October 2020 arXiv papers — page 120

Richard G. Freedman, Steven J. Levine, Brian C. Williams, Shlomo Zilberstein

As robotic teammates become more common in society, people will assess the robots' roles in their interactions along many dimensions. One such dimension is effectiveness: people will ask whether their robotic partners are trustworthy and effective collaborators. This begs a crucial question: how can we quantitatively measure the helpfulness of a robotic

Ruixue Tang, Chao Ma

There are two main lines of research on visual question answering (VQA): compositional model with explicit multi-hop reasoning, and monolithic network with implicit reasoning in the latent feature space. The former excels in interpretability and compositionality but fails on real-world images, while the latter usually achieves better performance due to model

Lijia Yu, Xiao-Shan Gao

In this paper, the robustness and accuracy of the deep neural network (DNN) was enhanced by introducing the $L_{2,\infty}$ normalization of the weight matrices of the DNN with Relu as the activation function. It is proved that the $L_{2,\infty}$ normalization leads to large dihedral angles between two adjacent faces of the polyhedron graph of the DNN functio

Jin-Yi Cai, Artem Govorov

We prove #P-completeness results for counting edge colorings on simple graphs. These strengthen the corresponding results on multigraphs from [4]. We prove that for any $κ\ge r \ge 3$ counting $κ$-edge colorings on $r$-regular simple graphs is #P-complete. Furthermore, we show that for planar $r$-regular simple graphs where $r \in \{3, 4, 5\}$ counting edge

George C. Hsiao, Tonatiuh Sánchez-Vizuet

We study boundary integral formulations for an interior/exterior initial boundary value problem arising from the thermo-elasto-dynamic equations in a homogeneous and isotropic domain. The time dependence is handled, based on Lubich's approach, through a passage to the Laplace domain. We focus on the cases where one of the unknown fields satisfies a Diric

Isaac Yaesh, Natan Grinfeld

A particle filtering approach is suggested for the training of multi-layer neural networks without utilizing gradients calculation. The network weights are considered to be the components of the estimated state-vector of a noise driven linear system, whereas the neural network serves as the measurement function in the estimation problem. A simple example is

Csilla Bujtás, Michael A. Henning, Vesna Iršič, Sandi Klavžar

The (total) connected domination game on a graph $G$ is played by two players, Dominator and Staller, according to the standard (total) domination game with the additional requirement that at each stage of the game the selected vertices induce a connected subgraph of $G$. If Dominator starts the game and both players play optimally, then the number of vertic

Yu-An Wang, Yun-Nung Chen

In recent years, pre-trained Transformers have dominated the majority of NLP benchmark tasks. Many variants of pre-trained Transformers have kept breaking out, and most focus on designing different pre-training objectives or variants of self-attention. Embedding the position information in the self-attention mechanism is also an indispensable factor in Trans

Shenyang Huang, Fanjie Wang, Guowei Zhang, Chaoyu Song

The temperature dependence of the band gap is crucial to a semiconductor. Bulk black phosphorus (BP) is known to exhibit an anomalous behavior. Through optical spectroscopy, here we show that the temperature effect on BP band gap gradually evolves with decreasing layer number, eventually turns into a normal one in the monolayer limit, rendering a crossover f

John Pougue Biyong, Bo Wang, Terry Lyons, Alejo J Nevado-Holgado

Relying on large pretrained language models such as Bidirectional Encoder Representations from Transformers (BERT) for encoding and adding a simple prediction layer has led to impressive performance in many clinical natural language processing (NLP) tasks. In this work, we present a novel extension to the Transformer architecture, by incorporating signature

Jeffrey W. Miller, Scott L. Carter

Latent factor models are widely used to discover and adjust for hidden variation in modern applications. However, most methods do not fully account for uncertainty in the latent factors, which can lead to miscalibrated inferences such as overconfident p-values. In this article, we develop a fast and accurate method of uncertainty quantification in generalize

Feiyang Pan, Jia He, Dandan Tu, Qing He

It is a popular belief that model-based Reinforcement Learning (RL) is more sample efficient than model-free RL, but in practice, it is not always true due to overweighed model errors. In complex and noisy settings, model-based RL tends to have trouble using the model if it does not know when to trust the model. In this work, we find that better model usage

