February 2022 arXiv papers

Jennie Rogers, Elizabeth Adetoro, Johes Bater, Talia Canter

Electronic health records represent a rich and growing source of clinical data for research. Privacy, regulatory, and institutional concerns limit the speed and ease of sharing this data. VaultDB is a framework for securely computing SQL queries over private data from two or more sources. It evaluates queries using secure multiparty computation: cryptographi

Olivier Danvy

"There and Back Again" (TABA) is a programming pattern where the recursive calls traverse one data structure and the subsequent returns traverse another. This article presents new TABA examples, refines existing ones, and formalizes both their control flow and their data flow using the Coq Proof Assistant. Each formalization mechanizes a pen-and-pape

Abdallah Alabdallah, Mattias Ohlsson, Sepideh Pashami, Thorsteinn Rögnvaldsson

The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored c

Adarsh Sehgal, Nicholas Ward, Hung Manh La, Christos Papachristos

Agents can base decisions made using reinforcement learning (RL) on a reward function. The selection of values for the learning algorithm parameters can, nevertheless, have a substantial impact on the overall learning process. In order to discover values for the learning parameters that are close to optimal, we extended our previously proposed genetic algori

Joaquin Grefa, Mauricio Hippert, Jorge Noronha, Jacquelyn Noronha-Hostler

A bottom-up Einstein-Maxwell-Dilaton holographic model is used to compute, for the first time, the behavior of several transport coefficients of the hot and baryon-rich strongly coupled quark-gluon plasma at the critical point and also across the first-order phase transition line in the phase diagram. The observables under study are of the shear and bulk vis

E. Rangel, B. Moura, J. Menezes

Disease outbreaks affect many ecosystems threatening species that also fight against other natural enemies. We investigate a cyclic game system with $5$ species, whose organisms outcompete according to the rules of a generalised spatial rock-paper-scissors game, during an epidemic. We study the effects of behavioural movement strategies that allow individual

Razieh Nabi, Rohit Bhattacharya

Significant progress has been made in developing identification and estimation techniques for missing data problems where modeling assumptions can be described via a directed acyclic graph. The validity of results using such techniques rely on the assumptions encoded by the graph holding true; however, verification of these assumptions has not received suffi

Yunhe Gao, Mu Zhou, Di Liu, Zhennan Yan

Transformers have demonstrated remarkable performance in natural language processing and computer vision. However, existing vision Transformers struggle to learn from limited medical data and are unable to generalize on diverse medical image tasks. To tackle these challenges, we present MedFormer, a data-scalable Transformer designed for generalizable 3D med

Xiucai Ding, Rong Ma

We propose a kernel-spectral embedding algorithm for learning low-dimensional nonlinear structures from high-dimensional and noisy observations, where the datasets are assumed to be sampled from an intrinsically low-dimensional manifold and corrupted by high-dimensional noise. The algorithm employs an adaptive bandwidth selection procedure which does not rel

Xiang Chen, Yu-Tsun Shao, Rui Chen, Sandhya Susarla

The existence of long range magnetic order in low dimensional magnetic systems, such as the quasi-two-dimensional (2D) van der Waals (vdW) magnets, has attracted intensive studies of new physical phenomena. The vdW Fe$_N$GeTe$_2$ ($N$ = 3, 4, 5; FGT) family is exceptional owing to its vast tunability of magnetic properties. Particularly, a ferromagnetic orde

Viorel Barbu, Michael Röckner

This work is concerned with the existence and uniqueness of generalized (mild or distributional) solutions to (possibly degenerate) Fokker-Planck equations $ρ_t-Δβ(ρ)+{\rm div}(Db(ρ)ρ)=0$ in $(0,\infty)\times\mathbb{R}^d,$ $ρ(0,x) \equiv ρ_0(x)$. Under suitable assumptions on $β:\mathbb{R}\to\mathbb{R},\,b:\mathbb{R}\to\mathbb{R}$ and $D:\mathbb{R}^d\to\math

Aowabin Rahman, Ján Drgoňa, Aaron Tuor, Jan Strube

Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare their predictive performance with current state-of-the-art nonlinear and classical linear methods. In particular, we present a quantitative study comparing NODE's performance

Matthew Ciolino, Dominick Hambrick, David Noever

The sensor to shooter timeline is affected by two main variables: satellite positioning and asset positioning. Speeding up satellite positioning by adding more sensors or by decreasing processing time is important only if there is a prepared shooter, otherwise the main source of time is getting the shooter into position. However, the intelligence community s

