July 2022 arXiv papers — page 64

Mark A. Rubin

We present techniques to construct the Deutsch-Hayden representation for quantum field operators and apply them to an entangled state of identical nonrelativistic spin-1/2 fermions localized in well-separated spatial regions. Using these entangled field operators we construct operators measuring spin in localized spatial regions, and verify that matrix eleme

Jingwang Ling, Zhibo Wang, Ming Lu, Quan Wang

Morphable models are essential for the statistical modeling of 3D faces. Previous works on morphable models mostly focus on large-scale facial geometry but ignore facial details. This paper augments morphable models in representing facial details by learning a Structure-aware Editable Morphable Model (SEMM). SEMM introduces a detail structure representation

Run Xiao, Junyi Zhang, Juan Chamorro, Jinwoong Kim

We investigate the integer quantum Hall effect in Cd3As2 thin films under conditions of strong to moderate quantum confinement (thicknesses of 10 nm, 12 nm, 15 nm). In all the films, we observe the integer quantum Hall effect in the spin-polarized lowest Landau level (filling factor {\nu} = 1) and at spin-degenerate higher index Landau levels with even filli

Jian-Jian Miao

The Majorana lattice gauge theory purely composed of Majorana fermions on square lattice is studied throughly. The ground state is obtained exactly and exhibits the coexistence of symmetry breaking and topological order. The $Z_2$ symmetry breaking of matter fields leads to the intertwined antiferromagnetic spin order and $\eta$-pairing order. The topologica

Amanda Coston, Edward H. Kennedy

Historically used in settings where the outcome is rare or data collection is expensive, outcome-dependent sampling is relevant to many modern settings where data is readily available for a biased sample of the target population, such as public administrative data. Under outcome-dependent sampling, common effect measures such as the average risk difference a

Robert F. Allen, Katherine Heller, Matthew A. Pons

We investigate composition-differentiation operators acting on the Dirichlet space of the unit disk. Specifically, we determine characterizations for bounded, compact, and Hilbert-Schmidt composition-differentiation operators. In addition, for particular classes of inducing maps, we derive an adjoint formula, compute the norm, and identify the spectrum.

Henning Schomerus

Utilizing scattering theory, we quantify the consequences of physical constraints that limit the visibility of non-Hermitian effects in passive devices. The constraints arise from the fundamental requirement that the system obeys causality, and can be captured concisely in terms of an internal time-delay operator, which furthermore provides a direct quantita

Vicente Cortés, Kazuyuki Hasegawa

Given a hypercomplex manifold with a rotating vector field (and additional data), we construct a conical hypercomplex manifold. As a consequence, we associate a quaternionic manifold to a hypercomplex manifold of the same dimension with a rotating vector field. This is a generalization of the HK/QK-correspondence. As an application, we show that a quaternion

Darshan Gera, Badveeti Naveen Siva Kumar, Bobbili Veerendra Raj Kumar, S Balasubramanian

Automatic affect recognition has applications in many areas such as education, gaming, software development, automotives, medical care, etc. but it is non trivial task to achieve appreciable performance on in-the-wild data sets. In-the-wild data sets though represent real-world scenarios better than synthetic data sets, the former ones suffer from the proble

S. J. Frank, J. P. Lee, J. C. Wright, I. H. Hutchinson

Raytracing/Fokker-Planck (FP) simulations used to model lower-hybrid current drive (LHCD) often fail to reproduce experimental results, particularly when LHCD is weakly damped. A proposed reason for this discrepancy is the lack of "full-wave" effects, such as diffraction and interference, in raytracing simulations and the breakdown of raytracing approximatio

Pedro A. Santos-Florez, Shi-Cheng Dai, Yi Yao, Howard Yanxon

We employ a machine-learning force field, trained by a neural network (NN) with bispectrum coefficients as descriptors, to investigate the short-range order (SRO) influences on the BCC NbMoTaW alloy strengthening mechanism. The NN interatomic potential provides a transferable force field with density functional theory accuracy. This novel NN potential is app

Robert F. Allen, Whitney George, Matthew A. Pons

We study properties of the topological space of composition operators on the Banach algebra of bounded functions on an unbounded, locally finite metric space in the operator norm topology and essential norm topology. Moreover, we characterize the compactness of differences of two such composition operators.

