October 2022 arXiv papers — page 28

Thuy Bui, Thomas Lidbetter

We present solutions to a continuous patrolling game played on network. In this zero-sum game, an Attacker chooses a time and place to attack a network for a fixed amount of time. A Patroller patrols the network with the aim of intercepting the attack with maximum probability. Our main result is the proof of a recent conjecture on the optimal patrolling stra

Abida Sanjana Shemonti, Joshua D. Eisenberg, Robert O. Heuckeroth, Marthe J. Howard

We describe a generative network model of the architecture of the enteric nervous system (ENS) in the colon employing data from images of human and mouse tissue samples obtained through confocal microscopy. Our models combine spatial point pattern analysis with graph generation to characterize the spatial and topological properties of the ganglia (clusters o

Wentao Chen, Fuzhou Wang, Matthew Edwards

As a major component of online crime, email-based fraud is a threat that causes substantial economic losses every year. To counteract these scammers, volunteers called scam-baiters play the roles of victims, reply to scammers, and try to waste their time and attention with long and unproductive conversations. To curb email fraud and magnify the effectiveness

Rouzbeh Behnia, Mohamamdreza Ebrahimi, Jason Pacheco, Balaji Padmanabhan

Pre-trained Large Language Models (LLMs) are an integral part of modern AI that have led to breakthrough performances in complex AI tasks. Major AI companies with expensive infrastructures are able to develop and train these large models with billions and millions of parameters from scratch. Third parties, researchers, and practitioners are increasingly adop

Swayamtrupta Panda, Paola Marziani

A landmark of accretion processes in active galactic nuclei (AGN) is the continuum originating from a complex structure, i.e., an accretion disk and a corona around a supermassive black hole. Modelling the broad-band spectral energy distribution (SED) effectively ionizing the gas-rich broad emission line region (BLR) is key to understanding the various radia

Andrés Casado-Elvira, Marc Comino Trinidad, Dan Casas

Clothing plays a fundamental role in digital humans. Current approaches to animate 3D garments are mostly based on realistic physics simulation, however, they typically suffer from two main issues: high computational run-time cost, which hinders their development; and simulation-to-real gap, which impedes the synthesis of specific real-world cloth samples. T

Henry A. Prager, Lee Anne Willson, Massimo Marengo, Michelle J. Creech-Eakman

Using the Riebel et al. (2012) data set for 6,889 pulsating AGB stars in the LMC, we have derived formulae for mass-loss rate as a function of luminosity and pulsation period or luminosity and mass in three ways, for each of five subsets of data: fundamental mode oxygen rich stars, first overtone mode oxygen rich stars stars, fundamental mode carbon stars, f

Jagoba Iturri, Julia Miholich, Spela Zemljic, Amsatou Andorfer-Sarr

Optimal characterization of the mechanical properties of both cells and their surrounding is an issue of major interest. Indeed, cell function and development are strongly influenced by external stimuli. Furthermore, a change in cell mechanics might, in some cases, associate with diseases or malfunctioning. In this work, atomic force microscopy (AFM) was app

Wang Zhu, Jesse Thomason, Robin Jia

For vision-and-language reasoning tasks, both fully connectionist, end-to-end methods and hybrid, neuro-symbolic methods have achieved high in-distribution performance. In which out-of-distribution settings does each paradigm excel? We investigate this question on both single-image and multi-image visual question-answering through four types of generalizatio

S. Ihnatsenka

Quantum-mechanical calculations of electron magnetotransport in graphene Fabry-P\'{e}rot interferometers are presented with a focus on the role of spatial structure of edge channels. For an interferometer that is made by removing carbon atoms, which is typically realized in nanolithography experiments, the constrictions are shown to cause strong inter-channe

Trivikram Dokka, Jorge Bruno, Sonali SenGupta, Chowdhury Mohammad Sakib Anwar

We study equilibria in an Electric Vehicle (EV) charging game, a cost minimization game inherent to decentralized charging control strategy for EV power demand management. In our model, each user optimizes its total cost which is sum of direct power cost and the indirect dissatisfaction cost. We show that, taking player specific price independent dissatisfac

Homa Esfahanizadeh, William Wu, Manya Ghobadi, Regina Barzilay

The use of mutual information as a tool in private data sharing has remained an open challenge due to the difficulty of its estimation in practice. In this paper, we propose InfoShape, a task-based encoder that aims to remove unnecessary sensitive information from training data while maintaining enough relevant information for a particular ML training task.

