January 2022 arXiv papers — page 18

W. Zhang, L. Tao, R. Soria, J. L. Qu

We present a spectral study of the black hole candidate MAXI J1348-630 during its 2019 outburst, based on monitoring observations with Insight-HXMT and Swift. Throughout the outburst, the spectra are well fitted with power-law plus disk-blackbody components. In the soft-intermediate and soft states, we observed the canonical relation L ~ T_in^4 between disk

Masaki Kashiwara, Se-jin Oh

The $(q,t)$-Cartan matrix specialized at $t=1$, usually called the quantum Cartan matrix, has deep connections with (i) the representation theory of its untwisted quantum affine algebra, and (ii) quantum unipotent coordinate algebra, root system and quantum cluster algebra of kew-symmetric type. In this paper, we study the $(q,t)$-Cartan matrix specialized a

Jiangnan Cheng, Sandeep Chinchali, Ao Tang

Network coding allows distributed information sources such as sensors to efficiently compress and transmit data to distributed receivers across a bandwidth-limited network. Classical network coding is largely task-agnostic -- the coding schemes mainly aim to faithfully reconstruct data at the receivers, regardless of what ultimate task the received data is u

Xijing Liu, Kevin Lam, Balsam Alkouz, Babar Shahzaad

Drone swarms are required for the simultaneous delivery of multiple packages. We demonstrate a multi-stop drone swarm-based delivery in a smart city. We leverage formation flying to conserve energy and increase the flight range of a drone swarm. An adaptive formation is presented in which a swarm adjusts to extrinsic constraints and changes the formation pat

Jason K. Eshraghian, Wei D. Lu

Spiking neural networks can compensate for quantization error by encoding information either in the temporal domain, or by processing discretized quantities in hidden states of higher precision. In theory, a wide dynamic range state-space enables multiple binarized inputs to be accumulated together, thus improving the representational capacity of individual

Caleb Stamper, David Cortie, Zengji Yue, Xiaolin Wang

An analytical model describing the vibrational phonon density of states (VDOS) of liquids has long been elusive, mainly due to the difficulty in dealing with the imaginary modes dominant in the low-energy region, as described by the instantaneous normal mode (INM) approach. Nevertheless, Zaccone and Baggioli have recently developed such a model based on over

Yun-Fang Cai, Xu Yang, Yong-Yuang Xiang, Xiao-Li Yan

The New Vacuum Solar Telescope (NVST) has been releasing its novel winged Ha data (WHD) since April 2021, namely the Ha imaging spectroscopic data. Compared with the prior released version, the new data are further co-aligned among the off-band images and packaged into a standard solar physics community format. In this study, we illustrate the alignment algo

Peishi S. Cheng, Jiace Sun, Shi-Ning Sun, Alexander Y. Choi

High-field charge transport in semiconductors is of fundamental interest and practical importance. While the \textit{ab initio} treatment of low-field transport is well-developed, the treatment of high-field transport is much less so, particularly for multi-phonon processes that are reported to be relevant in GaAs. Here, we report a calculation of the high-f

Taichi Uyama, Michihiro Takami, Gabriele Cugno, Vincent Deo

We present multi-epoch observations of the RY~Tau jet for H$\alpha$ and [\ion{Fe}{2}] 1.644 \micron~emission lines obtained with Subaru/SCExAO+VAMPIRES, Gemini/NIFS, and Keck/OSIRIS in 2019--2021. These data show a series of four knots within 1$\arcsec$ consistent with the proper motion of $\sim$0\farcs3~yr$^{-1}$, analogous to the jets associated with anoth

Zhenyu Wang, John H. L. Hansen

Audio analysis for forensic speaker verification offers unique challenges in system performance due in part to data collected in naturalistic field acoustic environments where location/scenario uncertainty is common in the forensic data collection process. Forensic speech data as potential evidence can be obtained in random naturalistic environments resultin

Zhijie Fan, Ji Li, Edward McDonald, Fedor Sukochev

Along the line of singular value estimates for commutators by Rochberg-Semmes, Lord-McDonald-Sukochev-Zanin and Fan-Lacey-Li, we establish the endpoint weak Schatten class estimate for commutators of Riesz transforms with multiplication operator $M_f$ on Heisenberg groups via homogeneous Sobolev norm of the symbol $f$. The new tool we exploit is the construc

Gurupraanesh Raman, Gururaghav Raman, Jimmy Chih-Hsien Peng

Increasing the inertia is widely considered to be the solution to resolving unstable interactions between coupled oscillators. In power grids, Virtual Synchronous Generators (VSGs) are proposed to compensate the reducing inertia as rotating synchronous generators are being phased out. Yet, modeling how VSGs and rotating generators simultaneously contribute e

Edward McDonald, Fedor Sukochev, Dmitriy Zanin

We study Cwikel-type estimates for the singular values and Schatten $\mathcal{L}_p$-norms of compositions of multiplication and convolution operators acting on stratified Lie groups. This enables us to obtain novel spectral asymptotic formulas for certain operators derived from sub-Laplacians.

