed as the product of at most $4$ special elliptic isometries with parameter $\alpha$. We also describe the isometries that can be written as the product of $2$ or $3$

Wei Zhan

We show that the set of entries generated by any finite set of doubly stochastic matrices is nowhere dense, in contrast to the cases of stochastic matrices or unitary matrices. In other words, there is no finite universal set of doubly stochastic matrices, even with the weakest notion of universality. Our proof is based on a theorem for topological semigroup

Vladimir Dotsenko, Pedro Tamaroff

The celebrated Diamond Lemma of Bergman gives an effectively verifiable criterion of uniqueness of normal forms for term rewriting in associative algebras. We present a new way to interpret and prove this result from the viewpoint of homotopical algebra. Our main result states that every multiplicative free resolution of an algebra with monomial relations gi

Ilya Shvartsman

In a recent paper it has been shown that if Cesaro and Abel limits for a certain discrete time optimal control problem are not equal, then there is a duality gap between a certain infinite-dimensional linear programming problem and its dual. In this paper we construct an example of a problem satisfying the assumptions of the aforementioned paper, where Cesar

Jeremy Dietrich, Dániel Apai

tau Ceti is the closest single Sun-like star to the solar system and hosts a multi-planet system with four confirmed planets. The possible presence of additional planets, especially potentially habitable worlds, remains of great interest. We analyze the structure of the tau Ceti planetary system via the DYNAMITE algorithm, combining information from exoplane

E. D. Cherotchenko, H. Sigurdsson, A. Askitopoulos, A. V. Nalitov

A condensed matter platform for analogue simulation of complex two-dimensional molecular bonding configurations, based on optically trapped exciton-polariton condensates is proposed. The stable occupation of polariton condensates in the excited states of their optically configurable potential traps permits emulation of excited atomic orbitals. A classical me

Mario Ghossoub, Jesse Hall, David Saunders

We consider the problem of determining an upper bound for the value of a spectral risk measure of a loss that is a general nonlinear function of two factors whose marginal distributions are known, but whose joint distribution is unknown. The factors may take values in complete separable metric spaces. We introduce the notion of Maximum Spectral Measure (MSP)

Liam Collins, Aryan Mokhtari, Sanjay Shakkottai

Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard non-adaptive learning (NAL), and little is understood about how much MAML improves over NAL i

Satoru Hayami, Yukitoshi Motome

We theoretically investigate the origin of the square-type skyrmion crystal in centrosymmetric itinerant magnets, motivated from the recent experimental finding in GdRu$_2$Si$_2$ [N. D. Khanh $\mathit{et}$ $\mathit{al.}$, Nat. Nanotech. $\mathbf{15}$, 444 (2020)]. By simulated annealing for an effective spin model derived from the Kondo lattice model on a sq

Avrim Blum, Han Shao

We study the problem of online learning with primary and secondary losses. For example, a recruiter making decisions of which job applicants to hire might weigh false positives and false negatives equally (the primary loss) but the applicants might weigh false negatives much higher (the secondary loss). We consider the following question: Can we combine "exp

John R. Moser

In this paper, I present a visual representation of the relationship between mean hourly total compensation divided by per-capita GDP, hours worked per capita, and the labor share, and show the represented labor equilibrium equation is the definition of the labor share. I also present visual examination of the productivity horizon and wage compression, and u

Alessandro Cantelmo, Giovanni Melina

How should central banks optimally aggregate sectoral inflation rates in the presence of imperfect labor mobility across sectors? We study this issue in a two-sector New-Keynesian model and show that a lower degree of sectoral labor mobility, ceteris paribus, increases the optimal weight on inflation in a sector that would otherwise receive a lower weight. W

Liding Huang

In this paper, we consider the adiabatic limit of Fu-Yau equations on a product of two Calabi-Yau manifolds. We prove that the adiabatic limit of Fu-Yau equations are quasilinear equations.

