December 2020 arXiv papers

Torin F. Stetina, Anthony Ciavarella, Xiaosong Li, Nathan Wiebe

In recent years simulations of chemistry and condensed materials has emerged as one of the preeminent applications of quantum computing, offering an exponential speedup for the solution of the electronic structure for certain strongly correlated electronic systems. To date, most treatments have ignored the question of whether relativistic effects, which are

Eric Jiang, Meng-Hsi Chen, Ben Liang, Min Dong

In this work, we consider a multi-user mobile edge computing system with multiple computing access points (CAPs). Each mobile user has multiple dependent tasks that must be processed in a round-by-round schedule. In every round, a user may process their individual task locally, or choose to offload their task to one of the $M$ CAPs or the remote cloud server

Ali H. Abdollahi Bafghi, Mahtab Mirmohseni, Mohammad Reza Aref

We study the problem of joint information and energy transfer in a binary two-hop channel with an energy harvesting relay. We consider a finite battery size at the relay and energy loss in transmitting energy. In other words, to be able to send an energy-contained symbol, the relay must receive multiple energy-contained symbols. Thus, we face a kind of chann

Wenchao Yang, Emad Masroor, Mark A. Stremler

An inclined gravity-driven soap film channel was used to study the wake patterns formed behind a transversely oscillating cylinder at $Re =235 \pm 14$. The natural frequency of vortex shedding from a stationary cylinder, $f_{\text{St}}$, was used to identify the oscillation frequencies of interest. The (dimensionless) frequency, $f^*=f/{f_{\text{St}}}$, and

Jake Koenig, Hoi Nguyen

A central question in random matrix theory is universality. When an emergent phenomena is observed from a large collection of chosen random variables it is natural to ask if this behavior is specific to the chosen random variable or if the behavior occurs for a larger class of random variables. The rank statistics of random matrices chosen uniformly from $\o

Asmaa Alrushoud, Catie McEntee, Ning Lu

This paper presents a zonal Volt/VAR control scheme that coordinates Photovoltaic (PV) inverters for providing voltage regulation on 3-phase unbalanced distribution feeders. Voltage sensitivity studies are conducted to uncover the dependency between nodal voltage changes and the reactive power injections at nodes with smart PV inverters. A fast incremental c

Branko Ćurgus, Volodymyr Derkach, Carsten Trunk

We consider the indefinite Sturm-Liouville differential expression \[\mathfrak{a}(f) := - \frac{1}{w}\left( \frac{1}{r} f' \right)',\] where $\mathfrak{a}$ is defined on a finite or infinite open interval $I$ with $0\in I$ and the coefficients $r$ and $w$ are locally summable and such that $r(x)$ and $(\operatorname{sgn} x) w(x)$ are positive a.e. on

Leonid A. Levin

NP (search) problems allow easy correctness tests for solutions. Climbing algorithms allow also easy assessment of how close to yielding the correct answer is the configuration at any stage of their run. This offers a great flexibility, as how sensible is any deviation from the standard procedures can be instantly assessed. An example is the Dual Matrix Algo

Marzieh Ershad, Robert Rege, Ann Majewicz Fey

Surgical skill directly affects surgical procedure outcomes; thus, effective training is needed to ensure satisfactory results. Many objective assessment metrics have been developed and some are widely used in surgical training simulators. These objective metrics provide the trainee with descriptive feedback about their performance however, often lack feedba

Mario Bonk, Alexandre Eremenko

We show that for given four points in the Riemann sphere and a given isotopy class of two disjoint arcs connecting these points in two pairs, there exists a unique configuration with the property that each arc is a hyperbolic geodesic segment in the complement of the other arc.