Ali Kefayati, Philip B. Allen, Vasili Perebeinos

Thermal transport in a quasi-ballistic regime is determined not only by the local temperature $T(r)$, or its gradient $\nabla T(r)$, but also by temperature distribution at neighboring points. For an accurate description of non-local effects on thermal transport, we employ the thermal distributor, $\Theta (r,r')$, which provides the temperature response of t

Shi Wang, Dingding Liang, Yang Chen

A photonics-assisted joint communication-radar system is proposed and experimentally demonstrated, by introducing a quadrature phase-shift keying (QPSK)-sliced linearly frequency-modulated (LFM) signal. An LFM signal is carrier-suppressed single-sideband modulated onto the optical carrier in one dual-parallel Mach-Zehnder modulator (DPMZM) of a dual-polariza

Qiye Guan, Hejin Yan, Yongqing Cai

MXene and its derivatives have attracted considerable attention for potential application in energy storage like batteries and supercapacitors owing to its ultrathin metallic structures. However, the complexity of the ionic and electronic dynamics in MXene based hybrids, which are normally needed for device integration, triggers both challenges and opportuni

Hye-Sung Lee, Jiheon Lee, Jaeok Yi

We introduce a new method to search for the dark matter sector particles using laser light. Some dark matter particles may have a small mixing or interaction with a photon. High-power lasers provide substantial test grounds for these hypothetical light particles of exploding interests in particle physics. We show that any light source can also emit a subfreq

Tong Li, Jiajun Liao, Rui-Jia Zhang

The fermionic dark matter (DM) absorption by nucleus or electron targets provides a distinctive signal to search for sub-GeV DM. We consider a Dirac fermion DM charged under a dark gauge group and with the dark magnetic dipole operator. The DM field mixes with right-handed neutrino and interacts with the ordinary electromagnetic charge current via the kineti

Hongfei Chen, Hejin Yan, Yongqing Cai

Two-dimensional (2D) layered lead iodide (PbI2) is an important precursor and common residual species during the synthesis of lead-halide perovskites. There currently exist some debates and uncertainties about the effect of excess PbI2 on the efficiency and stability of the solar cell with respect to its energy alignment and energetics of defects. Herein, by

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma

We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought prompting, where a few chai

Susama Agarwala, Ben Dees, Corey Lowman

We study the map learned by a family of autoencoders trained on MNIST, and evaluated on ten different data sets created by the random selection of pixel values according to ten different distributions. Specifically, we study the eigenvalues of the Jacobians defined by the weight matrices of the autoencoder at each training and evaluation point. For high enou

Pinhas Grossman, Masaki Izumi, Noah Snyder

We classify certain $\mathbb{Z}_2 $-graded extensions of generalized Haagerup categories in terms of numerical invariants satisfying polynomial equations. In particular, we construct a number of new examples of fusion categories, including: $\mathbb{Z}_2 $-graded extensions of $\mathbb{Z}_{2n} $ generalized Haagerup categories for all $n \leq 5 $; $\mathbb{Z

Changmeng Huan, Pu Wang, Binghan He, Yongqing Cai

Semiconducting oxides with reducible cations are ideal platforms for various functional applications in nanoelectronics and catalysts. Here we report an ultrathin monolayer alpha-MoO3 where tunable electronic properties and different gas adsorbing behaviors upon introducing the oxygen vacancies (VO). The unique property of alpha-MoO3 is that it contains thre

Riccardo Fantoni, John R. Klauder

We prove through Monte Carlo analysis that the covariant euclidean scalar field theory, $\varphi^r_n$, where $r$ denotes the power of the interaction term and $n = s + 1$ where $s$ is the spatial dimension and $1$ adds imaginary time, such that $r = n = 4$ can be acceptably quantized using scaled affine quantization and the resulting theory is nontrivial and

