June 2020 arXiv papers

Saeed Rashidi, Matthew Denton, Srinivas Sridharan, Sudarshan Srinivasan

Deep Learning (DL) training platforms are built by interconnecting multiple DL accelerators (e.g., GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandwidth. However, as we identify in this work, driving this bandwidth is quite challenging. This is because there is a pernicious balance between using the accelerator's compute a

Guanghui Lan, Edwin Romeijn, Zhiqiang Zhou

Conditional gradient methods have attracted much attention in both machine learning and optimization communities recently. These simple methods can guarantee the generation of sparse solutions. In addition, without the computation of full gradients, they can handle huge-scale problems sometimes even with an exponentially increasing number of decision variabl

Guantao Chen, Xiaowei Yu, Yi Zhao

For a given graph $H$, the Ramsey number $r(H)$ is the minimum $N$ such that any 2-edge-coloring of the complete graph $K_N$ yields a monochromatic copy of $H$. Given a positive integer $n$, a \emph{fan }$F_n$ is a graph formed by $n$ triangles that share one common vertex. We show that ${9n}/{2}-5\le r(F_n)\le {11n}/{2} + 6$ for any $n$. This improves previ

Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda

We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed to first fit the training data with clean labels during an "early learning" phase, before eventually memorizing the examples with false labels. We prove that early learnin

Ana M. Bianco, Graciela Boente, Wenceslao Gonzalez-Manteiga

The Receiver Operating Characteristic (ROC) curve is a useful tool that measures the discriminating power of a continuous variable or the accuracy of a pharmaceutical or medical test to distinguish between two conditions or classes. In certain situations, the practitioner may be able to measure some covariates related to the diagnostic variable which can inc

Antonino Marciano, Deen Chen, Filippo Fabrocini*, Chris Fields

Our work intends to show that: (1) Quantum Neural Networks (QNN) can be mapped onto spinnetworks, with the consequence that the level of analysis of their operation can be carried out on the side of Topological Quantum Field Theories (TQFT); (2) Deep Neural Networks (DNN) are a subcase of QNN, in the sense that they emerge as the semiclassical limit of QNN;

Kumar Pratik, Bhaskar D. Rao, Max Welling

In this paper, we present a novel neural network for MIMO symbol detection. It is motivated by several important considerations in wireless communication systems; permutation equivariance and a variable number of users. The neural detector learns an iterative decoding algorithm that is implemented as a stack of iterative units. Each iterative unit is a neura

Changchang Xi

We establish relations between representation dimensions of two algebras connected by a Frobenius bimodule or extension. Consequently, upper bounds and equality formulas for representation dimensions of group algebras, symmetric separably equivalent algebras and crossed products are obtained. Particularly, for any subgroup H of a finite group G, if [G:H] is

Enrico Speranza, Nora Weickgenannt

The relativistic treatment of spin is a fundamental subject which has an old history. In various physical contexts it is necessary to separate the relativistic total angular momentum into an orbital and spin contribution. However, such decomposition is affected by ambiguities since one can always redefine the orbital and spin part through the so-called pseud

Yassine El Ouahidi, Matis Feller, Matthieu Talagas, Bastien Pasdeloup

In this paper, we introduce a novel and interpretable methodology to cluster subjects suffering from cancer, based on features extracted from their biopsies. Contrary to existing approaches, we propose here to capture complex patterns in the repartitions of their cells using histograms, and compare subjects on the basis of these repartitions. We describe her

Kasra Hejazi, Xiao Chen, Leon Balents

We propose a method for studying the strong interaction regimes in twisted bilayer graphene using hybrid Wannier functions, that are Wannier-like in one direction and Bloch-like in the other. We focus on the active bands as given by the continuum model proposed by Bistritzer and MacDonald, and discuss the properties of corresponding hybrid Wannier functions.