Ioannis Gasteratos, Michael Salins, Konstantinos Spiliopoulos

The goal of this paper is to study the Moderate Deviation Principle (MDP) for a system of stochastic reaction-diffusion equations with a time-scale separation in slow and fast components and small noise in the slow component. Based on weak convergence methods in infinite dimensions and related stochastic control arguments, we obtain an exact form for the mod

Murat Kuscu, Ozgur B. Akan

Molecular Communications (MC) is a bio-inspired communication technique that uses molecules to transfer information among bio-nano devices. In this paper, we focus on the detection problem for biological MC receivers employing ligand receptors to infer the transmitted messages encoded into the concentration of molecules, i.e., ligands. In practice, receptors

Tasfia Shermin, Shyh Wei Teng, Ferdous Sohel, Manzur Murshed

Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neit

Anjalie Field, Chan Young Park, Kevin Z. Lin, Yulia Tsvetkov

Social biases on Wikipedia, a widely-read global platform, could greatly influence public opinion. While prior research has examined man/woman gender bias in biography articles, possible influences of other demographic attributes limit conclusions. In this work, we present a methodology for analyzing Wikipedia pages about people that isolates dimensions of i

Rhiannon Griffiths

In this paper we develop the theory of presentations for globular operads and construct presentations for the globular operads corresponding to several key theories of $n$-category for $n \leqslant 4$.

Yilun Xu, Gang Huang, Jan Balewski, Ravi Naik

As quantum information processors grow in quantum bit (qubit) count and functionality, the control and measurement system becomes a limiting factor to large scale extensibility. To tackle this challenge and keep pace with rapidly evolving classical control requirements, full control stack access is essential to system level optimization. We design a modular

Zhikai Yao, Jennie Si, Ruofan Wu, Jianyong Yao

This paper addresses reinforcement learning based, direct signal tracking control with an objective of developing mathematically suitable and practically useful design approaches. Specifically, we aim to provide reliable and easy to implement designs in order to reach reproducible neural network-based solutions. Our proposed new design takes advantage of two

Yilun Xu, Gang Huang, David I. Santiago, Irfan Siddiqi

As the number of qubits in nascent quantum processing units increases, the connectorized RF (radio frequency) analog circuits used in first generation experiments become exceedingly complex. The physical size, cost and electrical failure rate all become limiting factors in the extensibility of control systems. We have developed a series of compact RF mixing

Alec Helm, Ann S. Blevins, Danielle S. Bassett

Probing the developing neural circuitry in Caenorhabditis elegans has enhanced our understanding of nervous systems. The C. elegans connectome, like those of other species, is characterized by a rich club of densely connected neurons embedded within a small-world architecture. This organization of neuronal connections, captured by quantitative network statis

Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan

Heavily overparameterized language models such as BERT, XLNet and T5 have achieved impressive success in many NLP tasks. However, their high model complexity requires enormous computation resources and extremely long training time for both pre-training and fine-tuning. Many works have studied model compression on large NLP models, but only focusing on reduci

Zixiang Zhao, Jiangshe Zhang, Shuang Xu, Kai Sun

Pansharpening is a widely used image enhancement technique for remote sensing. Its principle is to fuse the input high-resolution single-channel panchromatic (PAN) image and low-resolution multi-spectral image and to obtain a high-resolution multi-spectral (HRMS) image. The existing deep learning pansharpening method has two shortcomings. First, features of

Simina Brânzei, Jiawei Li

We consider the query complexity of finding a local minimum of a function defined on a graph. This abstract problem is fundamental to many optimization tasks, such as finding a local minimum of the loss function when training deep neural networks. In such applications, each query is an expensive loss evaluation, making it crucial to parallelize computations.

Thomas E. Valles, Hannah Shoenhard, Joseph Zinski, Sarah Trick

A major strategy to prevent the spread of COVID-19 is the limiting of in-person contacts. However, this is impractical or impossible for the many disabled people who do not live in care facilities, but still require caregivers. We seek to determine which interventions can prevent infections among disabled people and their caregivers. We simulate transmission

Mark Law

The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples. Until recently, most research on ILP targeted learning Prolog programs. The ILASP system instead learns Answer Set Programs (ASP). Learning such expressive programs widens the applicability of ILP considerably; for example, enabling preference learning, learn

Junda Xiong, Xin Cai, Jinglai Li

Quantifying uncertainties in physical or engineering systems often requires a large number of simulations of the underlying computer models that are computationally intensive. Emulators or surrogate models are often used to accelerate the computation in such problems, and in this regard the Gaussian Process (GP) emulator is a popular choice for its ability t

Marek Zawadowski

We show that the positive zoom complexes, with fairly natural morphisms, form a dual category to the category of positive opetopes with contraction epimorphisms. We also show how this duality can be extended to opetopic cardinals.