Anthony Peruma, Christian D. Newman

Before any software maintenance can occur, developers must read the identifier names found in the code to be maintained. Thus, high-quality identifier names are essential for productive program comprehension and maintenance activities. With developers free to construct identifier names to their liking, it can be difficult to automatically reason about the qu

John Palowitch, Anton Tsitsulin, Brandon Mayer, Bryan Perozzi

Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets are currently used to evaluate new models. This continued reliance on a handful of datasets provides minimal insight into the performance differences between models, and is especially challenging for industrial practitioners who are likely to have datasets whi

Hugo Caselles-Dupré, Mohamed Chetouani, Olivier Sigaud

When demonstrating a task, human tutors pedagogically modify their behavior by either "showing" the task rather than just "doing" it (exaggerating on relevant parts of the demonstration) or by giving demonstrations that best disambiguate the communicated goal. Analogously, human learners pragmatically infer the communicative intent of the tut

Fatma Gouiaa, Arun Padakandla

We consider the scenario of communicating on a $3\mhyphen$user classical-quantum broadcast channel. We undertake an information theoretic study and focus on the problem of characterizing an inner bound to its capacity region. We design a new coding scheme based \textit{partitioned coset codes} - an ensemble of codes possessing algebraic properties. Analyzing

Joel Kostensalo, Jouni Suhonen, Kai Zuber

Large-scale shell-model calculations were carried out for the half-lives and branching ratios of the $2νββ$ decay of $^{76}$Ge to the ground state and the lowest three excited states $2_1^+$, $0_2^+$ and $2_2^+$ in $^{76}$Se. In total, the wave functions of more than 10,000 intermediate $1^+$ states in $^{76}$As were calculated in a three-step procedure allo

Chun-Xiao Liu, Guanzhong Wang, Tom Dvir, Michael Wimmer

Semiconductor quantum dots have proven to be a useful platform for quantum simulation in the solid state. However, implementing a superconducting coupling between quantum dots mediated by a Cooper pair has so far suffered from limited tunability and strong suppression. This has limited applications such as Cooper pair splitting and quantum dot simulation of

Héloïse Delaporte, Astrid Eichhorn, Aaron Held

We discuss parameterizations of black-hole spacetimes in and beyond General Relativity in view of their symmetry constraints: within the class of axisymmetric, stationary spacetimes, we propose a parameterization that includes non-circular spacetimes, both in Boyer-Lindquist as well as in horizon-penetrating coordinates. We show how existing parameterization

Juan Carlos Estrada Saldaña, Alexandros Vekris, Luka Pavešič, Rok Žitko

Condensed matter is composed of a small set of identical units, yet it shows an immense range of behaviour. Recently, an array of cold atoms was used to generate long-range quantum entanglement, a property of topological matter. Another approach to strong non-local correlations employs the macroscopic coherence of superconductors. Impurity spins in supercond

Mark A. Webster, Benjamin J. Brown, Stephen D. Bartlett

We propose an extension to the Pauli stabiliser formalism that includes fractional $2π/N$ rotations around the $Z$ axis for some integer $N$. The resulting generalised stabiliser formalism - denoted the XP stabiliser formalism - allows for a wider range of states and codespaces to be represented. We describe the states which arise in the formalism, and demon

Constantino Tsallis, Henrique Santos Lima, Ugur Tirnakli, Deniz Eroglu

We numerically study the thermal transport in the classical inertial nearest-neighbor XY ferromagnet in $d=1,2,3$, the total number of sites being given by $N=L^d$, where $L$ is the linear size of the system. For the thermal conductance $σ$, we obtain $σ(T,L)\, L^{δ(d)} = A(d)\, e_{q(d)}^{- B(d)\,[L^{γ(d)}T]^{η(d)}}$ (with $e_q^z \equiv [1+(1-q)z]^{1/(1-q)};

Hossein Keshavarz, Meiyappan Nagappan

In this paper, we present ApacheJIT, a large dataset for Just-In-Time defect prediction. ApacheJIT consists of clean and bug-inducing software changes in popular Apache projects. ApacheJIT has a total of 106,674 commits (28,239 bug-inducing and 78,435 clean commits). Having a large number of commits makes ApacheJIT a suitable dataset for machine learning mod