Jiakun Liu, Xin Xia, David Lo, Haoxiang Zhang

Stack Overflow hosts valuable programming-related knowledge with 11,926,354 links that reference to the third-party websites. The links that reference to the resources hosted outside the Stack Overflow websites extend the Stack Overflow knowledge base substantially. However, with the rapid development of programming-related knowledge, many resources hosted o

Yuanlu Bai, Zhiyuan Huang, Henry Lam, Ding Zhao

We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests. This problem is motivated from fast emerging studies on the safety evaluation of intelligent systems, robustness quantification of learning models, and other potential appli

Forrest Davis, Marten van Schijndel

Language models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations. Our work tests the robustness of these abstractions by focusing on the ability of LMs to learn interactions between different linguistic representations. In particular, we utilized stimuli from psycholinguistic studies showing that huma

Sanjib Sharma, Michael Gomez, Klaus Keller, Robert Nicholas

Flood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and implementation of flood risk management strategies. Here we develop an integrated modeling framework to assess projected cha

Mengyao Li, Areen Alsaid, Sofia I. Noejovich, Ernest V. Cross

The increasingly collaborative decision-making process between humans and agents demands a comprehensive, continuous, and unobtrusive measure of trust in agents. The gold standard format for measuring trust, a Likert-style survey, suffers from major limitations in dynamic human-agent interactions. We proposed a new approach to evaluate trust in a nondirectiv

Yurui Ming

In this paper we craft a cascaded fuzzy controlling system for the traditional Truck-and-Trailer Backer-Upper problem, which is a benchmarking for testing various intelligent controlling systems. Inspired by the most inclination of human operations, we decompose the original overall controlling problem into two sub-controlling problems. A first fuzzy control

Xinyin Ma, Yongliang Shen, Gongfan Fang, Chen Chen

Large pre-trained transformer-based language models have achieved impressive results on a wide range of NLP tasks. In the past few years, Knowledge Distillation(KD) has become a popular paradigm to compress a computationally expensive model to a resource-efficient lightweight model. However, most KD algorithms, especially in NLP, rely on the accessibility of

Shuai Hou, Yunhe Sheng, Rong Tang

In this paper, first we introduce the notion of a twilled 3-Lie algebra, and construct an $L_\infty$-algebra, whose Maurer-Cartan elements give rise to new twilled 3-Lie algebras by twisting. In particular, we recover the Lie $3$-algebra whose Maurer-Cartan elements are O-operators (also called relative Rota-Baxter operators) on 3-Lie algebras. Then we intro

Debasish Chakroborti, Banani Roy, Sristy Sumana Nath

To process a large amount of data sequentially and systematically, proper management of workflow components (i.e., modules, data, configurations, associations among ports and links) in a Scientific Workflow Management System (SWfMS) is inevitable. Managing data with provenance in a SWfMS to support reusability of workflows, modules, and data is not a simple

Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman

Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning. Discovering these sequences in an unsupervised manner is a longstanding problem in statistical neuroscience. Promising recent work utilized a convolutive nonnegative matrix factorization model to tackle this challenge. However, this model requ

Yueming Yin, Zhen Yang, Xiaofu Wu, Haifeng Hu

As a more practical setting for unsupervised domain adaptation, Universal Domain Adaptation (UDA) is recently introduced, where the target label set is unknown. One of the big challenges in UDA is how to determine the common label set shared by source and target domains, as there is simply no labeling available in the target domain. In this paper, we employ

Charles Lovering, Ellie Pavlick

When communicating, people behave consistently across conversational roles: People understand the words they say and are able to produce the words they hear. To date, artificial agents developed for language tasks have lacked such symmetry, meaning agents trained to produce language are unable to understand it and vice-versa. In this work, we exploit the sym

Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu

This paper formalizes the binarization operations over neural networks from a learning perspective. In contrast to classical hand crafted rules (\eg hard thresholding) to binarize full-precision neurons, we propose to learn a mapping from full-precision neurons to the target binary ones. Each individual weight entry will not be binarized independently. Inste