Axel Garcia-Vega, Ricardo Espinosa, Luis Ramirez-Guzman, Thomas Bazin

Endoscopy is the most widely used imaging technique for the diagnosis of cancerous lesions in hollow organs. However, endoscopic images are often affected by illumination artefacts: image parts may be over- or underexposed according to the light source pose and the tissue orientation. These artifacts have a strong negative impact on the performance of comput

S. D. Korolkov, V. V. Izmodenov

One of the important discoveries made by Voyager-2 is the nonadiabatic radial profile of the solar wind proton temperature. This phenomenon has been studied for several decades. The dissipation of turbulence energy has been proposed as the main physical process responsible for the temperature profile. The turbulence is both convected with the solar wind and

Pratyush Maini, Saurabh Garg, Zachary C. Lipton, J. Zico Kolter

Researchers investigating example hardness have increasingly focused on the dynamics by which neural networks learn and forget examples throughout training. Popular metrics derived from these dynamics include (i) the epoch at which examples are first correctly classified; (ii) the number of times their predictions flip during training; and (iii) whether thei

Raphael Olivier, Bhiksha Raj

Whisper is a recent Automatic Speech Recognition (ASR) model displaying impressive robustness to both out-of-distribution inputs and random noise. In this work, we show that this robustness does not carry over to adversarial noise. We show that we can degrade Whisper performance dramatically, or even transcribe a target sentence of our choice, by generating

Srishti Mishra, Tvarita Jain, Dinkar Sitaram

Anomaly detection to recognize unusual events in large scale systems in a time sensitive manner is critical in many industries, eg. bank fraud, enterprise systems, medical alerts, etc. Large-scale systems often grow in size and complexity over time, and anomaly detection algorithms need to adapt to changing structures. A hierarchical approach takes advantage

Christopher Barrett, Andrei Bura, Qijun He, Fenix Huang

We propose a novel mathematical paradigm for the study of genetic variation in sequence alignments. This framework originates from extending the notion of pairwise relations, upon which current analysis is based on, to k-ary dissimilarity. This dissimilarity naturally leads to a generalization of simplicial complexes by endowing simplices with weights, compa

Suvir Mirchandani, Licheng Yu, Mengjiao Wang, Animesh Sinha

Multimodal tasks in the fashion domain have significant potential for e-commerce, but involve challenging vision-and-language learning problems - e.g., retrieving a fashion item given a reference image plus text feedback from a user. Prior works on multimodal fashion tasks have either been limited by the data in individual benchmarks, or have leveraged gener

Asma Elmaizi, Hasna Nhaila, Elkebir Sarhrouni, Ahmed Hammouch

Recently, the hyperspectral sensors have improved our ability to monitor the earth surface with high spectral resolution. However, the high dimensionality of spectral data brings challenges for the image processing. Consequently, the dimensionality reduction is a necessary step in order to reduce the computational complexity and increase the classification a

Md Aminul Haque Palash, Akib Khan, Kawsarul Islam, MD Abdullah Al Nasim

Incongruity between news headlines and the body content is a common method of deception used to attract readers. Profitable headlines pique readers' interest and encourage them to visit a specific website. This is usually done by adding an element of dishonesty, using enticements that do not precisely reflect the content being delivered. As a result, automat

Caitlin S. Kengle, Samantha I. Rubeck, Melinda Rak, Jin Chen

Doped SrTi1-xNbxO3 exhibits superconductivity and a mid-infrared optical response reminiscent of copper-oxide superconductors. Strangely, its plasma frequency, omega_p, increases by a factor of ~3 when cooling from 300 K to 20 K, without any accepted explanation. Here, we present momentum-resolved electron energy loss spectroscopy (M-EELS) measurements of Sr

Haolin Yuan, Bo Hui, Yuchen Yang, Philippe Burlina

Federated learning (FL) allows multiple clients to collaboratively train a deep learning model. One major challenge of FL is when data distribution is heterogeneous, i.e., differs from one client to another. Existing personalized FL algorithms are only applicable to narrow cases, e.g., one or two data classes per client, and therefore they do not satisfactor

Mingyu Kang, Wesley C. Campbell, Kenneth R. Brown

Erasures, or errors with known locations, are a more favorable type of error for quantum error-correcting codes than Pauli errors. Converting physical noise into erasures can significantly improve the performance of quantum error correction. Here we apply the idea of performing erasure conversion by encoding qubits into metastable atomic states, proposed by