Jack W Shepherd, Alex L Payne-Dwyer, Ji-Eun Lee, Aisha Syeda

Super-resolution microscopy has catalyzed valuable insights into the sub-cellular, mechanistic details of many different biological processes across a wide range of cell types. Fluorescence polarization spectroscopy tools have also enabled important insights into cellular processes through identifying orientational changes of biological molecules typically a

Ettore Aldrovandi, Niranjan Ramachandran

Let $π: X \to S$ be a family of smooth projective curves, and let $L$ and $M$ be a pair of line bundles on $X$. We show that Deligne's line bundle $\langle{L,M}\rangle$ can be obtained from the $\mathcal{K}_2$-gerbe $G_{L,M}$ constructed in a previous work by the authors via an integration along the fiber map for gerbes that categorifies the well known o

Paavo Nevalainen, Parisa Movahedi, Jorge Peña Queralta, Tomi Westerlund

Vehicles with prolonged autonomous missions have to maintain environment awareness by simultaneous localization and mapping (SLAM). Closed loop correction is substituted by interpolation in rigid body transformation space in order to systematically reduce the accumulated error over different scales. The computation is divided to an edge computed lightweight

Philippe Casgrain, Anastasis Kratsios

The need for fast and robust optimization algorithms are of critical importance in all areas of machine learning. This paper treats the task of designing optimization algorithms as an optimal control problem. Using regret as a metric for an algorithm's performance, we study the existence, uniqueness and consistency of regret-optimal algorithms. By provid

Francesco Finelli, Silvio S. Cerri, Francesco Califano, Francesco Pucci

Magnetic reconnection (MR) plays a fundamental role in plasma dynamics under many different conditions, from space and astrophysical environments to laboratory devices. High-resolution in-situ measurements from space missions allow to study naturally occurring MR processes in great detail. Alongside direct measurements, numerical simulations play a key role

David Matejov, Jiri Podolsky

We systematically investigate axisymmetric extremal isolated horizons (EIHs) defined by vanishing surface gravity, corresponding to zero temperature. In the first part, using the Newman-Penrose and GHP formalism we derive the most general metric function for such EIHs in the Einstein-Maxwell theory, which complements the previous result of Lewandowski and Pa

Andreas Papaefstathiou, Tania Robens, Gilberto Tetlalmatzi-Xolocotzi

We investigate the production of three Higgs bosons in the Two Real Singlet extension of the Standard Model, where the scalar sector is augmented by two additional real scalar fields which are singlets under the Standard Model gauge group. The model contains three neutral CP-even scalars, allowing for resonant production and asymmetric decay chains. We focus

Yuta Nakamura, Shouhei Hanaoka, Yukihiro Nomura, Naoto Hayashi

For the safe sharing pre-trained language models, no guidelines exist at present owing to the difficulty in estimating the upper bound of the risk of privacy leakage. One problem is that previous studies have assessed the risk for different real-world privacy leakage scenarios and attack methods, which reduces the portability of the findings. To tackle this

Richard Carney, Monique Chyba, Chris Gray, Corey Shanbrom

Unmanned Aerial Vehicles (UAVs) have been increasingly used in the context of remote sensing missions such as target search and tracking, mapping, or surveillance monitoring. In the first part of our paper we consider agent dynamics, network topologies, and collective behaviors. The objective is to enable multiple UAVs to collaborate toward a common goal, as

Kushal Seetharam, Yulia Shchadilova, Fabian Grusdt, Mikhail B. Zvonarev

The challenge of understanding the dynamics of a mobile impurity in an interacting quantum many-body medium comes from the necessity of including entanglement between the impurity and excited states of the environment in a wide range of energy scales. In this paper, we investigate the motion of a finite mass impurity injected into a three-dimensional quantum

Mario Herrero-Valea, Stefano Liberati, Raquel Santos-Garcia

The persistence of a suitable notion of black hole thermodynamics in Lorentz breaking theories of gravity is not only a non-trivial consistency test for such theories, it is also an interesting investigation {\em per se}, as it might help us identifying the crucial features at the root of these surprising laws governing such purely gravitational objects. In

