January 2022 arXiv papers — page 3

Liyi Li, Yiyun Liu, Deena L. Postol, Leonidas Lampropoulos

We present a formal model of Checked C, a dialect of C that aims to enforce spatial memory safety. Our model pays particular attention to the semantics of dynamically sized, potentially null-terminated arrays. We formalize this model in Coq, and prove that any spatial memory safety errors can be blamed on portions of the program labeled unchecked; this is a

Christine Ruey Shan Lee

We show that colored Khovanov homology detects classes of essential surfaces as a direct analogue of the slope conjectures for the colored Jones polynomial. We do this by identifying certain generators of the colored Khovanov chain complex with normal surfaces in the complement of the knot using an ideal triangulation from a diagram.

Juanyun Mai, Minghao Wang, Jiayin Zheng, Yanbo Shao

The mortality of lung cancer has ranked high among cancers for many years. Early detection of lung cancer is critical for disease prevention, cure, and mortality rate reduction. However, existing detection methods on pulmonary nodules introduce an excessive number of false positive proposals in order to achieve high sensitivity, which is not practical in cli

Tomasz M. Tyranowski

In this work we demonstrate that SVD-based model reduction techniques known for ordinary differential equations, such as the proper orthogonal decomposition, can be extended to stochastic differential equations in order to reduce the computational cost arising from both the high dimension of the considered stochastic system and the large number of independen

Swarnendu Mandal, Sudeshna Sinha, Manish Dev Shrimali

Reservoir Computing offers a great computational framework where a physical system can directly be used as computational substrate. Typically a "reservoir" is comprised of a large number of dynamical systems, and is consequently high-dimensional. In this work, we use just a single simple low-dimensional dynamical system, namely a driven pendulum, as a potent

Thi Nguyen Khoa Nguyen, Thibault Dairay, Raphaël Meunier, Mathilde Mougeot

Physics-Informed Neural Networks (PINNs) have gained much attention in various fields of engineering thanks to their capability of incorporating physical laws into the models. However, the assessment of PINNs in industrial applications involving coupling between mechanical and thermal fields is still an active research topic. In this work, we present an appl

Paul N. Patrone, Prajakta Bedekar, Nora Pisanic, Yukari C. Manabe

In diagnostic testing, establishing an indeterminate class is an effective way to identify samples that cannot be accurately classified. However, such approaches also make testing less efficient and must be balanced against overall assay performance. We address this problem by reformulating data classification in terms of a constrained optimization problem t

Davide Mambelli, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf

Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge. In this paper, we focus on models that can learn manipulation tasks in fixed multi-object settings and extrapolate this skill zero-shot without any drop in performance when the numb

Shaymaa Abdulla Al-Delayel

This paper discusses the security posture of Android m-banking applications in Qatar. Since technology has developed over the years and more security methods are provided, banking is now heavily reliant on mobile applications for prompt service delivery to clients, thus enabling a seamless and remote transaction. However, such mobile banking applications hav

Boxin Zhao, Boxiang Lyu, Mladen Kolar

Stochastic gradient-based optimization methods, such as L-SVRG and its accelerated variant L-Katyusha (Kovalev et al., 2020), are widely used to train machine learning models.The theoretical and empirical performance of L-SVRG and L-Katyusha can be improved by sampling observations from a non-uniform distribution (Qian et al., 2021). However,designing a desi

Philip Greengard, Jeremy G. Hoskins, Nicholas F. Marshall, Amit Singer

This paper studies the problem of computing a linear approximation of quadratic Wasserstein distance $W_2$. In particular, we compute an approximation of the negative homogeneous weighted Sobolev norm whose connection to Wasserstein distance follows from a classic linearization of a general Monge-Amp\'ere equation. Our contribution is threefold. First, we pr

Jonas Deré, Thomas Witdouck

Given a simple undirected graph, one can construct from it a $c$-step nilpotent Lie algebra for every $c \geq 2$ and over any field $K$, in particular also over the real and complex numbers. These Lie algebras form an important class of examples in geometry and algebra, and it is interesting to link their properties to the defining graph. In this paper, we c

Sourav Medya, Mohammad Rasoolinejad, Yang Yang, Brian Uzzi

Financial market analysis has focused primarily on extracting signals from accounting, stock price, and other numerical hard data reported in P&L statements or earnings per share reports. Yet, it is well-known that the decision-makers routinely use soft text-based documents that interpret the hard data they narrate. Recent advances in computational methods f