Kailiang Zhong, Fengtong Xiao, Yan Ren, Yaorong Liang

Causal Inference has wide applications in various areas such as E-commerce and precision medicine, and its performance heavily relies on the accurate estimation of the Individual Treatment Effect (ITE). Conventionally, ITE is predicted by modeling the treated and control response functions separately in their individual sample spaces. However, such an approa

Yanghao Fang, Tsampikos Kottos, Ramathasan Thevamaran

We study the elastodynamics of a periodic metastructure incorporating a defect pair that enforces a parity-time (PT) symmetry due to a judiciously engineered imaginary impedance elements - one having energy amplification (gain) and the other having an equivalent attenuation (loss) mechanism. We show that their presence affects the initial band structure of t

Yue Bai, Abolfazl Safikhani

In recent years, change point detection for high dimensional data has become increasingly important in many scientific fields. Most literature develop a variety of separate methods designed for specified models (e.g. mean shift model, vector auto-regressive model, graphical model). In this paper, we provide a unified framework for structural break detection

Robert F. Allen, Matthew A. Pons

We present the current results in the study of weighted composition operators on weighted Banach spaces of an unbounded, locally finite metric space. Specifically, we determine characterizations of bounded and compact weighted composition operators, including the operator and essential norms. In addition, we characterize the weighted composition operators th

Paulo Montero-Camacho, Yuchen Liu, Yi Mao

It has been recently shown that the astrophysics of reionization can be extracted from the Ly$\alpha$ forest power spectrum by marginalizing the memory of reionization over cosmological information. This impact of cosmic reionization on the Ly$\alpha$ forest power spectrum can survive cosmological time scales because cosmic reionization, which is inhomogeneo

Grigory Franguridi

For the kernel estimator of the quantile density function (the derivative of the quantile function), I show how to perform the boundary bias correction, establish the rate of strong uniform consistency of the bias-corrected estimator, and construct the confidence bands that are asymptotically exact uniformly over the entire domain $[0,1]$. The proposed proce

Indrani Banerjee, Sumanta Chakraborty, Soumitra SenGupta

We show that the observed angular diameter of the shadow of the ultra compact object Sgr A*, favours the existence of an extra spatial dimension. This holds irrespective of the nature of the ultra compact object, i.e., whether it is a wormhole or, a black hole mimicker, but with the common feature that both of them have an extra dimensional origin. This resu

Shreyas Ramakrishna, Baiting Luo, Christopher Kuhn, Gabor Karsai

Despite recent advances in autonomous driving systems, accidents such as the fatal Uber crash in 2018 show these systems are still susceptible to edge cases. Such systems must be thoroughly tested and validated before being deployed in the real world to avoid such events. Testing in open-world scenarios can be difficult, time-consuming, and expensive. These

Zhao Song, Zhaozhuo Xu, Yuanyuan Yang, Lichen Zhang

In this paper, we study the problem of speeding up a type of optimization algorithms called Frank-Wolfe, a conditional gradient method. We develop and employ two novel inner product search data structures, improving the prior fastest algorithm in [Shrivastava, Song and Xu, NeurIPS 2021]. * The first data structure uses low-dimensional random projection to re

Robert F. Allen, Matthew A. Pons

We study composition operators on the weighted Banach spaces of an infinite tree. We characterize the bounded and the compact operators, as well as determine the operator norm and the essential norm. In addition, we study the isometric composition operators.

Siddhartha Paul Tiwari

Mobile technology creates value on three fundamental pillars: productivity, coordination, and transformation. Mobile apps are becoming an increasingly important aspect of teaching and learning in many countries. The use of mobile applications in education is not only beneficial, but also provides students with an enjoyable and interactive experience. For a m

Vadim Gorin, Jiaming Xu

A sorting network is a shortest path from $12\dots n$ to $n\dots 21$ in the Cayley graph of the symmetric group $\mathfrak S_n$ spanned by adjacent transpositions. The paper computes the edge local limit of the uniformly random sorting networks as $n\to\infty$. We find the asymptotic distribution of the first occurrence of a given swap $(k,k+1)$ and identify

Robert F. Allen, Katherine Heller, Matthew A. Pons

We propose two SIR models which incorporate sociological behavior of groups of individuals. It is these differences in behaviors which impose different infection rates on the individual susceptible populations, rather than biological differences. We compute the basic reproduction number for each model, as well as analyze the sensitivity of $R_0$ to changes i