Zhan-Feng Mai, Run-Qiu Yang

It has been shown recently that the charged black hole can be scalarized if Maxwell field minimally couples with a complex scalar which has nonnegative nonlinear potential. We firstly prove that such scalarization cannot be a result of continuous phase transition for general scalar potential. Furthermore, we numerically find that it is possible that the RN b

Georgios K. Karananas, Alex Kehagias, John Taskas

We derive a novel four-dimensional black hole with planar horizon that asymptotes to the linear dilaton background. The usual growth of its entanglement entropy before Page's time is established. After that, emergent islands modify to a large extent the entropy, which becomes finite and is saturated by its Bekenstein-Hawking value in accordance with the

Hee-Cheol Kim, Minsung Kim, Sung-Soo Kim, Ki-Hong Lee

We propose a systematic approach to computing the BPS spectra of any 5d/6d supersymmetric quantum field theory in Coulomb phases, which admits either gauge theory descriptions or geometric descriptions, based on the Nakajima-Yoshioka's blowup equations. We provide a significant generalization of the blowup equation approach in terms of both properly quan

Luuk Coopmans, Shane Dooley, Ian Jubb, Kevin Kavanagh

A local Hamiltonian with Topological Quantum Order (TQO) has a robust ground state degeneracy that makes it an excellent quantum memory candidate. This memory can be corrupted however if part of the state leaves the protected ground state manifold and returns later with a dynamically accrued phase error. Here we analyse how TQO suppresses this process and us

Andrzej J. Buras

The ratio $ε'/ε$ measures the size of the direct CP violation in $K_L\toππ$ decays $(ε^\prime)$ relative to the indirect one described by $ε$ and is very sensitive to new sources of CP violation. As such it played a prominent role in particle physics already for 45 years. Due to the smallness of $ε'/ε$ its measurement required heroic efforts in the 1

Sudarshan Ananth, Lars Brink, Sucheta Majumdar

We analyze the residual gauge freedom in gravity, in four dimensions, in the light-cone gauge, in a formulation where unphysical fields are integrated out. By checking the invariance of the light-cone Hamiltonian, we obtain a set of residual gauge transformations, which satisfy the BMS algebra realized on the two physical fields in the theory. Hence, the BMS

Shi-Fa Guo, Hai-Shan Liu, H. Lu, Yi Pang

It was recently shown that charged AdS boson stars can reproduce the universal structure of the lowest scaling dimension in the subsector of a CFT with fixed large global $U(1)$ charge $Q$. Using the model consisting of Einstein-Maxwell gravity with a negative cosmological constant, coupled to a $U(1)$-charged conformally massless scalar with the fourth-orde

Artur Czerwinski

In this article, we investigate the problem of state reconstruction of four-level quantum systems. A realistic scenario is considered with measurement results distorted by random unitary operators. Two frames which define injective measurements are applied and compared. By introducing arbitrary rotations, we can test the performance of the framework versus t

Salini Karuvade, Abhijeet Alase, Barry C. Sanders

In quantum mechanics, physical states are represented by rays in Hilbert space $\mathscr H$, which is a vector space imbued by an inner product $\langle\,|\,\rangle$, whose physical meaning arises as the overlap $\langleϕ|ψ\rangle$ for $|ψ\rangle$ a pure state (description of preparation) and $\langleϕ|$ a projective measurement. However, current quantum the

J. F. Mendez-Valderrama, Debanjan Chowdhury

We study the transport properties for a family of geometrically frustrated models on the triangular lattice with an interaction scale far exceeding the single-particle bandwidth. Starting from the interaction-only limit, which can be solved exactly, we analyze the transport and thermodynamic behavior as a function of filling and temperature at the leading no

Sydney Jenkins, Ting S. Li, Andrew B. Pace, Alexander P. Ji

We perform consistent reductions and measurements for three ultra-faint dwarf galaxies (UFDs): Boötes I, Leo IV and Leo V. Using the public archival data from the GIRAFFE spectrograph on the Very Large Telescope (VLT), we locate new members and provide refined measurements of physical parameters for these dwarf galaxies. We identify nine new Leo IV members a

Juven Wang

Strong, electromagnetic, and weak forces were unified in the Standard Model (SM) with spontaneous gauge symmetry breaking. These forces were further conjectured to be unified in a simple Lie group gauge interaction in the Grand Unification (GUT). In this work, we propose a theory beyond the SM and GUT by adding new gapped Topological Phase Sectors consistent

Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sanchez, Mugdha Pandya

Natural Language Processing (NLP) systems learn harmful societal biases that cause them to amplify inequality as they are deployed in more and more situations. To guide efforts at debiasing these systems, the NLP community relies on a variety of metrics that quantify bias in models. Some of these metrics are intrinsic, measuring bias in word embedding spaces

Nirmalya Brahma, Chethan Krishnan

We consider gauge theories of non-Abelian $finite$ groups, and discuss the 1+1 dimensional lattice gauge theory of the permutation group $S_N$ as an illustrative example. The partition function at finite $N$ can be written explicitly in a compact form using properties of $S_N$ conjugacy classes. A natural large-$N$ limit exists with a new 't Hooft coupli

Qinyuan Ye, Belinda Z. Li, Sinong Wang, Benjamin Bolte

Closed-book question-answering (QA) is a challenging task that requires a model to directly answer questions without access to external knowledge. It has been shown that directly fine-tuning pre-trained language models with (question, answer) examples yields surprisingly competitive performance, which is further improved upon through adding an intermediate p

Marcos A. Ramirez

The thermodynamic stability of braneworld cosmological solutions in five-dimensional Einstein-Gauss-Bonnet gravity is addressed, particularly in the context of the splitting vacuum thin shells in which the shells represent either an ephemeral or a branch-changing false vacuum bubble spontaneously emerging from the braneworld and there is more than one possib

Jonathan P. Keating, Mo Dick Wong

We study the 'critical moments' of subcritical Gaussian multiplicative chaos (GMCs) in dimensions $d \leq 2$. In particular, we establish a fully explicit formula for the leading order asymptotics, which is closely related to large deviation results for GMCs and demonstrates a similar universality feature. We conjecture that our result correctly desc

Nima Arkani-Hamed, Tzu-Chen Huang, Yu-tin Huang

We re-examine the constraints imposed by causality and unitarity on the low-energy effective field theory expansion of four-particle scattering amplitudes, exposing a hidden "totally positive" structure strikingly similar to the positive geometries associated with grassmannians and amplituhedra. This forces the infinite tower of higher-dimension oper

Gaber Faisel, Jhih-Ying Su, Jusak Tandean

We investigate the possibility that scalar leptoquarks generate consequential effects on the flavor-changing neutral-current decays of charmed hadrons into final states with missing energy ($\not\!\!E$) carried away by either standard model or sterile neutrinos. We focus on scenarios involving the $R_2$, $\tilde R_2$, and $\bar S_1$ leptoquarks and take into

Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger, Beidi Chen

Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval. The calculation of full softmax is costly from the computational and energy perspective. There have been various sampling approaches to overcome this challenge, popularly known as negative sampling (NS).

Sarah E. Grefe, Hsin-Hua Lai, Silke Paschen, Qimiao Si

There is considerable interest in the intersection of correlations and topology, especially in metallic systems. Among the outstanding questions are how strong correlations drive novel topological states and whether such states can be readily controlled. Here we study the effect of a Zeeman coupling on a Weyl-Kondo semimetal in a nonsymmorphic and noncentros

Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu

Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract/semantic visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the latest efforts have been focused on increasi

Manoj Mandal, Sabyasachi Pal

We made a detailed study of the timing and spectral properties of the X-ray pulsar 1A 0535+262 during the recent giant outburst in 2020 November and December. The flux of the pulsar reached a record value of $\sim$12.5 Crab as observed by Swift/BAT (15--50 keV) and the corresponding mass accretion rate was $\sim6.67\times10^{17}$ g s$^{-1}$ near the peak of

Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang

This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly spar

Robert B. Allen

We explore using the Suggested Upper Merged Ontology (SUMO) to develop a semantic simulation. We provide two proof-of-concept demonstrations modeling transitions in a simulated gasoline engine using a general-purpose programming language. Rather than focusing on computationally highly intensive techniques, we explore a less computationally intensive approach