Chethan Krishnan, Ranjini Mondol

We reverse the logic behind the apparent existence of $H_0$-tension, to design diagnostics for cosmological models. The basic idea is that the non-constancy of $H_0$ inferred from observations at different redshifts is a null hypothesis test for models within the FLRW paradigm -- if $H_0$ runs, the model is wrong. Depending on the kind of observational data,

Bruno Loureiro, Cédric Gerbelot, Maria Refinetti, Gabriele Sicuro

From the sampling of data to the initialisation of parameters, randomness is ubiquitous in modern Machine Learning practice. Understanding the statistical fluctuations engendered by the different sources of randomness in prediction is therefore key to understanding robust generalisation. In this manuscript we develop a quantitative and rigorous theory for th

Daniel López Neumann

In this paper, we consider the Reshetikhin-Turaev invariants of knots in the three-sphere obtained from a twisted Drinfeld double of a Hopf algebra, or equivalently, the relative Drinfeld center of the crossed product $\text{Rep}(H)\rtimes\text{Aut}(H)$. These are quantum invariants of knots endowed with a homomorphism of the knot group to $\text{Aut}(H)$. W

Špela Špenko

The derived category of an algebraic variety might be a source of a myriad of new (categorical) symmetries. Some are predicted by homological mirror symmetry, to be obtained from the fundamental group of the space of complex structures of its mirror partner. These finally lead to differential equations. We expositorily unravel a part of this conjectural mast

Rafael R. S. Guimaraes

Limited datasets and complex nonlinear relationships are among the challenges that may emerge when applying econometrics to macroeconomic problems. This research proposes deep learning as an approach to transfer learning in the former case and to map relationships between variables in the latter case. Although macroeconomists already apply transfer learning

B. Cseh, B. Világos, M. P. Roriz, C. B. Pereira

Barium (Ba) stars are polluted by material enriched in the slow neutron capture (s-process) elements synthesised in the interior of their former asymptotic giant branch (AGB) companion star, which is now a white dwarf. We compare individual Ba star abundance patterns to AGB nucleosynthesis models to verify if the AGB model mass is compatible with independent

Cooper J. Mellema, Albert Montillo

Objective: New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity between pairs of brain regions in functional MRI, such as correlation and partial correla

Murat Onen, Tayfun Gokmen, Teodor K. Todorov, Tomasz Nowicki

Analog crossbar arrays comprising programmable nonvolatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymmetric conductance modulation of practical resistive devices critically degrades the classification performance of networks trained with conventional algorithms. Here, we describe a

Michael Shekelyan, Grigorios Loukides

Selecting the top-$k$ highest scoring items under differential privacy (DP) is a fundamental task with many applications. This work presents three new results. First, the exponential mechanism, permute-and-flip and report-noisy-max, as well as their oneshot variants, are unified into the Lipschitz mechanism, an additive noise mechanism with a single DP-proof

Corentin Briat, Mustafa Khammash

Perfect adaptation is a well-studied biochemical homeostatic behavior lying at the core of biochemical regulation. While the concepts of homeostasis and perfect adaptation are not new, their underlying mechanisms and associated biochemical regulation motifs are not yet fully understood. Insights from control theory unraveled the connections between perfect a

Viktar Kireyeu, Jan Steinheimer, Jörg Aichelin, Marcus Bleicher

The formation of deuterons in heavy-ion collisions at relativistic energies is investigated by employing two recently advanced models -- the Minimum Spanning Tree (MST) method and the coalescence model by embedding them in the PHQMD and the UrQMD transport approaches. While the coalescence mechanism combines nucleons into deuterons at the kinetic freeze-out

Antonio Jiménez-Pastor, Joshua Paul Jacob, Gleb Pogudin

Detailed dynamical systems models used in life sciences may include dozens or even hundreds of state variables. Models of large dimension are not only harder from the numerical perspective (e.g., for parameter estimation or simulation), but it is also becoming challenging to derive mechanistic insights from such models. Exact model reduction is a way to addr

Stéphane Gaïffas, Ibrahim Merad

This paper considers the problem of supervised learning with linear methods when both features and labels can be corrupted, either in the form of heavy tailed data and/or corrupted rows. We introduce a combination of coordinate gradient descent as a learning algorithm together with robust estimators of the partial derivatives. This leads to robust statistica

Condell Eastmond, Aseem Subedi, Suvranu De, Xavier Intes

Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional Near-InfraRed Spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, Deep Learning (DL) methodologies have d