Sapan Karki, Brett Altschul

Lorentz- and CPT-violating models of electrodynamics with Chern-Simons terms are typically plagued by various sorts of instabilities. However, when the Chern-Simons term arises from a slow time variation in a pseudoscalar field with an axion-like electromagnetic coupling, the total energy of the theory is bounded below. We examine the behavior of such a theo

Mazen Ali, Anthony Nouy

We study the approximation by tensor networks (TNs) of functions from classical smoothness classes. The considered approximation tool combines a tensorization of functions in $L^p([0,1))$, which allows to identify a univariate function with a multivariate function (or tensor), and the use of tree tensor networks (the tensor train format) for exploiting low-r

André Reggio, Robin Delabays, Philippe Jacquod

Inspired by the Deffuant and Hegselmann-Krause models of opinion dynamics, we extend the Kuramoto model to account for confidence bounds, i.e., vanishing interactions between pairs of oscillators when their phases differ by more than a certain value. We focus on Kuramoto oscillators with peaked, bimodal distribution of natural frequencies. We show that, in t

Heather Z. Brooks, Unchitta Kanjanasaratool, Yacoub H. Kureh, Mason A. Porter

The COVID-19 pandemic has led to significant changes in how people are currently living their lives. To determine how to best reduce the effects of the pandemic and start reopening societies, governments have drawn insights from mathematical models of the spread of infectious diseases. In this article, we give an introduction to a family of mathematical mode

Ivan Voitalov, Pim van der Hoorn, Maksim Kitsak, Fragkiskos Papadopoulos

Maximum entropy null models of networks come in different flavors that depend on the type of constraints under which entropy is maximized. If the constraints are on degree sequences or distributions, we are dealing with configuration models. If the degree sequence is constrained exactly, the corresponding microcanonical ensemble of random graphs with a given

Juliane Capaverde, Ariane M. Masuda, Virgínia M. Rodrigues

Let $\mathbb{F}_q$ be a finite field of odd characteristic. We study Rédei functions that induce permutations over $\mathbb{P}^1(\mathbb{F}_q)$ whose cycle decomposition contains only cycles of length $1$ and $j$, for an integer $j\geq 2$. When $j$ is $4$ or a prime number, we give necessary and sufficient conditions for a Rédei permutation of this type to e

Filomena Feo, Juan Luis Vázquez, Bruno Volzone

We prove the existence of self-similar fundamental solutions (SSF) of the anisotropic porous medium equation in the suitable fast diffusion range. Each of such SSF solutions is uniquely determined by its mass. We also obtain the asymptotic behaviour of all finite-mass solutions in terms of the family of self-similar fundamental solutions. Time decay rates ar

Cheng Shu

We compute the character table of $\text{GL}_n(\mathbb{F}_q)\rtimes\!<\!σ\!>\!$, $σ$ being an order 2 exterior automorphism.

Mazen Ali, Anthony Nouy

We study the approximation of functions by tensor networks (TNs). We show that Lebesgue $L^p$-spaces in one dimension can be identified with tensor product spaces of arbitrary order through tensorization. We use this tensor product structure to define subsets of $L^p$ of rank-structured functions of finite representation complexity. These subsets are then us

Seong Soon Jo, Akshay Singh, Liqiu Yang, Subodh C. Tiwari

A thorough understanding of native oxides is essential for designing semiconductor devices. Here we report a study of the rate and mechanisms of spontaneous oxidation of bulk single crystals of ZrS$_x$Se$_{2-x}$ alloys and MoS$_2$. ZrS$_x$Se$_{2-x}$ alloys oxidize rapidly, and the oxidation rate increases with Se content. Oxidation of basal surfaces is initi

Yacine Aoun

For a family of random-cluster models with cluster weights $q\geq 1$, we prove that the probability that $0$ is connected to $x$ is asymptotically equal to $\tfrac{1}{q}χ(β)^{2}βJ_{0,x}$. The method developed in this article can be applied to any spin model for which there exists a random-cluster representation which is one-monotonic.

Luis Enrique Adame, Luis Manuel Rivera, Ana Laura Trujillo-Negrete

Let $G$ be a simple graph of order $n$. The double vertex graph $F_2(G)$ of $G$ is the graph whose vertices are the $2$-subsets of $V(G)$, where two vertices are adjacent in $F_2(G)$ if their symmetric difference is a pair of adjacent vertices in $G$. A generalization of this graph is the complete double vertex graph $M_2(G)$ of $G$, defined as the graph who

Océane Boulais, Ben Woodward, Brian Schlining, Lonny Lundsten

Thousands of hours of marine video data are collected annually from remotely operated vehicles (ROVs) and other underwater assets. However, current manual methods of analysis impede the full utilization of collected data for real time algorithms for ROV and large biodiversity analyses. FathomNet is a novel baseline image training set, optimized to accelerate