Remi A. Chou

We study private classical communication over quantum multiple-access channels. For an arbitrary number of transmitters, we derive a regularized expression of the capacity region. In the case of degradable channels, we establish a single-letter expression for the best achievable sum-rate and prove that this quantity also corresponds to the best achievable su

Xulu Zhang, Zhenqun Yang, Hao Tian, Qing Li

Deep learning became the game changer for image retrieval soon after it was introduced. It promotes the feature extraction (by representation learning) as the core of image retrieval, with the relevance/matching evaluation being degenerated into simple similarity metrics. In many applications, we need the matching evidence to be indicated rather than just ha

Yuekai Huang, Ye Yang, Junjie Wang, Wei Zheng

In open source software (OSS) communities, existing leadership indicators are dominantly measured by code contribution or community influence. Recent studies on emergent leadership shed light on additional dimensions such as intellectual stimulation in collaborative communications. To that end, this paper proposes an automated approach, named iLead, to mine

Keiichi Kato, Ryo Muramatsu

In this paper, we give estimates of the solutions to Schr\"{o}dinger equation on modulation spaces with vector potential of sub-linear growth.

Jane Dwivedi-Yu, Alon Y. Halevy

Social media plays an increasing role in our communication with friends and family, and our consumption of information and entertainment. Hence, to design effective ranking functions for posts on social media, it would be useful to predict the affective response to a post (e.g., whether the user is likely to be humored, inspired, angered, informed). Similar

L. Jin

The Hermitian systems possess unitary scattering; however, the Hermiticity is unnecessary for a unitary scattering although the scattering under the influence of non-Hermiticity is mostly non-unitary. Here we prove that the unitary scattering is protected by certain type of pseudo-Hermiticity and unaffected by the degree of non-Hermiticity. The energy conser

Kang Liu, Zaichen Zhang, Jian Dang, Liang Wu

Energy efficiency (EE) plays a key role in future wireless communication network and it is easily to achieve high EE performance in low SNR regime. In this paper, a new high EE scheme is proposed for a MIMO wireless communication system working in the low SNR regime by using two dimension resource allocation. First, we define the high EE area based on the re

Zheng Shu, Yongqing Cai

Nitrogen reduction reaction (NRR) which converts nitrogen (N2) to ammonia (NH3) normally requires harsh conditions to break the bound nitrogen bond. Herein, via first-principles calculation we reveal that a superior NRR catalytic activity could be obtained through anchoring atomic catalyst above a phosphorene-like puckering surface of germanium selenide (GeS

Remi A. Chou, Joerg Kliewer

Consider $L$ users, who each hold private data, and one fusion center who must compute a function of the private data of the $L$ users. To accomplish this task, each user may utilize a public and noiseless broadcast channel in a non-interactive manner. In this setting, and in the absence of any additional resources such as secure links, we study the optimal

Zheng Shu, Yongqing Cai

Layered chalcogenide materials have a wealth of nanoelectronics applications like resistive switching and energy-harvesting such as photocatalyst owing to rich electronic, orbital, and lattice excitations. In this work, we explore monochalcogenide germanium selenide GeSe with respect to substitutional doping with 13 metallic cations by using first-principles

Jiaren Yuan, Yuanping Chen, Yuee Xie, Xiaoyu Zhang

Shrinking the size of a bulk metal into nanoscale leads to the discreteness of electronic energy levels, the so-called Kubo gap. Renormalization of the electronic properties with a tunable and size-dependent Kubo gap renders fascinating photon emission and electron tunneling. In contrast with usual three-dimensional (3D) metal clusters, here we demonstrate t

Hyosub Kim, Katarzyna Krzyzanowska, K. C. Henderson, C. Ryu

We report a multiple-loop guided atom interferometer in which the atoms make 200 small-amplitude roundtrips, instead of one large single orbit. The approach is enabled by using ultracold 39K gas and a magnetic Feshbach resonance that can tune the s-wave scattering length across zero to significantly reduce the atom loss from cold collisions. This scheme is r

R. Doran, A. J. Groszek, T. P. Billam

Superfluid flow past a potential barrier is a well studied problem in ultracold Bose gases, however, fewer studies have considered the case of flow through a disordered potential. Here we consider the case of a superfluid flowing through a channel containing multiple point-like barriers, randomly placed to form a disordered potential. We begin by identifying