Tomi K. Baikie, Laura T. Wey, Hitesh Medipally, Erwin Reisner

Photosystems II and I (PSII and PSI) are the reaction centre complexes that drive the light reactions of photosynthesis. PSII performs light-driven water oxidation (quantum efficiencies and catalysis rates of up to 80% and 1000 $e^{-}\text{s}^{-1}$, respectively) and PSI further photo-energises the harvested electrons (quantum efficiencies of ~100%). The imp

E. A. Ghioldi, Shang-Shun Zhang, Yoshitomo Kamiya, L. O. Manuel

Recent inelastic neutron scattering (INS) experiments of the triangular antiferromagnet Ba$_3$CoSb$_2$O$_9$ revealed strong deviations from semiclassical theories. We demonstrate that key features of the INS data are well reproduced by a parton Schwinger boson theory beyond the saddle-point approximation. The measured magnon dispersion is well reproduced by

Nicholas Werren, Erik Gauger, Peter Kirton

Starting from a quantum description of multiple Lambda-type 3-level atoms driven with a coherent microwave field and incoherent optical pumping, we derive a microscopic model of lasing from which we move towards a consistent macroscopic picture. Our analysis applies across the range of system sizes from nanolasers to the thermodynamic limit of conventional l

Jad Salem, Deven R. Desai, Swati Gupta

Companies that try to address inequality in employment face a hiring paradox. Failing to address workforce imbalance can result in legal sanctions and scrutiny, but proactive measures to address these issues might result in the same legal conflict. Recent run-ins of Microsoft and Wells Fargo with the Labor Department's Office of Federal Contract Compliance P

Hank Chen, Florian Girelli

The gauge symmetry and shift/translational symmetry of a 3D BF action, which are associated to a pair of dual Lie algebras, can be combined to form the Drinfel'd double. This combined symmetry is the gauge symmetry of the Chern-Simons action which is equivalent to the BF action, up to some boundary term. We show that something similar happens in 4D when cons

Matteo Piccolini, Farzam Nosrati, Roberto Morandotti, Rosario Lo Franco

We extend a procedure exploiting spatial indistinguishability of identical particles to recover the spoiled entanglement between two qubits interacting with Markovian noisy environments. Here, the spatially localized operations and classical communication (sLOCC) operational framework is used to activate the entanglement restoration from the indistinguishabl

David E. Evans, Ulrich Pennig

We develop an equivariant Dixmier-Douady theory for locally trivial bundles of $C^*$-algebras with fibre $D \otimes \mathbb{K}$ equipped with a fibrewise $\mathbb{T}$-action, where $\mathbb{T}$ denotes the circle group and $D = \operatorname{End}\left(V\right)^{\otimes \infty}$ for a $\mathbb{T}$-representation $V$. In particular, we show that the group of $

Zouheir Amara, Mourad Oudghiri

For a separable complex Hilbert space $H$, we say that a bounded linear operator $T$ acting on $H$ is $C$-normal, where $C$ is a conjugation on $H$, if it satisfies $CT^*TC=TT^*$. For a normal operator, we give geometric conditions which guarantee that its rank-one perturbation is a $C$-normal for some conjugation $C$.

M. V. Salis, J. P. Lorenz, Y. K. Huang, A. de Visser

We report a doping study directed to intentionally induce disorder in PdTe$_2$ by the isoelectronic substitution of Pt. Two single-crystalline batches Pd$_{1-x}$Pt$_x$Te$_2$ have been prepared with nominal doping concentrations x = 0.05 and x = 0.10. Sample characterization by energy dispersive x-ray spectroscopy (EDX) revealed Pt did not dissolve homogeneou

Hugo A. Camargo, Pratik Nandy, Qiang Wen, Haocheng Zhong

Recently in Ref.\cite{Wen:2021qgx}, one of the authors introduced the balanced partial entanglement (BPE), which has been proposed to be dual to the entanglement wedge cross-section (EWCS). In this paper, we explicitly demonstrate that the BPE could be considered as a proper measure of the total intrinsic correlation between two subsystems in a mixed state.