Zhe Huang, Aamir Hasan, Kazuki Shin, Ruohua Li

Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians. Critical insights from human intention and behavioral patterns need to be integrated to effectively forecast long-term pedestrian behavior. Thus, we propose a framework incorporating a Mutable Intention Filter and a Warp LSTM (MIF-WLSTM) to simul

Hojin Jang, Syed Suleman Abbas Zaidi, Xavier Boix, Neeraj Prasad

Deep Convolutional Neural Networks (DCNNs) have demonstrated impressive robustness to recognize objects under transformations (eg. blur or noise) when these transformations are included in the training set. A hypothesis to explain such robustness is that DCNNs develop invariant neural representations that remain unaltered when the image is transformed. Howev

Igor Protasov, Ksenia Protasova

Given a discrete group $G$, we identify the Stone-$\check C$ech compactification $βG$ with the set of all ultrafilters on $G$ and put $G^\ast =βG\setminus G$. The action $G$ on $G$ by the conjugations $(g,x)\mapsto g^{-1}xg$ induces the action of $G$ on $G^\ast$ by $(g, p)\mapsto p^g $, $p^g = \{ g^{-1} Pg: P\in p\}$. We study interplays between the algebrai

Imene Trigui, Sofiene Affes, Marco Di Renzo, Dushantha Nalin K. Jayakody

In this paper, we develop an innovative approach to quantitatively characterize the performance of ultra-dense wireless networks in a plethora of propagation environments. The proposed framework has the potential of significantly simplifying the cumbersome procedure of analyzing the coverage probability and allowing the remarkable unification of single- and

Linchuan Wei, Andres Gomez, Simge Kucukyavuz

Motivated by modern regression applications, in this paper, we study the convexification of a class of convex optimization problems with indicator variables and combinatorial constraints on the indicators. Unlike most of the previous work on convexification of sparse regression problems, we simultaneously consider the nonlinear non-separable objective, indic

Noam R. Izenberg, Diana M. Gentry, David J. Smith, Martha S. Gilmore

Ancient Venus and Earth may have been similar in crucial ways for the development of life, such as liquid water oceans, land-ocean interfaces, favorable chemical ingredients and energy pathways. If life ever developed on, or was transported to, early Venus from elsewhere, it might have thrived, expanded and then survived the changes that have led to an inhos

Goodarz Mehr, Azim Eskandarian

This paper presents an on-board advance warning system for vehicles based on a probabilistic prediction model that advises them on when to change lanes to reach a highway diverge on time. The system is based on a model that estimates the probability of reaching a goal state on the road using one or multiple lane changes. This estimate is based on several tra

Suzie Brown, Paul A. Jenkins, Adam M. Johansen, Jere Koskela

We present simple conditions under which the limiting genealogical process associated with a class of interacting particle systems with non-neutral selection mechanisms, as the number of particles grows, is a time-rescaled Kingman coalescent. Sequential Monte Carlo algorithms are popular methods for approximating integrals in problems such as non-linear filt

Amir Rasouli

Vision-based prediction algorithms have a wide range of applications including autonomous driving, surveillance, human-robot interaction, weather prediction. The objective of this paper is to provide an overview of the field in the past five years with a particular focus on deep learning approaches. For this purpose, we categorize these algorithms into video

Matthias Maier, Martin Kronbichler

We discuss the efficient implementation of a high-performance second-order collocation-type finite-element scheme for solving the compressible Euler equations of gas dynamics on unstructured meshes. The solver is based on the convex limiting technique introduced by Guermond et al. (SIAM J. Sci. Comput. 40, A3211-A3239, 2018). As such it is invariant-domain p

Abhinav Sagar, RajKumar Soundrapandiyan

In this paper, we present a novel neural network using multi scale feature fusion at various scales for accurate and efficient semantic image segmentation. We used ResNet based feature extractor, dilated convolutional layers in downsampling part, atrous convolutional layers in the upsampling part and used concat operation to merge them. A new attention modul

Tajdar Mufti

Studying extent of dynamical generation of mass in a quantum field theory, which may have non-perturbative attributes, is an essential step towards complete understanding of the theory. It has historic relevance to interactions including fermions. However, as search of further fundamental scalars continues, inquiring into the possibility of dynamical generat

Szabolcs Ivan

A linear ordering is called context-free if it is the lexicographic ordering of some context-free language and is called scattered if it has no dense subordering. Each scattered ordering has an associated ordinal, called its rank. It is known that scattered context-free (regular, resp.) orderings have rank less than $ω^ω$ ($ω$, resp). In this paper we confir

Biao Ma

In this paper, we study the curve shortening flow (CSF) on Riemann surfaces. We generalize Huisken&#39;s comparison function to Riemann surfaces and surfaces with conic singularities. We reprove the Gage-Hamilton-Grayson theorem on surfaces. We also prove that for embedded simple closed curves, CSF can not touch conic singularities with cone angles $\leq π$.