Roi Holtzman, Marco Giulini, Raffaello Potestio

Complex systems are characterised by a tight, nontrivial interplay of their constituents, which gives rise to a multi-scale spectrum of emergent properties. In this scenario, it is practically and conceptually difficult to identify those degrees of freedom that mostly determine the behaviour of the system and separate them from less prominent players. Here,

Lucas D. Wittwer, Sebastian Aland

Mechanochemical processes on surfaces such as the cellular cortex or epithelial sheets, play a key role in determining patterns and shape changes of biological systems. To understand the complex interplay of hydrodynamics and material flows on such active surfaces requires novel numerical tools. Here, we present a finite-element method for an active deformab

Havva Yoldaş

This review concerns recent results on the quantitative study of convergence towards the stationary state for spatially inhomogeneous kinetic equations. We focus on analytical results obtained by means of certain probabilistic techniques from the ergodic theory of Markov processes. These techniques are sometimes referred to as Harris-type theorems. They prov

Timothy C. Y. Chan, Simon Y. Huang, Vahid Sarhangian

We study a control problem for queueing systems where customers may return for additional episodes of service after their initial service completion. At each service completion epoch, the decision maker can choose to reduce the probability of return for the departing customer but at a cost that is convex increasing in the amount of reduction in the return pr

Dominik Rist, Christian Saemann, Martin Wolf

We define the notion of adjustment for strict Lie 2-groups and provide the complete cocycle description for non-Abelian gerbes with connections whose structure 2-group is an adjusted 2-group. Most importantly, we depart from the common fake-flat connections and employ adjusted connections. This is an important generalisation that is needed for physical appli

Ivan Damnjanović

We investigate the spectral properties of rooted trees with the intention of improving the currently existing results that deal with this matter. The concept of an assigned rational function is recursively defined for each vertex of a rooted tree. Afterwards, two mathematical formulas are given which show how the characteristic polynomials of the adjacency a

William D'Amico, Marcello Farina

In this paper we propose a novel methodology that allows to design, in a purely data-based fashion and for linear single-input and single-output systems, both robustly stable and performing control systems for tracking piecewise constant reference signals. The approach uses both (i) Virtual Reference Feedback Tuning for enforcing suitable performances and (i

Seth A. Major

Quasi-local energies for constantly accelerating observers in Bañados, Teitelboim, and Zanelli (BTZ), Schwarzschild and Schwarzschild-de Sitter spacetimes are derived. The energies are expressed in terms of acceleration, cosmological constant, and area, quantities measurable by the observers. Based on results from quantum fields in curved spacetime for the r

Yongli Sang, Xin Dang

The categorical Gini correlation proposed by Dang et al. is a dependence measure to characterize independence between categorical and numerical variables. The asymptotic distributions of the sample correlation under dependence and independence have been established when the dimension of the numerical variable is fixed. However, its asymptotic behavior for hi

Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Shan E Ahmed Raza

The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially localise various structures in the tissue and hence facilitate the extraction of interpretable features for biomarker discovery. However, these models are typically trained for a sin

Daniel Vial, Sanjay Shakkottai, R. Srikant

We consider a multi-agent multi-armed bandit setting in which $n$ honest agents collaborate over a network to minimize regret but $m$ malicious agents can disrupt learning arbitrarily. Assuming the network is the complete graph, existing algorithms incur $O( (m + K/n) \log (T) / Δ)$ regret in this setting, where $K$ is the number of arms and $Δ$ is the arm g

Liang Qiu, Chien-Sheng Wu, Wenhao Liu, Caiming Xiong

Extracting structure information from dialogue data can help us better understand user and system behaviors. In task-oriented dialogues, dialogue structure has often been considered as transition graphs among dialogue states. However, annotating dialogue states manually is expensive and time-consuming. In this paper, we propose a simple yet effective approac

Carl Mueller, Eyal Neuman

We consider self-repelling elastic manifolds with a domain $[-N,N]^d \cap \mathbb{Z}^d$, that take values in $\mathbb{R}^D$. Our main result states that when the dimension of the domain is $d=2$ and the dimension of the range is $D=1$, the effective radius $R_N$ of the manifold is approximately $N^{4/3}$. This verifies the conjecture of Kantor, Kardar and Ne

Niharika Thakuria, Reena Elangovan, Anand Raghunathan, Sumeet K. Gupta

We propose non-volatile memory (NVM) designs based on Piezoelectric Strain FET (PeFET) utilizing a piezoelectric/ferroelectric (PE/FE such as PZT) coupled with 2D Transition Metal Dichalcogenide (2D-TMD such as MoS2) transistor. The proposed NVMs store bit information in the form of polarization (P) of the FE/PE, use electric-field driven P-switching for wri