Clémence Alasseur, Matteo Basei, Charles Bertucci, Alekos Cecchin

We propose a model based on a large number of small competitive producers of renewable energies, to study the effect of subsidies on the aggregate level of capacity, taking into account a cannibalization effect. We first derive a model to explain how long-time equilibrium can be reached on the market of production of renewable electricity and compare this eq

Michael Wan, Xiaofei Huang, Bethany Tunik, Sarah Ostadabbas

We apply computer vision pose estimation techniques developed expressly for the data-scarce infant domain to the study of torticollis, a common condition in infants for which early identification and treatment is critical. Specifically, we use a combination of facial landmark and body joint estimation techniques designed for infants to estimate a range of ge

Changhun Oh, Liang Jiang, Bill Fefferman

Cross entropy (XE) measure is a widely used benchmarking to demonstrate quantum computational advantage from sampling problems, such as random circuit sampling using superconducting qubits and boson sampling (BS). We present a heuristic classical algorithm that attains a better XE than the current BS experiments in a verifiable regime and is likely to attain

Claudia Toci, Giuseppe Lodato, Francesco Gerardo Livio, Giovanni Rosotti

In order to understand which mechanism is responsible for accretion in protoplanetary discs, a robust knowledge of the observed disc radius using gas tracers such as $^{12}$CO and other CO isotopologues is pivotal. Indeed, the two main theories proposed, viscous accretion and wind-driven accretion, predict different time evolution for the disc radii. In this

H. -G. Jason Chou, Anna Grassi, Siegfried H. Glenzer, Frederico Fiuza

We study the onset of electron heating in intense laser-solid interactions and its impact on the spectral quality of radiation pressure accelerated ions in both hole boring and light sail regimes. Two- and three-dimensional particle-in-cell (PIC) simulations are performed over a wide range of laser and target parameters and reveal how the pulse duration, pro

Mozhgan Pourmoradnasseri, Kaveh Khoshkhah, Amnir Hadachi

This paper presents a simulation-based optimization framework for city-scale real-time estimation and calibration of dynamic demand models by focusing on disaggregated microsimulation in congested networks. The calibration approach is based on sequential optimization demand estimation for short time frames and uses a stream of traffic count data from IoT sen

Ertem Nusret Tas, John Adler, Mustafa Al-Bassam, Ismail Khoffi

Accountability, the ability to provably identify protocol violators, gained prominence as the main economic argument for the security of proof-of-stake (PoS) protocols. Rollups, the most popular scaling solution for blockchains, typically use PoS protocols as their parent chain. We define accountability for rollups, and present an attack that shows the absen

Jianhui Li

We prove sharp $L^2$ Fourier restriction inequalities for compact, smooth surfaces in $\mathbb{R}^3$ equipped with the affine surface measure or a power thereof. The results are valid for all smooth surfaces and the bounds are uniform for all surfaces defined by the graph of polynomials of degrees up to $d$ with bounded coefficients. The primary tool is a de

Nicolas El Maalouly, Yanheng Wang

We show that the problem of counting perfect matchings remains #P-complete even if we restrict the input to very dense graphs, proving the conjecture in [5]. Here "dense graphs" refer to bipartite graphs of bipartite independence number $\leq 2$, or general graphs of independence number $\leq 2$. Our proof is by reduction from counting perfect matchings in b

Shuyu Wang, Lina Necib, Alexander P. Ji, Xiaowei Ou

Nyx is a nearby, prograde, and high-eccentricity stellar stream physically contained in the thick disk but with an unknown origin. Nyx could be the remnant of a disrupted dwarf galaxy, in which case the associated dark matter substructure could affect terrestrial dark matter direct detection experiments. Alternatively, Nyx could be a signature of the Milky W

Marius Pesavento, Minh Trinh-Hoang, Mats Viberg

The signal processing community currently witnesses the emergence of sensor array processing and Direction-of-Arrival (DoA) estimation in various modern applications, such as automotive radar, mobile user and millimeter wave indoor localization, drone surveillance, as well as in new paradigms, such as joint sensing and communication in future wireless system

Siddhant Bhambri, Purv Chauhan, Frederico Araujo, Adam Doupé

Identifying the actual adversarial threat against a system vulnerability has been a long-standing challenge for cybersecurity research. To determine an optimal strategy for the defender, game-theoretic based decision models have been widely used to simulate the real-world attacker-defender scenarios while taking the defender's constraints into consideration.