Serguei Barannikov, Daria Voronkova, Alexander Mironenko, Ilya Trofimov

Neural network training is commonly based on SGD. However, the understanding of SGD's ability to converge to good local minima, given the non-convex nature of loss functions and the intricate geometric characteristics of loss landscapes, remains limited. In this paper, we apply topological data analysis methods to loss landscapes to gain insights into th

Ofir Press, Noah A. Smith, Mike Lewis

Increasing the input length has been a driver of progress in language modeling with transformers. We identify conditions where shorter inputs are not harmful, and achieve perplexity and efficiency improvements through two new methods that decrease input length. First, we show that initially training a model on short subsequences before moving on to longer on

Baturalp Buyukates, Sennur Ulukus

We consider a federated learning framework in which a parameter server (PS) trains a global model by using $n$ clients without actually storing the client data centrally at a cloud server. Focusing on a setting where the client datasets are fast changing and highly temporal in nature, we investigate the timeliness of model updates and propose a novel timely

Anatoly Dymarsky, Alfred Shapere

We discuss the holographic description of Narain $U(1)^c\times U(1)^c$ conformal field theories, and their potential similarity to conventional weakly coupled gravity in the bulk, in the sense that the effective IR bulk description includes "$U(1)$ gravity" amended with additional light degrees of freedom. Starting from this picture, we formulate the

Aolin Xu

We study the continuity property of the generalized entropy as a function of the underlying probability distribution, defined with an action space and a loss function, and use this property to answer the basic questions in statistical learning theory: the excess risk analyses for various learning methods. We first derive upper and lower bounds for the entrop

Wenhui Wang, Hangbo Bao, Shaohan Huang, Li Dong

We generalize deep self-attention distillation in MiniLM (Wang et al., 2020) by only using self-attention relation distillation for task-agnostic compression of pretrained Transformers. In particular, we define multi-head self-attention relations as scaled dot-product between the pairs of query, key, and value vectors within each self-attention module. Then

Lucas Prado Osco, Mauro dos Santos de Arruda, Diogo Nunes Gonçalves, Alexandre Dias

In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The experimental setup was evaluated in a cornfield with different growth stages and in a Citrus orchard. Both datasets ch

Shang-Wen Li

Since its introduction in 2011, there have been over 4000 MOOCs on various subjects on the Web, serving over 35 million learners. MOOCs have shown the ability to democratize knowledge dissemination and bring the best education in the world to every learner. However, the disparate distances between participants, the size of the learner population, and the het

Michał Oszmaniec, Ninnat Dangniam, Mauro E. S. Morales, Zoltán Zimborás

Fermionic Linear Optics (FLO) is a restricted model of quantum computation which in its original form is known to be efficiently classically simulable. We show that, when initialized with suitable input states, FLO circuits can be used to demonstrate quantum computational advantage with strong hardness guarantees. Based on this, we propose a quantum advantag

Gregory Gold, David A. McGady, Subodh P. Patil, Valeri Vardanyan

Particle-antiparticle pairs can be produced by background electric fields via the Schwinger mechanism provided they are unconfined. If, as in QED in (3+1)-$d$ these particles are massive, the particle production rate is exponentially suppressed below a threshold field strength. Above this threshold, the energy for pair creation must come from the electric fi

Mehdi Bahri, Gaétan Bahl, Stefanos Zafeiriou

Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. As they generalize the operations of classical CNNs on grids to arbitrary topologies, GNNs also bring much of the implementation challenges of their Euclidean counterparts. Model size, memory footprint, and energy consumption are comm

Marko Toroš, Uroš Delić, Fagin Hales, Tania S. Monteiro

The strong light-matter optomechanical coupling offered by Coherent Scattering (CS) set-ups have allowed the experimental realisation of quantum ground state cavity cooling of the axial motion of a levitated nanoparticle [U. Delić et al., Science 367, 892 (2020)]. An appealing milestone is now quantum 2D cooling of the full in-plane motion, in any direction

Vishnu Jejjala, Damian Kaloni Mayorga Pena, Challenger Mishra

Ricci flat metrics for Calabi-Yau threefolds are not known analytically. In this work, we employ techniques from machine learning to deduce numerical flat metrics for the Fermat quintic, for the Dwork quintic, and for the Tian-Yau manifold. This investigation employs a single neural network architecture that is capable of approximating Ricci flat Kaehler met