Robi Bhattacharjee, Alex Cloninger, Yoav Freund, Andreas Oslandsbotn

Effective resistance (ER) is an attractive way to interrogate the structure of graphs. It is an alternative to computing the eigen-vectors of the graph Laplacian. Graph laplacians are used to find low dimensional structures in high dimensional data. Here too, ER based analysis has advantages over eign-vector based methods. Unfortunately Von Luxburg et al. (2

Di Shu, Peisong Han, Sean Hennessy, Todd A Miano

There is growing interest in developing causal inference methods for multi-valued treatments with a focus on pairwise average treatment effects. Here we focus on a clinically important, yet less-studied estimand: causal drug-drug interactions (DDIs), which quantifies the degree to which the causal effect of drug A is altered by the presence versus the absenc

Yang Zhang, Ling-Fang Lin, Adriana Moreo, Thomas A. Maier

Here, using density functional theory and density matrix renormalization group methods, we investigate the electronic and magnetic properties of RuOCl$_2$ and OsOCl$_2$ with $d^4$ electronic configurations. Different from a previous study using VOI$_2$ with $d^1$ configuration, these systems with $4d^4$ or $5d^4$ do not exhibit a ferroelectric instability al

C. J. Cotter, D. D. Holm, T. Pryer

This paper introduces the r-Camassa-Holm (r-CH) equation, which describes a geodesic flow on the manifold of diffeomorphisms acting on the real line induced by the W1,r metric. The conserved energy is for the problem is given by the full W1,r norm and the for r = 2, we recover the Camassa-Holm equation. We compute the Lie symmetries for r-CH and study variou

Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song

Learning policies that effectively utilize language instructions in complex, multi-task environments is an important problem in sequential decision-making. While it is possible to condition on the entire language instruction directly, such an approach could suffer from generalization issues. In our work, we propose \emph{Learning Interpretable Skill Abstract

Alfonso Ballon-Bayona, Jonathan P. Shock, Dimitrios Zoakos

We investigate the thermodynamics of the anisotropic magnetic $\rm AdS_5$ black brane solution found by D'Hoker and Kraus arXiv:0908.3875. This solution is the gravity dual of a strongly coupled ${\cal N}=4$ Super Yang-Mills plasma in ${\mathbb R}^{3,1}$, with temperature $T$, in the presence of a magnetic field ${\cal B}$. Following the procedure of hol

Jørgen A. Agersborg, Luigi T. Luppino, Stian Normann Anfinsen, Jane Uhd Jepsen

Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well suited to detect weak signatures of certain disturbances of ecological systems. To resolve this problem we propose a new approach based on

Jiali Duan, Liqun Chen, Son Tran, Jinyu Yang

Aligning signals from different modalities is an important step in vision-language representation learning as it affects the performance of later stages such as cross-modality fusion. Since image and text typically reside in different regions of the feature space, directly aligning them at instance level is challenging especially when features are still evol

Rushi Gong, Shun-Li Shang, Hui Sun, Michael J. Janik

Pd-Zn intermetallic catalysts show encouraging combinations of activity and selectivity on well-defined active site ensembles. Thermodynamic description of the Pd-Zn system, delineating phase boundaries, and enumerating site occupancies within intermediate alloy phases, are essential to determining the ensembles of Pd-Zn atoms as a function of composition an

T. Akindele, T. Anderson, E. Anderssen, M. Askins

The High Energy Physics community can benefit from a natural synergy in research activities into next-generation large-scale water and scintillator neutrino detectors, now being studied for remote reactor monitoring, discovery and exclusion applications in cooperative nonproliferation contexts. Since approximately 2010, US nonproliferation researchers, suppo

Kun Wang, Mridul Aanjaneya, Kostas Bekris

Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differentiable physics engines have been recently proposed as a data-driven approach for model identification of such complex robotic systems. These engines are often executed at a high-f

Peter F. Barker, Sougato Bose, Ryan J. Marshman, Anupam Mazumdar

Quantum entanglement provides a novel way to test short distance physics in the non-relativistic regime. We will provide a protocol to {\it potentially} test new physics by bringing two charged massive particle interferometers adjacent to each other. Being charged, the two superpositions will be entangled via electromagnetic interactions mediated by the phot