Linh Anh Nguyen, Dat Xuan Tran

Fuzzy structures such as fuzzy automata, fuzzy transition systems, weighted social networks and fuzzy interpretations in fuzzy description logics have been widely studied. For such structures, bisimulation is a natural notion for characterizing indiscernibility between states or individuals. There are two kinds of bisimulations for fuzzy structures: crisp bi

Pieter van Goor, Tarek Hamel, Robert Mahony

The kinematics of many systems encountered in robotics, mechatronics, and avionics are naturally posed on homogeneous spaces; that is, their state lies in a smooth manifold equipped with a transitive Lie group symmetry. This paper proposes a novel filter, the Equivariant Filter (EqF), by posing the observer state on the symmetry group, linearising global err

Yongqiang Wang, Yangyang Shi, Frank Zhang, Chunyang Wu

In this paper, we summarize the application of transformer and its streamable variant, Emformer based acoustic model for large scale speech recognition applications. We compare the transformer based acoustic models with their LSTM counterparts on industrial scale tasks. Specifically, we compare Emformer with latency-controlled BLSTM (LCBLSTM) on medium laten

Sen Lin, Hang Wang, Junshan Zhang

System identification is a fundamental problem in reinforcement learning, control theory and signal processing, and the non-asymptotic analysis of the corresponding sample complexity is challenging and elusive, even for linear time-varying (LTV) systems. To tackle this challenge, we develop an episodic block model for the LTV system where the model parameter

Daniel Gabric

A word is said to be \emph{bordered} if it contains a non-empty proper prefix that is also a suffix. We can naturally extend this definition to pairs of non-empty words. A pair of words $(u,v)$ is said to be \emph{mutually bordered} if there exists a word that is a non-empty proper prefix of $u$ and suffix of $v$, and there exists a word that is a non-empty

Yanan Wang, Long Ji, Javier A. Garcia, Thomas Dauser

After 34 years, the black-hole candidate EXO 1846-031 went into outburst again in 2019. We investigate its spectral properties in the hard intermediate and the soft states with NuSTAR and Insight-HXMT. A reflection component has been detected in the two spectral states but possibly originating from different illumination spectra: in the intermediate state, t

Julia Gaudio, Elchanan Mossel

Graph shotgun assembly refers to the problem of reconstructing a graph from a collection of local neighborhoods. In this paper, we consider shotgun assembly of \ER random graphs $G(n, p_n)$, where $p_n = n^{-\alpha}$ for $0 < \alpha < 1$. We consider both reconstruction up to isomorphism as well as exact reconstruction (recovering the vertex labels as well a

Pierre L. Dognin, Igor Melnyk, Inkit Padhi, Cicero Nogueira dos Santos

In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs). We investigate the impact of weak supervision by creating a weakly supervised dataset and show that even a slight amount of supervision can significantly improve the model performance and enable better-quality trans

S. James Gates,, Yangrui Hu, Kory Stiffler

The mathematical concept of a "Banchoff index" associated with discrete Morse functions for oriented triangular meshes has been shown to correspond to the height assignments of nodes in adinkras. In recent work there has been introduced the concept of "Banchoff matrices" leading to HYMNs - height yielding matrix numbers. HYMNs map the shape of an adinkra to

Arun Ganesh, Kunal Talwar

Various differentially private algorithms instantiate the exponential mechanism, and require sampling from the distribution $\exp(-f)$ for a suitable function $f$. When the domain of the distribution is high-dimensional, this sampling can be computationally challenging. Using heuristic sampling schemes such as Gibbs sampling does not necessarily lead to prov

Rui Liu, Alex Olshevsky

Temporal difference learning with linear function approximation is a popular method to obtain a low-dimensional approximation of the value function of a policy in a Markov Decision Process. We give a new interpretation of this method in terms of a splitting of the gradient of an appropriately chosen function. As a consequence of this interpretation, converge

Kevin Patrick Griffin, Lin Fu, Parviz Moin

While the computation of the boundary-layer thickness is straightforward for canonical equilibrium flows, there are no established definitions for general non-equilibrium flows. In this work, a method is developed based on a local reconstruction of the "inviscid" velocity profile $U_I[y]$ resulting from the application of the Bernoulli equation in the wall-n

Ugur Atli, Omer Guleryuz, Mehmet Ozkan

We provide a tensor calculus for $n$-number of ${\mathcal N}=(1,0)$ linear multiplets in six dimensions. The coupling of linear multiplets is encoded in a function ${\mathcal F}_{IJ}$ that is subject to certain constraints. We provide various rigid and local supersymmetric models depending on the choice of the function ${\mathcal F}_{IJ}$ and provide an inte