Shutian Liu, Quanyan Zhu

Risk perceptions are essential in cyber insurance contracts. With the recent surge of information, human risk perceptions are exposed to the influences from both beneficial knowledge and fake news. In this paper, we study the role of the risk perceptions of the insurer and the user in cyber insurance contracts. We formulate the cyber insurance problem into a

Bogumił Kamiński, Paweł Prałat, François Théberge

The Artificial Benchmark for Community Detection (ABCD) graph is a recently introduced random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known LFR one, and its main parameter can be tuned to mimic its counterpart in the LFR model, the mix

Xin Ma, Suprateek Kundu

Recently emerging large-scale biomedical data pose exciting opportunities for scientific discoveries. However, the ultrahigh dimensionality and non-negligible measurement errors in the data may create difficulties in estimation. There are limited methods for high-dimensional covariates with measurement error, that usually require knowledge of the noise distr

Mustafa Halilsoy, Mert Mangut, Chia-Li Hsieh

Through the Ernst formalism we provide expression for a class of colliding Einstein-Maxwell (EM) metrics with cross polarization. Local isometry is imposed as a means to transform interaction region of the spacetime into stationary, charged Zipoy-Voorhees (ZV) metric in Schwarzschild coordinates. The ZV-metric is known to describe planetary/stellar objects w

Chris Akers, Thomas Faulkner, Simon Lin, Pratik Rath

In arXiv:2112.09122, we analyzed the reflected entropy ($S_R$) in random tensor networks motivated by its proposed duality to the entanglement wedge cross section (EW) in holographic theories, $S_R=2 \frac{EW}{4G}$. In this paper, we discover further details of this duality by analyzing a simple network consisting of a chain of two random tensors. This setup

Yevgeniya Tarasova

If $I$ is a perfect ideal in a local Cohen-Macaulay ring, the generators of ideals linked to $I$ are well understood. However, the generators of the residual intersections of $I$ have only been computed in a few special cases. In this paper, we show that the $n$-residual intersections of determinantal ideals of generic $2\times n$ matrices are sums of links.

Felipe García-Ramos, Victor Muñoz-López

We characterize measure-theoretic sequence entropy pairs of continuous abelian group actions using mean sensitivity. This solves an open question mentioned by Li and Yu. As a consequence of our results we provide a simpler characterization of Kerr and Li's independence sequence entropy pairs ($\mu$-IN-pairs) when the measure is ergodic and the group is abeli

Fabiano F. Santos, F. A. Brito

We investigate thick brane solutions in the Horndeski gravity. In this setup, we found analytical solutions, applying the first-order formalism to two scalar fields where the first field comes from the non-minimal scalar-tensor coupling and the second is due to the matter contribution sector. With these analytical solutions, we evaluate the symmetric thick b

Andrea Guizzo, Alessandro Vezzani, Andrea Barontini, Fabrizio Russo

Wireless networks are commonly used in public spaces, universities and public institutions and provide accurate and easily accessible information to monitor the mobility and behavior of users. Following the application of containment measures during the recent pandemic, we analyse extensive data from the WiFi network in a University Campus in Italy during th

Camille Noufi, Jonathan Berger, Karen J. Parker, Daniel L. Bowling

In this paper, we propose a method for removing linguistic information from speech for the purpose of isolating paralinguistic indicators of affect. The immediate utility of this method lies in clinical tests of sensitivity to vocal affect that are not confounded by language, which is impaired in a variety of clinical populations. The method is based on simu

Thuan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias Scheutz

Domain generalization (DG) is a branch of transfer learning that aims to train the learning models on several seen domains and subsequently apply these pre-trained models to other unseen (unknown but related) domains. To deal with challenging settings in DG where both data and label of the unseen domain are not available at training time, the most common app

Eric Pauley, Kyle Domico, Blaine Hoak, Ryan Sheatsley

Public clouds necessitate dynamic resource allocation and sharing. However, the dynamic allocation of IP addresses can be abused by adversaries to source malicious traffic, bypass rate limiting systems, and even capture traffic intended for other cloud tenants. As a result, both the cloud provider and their customers are put at risk, and defending against th

Pierre Gaillard, Aadirupa Saha, Soham Dan

We address the problem of \emph{`Internal Regret'} in \emph{Sleeping Bandits} in the fully adversarial setup, as well as draw connections between different existing notions of sleeping regrets in the multiarmed bandits (MAB) literature and consequently analyze the implications: Our first contribution is to propose the new notion of \emph{Internal Regret} for