Sebastian Junges, Nils Jansen, Sanjit A. Seshia

Partially-Observable Markov Decision Processes (POMDPs) are a well-known stochastic model for sequential decision making under limited information. We consider the EXPTIME-hard problem of synthesising policies that almost-surely reach some goal state without ever visiting a bad state. In particular, we are interested in computing the winning region, that is,

Constantinos Skordis, Tom Zlosnik

We propose a relativistic gravitational theory leading to modified Newtonian dynamics, a paradigm that explains the observed universal galactic acceleration scale and related phenomenology. We discuss phenomenological requirements leading to its construction and demonstrate its agreement with the observed cosmic microwave background and matter power spectra

Xiao-Yue Gong, David Simchi-Levi

Motivated by the episodic version of the classical inventory control problem, we propose a new Q-learning-based algorithm, Elimination-Based Half-Q-Learning (HQL), that enjoys improved efficiency over existing algorithms for a wide variety of problems in the one-sided-feedback setting. We also provide a simpler variant of the algorithm, Full-Q-Learning (FQL)

Cody Coleman, Edward Chou, Julian Katz-Samuels, Sean Culatana

Many active learning and search approaches are intractable for large-scale industrial settings with billions of unlabeled examples. Existing approaches search globally for the optimal examples to label, scaling linearly or even quadratically with the unlabeled data. In this paper, we improve the computational efficiency of active learning and search methods

Dinuka Sahabandu, Shana Moothedath, Joey Allen, Linda Bushnell

Advanced Persistent Threats (APTs) are stealthy attacks that threaten the security and privacy of sensitive information. Interactions of APTs with victim system introduce information flows that are recorded in the system logs. Dynamic Information Flow Tracking (DIFT) is a promising detection mechanism for detecting APTs. DIFT taints information flows origina

Peter C. Sercel, Zeev Valy Vardeny, Alexander L. Efros

We demonstrate theoretically that non-chiral perovskite layers can exhibit circular dichroism (CD) in the absence of a magnetic field and without chiral activation by chiral molecules. The effect is shown to be due to splitting of helical excitonic states which can form in structures of orthorhombic or lower symmetry that exhibit Rashba spin effects. The sel

T. Byvank, D. A. Endrizzi, C. B. Forest, S. J. Langendorf

We present experimental data providing evidence for the formation of transient ($\sim 20~μ$s) plasmas that are simultaneously weakly magnetized (i.e., Hall magnetization parameter $ωτ> 1$) and dominated by thermal pressure (i.e., ratio of thermal-to-magnetic pressure $β> 1$). Particle collisional mean free paths are an appreciable fraction of the overall sys

Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li

Transformers are one of the most important machine learning workloads today. Training one is a very compute-intensive task, often taking days or weeks, and significant attention has been given to optimizing transformers. Despite this, existing implementations do not efficiently utilize GPUs. We find that data movement is the key bottleneck when training. Due

Amir Babak Aazami

On a smooth $n$-manifold $M$ with $n \geq 3$, we study pairs $(g,T)$ consisting of a Riemannian metric $g$ and a unit length closed vector field $T$. Motivated by how Ricci solitons generalize Einstein metrics via a distinguished vector field, we propose to generalize space forms by considering those pairs $(g,T)$ whose corresponding Lorentzian metric $g_{\s

Christopher D. C. Hawthorne

The problem of characterizing which automatic sets of integers are stable is here solved. Given a positive integer $d$ and a subset $A\subseteq \mathbb{Z}$ whose set of representations base $d$ is recognized by a finite automaton, a necessary condition is found for $x+y\in A$ to be a stable formula in $\operatorname{Th}(\mathbb{Z},+,A)$. Combined with a theo