Akshay Suresh, James M. Cordes, Shami Chatterjee, Vishal Gajjar

The Galactic Center (GC), with its high density of massive stars, is a promising target for radio transient searches. In particular, the discovery and timing of a pulsar orbiting the central supermassive black hole (SMBH) of our Galaxy will enable stringent strong-field tests of gravity and accurate measurements of SMBH properties. We performed multi-epoch 4

Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri

Mean-Field Control (MFC) is a powerful tool to solve Multi-Agent Reinforcement Learning (MARL) problems. Recent studies have shown that MFC can well-approximate MARL when the population size is large and the agents are exchangeable. Unfortunately, the presumption of exchangeability implies that all agents uniformly interact with one another which is not true

Lucas Hellström, Abbas Askar, Alessandro A. Trani, Mirek Giersz

In the cores of dense stellar clusters, close gravitational encounters between binary and single stars can frequently occur. Using the Tsunami code, we computed the outcome of a large number of binary-single interactions involving two black holes (BHs) and a star to check how the inclusion of orbital energy losses due to tidal dissipation can change the outc

Susan Gardner, Girish Muralidhara

We present a QCD analysis of the effective weak Hamiltonian at hadronic energy scales for strangeness-nonchanging ($ΔS=0$) hadronic processes. Performing a leading-order renormalization group analysis in QCD from the $W$ to the ${\cal O}(2\,\rm GeV)$ energy scale, we derive the pertinent effective Hamiltonian for hadronic parity violation, including the effe

Gian Gentinetta, Arne Thomsen, David Sutter, Stefan Woerner

Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models corresponds to solving a convex optimization problem either via its primal or dual formulation. Due to th

B. Ladewig, S. Diehl, M. Buchhold

The interplay of unitary evolution and local measurements in many-body systems gives rise to a stochastic state evolution and to measurement-induced phase transitions in the pure state entanglement. In realistic settings, however, this dynamics may be spoiled by decoherence, e.g., dephasing, due to coupling to an environment or measurement imperfections. We

Leander Thiele, Miles Cranmer, William Coulton, Shirley Ho

Theoretical uncertainty limits our ability to extract cosmological information from baryonic fields such as the thermal Sunyaev-Zel'dovich (tSZ) effect. Being sourced by the electron pressure field, the tSZ effect depends on baryonic physics that is usually modeled by expensive hydrodynamic simulations. We train neural networks on the IllustrisTNG-300 co

Federica Pompa, Francesco Capozzi, Olga Mena, Michel Sorel

Time of flight delay in the supernova neutrino signal offers a unique tool to set model-independent constraints on the absolute neutrino mass. The presence of a sharp time structure during a first emission phase, the so-called neutronization burst in the electron neutrino flavor time distribution, makes this channel a very powerful one. Large liquid argon un

Tomáš Brauner, Angelo Esposito, Riccardo Penco

It is a common lore that the amplitude for a scattering process involving one soft Nambu--Goldstone boson should scale like an integer power of the soft momentum. We revisit this expectation by considering the $2 \to 2$ scattering of phonons in solids. We show that, depending on the helicities of the phonons involved in the scattering process, the scattering

Michele Cicoli, Francisco G. Pedro, Nicola Pedron

We derive the spectrum and analyse the detectability prospects of secondary gravity waves (GWs) associated to primordial black hole (PBH) production in a class of string inflationary models called Fibre Inflation. The inflationary potential features a near inflection point that induces a period of ultra slow-roll responsible for an enhancement of the scalar

Xiao-Qi Sun, Tamra Nebabu, Xizhi Han, Michael O. Flynn

Restricted Boltzmann machines (RBMs) are a class of neural networks that have been successfully employed as a variational ansatz for quantum many-body wave functions. Here, we develop an analytic method to study quantum many-body spin states encoded by random RBMs with independent and identically distributed complex Gaussian weights. By mapping the computati

A. Fedotov, A. Ilderton, F. Karbstein, B. King

Upcoming and planned experiments combining increasingly intense lasers and energetic particle beams will access new regimes of nonlinear, relativistic, quantum effects. This improved experimental capability has driven substantial progress in QED in intense background fields. We review here the advances made during the last decade, with a focus on theory and

Seoyoung Lyla Jung, Douglas Rennehan, Vida Saeedzadeh, Arif Babul

Contrary to many stereotypes about massive galaxies, observed brightest group galaxies (BGGs) are diverse in their star formation rates, kinematic properties, and morphologies. Studying how they evolve into and express such diverse characteristics is an important piece of the galaxy formation puzzle. We use a high-resolution cosmological suite of simulations