Doug Pickrell

Let $K$ denote a simply connected compact Lie group and let $G=K^{\mathbb C}$, the complexification. It is known that there exists an $LK$ bi-invariant probability measure on a natural hyperfunction completion of the complex loop group $LG$. There are various generalizations, involving positive line bundle valued measures on the hyperfunction completion, rep

Boris Babenko, Ilana Traynis, Christina Chen, Preeti Singh

External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about additional systemic medical conditions. We developed a deep learning system (DLS) that takes external eye photos as input and predicts multiple systemic parameters, such as those r

Neil Nie, Samir Yitzhak Gadre, Kiana Ehsani, Shuran Song

We introduce Structure from Action (SfA), a framework to discover 3D part geometry and joint parameters of unseen articulated objects via a sequence of inferred interactions. Our key insight is that 3D interaction and perception should be considered in conjunction to construct 3D articulated CAD models, especially for categories not seen during training. By

Saeid Sadeghi Vilni, Mohammad Moltafet, Markus Leinonen, Marian Codreanu

We consider a multi-source hybrid automatic repeat request (HARQ) based system, where a transmitter sends status update packets of random arrival (i.e., uncontrollable sampling) and generate-atwill (i.e., controllable sampling) sources to a destination through an error-prone channel. We develop transmission scheduling policies to minimize the average number

C. D. Pemmaraju, Amol Deshmukh

The constrained-search formulation of Levy and Lieb provides a concrete mapping from N-representable densities to the space of N-particle wavefunctions and explicitly defines the universal functional of density functional theory. We numerically implement the Levy-Lieb procedure for a paradigmatic lattice system, the Hubbard dimer, using a modified variationa

Wen-Wei Li

For a connected reductive group $G$ and an affine smooth $G$-variety $X$ over the complex numbers, the localization functor takes $\mathfrak{g}$-modules to $D_X$-modules. We extend this construction to an equivariant and derived setting using the formalism of h-complexes due to Beilinson-Ginzburg, and show that the localizations of Harish-Chandra $(\mathfrak

Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang

While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based profile pollution attacks. To demonstrate our hypothesis, we propose a substitution-based adversarial attack algorithm, which modifies the input sequence by selecting certain vulnera

Francisco Caldas, Cláudia Soares

Since the late 1950s, when the first artificial satellite was launched, the number of Resident Space Objects has steadily increased. It is estimated that around one million objects larger than one cm are currently orbiting the Earth, with only thirty thousand larger than ten cm being tracked. To avert a chain reaction of collisions, known as Kessler Syndrome

Robert F. Allen, Thong M. Le, Matthew A. Pons

We give a complete characterization of the spectrum of composition operators, induced by an automorphism of the open unit disk, acting on a family of Banach spaces of analytic functions that includes the Bloch space and BMOA. We show that for parabolic and hyperbolic automorphisms, the spectrum is the unit circle. For the case of elliptic automorphisms, the

Sébastien Breteaux, Jérémy Faupin, Marius Lemm, Israel Michael Sigal

We prove a maximal velocity bound for the dynamics of Markovian open quantum systems. The dynamics are described by one-parameter semi-groups of quantum channels satisfying the von Neumann-Lindblad equation. Our result says that dynamically evolving states are contained inside a suitable light cone up to polynomial errors. We also give a bound on the slope o

Evelyn Pratami Sinaga, Michael P. Adams, Mathias Bersweiler, Laura G. Vivas

We employ micromagnetic simulations to model the effect of pore-type microstructural defects on the magnetic small-angle neutron scattering cross section and the related pair-distance distribution function of spherical magnetic nanoparticles. Our expression for the magnetic energy takes into account the isotropic exchange interaction, the magnetocrystalline

Philip Harris, Philip Schuster, Jure Zupan

Dark matter can be part of a dark sector with non-minimal couplings to the Standard Model. Compared to many (minimal) benchmark models, such scenarios can result in significant modifications in experimental signatures and strongly impact experimental search sensitivity. In this white paper, we review several non-minimal dark sector models, including phenomen