Thomas Stuttard, Mikkel Jensen

Neutrinos undergoing stochastic perturbations as they propagate experience decoherence, damping neutrino oscillations over distance. Such perturbations may result from fluctuations in space-time itself if gravity is a quantum force, including interactions between neutrinos and virtual black holes. In this work we model the influence of heuristic neutrino-vir

Scott Schoen, Costas D. Arvanitis

The angular spectrum approach (ASA)---a fast, frequency domain method for calculation of the acoustic field---enables passive source localization and modeling forward propagation in homogeneous media with high computational efficiency. Here we show that, if the medium is continuously stratified, a first-order analytical solution may be obtained for the field

Matthew Wright, Xiaojun Zheng

Single-parameter persistent homology, a key tool in topological data analysis, has been widely applied to data problems along with statistical techniques that quantify the significance of the results. In contrast, statistical techniques for two-parameter persistence, while highly desirable for real-world applications, have scarcely been considered. We presen

Ioannis Kansizoglou, Loukas Bampis, Antonios Gasteratos

One of the most prominent attributes of Neural Networks (NNs) constitutes their capability of learning to extract robust and descriptive features from high dimensional data, like images. Hence, such an ability renders their exploitation as feature extractors particularly frequent in an abundant of modern reasoning systems. Their application scope mainly incl

Hugues Beauchesne

In addition to being a solution to the little hierarchy problem, the Mirror Twin Higgs provides a natural setting for Asymmetric Dark Matter. In its incarnation with only one Higgs doublet and its mirror copy, dark matter would however almost certainly consist mostly of mirror atoms, which is severely ruled out by constraints on dark matter self-interactions

Hadi Pouransari, Mojan Javaheripi, Vinay Sharma, Oncel Tuzel

Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model complexity. However, in most applications the compressed student model suffers from an accuracy gap with its teacher. We propose extracurricular learning, a novel knowledge distill

Sunipa Dev, Tao Li, Jeff M Phillips, Vivek Srikumar

Language representations are known to carry stereotypical biases and, as a result, lead to biased predictions in downstream tasks. While existing methods are effective at mitigating biases by linear projection, such methods are too aggressive: they not only remove bias, but also erase valuable information from word embeddings. We develop new measures for eva

Naoki Koseki

In this paper, we prove a stronger form of the Bogomolov-Gieseker (BG) inequality for stable sheaves on two classes of Calabi-Yau threefolds, namely, weighted hypersurfaces inside the weighted projective spaces $\mathbb{P}(1, 1, 1, 1, 2)$ and $\mathbb{P}(1, 1, 1, 1, 4)$. Using the stronger BG inequality as a main technical tool, we construct open subsets in

Yuri N. Obukhov, Friedrich W. Hehl

We demonstrate that Einstein&#39;s general relativity theory arises as a special case in the framework of the Poincaré gauge theory of gravity under the assumption of a suitable nonminimal coupling of matter to the Riemann-Cartan geometry of spacetime.

Jay S. Stanley, Eric C. Chi, Gal Mishne

Graph signal processing (GSP) is an important methodology for studying data residing on irregular structures. As acquired data is increasingly taking the form of multi-way tensors, new signal processing tools are needed to maximally utilize the multi-way structure within the data. In this paper, we review modern signal processing frameworks generalizing GSP

Jyri J. Lehtinen, Maarit J. Käpylä, Nigul Olspert, Federico Spada

The magnetic activity of late-type stars is correlated with their rotation rates. Up to a certain limit, stars with smaller Rossby numbers, defined as the rotation period divided by the convective turnover time, have higher activity. A more detailed look at this rotation-activity relation reveals that, rather than being a simple power law relation, the activ

Lorenzo Zanisi, Marc Huertas-Company, Francois Lanusse, Connor Bottrell

Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed across galaxy scaling relations are important probes of our knowledge of galaxy formation physics. Here we propose an uns

Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon, Francois Leduc-Primeau

Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based u

D. J. Eden, T. J. T. Moore, R. Plume, A. J. Rigby

We have used the ratio of column densities (CDR) derived independently from the 850-$μ$m continuum JCMT Plane Survey (JPS) and the $^{13}$CO/C$^{18}$O $(J=3-2)$ Heterodyne Inner Milky Way Plane Survey (CHIMPS) to produce maps of the dense-gas mass fraction (DGMF) in two slices of the Galactic Plane centred at $\ell$=30$^{\circ}$ and $\ell$=40$^{\circ}$. The