Tobias Foller, Lukas Madauss, Dali Ji, Xiaojun Ren

Angstrom confined solvents in two-dimensional laminates travel through interlayer spacings, gaps between adjacent sheets, and via in plane pores. Among these, experimental access to investigate the mass transport through in plane pores is lacking. Here, we create these nanopores in graphene oxide membranes via ion irradiation with precise control over functi

Satadisha Saha Bhowmick, Eduard C. Dragut, Weiyi Meng

Microblogging sites, like Twitter, have emerged as ubiquitous sources of information. Two important tasks related to the automatic extraction and analysis of information in Microblogs are Entity Mention Detection (EMD) and Entity Detection (ED). The state-of-the-art EMD systems aim to model the non-literary nature of microblog text by training upon offline s

A. Feder Cooper, Gili Vidan

Contemporary concerns over the governance of technological systems often run up against narratives about the technical infeasibility of designing mechanisms for accountability. While in recent AI ethics literature these concerns have been deliberated predominantly in relation to ML, other instances in computing history also presented circumstances in which c

Ranga P. Dias, Ashkan Salamat

In this paper, we respond to a recent criticism of our work on CSH, (1,2) raised by Hirsch and van der Marel on the arXiv (3). We point out that their non-peer reviewed critique (3) stems from either a lack of scientific understanding or a failure to appropriately analyze raw data. We explain that the use of their "Unwrapped" method was inappropriately perfo

Helen Zhi Jie Zeng, Minh Anh Phan Ngyuen, Xiaoyu Ai, Adam Bennet

High-purity single photon sources (SPS) that can operate at room temperature are highly desirable for a myriad of applications, including quantum photonics and quantum key distribution. In this work, we realise an ultra-bright solid-state SPS based on an atomic defect in hexagonal boron nitride (hBN) integrated with a solid immersion lens (SIL). The SIL incr

Gaurav Arya, William F. Li, Charles Roques-Carmes, Marin Soljačić

We present a framework for the end-to-end optimization of metasurface imaging systems that reconstruct targets using compressed sensing, a technique for solving underdetermined imaging problems when the target object exhibits sparsity (i.e. the object can be described by a small number of non-zero values, but the positions of these values are unknown). We ne

James K. Freericks

The chemistry community has long sought the exact relationship between the conventional and the unitary coupled cluster ansatz for a single-reference system, especially given the interest in performing quantum chemistry on quantum computers. In this work, we show how one can use the operator manipulations given by the exponential disentangling identity and t

Isabela Villamil, János Kertész, Johannes Wachs

Studying corruption presents unique challenges. Recent work in the spirit of computational social science exploits newly available data and methods to give a fresh perspective on this important topic. In this chapter we highlight some of these works, describing how they provide insights into classic social scientific questions about the structure and dynamic

Tianming Feng, Xuemai Gu, Ben Liang

The strong interference suffered by users can be a severe problem in cache-enabled networks (CENs) due to the content-centric user association mechanism. To tackle this issue, multi-antenna technology may be employed for interference management. In this paper, we consider a user-centric interference nulling (IN) scheme in two-tier multi-user multi-antenna CE

Sharon Lynn Chu, Brittany M. Garcia, Neha Rani

A good amount of research has explored the use of wearables for educational or learning purposes. We have now reached a point when much literature can be found on that topic, but few attempts have been made to make sense of that literature from a holistic perspective. This paper presents a systematic review of the literature on wearables for learning. Litera

Takayuki Myo, Mengjiao Lyu, Hiroshi Toki, Hisashi Horiuchi

We propose a new many-body method based on the correlation functions, in which the multiple products of the correlation functions are expanded into the many-body diagrams using the cluster expansion method and every diagram is independently optimized in the total-energy variation. We apply this idea to the tensor-optimized antisymmetrized molecular dynamics

Grégoire Sergeant-Perthuis

We propose a theoretical framework for non redundant reconstruction of a global loss from a collection of local ones under constraints given by a functor; we call this loss the regionalized loss in honor to Yedidia, Freeman, Weiss' celebrated article `Constructing free-energy approximations and generalized belief propagation algorithms' where a first example

Zh. Myrzakulova, G. Nugmanova, K. Yesmakhanova, R. Myrzakulov

In this paper, we have studied the geometrical formulation of the Landau-Lifshitz equation (LLE) and established its geometrical equivalent counterpart as some generalized nonlinear Schr\"{o}dinger equation. When the anisotropy vanishes, from this result follows the well-known results corresponding for the isotropic case, i.e. to the Heisenberg ferromagnet e