Manthan Patel, Gabriel Waibel, Shehryar Khattak, Marco Hutter

Detecting objects of interest, such as human survivors, safety equipment, and structure access points, is critical to any search-and-rescue operation. Robots deployed for such time-sensitive efforts rely on their onboard sensors to perform their designated tasks. However, as disaster response operations are predominantly conducted under perceptually degraded

Yuji Takubo, Damon Landau, Brian Anderson

Future missions to Enceladus would benefit from multi-moon tours that leverage V-infinity on resonant orbits to progressively transfer between moons. Such "resonance family hopping" trajectories present a vast search space for global optimization due to the different combinations of available resonances and flyby speeds. The proposed multi-objective tour des

Francisco Teixeira, Alberto Abad, Bhiksha Raj, Isabel Trancoso

Automatic Speaker Diarization (ASD) is an enabling technology with numerous applications, which deals with recordings of multiple speakers, raising special concerns in terms of privacy. In fact, in remote settings, where recordings are shared with a server, clients relinquish not only the privacy of their conversation, but also of all the information that ca

Jiaxuan Li, Jenny E. Greene, Johnny P. Greco, Song Huang

Large diffuse galaxies are hard to find, but understanding the environments where they live, their numbers, and ultimately their origins, is of intense interest and importance for galaxy formation and evolution. Using Subaru's Hyper Suprime-Cam Strategic Survey Program, we perform a systematic search for low surface brightness galaxies and present novel and

Fahimeh Ebrahimi, Miroslav Tushev, Anas Mahmoud

Applications (apps) of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit, have become a main enabler of economic growth and shared prosperity in modern-day societies. However, the complex exchange of goods, services, and data that takes place over these apps frequently puts their end-users' privacy at risk. Privacy policies of DSE apps

Andrey Kharitenko, Carsten W. Scherer

In this note it is shown that the famous multiplier absolute stability test of R. O'Shea, G. Zames and P. Falb is necessary and sufficient if the set of Lur'e interconnections is lifted to a Kronecker structure and an explicit method to construct the destabilizing static nonlinearity is presented.

Yi Dong, Xingyu Zhao, Sen Wang, Xiaowei Huang

Deep Reinforcement Learning (DRL) has achieved impressive performance in robotics and autonomous systems (RAS). A key challenge to its deployment in real-life operations is the presence of spuriously unsafe DRL policies. Unexplored states may lead the agent to make wrong decisions that could result in hazards, especially in applications where DRL-trained end

Alessandro Carderi, Damien Gaboriau, François Le Maître, Yves Stalder

Given a Baumslag-Solitar group, we study its space of subgroups from a topological and dynamical perspective. We first determine its perfect kernel (the largest closed subset without isolated points). We then bring to light a natural partition of the space of subgroups into one closed subset and countably many open subsets that are invariant under the action

Sylvain Crovisier, Mauricio Poletti

We prove a generalization of a so called "invariance principle" for partially hyperbolic diffeomorphisms: if an invariant probability measure has all its center Lyapunov exponents equal to zero then the measure admits a center disintegration that is invariant by stable and unstable holonomies. This was known for systems admitting a foliation by compact cente

Joseph Feldman, Daniel R. Kowal

Modern datasets commonly feature both substantial missingness and many variables of mixed data types, which present significant challenges for estimation and inference. Complete case analysis, which proceeds using only the observations with fully-observed variables, is often severely biased, while model-based imputation of missing values is limited by the ab

Guangrui Sun, J. Andrzej Domaradzki

A previously developed modeling procedure for large eddy simulations (LESs) is extended to allow physical space implementations for inhomogeneous flows. The method is inspired by the well-established theoretical analyses and numerical investigations of homogeneous, isotropic turbulence. A general procedure that focuses on recovering the full subgrid scale (S

Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker

Despite widespread use of LLMs as conversational agents, evaluations of performance fail to capture a crucial aspect of communication: interpreting language in context -- incorporating its pragmatics. Humans interpret language using beliefs and prior knowledge about the world. For example, we intuitively understand the response "I wore gloves" to the questio

Jiawei Fu, Yunlong Song, Yan Wu, Fisher Yu

Autonomous drones can operate in remote and unstructured environments, enabling various real-world applications. However, the lack of effective vision-based algorithms has been a stumbling block to achieving this goal. Existing systems often require hand-engineered components for state estimation, planning, and control. Such a sequential design involves labo