Tomas Cabezon Pedroso, Javier Del Ser, Natalia Diaz-Rodrıguez

Rendering programs have changed the design process completely as they permit to see how the products will look before they are fabricated. However, the rendering process is complicated and takes a significant amount of time, not only in the rendering itself but in the setting of the scene as well. Materials, lights and cameras need to be set in order to get

Mingbin Xu, Congzheng Song, Ye Tian, Neha Agrawal

Federated Learning (FL) is a technique to train models using data distributed across devices. Differential Privacy (DP) provides a formal privacy guarantee for sensitive data. Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP. However, the DP-noise introduced to the model

Arthur B. Congdon, Savitri V. Iyer, Charles R. Keeton

The observation of the bending of light by mass, now known as gravitational lensing, was key in establishing general relativity as one of the pillars of modern physics. In the past couple of decades, there has been increasing interest in using gravitational lensing to test general relativity beyond the weak deflection limit. Black holes and neutron stars pro

Citian Wang, Feng Liu, Huaqing Huang

High-order topological insulators (HOTIs), as generalized from topological crystalline insulators (TCIs), are characterized with lower-dimensional metallic boundary states protected by spatial symmetries of a crystal, whose theoretical framework based on band inversion at special $k$-points cannot be readily extended to quasicrystals because quasicrystals co

Anton Baranov, Timur Batenev

We give an elementary construction of representing systems of the Cauchy kernels in the Hardy spaces $H^p$, $1 \le p <\infty$, as well as of representing systems of reproducing kernels in weighted Hardy spaces.

Alan A. Kaptanoglu, Tony Qian, Florian Wechsung, Matt Landreman

A common scientific inverse problem is the placement of magnets that produce a desired magnetic field inside a prescribed volume. This is a key component of stellarator design, and recently permanent magnets have been proposed as a potentially useful tool for magnetic field shaping. Here, we take a closer look at possible objective functions for permanent ma

Bin Guo, Duong H. Phong

Energy bounds which are uniform in the background metric are obtained from upper bounds for entropy-like quantities. The argument is based on auxiliary Monge-Amp\`ere equations involving sublevel sets, and bypasses the Alexandrov-Bakelman-Pucci maximum principle. In particular, it implies uniform $L^\infty$ bounds for systems coupling a fully non-linear equa

Emily McMilin

In this work we show how large language models (LLMs) can learn statistical dependencies between otherwise unconditionally independent variables due to dataset selection bias. To demonstrate the effect, we developed a masked gender task that can be applied to BERT-family models to reveal spurious correlations between predicted gender pronouns and a variety o

George Drummond, Charles Semple

An element $e$ of a $3$-connected matroid $M$ is elastic if ${\rm si}(M/e)$, the simplification of $M/e$, and ${\rm co}(M\backslash e)$, the cosimplification of $M\backslash e$, are both $3$-connected. It was recently shown that if $|E(M)|\geq 4$, then $M$ has at least four elastic elements provided $M$ has no $4$-element fans and no member of a specific fam

Siamul Karim Khan, Patrick Tinsley, Adam Czajka

Nonlinear iris texture deformations due to pupil size variations are one of the main factors responsible for within-class variance of genuine comparison scores in iris recognition. In dominant approaches to iris recognition, the size of a ring-shaped iris region is linearly scaled to a canonical rectangle, used further in encoding and matching. However, the

David Hart, Michael Whitney, Bryan Morse

Convolutional Neural Networks have revolutionized vision applications. There are image domains and representations, however, that cannot be handled by standard CNNs (e.g., spherical images, superpixels). Such data are usually processed using networks and algorithms specialized for each type. In this work, we show that it may not always be necessary to use sp

Joydeep Mitra

With the onset of COVID-19, governments worldwide planned to develop and deploy contact tracing (CT) apps to help speed up the contact tracing process. However, experts raised concerns about the long-term privacy and security implications of using these apps. Consequently, several proposals were made to design privacy-preserving CT apps. To this end, Google

Ananya Kumar, Tengyu Ma, Percy Liang, Aditi Raghunathan

We often see undesirable tradeoffs in robust machine learning where out-of-distribution (OOD) accuracy is at odds with in-distribution (ID) accuracy: a robust classifier obtained via specialized techniques such as removing spurious features often has better OOD but worse ID accuracy compared to a standard classifier trained via ERM. In this paper, we find th