Luis Duarte, Jonathan Torres, Vitor Ribeiro, Inês Moreira

Artificial Intelligence (AI) has been improving the performance of systems for a diverse set of tasks and introduced a more interactive generation of personal agents. Despite the current trend of applying AI for a great amount of areas, we have not seen the same quantity of work being developed for the tourism sector. This paper reports on the main applicati

Cristian Tatino, Nikolaos Pappas, Di Yuan

This paper considers the joint optimization of trajectory and beamforming of a wirelessly connected robot using intelligent reflective surface (IRS)-assisted millimeter-wave (mm-wave) communications. The goal is to minimize the motion energy consumption subject to time and communication quality of service (QoS) constraints. This is a fundamental problem for

Maryam Ashrafi, Kyle J. Ray, Fabio Anza, James P. Crutchfield

We analyze an engine whose working fluid consists of a single quantum particle, paralleling Szilard's construction of a classical single-particle engine. Following his resolution of Maxwell's Second Law paradox using the latter, which turned on physically instantiating the demon (control subsystem), the quantum engine's design mirrors the classically-chaotic

Frederik Plesner Lyngse

Are low-income individuals relying on government transfers liquidity constrained by the end of the month to a degree that they postpone medical treatment? I investigate this question using Danish administrative data comprising the universe of welfare recipients and the filling of all prescription drugs. I find that on transfer income payday, recipients have

Martino Fassina, Yifei Pan, Yuan Zhang

We study the minimal regularity required on the datum to guarantee the existence of classical $C^1$ solutions to the inhomogeneous Cauchy-Riemann equations on planar domains.

Takashi Wada, Tomoharu Iwata, Yuji Matsumoto, Timothy Baldwin

We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence. Through sharing model parameters among different languages, our model

Mohammad Abdulaziz, Friedrich Kurz

We present an executable formally verified SAT encoding of classical AI planning. We use the theorem prover Isabelle/HOL to perform the verification. We experimentally test the verified encoding and show that it can be used for reasonably sized standard planning benchmarks. We also use it as a reference to test a state-of-the-art SAT-based planner, showing t

Apostolos Christou, Jeremie Vaubaillon, Paul Withers, Ricardo Hueso

All planets and satellites of our solar system are subject to a continuous rain of material, ranging in size from specks of dust to objects the size of boulders. Upon impact, these objects deposit their kinetic energy into the incident surface or atmosphere and affect the environment of the target body in ways not yet well understood. Recent high-profile eve

Erhan Bayraktar, Gaoyue Guo, Wenpin Tang, Yuming Zhang

This paper is concerned with the analysis of blow-ups for two McKean-Vlasov equations involving hitting times. Let $(B(t); \, t \ge 0)$ be standard Brownian motion, and $\tau:= \inf\{t \ge 0: X(t) \le 0\}$ be the hitting time to zero of a given process $X$. The first equation is $X(t) = X(0) + B(t) - \alpha \mathbb{P}(\tau \le t)$. We provide a simple condit

Shiliang Gao, Reuven Hodges, Gidon Orelowitz

We provide a non-recursive, combinatorial classification of multiplicity-free skew Schur polynomials. These polynomials are $GL_n$, and $SL_n$, characters of the skew Schur modules. Our result extends work of H. Thomas--A. Yong, and C. Gutschwager, in which they classify the multiplicity-free skew Schur functions.

Ishan Banerjee

In this paper we show that in a stable range the cohomology of the space of regular algebraic sections of a line bundle $\mathscr{L}$on a curve $X$ is isomorphic to the cohomology of the space of regular $C^{\infty}$sections of the same line bundle.