Saiph Savage, Claudia Flores-Saviaga, Rachel Rodney, Liliana Savage

The popularity of 3D printed assistive technology has led to the emergence of new ecosystems of care, where multiple stakeholders (makers, clinicians, and recipients with disabilities) work toward creating new upper limb prosthetic devices. However, despite the increasing growth, we currently know little about the differences between these care ecosystems. M

Nondh Panithanpaisal, Robyn E. Sanderson, Arpit Arora, Emily C. Cunningham

Recent studies have suggested that the Milky Way (MW)'s Dark Matter (DM) halo may be significantly tilted with respect to its central stellar disk, a feature that might be linked to its formation history. In this work, we demonstrate a method of constraining the orientation of the minor axis of the DM halo using the angle and frequency variables. This method

He Zhang, Sizhen Li, Liang Zhang, David H. Mathews

Many ncRNAs function through RNA-RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA-RNA interaction is useful. Some existing tools are less accurate due to omitting the competing of intermolecular and intramolecular base pairs, or focus more on predicting the binding region rather than predicting the complete secondary str

Jialiang Yu, Dhruv Kedar, Sebastian Häfner, Thomas Legero

Thermodynamically induced length fluctuations of high-reflectivity mirror coatings put a fundamental limit on sensitivity and stability of precision optical interferometers like gravitational wave detectors and ultra-stable lasers. The main contribution - Brownian thermal noise - is related to the mechanical loss of the coating material. Owing to their low m

Saravanabalagi Ramachandran, Jonathan Horgan, Ganesh Sistu, John McDonald

Scene categorization is a useful precursor task that provides prior knowledge for many advanced computer vision tasks with a broad range of applications in content-based image indexing and retrieval systems. Despite the success of data driven approaches in the field of computer vision such as object detection, semantic segmentation, etc., their application i

Benjamin P. S. Donitz, Declan Mages, Hiroyasu Tsukamoto, Peter Dixon

Interstellar objects (ISOs) represent a compelling and under-explored category of celestial bodies, providing physical laboratories to understand the formation of our solar system and probe the composition and properties of material formed in exoplanetary systems. In this work, we investigate existing approaches to designing successful flyby missions to ISOs

Mathieu Grosso, Pirashanth Ratnamogan, Alexis Mathey, William Vanhuffel

Recent literature has demonstrated the potential of multilingual Neural Machine Translation (mNMT) models. However, the most efficient models are not well suited to specialized industries. In these cases, internal data is scarce and expensive to find in all language pairs. Therefore, fine-tuning a mNMT model on a specialized domain is hard. In this context,

Myungsoo Yoo, Christopher K. Wikle

Intense wildfires impact nature, humans, and society, causing catastrophic damage to property and the ecosystem, as well as the loss of life. Forecasting wildfire front propagation is essential in order to support fire fighting efforts and plan evacuations. The level set method has been widely used to analyze the change in surfaces, shapes, and boundaries. I

Sahisnu Mazumder, Bing Liu, Shuai Wang, Yingxuan Zhu

This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of state-action permissibility (SAP). Two types of permissibility are defined under SAP. The first type says that after an action $a_t$ is performed in a state $s_t$ and the agent has reached the new state $s_{t+1}$, the

Yi Chang, Zhao Ren, Thanh Tam Nguyen, Kun Qian

Speech emotion recognition (SER) has been a popular research topic in human-computer interaction (HCI). As edge devices are rapidly springing up, applying SER to edge devices is promising for a huge number of HCI applications. Although deep learning has been investigated to improve the performance of SER by training complex models, the memory space and compu

Jizhe Cai, Claire Griesbach, Savannah G. Ahnen, Ramathasan Thevamaran

A clear understanding of the dynamic behavior of metals is critical for developing superior structural materials as well as for improving material processing techniques such as cold spray and shot peening. Using a high velocity (from 120 m/s to 700 m/s; strain rates >10^7 1/s) micro-projectile impact testing and quasistatic (strain rates: 10^-2 1/s) nanoinde

Jacqueline He, Mengzhou Xia, Christiane Fellbaum, Danqi Chen

Pre-trained language models encode undesirable social biases, which are further exacerbated in downstream use. To this end, we propose MABEL (a Method for Attenuating Gender Bias using Entailment Labels), an intermediate pre-training approach for mitigating gender bias in contextualized representations. Key to our approach is the use of a contrastive learnin

Harry Gao, Weijie Gan, Zhixin Sun, Ulugbek S. Kamilov

Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression. An image can be compressed by training an INR model with fewer weights than the number of image pixels to map the coordinates of the image to corresponding pixel values. While traditional training approaches for INRs are based on enf