Qi Zhang, Yun Ma, Qi Liu, Xinchen Zhang

Parity-time (PT) symmetry and broken in micro/nano photonic structures have been investigated extensively as they bring new opportunities to control the flow of light based on non-Hermitian optics. Previous studies have focused on the situations of PT-symmetry broken in loss-loss or gain-loss coupling systems. Here, we theoretically predict the gain-gain and

Tengfei Xue, Fan Zhang, Chaoyi Zhang, Yuqian Chen

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on

Griffin Dietz, Jennifer King Chen, Jazbo Beason, Matthew Tarrow

Typical educational robotics approaches rely on imperative programming for robot navigation. However, with the increasing presence of AI in everyday life, these approaches miss an opportunity to introduce machine learning (ML) techniques grounded in an authentic and engaging learning context. Furthermore, the needs for costly specialized equipment and ample

Minu J. Bae

The semi-source independent quantum walk random number generator (SI-QW-QRNG) is a cryptographic protocol that extracts a string of true random bits from a quantum random walk with an adversary controls a randomness source, but the dimension of the system is known. This paper analyzes SI-QW-QRNG protocols with a memory-based quantum walk state. The new proto

Navas-López, Eduardo Adam

This article describes the features of a compiler for a superset language of the well-known PL/0 created by Niklaus Wirth. The main feature is that it implements the build phases in such a way that the information passed between each one is reflected as an XML file.

Anne Theurkauf, Nisar Ahmed, Morteza Lahijanian

Although resource-limited networked autonomous systems must be able to efficiently and effectively accomplish tasks, better conservation of resources often results in worse task performance. We specifically address the problem of finding strategies for managing measurement communication costs between agents. A well understood technique for trading off commun

Prasad Sonar, Hiroaki Katsuragi

We experimentally study the crack formation and decompaction-wave propagating in a vibrated powder bed consisting of glass beads of 5 {\mu}m in diameter. The vibrated powder bed exhibits three distinct phases depending on the vibration conditions: consolidation (CS), static fracture (SF), and dynamic fracture (DF). Particularly, we found an upward wave propa

J. G. Sereni, I. Čurlik, M. Reiffers, M. Goivannini

Magnetic, thermal and transport properties of the non-centrosymmetric compound Eu$_2$Pd$_2$Sn are revisited after including new measurements. In its paramagnetic phase, the outstanding feature of this compound is the formation of Eu$^{2+}$ dimers that allows to understand the deviation of the magnetic susceptibility $\chi(T)$ from the C-W law below about 70\

Eric MacLennan, Mikael Granvik

Near-Earth asteroid (3200) Phaethon is an active asteroid with a dust tail repeatedly observed over the past decade for 3 days during each perihelion passage down to a heliocentric distance of 0.14 au. The mechanism causing the activity is still debated, and the suggested mechanisms lack clear supporting evidence. Phaethon has been identified as the likely p

Navas-López, Eduardo Adam

This article describes an implementation of low-cost portable touch screen technology, applied to university teaching, using as a base the remote control of the Nintendo Wii console (known as Wiimote), a normal projector, a computer and free software. The purpose is to show the feasibility of such implementation to improve teaching/learning processes, withou

Alireza Ghaffari, Marzieh S. Tahaei, Mohammadreza Tayaranian, Masoud Asgharian

The ever-increasing computational complexity of deep learning models makes their training and deployment difficult on various cloud and edge platforms. Replacing floating-point arithmetic with low-bit integer arithmetic is a promising approach to save energy, memory footprint, and latency of deep learning models. As such, quantization has attracted the atten

Rad Niazadeh, Renato Paes Leme, Jon Schneider

We construct explicit combinatorial Bernoulli factories for the class of \emph{flow-based polytopes}; integral 0/1-polytopes defined by a set of network flow constraints. This generalizes the results of Niazadeh et al. (who constructed an explicit factory for the specific case of bipartite perfect matchings) and provides novel exact sampling procedures for s

Cuong T. Pham, Kevin G. Lynch, James R. McKay, Ashkan Ertefaie

The current body of research on developing optimal treatment strategies often places emphasis on intention-to-treat analyses, which fail to take into account the compliance behavior of individuals. Methods based on instrumental variables have been developed to determine optimal treatment strategies in the presence of endogeneity. However, these existing meth