Xiaoyu Mao, Rajeev Jaiman

We present an interface and geometry preserving (IGP) method for the modeling of fully Eulerian fluid-structure interaction via phase-field formulation. While the hyperbolic tangent interface profile is preserved by the time-dependent mobility model, the proposed method maintains the geometry of the solid-fluid interface by reducing the volume-conserved mean

Michael W. Werner, Patrick J. Lowrance, Tom Roellig, Varoujan Gorjian

The Spitzer Space Telescope operated for over 16 years in an Earth-trailing solar orbit, returning not only a wealth of scientific data but, as a by-product, spacecraft and instrument engineering data which will be of interest to future mission planners. These data will be particularly useful because Spitzer operated in an environment essentially identical t

Mayu Ishii, Ryuji Okazaki, Masafumi Tamura

We performed third-order electrical conductivity measurements on the organic conductor $\alpha$-(BEDT-TTF)$_2$I$_3$ using an ac bridge technique sensitive to nonlinear signals. Third-order conductance $G_3$ is clearly observed even at low electric fields, and interestingly, $G_3$ is critically enhanced above the charge-order transition temperature $T_{\rm CO

Natalie Maus, Haydn T. Jones, Juston S. Moore, Matt J. Kusner

Bayesian optimization over the latent spaces of deep autoencoder models (DAEs) has recently emerged as a promising new approach for optimizing challenging black-box functions over structured, discrete, hard-to-enumerate search spaces (e.g., molecules). Here the DAE dramatically simplifies the search space by mapping inputs into a continuous latent space wher

Zhengwei Bai, Guoyuan Wu, Xuewei Qi, Yongkang Liu

Object detection plays a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of contemporary transportation systems. Although current computer vision technologies could provide satisfactory object detection results in occlusion-free scenar

Payam Karisani

We present a novel multiple-source unsupervised model for text classification under domain shift. Our model exploits the update rates in document representations to dynamically integrate domain encoders. It also employs a probabilistic heuristic to infer the error rate in the target domain in order to pair source classifiers. Our heuristic exploits data tran

César Ivan Victoria-Ceballos, Omaira González-Martín, Jacopo Fritz, Cristina Ramos Almeida

Thanks to ground-based infrared and sub-mm observations the study of the dusty torus of nearby AGN has greatly advanced in the last years. With the aim of further investigating the nuclear mid-infrared emission of the archetypal Seyfert 2 galaxy NGC1068, here we present a fitting to the N- and Q-band Michelle/Gemini spectra. We initially test several availab

Antoine Bruguier, Duc Le, Rohit Prabhavalkar, Dangna Li

We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transducers (FSTs) in a mathematically consistent framework. Our method utilizes a background NNLM which models generic background text together with a collection of domain-specific entitie

Jerry Wei, Lorenzo Torresani, Jason Wei, Saeed Hassanpour

The classification of histopathology images fundamentally differs from traditional image classification tasks because histopathology images naturally exhibit a range of diagnostic features, resulting in a diverse range of annotator agreement levels. However, examples with high annotator disagreement are often either assigned the majority label or discarded e

Jianyu Wang, Hang Qi, Ankit Singh Rawat, Sashank Reddi

In classical federated learning, the clients contribute to the overall training by communicating local updates for the underlying model on their private data to a coordinating server. However, updating and communicating the entire model becomes prohibitively expensive when resource-constrained clients collectively aim to train a large machine learning model.

William Franz Lamberti

White Blood Cell (WBC) Leukaemia is detected through image-based classification. Convolutional Neural Networks are used to learn the features needed to classify images of cells a malignant or healthy. However, this type of model requires learning a large number of parameters and is difficult to interpret and explain. Explainable AI (XAI) attempts to alleviat

Matthew Baker, Bhumika Mittal, Haran Mouli, Eric Tang

A balanced generalized de Bruijn sequence with parameters $(n,l,k)$ is a cyclic sequence of $n$ bits such that (a) the number of 0's equals the number of 1's, and (b) each substring of length $l$ occurs at most $k$ times. We determine necessary and sufficient conditions on $n,l$, and $k$ for the existence of such a sequence.