Guo-Yi Zhu, Ji-Yao Chen, Peng Ye, Simon Trebst

Gapped fracton phases of matter generalize the concept of topological order and broaden our fundamental understanding of entanglement in quantum many-body systems. However, their analytical or numerical description beyond exactly solvable models remains a formidable challenge. Here we employ an exact 3D quantum tensor-network approach that allows us to study

B. Parazin, Michael W. Coughlin, Leo P. Singer, Vaidehi Gupta

Electromagnetic follow-up of gravitational wave detections is very resource intensive, taking up hours of limited observation time on dozens of telescopes. Creating more efficient schedules for follow-up will lead to a commensurate increase in counterpart location efficiency without using more telescope time. Widely used in operations research and telescope

Chiao-Hsuan Wang, Fangxin Li, Liang Jiang

High-performance quantum transducers, which faithfully convert quantum information between disparate physical carriers, are essential in quantum science and technology. Different figures of merit, including efficiency, bandwidth, and added noise, are typically used to characterize the transducers' ability to transfer quantum information. Here we utilize

Justin C. Feng, Shinji Mukohyama, Sante Carloni

In this article, we develop the formalism for singular hypersurfaces and junction conditions in generalized coupling theories using a variational approach. We then employ this formalism to examine the behavior of sharp matter density gradients in generalized coupling theories. We find that such gradients do not necessarily lead to the pathologies present in

G. Spada, S. Giorgini, S. Pilati

We provide a detailed description of the path-integral Monte Carlo worm algorithm used to exactly calculate the thermodynamics of Bose systems in the canonical ensemble. The algorithm is fully consistent with periodic boundary conditions, that are applied to simulate homogeneous phases of bulk systems, and it does not require any limitation in the length of

Weiguang Cui, Romeel Dave, Alexander Knebe, Elena Rasia

We introduce \textsc{Gizmo-Simba}, a new suite of galaxy cluster simulations within \textsc{The Three Hundred} project. \textsc{The Three Hundred} consists of zoom re-simulations of 324 clusters with $M_{200}\gtrsim 10^{14.8}M_\odot$ drawn from the MultiDark-Planck $N$-body simulation, run using several hydrodynamic and semi-analytic codes. The \textsc{Gizmo

Jenny Tang, Eleanor Birrell, Ada Lerner

Privacy and security researchers often rely on data collected through online crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) and Prolific. Prior work -- which used data collected in the United States between 2013 and 2017 -- found that MTurk responses regarding security and privacy were generally representative for people under 50 or with some

Jonne Sälevä, Constantine Lignos

We introduce ParaNames, a multilingual parallel name resource consisting of 118 million names spanning across 400 languages. Names are provided for 13.6 million entities which are mapped to standardized entity types (PER/LOC/ORG). Using Wikidata as a source, we create the largest resource of this type to-date. We describe our approach to filtering and standa

Yue Yao, Liang Zheng, Xiaodong Yang, Milind Napthade

This article aims to use graphic engines to simulate a large number of training data that have free annotations and possibly strongly resemble to real-world data. Between synthetic and real, a two-level domain gap exists, involving content level and appearance level. While the latter is concerned with appearance style, the former problem arises from a differ

Madhumita Saha, B. Prasanna Venkatesh, Bijay Kumar Agarwalla

Quasi-periodic lattice systems offer diverse transport properties. In this work, we investigate the environment induced effects on transport properties for quasi-periodic systems, namely the one-dimensional Aubry-André-Harper (AAH) lattice chain and its generalized version (GAAH) by considering the Büttiker probe approach. We first consider voltage probe sit

Quentin Dubroff, António Girão, Eoin Hurley, Corrine Yap

Resolving a problem of Conlon, Fox, and Rödl, we construct a family of hypergraphs with arbitrarily large tower height separation between their $2$-colour and $q$-colour Ramsey numbers. The main lemma underlying this construction is a new variant of the Erdős--Hajnal stepping-up lemma for a generalized Ramsey number $r_k(t;q,p)$, which we define as the small

Akhil Antony, Fabio Finelli, Dhiraj Kumar Hazra, Arman Shafieloo

We identify examples of single field inflationary trajectories beyond the slow-roll regime which improve the fit to Planck 2018 data compared to baseline $Λ$CDM model with power law form of primordial spectrum and at the same time alleviate existing tensions between different data sets in the estimate of cosmological parameters such as $H_0$ and $S_8$. A dam