Bryan Andrews, Gregory F. Cooper, Thomas S. Richardson, Peter Spirtes

Directed acyclic graph (DAG) models have become widely studied and applied in statistics and machine learning -- indeed, their simplicity facilitates efficient procedures for learning and inference. Unfortunately, these models are not closed under marginalization, making them poorly equipped to handle systems with latent confounding. Acyclic directed mixed g

Takao Komatsu, Haotian Ying

The so-called Frobenius number in the famous linear Diophantine problem of Frobenius is the largest integer such that the linear equation $a_1 x_1+\cdots+a_k x_k=n$ ($a_1,\dots,a_k$ are given positive integers with $\gcd(a_1,\dots,a_k)=1$) does not have a non-negative integer solution $(x_1,\dots,x_k)$. The generalized Frobenius number (called the $p$-Froben

Alan Sola

We analyze certain compositions of rational inner functions in the unit polydisk $\mathbb{D}^{d}$ with polydegree $(n,1)$, $n\in \mathbb{N}^{d-1}$, and isolated singularities in $\mathbb{T}^d$. Provided an irreducibility condition is met, such a composition is shown to be a rational inner function with singularities in precisely the same location as those of

Zijie Yue, Miaojing Shi

The target of space-time video super-resolution (STVSR) is to increase both the frame rate (also referred to as the temporal resolution) and the spatial resolution of a given video. Recent approaches solve STVSR using end-to-end deep neural networks. A popular solution is to first increase the frame rate of the video; then perform feature refinement among di

Miloslav Torda, John Y. Goulermas, Vitaliy Kurlin, Graeme M. Day

Packings of regular convex polygons ($n$-gons) that are sufficiently dense have been studied extensively in the context of modeling physical and biological systems as well as discrete and computational geometry. Former results were mainly regarding densest lattice or double-lattice configurations. Here we consider all two-dimensional crystallographic symmetr

Kiran Tomlinson, Johan Ugander, Jon Kleinberg

Instant runoff voting (IRV) is an increasingly-popular alternative to traditional plurality voting in which voters submit rankings over the candidates rather than single votes. In practice, elections using IRV often restrict the ballot length, the number of candidates a voter is allowed to rank on their ballot. We theoretically and empirically analyze how ba

Wodson Mendson, Jorge Vitório Pereira

We investigate the geometry of codimension one foliations on smooth projective varieties defined over fields of positive characteristic with an eye toward applications to the structure of codimension one holomorphic foliations on projective manifolds.

Germano Gabbianelli, Matteo Papini, Gergely Neu

We study the problem of online learning in adversarial bandit problems under a partial observability model called off-policy feedback. In this sequential decision making problem, the learner cannot directly observe its rewards, but instead sees the ones obtained by another unknown policy run in parallel (behavior policy). Instead of a standard exploration-ex

Arvind U Raghunathan, Carlos Cardonha, David Bergman, Carlos J Nohra

Linear programming (LP) relaxations are widely employed in exact solution methods for multilinear programs (MLP). One example is the family of Recursive McCormick Linearization (RML) strategies, where bilinear products are substituted for artificial variables, which deliver a relaxation of the original problem when introduced together with concave and convex

Shiyu Zhao, Zhixing Zhang, Samuel Schulter, Long Zhao

Building robust and generic object detection frameworks requires scaling to larger label spaces and bigger training datasets. However, it is prohibitively costly to acquire annotations for thousands of categories at a large scale. We propose a novel method that leverages the rich semantics available in recent vision and language models to localize and classi

Wilkie Olin-Ammentorp Maxim Bazhenov

Vector-symbolic architectures (VSAs) provide methods for computing which are highly flexible and carry unique advantages. Concepts in VSAs are represented by 'symbols,' long vectors of values which utilize properties of high-dimensional spaces to represent and manipulate information. In this new work, we combine efficiency of the operations provided within t

F. Fraschetti, J. D. Alvarado-Gómez, J. J. Drake, O. CoheN

Energetic particles emitted by active stars are likely to propagate in astrospheric magnetized plasma turbulent and disrupted by the prior passage of energetic Coronal Mass Ejections (CMEs). We carried out test-particle simulations of $\sim$ GeV protons produced at a variety of distances from the M1Ve star AU~Microscopii by coronal flares or travelling shock

Runze Li, Pan Ji, Yi Xu, Bir Bhanu

Self-supervised monocular depth estimation has seen significant progress in recent years, especially in outdoor environments. However, depth prediction results are not satisfying in indoor scenes where most of the existing data are captured with hand-held devices. As compared to outdoor environments, estimating depth of monocular videos for indoor environmen

Rostislav Korst, Arip Asadulaev

We propose the novel framework for generative modelling using hybrid energy-based models. In our method we combine the interpretable input gradients of the robust classifier and Langevin Dynamics for sampling. Using the adversarial training we improve not only the training stability, but robustness and generative modelling of the joint energy-based models.