Ruijun Wang, Philipp Taschler, Filippos Kapsalidis, Mehran Shahmohammadi

We present quantum cascade laser (QCL) frequency comb devices with engineered waveguides for managing the dispersion. The QCL waveguide consists of multiple sections with different waveguide widths. The narrow and wide sections of the waveguide are designed in a way to compensate the group velocity dispersion (GVD) of each other and thereby produce a flat an

Aram Bahroz Brzo, David Alvarez-Castillo

In this study we work out thermodynamic functions for a quantum gas of mesons described as color-electric charge dipoles. They refer to a particular parametrization of the trigonometric Rosen-Morse potential which allows to be transformed to a perturbation of free quantum motion on the three-dimensional hyper-sphere, $S^{3}$, a manifold that can host only ch

Tim Seyde, Wilko Schwarting, Sertac Karaman, Daniela Rus

Learning complex robot behaviors through interaction requires structured exploration. Planning should target interactions with the potential to optimize long-term performance, while only reducing uncertainty where conducive to this objective. This paper presents Latent Optimistic Value Exploration (LOVE), a strategy that enables deep exploration through opti

Peter Organisciak, Maggie Ryan

Data augmentation uses artificially-created examples to support supervised machine learning, adding robustness to the resulting models and helping to account for limited availability of labelled data. We apply and evaluate a synthetic data approach to relationship classification in digital libraries, generating artificial books with relationships that are co

Jessica S. Martins, Oliver F. Piattella, Ilya L. Shapiro, Alexei A. Starobinsky

In the recent paper [3] it was shown that the consistency of the quantum theory of a sterile scalar coupled to massive fermions requires the inclusion of odd-power terms in the potential of scalar self-interaction. One of the most important examples of a sterile scalar is the inflaton, that is typically a real scalar field which does not belong to representa

Yabo Niu, Nilabja Guha, Debkumar De, Anindya Bhadra

Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices. We develop a Bayesian method to incorporate covariate information in this GGMs setup in a nonlinear seemingly unrelated regression framework. We propose a joint predictor and graph selection model and develop an efficie

Jason W. Rocks, Sean A. Ridout, Andrea J. Liu

The plasticity of amorphous solids undergoing shear is characterized by quasi-localized rearrangements of particles. While many models of plasticity exist, the precise relationship between plastic dynamics and the structure of a particle's local environment remains an open question. Previously, machine learning was used to identify a structural predictor of

Ricky Ini Liu, Christian Smith

The up-operators $u_i$ and down-operators $d_i$ (introduced as Schur operators by Fomin) act on partitions by adding/removing a box to/from the $i$th column if possible. It is well known that the $u_i$ alone satisfy the relations of the (local) plactic monoid, and the present authors recently showed that relations of degree at most 4 suffice to describe all

Adar Sharon, Tal Sheaffer

We study the large $N$ phase diagram of an asymptotically free UV completion of $\mathcal{N}=1$ $SU(N)$ super-Yang-Mills-Chern-Simons theory coupled to a single massive fundamental scalar multiplet with a quartic superpotential coupling. We compute the effective superpotential at small gauge coupling $\lambda\equiv N/k$, and combine this with previous result

Zin Lin, Charles Roques-Carmes, Rasmus E. Christiansen, Marin Soljačić

We present full-Maxwell topology-optimization design of a single-piece multlayer metalens, about 10 wavelengths~$\lambda$ in thickness, that simultaneously focuses over a $60^\circ$ angular range and a 23\% spectral bandwidth without suffering chromatic or angular aberration, a "plan-achromat." At all angles and frequencies it achieves diffraction-limited fo

Maxwell Levit

In 1985, Arjeh Cohen and Jacques Tits proved the existence of a 4-cycle-free 2-fold cover of the hypercube. This Cohen-Tits cover is closely related to the signed adjacency matrix that Hao Huang used last year in his proof of the Sensitivity Conjecture. Terence Tao observed that Huang's signed adjacency matrix can be understood by lifting functions on an ele

Mucyo Karemera

We construct quantum invariants of 3-manifolds based on a $\mathfrak{sl}_3$ matrix dilogarithm proposed by Kashaev. This matrix dilogarithm is an $\mathfrak{sl}_3$ analogue of the (cyclic) quantum dilogarithm used to define Kashaev's invariants as well as Baseilhac and Benedetti's quantum hyperbolic invariants. % In this article, we show that the $\mathfrak{

Ben Graul, Matthew L. Rollins, Nathan Powers, Dennis Della Corte

This paper outlines the feasibility of replacing a midterm assessment with a student-created digital media project. We examine the benefits of a peer review process for student-created pedagogy, the effectiveness of the student-generated digital media, and self-evaluations as a replacement for accepted forms of assessment in terms of: learning, assessment, a