Paul Ross McWhirter, Marco C. Lam

Blue Large Amplitude Pulsators (BLAPs) are hot, subluminous stars undergoing rapid variability with periods of under 60 mins. They have been linked with the early stages of pre-white dwarfs and hot subdwarfs. They are a rare class of variable star due to their evolutionary history within interacting binary systems and the short timescales relative to their l

F Sgaramella, M Miliucci, M Bazzi, D Bosnar

The SIDDHARTA-2 experiment at the DA$Φ$NE collider aims to perform the first kaonic deuterium X-ray transitions to the fundamental level measurement, with a systematic error at the level of a few eV. To achieve this challenging goal the experimental apparatus is equipped with 384 Silicon Drift Detectors (SDDs) distributed around its cryogenic gaseous target.

Nathan Lambert, Markus Wulfmeier, William Whitney, Arunkumar Byravan

Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked processes of reinforcement learning: collecting informative experience and inferring optimal behaviour. The second step has been widely studied in the offline setting, but just as critical to data-efficient RL is the collection of informative data. The task-agnostic settin

Piyush Kumar Sharma, Devashish Gosain, Claudia Diaz

Cryptocurrency systems can be subject to deanonimization attacks by exploiting the network-level communication on their peer-to-peer network. Adversaries who control a set of colluding node(s) within the peer-to-peer network can observe transactions being exchanged and infer the parties involved. Thus, various network anonymity schemes have been proposed to

Mehmet Fatih Ozkan, Yao Ma

Human-leading truck platooning systems have been proposed to leverage the benefits of both human supervision and vehicle autonomy. Equipped with human guidance and autonomous technology, human-leading truck platooning systems are more versatile to handle uncertain traffic conditions than fully automated platooning systems. This paper presents a novel distrib

Sophia Gershman, Henry Fetsch, Fnu Gorky, Maria L. Carreon

Herein we demonstrate that the performance of mesoporous silica SBA-15 and SBA-15-Ag impregnated during plasma ammonia synthesis depend on the plasma conditions. At high power the mesoporous silica SBA-15 without Ag produces the largest amount of ammonia observed in our experiments, but the addition of Ag provides a minor benefit at lower powers. Plasma cond

Tai Hoang

Autonomous grasping remains challenging as unlike humans, robots do not possess a sophisticated sensing nor delicate interaction capability with the real environment. Among other efforts that tried to close the gap between them, anthropomorphic robotic hands is the most prominent direction. However, exactly following human hand design might be unnecessary as

William Franz Lamberti

Traditional machine learning (ML) algorithms, such as multiple regression, require human analysts to make decisions on how to treat the data. These decisions can make the model building process subjective and difficult to replicate for those who did not build the model. Deep learning approaches benefit by allowing the model to learn what features are importa

Francesco Armand Pilon, Yann-Michel Niquet, Jeremie Chretien, Nicolas Pauc

Efficient and cost-effective Si-compatible lasers are a long standing wish of the optoelectronic industry. In principle, there are two options. For many applications, lasers based on III-V compounds provide compelling solutions, even if the integration is complex and therefore costly. However, where low costs and also high integration density are crucial, gr

Yunfei Ge, Quanyan Zhu

Supply chain security has become a growing concern in security risk analysis of the Internet of Things (IoT) systems. Their highly connected structures have significantly enlarged the attack surface, making it difficult to track the source of the risk posed by malicious or compromised suppliers. This chapter presents a system-scientific framework to study th

Sarper Aydin, Sina Arefizadeh, Ceyhun Eksin

We investigate convergence of decentralized fictitious play (DFP) in near-potential games, wherein agents preferences can almost be captured by a potential function. In DFP agents keep local estimates of other agents' empirical frequencies, best-respond against these estimates, and receive information over a time-varying communication network. We prove that

Heting Liu, Zhichao Li, Cheng Tan, Rongqiu Yang

Graphics processing units (GPUs) are the de facto standard for processing deep learning (DL) tasks. Meanwhile, GPU failures, which are inevitable, cause severe consequences in DL tasks: they disrupt distributed trainings, crash inference services, and result in service level agreement violations. To mitigate the problem caused by GPU failures, we propose to

C. V. Vletter, H. L. Burger, H. Alers, N. Sourlos

Central palsy is a form of facial paralysis that requires urgent medical attention and has to be differentiated from other, similar conditions such as peripheral palsy. To aid in fast and accurate diagnosis of this condition, we propose a machine learning approach to automatically classify peripheral and central facial palsy. The Palda dataset is used, which

Lingfei Yi

We show that the Frenkel-Gross connection on $\mathbb{G}_m$ is physically rigid as $\check{G}$-connection, thus confirming the de Rham version of a conjecture of Heinloth-Ng\^o-Yun. The proof is based on the construction of the Hecke eigensheaf of a connection with only generic oper structure, using the localization of Weyl modules.