Sheng Liu, Zhihui Zhu, Qing Qu, Chong You

Recently, over-parameterized deep networks, with increasingly more network parameters than training samples, have dominated the performances of modern machine learning. However, when the training data is corrupted, it has been well-known that over-parameterized networks tend to overfit and do not generalize. In this work, we propose a principled approach for

Marco Frasca, Anish Ghoshal, Stefan Groote

We derive the low-energy limit of quantum chromodynamics (QCD) and provide evidence that in the 't Hooft limit, i.e. for a very large number of colors and increasing 't Hooft coupling, quark confinement is recovered. The low energy limit of the theory turns out to be a non-local Nambu-Jona-Lasinio (NJL) model. The effect of non-locality, arising from

Lambert A'Campo

We show the vanishing of adjoint Bloch-Kato Selmer groups of automorphic Galois representations over CM fields. This proves their rigidity in the sense that they have no deformations which are de Rham. In order for this to make sense we also prove that automorphic Galois representations over CM fields are de Rham themselves. Our methods draw heavily from the

Patrizio Frosini, Ivan Gridelli, Andrea Pascucci

In recent years, group equivariant non-expansive operators (GENEOs) have attracted attention in the fields of Topological Data Analysis and Machine Learning. In this paper we show how these operators can be of use also for the removal of impulsive noise and to increase the stability of TDA in the presence of noisy data. In particular, we prove that GENEOs ca

Ramij R. Hossain, Ratnesh Kumar

This article presents a distributed model-predictive control (MPC) design for real-time voltage control in power systems, including an online method to estimate the bus admittance matrix $\mathbf{Y}$ to let it be time-varying and unknown a priori. The prevalent control designs are either (a) centralized, providing optimal solutions but less scalable and susc

Robert Chang, Abraham Rabinowitz

We work on the boundary $\partial M_τ$ of a Grauert tube of a closed, real analytic Riemannian manifold $M$. The Toeplitz operator $Π_τD_{\sqrtρ} Π_τ$ associated to the Reeb vector field is a positive, self-adjoint, elliptic operator on $H^2(\partial M_τ)$. We compute $λ\to \infty$ asymptotics under parabolic rescaling in a neighborhood of the geodesic (Reeb

James Holt, Edward Raff, Ahmad Ridley, Dennis Ross

The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities. Additionally, adversaries continue to develop new attacks. Hence, AI methods are required to understand and protect the cyber domain. These challenges are widely studied in ente

Heng Dai, Zixu Zhao, Shuhang Zhang

We study the first order phase transition of Euler-Heisenberg-AdS black hole based on free energy landscape. By solving the Fokker-Planck equation, we research the probability distribution of the system states. The small (large) black hole can have the chance to switch to the large (small) black hole due to the change of the temperature $T$ or Euler-Heisenbe

Richard Stiskalek, Deaglan J. Bartlett, Harry Desmond, Dhayaa Anbajagane

We apply machine learning, a powerful method for uncovering complex correlations in high-dimensional data, to the galaxy-halo connection of cosmological hydrodynamical simulations. The mapping between galaxy and halo variables is stochastic in the absence of perfect information, but conventional machine learning models are deterministic and hence cannot capt

Moritz Blumenthal, Guanxiong Luo, Martin Schilling, H. Christian M. Holme

Purpose: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. Methods: The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a non-linear operator framework that provides automatic different

Michael G. Rawson

Brain function and connectivity is a pressing mystery in medicine related to many diseases. Neural connectomes have been studied as graphs with graph theory methods including topological methods. Work has started on hypergraph models and methods where the geometry and topology is significantly different. We define a hypergraph called the hyper-connectome wit

Pablo Alvarez, Matthieu Chabanas, Stéphane Sikora, Simon Rouzé

Video-assisted thoracoscopic surgery (VATS) is a minimally invasive surgical technique for the diagnosis and treatment of early-stage lung cancer. During VATS, large lung deformation occurs as a result of a change of patient position and a pneumothorax (lung deflation), which hinders the intraoperative localization of pulmonary nodules. Modeling lung deforma

Théo Nancy, Vassili Maillet, Johann Barbier

Neural networks are a convenient way to automatically fit functions that are too complex to be described by hand. The downside of this approach is that it leads to build a black-box without understanding what happened inside. Finding the preimage would help to better understand how and why such neural networks had given such outputs. Because most of the neur