Nils Koster, Oliver Grothe, Achim Rettinger

The exponential growth in numbers of parameters of neural networks over the past years has been accompanied by an increase in performance across several fields. However, due to their sheer size, the networks not only became difficult to interpret but also problematic to train and use in real-world applications, since hardware requirements increased according

Ogulcan E. Orsel, Sasha S. Yamada

In this work, we apply machine learning techniques to historical stock prices to forecast future prices. To achieve this, we use recursive approaches that are appropriate for handling time series data. In particular, we apply a linear Kalman filter and different varieties of long short-term memory (LSTM) architectures to historical stock prices over a 10-yea

Nathan Hughes, Yun Chang, Luca Carlone

3D scene graphs have recently emerged as a powerful high-level representation of 3D environments. A 3D scene graph describes the environment as a layered graph where nodes represent spatial concepts at multiple levels of abstraction and edges represent relations between concepts. While 3D scene graphs can serve as an advanced "mental model" for robots, how t

Hans Bantilan, Yago Bea, Pau Figueras

Motivated by the physics of the quark-gluon plasma created in heavy-ion collision experiments, we use holography to study the regime of applicability of various theories of relativistic viscous hydrodynamics. Using the microscopic description provided by holography of a system that relaxes to equilibrium, we obtain initial data with which we perform real-tim

J. S. Dowker

The effect of a spherical monodromy defect on the entanglement entropy and central charge $C_T$ of a free conformal scalar field propagating on an odd-dimensional sphere is investigated. As on even spheres the central charge becomes negative for a range of the flux parameter, $\delta$, with possible implications for reflection positivity. The work is mostly

Hassam Sheikh, Kizza Frisbee, Mariano Phielipp

Application of ensemble of neural networks is becoming an imminent tool for advancing the state-of-the-art in deep reinforcement learning algorithms. However, training these large numbers of neural networks in the ensemble has an exceedingly high computation cost which may become a hindrance in training large-scale systems. In this paper, we propose DNS: a D

Isnaldi R. Souza Filho1, Hauke Springer, Yan Ma, Ankita Mahajan

Iron- and steelmaking cause ~7% of the global CO2 emissions, due to the use of carbon for the reduction of iron ores. Replacing carbon by hydrogen as the reductant offers a pathway to reduce emissions. However, production of hydrogen using renewable energy will remain a bottlenecks, because making the annual crude steel production of 1.8 billion tons sustain

Gabriele Casciaro, Mattia Cavaiola, Andrea Mazzino

The CAMS air quality multi-model forecasts have been assessed and calibrated for PM10, PM2.5, O3, NO2, and CO against observations collected by the Regional Monitoring Network of the Liguria region (northwestern Italy) in the years 2019 and 2020. The calibration strategy used in the present work has its roots in the well-established Ensemble Model Output Sta

Bing Yao, Fei Ma

In order to make more complex number-based strings from topological coding for defending against the intelligent attacks equipped with quantum computing and providing effective protection technology for the age of quantum computing, we will introduce set-colored graphs admitting set-colorings that has been considerable cryptanalytic significance, and especia

Andrea Bianchi, Alexander Mangulad Christgau, Jonathan Sejr Pedersen

We consider for $d\geq 1$ the graded commutative $\mathbb{Q}$-algebra $\mathcal{A}(d):=H^*(\operatorname{Hilb}^d(\mathbb{C}^2);\mathbb{Q})$, which is also connected to the study of generalised Hurwitz spaces by work of the first author. These Hurwitz spaces are in turn related to the moduli spaces of Riemann surfaces with boundary. We determine two distinct,

Nicolas Roisin, Thibault Delhaye, Nicolas André, Jean-Pierre Raskin

A strain sensor inspired by a Widlar self-biased current source topology called $\beta$-multiplier is developed to obtain a strain-dependent reference current with high supply rejection. The sensor relies on the piezoresistive effect in the silicon MOS transistors that form the current reference circuit. The device behavior is analytically computed and verif

Brandon Paulsen, Chao Wang

The most scalable approaches to certifying neural network robustness depend on computing sound linear lower and upper bounds for the network's activation functions. Current approaches are limited in that the linear bounds must be handcrafted by an expert, and can be sub-optimal, especially when the network's architecture composes operations using, for exampl

Francisco J. Palomo, José A. S. Pelegrín, Alfonso Romero

New rigidity results for complete non-compact spacelike submanifolds of arbitrary codimension in plane fronted waves are obtained. Under appropriate assumptions, we prove that a complete spacelike submanifold in these spacetimes is contained in a characteristic lightlike hypersurface. Moreover, for a complete codimension two extremal submanifold in a plane f