E. Naghd Mezerji, J. Sadeghi

In this paper, we focus on the possible correlation between conjectures KSS bound and weak gravity conjecture (WGC). The hydrodynamic values KSS bound and weak gravity conjecture constraint the low-energy effective field theory. These conjectures identify UV complete theories. We give four, six and eight order derivative corrections to corresponding action a

Gabriele Carcassi, Andrea Oldofredi, Christine A. Aidala

In this paper we show that $\psi$-ontic models, as defined by Harrigan and Spekkens (HS), cannot reproduce quantum theory. Instead of focusing on probability, we use information theoretic considerations to show that all pure states of $\psi$-ontic models must be orthogonal to each other, in clear violation of quantum mechanics. Given that (i) Pusey, Barrett

Ryan Magee, Ssohrab Borhanian

We describe realistic observing scenarios for early warning detection of binary neutron star mergers with the current generation of ground-based gravitational-wave detectors as these approach design sensitivity. Using Fisher analysis, we estimate that Advanced LIGO and Advanced Virgo will detect one signal before merger in their fourth observing run provided

Meghan Cowan, Saeed Maleki, Madanlal Musuvathi, Olli Saarikivi

Machine learning models made up of millions or billions of parameters are trained and served on large multi-GPU systems. As models grow in size and execute on more GPUs, the collective communications used in these applications become a bottleneck. Custom collective algorithms optimized for both particular network topologies and application specific communica

Brendan Creutz, Sheng Lu

We consider the local-global principle for divisibility in the Mordell-Weil group of a CM elliptic curve defined over a number field. For each prime $p$ we give sharp lower bounds on the degree $d$ of a number field over which there exists a CM elliptic curve which gives a counterexample to the local-global principle for divisibility by a power of $p$. As a

Yikuan Li, Ramsey M. Wehbe, Faraz S. Ahmad, Hanyin Wang

Transformers-based models, such as BERT, have dramatically improved the performance for various natural language processing tasks. The clinical knowledge enriched model, namely ClinicalBERT, also achieved state-of-the-art results when performed on clinical named entity recognition and natural language inference tasks. One of the core limitations of these tra

Amine Abouaomar, Soumaya Cherkaoui, Zoubeir Mlika, Abdellatif Kobbane

Low-Latency IoT applications such as autonomous vehicles, augmented/virtual reality devices and security applications require high computation resources to make decisions on the fly. However, these kinds of applications cannot tolerate offloading their tasks to be processed on a cloud infrastructure due to the experienced latency. Therefore, edge computing i

Pierre Mergny, Marc Potters

In this note we study the right large deviation of the top eigenvalue (or singular value) of the sum or product of two random matrices $\mathbf{A}$ and $\mathbf{B}$ as their dimensions goes to infinity. The matrices $\mathbf{A}$ and $\mathbf{B}$ are each assumed to be taken from an invariant (or bi-invariant) ensemble with a confining potential with a possib

Chao Zhang, Ling Tang, Yang Sun, Kai-Ming Ho

Using artificial neural-network machine learning (ANN-ML) to generate interatomic potentials has been demonstrated to be a promising approach to address the long-standing challenge of accuracy versus efficiency in molecular dynamics (MD) simulations. Here, taking the Fe-Si-O system as a prototype, we show that accurate and transferable ANN-ML potentials can

Jonas Wessén, Tanmoy Pal, Hue Sun Chan

Phase separation of several different overall neutral polyampholyte species (with zero net charge) is studied in solution with two oppositely charged ion species that can form ion-pairs through an association reaction. A field theory description of the system, that treats polyampholyte charge sequence dependent electrostatic interactions as well as excluded

Yuriy Drozd, Andriana Plakosh

We calculate explicitly cohomologies of the lattices over the Kleinian 4-group belonging to the regular components of the Auslander-Reiten quiver as well as of their dual modules. The result is applied to the classification of some crystallographic and Chernikov groups.