Reazul Hasan Russel, Mouhacine Benosman, Jeroen Van Baar

In this paper, we focus on the problem of robustifying reinforcement learning (RL) algorithms with respect to model uncertainties. Indeed, in the framework of model-based RL, we propose to merge the theory of constrained Markov decision process (CMDP), with the theory of robust Markov decision process (RMDP), leading to a formulation of robust constrained-MD

Dohyun Kim, Jongwoo Kim, Jonggeun Hwang, Dongha Shin

Surface tension is a key parameter for understanding nucleation from the very initial stage of phase transformation. Although surface tension has been predicted to vary with the curvature of the liquid-vapor interface, particularly at the large curvature of, e.g., the subnanometric critical nucleus, experimental study still remains challenging due to inacces

Liang Yuan, Hang Cao, Yunquan Zhang, Kun Li

Stencil computations represent a very common class of nested loops in scientific and engineering applications. Exploiting vector units in modern CPUs is crucial to achieving peak performance. Previous vectorization approaches often consider the data space, in particular the innermost unit-strided loop. It leads to the well-known data alignment conflict probl

Liang Chen, Ming Mei, Guojing Zhang, Kaijun Zhang

The purpose of this paper is to study radial solutions for steady hydrodynamic model of semiconductors represented by Euler-Poisson equations with sonic boundary. The existence and uniqueness of radial subsonic solution, and the existence of radial supersonic solutions are derived by using the energy method and the compactness method, but under a general con

Masahiro N. Machida, Takashi Hosokawa

The evolution of protostellar outflows is investigated under different mass accretion rates in the range $\sim10^{-5}-10^{-2} {\rm M}_\odot$ yr$^{-1}$ with three-dimensional magnetohydrodynamic simulations. A powerful outflow always appears in strongly magnetized clouds with $B_0 \gtrsim B_{\rm 0, cr}$ $=10^{-4} (M_{\rm cl}/100 {\rm M}_\odot){\rm G}$, where

Arun V. Sathanur, Nathan A. Baker

In this work, we developed an efficient approach to compute ensemble averages in systems with pairwise-additive energetic interactions between the entities. Methods involving full enumeration of the configuration space result in exponential complexity. Sampling methods such as Markov Chain Monte Carlo (MCMC) algorithms have been proposed to tackle the expone

Hao Huang, Guodong Long, Tao Shen, Jing Jiang

Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of light-weight structure, high efficiency and great interpretability. Especially when extended to complex vector space, they show the capability in handling various relation patterns includ

R. Mincigrucci, Jeremy Rouxel, Barbara Rossi, Emiliano Principi

In chemistry, biology and materials science, the ability to access interatomic interactions and their dynamical evolution has become possible with the advent of femtosecond lasers. In particular, the observation of vibrational wave packets via optical (UV-visible-IR) spectroscopies has been a major achievement as it can track the motion of nuclei within the

Yuanhang Zhang, He Wen, Alireza Fardoost, Shengli Fan

We propose a wavelength-mode sorter realized by multi-plane light conversion (MPLC). For the first time, to our best knowledge, wavelengths and spatial modes can be sorted simultaneously. We first demonstrate pure wavelength sorting by a series of phase masks, which could find applications in high-power wavelength beam combining (WBC) or coarse wavelength-di

Pedro Cañate, Santiago Esteban Perez Bergliaffa

In this work the first exact asymptotically flat static and spherically symmetric black hole solution for $(3+1)$-dimensional ESTGB is presented, with a model of nonlinear electrodynamics -- that reduces to Maxwell's theory in the weak field limit and satisfies the weak energy condition -- as a source. The solution has a nonzero magnetic charge, and scal

J. W. Maluf, S. C. Ulhoa

We present the Lagrangian and Hamiltonian formulations of a theory for spin 2 fields. The construction is developed in flat space-time. The construction in curved space-time is conceptually straightforward, although it is not unique. The theory is based on a symmetric tensor $S_{μν}$, contains two degrees of freedom of radiation, is motivated by the telepara

Elena Gaburro, Michael Dumbser

In this work, we consider the general family of the so called ADER PNPM schemes for the numerical solution of hyperbolic partial differential equations with \textit{arbitrary} high order of accuracy in space and time. The family of one-step PNPM schemes was introduced in [Dumbser et al., JCP, 2008] and represents a unified framework for classical high order