Adam F. Kowalski, Joel C. Allred, Mats Carlsson, Graham S. Kerr

Red-shifted components of chromospheric emission lines in the hard X-ray impulsive phase of solar flares have recently been studied through their 30 s evolution with the high resolution of IRIS. Radiative-hydrodynamic flare models show that these redshifts are generally reproduced by electron-beam generated chromospheric condensations. The models produce lar

Stefan Höppner, Yves Haas, Matthias Tichy, Katharina Juhnke

Model driven development envisages the use of model transformations to evolve models. Model transformation languages, developed for this task, are touted with many benefits over general purpose programming languages. However, a large number of these claims have not yet been substantiated. They are also made without the context necessary to be able to critica

J. R. Eskilt

We present new constraints on the frequency dependence of the cosmic birefringence angle from the Planck data release 4 polarization maps. An axion field coupled to electromagnetism predicts a nearly frequency-independent birefringence angle, $\beta_\nu = \beta$, while Faraday rotation from local magnetic fields and Lorentz violating theories predict a cosmi

Gholam Hossein Bordbar, Mohammad Karami

As we know, the effect of strong magnetic field causes the anisotropy for the magnetized compact objects. Therefore, in this paper, we have studied the structure properties of anisotropic case of magnetized neutron star. We have derived the equation of state (EoS) of neutron star matter for two forms of magnetic fields, one uniform and one density dependent.

Martha Precup, Eric Sommers

We consider generalizations of the Springer resolution of the nilpotent cone of a simple Lie algebra by replacing the cotangent bundle with certain other vector bundles over the flag variety. We show that the analogue of the Springer sheaf has as direct summands only intersection cohomology sheaves that arise in the Springer correspondence. The fibers of the

Sam Young

In light of recent developments in the field, we re-evaluate the effect of local-type non-Gaussianity on the primordial black hole (PBH) abundance (and consequently, upon constraints on the primordial power spectrum arising from PBHs). We apply peaks theory to the full, non-linear compaction, finding that, whilst the effect of non-Gaussianity is qualitativel

Sanjukta Krishnagopal

Mountaineering is a sport of contrary forces: teamwork plays a large role in mental fortitude and skills, but the actual act of climbing, and indeed survival, is largely individualistic. This work studies the effects of the structure and topology of relationships within climbers on the level of cooperation and success. It does so using simplicial complexes,

George Grätzer

Let $L$ be a slim, planar, semimodular lattice (slim means that it does not contain ${\mathsf M}_3$-sublattices). We call the interval $I = [o, i]$ of $L$ \emph{rectangular}, if there are $u_l, u_r \in [o, i] - \{o,i\}$ such that $i = u_l \vee u_r$ and $o = u_l \wedge u_r$ where $u_l$ is to the left of $u_r$. \emph{The first result}: a rectangular interval o

Hyungyeong Jung, Sunghwan Moon

Photoacoustic tomography (PAT) is a novel and rapidly promising technique in the field of medical imaging, based on the generation of acoustic waves inside an object of interest by stimulating non-ionizing laser pulses. This acoustic wave is measured using the detector on the outside of the object and converted into an image of the human body by several inve

Sarah E. Dodson-Robinson, Victor Ramirez Delgado, Justin Harrell, Charlotte Haley

If Doppler searches for earth-mass, habitable planets are to succeed, observers must be able to identify and model out stellar activity signals. Here we demonstrate how to diagnose activity signals by calculating the magnitude-squared coherence $\hat{C}^2_{xy}(f)$ between an activity indicator time series $x_t$ and the radial velocity (RV) time series $y_t$.

J. Šubjak, M. Endl, P. Chaturvedi, R. Karjalainen

We report the discovery of TOI-1268b, a transiting Saturn-mass planet from the TESS space mission. With an age of less than one Gyr, derived from various age indicators, TOI-1268b is the youngest Saturn-mass planet known to date and contributes to the small sample of well characterised young planets. It has an orbital period of $P\,=\,8.1577080\pm0.0000044$

Ali K. Z. Tehrani, Mostafa Sharifzadeh, Emad Boctor, Hassan Rivaz

The performance of ultrasound elastography (USE) heavily depends on the accuracy of displacement estimation. Recently, Convolutional Neural Networks (CNN) have shown promising performance in optical flow estimation and have been adopted for USE displacement estimation. Networks trained on computer vision images are not optimized for USE displacement estimati

Vincent Hickl, Gabriel Juarez

Bacteria biodegradation of immiscible oil requires cell-droplet encounters, surface attachment, and hydrocarbon metabolism. Chemical dispersants are applied to oil spills to reduce the mean dispersed droplet size, thereby increasing the available surface area for attachment, in attempts to facilitate bacterial biodegradation. However, their effectiveness rem

Xiuxiong Chen, Jingrui Cheng

In this paper, we consider a version of parabolic complex Monge-Ampere equations, and use a PDE approach similar to Phong et al to establish $L^{\infty}$ and H\"older estimates. We also generalize the $L^{\infty}$ estimates to parabolic Hessian equations.