Navid Abbasi, Matthias Kaminski

We consider a holographic thermal state and perturb it by a scalar operator whose associated real-time Green's function has only gapped poles. These gapped poles correspond to the non-hydrodynamic quasinormal modes of a massive scalar perturbation around a Schwarzschild black brane. Relations between pole-skipping points, critical points and quasinormal

D. V. Bulgakova, N. Buzhinsky, Y. O. Goncharov

We revisit the question of classification of balanced circular words and focus on the case of a ternary alphabet. We propose a $3$-dimensional generalisation of the discrete approximation representation of Christoffel words. By considering the minimal bound $3$ for abelian complexity of balanced circular words over a ternary alphabet, we provide a classifica

Brett T. Lopez, Jean-Jacques E. Slotine

This work develops a new direct adaptive control framework that extends the certainty equivalence principle to general nonlinear systems with unmatched model uncertainties. The approach adjusts the rate of adaptation online to eliminate the effects of parameter estimation transients on closed-loop stability. The method can be immediately combined with a prev

Ruocheng Wang, Jiayuan Mao, Samuel J. Gershman, Jiajun Wu

We present Language-mediated, Object-centric Representation Learning (LORL), a paradigm for learning disentangled, object-centric scene representations from vision and language. LORL builds upon recent advances in unsupervised object discovery and segmentation, notably MONet and Slot Attention. While these algorithms learn an object-centric representation ju

John Huerta

We show that every bundle gerbe on a supermanifold decomposes into a bundle gerbe over the underlying manifold and a 2-form on the supermanifold. This decomposition is not canonical, but is determined by the choice of a projection map to the underlying manifold. Along the way, we prove the familiar cohomological classification theorems for bundle gerbes and

Jorge Mago, Anders Schreiber, Marcus Spradlin, Akshay Yelleshpur Srikant

Symbol alphabets of n-particle amplitudes in N=4 super-Yang-Mills theory are known to contain certain cluster variables of Gr(4,n) as well as certain algebraic functions of cluster variables. The first paper arXiv:2007.00646 in this series focused on n=8 algebraic letters. In this paper we show that it is possible to obtain all rational symbol letters (in fa

Nicolò Defenu

Long lived quasi-stationary states (QSSs) are a signature characteristic of long-range interacting systems both in the classical and in the quantum realms. Often, they emerge after a sudden quench of the Hamiltonian internal parameters and present a macroscopic life-time, which increases with the system size. Despite their ubiquity, the fundamental mechanism

Hamid Afshar, Erfan Esmaeili, Hamid Safari

We present an interacting spin-2 gauge theory coupled to the two-dimensional dilaton-gravity in flat spacetime. The asymptotic symmetry group is enhanced to the central extension of Diff$(S^1)\ltimes C^\infty(S^1)\ltimes$Vec($S^1$) when the central element of the Heisenberg subgroup is zero (vanishing $U(1)$ level). Using the BF-formulation of the model we d

Monica Guica

It has been recently shown that classical $J\bar T$ - deformed CFTs possess an infinite-dimensional Witt-Kač-Moody symmetry, generated by certain field-dependent coordinate and gauge transformations. On a cylinder, however, the equal spacing of the descendants' energies predicted by such a symmetry algebra is inconsistent with the known finite-size spect

Marcin M. Wysokiński, Marcin Płodzień, Mircea Trif

Hole-spins localized in semiconductor structures, such as quantum dots or defects, serve to the realization of efficient gate-tunable solid-state quantum bits. Here we study two electrically driven spin $3/2$ holes coupled to the electromagnetic field of a microwave cavity. We show that the interplay between the non-Abelian Berry phases generated by local ti

Bhargav Bhatt, Linquan Ma, Zsolt Patakfalvi, Karl Schwede

We establish the Minimal Model Program for arithmetic threefolds whose residue characteristics are greater than five. In doing this, we generalize the theory of global $F$-regularity to mixed characteristic and identify certain stable sections of adjoint line bundles. Finally, by passing to graded rings, we generalize a special case of Fujita's conjectur

David Doty, Mahsa Eftekhari, Eric Severson

We study population protocols, a model of distributed computing appropriate for modeling well-mixed chemical reaction networks and other physical systems where agents exchange information in pairwise interactions, but have no control over their schedule of interaction partners. The well-studied *majority* problem is that of determining in an initial populati