Esther Rolf, Nikolay Malkin, Alexandros Graikos, Ana Jojic

We propose a method for jointly inferring labels across a collection of data samples, where each sample consists of an observation and a prior belief about the label. By implicitly assuming the existence of a generative model for which a differentiable predictor is the posterior, we derive a training objective that allows learning under weak beliefs. This fo

Jacky J. Chong, Laurent Lafleche, Chiara Saffirio

For arbitrarily large times $T>0$, we prove the uniform-in-$\hbar$ propagation of semiclassical regularity for the solutions to the Hartree$\unicode{x2013}$Fock equation with singular interactions of the form $V(x)=\pm\,|x|^{-a}$ where $a\in(0,\frac12)$. As a byproduct of this result, we extend to arbitrarily long times the derivation of the Hartree$\unicode

Jiayu Zhang

In the quantum computation verification problem, a quantum server wants to convince a client that the output of evaluating a quantum circuit $C$ is some result that it claims. This problem is considered very important both theoretically and practically in quantum computation [arXiv:1709.06984], [arXiv:1704.04487], [arXiv:1209.0449]. The client is considered

Matthew P. Leighton, David A. Sivak

Molecular motors work collectively to transport cargo within cells, with anywhere from one to several hundred motors towing a single cargo. For a broad class of collective-transport systems, we use tools from stochastic thermodynamics to derive a new lower bound for the entropy production rate which is tighter than the second law. This implies new bounds on

S. Arthamonov, J. Harnad, J. Hurtubise

This work concerns the relation between the geometry of Lagrangian Grassmannians and the CKP integrable hierarchy. The Lagrange map from the Lagrangian Grassmannian of maximal isotropic (Lagrangian) subspaces of a finite dimensional symplectic vector space $V\oplus V^*$ into the projectivization of the exterior space $ΛV$ is defined by restricting the Plücke

Sophie Libkind, Andrew Baas, Micah Halter, Evan Patterson

Pandemic management requires that scientists rapidly formulate and analyze epidemiological models in order to forecast the spread of disease and the effects of mitigation strategies. Scientists must modify existing models and create novel ones in light of new biological data and policy changes such as social distancing and vaccination. Traditional scientific

Vince Kurtz, Hai Lin

Contact-implicit trajectory optimization offers an appealing method of automatically generating complex and contact-rich behaviors for robot manipulation and locomotion. The scalability of such techniques has been limited, however, by the challenge of ensuring both numerical reliability and physical realism. In this paper, we present preliminary results sugg

Rebecca Gorman, Stuart Armstrong

For an artificial intelligence (AI) to be aligned with human values (or human preferences), it must first learn those values. AI systems that are trained on human behavior, risk miscategorising human irrationalities as human values -- and then optimising for these irrationalities. Simply learning human values still carries risks: AI learning them will inevit

Julian Petzold, Mostafa Wahby, Franek Stark, Ulrich Behrje

Pedestrians are particularly vulnerable road users in urban traffic. With the arrival of autonomous driving, novel technologies can be developed specifically to protect pedestrians. We propose a machine learning toolchain to train artificial neural networks as models of pedestrian behavior. In a preliminary study, we use synthetic data from simulations of a

Qi Fang, Ya-Nan Feng, Shi-Mei Ma

Let R(n,k) be the number of permutations of $\{1,2,\ldots,n\}$ with k alternating runs. In this paper, we establish the relationships between R(n,k) and the central factorial numbers of even indices as well as the number of signed permutations with a given number of alternating runs and the central factorial numbers of odd indices. The explicit formulas of t

Maria Chudnovsky, Alex Scott, Paul Seymour, Sophie Spirkl

A pure pair in a tournament $G$ is an ordered pair $(A,B)$ of disjoint subsets of $V(G)$ such that every vertex in $B$ is adjacent from every vertex in $A$. Which tournaments $H$ have the property that if $G$ is a tournament not containing $H$ as a subtournament, and $|G|>1$, there is a pure pair $(A,B)$ in $G$ with $|A|,|B|\ge c|G|$, where $c>0$ is a consta

András Strausz, Flavio Vella, Salvatore Di Girolamo, Maciej Besta

Triangle count and local clustering coefficient are two core metrics for graph analysis. They find broad application in analyses such as community detection and link recommendation. Current state-of-the-art solutions suffer from synchronization overheads or expensive pre-computations needed to distribute the graph, achieving limited scaling capabilities. We