Thomas Kleine Buening, Victor Villin, Christos Dimitrakakis

Learning a reward function from demonstrations suffers from low sample-efficiency. Even with abundant data, current inverse reinforcement learning methods that focus on learning from a single environment can fail to handle slight changes in the environment dynamics. We tackle these challenges through adaptive environment design. In our framework, the learner

Abhinav Goyal, Anupam Singh, Nikesh Garera

Automation of on-call customer support relies heavily on accurate and efficient speech-to-intent (S2I) systems. Building such systems using multi-component pipelines can pose various challenges because they require large annotated datasets, have higher latency, and have complex deployment. These pipelines are also prone to compounding errors. To overcome the

Bowei Zhou, Pravin Khanal, Onri Jay Benally, Deyuan Lyu

Nb and its compounds are widely used in quantum computing due to their high superconducting transition temperatures and high critical fields. Devices that combine superconducting performance and spintronic non-volatility could deliver unique functionality. Here we report the study of magnetic tunnel junctions with Nb as the heavy metal layers. An interfacial

Peter A. Hogan, Dirk Puetzfeld

We present a formulation of Einstein--Maxwell vacuum fields due to plane fronted electromagnetic waves sharing their wave fronts with gravitational waves. This is based on a recent geometrical reconstruction of plane fronted wave fields by the authors which clearly identifies the cases in which the wave fronts collide or do not collide. In the former case ou

Ryan O. Behunin, Peter T. Rakich

Owing to their long-lifetimes at cryogenic temperatures, mechanical oscillators are recognized as an attractive resource for quantum information science and as a testbed for fundamental physics. Key to these applications is the ability to prepare, manipulate and measure quantum states of mechanical motion. Through an exact formal solution to the Schrodinger

Yang Trista Cao, Kyle Seelman, Kyungjun Lee, Hal Daumé

In visual question answering (VQA), a machine must answer a question given an associated image. Recently, accessibility researchers have explored whether VQA can be deployed in a real-world setting where users with visual impairments learn about their environment by capturing their visual surroundings and asking questions. However, most of the existing bench

Shivang Srivastava, Dmitri B. Horoshko, Mikhail I. Kolobov

We propose an application of quantum temporal imaging to restoring the indistinguishability of the signal and the idler photons produced in type-II spontaneous parametric down-conversion with a pulsed broadband pump. It is known that in this case, the signal and the idler photons have different spectral and temporal properties. This effect deteriorates their

Jacek Jendrej, Andrew Lawrie

We consider the harmonic map heat flow for maps from the plane taking values in the sphere, under equivariant symmetry. It is known that solutions to the initial value problem can exhibit bubbling along a sequence of times -- the solution decouples into a superposition of harmonic maps concentrating at different scales and a body map that accounts for the re

Fariha Nazneen Rista, Khondhaker Al Momin, Arif Mohaimin Sadri

The adoption of transportation policies that prioritized highway expansion over public transportation has disproportionately impacted minorities and low-income people by restricting their access to social and economic opportunities and thus resulting in residential segregation. Policymakers, transportation researchers, planners, and practitioners have starte

Tiziana Di Matteo, Yueying Ni, Nianyi Chen, Rupert Croft

Massive black holes in the centers of galaxies today must have grown by several orders of magnitude from seed black holes formed at early times. Detecting a population of intermediate mass black holes (IMBHs) can provide constraints on these elusive BH seeds. Here we use the large volume, cosmological hydrodynamical simulation Astrid, which includes IMBH see

Hooman Davoudiasl, Robert Szafron

Light scalars can in principle couple to both bulk matter and fermion spin, with hierarchically disparate strengths. Storage ring measurements of fermion electromagnetic moments via spin precession can be sensitive to such a force, sourced by the Earth. We discuss how this force could lead to a deviation of the measured muon anomalous magnetic moment, $g-2$,

Weijie Zhao, Shulong Tan, Ping Li

Search engines and recommendation systems are built to efficiently display relevant information from those massive amounts of candidates. Typically a three-stage mechanism is employed in those systems: (i) a small collection of items are first retrieved by (e.g.,) approximate near neighbor search algorithms; (ii) then a collection of constraints are applied

Jiahao Zhao, Wenji Mao

Adversarial vulnerability remains a major obstacle to constructing reliable NLP systems. When imperceptible perturbations are added to raw input text, the performance of a deep learning model may drop dramatically under attacks. Recent work argues the adversarial vulnerability of the model is caused by the non-robust features in supervised training. Thus in

Aman Chawla, Salvatore Domenic Morgera

In this paper the authors extend [1] and provide more details of how the brain may act like a quantum computer. In particular, positing the difference between voltages on two axons as the environment for ions undergoing spatial superposition, we argue that evolution in the presence of metric perturbations will differ from that in the absence of these waves.