Zeki Hayran, Aobo Chen, Francesco Monticone

Causality - the principle stating that the output of a system cannot temporally precede the input - is a universal property of nature. Here, we show that analogous input-output relations can also be realized in the spectral domain by leveraging the peculiar properties of time-modulated non-Hermitian photonic systems. Specifically, we uncover the existence of

Sam Hopkins, Michael Joseph

The Lalanne-Kreweras involution is an involution on the set of Dyck paths which combinatorially exhibits the symmetry of the number of valleys and major index statistics. We define piecewise-linear and birational extensions of the Lalanne-Kreweras involution. Actually, we show that the Lalanne-Kreweras involution is a special case of a more general operator,

Michael J. Schlosser, Meesue Yoo

We study two types of dynamical extensions of Lucas sequences and give elliptic solutions for them. The first type concerns a level-dependent (or discrete time-dependent) version involving commuting variables. We show that a nice solution for this system is given by elliptic numbers. The second type involves a non-commutative version of Lucas sequences which

Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart

Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agents. Recent research has introduced successful methods to scale multi-agent reinforcement learning algorithms to many agent scenarios using mean field theory. Previous work in this

Christopher Yeung, Ryan Tsai, Benjamin Pham, Brian King

Understanding how nano- or micro-scale structures and material properties can be optimally configured to attain specific functionalities remains a fundamental challenge. Photonic metasurfaces, for instance, can be spectrally tuned through material choice and structural geometry to achieve unique optical responses. However, existing numerical design methods r

James Thorne, Andreas Vlachos

This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. This extends the well-studied task of fact verification by providing a mechanism to correct written texts that are refuted or only partially supported by evidence. We demonstrate that it is feasible to train f

Michael C. Strayer

The $Γ$-colored $d$-complete and $Γ$-colored minuscule posets unify and generalize multiple classes of colored posets introduced by R.A. Proctor, J.R. Stembridge, and R.M. Green. In previous work, we showed that $Γ$-colored minuscule posets are necessary and sufficient to build from colored posets certain representations of Kac--Moody algebras that generaliz

Shih-Ting Lin, Nathanael Chambers, Greg Durrett

Models of narrative schema knowledge have proven useful for a range of event-related tasks, but they typically do not capture the temporal relationships between events. We propose a single model that addresses both temporal ordering, sorting given events into the order they occurred, and event infilling, predicting new events which fit into an existing tempo

David Blaschke, Mateusz Cierniak

With the first multi-messenger observation of a binary neutron star merger (GW170817) new constraints became available for masses and radii of neutron stars. We introduce a class of hybrid EoS that fulfils all these constraints and predicts a region in the mass-radius diagram that could be populated only by hybrid neutron stars with quark matter cores. A con

Rajkumar Pujari, Dan Goldwasser

Politicians often have underlying agendas when reacting to events. Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity. In spite of recent advances in Pretrained Language Models (PLMs), those text representations are not designed to capture such nuanced patterns. In this paper, we propose a Compositional Read

Saeed Khorram, Tyler Lawson, Fuxin Li

The black-box nature of the deep networks makes the explanation for "why" they make certain predictions extremely challenging. Saliency maps are one of the most widely-used local explanation tools to alleviate this problem. One of the primary approaches for generating saliency maps is by optimizing a mask over the input dimensions so that the output

Han Guo, Nazneen Fatema Rajani, Peter Hase, Mohit Bansal

Influence functions approximate the "influences" of training data-points for test predictions and have a wide variety of applications. Despite the popularity, their computational cost does not scale well with model and training data size. We present FastIF, a set of simple modifications to influence functions that significantly improves their run-tim

Ben Brubaker, Valentin Buciumas, Daniel Bump, Henrik P. A. Gustafsson

In this paper we compute new values of Iwahori Whittaker functions on $n$-fold metaplectic covers $\widetilde{G}$ of $\mathbf{G}(F)$ with $\mathbf{G}$ a split reductive group over a non-archimedean local field $F$. For every Iwahori Whittaker function $ϕ$, and for every $g\in\widetilde{G}$, we evaluate $ϕ(g)$ by recurrence relations over the Weyl group using