Julian Wechs, Cyril Branciard, Ognyan Oreshkov

It has been shown that it is theoretically possible for there to exist quantum and classical processes in which the operations performed by separate parties do not occur in a well-defined causal order. A central question is whether and how such processes can be realised in practice. In order to provide a rigorous argument for the notion that certain such pro

Amine Abouaomar, Zoubeir Mlika, Abderrahime Filali, Soumaya Cherkaoui

Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in

Amine Abouaomar, Soumaya Cherkaoui, Zoubeir Mlika, Abdellatif Kobbane

In this paper, we address the resource provisioning problem for service function chaining (SFC) in terms of the placement and chaining of virtual network functions (VNFs) within a multi-access edge computing (MEC) infrastructure to reduce service delay. We consider the VNFs as the main entities of the system and propose a mean-field game (MFG) framework to m

Amine Abouaomar, Soumaya Cherkaoui, Abdellatif Kobbane, Oussama Abderrahmane Dambri

Fog computing is emerging as a new paradigm to deal with latency-sensitive applications, by making data processing and analysis close to their source. Due to the heterogeneity of devices in the fog, it is important to devise novel solutions which take into account the diverse physical resources available in each device to efficiently and dynamically distribu

Shuangjun Liu, Sarah Ostadabbas

Computer vision has achieved great success in interpreting semantic meanings from images, yet estimating underlying (non-visual) physical properties of an object is often limited to their bulk values rather than reconstructing a dense map. In this work, we present our pressure eye (PEye) approach to estimate contact pressure between a human body and the surf

C. R. Hoffman, T. L. Tang, M. Avila, Y. Ayyad

An in-flight beam of $^{16}$N was produced via the single-neutron adding ($d$,$p$) reaction in inverse kinematics at the recently upgraded Argonne Tandem Linear Accelerator System (ATLAS) in-flight system. The amount of the $^{16}$N beam which resided in its excited 0.120-MeV $J^{\pi}=0^-$ isomeric state (T$_{1/2}\approx5$ $\mu$s) was determined to be 40(5)%

Amine Abouaomar, Abdellatif Kobbane, Soumaya Cherkaoui

Fog computing has emerged as a new paradigm in mobile network communications, aiming to equip the edge of the network with the computing and storing capabilities to deal with the huge amount of data and processing needs generated by the users' devices and sensors. Optimizing the assignment of users to fogs is, however, still an open issue. In this paper, we

Ayoub Ghriss, Bo Yang, Viktor Rozgic, Elizabeth Shriberg

We propose a novel multi-task pre-training method for Speech Emotion Recognition (SER). We pre-train SER model simultaneously on Automatic Speech Recognition (ASR) and sentiment classification tasks to make the acoustic ASR model more ``emotion aware''. We generate targets for the sentiment classification using text-to-sentiment model trained on publicly ava

Daniel Arnold, Sy-Toan Ngo, Ciaran Roberts, Yize Chen

Volt-VAR and Volt-Watt control functions are mechanisms that are included in distributed energy resource (DER) power electronic inverters to mitigate excessively high or low voltages in distribution systems. In the event that a subset of DER have had their Volt-VAR and Volt-Watt settings compromised as part of a cyber-attack, we propose a mechanism to contro

Alexander Wich, Holger Schultheis, Michael Beetz

A key challenge for robotic systems is to figure out the behavior of another agent. The capability to draw correct inferences is crucial to derive human behavior from examples. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally (observational evidence). For this reason, robots that rely on inf

Philip D. Powell, Gordon Baym, Carlos Sa de Melo

We obtain the superfluid transition temperature of equal Rashba-Dresselhaus spin-orbit and Rabi-coupled Fermi superfluids, from the Bardeen-Cooper-Schrieffer (BCS) to Bose-Einstein condensate (BEC) regimes in three dimensions for tunable $s$-wave interactions. In the presence of Rabi coupling, we find that spin-orbit coupling enhances (reduces) the critical

CHIME Collaboration, Mandana Amiri, Kevin Bandura, Anja Boskovic

We present a beam pattern measurement of the Canadian Hydrogen Intensity Mapping Experiment (CHIME) made using the Sun as a calibration source. As CHIME is a pure drift scan instrument, we rely on the seasonal North-South motion of the Sun to probe the beam at different elevations. This semiannual range in elevation, combined with the radio brightness of the

Hao Li, Filipe R. Cogo, Cor-Paul Bezemer

Cargo, the software packaging manager of Rust, provides a yank mechanism to support release-level deprecation, which can prevent packages from depending on yanked releases. Most prior studies focused on code-level (i.e., deprecated APIs) and package-level deprecation (i.e., deprecated packages). However, few studies have focused on release-level deprecation.