Yuqing Zhu, Xiang Yu, Yi-Hsuan Tsai, Francesco Pittaluga

Differentially Private Federated Learning (DPFL) is an emerging field with many applications. Gradient averaging based DPFL methods require costly communication rounds and hardly work with large-capacity models, due to the explicit dimension dependence in its added noise. In this work, inspired by knowledge transfer non-federated privacy learning from Papern

Tousif Islam, Scott E. Field, Carl-Johan Haster, Rory Smith

The recent observation of GW190412, the first high-mass ratio binary black-hole (BBH) merger, by the LIGO-Virgo Collaboration (LVC) provides a unique opportunity to probe the impact of subdominant harmonics and precession effects encoded in a gravitational wave signal. We present refined estimates of source parameters for GW190412 using \texttt{NRSur7dq4}, a

Ioannis Karatzas, Bertram Tschiderer

We show that Langevin$-$Smoluchowski measure on path space is invariant under time-reversal, followed by stochastic control of the drift with a novel entropic-type criterion. Repeated application of these forward-backward steps leads to a sequence of stochastic control problems, whose initial/terminal distributions converge to the Gibbs probability measure o

Cristian Lenart, Satoshi Naito, Daisuke Sagaki

We give a Chevalley formula for an arbitrary weight for the torus-equivariant $K$-group of semi-infinite flag manifolds, which is expressed in terms of the quantum alcove model. As an application, we prove the Chevalley formula for an anti-dominant fundamental weight for the (small) torus-equivariant quantum $K$-theory $QK_{T}(G/B)$ of an (ordinary) flag man

Orit E. Raz, Joshua Zahl

We characterize when bivariate real analytic functions are &#34;dimension expanding&#34; when applied to a Cartesian product. If $P$ is a bivariate real analytic function that is not locally of the form $P(x,y) = h(a(x) + b(y))$, then whenever $A$ and $B$ are Borel subsets of $\mathbb{R}$ with Hausdorff dimension $0<α<1$, we have that $P(A,B)$ has Hausdorff

Lou Zonca, David Holcman

Neuronal networks can generate burst events. It remains unclear how to analyse interburst periods and their statistics. We study here the phase-space of a mean-field model, based on synaptic short-term changes, that exhibit burst and interburst dynamics and we identify that interburst corresponds to the escape from a basin of attraction. Using stochastic sim

Francois Leyvraz

We present in a detailed manner the scaling theory of irreversible aggregation characterized by the set of reaction rates $K(k,l)=1/k+1/l$, as well as a minor generalisation thereof. In this case, it is possible to evaluate the scaling function exactly. By this we mean that it is expressed as the unique solution of an ordinary differential equation with give

Maximilian Mörchen, Leon Freitag, Markus Reiher

The tailored coupled cluster (TCC) approach is a promising ansatz that preserves the simplicity of single-reference coupled cluster theory, while incorporating a multi-reference wave function through amplitudes obtained from a preceding multi-configurational calculation. Here, we present a detailed analysis of the TCC wave function based on model systems, wh

Wayne B. Hayes

Network alignment aims to uncover topologically similar regions in the protein-protein interaction (PPI) networks of two or more species under the assumption that topologically similar regions tend to perform similar functions. Although there exist a plethora of both network alignment algorithms and measures of topological similarity, currently no gold stand

Iljoo Baek, Tzu-Chieh Tai, Manoj Bhat, Karun Ellango

Reliable curb detection is critical for safe autonomous driving in urban contexts. Curb detection and tracking are also useful in vehicle localization and path planning. Past work utilized a 3D LiDAR sensor to determine accurate distance information and the geometric attributes of curbs. However, such an approach requires dense point cloud data and is also v

Domingos S. P. Salazar

Fluctuation theorems impose fundamental bounds in the statistics of the entropy production, with the second law of thermodynamics being the most famous. Using information theory, we quantify the information of entropy production and find an upper tight bound as a function of its mean from the strong detailed fluctuation theorem. The bound is given in terms o