Sacha Jennifer van Albada, Aitor Morales-Gregorio, Timo Dickscheid, Alexandros Goulas

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a

Ohad Shamir

A line of recent works established that when training linear predictors over separable data, using gradient methods and exponentially-tailed losses, the predictors asymptotically converge in direction to the max-margin predictor. As a consequence, the predictors asymptotically do not overfit. However, this does not address the question of whether overfitting

Enrico Pajer, David Stefanyszyn, Jakub Supeł

Poincaré invariance is a well-tested symmetry of nature and sits at the core of our description of relativistic particles and gravity. At the same time, in most systems Poincaré invariance is not a symmetry of the ground state and is hence broken spontaneously. This phenomenon is ubiquitous in cosmology where Lorentz boosts are spontaneously broken by the ex

Thomas Benjamin Smith, Coskun Kocabas, Alessandro Principi

We study topological surface-plasmon-polaritons at optical frequencies in diffraction gratings formed by bipartite corrugated metal-dielectric gratings. To do so we implement the theory as developed in Della Valle and Longhi to describe the amplitude of the field by an emergent Schrödinger-like equation. The tri-harmonic grating generates a bipartite Kronig-

Telescope Array Collaboration, R. U. Abbasi, M. Abe, T. Abu-Zayyad

Motivated by the detection of a significant dipole structure in the arrival directions of ultrahigh-energy cosmic rays above 8 EeV reported by the Pierre Auger Observatory (Auger), we search for a large-scale anisotropy using data collected with the surface detector array of the Telescope Array Experiment (TA). With 11 years of TA data, a dipole structure in

M. Cao, F. Hoffet, S. Qiu, A. S. Sheremet

Reversible entanglement transfer between light and matter is a crucial requisite for the ongoing developments of quantum information technologies. Quantum networks and their envisioned applications, e.g., secure communications beyond direct transmission, distributed quantum computing or enhanced sensing, rely on entanglement distribution between nodes. Altho

Benjamin V. Lehmann, Stefano Profumo, Jackson Yant

Primordial black holes may encode the conditions of the early universe, and may even constitute a significant fraction of cosmological dark matter. Their existence has yet to be established. However, black holes with masses below $\sim1~\mathrm{M}_\odot$ cannot form as an endpoint of stellar evolution, so the detection of even one such object would be a smok

Shabnam Iyyani, Vidushi Sharma

The prompt emission of short gamma ray bursts (sGRBs) with known redshifts are analyzed using the model of multi-color blackbody which is interpreted as the emission from a non-dissipative photosphere taking into account a power law jet structure and the viewing geometry of the jet. We find nearly $69\%$ and $26\%$ of the sample are consistent with multi-col

Thomas Berlok, Eliot Quataert, Martin E. Pessah, Christoph Pfrommer

In the outskirts of the intracluster medium (ICM) in galaxy clusters, the temperature decreases with radius. Due to the weakly collisional nature of the plasma, these regions are susceptible to the magneto-thermal instability (MTI), which can sustain turbulence and provide turbulent pressure support in the ICM. This instability arises due to heat conduction

Samuel Mugel, Carlos Kuchkovsky, Escolastico Sanchez, Samuel Fernandez-Lorenzo

In this paper we tackle the problem of dynamic portfolio optimization, i.e., determining the optimal trading trajectory for an investment portfolio of assets over a period of time, taking into account transaction costs and other possible constraints. This problem is central to quantitative finance. After a detailed introduction to the problem, we implement a

Kiwon Um, Robert Brand, Yun, Fei

Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all scientific and engineering disciplines. It has recently been shown that machine learning methods can improve the solution accuracy by correcting for effects not captured by the discretized PDE. We target the problem of reducing numerical errors of iterative PDE solve

Pedro Machado, Holger Schulz, Jessica Turner

We propose a novel analysis strategy, that leverages the unique capabilities of the DUNE experiment, to study tau neutrinos. We integrate collider physics ideas, such as jet clustering algorithms in combination with machine learning techniques, into neutrino measurements. Through the construction of a set of observables and kinematic cuts, we obtain a superi