Fabio Cermelli, Massimiliano Mancini, Samuel Rota Buló, Elisa Ricci

Deep neural networks have enabled major progresses in semantic segmentation. However, even the most advanced neural architectures suffer from important limitations. First, they are vulnerable to catastrophic forgetting, i.e. they perform poorly when they are required to incrementally update their model as new classes are available. Second, they rely on large

Weijie Lu, Manuel Pinto, Y. H Xia

Hein and Pr\"{u}ss [J. Differential Equations, 261(2016)4709-4727] presented a version of Hartman-Grobman type $C^{0}$ linearization result for semilinear hyperbolic evolution equations. They showed that the linearising map (homomorphism) and its inverse are H\"{o}lder continuous. An important question: is it possible to improve the regularity of the homomor

P. T. Yang, Q. X. Dong, P. F. Shan, Z. Y. Liu

The advances in the field of unconventional superconductivity are largely driven by the discovery of novel superconducting systems. Here we report on the discovery of superconductivity on the border of antiferromagnetic order in a quasi-one-dimensional RbMn6Bi5 via measurements of resistivity and magnetic susceptibility under high pressures. With increasing

Lisa V. Drummond, Scott A. Hughes

In this paper, we continue our study of the motion of spinning test bodies orbiting Kerr black holes. Non-spinning test bodies follow geodesics of the spacetime in which they move. A test body's spin couples to the curvature of that spacetime, introducing a "spin-curvature force" which pushes the body's worldline away from a geodesic trajectory. The spin-cur

Lisa V. Drummond, Scott A. Hughes

Very large mass ratio binary black hole systems are of interest both as a clean limit of the two-body problem in general relativity, as well as for their importance as sources of low-frequency gravitational waves. At lowest order, the smaller body moves along a geodesic of the larger black hole's spacetime. Post-geodesic effects include the gravitational sel

Mathias Pont, Riccardo Albiero, Sarah E. Thomas, Nicolò Spagnolo

We report on a universal method to measure the genuine indistinguishability of n-photons - a crucial parameter that determines the accuracy of optical quantum computing. Our approach relies on a low-depth cyclic multiport interferometer with N = 2n modes, leading to a quantum interference fringe whose visibility is a direct measurement of the genuine n-photo

Ioannis Caragiannis, Nisarg Shah, Alexandros A. Voudouris

We extend the recently introduced framework of metric distortion to multiwinner voting. In this framework, $n$ agents and $m$ alternatives are located in an underlying metric space. The exact distances between agents and alternatives are unknown. Instead, each agent provides a ranking of the alternatives, ordered from the closest to the farthest. Typically,

Daniel Weber, Maximilian Schenke, Oliver Wallscheid

Reinforcement learning (RL) is a promising, upcoming topic in automatic control applications. Where classical control approaches require a priori system knowledge, data-driven control approaches like RL allow a model-free controller design procedure, rendering them emergent techniques for systems with changing plant structures and varying parameters. While i

Xiuxiong Chen, Jingrui Cheng

In this note, we consider complex Monge-Ampere equation posed on a compact K\"ahler manifold. We show how to get $L^p$($p<\infty$) and $L^{\infty}$ estimate for the gradient of the solution in terms of the continuity of the right hand side.