Pradipta Banerjee, Subhrabrata Choudhury

COVID-19 pandemic has sharply projected the globally persistent multi-dimensional fundamental challenges in securing general socio-economic wellbeing of the society. The problems intensify with increasing population densities and also vary with several socio-economic-geo-cultural activity parameters. These problems directly highlight the urgent need for acco

Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis

Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings, where objects are represented by n-dimensional hyperrectangles, are a particularly promising example of such an embedding as they are closed under intersection and their volume can be calculated easily,

Amir DN Cohen, Shachar Rosenman, Yoav Goldberg

The current supervised relation classification (RC) task uses a single embedding to represent the relation between a pair of entities. We argue that a better approach is to treat the RC task as span-prediction (SP) problem, similar to Question answering (QA). We present a span-prediction based system for RC and evaluate its performance compared to the embedd

Feng Li, Dali Wang, Feng Yan, Fengguang Song

For large-scale scientific simulations, it is expensive to store raw simulation results to perform post-analysis. To minimize expensive I/O, &#34;in-situ&#34; analysis is often used, where analysis applications are tightly coupled with scientific simulations and can access and process the simulation results in memory. Increasingly, scientific domains employ

Qingyu Chen, Robert Leaman, Alexis Allot, Ling Luo

The COVID-19 pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Nat

Ziyuan Zhong, Yuchi Tian, Baishakhi Ray

Deep Neural Networks (DNNs) are being deployed in a wide range of settings today, from safety-critical applications like autonomous driving to commercial applications involving image classifications. However, recent research has shown that DNNs can be brittle to even slight variations of the input data. Therefore, rigorous testing of DNNs has gained widespre

Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani

Mixup is a popular data augmentation technique based on taking convex combinations of pairs of examples and their labels. This simple technique has been shown to substantially improve both the robustness and the generalization of the trained model. However, it is not well-understood why such improvement occurs. In this paper, we provide theoretical analysis

Edward H. Chen, Kate Raach, Andrew Pan, Andrey A. Kiselev

Silicon quantum dot qubits must contend with low-lying valley excited states which are sensitive functions of the quantum well heterostructure and disorder; quantifying and maximizing the energies of these states are critical to improving device performance. We describe a spectroscopic method for probing excited states in isolated Si/SiGe double quantum dots

Jacob C. Curtis, Connor T. Hann, Salvatore S. Elder, Christopher S. Wang

Single-photon detectors are ubiquitous and integral components of photonic quantum cryptography, communication, and computation. Many applications, however, require not only detecting the presence of any photons, but distinguishing the number present with a single shot. Here, we implement a single-shot, high-fidelity photon number-resolving detector of up to

Heejung Chung, Rodrigo Freitas, Gowoon Cheon, Evan J. Reed

Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are required to aid interpretation. These have h

Jonathan Roth, Pedro H. C. Sant&#39;Anna

This paper assesses when the validity of difference-in-differences depends on functional form. We provide a novel characterization: the parallel trends assumption holds under all strictly monotonic transformations of the outcome if and only if a stronger ``parallel trends&#39;&#39;-type condition holds for the cumulative distribution function of untreated po

Xiang Deng, Zhongfei, Zhang

Knowledge distillation (KD) is an effective technique to transfer knowledge from one neural network (teacher) to another (student), thus improving the performance of the student. To make the student better mimic the behavior of the teacher, the existing work focuses on designing different criteria to align their logits or representations. Different from thes

Shreya Banerjee, David Eichler, Dafne Guetta

There exists an inevitable scatter in intrinsic luminosity of Gamma Ray Bursts(GRBs). If there is relativistic beaming in the source, viewing angle variation necessarily introduces variation in the intrinsic luminosity function(ILF). Scatter in the ILF can cause a selection bias where distant sources that are detected have a larger median luminosity than tho

Ethan Mook, Chris Peikert

Minkowski proved that any $n$-dimensional lattice of unit determinant has a nonzero vector of Euclidean norm at most $\sqrt{n}$; in fact, there are $2^{Ω(n)}$ such lattice vectors. Lattices whose minimum distances come close to Minkowski&#39;s bound provide excellent sphere packings and error-correcting codes in $\mathbb{R}^{n}$. The focus of this work is a