Philip C. Argyres, Mario Martone

We study the stratification of the singular locus of four dimensional $\mathcal{N}=2$ Coulomb branches. We present a set of self-consistency conditions on this stratification which can be used to extend the classification of scale-invariant rank 1 Coulomb branch geometries to two complex dimensions, and beyond. The calculational simplicity of the arguments p

Niko Jokela, Arttu Pönni

A minimal requirement for any strongly coupled gauge field theory to have a classical dual bulk gravity description is that one should in principle be able to recover the full geometry as encoded on the asymptotics of the spacetime. Even this requirement cannot be fulfilled with arbitrary precision simply due to the fact that the boundary data is inherently

Subhash Bose, Subo Dong, C. S. Kochanek, M. D. Stritzinger

ASASSN-18am/SN 2018gk is a newly discovered member of the rare group of luminous, hydrogen-rich supernovae (SNe) with a peak absolute magnitude of $M_V \approx -20$ mag that is in between normal core-collapse SNe and superluminous SNe. These SNe show no prominent spectroscopic signatures of ejecta interacting with circumstellar material (CSM), and their powe

Maine Christos, Subir Sachdev, Mathias S. Scheurer

Recent experiments on twisted bilayer graphene have shown a high-temperature parent state with massless Dirac fermions and broken electronic flavor symmetry; superconductivity and correlated insulators emerge from this parent state at lower temperatures. We propose that the superconducting and correlated insulating orders are connected by Wess-Zumino-Witten

Anish Ghoshal, Alberto Salvio

A totally asymptotically free QCD axion model, where all couplings flow to zero in the infinite energy limit, was recently formulated. A very interesting feature of this fundamental theory is the ability to predict some low-energy observables, like the masses of the extra fermions and scalars. Here we find and investigate a region of the parameter space wher

Tarek Anous, Jorrit Kruthoff, Raghu Mahajan

We study density matrices in quantum gravity, focusing on topology change. We argue that the inclusion of bra-ket wormholes in the gravity path integral is not a free choice, but is dictated by the specification of a global state in the multi-universe Hilbert space. Specifically, the Hartle-Hawking (HH) state does not contain bra-ket wormholes. It has recent

Utkarsh Bajpai, Mark J. H. Ku, Branislav K. Nikolic

The theoretical analysis of topological insulators (TIs) has been traditionally focused on infinite homogeneous crystals with band gap in the bulk and nontrivial topology of their wavefunctions, or infinite wires whose boundaries host surface or edge metallic states. However, experimental devices contain finite-size topological region attached to normal meta

Juven Wang

Standard lore uses local anomalies to check the kinematic consistency of gauge theories coupled to chiral fermions, e.g. Standard Models (SM). Based on a systematic cobordism classification, we examine constraints from invertible quantum anomalies (including all perturbative local and nonperturbative global anomalies) for gauge theories. We also clarify the

Evgeny Smirnov, Anna Tutubalina

Subword complexes were defined by A.Knutson and E.Miller in 2004 for describing Gröbner degenerations of matrix Schubert varieties. The facets of such a complex are indexed by pipe dreams, or, equivalently, by the monomials in the corresponding Schubert polynomial. In 2017 S.Assaf and D.Searles defined a basis of slide polynomials, generalizing Stanley symme

Kun Yang, Samory Kpotufe, Nick Feamster

Data representation plays a critical role in the performance of novelty detection (or ``anomaly detection&#39;&#39;) methods in machine learning. The data representation of network traffic often determines the effectiveness of these models as much as the model itself. The wide range of novel events that network operators need to detect (e.g., attacks, malwar

Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma

Initialization, normalization, and skip connections are believed to be three indispensable techniques for training very deep convolutional neural networks and obtaining state-of-the-art performance. This paper shows that deep vanilla ConvNets without normalization nor skip connections can also be trained to achieve surprisingly good performance on standard i

Chunyan Niu, Yunhui He, Xiaozhe Hu

In this paper, we investigate the AMLI-cycle method and make two contributions. First, we revisit the AMLI-cycle using the Chebyshev polynomials and establish a theory for its uniform convergence, assuming the two-grid method converges uniformly. This removes the need for estimating extreme eigenvalues at all coarse levels. Only an estimation of the two-grid

Guillaume Baudart, Peter D. Kirchner, Martin Hirzel, Kiran Kate

AI automation tools need machine-readable hyperparameter schemas to define their search spaces. At the same time, AI libraries often come with good human-readable documentation. While such documentation contains most of the necessary information, it is unfortunately not ready to consume by tools. This paper describes how to automatically mine Python docstrin