Kevin R. Chaves, Xian Wu, Yaniv J. Rosen, Jonathan L DuBois

Having accurate gate generation is essential for precise control of a quantum system. The generated gate usually suffers from linear and nonlinear distortion. Previous works have demonstrated how to use a qubit to correct linear frequency distortions but have not commented on how to handle nonlinear distortions. This is an important issue as we show that non

Samuel Heroy, Isabella Loaiza, Alexander Pentland, Neave O'Clery

Countries and cities around the world have resorted to unprecedented mobility restrictions to combat Covid-19 transmission. Here we exploit a natural experiment whereby Colombian cities implemented varied lockdown policies based on ID number and gender to analyse the impact of these policies on urban mobility. Using mobile phone data, we find that the restri

Yau Wing Li

We show that the neutral block of the affine monodromic Hecke category for a reductive group is monoidally equivalent to the neutral block of the affine Hecke category for the endoscopic group. The semisimple complexes of both categories can be identified with the generalized Soergel bimodules via the Soergel functor. We extend this identification of semisim

Helmut Eberl, Ioannis D. Gialamas, Vassilis C. Spanos

We calculate the gravitino production rate, computing its one-loop thermal self-energy. Gravitino production processes that do not result through thermal cuts of its self-energy, have been identified and taken into account. Correcting analytical errors and numerical approximations in the previous calculations, we present our result. This deviates from the la

F. Linares, A. J. Mendez

We are concerned with the decay of long time solutions of the initial value problem associated with the Schr\"odinger-Korteweg-de Vries system. We use recent techniques in order to show that solutions of this system decay to zero in the energy space. The result is independent of the integrability of the equations involved and it does not require any size ass

Jürgen Dölz, Helmut Harbrecht, Carlos Jerez-Hanckes, Michael Multerer

We study the numerical solution of forward and inverse acoustic scattering problems by randomly shaped obstacles in three-dimensional space using a fast isogeometric boundary element method. Within the isogeometric framework, realizations of the random scatterer can efficiently be computed by simply updating the NURBS mappings which represent the scatterer.

Byon N. Jayawiguna

We present a novel solution describing four dimensional rotating regular charged black hole in the low energy heterotic string theory. This present solution is characterized by its mass, charged, as well as angular momentum. Some aspect including event horizon, ergosphere, and the angular velocity are discussed. It is shown that later, our expression for eve

Matthew Evanusa, Snehesh Shrestha, Michelle Girvan, Cornelia Fermüller

In many real-world applications, fully-differentiable RNNs such as LSTMs and GRUs have been widely deployed to solve time series learning tasks. These networks train via Backpropagation Through Time, which can work well in practice but involves a biologically unrealistic unrolling of the network in time for gradient updates, are computationally expensive, an

Baoxiang Pan, Gemma J. Anderson, AndrE Goncalves, Donald D. Lucas

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal forecast, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation defic

József Balogh, Michelle Delcourt, Emily Heath, Lina Li

Given graphs $F$ and $H$, the generalized rainbow Tur\'an number $\text{ex}(n,F,\text{rainbow-}H)$ is the maximum number of copies of $F$ in an $n$-vertex graph with a proper edge-coloring that contains no rainbow copy of $H$. B. Janzer determined the order of magnitude of $\text{ex}(n,C_s,\text{rainbow-}C_t)$ for all $s\geq 4$ and $t\geq 3$, and a recent re

Jonathan Rodden, Thomas Weighill

This preprint offers a detailed look, both qualitative and quantitative, at districting with respect to recent voting patterns in one state: Pennsylvania. We investigate how much the partisan playing field is tilted by political geography. In particular we closely examine the role of scale. We find that partisan-neutral maps rarely give seats proportional to

Peyman Tahghighi, Abbas Koochari, Masoume Jalali

People represent their emotions in a myriad of ways. Among the most important ones is whole body expressions which have many applications in different fields such as human-computer interaction (HCI). One of the most important challenges in human emotion recognition is that people express the same feeling in various ways using their face and their body. Recen