Yipeng Kang, Tonghan Wang, Xiaoran Wu, Qianlan Yang

Value decomposition multi-agent reinforcement learning methods learn the global value function as a mixing of each agent's individual utility functions. Coordination graphs (CGs) represent a higher-order decomposition by incorporating pairwise payoff functions and thus is supposed to have a more powerful representational capacity. However, CGs decompose the

Diego Subero, Olivier Maillet, Dmitry S. Golubev, George Thomas

The Josephson junction is a building block of quantum circuits. Its behavior, well understood when treated as an isolated entity, is strongly affected by coupling to an electromagnetic environment. In 1983, Schmid predicted that a Josephson junction shunted by a resistance exceeding the resistance quantum $\mathbf{\textit{R}}_\mathrm{Q} = h/4e^2 \approx 6.45

Zheyu Lin, Ning Jiang, Xu Kong, Shifeng Huang

The high-cadence survey of Zwicky Transient Facility (ZTF) has completely dominated the discovery of tidal disruption events (TDEs) in the past few years and resulted in the largest sample of TDEs with optical/UV light curves well-sampled around their peaks, providing us an excellent opportunity to construct a peak luminosity function (LF) of tidal disruptio

Ferdinand Jost, Vassillen Chizhov, Joachim Weickert

Inpainting-based image compression is a promising alternative to classical transform-based lossy codecs. Typically it stores a carefully selected subset of all pixel locations and their colour values. In the decoding phase the missing information is reconstructed by an inpainting process such as homogeneous diffusion inpainting. Optimising the stored data is

James Kempf

Managing the energy consumption of the built environment is an important source of flexible load and decarbonization, enabling building managers and utilities to schedule consumption to avoid costly demand charges and peak times when carbon emissions from grid generated electricity are highest. A key technology component in building energy management is the

Gia Dvali, Juan Valbuena-Bermudez, Michael Zantedeschi

In this work, we study the annihilation of a pair of `t Hooft-Polyakov monopoles due to confinement by a string. We analyze the regime in which the scales of monopoles and strings are comparable. We compute the spectrum of the emitted gravitational waves and find it to agree with the previously calculated point-like case for wavelengths longer than the syste

S. Belladitta, A. Moretti, A. Caccianiga, D. Dallacasa

We present the discovery of PSO J191.05696$+$86.43172 (hereafter PSO J191$+$86), a new powerful radio-loud quasar (QSO) in the early Universe (z = 5.32). We discovered it by cross-matching the NRAO VLA Sky Survey (NVSS) radio catalog at 1.4 GHz with the first data release of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS PS1) in the opt

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The first observation of the $B^0_s\!\to D^{*+}D^{*-}$ decay and the measurement of its branching ratio relative to the $B^0\!\to D^{*+}D^{*-}$ decay are presented. The data sample used corresponds to an integrated luminosity of $9\,\text{fb}^{-1}$ of proton-proton collisions recorded by the LHCb experiment at centre-of-mass energies of 7, 8 and $13\,\text{T

Arman Babakhani, Parsa Bonderson

The universal properties of (2 + 1)D topological phases of matter enriched by a symmetry group G are described by G-crossed extensions of unitary modular tensor categories (UMTCs). While the fusion and braiding properties of quasiparticles associated with the topological order are described by a UMTC, the G-crossed extensions further capture the properties o

Mark A. Siebert, Marie Van de Sande, Thomas J. Millar, Anthony J. Remijan

In recent years, many questions have arisen regarding the chemistry of photochemical products in the carbon-rich winds of evolved stars. To address them, it is imperative to constrain the distributions of such species through high angular resolution interferometric observations covering multiple rotational transitions. We used archival ALMA observations to m

G. González-Torà, M. Wittkowski, B. Davies, B. Plez

Red supergiants (RSGs) are evolved massive stars in a stage preceding core-collapse supernova. The physical processes that trigger mass loss in their atmospheres are still not fully understood. Based on observations of $\alpha$ Ori, a new semi-empirical method to add a wind to hydrostatic model atmospheres of RSGs was recently developed. We use this method o