Lauren J. Saunders, Matthew Hasselfield, Brian J. Koopman, Laura Newburgh

The Simons Observatory (SO) is a ground-based cosmic microwave background survey experiment that consists of three 0.5 m small-aperture telescopes and one 6 m large-aperture telescope, sited at an elevation of 5200 m in the Atacama Desert in Chile. SO will study the polarization and temperature anisotropies of the Cosmic Microwave Background (CMB). The obser

Arip Asadulaev, Alexander Panfilov, Andrey Filchenkov

Adversarial examples are transferable between different models. In our paper, we propose to use this property for multi-step domain adaptation. In unsupervised domain adaptation settings, we demonstrate that replacing the source domain with adversarial examples to $\mathcal{H} \Delta \mathcal{H}$-divergence can improve source classifier accuracy on the targe

Mario Geiger, Tess Smidt

We present e3nn, a generalized framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks. e3nn naturally operates on geometry and geometric tensors that describe systems in 3D and transform predictably under a change of coordinate system. The core of e3nn are equivariant operations such as the TensorProduct class or

Charles J. Aouad, Paolo A. Mazzali, Stephan Hachinger, Jacob Teffs

The abundance distribution in the ejecta of the peculiar slowly declining Type Ia supernova (SN\,Ia) SN\,1999aa is obtained by modelling a time series of optical spectra. Similar to SN\,1991T, SN\,1999aa was characterised by early-time spectra dominated by \FeIII\ features and a weak \SiII\,6355\,\AA\ line, but it exhibited a high-velocity \CaII\,H\&K line a

Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer

This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant. This is the default approach in state-of-the-art lattice field theory calculations, making this development critical to the practical application of flow models to theories

Chonghan Chen, Haohan Wang, Leyang Hu, Yuhao Zhang

We introduce the initial release of our software Robustar, which aims to improve the robustness of vision classification machine learning models through a data-driven perspective. Building upon the recent understanding that the lack of machine learning model's robustness is the tendency of the model's learning of spurious features, we aim to solve this probl

Yifan Zhong, Haohan Wang, Eric P. Xing

Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to predict and fail to generalize on out-of-sample data. While many other approaches have been proposed to address this issue from the computation perspective such as new architectures

Kais Hariz, Hachem Kadri, Stéphane Ayache, Maher Moakher

We study the implicit regularization effects of deep learning in tensor factorization. While implicit regularization in deep matrix and 'shallow' tensor factorization via linear and certain type of non-linear neural networks promotes low-rank solutions with at most quadratic growth, we show that its effect in deep tensor factorization grows polynomially with

Carolina E S Mattsson, Teodoro Criscione, Frank W Takes

Circulation is the characteristic feature of successful currency systems, from community currencies to cryptocurrencies to national currencies. In this paper, we propose a network analysis approach especially suited for studying circulation given a system's digital transaction records. Sarafu is a digital community currency that was active in Kenya over a pe

Arip Asadulaev, Alexander Panfilov, Andrey Filchenkov

It was shown that adversarial examples improve object recognition. But what about their opposite side, easy examples? Easy examples are samples that the machine learning model classifies correctly with high confidence. In our paper, we are making the first step toward exploring the potential benefits of using easy examples in the training procedure of neural

Avrajit Ghosh, Michael T. McCann, Madeline Mitchell, Saiprasad Ravishankar

We present a method for supervised learning of sparsity-promoting regularizers for denoising signals and images. Sparsity-promoting regularization is a key ingredient in solving modern signal reconstruction problems; however, the operators underlying these regularizers are usually either designed by hand or learned from data in an unsupervised way. The recen