Lue Tao, Lei Feng, Hongxin Wei, Jinfeng Yi

Adversarial training, originally designed to resist test-time adversarial examples, has shown to be promising in mitigating training-time availability attacks. This defense ability, however, is challenged in this paper. We identify a novel threat model named stability attack, which aims to hinder robust availability by slightly manipulating the training data

Rodion Kononchuk, Jizhe Cai, Fred Ellis, Ramathasan Thevamaran

Exceptional points (EP) are non-Hermitian spectral degeneracies where both eigenvalues and their corresponding eigenvectors coalesce. Recently, EPs have attracted a lot of attention as a means to enhance the responsivity of sensors, via the abrupt resonant detuning occurring in their proximity. In many cases, however, the EP implementation is accompanied by

Jonas Deré, Michal Ferov, Mark Pengitore

Separability for groups refers to the question which subsets of a group can be detected in its finite quotients. Classically, separability is studied in terms of which classes have a certain separability property, and this question is related to algorithmic problems in groups such as the word problem. A more recent perspective tries to study the order of the

S. M. Haroon, A. K. Bhakta, M. Shahabuddin, N. Rahman

This study aims to find out the current status and prospects of using a bicycle as a mode for commuting within Dhaka city. Bicycling is a very sustainable mode of transport but unfortunately is used very less by the commuters of Dhaka. There has been a lot of factors affecting the choice of bicycle to commute. This study was aimed to find out what factors co

William Schueller, Christian Diem, Melanie Hinterplattner, Johannes Stangl

The Covid-19 pandemic drastically emphasized the fragility of national and international supply networks (SNs),leading to significant supply shortages of essential goods for people, such as food and medical equipment. Severe disruptions that propagate along complex SNs can expose the population of entire regions or even countries to these risks. A lack of bo

Pengyu Li, Christine Tseng, Yaxuan Zheng, Joyce A. Chew

Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform classification and topic modeling tasks; however, most methods that can perform both do not allow for guidance of th

Evangelos Galaris, Gianluca Fabiani, Ioannis Gallos, Ioannis Kevrekidis

We address a three-tier data-driven approach to solve the inverse problem in complex systems modelling from spatio-temporal data produced by microscopic simulators using machine learning. In the first step, we exploit manifold learning and in particular parsimonious Diffusion Maps using leave-one-out cross-validation (LOOCV) to both identify the intrinsic di

Jiaguo Yu, Yuming Shen, Menghan Wang, Haofeng Zhang

Learning to hash pictures a list-wise sorting problem. Its testing metrics, e.g., mean-average precision, count on a sorted candidate list ordered by pair-wise code similarity. However, scarcely does one train a deep hashing model with the sorted results end-to-end because of the non-differentiable nature of the sorting operation. This inconsistency in the o

Youngeun Kim, Hyoungseob Park, Abhishek Moitra, Abhiroop Bhattacharjee

Recent Spiking Neural Networks (SNNs) works focus on an image classification task, therefore various coding techniques have been proposed to convert an image into temporal binary spikes. Among them, rate coding and direct coding are regarded as prospective candidates for building a practical SNN system as they show state-of-the-art performance on large-scale

Sarah M. Harrison, Jeffrey A. Harvey, Natalie M. Paquette

We present a brief overview of Moonshine with an emphasis on connections to physics. Moonshine collectively refers to a set of phenomena connecting group theory, analytic number theory, and vertex operator algebras or conformal field theories. Modern incarnations of Moonshine arise in various BPS observables in string theory and, via dualities, invariants in

Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtárik

Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design communication-compressed algorithms for decentralized nonconvex optimization, where the clients are only allo

Andrea Solfanelli, Alessandro Santini, Michele Campisi

We report on a quantum thermodynamic method to purify a qubit on a quantum processing unit (QPU) equipped with (nearly) identical qubits. Our starting point is a three qubit design that emulates the well known two qubit swap engine. Similar to standard fridges, the method would allow to cool down a qubit at the expense of heating two other qubits. A minimal

Shan-Chi Huang

We construct the ASDYM 1-solitons and multi-solitons for split signature and interpret them as soliton walls. We show that the gauge group is $\mathrm{G=SU(2)}$ for the entire intersecting soliton walls, and $\mathrm{SU(3)}$ for each soliton walls in the asymptotic region. This is joint research partially with Masashi Hamanaka, Claire R. Gilson, and Jonathan

Shan Jiang, Xiaokang Li, Benoît Fauqué, Kamran Behnia

SrTiO$_3$, a quantum paralectric, displays a detectable phonon thermal Hall effect (THE). Here we show that the amplitude of THE is extremely sensitive to stoichiometry. It drastically decreases upon substitution of a tiny fraction of Sr atoms with Ca, which stabilizes the ferroelectric order. It drastically increases by an even lower density of oxygen vacan

Enno Diekema

Szmytkowski derived a certain integral with Gegenbauer polynomials. A natural generalization is to derive lookalike integrals with Jacobi polynomials. Six methods are treated to derive the first integral. The first method should be enough to prove the first integral, but by the other methods there arises remarkable formula such as par example a zero-balanced