Alexander Moretó

We study finite groups $G$ with the property that for any subgroup $M$ maximal in $G$ whose order is divisible by all the prime divisors of $|G|$, $M$ is supersolvable. We show that any nonabelian simple group can occur as a composition factor of such a group and that, if $G$ is solvable, then the nilpotency length and the rank are arbitrarily large. On the

Silei Xu, Sina J. Semnani, Giovanni Campagna, Monica S. Lam

We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort. Given a database schema and its data, AutoQA automatically generates a large set of high-quality questions for training that covers different database operations. It uses automatic paraphrasing combined with template-based pars

Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos

Machine Learning (ML) adoption in the enterprise requires simpler and more efficient software infrastructure---the bespoke solutions typical in large web companies are simply untenable. Model scoring, the process of obtaining predictions from a trained model over new data, is a primary contributor to infrastructure complexity and cost as models are trained o

Jane Yen, Tamás Lévai, Qinyuan Ye, Xiang Ren

For decades, Internet protocols have been specified using natural language. Given the ambiguity inherent in such text, it is not surprising that protocol implementations have long exhibited bugs. In this paper, we apply natural language processing (NLP) to effect semi-automated generation of protocol implementations from specification text. Our system, SAGE,

Reed Oei, Michael Coblenz, Jonathan Aldrich

Blockchains host smart contracts for crowdfunding, tokens, and many other purposes. Vulnerabilities in contracts are often discovered, leading to the loss of large quantities of money. Psamathe is a new language we are designing around a new flow abstraction, reducing asset bugs and making contracts more concise than in existing languages. We present an over

Ignacio Perito, Guido Bellomo, Daniel Galicer, Santiago Figueira

We present a characterization of the set of non-signaling correlations in terms of a two dimensional representation that involves the maximal value of a Bell functional and the mutual information between the parties. In particular, we apply this representation to the bipartite Bell scenario with two measurements and two outcomes. In terms of these physically

Zhiyi Chi

Series representations consisting of spherical harmonics are obtained for characteristic exponents and probability density functions of multivariate stable distributions under various conditions. A esult potentially applicable in a practical setting is that for any distribution with stability index not equal to 1 and with a polynomial spectral spherical dens

Sanjib Sharma, Ganesh Raj Ghimire, Rocky Talchabhadel, Jeeban Panthi

Fluvial floods drive severe risk to riverine communities. There is a strong evidence of increasing flood hazards in many regions around the world. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies. In this study, we characterize the expected flood hazards conditioned on the uncertain mode

Haoyue Tang, Philippe Ciblat, Jintao Wang, Michele Wigger

We consider updating strategies for a local cache which downloads time-sensitive files from a remote server through a bandwidth-constrained link. The files are requested randomly from the cache by local users according to a popularity distribution which varies over time according to a Markov chain structure. We measure the freshness of the requested time-sen

Adina Luican-Mayer, Justin Boddison-Chouinard, Samantha Scarfe, K. Watanabe

Fabrication of custom-built heterostructures based on stacked 2D materials provides an effective method to controllably tune electronic and optical properties. To that end, optimizing fabrication techniques for building these heterostructures is imperative. A common challenge in layer-by-layer assembly of 2D materials is the formation of bubbles at the atomi

Timo Kötzing, Karen Seidel

We investigate learning collections of languages from texts by an inductive inference machine with access to the current datum and a bounded memory in form of states. Such a bounded memory states (BMS) learner is considered successful in case it eventually settles on a correct hypothesis while exploiting only finitely many different states. We give the compl

Michel Cahen, Simone Gutt, John Rawnsley

In this paper we look at the question of integrability, or not, of the two natural almost complex structures $J^{\pm}_\nabla$ defined on the twistor space $J(M,g)$ of an even-dimensional manifold $M$ with additional structures $g$ and $\nabla$ a $g$-connection. We also look at the question of the compatibility of $J^{\pm}_\nabla$ with a natural closed $2$-fo

Megan J. McCarthy, Timothy J. Rupert

Faceted grain boundaries exhibit unusual segregation and migration tendencies. To gain a deeper understanding of how solute atoms interact with faceted interfacial structures during migration, this study probes the migration behavior of a faceted $Σ$11 boundary in Cu doped with Ag atoms. The solutes are found to segregate to the facet with more free volume a

