arXiv papers from May 2022

Yue Zheng, Imtiar Niloy, Ian Tobasco, Paolo Celli

Kirigami metamaterials dramatically change their shape through a coordinated motion of nearly rigid panels and flexible slits. Here, we study a model system for mechanism-based planar kirigami featuring periodic patterns of quadrilateral panels and rhombi slits, with the goal of predicting their engineering scale response to a broad range of loads. We develo

Quanyi Li, Zhenghao Peng, Haibin Wu, Lan Feng

Human-AI shared control allows human to interact and collaborate with AI to accomplish control tasks in complex environments. Previous Reinforcement Learning (RL) methods attempt the goal-conditioned design to achieve human-controllable policies at the cost of redesigning the reward function and training paradigm. Inspired by the neuroscience approach to inv

Hao Wang

Cold-start and sparsity problem are two key intrinsic problems to recommender systems. During the past two decades, researchers and industrial practitioners have spent considerable amount of efforts trying to solve the problems. However, for cold-start problem, most research relies on importing side information to transfer knowledge. A notable exception is Z

Amit Samanta, Stephan Friedrich, Kyle G. Leach, Vincenzo Lordi

Several current searches for physics beyond the standard model are based on measuring the electron capture (EC) decay of radionuclides implanted into cryogenic high-resolution sensors. The sensitivity of these experiments has already reached the level where systematic effects related to atomic-state energy changes from the host material are a limiting factor

Wenkai Xu, Gesine Reinert

Synthetic data generation has become a key ingredient for training machine learning procedures, addressing tasks such as data augmentation, analysing privacy-sensitive data, or visualising representative samples. Assessing the quality of such synthetic data generators hence has to be addressed. As (deep) generative models for synthetic data often do not admi

Paul Yudkin, Eli Friedman, Orly Zvitia, Gil Elbaz

Over the past few years there has been major progress in the field of synthetic data generation using simulation based techniques. These methods use high-end graphics engines and physics-based ray-tracing rendering in order to represent the world in 3D and create highly realistic images. Datagen has specialized in the generation of high-quality 3D humans, re

Can Chen, Chen Ma, Xi Chen, Sirui Song

Implicit feedback is widely leveraged in recommender systems since it is easy to collect and provides weak supervision signals. Recent works reveal a huge gap between the implicit feedback and user-item relevance due to the fact that implicit feedback is also closely related to the item exposure. To bridge this gap, existing approaches explicitly model the e

A. D. Alhaidari

Conventional quantum field theory is a method for studying structureless elementary particles. Non-elementary particles, on the other hand, are those with internal structure or particles that are made up of elementary constituents like the hadrons, which contain quarks and gluons. We introduce a structure-inclusive algebraic formulation of quantum field theo

Shang Wang, Yansong Gao, Anmin Fu, Zhi Zhang

As a critical threat to deep neural networks (DNNs), backdoor attacks can be categorized into two types, i.e., source-agnostic backdoor attacks (SABAs) and source-specific backdoor attacks (SSBAs). Compared to traditional SABAs, SSBAs are more advanced in that they have superior stealthier in bypassing mainstream countermeasures that are effective against SA

Matthew N. H. Chow, Bethany J. Little, Yuan-Yu Jau

We demonstrate discrimination of ground-state hyperfine manifolds of a cesium atom in an optical tweezer using a simple probe beam with 99.91(2)% detection fidelity and 0.9(2)% detection-driven loss of bright state atoms. Our detection infidelity of 0.09(2)% is an order of magnitude better than previously published low-loss readout results for alkali-metal a

Kazushi Aoyama

We theoretically investigate the critical bias current $j_c$ for a superconducting (SC) ring in a magnetic field. Based on the Ginzburg-Landau theory, we show that $j_c$ exhibits a Little-Parks (LP) oscillation as a function of the magnetic flux passing through the ring, similarly to the LP oscillation in the SC transition temperature. It is also found that

Artem Zholus, Alexey Skrynnik, Shrestha Mohanty, Zoya Volovikova

We present the IGLU Gridworld: a reinforcement learning environment for building and evaluating language conditioned embodied agents in a scalable way. The environment features visual agent embodiment, interactive learning through collaboration, language conditioned RL, and combinatorically hard task (3d blocks building) space.

Kevin Keomanee-Dizon, Matt Jones, Peter Luu, Scott E. Fraser

Light-sheet microscopes must compromise between field of view, optical sectioning, resolution, and detection efficiency. High-numerical-aperture (NA) detection objective lenses provide high resolution but their narrow depth of field fails to capture effectively the fluorescence signal generated by the illumination light sheets, in imaging large volumes. Here

Damian Arellanes

Compositionality is a key property for dealing with complexity, which has been studied from many points of view in diverse fields. Particularly, the composition of individual computations (or programs) has been widely studied almost since the inception of computer science. Unlike existing composition theories, this paper presents an algebraic model not for c

Heba Aamer, Marco Montali, Jan Van den Bussche

In the last decade, the term instance-spanning constraint has been introduced in the process mining field to refer to constraints that span multiple process instances of one or several processes. Of particular relevance, in this setting, is checking whether process executions comply with constraints of interest, which at runtime calls for suitable monitoring

Emil Mottola

Two of the most fundamental problems at the nexus of Einstein's classical General Relativity (GR) and Quantum Field Theory (QFT) are: (1) complete gravitational collapse, presumed in classical GR to lead to a Black Hole (BH) horizon and interior singularity, which generate a number of paradoxes for quantum theory; and (2) the origin and magnitude of the cosm

Derek A. Berman, Min S. Yun, K. C. Harrington, P. Kamieneski

The Planck All-Sky Survey to Analyze Gravitationally-lensed Extreme Starbursts (PASSAGES) project aims to identify a population of extremely luminous galaxies using the Planck All-Sky Survey and to explore the nature of their gas fuelling, induced starburst, and the resulting feedback that shape their evolution. Here, we report the identification of 22 high

Yiqiao Liao, Parinaz Naghizadeh

Although many fairness criteria have been proposed to ensure that machine learning algorithms do not exhibit or amplify our existing social biases, these algorithms are trained on datasets that can themselves be statistically biased. In this paper, we investigate the robustness of a number of existing (demographic) fairness criteria when the algorithm is tra

Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen

Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, cleaning, featurizing the datasets) and a machine learning (ML) part invoking one or more trained models to perform predictions. These parts have so far been optimized in isolation,

Kathryn Dover, Zixuan Cang, Anna Ma, Qing Nie

High-dimensional multimodal data arises in many scientific fields. The integration of multimodal data becomes challenging when there is no known correspondence between the samples and the features of different datasets. To tackle this challenge, we introduce AVIDA, a framework for simultaneously performing data alignment and dimension reduction. In the numer

John A. Rock

The kernel of analysis, to me anyway, is the following idea: A point is arbitrarily close to a set if every neighborhood of the point intersects the set. Defining ``arbitrarily close'' in this way provides a foundation for classical results in calculus and real analysis dealing with convergence, limits, connectedness, limits, continuity, differentiation, int

Nicholas Pippenger

We give a formula for the determinant of an $n\times n$ matrix with entries from a commutative ring with unit. The formula can be evaluated by a "straight-line program" performing only additions, subtractions and multiplications of ring elements; in particular it requires no divisions or conditional branching (as are required, for example, by Gaussian elimin

Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim

Many important problems involving molecular property prediction from 3D structures have limited data, posing a generalization challenge for neural networks. In this paper, we describe a pre-training technique based on denoising that achieves a new state-of-the-art in molecular property prediction by utilizing large datasets of 3D molecular structures at equi

Alan Dasilva, Helton Saulo, Roberto Vila, Jose A. Fiorucci

Parametric autoregressive moving average models with exogenous terms (ARMAX) have been widely used in the literature. Usually, these models consider a conditional mean or median dynamics, which limits the analysis. In this paper, we introduce a class of quantile ARMAX models based on log-symmetric distributions. This class is indexed by quantile and dispersi

Alessandro Iraci, Roberto Pagaria, Giovanni Paolini, Anna Vanden Wyngaerd

In this paper, we extend the rectangular side of the shuffle conjecture by stating a rectangular analogue of the square paths conjecture. In addition, we describe a set of combinatorial objects and one statistic that are a first step towards a rectangular extension of (the rise version of) the Delta conjecture, and of (the rise version of) the Delta square c

Jaein Lim, Panagiotis Tsiotras

Multi-Agent Path Finding (MAPF) is the problem of finding a collection of collision-free paths for a team of multiple agents while minimizing some global cost, such as the sum of the time travelled by all agents, or the time travelled by the last agent. Conflict Based Search (CBS) is a leading complete and optimal MAPF solver which lazily explores the joint

Yatong Chen, Reilly Raab, Jialu Wang, Yang Liu

Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound? In this paper, we study the transferability of statistical group fairness for machine learning predictors (i.e., classifiers or regressors) subject to bounded distribution shifts. S

Brian Liu, Rahul Mazumder

Tree ensembles are powerful models that achieve excellent predictive performances, but can grow to unwieldy sizes. These ensembles are often post-processed (pruned) to reduce memory footprint and improve interpretability. We present ForestPrune, a novel optimization framework to post-process tree ensembles by pruning depth layers from individual trees. Since

Vasileios Charisopoulos, Anil Damle

We develop an eigenspace estimation algorithm for distributed environments with arbitrary node failures, where a subset of computing nodes can return structurally valid but otherwise arbitrarily chosen responses. Notably, this setting encompasses several important scenarios that arise in distributed computing and data-collection environments such as silent/s

Ikuya Kaneko, Shin-ya Koyama, Nobushige Kurokawa

We explicate the deep Riemann hypothesis for the general linear group $\mathrm{GL}_{n}$ on the convergence of normalised Euler products of standard $L$-functions on the critical line. It conditionally improves upon the error term in the prime number theorem beyond what the grand Riemann hypothesis predicts. Furthermore, we discuss the Chebyshev bias for Sata

Charles J. Sayers, Stefano Dal Conte, Daniel Wolverson, Christoph Gadermaier

The excitation and detection of coherent phonons has given unique insights into condensed matter, in particular for materials with strong electron-phonon coupling. We report a study of coherent phonons in the layered charge density wave (CDW) compound 1$T$-TaSe$_2$ performed using transient broadband reflectivity spectroscopy, in the photon energy range 1.75

Hristu Culetu

Following a previous idea, a curved geometry is proposed as being valid in accelerated systems, in Minkowski space. The curvature turns out to be generated by the source of the accelerated motion. An exponential factor depending on $\rho$ (the coordinate along the acceleration) and a constant length is introduced in the metric. The source stress tensor appea

Sebastien Prince, Pratyush Anand, James Battat, Russell Farnsworth

A digital instrument that allows the measurement of the mechanical tension of an array of wires of known length and density, and the testing of their electrical continuity and isolation, has been developed. The instrument measures wire tension by measuring the fundamental frequency of the wire. Its working principle is to apply direct high voltages on neighb

V. A. Coutinho, F. M. Bayer, R. J. Cintra

The discrete Hartley transform (DHT) is a useful tool for medical image coding. The three-dimensional DHT (3D DHT) can be employed to compress medical image data, such as magnetic resonance and X-ray angiography. However, the computation of the 3D DHT involves several multiplications by irrational quantities, which require floating-point arithmetic and inher

Tianyuan Yao, Yuzhe Lu, Jun Long, Aadarsh Jha

The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills in order to be able to handle heterogeneous

T. L. T. da Silveira, D. R. Canterle, D. F. G. Coelho, V. A. Coutinho

The discrete cosine transform (DCT) is a relevant tool in signal processing applications, mainly known for its good decorrelation properties. Current image and video coding standards -- such as JPEG and HEVC -- adopt the DCT as a fundamental building block for compression. Recent works have introduced low-complexity approximations for the DCT, which become p

Clémence Réda, Sattar Vakili, Emilie Kaufmann

This paper introduces a general multi-agent bandit model in which each agent is facing a finite set of arms and may communicate with other agents through a central controller in order to identify, in pure exploration, or play, in regret minimization, its optimal arm. The twist is that the optimal arm for each agent is the arm with largest expected mixed rewa

Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran, Tara Javidi

Understanding complex dynamics of two-sided online matching markets, where the demand-side agents compete to match with the supply-side (arms), has recently received substantial interest. To that end, in this paper, we introduce the framework of decentralized two-sided matching market under non stationary (dynamic) environments. We adhere to the serial dicta

Pak Shing Li, Enrique Lopez-Rodriguez, Archana Soam, Richard I. Klein

We present the stability analysis of two regions, OMC-3 and OMC-4, in the massive and long molecular cloud complex of Orion A. We obtained $214~\mu$m HAWC+/SOFIA polarization data, and we make use of archival data for the column density and C$^{18}$O (1-0) emission line. We find clear depolarization in both observed regions and that the polarization fraction

Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen

Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties. In this paper, we propose the first general and effective information-theoretic formulation of graph sparsification, by taking inspiration from the Principle of Relevant Information (PRI). To this end, we extend the PRI from a standard scalar rando

Xiaoqing Wan, Nichole R. Lighthall

Recently enacted regulations aimed to enhance retail investors' understanding about different types of investment accounts. Toward this goal, the Securities and Exchange Commission (SEC) mandated that SEC-registered investment advisors and broker-dealers provide a brief relationship summary (Form CRS) to retail investors. The present study examines the impac

John C. Stevenson

The emergence of eusocial species is both very rare in evolutionary history and results in remarkably successful species. By inverting an agent based model, agent rules are discovered that display behaviors characteristic of eusocial species as well as other behaviors that lead to unexpected population dynamics. By holding the agents' genome constant across

Gregory Dresden

We start with new convolution formulas for $F_n - n^p$ involving only the binomial coefficients. Then, we use those to find direct formulas for the sums $\sum_{i=1}^n i^p F_{n-i}$ and $\sum_{i=1}^n i^p F_i$, and we show how our formulas connect to work in earlier papers by Ledin, Brousseau, Zeitlin, Adegoke, Shannon and Ollerton, and Kinlaw, Morris, and Thia

László Györfi, Martin Kroll

We consider the problem of estimating a regression function from anonymized data in the framework of local differential privacy. We propose a novel partitioning estimate of the regression function, derive a rate of convergence for the excess prediction risk over H\"older classes, and prove a matching lower bound. In contrast to the existing literature on the

Daniel Hernandez, Hendrik Baier, Michael Kaisers

Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies). We propose Best Response Expert Iteration (BRExIt), which accel

Beren Millidge, Yuhang Song, Tommaso Salvatori, Thomas Lukasiewicz

How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients that approximate those computed by backpropagation (BP), and which operate in ways that more closely satisfy the constraints imposed by neural circuitry. Many such algorithms utilize

Isabelle Bouchoule, Jérôme Dubail, Léa Dubois, Dimitri M. Gangardt

We investigate the Lieb-Liniger gas initially prepared in an out-of-equilibrium state that is Gaussian in terms of the phonons. Because the phonons are not exact eigenstates of the Hamiltonian, the gas relaxes to a stationary state at very long times. Thanks to integrability, that stationary state needs not be a thermal state. We characterize the stationary

Yu-Zhen Janice Chen, Daniel S. Menasche, Don Towsley

We study how the amount of correlation between observations collected by distinct sensors/learners affects data collection and collaboration strategies by analyzing Fisher information and the Cramer-Rao bound. In particular, we consider a simple setting wherein two sensors sample from a bivariate Gaussian distribution, which already motivates the adoption of

I. A. Aleksandrov, D. A. Tumakov, A. Kudlis, V. A. Zaytsev

The behavior of a twisted electron colliding with a linearly polarized laser pulse is investigated within relativistic quantum mechanics. In order to better fit the real experimental conditions, we introduce a Gaussian spatial profile for the initial electron state as well as an envelope function for the laser pulse, so the both interacting objects have a fi

William F Godoy, Jenna Delozier, Gregory R Watson

The present work investigates the modeling of pre-exascale input/output (I/O) workloads of Adaptive Mesh Refinement (AMR) simulations through a simple proxy application. We collect data from the AMReX Castro framework running on the Summit supercomputer for a wide range of scales and mesh partitions for the hydrodynamic Sedov case as a baseline to provide su

Daniel Ebler, Michał Horodecki, Marcin Marciniak, Tomasz Młynik

Let $U_d$ be a unitary operator representing an arbitrary $d$-dimensional unitary quantum operation. This work presents optimal quantum circuits for transforming a number $k$ of calls of $U_d$ into its complex conjugate $\bar{U_d}$. Our circuits admit a parallel implementation and are proven to be optimal for any $k$ and $d$ with an average fidelity of $\lef

Miguel González-Duque, Rasmus Berg Palm, Søren Hauberg, Sebastian Risi

Deep generative models can automatically create content of diverse types. However, there are no guarantees that such content will satisfy the criteria necessary to present it to end-users and be functional, e.g. the generated levels could be unsolvable or incoherent. In this paper we study this problem from a geometric perspective, and provide a method for r

Muhammad Muneeb, Samuel F. Feng, Andreas Henschel

This article proposes and documents a machine-learning framework and tutorial for classifying images using mobile phones. Compared to computers, the performance of deep learning model performance degrades when deployed on a mobile phone and requires a systematic approach to find a model that performs optimally on both computers and mobile phones. By followin

Armando Alves Neto, Leonardo Amaral Mozelli

In the last few years, researchers have applied machine learning strategies in the context of vehicular platoons to increase the safety and efficiency of cooperative transportation. Reinforcement Learning methods have been employed in the longitudinal spacing control of Cooperative Adaptive Cruise Control systems, but to date, none of those studies have addr

L. L. Lage, A. Latge

The Sierpinski Triangle (ST) is a fractal mathematical structure that has been used to explore the emergence of flat bands in lattices of different geometries and dimensions in condensed matter. Here we look into fractal features in the electronic properties of ST flakes and molecular chains simulating experimental synthesized fractal nanostructures. We use

Yuan Yang, Jian Kang, Yi Li

We consider a class of Cox models with time-dependent effects that may be zero over certain unknown time regions or, in short, sparse time-varying effects. The model is particularly useful for biomedical studies as it conveniently depicts the gradual evolution of effects of risk factors on survival. Statistically, estimating and drawing inference on infinite

Debopriya Roy Dipta, Berk Gulmezoglu

Microarchitectural attacks have become more threatening the hardware security than before with the increasing diversity of attacks such as Spectre and Meltdown. Vendor patches cannot keep up with the pace of the new threats, which makes the dynamic anomaly detection tools more evident than before. Unfortunately, previous studies utilize hardware performance

Yi Li, Rameswar Panda, Yoon Kim, Chun-Fu Chen

Designing better machine translation systems by considering auxiliary inputs such as images has attracted much attention in recent years. While existing methods show promising performance over the conventional text-only translation systems, they typically require paired text and image as input during inference, which limits their applicability to real-world

Sudeep Salgia, Sattar Vakili, Qing Zhao

We consider the neural contextual bandit problem. In contrast to the existing work which primarily focuses on ReLU neural nets, we consider a general set of smooth activation functions. Under this more general setting, (i) we derive non-asymptotic error bounds on the difference between an overparameterized neural net and its corresponding neural tangent kern

J. Hlavacek-Larrondo, Y. Li, E. Churazov

AGN feedback stands for the dramatic impact that a SMBH can make on its environment. It has become an essential element of models that describe the formation and evolution of baryons in massive virialized halos. The baryons' radiative losses in the cores of these halos might lead to massive cooling and vigorous star formation on the order of 10-1000 Msun/yr,

Angelo Caregnato-Neto, Marcos Ricardo Omena de Albuquerque Maximo, Rubens Junqueira Magalhães Afonso

This work is concerned with the problem of planning trajectories and assigning tasks for a Multi-Agent System (MAS) comprised of differential drive robots. We propose a multirate hierarchical control structure that employs a planner based on robust Model Predictive Control (MPC) with mixed-integer programming (MIP) encoding. The planner computes trajectories

Joshua C. Hill, William K. Holland, Paul D. Kunz, Kevin C. Cox

Vertical external-cavity surface-emitting lasers (VECSELs) augmented by intra-cavity nonlinear optical frequency conversion have emerged as an attractive light source of ultraviolet to visible light for demanding scientific applications, relative to other laser technologies. They offer high power, low phase noise, wide frequency tunability, and excellent bea

Aaron V. Diebold, Divya Pande, Christine Gregg, David R. Smith

We propose and numerically validate a patch reflectarray modeling approach suitable for small patches that describes each patch as a pair of polarizable magnetic dipoles. We introduce an extraction technique to obtain the effective polarizability of the patch dipoles via full-wave simulations on individual patches, followed by a beamforming design routine va

Eddie Nijholt, Nándor Sieben, James W. Swift

The internal state of a cell in a coupled cell network is often described by an element of a vector space. Synchrony or anti-synchrony occurs when some of the cells are in the same or the opposite state. Subspaces of the state space containing cells in synchrony or anti-synchrony are called polydiagonal subspaces. We study the properties of several types of

Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes

We pursue tractable Bayesian analysis of generalized linear models (GLMs) for categorical data. Thus far, GLMs are difficult to scale to more than a few dozen categories due to non-conjugacy or strong posterior dependencies when using conjugate auxiliary variable methods. We define a new class of GLMs for categorical data called categorical-from-binary (CB)

Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi, Ivaxi Sheth

Few-shot learning has recently attracted wide interest in image classification, but almost all the current public benchmarks are focused on natural images. The few-shot paradigm is highly relevant in medical-imaging applications due to the scarcity of labeled data, as annotations are expensive and require specialized expertise. However, in medical imaging, f

Jack Sayers, Adam B. Mantz, Elena Rasia, Steven W. Allen

We have combined X-ray observations from Chandra with Sunyaev-Zel'dovich (SZ) effect data from Planck and Bolocam to measure intra-cluster medium pressure profiles from 0.03R$_{500}$ $\le$ R $\le$ 5R$_{500}$ for a sample of 21 low-$z$ galaxy clusters with a median redshift $\langle z \rangle = 0.08$ and a median mass $\langle \textrm{M}_{500} \rangle = 6.1 \

Dmitriy Metelev, Alexander Rogozin, Alexander Gasnikov, Dmitry Kovalev

Large-scale saddle-point problems arise in such machine learning tasks as GANs and linear models with affine constraints. In this paper, we study distributed saddle-point problems (SPP) with strongly-convex-strongly-concave smooth objectives that have different strong convexity and strong concavity parameters of composite terms, which correspond to min and m

Laura Piispanen, Marcel Pfaffhauser, James Wootton, Julian Togelius

In this research article, we survey existing quantum physics-related games and, based on this survey, propose a definition for the concept of quantum games. We define a quantum game as any type of rule-based game that either employs the principles of quantum physics or references quantum phenomena or the theory of quantum physics through any of three propose

Shahrzad Naseri, Sravana Reddy, Joana Correia, Jussi Karlgren

In this work, we study the association between song lyrics and mood through a data-driven analysis. Our data set consists of nearly one million songs, with song-mood associations derived from user playlists on the Spotify streaming platform. We take advantage of state-of-the-art natural language processing models based on transformers to learn the associatio

Iztok Banic, Goran Erceg, Judy Kennedy

We consider a family of inverse limits of inverse sequences of closed unit intervals with a single upper semi-continuous set-valued bonding function whose graph is an arc; it is the union of two line segments in $[0,1]^2$, both of them contain the origin $(0, 0)$, have positive slope, and extend to the opposite boundary of $[0,1]^2$. We show that there is a

Marcel Robitaille, HeeBong Yang, Lu Wang, Na Young Kim

Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. Reliable RTS analyses are crucial prerequisite to identify underlying mechanisms related to performa

Maliheh Izadi, Mahtab Nejati, Abbas Heydarnoori

Software-related platforms have enabled their users to collaboratively label software entities with topics. Tagging software repositories with relevant topics can be exploited for facilitating various downstream tasks. For instance, a correct and complete set of topics assigned to a repository can increase its visibility. Consequently, this improves the outc

Hideki Tanimura, Nabila Aghanim, Marian Douspis, Nicola Malavasi

Using the publicly available eROSITA Final Equatorial Depth Survey (eFEDS) data, we detected the stacked X-ray emissions at the position of 463 filaments at a significance of 3.8 sigma based on the combination of all energy bands. In parallel, we found that the probability of the measurement under the null hypothesis is ~0.0017. The filaments were identified

S. Jalalzadeh, S. M. M. Rasouli, P. V. Moniz

In this brief review, we comment on the concept of shape invariant potentials, which is an essential feature in many settings of $N=2$ supersymmetric quantum mechanics. To motivate its application within supersymmetric quantum cosmology, we present a case study to illustrate the value of this promising tool. Concretely, we take a spatially flat FRW model in

Elisabeth Cuervo Lumbaque, Luis Baptista-Piresa, Jelena Radjenovic

Low-cost graphene sponge electrodes were functionalized with two-dimensional (2D) materials, i.e., borophene and hexagonal boron nitride (hBN), using a one-step, hydrothermal self-assembly method. Borophene and hBN-modified graphene sponge anode and N-doped graphene sponge cathode were employed for electrochemical degradation of model persistent contaminants

Zhongshan An, Lan-Hsuan Huang

In our prior work toward Bartnik's static vacuum extension conjecture for near Euclidean boundary data, we establish a sufficient condition, called static regular, and confirm large classes of boundary hypersurfaces are static regular. In this note, we further improve some of those prior results. Specifically, we show that any hypersurface in an open and den

Jorge Cayao, Paramita Dutta, Pablo Burset, Annica M. Black-Schaffer

We consider a finite-size topological Josephson junction formed at the edge of a two-dimensional topological insulator in proximity to conventional superconductors and study the impact of Cooper pair symmetries on the electron transport. We find that, due to the finite junction size, electron transport is highly tunable by the superconducting phase differenc

Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell

Sampling from complex target distributions is a challenging task fundamental to Bayesian inference. Parallel tempering (PT) addresses this problem by constructing a Markov chain on the expanded state space of a sequence of distributions interpolating between the posterior distribution and a fixed reference distribution, which is typically chosen to be the pr

Zhongshan An, Lan-Hsuan Huang

We introduce the notions of static regular of type (I) and type (II) and show that they are sufficient conditions for local well-posedness of solving asymptotically flat, static vacuum metrics with prescribed Bartnik boundary data. We then show that hypersurfaces in a very general open and dense family of hypersurfaces are static regular of type (II). As app

Yating Hu, Xiangang Wan, Feng Tang

Many topological band crossings (BCs) have been predicted efficiently utilizing the symmetry properties of wave-functions at high-symmetry points. Among various BCs, the so-called hourglass BCs (with the low-energy excitations dubbed as hourglass fermions) are fascinating since they can be guaranteed to exist under specific symmetry conditions even without r

R. S. Markiewicz, A. Bansil

While magnetic fields generally compete with superconductivity, a type II superconductor can persist to very high fields by confining the field in topological defects, namely vortices. We propose that a similar physics underlies the pseudogap phase in cuprates, where the relevant topological defects are the antiphase domain walls of an underlying antiferroma

Bo Gyu Jang, Changhoon Lee, Jian-Xin Zhu, Ji Hoon Shim

The discovery of two-dimensional (2D) van der Waals (vdW) materials often provides interesting playgrounds to explore novel phenomena. One of the missing components in 2D vdW materials is the intrinsic heavy-fermion systems, which can provide an additional degree of freedom to study quantum critical point (QCP), unconventional superconductivity, and emergent

Andy Wilson

Suppose $M$ and $N$ are positive integers and let $k = \gcd(M, N)$, $m = M/k$, and $n=N/k$. We define a symmetric function $L_{M,N}$ as a weighted sum over certain tuples of lattice paths. We show that $L_{M,N}$ satisfies a generalization of Mellit and Hogancamp's recursion for the triply-graded Khovanov--Rozansky homology of the $M,N$-torus link. As a corol

Camille Olivia Little, Michael Weylandt, Genevera I Allen

Algorithmic fairness has emerged as an important consideration when using machine learning to make high-stakes societal decisions. Yet, improved fairness often comes at the expense of model accuracy. While aspects of the fairness-accuracy tradeoff have been studied, most work reports the fairness and accuracy of various models separately; this makes model co

Alex Abreu, Antonio Nigro

We revisit Haiman's conjecture on the relations between characters of Kazdhan-Lusztig basis elements of the Hecke algebra over the symmetric group. The conjecture asserts that, for purposes of character evaluation, any Kazhdan-Lusztig basis element is reducible to a sum of the simplest possible ones (those associated to so-called codominant permutations). Wh

Yibo Ji

In this paper, we count the number of matrices $A = (A_{i,j} )\in \mathcal{O} \subset Mat_{n\times n}(\mathbb{F}_q[x])$ where $deg(A_{i,j})\leq k, 1\leq i,j\leq n$, $deg(\det A) = t$, and $\mathcal{O}$ a given orbit of $GL_n(\mathbb{F}_q[x])$. By an elementary argument, we show that the above number is exactly $\# GL_n(\mathbb{F}_q)\cdot q^{(n-1)(nk-t)}$. Th

Parisa Hassanzadeh, Robert E. Tillman

Deep generative models, such as Generative Adversarial Networks (GANs), synthesize diverse high-fidelity data samples by estimating the underlying distribution of high dimensional data. Despite their success, GANs may disclose private information from the data they are trained on, making them susceptible to adversarial attacks such as membership inference at

Kamil Deja, Anna Kuzina, Tomasz Trzciński, Jakub M. Tomczak

Diffusion-based Deep Generative Models (DDGMs) offer state-of-the-art performance in generative modeling. Their main strength comes from their unique setup in which a model (the backward diffusion process) is trained to reverse the forward diffusion process, which gradually adds noise to the input signal. Although DDGMs are well studied, it is still unclear

Elias Villalvazo-Avila, Francisco Lopez-Tiro, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz

This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints with the aim to produce more discriminant object features. Our approach was specifically designed to mimic the morpho-constitutional analysis used by urologists to visually classify kidney stones by inspecting the sections and surf

Hugo Caerols-Palma, Katia Vogt-Geisse

In this article we describe special type of mathematical problems that may help develop teaching methods that motivate students to explore patterns, formulate conjectures and find solutions without only memorizing formulas and procedures. These are problems that either their solutions do not make sense in a real life context and/or provide a contradiction du

Trey McNeely, Pavel Khokhlov, Niccolo Dalmasso, Kimberly M. Wood

Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model which is trained solely on two inputs: Geo infra

Yuhan Sun

We use relative symplectic cohomology to detect heavy sets, with the help of index bounded contact forms. This establishes a relation between two notions SH-heaviness and heaviness, which partly answers a conjecture of Dickstein-Ganor-Polterovich-Zapolsky in the symplectically aspherical setting.

Ali Mokhtari, Md Abir Hossen, Pooyan Jamshidi, Mohsen Amini Salehi

Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGAs) to fulfill the latency co

Tanayveer S. Bhatia, Robert H. Cameron, Sami K. Solanki, Hardi Peter

Some of the small-scale solar magnetic flux can be attributed to a small-scale dynamo (SSD) operating in the near-surface convection. The SSD fields have consequences for solar granular convection, basal flux, as well as chromospheric heating. A similar SSD mechanism is expected to be active in the near-surface convection of other cool main-sequence stars, b

Thierry Alboussière, Franck Plunian, Marc Moulin

We report measurements of dynamo action in a new experimental setup, named Fury, based on the use of an anisotropic electrical conductivity. It consists in a copper rotor rotating inside a copper stator, electrically connected with a thin layer of liquid metal, galinstan. Grooves have been cut in the copper so that, everywhere, electrical conductivity can be

Marcus Engsig, Alejandro Tejedor, Yamir Moreno

Network robustness is an essential system property to sustain functionality in the face of failures or targeted attacks. Currently, only the connectivity of the nodes unaffected by an attack is utilized to assess robustness. We propose to incorporate the properties of the emerging connectivity of the nodes affected by the attack (Idle Network), which is demo

Tyler Perini, Amy Langville, Glenn Kramer, Jeff Shrager

The problem of interpreting or aggregating multiple rankings is common to many real-world applications. Perhaps the simplest and most common approach is a weighted rank aggregation, wherein a (convex) weight is applied to each input ranking and then ordered. This paper describes a new tool for visualizing and displaying ranking information for the weighted r

Bo Peng, Daniel Bennett, Ivona Bravić, Bartomeu Monserrat

Halide perovskites exhibit giant photostriction, that is, volume or shape changes upon illumination. However, the microscopic origin of this phenomenon remains unclear and there are experimental reports of both light-induced lattice expansion and contraction. In this paper we establish a general method, based on first-principles calculations and molecular or

Daniel Dylewsky, Timothy M. Lenton, Marten Scheffer, Thomas M. Bury

The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using standard forecast modeling techniques is quite difficult. This has led to the development of an alternative suite of methods that seek to identify signatures of cri