Amjad Ashoorioon, Mohammad Bagher Jahani Poshteh

We investigate the pair creation of magnetically charged black holes on a cosmic string in the presence of a background magnetic field. The string may either break or fray to produce a pair of accelerating black holes described by Ernst metric. By using the instanton action we obtain the rate of such production. For large values of background magnetic field

Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti

Robust perception relies on both bottom-up and top-down signals. Bottom-up signals consist of what&#39;s directly observed through sensation. Top-down signals consist of beliefs and expectations based on past experience and short-term memory, such as how the phrase `peanut butter and~...&#39; will be completed. The optimal combination of bottom-up and top-do

Rodrigo Treviño

For $N$ compatible substitution rules on $M$ prototiles $t_1,\dots,t_M$, consider tilings and tiling spaces constructed by applying the different substitution rules at random. These give (globally) random substitution tilings. In this paper I obtain bounds for the growth on twisted ergodic integrals for the $\mathbb{R}^d$ action on the tiling space which giv

Stan Zachary, Simon Tindemans, Michael Evans, James Cruise

The increasing reliance on renewable energy generation means that storage may well play a much greater role in the balancing of future electricity systems. We show how heterogeneous stores, differing in capacity and rate constraints, may be optimally, or nearly optimally, scheduled to assist in such balancing, with the aim of minimising the total imbalance (

Stefan Steinerberger

Randomized Kaczmarz is a simple iterative method for finding solutions of linear systems $Ax = b$. We point out that the arising sequence $(x_k)_{k=1}^{\infty}$ tends to converge to the solution $x$ in an interesting way: generically, as $k \rightarrow \infty$, $x_k - x$ tends to the singular vector of $A$ corresponding to the smallest singular value. This h

Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu

State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models. The lack of transparency has highlighted the critical importance of improving the explainability of recommender systems. In this paper, we propose to extra

Konstantin T. Matchev, Prasanth Shyamsundar

We propose a technique called Optimal Analysis-Specific Importance Sampling (OASIS) to reduce the number of simulated events required for a high-energy experimental analysis to reach a target sensitivity. We provide recipes to obtain the optimal sampling distributions which preferentially focus the event generation on the regions of phase space with high uti

Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann

Today&#39;s state-of-the-art machine vision models are vulnerable to image corruptions like blurring or compression artefacts, limiting their performance in many real-world applications. We here argue that popular benchmarks to measure model robustness against common corruptions (like ImageNet-C) underestimate model robustness in many (but not all) applicati

Marta Fiori-Carones, Paul Shafer, Giovanni Soldà

Inspired by Ramsey&#39;s theorem for pairs, Rival and Sands proved what we refer to as an inside/outside Ramsey theorem: every infinite graph $G$ contains an infinite subset $H$ such that every vertex of $G$ is adjacent to precisely none, one, or infinitely many vertices of $H$. We analyze the Rival-Sands theorem from the perspective of reverse mathematics a

Ricardo Augusto Borsoi, Clémence Prévost, Konstantin Usevich, David Brie

Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with strong theoretical guarantees. However, tensor-based approaches previously proposed assume that the different observed images are acquired under exactly the same conditions. A recent

Matthias Christandl, Fulvio Gesmundo, Daniel Stilck Franca, Albert H. Werner

Tensor network states form a variational ansatz class widely used, both analytically and numerically, in the study of quantum many-body systems. It is known that if the underlying graph contains a cycle, e.g. as in projected entangled pair states (PEPS), then the set of tensor network states of given bond dimension is not closed. Its closure is the tensor ne

Avishek Dusoye, Alvaro de la Cruz-Dombriz, Peter Dunsby, Nelson J. Nunes

The coupled quintessence model with disformal couplings is treated here to mimic the $Λ$CDM background. Using this approach, the quintessence potential does not have to be specified. The model considers a generic fluid coupled to the quintessence, which is specified to be either dark matter or a relativistic fluid. The background consists of a cosmological c

Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang

Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning. Recent reproducibility analyses have shown that reported performance results are often inconsistent and difficult to replicate. In this work, we argue that the inconsistency of performance stems from the use of flawed evaluation metrics. Taking a step towards