Michael Creutz

QCD, the theory of the strong interactions, involves quarks interacting with non-Abelian gluon fields. This theory has many features that are difficult to impossible to see in conventional diagrammatic perturbation theory. This includes quark confinement, mass generation, and chiral symmetry breaking. This paper is a colloquium level overview of the framewor

Twisha Titirsha, Anup Das

Hardware implementation of neuromorphic computing can significantly improve performance and energy efficiency of machine learning tasks implemented with spiking neural networks (SNNs), making these hardware platforms particularly suitable for embedded systems and other energy-constrained environments. We observe that the long bitlines and wordlines in a cros

Fei Xue, Paul M. Haney

The intrinsic spin Hall effect plays an important role in spintronics applications, such as spin-orbit torque-based memory. The bulk space group symmetry determines the form of the bulk spin current conductivity tensor. This paper considers materials for which the local point group symmetry of individual atoms is lower than the global (bulk) symmetry. This e

Roisin Braddell, Anna Kiesenhofer, Eva Miranda

Motivated by the group of Galilean transformations and the subgroup of Galilean transformations which fix time zero, we introduce the notion of a $b$-Lie group as a pair $(G,H)$ where $G$ is a Lie group and $H$ is a codimension-one Lie subgroup. Such a notion allows us to give a theoretical framework for transformations of space-time where the initial time c

Birgir Hrafnkelsson, Helgi Sigurdarson, Sölvi Rögnvaldsson, Axel Ö. Jansson

The power-law rating curve has been used extensively in hydraulic practice and hydrology. It is given by $Q(h)=a(h-c)^b$, where $Q$ is discharge, $h$ is water elevation, $a$, $b$ and $c$ are unknown parameters. We propose a novel extension of the power-law rating curve, referred to as the generalized power-law rating curve. It is constructed by linking the p

Bo Peng, Ajay Panyala, Karol Kowalski, Sriram Krishnamoorthy

Coupled cluster Green&#39;s function (GFCC) calculation has drawn much attention in the recent years for targeting the molecular and material electronic structure problems from a many body perspective in a systematically improvable way. However, GFCC calculations on scientific computing clusters usually suffer from expensive higher dimensional tensor contrac

Tanmay Vilas Samak, Chinmay Vilas Samak, Sivanathan Kandhasamy

In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning. The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle. The training phase comprised of data collection, balancing, augmentation, preprocessing and training a neur

Geng Chen, Sam G. Krupa, Alexis F. Vasseur

Let a 1-d system of hyperbolic conservation laws, with two unknowns, be endowed with a convex entropy. We consider the family of small $BV$ functions which are global solutions of this equation. For any small $BV$ initial data, such global solutions are known to exist. Moreover, they are known to be unique among $BV$ solutions verifying either the so-called

Scott E. Field, Sigal Gottlieb, Zachary J. Grant, Leah F. Isherwood

We develop and use a novel mixed-precision weighted essentially non-oscillatory (WENO) method for solving the Teukolsky equation, which arises when modeling perturbations of Kerr black holes. We show that WENO methods outperform higher-order finite-difference methods, standard in the discretization of the Teukolsky equation, due to the need to add dissipatio

Norihiro Someyama

We give some inclusion relations for arbitrary fuzzy sets with reference to famous inequalities. In particular, we can know that the bounded sum and the algebraic product go well together. We would like to propose the concept of `Fuzzy Set Inequalities&#39; through the present note.

Armin Tavakoli, Emmanuel Zambrini Cruzeiro, Roope Uola, Alastair A. Abbott

We introduce a hierarchy of semidefinite relaxations of the set of quantum correlations in generalised contextuality scenarios. This constitutes a simple and versatile tool for bounding the magnitude of quantum contextuality. To illustrate its utility, we use it to determine the maximal quantum violation of several noncontextuality inequalities whose maximum

Zack Carson

The explosive coalescence of two black holes 1.3 billion light years away has for the very first time allowed us to peer into the extreme gravity region of spacetime surrounding these events. With these maximally compact objects reaching speeds up to 60% the speed of light, collision events such as these create harsh spacetime environments where the fields a