Yvonne Alama Bronsard

We analyse a class of time discretizations for solving the nonlinear Schr\"odinger equation with non-smooth potential and at low-regularity on an arbitrary Lipschitz domain $\Omega \subset \mathbb{R}^d$, $d \le 3$. We show that these schemes, together with their optimal local error structure, allow for convergence under lower regularity assumptions on both t

Horacio Casini, Marina Huerta

These notes grew from a series of lectures given by the authors during the last decade. They will be published in the proceedings of TASI 2021. After a brief introduction to quantum information theory tools, they are organized in four chapters covering the following subjects: Entanglement in quantum field theory, Irreversibility theorems, Energy-entropy boun

Geoffrey Powell

For $\mathcal{O}$ an operad in $k$-vector spaces, the category $\mathcal{F}_\mathcal{O}$ is defined to be the category of $k$-linear functors from the PROP associated to $\mathcal{O}$ to $k$-vector spaces. Given $\mu \in \mathcal{O} (2)$ that satisfies a right Leibniz condition, the full subcategory $\mathcal{F}_\mathcal{O}^\mu \subset \mathcal{F}_\mathcal{O

Kimet Jusufi

We construct a novel class of spherically symmetric and asymptotically flat black holes and naked singularities surrounded by anisotropic dark matter fluid with the equation of state (EoS) of the form $P_t=\omega \rho$. We assume that dark matter is made of weakly interacting particles orbiting around the supermassive black hole in the galactic center and th

Alejandro José Uría-Álvarez, Daniel Molpeceres-Mingo, Juan José Palacios

Calculation of topological invariants for crystalline systems is well understood in reciprocal space, allowing for the topological classification of a wide spectrum of materials. In this work, we present a new technique based on the entanglement spectrum, which can be used to identify the hidden topology of systems without translational invariance. By traini

Hannah Peeler, Shuyue Stella Li, Andrew N. Sloss, Kenneth N. Reid

In this paper we introduce Shackleton as a generalized framework enabling the application of linear genetic programming -- a technique under the umbrella of evolutionary algorithms -- to a variety of use cases. We also explore here a novel application for this class of methods: optimizing sequences of LLVM optimization passes. The algorithm underpinning Shac

Zongkang Zhang, Yongdan Yang, Xiaosi Xu, Ying Li

The Schrieffer-Wolff transformation aims to solve degenerate perturbation problems and give an effective Hamiltonian that describes the low-energy dynamics of the exact Hamiltonian in the low-energy subspace of unperturbed Hamiltonian. This unitary transformation decoupling the low-energy and high-energy subspaces for the transformed Hamiltonian can be reali

Benjamin Braun, Kaitlin Bruegge

Symmetric edge polytopes, also called adjacency polytopes, are lattice polytopes determined by simple undirected graphs. We introduce the integer array \(\mathrm{maxf}(n,m)\) giving the maximum number of facets of a symmetric edge polytope for a connected graph having \(n\) vertices and \(m\) edges, and the corresponding sequence \(\mathrm{minf}(n,m)\) of mi

Alberto Abello, James Cheney

Data integration is a classical problem in databases, typically decomposed into schema matching, entity matching and data fusion. To solve the latter, it is mostly assumed that ground truth can be determined. However, in general, the data gathering processes in the different sources are imperfect and cannot provide an accurate merging of values. Thus, in the

Kimberly Glasgow, Jonathan Kopecky, John Gersh, Adam Crego

Humans should be able work more effectively with artificial intelligence-based systems when they can predict likely failures and form useful mental models of how the systems work. We conducted a study of human's mental models of artificial intelligence systems using a high-performing image classifier, focusing on participants' ability to predict the classifi

Van Sy Mai, Richard J. La, Tao Zhang, Abdella Battou

Providing differentiated services to meet the unique requirements of different use cases is a major goal of the fifth generation (5G) telecommunication networks and will be even more critical for future 6G systems. Fulfilling this goal requires the ability to assure quality of service (QoS) end to end (E2E), which remains a challenge. A key factor that makes

Maryam Nezami, Georg Maennel, Hossam Seddik Abbas, Georg Schildbach

This paper presents a novel, safe control architecture (SCA) for controlling an important class of systems: safety-critical systems. Ensuring the safety of control decisions has always been a challenge in automatic control. The proposed SCA aims to address this challenge by using a Model Predictive Controller (MPC) that acts as a supervisor for the operating