Jorge Bellorin, Claudio Borquez, Byron Droguett

We perform an analysis of the ultraviolet divergences of the quantum nonprojectable Horava gravity. We work the quantum field theory directly in the Hamiltonian formalism provided by the Batalin-Fradkin-Vilkovisky quantization. In this way the second-class constraints can be incorporated to the quantization. A known local gauge-fixing condition leads to a lo

M A Khalighi, Z Ghassemlooy, S Zvanovec, N Stevens

The EU COST Action NEWFOCUS is focused on investigating radical solutions with the potential to impact the design of future wireless networks. It aims to address some of the challenges in OWC and establish it as an efficient technology that can satisfy the demanding requirements of backhaul and access network levels in 5G networks. This also includes the use

Abhishek K. Umrawal, Christopher J. Quinn, Vaneet Aggarwal

We consider the problem of Influence Maximization (IM), the task of selecting $k$ seed nodes in a social network such that the expected number of nodes influenced is maximized. We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by sol

Heqing Huang, Adam C. Overvig, Yuan Xu, Stephanie C. Malek

Metasurfaces have been rapidly advancing our command over the many degrees of freedom of light within compact, lightweight devices. However, so far, they have mostly been limited to manipulating light in free space. Grating couplers provide the opportunity of bridging far-field optical radiation and in-plane guided waves, and thus have become fundamental bui

Cezary Gonera, Joanna Gonera, Javier de Lucas, Wioletta Szczesek

A known general class of superintegrable systems on 2D spaces of constant curvature can be defined by potentials separating in (geodesic) polar coordinates. The radial parts of these potentials correspond either to an isotropic harmonic oscillator or a generalised Kepler potential. The angular components, on the contrary, are given implicitly by a transcende

L. E. Golub, M. M. Glazov

We show theoretically that electromagnetic waves propagating in the transparency region of a non-centrosymmetric medium can induce a dc electric current. The origin of the effect is the Raman scattering of light by free carriers in the system. Due to the photon scattering, electrons undergo real quantum transitions resulting in the formation of their anisotr

B. Cooper Boniece, Lajos Horváth, Peter Jacobs

We consider the problem of detecting distributional changes in a sequence of high dimensional data. Our approach combines two separate statistics stemming from $L_p$ norms whose behavior is similar under $H_0$ but potentially different under $H_A$, leading to a testing procedure that that is flexible against a variety of alternatives. We establish the asympt

D. A. Román-García, F. H. Maldonado-Villamizar, B. Jaramillo-Ávila, B. M. Rodríguez-Lara

We demonstrate scattering control of Gaussian-like wave packets propagating with constant envelope velocity and invariant waist through coupled resonator optical waveguides (CROW) via an external resonator coupled to multiple sites of the CROW. We calculate the analytical reflectance and transmittance using standard scattering methods from waveguide quantum

Carlos Bercini, Vasco Goncalves, Alexandre Homrich, Pedro Vieira

We reduce the computation of three point function of three spinning operators with arbitrary polarizations to a statistical mechanics problem via the hexagon formalism. The central building block of these correlation functions is the hexagon partition function. We explore its analytic structure and use it to generate perturbative data for spinning three poin

Fawad Ahmad, Christina Suyong Shin, Weiwu Pang, Branden Leong

Recent works have considered two qualitatively different approaches to overcome line-of-sight limitations of 3D sensors used for perception: cooperative perception and infrastructure-augmented perception. In this paper, motivated by increasing deployments of infrastructure LiDARs, we explore a third approach, cooperative infrastructure perception. This appro

Subhrata Dey, Arti Goyal, Katarzyna Małek, Timothy J. Galvin

We present the detailed analysis of 11 local luminous infrared galaxies (LIRGs) from ultraviolet through far-infrared to radio ($\sim$70 MHz to $\sim$15 GHz) bands. We derive the astrophysical properties through spectral energy distribution (SED) modeling using the Code Investigating GALaxy Emission (CIGALE) and UltraNest codes. The radio SEDs include our ne

Duiliu-Emanuel Diaconescu, Mauro Porta, Francesco Sala

In this paper we provide a systematic way of producing representations of cohomological, K-theoretical and categorified Hall algebras, and study the output of our construction in several cases. We thus recover and categorify in a unified framework the action of the COHA of a quiver on the cohomology of Nakajima quiver variety, the action of the COHA of zero-