T. Tula, G. Möller, J. Quintanilla, S. R. Giblin

In recent years, Artificial Intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised Machine Learning (ML) algorithm called Principal Component Analysis (PCA) as a tool to analyse the data from muon spectroscopy experiments. Specifically, we apply the ML technique to detect phase t

Navid Naderializadeh, Fan H. Hung, Sean Soleyman, Deepak Khosla

We propose a novel framework for value function factorization in multi-agent deep reinforcement learning (MARL) using graph neural networks (GNNs). In particular, we consider the team of agents as the set of nodes of a complete directed graph, whose edge weights are governed by an attention mechanism. Building upon this underlying graph, we introduce a mixin

Damiano Anselmi

We compute the inflationary perturbation spectra and the quantity $r+8n_{T}$ to the next-to-next-to-leading log order in quantum gravity with purely virtual particles (which means the theory $R+R^{2}+C^{2}$ with the fakeon prescription/projection for $C^{2}$). The spectra are functions of the inflationary running coupling $α(1/k)$ and satisfy the cosmic reno

Oliver J. Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker, Augustin Marignier

Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance to rotational symmetries. We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to be leveraged alongside each other. The on

Anna R. Gallazzi, Anna Pasquali, Stefano Zibetti, Francesco La Barbera

We explore how the star formation and metal enrichment histories of present-day galaxies have been affected by environment combining stellar population parameter estimates and group environment characterization for SDSS DR7. We compare stellar ages, stellar metallicities and element abundance ratios [alpha/Fe] of satellite and central galaxies, as a function

Aaron Z. Goldberg, Andrei B. Klimov, Markus Grassl, Gerd Leuchs

The striking differences between quantum and classical systems predicate disruptive quantum technologies. We peruse quantumness from a variety of viewpoints, concentrating on phase-space formulations because they can be applied beyond particular symmetry groups. The symmetry-transcending properties of the Husimi $Q$ function make it our basic tool. In terms

Daniel J. Schultz, Adarsh S. Patri, Yong Baek Kim

Recently it was shown that the multipolar Kondo problem, wherein a quantum impurity carrying higher-rank multipolar moments interacts with conduction electrons, leads to novel non-Fermi liquid states. Because of the multipolar character of the local moments, the form of the interaction with conduction electrons is strongly dependent on the orbital-symmetry o

Panos Betzios, Elias Kiritsis, Vasilis Niarchos

We investigate emergent gravity extending the paradigm of the AdS/CFT correspondence. The emergent graviton is associated to the (dynamical) expectation value of the energy-momentum tensor. We derive the general effective description of such dynamics, and apply it to the case where a hidden theory generates gravity that is coupled to the Standard Model. In t

Daniel Kresse, Thomas Ertl, Hans-Thomas Janka

The diffuse cosmic supernova neutrino background (DSNB) is observational target of the gadolinium-loaded Super-Kamiokande (SK) detector and the forthcoming JUNO and Hyper-Kamiokande detectors. Current predictions are hampered by our still incomplete understanding of the supernova (SN) explosion mechanism and of the neutron star (NS) equation of state and max

Arushi Bodas, Soubhik Kumar, Raman Sundrum

Non-analyticity in co-moving momenta within the non-Gaussian bispectrum is a distinctive sign of on-shell particle production during inflation, presenting a unique opportunity for the &#34;direct detection&#34; of particles with masses as large as the inflationary Hubble scale ($H$). However, the strength of such non-analyticity ordinarily drops exponentiall

Grant N. Remmen, Nicholas L. Rodd

We place theoretical constraints on the leading deviations to four-fermion standard model interactions. Invoking S-matrix analyticity and partial wave unitarity, we develop new dispersion relations that yield either spin-dependent sum rules on dimension-six fermionic operators or information about the amplitude&#39;s behavior at large momentum. The pattern o

Jaime Simarro, Ezequiel de la Rosa, Thijs Vande Vyvere, David Robben

Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. As such, AD systems can help physicians to determine the presence, severity, and extension of a pathology. Deep generative models, such as Generative Adversarial Networks (GANs), can be exploited to capture anatomical variability. Consequently, any outli

Eric Zelikman, Sharon Zhou, Jeremy Irvin, Cooper Raterink

Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions.