May 2022 arXiv papers — page 93

Itzhak Fouxon, Seulgi Lee, Changhoon Lee

We study theoretically and numerically spatial distribution and collision rate of droplets that sediment in homogeneous isotropic Navier-Stokes turbulence. It is assumed that typical turbulent accelerations of fluid particles are much smaller than gravity. This was shown to imply that the particles interact weakly with individual vortices and, as a result, f

Gabriele U. Varieschi

We expand our analysis of Newtonian Fractional-Dimension Gravity (NFDG), an extension of the classical laws of Newtonian gravity to lower dimensional spaces, including those with fractional (i.e., non-integer) dimension. We apply our model to four rotationally supported galaxies (NGC 5033, NGC 6674, NGC 5055, NGC 1090), in addition to other three galaxies (N

Yi Fang, Yiwei Tao, Huan Ma, Yonghui Li

In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted frequency-modulated (FM) differential chaos shift keying (DCSK) scheme with simultaneous wireless information and power transfer (SWIPT), called RIS-FM-DCSK-SWIPT scheme, for low-power, low-cost, and high-reliability wireless communication networks. In particular, the proposed sche

Soham S. Sarpotdar

Face Recognition (FR) systems are being used in a variety of applications, including road crossings, banking, and mobile banking. The widespread use of FR systems has raised concerns about the safety of face biometrics against spoofing attacks, which use the use of a photo or video of a legitimate user's face to gain illegal access to the resources or activi

Peng Xu, Hu Cheng, Jiankun Wang, Max Q. -H. Meng

Reorienting objects by using supports is a practical yet challenging manipulation task. Owing to the intricate geometry of objects and the constrained feasible motions of the robot, multiple manipulation steps are required for object reorientation. In this work, we propose a pipeline for predicting various object placements from point clouds. This pipeline c

Minfa Wang

We propose Mask CycleGAN, a novel architecture for unpaired image domain translation built based on CycleGAN, with an aim to address two issues: 1) unimodality in image translation and 2) lack of interpretability of latent variables. Our innovation in the technical approach is comprised of three key components: masking scheme, generator and objective. Experi

Wenting Liu, Jinlong Lei, Peng Yi, Yiguang Hong

This paper considers no-regret learning for repeated continuous-kernel games with lossy bandit feedback. Since it is difficult to give the explicit model of the utility functions in dynamic environments, the players' action can only be learned with bandit feedback. Moreover, because of unreliable communication channels or privacy protection, the bandit feedb

Gibran Morales, Nissim Fraija

Gamma-ray bursts (GRB) are the most intense electromagnetic (EM) sources in the Universe. Long GRB (LGRB) correspond to those events with a typical prompt emission of more than a few seconds. It is generally assumed that they are originated after an implosion of a very massive star within a central compact object engine that can be either a black hole (BH) o

Arka Bandyopadhyay, Nesta Benno Joseph, Awadhesh Narayan

The anomalous Hall effect in time-reversal symmetry broken systems is underpinned by the concept of Berry curvature in band theory. However, recent experiments reveal that the nonlinear Hall effect can be observed in non-magnetic systems without applying an external magnetic field. The emergence of nonlinear Hall effect under time-reversal symmetric conditio

Thomas Wilkason, Megan Nantel, Jan Rudolph, Yijun Jiang

Floquet engineering offers a compelling approach for designing the time evolution of periodically driven systems. We implement a periodic atom-light coupling to realize Floquet atom optics on the strontium ${}^1\!S_0\,\text{-}\, {}^3\!P_1$ transition. These atom optics reach pulse efficiencies above $99.4\%$ over a wide range of frequency offsets between lig

Mitsutoshi Fujita, Jun Zhang

In this paper, we analyze the holographic covariant entanglement entropy in the double Wick rotated version of a rotating BTZ black hole (3 dimensional Kerr-AdS solution), where the periodicity of Euclidean time and spatial direction are changed. The dual field theory has negative energy in the Lorentzian signature. The holographic entanglement entropy agree

Heli Qi, Sashi Novitasari, Sakriani Sakti, Satoshi Nakamura

Consistency regularization has recently been applied to semi-supervised sequence-to-sequence (S2S) automatic speech recognition (ASR). This principle encourages an ASR model to output similar predictions for the same input speech with different perturbations. The existing paradigm of semi-supervised S2S ASR utilizes SpecAugment as data augmentation and requi

Che-Yu Chen

The recently released images of the supermassive black holes in the M87 galaxy and the galaxy of our own make probing black hole spacetimes and testing general relativity (GR) possible. The violation of equatorial reflection symmetry of black hole spacetimes is clearly a smoking gun of physics beyond GR. In this paper, we place constraints on the violation o

Bruce W. Lee, Jason H. Lee

We show a method to auto-select reading passages in English assessment tests and share some key insights that can be helpful in related fields. In specifics, we prove that finding a similar passage (to a passage that already appeared in the test) can give a suitable passage for test development. In the process, we create a simple database-tagger-filter algor

Xinzhou Guo, Waverly Wei, Molei Liu, Tianxi Cai

There have been increased concerns that the use of statins, one of the most commonly prescribed drugs for treating coronary artery disease, is potentially associated with the increased risk of new-onset type II diabetes (T2D). Nevertheless, to date, there is no robust evidence supporting as to whether and what kind of populations are indeed vulnerable for de

Xing Wei

Thermal convection is commonly believed to be the energy source of stellar or planetary dynamo. In this short paper we provide another possibility, namely large-scale tidal flow. In close binary stars, say, solar-like stars with orbital period at 2 or 3 days, large-scale tidal flow is comparable to or even stronger than convective flow, and it can induce mag

Stuart Robert Anderson, Victor P. Debattista, Peter Erwin, David J. Liddicott

The major-axis density profiles of bars are known to be either exponential or 'flat'. We develop an automated non-parametric algorithm to detect flat profiles and apply it to a suite of simulations (with and without gas). We demonstrate that flat profiles are a manifestation of a bar's secular growth, producing a 'shoulder' region (an overdensity above an ex

Yuta Kochi, Sunao Kurimura, Junko Ishi-Hayase

Time-bin qubits, in which quantum information is encoded in a single photon at different times $\Delta$t, are suitable for long-distance transmission via optical fibers. However, detection of time-bin qubits has been limited to the nanosecond range owing to the limited temporal resolution of single-photon detectors. In this study, we developed an up-conversi

Joshua Carlson, Meghan Halloran, Carolyn Reinhart

In many variants of the game of Cops and Robbers on graphs, multiple cops play against a single robber. In 2019, Cox and Sanaei introduced a variant of the game that gives the robber a more active role than simply evading the cop. In their version, the robber tries to damage as many vertices as possible and the cop attempts to minimize this damage. While the

Qiao Qi, Xiaoming Chen

Mass data traffics, low-latency wireless services and advanced artificial intelligence (AI) technologies have driven the emergence of a new paradigm for wireless networks, namely edge-intelligent networks, which are more efficient and flexible than traditional cloud-intelligent networks. Considering users' privacy, model sharing-based federated learning (FL)

Tsz On Mario Chan, Young-Jun Choi

As an application of the residue functions corresponding to the lc-measures developed by the authors, the proof of the injectivity theorem on compact K\"ahler manifolds for plt pairs by Matsumura is improved in this article to allow multiplier ideal sheaves of plurisubharmonic functions with neat analytic singularities in the coefficients of the relevant coh

Yoshimasa Kurihara

This report investigates general relativity and the Yang-Mills theory in four-dimensional space-time using a common mathematical framework, the Chern-Weil theory for principal bundles. The whole theory is described owing to the fibre bundle with the GL(4) symmetry by twisting several principal bundles with the gauge symmetry. In addition to the principal con

Emma Laird, Francesca M. Marchetti, Dmitry K. Efimkin, Meera M. Parish

We theoretically investigate exciton-polaritons in a two-dimensional (2D) semiconductor heterostructure, where a static magnetic field is applied perpendicular to the plane. To explore the interplay between magnetic field and a strong light-matter coupling, we employ a fully microscopic theory that explicitly incorporates electrons, holes and photons in a se

Fei Meng, Liangliang Chen, Han Ma, Jiankun Wang

Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. Many risk-aware path planners have been developed to explicitly limit the probability of collision to an acceptable bound in uncertain environments. However, convex obstacles or Gaussian uncertainties are usually assumed to make the problem

Mathias Braun

Let $(M,\mathsf{d},\mathfrak{m},\ll,\leq,\tau)$ be a causally closed, $\mathscr{K}$-globally hyperbolic, regular measured Lorentzian geodesic space satisfying the weak timelike curvature-dimension condition $\smash{\mathrm{wTCD}_p^e(K,N)}$ in the sense of Cavalletti and Mondino. We prove the existence of geodesics of probability measures on $M$ which satisfy

Patricio Peralta-Braz, Mehrisadat Makki Alamdari, Rafael O. Ruiz, Elena Atroshchenko

Vibrational energy harvested from the bridge excitation due to the traffic flow or the wind load can be supplied to sensors in a Structural Health Monitoring (SHM) system and help prolong its service life, reduce chemical battery waste and enable its use in remote locations. A common approach to designing a Piezoelectric Energy Harvester (PEH) consists in tu

Xu Liu, Wen Yao, Wei Peng, Weien Zhou

Physics-informed extreme learning machine (PIELM) has recently received significant attention as a rapid version of physics-informed neural network (PINN) for solving partial differential equations (PDEs). The key characteristic is to fix the input layer weights with random values and use Moore-Penrose generalized inverse for the output layer weights. The fr

Woong Gyu La, Sunil Muralidhara, Lingjie Kong, Pratik Nichat

We propose Unified Distributed Environment (UDE), an environment virtualization toolkit for reinforcement learning research. UDE is designed to integrate environments built on any simulation platform such as Gazebo, Unity, Unreal, and OpenAI Gym. Through environment virtualization, UDE enables offloading the environment for execution on a remote machine whil

Arian Vezvaee, Evangelia Takou, Paul Hilaire, Matthew F. Doty

Three-level Lambda systems appear in various quantum information processing platforms. In several control schemes, the excited level serves as an auxiliary state for implementing gate operations between the lower qubit states. However, extra excited levels give rise to unwanted transitions that cause leakage and other errors, degrading the gate fidelity. We

Chao Yao, Shuo Jin, Meiqin Liu, Xiaojuan Ban

Image denoising is an important low-level computer vision task, which aims to reconstruct a noise-free and high-quality image from a noisy image. With the development of deep learning, convolutional neural network (CNN) has been gradually applied and achieved great success in image denoising, image compression, image enhancement, etc. Recently, Transformer h

Nicholas D. Heller, Grigor H. Sargsyan, Kristina D. Launey, Calvin W. Johnson

We provide new insights into backbending phenomenon within the symmetry-adapted framework which naturally describes the intrinsic deformation of atomic nuclei. For $^{20}\text{Ne}$, the canonical example of backbending in light nuclei, the ab initio symmetry-adapted no-core shell model shows that while the energy spectrum replicates the backbending from expe

Yuning Zhang, Jiayang Wu, Yunyi Yang, Yang Qu

We experimentally demonstrate enhanced spectral broadening of femtosecond optical pulses af-ter propagation through silicon-on-insulator (SOI) nanowire waveguides integrated with two-dimensional (2D) graphene oxide (GO) films. Owing to the strong mode overlap between the SOI nanowires and the GO films with a high Kerr nonlinearity, the self-phase modulation

Dumitrel Loghin, Tien Tuan Anh Dinh, Aung Maw, Chen Gang

While state-of-the-art permissioned blockchains can achieve thousands of transactions per second on commodity hardware with x86/64 architecture, their performance when running on different architectures is not clear. The goal of this work is to characterize the performance and cost of permissioned blockchains on different hardware systems, which is important

Chenming Li, Han Ma, Peng Xu, Jiankun Wang

Adaptively Informed Trees (AIT*) is an algorithm that uses the problem-specific heuristic to avoid unnecessary searches, which significantly improves its performance, especially when collision checking is expensive. However, the heuristic estimation in AIT* consumes lots of computational resources, and its asymmetric bidirectional searching strategy cannot f

Feng Tian, Jian Zou, Hai Li, Bin Shao

Quantum system interacting with environment can induce redundant encoding of the information of system into a multipartite environment, which is the essence of quantum Darwinism. At the same time, environment may scramble the initially localized information about the system. We mainly investigate the relevance between information scrambling in environment an

Thierry Kirat, Olivia Tambou, Virginie Do, Alexis Tsoukiàs

The paper offers a contribution to the interdisciplinary constructs of analyzing fairness issues in automatic algorithmic decisions. Section 1 shows that technical choices in supervised learning have social implications that need to be considered. Section 2 proposes a contextual approach to the issue of unintended group discrimination, i.e. decision rules th

Jifan Chen, Aniruddh Sriram, Eunsol Choi, Greg Durrett

Verifying complex political claims is a challenging task, especially when politicians use various tactics to subtly misrepresent the facts. Automatic fact-checking systems fall short here, and their predictions like "half-true" are not very useful in isolation, since we have no idea which parts of the claim are true and which are not. In this work, we focus

Cori Faklaris, Laura Dabbish, Jason I. Hong

Behavior change ideas from health psychology can also help boost end user compliance with security recommendations, such as adopting two-factor authentication (2FA). Our research adapts the Transtheoretical Model Stages of Change from health and wellness research to a cybersecurity context. We first create and validate an assessment to identify workers on Am

Bishwamittra Ghosh, Dmitry Malioutov, Kuldeep S. Meel

Machine learning has become omnipresent with applications in various safety-critical domains such as medical, law, and transportation. In these domains, high-stake decisions provided by machine learning necessitate researchers to design interpretable models, where the prediction is understandable to a human. In interpretable machine learning, rule-based clas

Donald Bertucci, Md Montaser Hamid, Yashwanthi Anand, Anita Ruangrotsakun

In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML). ML practitioners often explore image datasets by generating a grid of images or projecting high-dimensional representations of images into 2-D using dimensionality reduction techniques (e.g., t-SNE). However, neither approach

Chuanbo Hu, Shan Jia, Fan Zhang, Xin Li

Global Positioning Systems (GPS) have played a crucial role in various navigation applications. Nevertheless, localizing the perfect destination within the last few meters remains an important but unresolved problem. Limited by the GPS positioning accuracy, navigation systems always show users a vicinity of a destination, but not its exact location. Street v

Kamala Ramasubramanian, Ashutosh Raina, Jonathan Mace, Peter Alvaro

Incidents in production systems are common and downtime is expensive. Applying an appropriate mitigating action quickly, such as changing a specific firewall rule, reverting a change, or diverting traffic to a different availability zone, saves money. Incident localization is time-consuming since a single failure can have many effects, extending far from the

Tian-Yu Ye, Jia-Li Hu

In this paper, a multi-party quantum private comparison (MQPC) scheme is suggested based on entanglement swapping of Bell entangled states within d-level quantum system, which can accomplish the equality comparison of secret binary sequences from n users via one execution of scheme. Detailed security analysis shows that both the outside attack and the partic

Sebastian Braun, Maria Luis Valero

Neural networks have led to tremendous performance gains for single-task speech enhancement, such as noise suppression and acoustic echo cancellation (AEC). In this work, we evaluate whether it is more useful to use a single joint or separate modules to tackle these problems. We describe different possible implementations and give insights into their perform

Tian-Yu Ye, Hong-Kun Li, Jia-Li Hu

In this paper, a novel quantum dialogue (QD) protocol is proposed based on single photons in both polarization and spatial-mode degrees of freedom. In the proposed QD protocol, the initial states of single photons in both polarization and spatial-mode degrees of freedom used for encoding are privately shared between two communicants through the direct transm

Mohamed R. Ibrahim, Terry Lyons

This paper introduces a new lightweight method for image recognition. ImageSig is based on computing signatures and does not require a convolutional structure or an attention-based encoder. It is striking to the authors that it achieves: a) an accuracy for 64 X 64 RGB images that exceeds many of the state-of-the-art methods and simultaneously b) requires ord

Chong-Qiang Ye, Tian-Yu Ye

In this paper, a novel multi-party quantum private comparison (MQPC) protocol for equality comparison with n-level single-particle states is constructed, where the encoded particles are transmitted in a circular way. Here, n parties employ the qudit shifting operation to encode their private secrets and can compare the equality of their private secrets withi

Sergio Machaca, Eric Cao, Amy Chi, Gina Adrales

Current robotic minimally invasive surgery (RMIS) platforms provide surgeons with no haptic feedback of the robot's physical interactions. This limitation forces surgeons to rely heavily on visual feedback and can make it challenging for surgical trainees to manipulate tissue gently. Prior research has demonstrated that haptic feedback can increase task accu

Romain Cosentino, Anirvan Sengupta, Salman Avestimehr, Mahdi Soltanolkotabi

Self-supervised learning (SSL) is currently one of the premier techniques to create data representations that are actionable for transfer learning in the absence of human annotations. Despite their success, the underlying geometry of these representations remains elusive, which obfuscates the quest for more robust, trustworthy, and interpretable models. In p

Aleksei Sholokhov, James V. Burke, Damian F. Santomauro, Peng Zheng

Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis of variable selection methods for LMEs is more difficult than for linear regression because LME models are nonlinear. In this work we propose a relaxation strategy and optimization

Jianwen Xie, Yaxuan Zhu, Jun Li, Ping Li

This paper studies the cooperative learning of two generative flow models, in which the two models are iteratively updated based on the jointly synthesized examples. The first flow model is a normalizing flow that transforms an initial simple density to a target density by applying a sequence of invertible transformations. The second flow model is a Langevin

Nikolai Kriukov

In this paper we consider some generalizations of the classical d-dimensional Brownian risk model. This contribution derives some non-asymptotic bounds for simultaneous ruin probabilities of interest. In addition, we obtain non-asymptotic bounds also for the case of general trend functions and convolutions of our original risk model.

Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh

Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has been little research investigating how ML practitioners actually use these toolkits in practice. In this paper, we conducted the first in-depth empirical exploration of how indust

Ruo Fei Chen, Stephanie Balzer, Bernardo Toninho

\emph{Session types} have proved viable in expressing and verifying the protocols of message-passing systems. While message passing is a dominant concurrency paradigm in practice, real world software is written without session types. A limitation of existing session type libraries in mainstream languages is their restriction to linear session types, precludi

Wesley Hanwen Deng, Nikita Mehandru, Samantha Robertson, Niloufar Salehi

Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals. However, in order to use MT reliably and safely, users need to understand when to trust MT outputs and how to assess the quality of often imperfect translation results. In this paper, we discuss research dire

Deepa Singh, Michal Slupczynski, Ajit G. Pillai, Vinoth Pandian Sermuga Pandian

In this position paper, we propose building a broader and deeper understanding around Explainability in AI by 'grounding' it in social contexts, the socio-technical systems operate in. We situate our understanding of grounded explainability in the 'Global South' in general and India in particular and express the need for more research within the global south

Lampros Svolos, Hashem M. Mourad, Gianmarco Manzini, Krishna Garikipati

Modeling crack initiation and propagation in brittle materials is of great importance to be able to predict sudden loss of load-carrying capacity and prevent catastrophic failure under severe dynamic loading conditions. Second-order phase-field fracture models have gained wide adoption given their ability to capture the formation of complex fracture patterns

Jingyun Jia, Philip K. Chan

Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples that cover all the classes for the training process in ML systems. An advanced malware classification system should classify the known classes correctly while

André Malavazi, Frederico Brito

The development of a self-consistent thermodynamic theory of quantum systems is of fundamental importance for modern physics. Still, despite its essential role in quantum science and technology, there is no unifying formalism for characterizing the thermodynamics within general autonomous quantum systems, and many fundamental open questions remain unanswered

Bochuan Lyu, Illya V. Hicks, Joey Huchette

We introduce techniques to build small ideal mixed-integer programming (MIP) formulations of combinatorial disjunctive constraints (CDCs) via the independent branching scheme. We present a novel pairwise IB-representable class of CDCs, CDCs admitting junction trees, and provide a combinatorial procedure to build MIP formulations for those constraints. Genera

Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan

Some of the tightest information-theoretic generalization bounds depend on the average information between the learned hypothesis and a single training example. However, these sample-wise bounds were derived only for expected generalization gap. We show that even for expected squared generalization gap no such sample-wise information-theoretic bounds exist.

Sushil K. Atreya, Aurélien Crida, Tristan Guillot, Cheng Li

The Saturn System has been studied in detail by the Cassini-Huygens Mission. A major thrust of those investigations has been to understand how Saturn formed and evolved and to place Saturn in the context of other gas giants and planetary systems in general. Two models have been proposed for the formation of the giant planets,the core accretion model and the

Nicolas Kardous, Amaury Hayat, Sean T. McQuade, Xiaoqian Gong

In this paper, a multi-lane multi-population microscopic model, which presents stop and go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are designed with the ultimate goal of using a small number of AVs (less than 5\% penetration rate) to represent Lagrangian

Joshua E. Castro, Trevor J. Steiner, Lillian Thiel, Alex Dinkelacker

Aluminum gallium arsenide-on-insulator (AlGaAsOI) exhibits large $\chi^\left(2\right)$ and $\chi^\left(3\right)$ optical nonlinearities, a wide tunable bandgap, low waveguide propagation loss, and a large thermo-optic coefficient, making it an exciting platform for integrated quantum photonics. With ultrabright sources of quantum light established in AlGaAsO

Wen Zhang, Eric Sheng, Michael Chang, Aurojit Panda

Modern web applications serve large amounts of sensitive user data, access to which is typically governed by data-access policies. Enforcing such policies is crucial to preventing improper data access, and prior work has proposed many enforcement mechanisms. However, these prior methods either alter application semantics or require adopting a new programming

Kanishka Misra, Julia Taylor Rayz, Allyson Ettinger

To what extent can experience from language contribute to our conceptual knowledge? Computational explorations of this question have shed light on the ability of powerful neural language models (LMs) -- informed solely through text input -- to encode and elicit information about concepts and properties. To extend this line of research, we present a framework

Yau-Chuen Yam, George A. Sawatzky, Mona Berciu

We investigate the difference between the coupling of a bare carrier to phonons versus the coupling of a correlations-dressed quasiparticle to phonons, and show that latter may be weak even if the former is strong. Specifically, we analyze the effect of the hole-phonon coupling on the dispersion of the quasiparticle that forms when a single hole is doped int

Michael O'Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli

Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the relationship between various wind conditions and its effect on aircraft maneuverability is not well understood, it is challenging to design effective robot controllers using tradition

Anirudh Sundar, Larry Heck

As humans, we experience the world with all our senses or modalities (sound, sight, touch, smell, and taste). We use these modalities, particularly sight and touch, to convey and interpret specific meanings. Multimodal expressions are central to conversations; a rich set of modalities amplify and often compensate for each other. A multimodal conversational A

Julian Knodt

Existing ML models are known to be highly over-parametrized, and use significantly more resources than required for a given task. Prior work has explored compressing models offline, such as by distilling knowledge from larger models into much smaller ones. This is effective for compression, but does not give an empirical method for measuring how much the mod

Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller

Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficient consideration on how practitioners should translate domain expertise into ML updates. In this paper, we consider how to capture interactions between practitioners and experts sy

Elena Khasanova, Pooja Hiranandani, Shayna Gardiner, Cheng Chen

For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not know why a customer is calling. In this paper we describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real t

Mark Titleman

E. T. Whittaker produced two papers in 1903 and 1904 that, although sometimes considered mere mathematical statements (Barrett, 1993), held important implications for physical theory. The Whittaker 1903 paper united electrostatic and gravitational attraction as resulting from longitudinal waves - waves whose wavefronts propagate parallel to their direction.

Mark J Reid

Over the past decade, the BeSSeL Survey and the VERA project have measured trigonometric parallaxes to approximately 250 massive, young stars using VLBI techniques. These sources trace spiral arms over nearly half of the Milky Way. What is now needed are accurate distances to such stars which are well past the Galactic center. Here we analyze the potential f

Guangye Chen, Luis Chacón

We introduce a new electrostatic particle-in-cell algorithm capable of using large timesteps compared to particle gyro-period under a uniform external magnetic field. The algorithm extends earlier electrostatic fully implicit PIC implementations with a new asymptotic-preserving particle-push scheme that allows timesteps much larger than particle gyroperiods.

Shuwen Lou

In this article, we derive the explicit transition density functions of skew Brownian motion (SBM in abbreviation) with two-valued drift for all $t>0$. As an important step of this result, it is also shown in this paper that SBM with two-valued drift is a strong Markov process by finding its symmetrizing measure and canonical scale function, from which one c

John W. Appel, Charles L. Bennett, Michael K. Brewer, Ricardo Bustos

The current and future cosmic microwave background (CMB) experiments fielding kilo-pixel arrays of transition-edge sensor (TES) bolometers require accurate and robust gain calibration methods. We simplify and refactor the standard TES model to directly relate the detector responsivity calibration and optical time constant to the measured TES current $I$ and

Hang Wang, Zhen Xiang, David J. Miller, George Kesidis

Backdoor attacks are an important type of adversarial threat against deep neural network classifiers, wherein test samples from one or more source classes will be (mis)classified to the attacker's target class when a backdoor pattern is embedded. In this paper, we focus on the post-training backdoor defense scenario commonly considered in the literature, whe

Kim Miskovetz, Trent J. Dupuy, Jessica Schonhut-Stasik, Keivan G. Stassun

The majority of stars have one or more stellar companions. As exoplanets continue to be discovered, it is crucial to examine planetary systems to identify their stellar companions. By observing a change in proper motion, companions can be detected by the acceleration they induce on their host stars. We selected 701 stars from the Hipparcos-Gaia Catalog of Ac

Adrián Hernández, Gilles Millerioux, José M. Amigó

In the past years, deep learning models have been successfully applied in several cognitive tasks. Originally inspired by neuroscience, these models are specific examples of differentiable programs. In this paper we define and motivate differentiable programming, as well as specify some program characteristics that allow us to incorporate the structure of th

Franco Mayo, Augusto J. Roncaglia

We consider the dissipative charging process of quantum batteries in terms of a collisional model, where the batteries are coupled to a heat bath using non-energy preserving interactions. First, we show that for low temperatures the collective process can attain a charging power that increases polynomically with the number of batteries. The scaling we find i

Nardos Ayele Ashenafi, Wankun Sirichotiyakul, Aykut C. Satici

In the realm of supervised learning, Bayesian learning has shown robust predictive capabilities under input and parameter perturbations. Inspired by these findings, we demonstrate the robustness properties of Bayesian learning in the control search task. We seek to find a linear controller that stabilizes a one-dimensional open-loop unstable stochastic syste

Anthi Papadopoulou, Pierre Lison, Lilja Øvrelid, Ildikó Pilán

We propose a novel method to bootstrap text anonymization models based on distant supervision. Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals. This knowledge graph is employed to automatically annotate text documents

Sourav Chatterjee, Persi Diaconis, Laurent Miclo

The Rado graph, also known as the random graph $G(\infty, p)$, is a classical limit object for finite graphs. We study natural ball walks as a way of understanding the geometry of this graph. For the walk started at $i$, we show that order $\log_2^*i$ steps are sufficient, and for infinitely many $i$, necessary for convergence to stationarity. The proof invo

Aleksandra Burashnikova

In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. To demonstrate the empirical efficiency of the proposed approaches we investigate their applications for decision making in recommender systems and energy systems domains. For the former, we propose novel algorithm called

Tobias Fritz, Fabio Gadducci, Davide Trotta, Andrea Corradini

Originally introduced in the context of the algebraic approach to term graph rewriting, the notion of gs-monoidal category has surfaced a few times under different monikers in the last decades. They can be thought of as symmetric monoidal categories whose arrows are generalised relations, with enough structure to talk about domains and partial functions, but

Jianan Liu, Hao Li, Tao Huang, Euijoon Ahn

High-resolution (HR) magnetic resonance imaging is critical in aiding doctors in their diagnoses and image-guided treatments. However, acquiring HR images can be time-consuming and costly. Consequently, deep learning-based super-resolution reconstruction (SRR) has emerged as a promising solution for generating super-resolution (SR) images from low-resolution

Marisa Ponti, Anastasia Skarpeti, Bruno Kestemont

It has been argued that introducing AI to creative practices destroys spontaneity, intuition and serendipity. However, the design of systems that leverage complex interactions between citizen scientists (members of the public engaged in research tasks) and computational AI methods have the potential to facilitate creative exploration and chance encounters. D

Csaba Biró, Beth Novick, Daniela Olejnikova

We investigate how the metric dimension of infinite graphs change when we add edges to the graph. Our two main results: (1) there exists a growing sequence of graphs (under the subgraph relation, but without adding vertices) for which the metric dimension changes between finite and infinite infinitely many times; (2) finite changes in the edge set can not ch

Y. Li, C. Bromberg, M. Diwan, S. Kettell

The ability of free electrons to drift long distances at high velocities in pure liquid argon under an applied electric field has been exploited for the past forty years to implement detectors with increasingly large volumes for high energy physics research. In this paper, we summarize the electron attachment rate constants as a function of the external elec

Pritam Sarkar, Aaron Posen, Ali Etemad

We introduce AVCAffe, the first Audio-Visual dataset consisting of Cognitive load and Affect attributes. We record AVCAffe by simulating remote work scenarios over a video-conferencing platform, where subjects collaborate to complete a number of cognitively engaging tasks. AVCAffe is the largest originally collected (not collected from the Internet) affectiv

S. Rodríguez, D. Garcia Lambas, N. D. Padilla, P. Tissera

We study the impact of the environment on galaxies as they fall in and orbit in the potential well of a Local Group (LG) analogue, following them with high cadence. The analysis is performed on eight disc satellite galaxies from the CIELO suite of hydrodynamical simulations. All galaxies have stellar masses within the range $[10^{8.1} - 10^{9.56}] M_{\sun} $

Thiago Santos, Amara Tariq, Susmita Das, Kavyasree Vayalpati

Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions. However, successful text mining of a large pathology database can play a critical role to advance 'big data' cancer research like similarity-based treatment selection, case identification, prognostication, surveillance, clinic

Shun Ogawa, Francesco Fumarola, Luca Mazzucato

Changes in an animal's behavioral state, such as arousal and movements, induce {complex modulations of the baseline input currents to sensory areas, eliciting sensory modality-specific effects. A simple computational principle explaining the effects of baseline modulations to recurrent cortical circuits is lacking. We investigate the benefits of baseline mod

Donald V. Reames

One of the earliest indicators of the importance of shock acceleration of solar energetic particles (SEPs) was the broad spatial extent of the "gradual" SEP events produced as the shock waves, driven by wide, fast coronal mass ejections (CMEs), expand across the Sun with cross-field transport mediated by the shocks. Contrasting "impulsive" SEP events, with c

Bogdan V. Semak, Yaroslav M. Beltukov, Oleg S. Vasyutinskii

The dichroism and birefringence effects in stumulated Raman scattering (SRS) in polyatomic molecules were studied theoretically. General expressions have been derived describing the change of the intensity and polarization of the probe pulse after transmission through a solution of arbitrary polyatomic molecules for any initial polarization of each of the la

Bruno M. Miranda, Mateus C. P. dos Santos, Wesley B. Cardoso

In this work, we consider a Bose-Einstein condensate in the self-focusing regime, confined transversely by a funnel-like potential and axially by a double-well potential formed by the combination of two inverted P\"oschl-Teller potentials. The system is well described by a one-dimensional nonpolynomial Schr\"odinger equation, for which we analyze the symmetr

Jeremy Darling

An observer moving with respect to the cosmic rest frame should observe a concentration and brightening of galaxies in the direction of motion and a spreading and dimming in the opposite direction. The velocity inferred from this dipole should match that of the cosmic microwave background (CMB) temperature dipole if galaxies are on average at rest with respe

J. Pratt, A. Busse, W. -C. Müller

Large-scale magnetic fields thread through the electrically conducting matter of the interplanetary and interstellar medium, stellar interiors, and other astrophysical plasmas, producing anisotropic flows with regions of high-Reynolds-number turbulence. It is common to encounter turbulent flows structured by a magnetic field with a strength approximately equ

Vasil Todorinov, Saurya Das, Pasquale Bosso

Theories of Quantum Gravity predict a minimum measurable length and a corresponding modification of the Heisenberg Uncertainty Principle to the so-called Generalized Uncertainty Principle (GUP). However, this modification is usually formulated in non-relativistic language, making it unclear whether the minimum length is Lorentz invariant. We have formulated