May 2022 arXiv papers — page 153

D. Alina, A. Shomanov, S. Baimukhametova

Filament identification became a key step to tackling fundamental problems in various fields of Astronomy. Nevertheless, existing filament identification algorithms are critically user-dependent and require individual parametrization. In this study, we aimed at adapting the neural networks approach to elaborate the best model for filament identification that

ATLAS Collaboration

Studies of the correlations of the two highest transverse momentum (leading) jets in individual Pb+Pb collision events can provide information about the mechanism of jet quenching by the hot and dense matter created in such collisions. In Pb+Pb and pp collisions at $\sqrt{s_{_\text{NN}}}$ = 5.02 TeV, measurements of the leading dijet transverse momentum ($p_

Bas van den Heuvel, Joseph W. N. Paulus, Daniele Nantes-Sobrinho, Jorge A. Pérez

We study functional and concurrent calculi with non-determinism, along with type systems to control resources based on linearity. The interplay between non-determinism and linearity is delicate: careless handling of branches can discard resources meant to be used exactly once. Here we go beyond prior work by considering non-determinism in its standard sense:

Antoine Bienvenu, Jonas Feldt, Julien Toulouse, Roland Assaraf

We propose to compute physical properties by Monte Carlo calculations using conditional expectation values. The latter are obtained on top of the usual Monte Carlo sampling by partitioning the physical space in several subspaces or fragments, and subsampling each fragment (i.e., performing side walks) while freezing the environment. No bias is introduced and

Han Xiang Choong, Yew-Soon Ong, Abhishek Gupta, Caishun Chen

For deep learning, size is power. Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These large pre-trained models or Jacks of All Trades (JATs), when fine-tuned for downstream tasks, are gaining importance in driving deep learning advancements. However, environments with tight resource constra

Sayed Kamaledin Ghiasi-Shirazi

Linear discriminant analysis (LDA) is a fundamental method for feature extraction and dimensionality reduction. Despite having many variants, classical LDA has its own importance, as it is a keystone in human knowledge about statistical pattern recognition. For a dataset containing C clusters, the classical solution to LDA extracts at most C-1 features. Here

Yixin Cheng

Artificial intelligence and semantic technologies are evolving and have been applied in various research areas, including the education domain. Higher Education institutions strive to improve students' academic performance. Early intervention to at-risk students and a reasonable curriculum is vital for students' success. Prior research opted for depl

Michael Weiss, Paolo Tonella

Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important technique to handle the typically very large test datasets efficiently, saving computation and labeling costs. This is particularly true for large-scale, deployed systems, where inputs observed in production are recorded to serve as potential test or training data for the next vers

Janmey Jay Panda, Krishna Rani Sahoo, Aparna Praturi, Ashique Lal

Magnetic/non-magnetic/heterostructured ultra-thin films' characterisation is highly demanding due to the emerging diverse applications of such films. Diverse measurements are usually performed on such systems to infer their electrical, optical and magnetic properties. We demonstrate that MOKE-based spin-Hall effect of light (SHEL) is a versatile surface char

Yeqi Gao, Zhao Song, Baocheng Sun, Omri Weinstein

We revisit the classical problem of Fourier-sparse signal reconstruction -- a variant of the \emph{Set Query} problem -- which asks to efficiently reconstruct (a subset of) a $d$-dimensional Fourier-sparse signal ($\|\hat{x}(t)\|_0 \leq k$), from minimum \emph{noisy} samples of $x(t)$ in the time domain. We present a unified framework for this problem by dev

Daisuke Maekawa, Hal Tasaki

The qualitative difference in low-energy properties of spin $S$ quantum antiferromagnetic chains with integer $S$ and half-odd-integer $S$ discovered by Haldane can be intuitively understood in terms of the valence-bond picture proposed by Affleck, Kennedy, Lieb, and Tasaki. Here we develop a similarly intuitive diagrammatic explanation of the qualitative di

Masafumi Hayashi, So Oshiro, Masato Takei

The one-dimensional elephant random walk is a typical model of discrete-time random walk with step-reinforcement, and is introduced by Schütz and Trimper (2004). It has a parameter $α\in (-1,1)$: The case $α=0$ corresponds to the simple symmetric random walk, and when $α>0$ (resp. $α<0$), the mean displacement of the walker at time $n$ grows (resp. vanishes)

Ashwin Verma, Marcos M. Vasconcelos, Urbashi Mitra, Behrouz Touri

Consider a set of agents collaboratively solving a distributed convex optimization problem, asynchronously, under stringent communication constraints. In such situations, when an agent is activated and is allowed to communicate with only one of its neighbors, we would like to pick the one holding the most informative local estimate. We propose new algorithms

Dejia Kong, Wei-Ren Chen, Ke-Qi Zeng, Lionel Porcar

We propose a microscopic picture for understanding the nonlinear rheology of supercooled liquids with soft-repulsive potentials. Based on Brownian dynamics simulations of supercooled charge-stabilized colloidal suspensions, our analysis shows that the shear thinning of viscosity (eta) at large enough shear rates (sr), expressed as eta~sr^(-lambda), originate

Daniel O'Malley, Jessie M. Henderson, Elijah Pelofske, Sarah Greer

Quantum algorithms for solving linear systems of equations have generated excitement because of the potential speed-ups involved and the importance of solving linear equations in many applications. However, applying these algorithms can be challenging. The Harrow-Hassidim-Lloyd algorithm and improvements thereof require complex subroutines suitable for fault

Jason Gaitonde, Max Hopkins, Tali Kaufman, Shachar Lovett

We study the relationship between the underlying structure of posets and the spectral and combinatorial properties of their higher-order random walks. While fast mixing of random walks on hypergraphs has led to myriad breakthroughs throughout theoretical computer science in the last five years, many other important applications (e.g. locally testable codes,

Jay Prakash Singh, Partha Sarathi Mondal, Vivek Semwal, Shradha Mishra

We studied a system of polar self-propelled particles (SPPs) on a thin rectangular channel designed into three regions of order-disorder-order. The division of the three regions is made on the basis of the noise SPPs experience in the respective regions. The noise in the two wide region is chosen lower than the critical noise of order-disorder transition and

Hadeel Soliman, Lingfei Zhao, Zhipeng Huang, Subhadeep Paul

The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or events between all pairs of nodes are conditionally independent given the block or community memberships, which prevents them from reproducing higher-order moti

Mohsen Sabbaghi, Tobias Stauber, Hyun-Woo Lee, J. Sebastian Gomez-Diaz

In this work, we study the in-plane optical phonon modes of current-carrying single-layer graphene whose coupling to the $π$ electron gas is strong. Such modes are expected to undergo a frequency shift compared to the non-current-carrying state due to the non-equilibrium occupation of the Dirac cone electronic eigen-states with the flowing $π$ electron gas.

Cheng Luo, Siyang Song, Weicheng Xie, Linlin Shen

The activations of Facial Action Units (AUs) mutually influence one another. While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display. This paper proposes an AU relationship modelling approach that deep learns a unique graph

Robin Koytcheff

We study homotopy groups of spaces of long links in Euclidean space of codimension at least three. With multiple components, they admit split injections from homotopy groups of spheres. We show that, up to knotting, these account for all the homotopy groups in a range which depends on the dimensions of the source manifolds and target manifold and which rough

Qinghui Sun, Constantine P. Deliyannis, Bruce A. Twarog, Barbara J. Anthony-Twarog

We present WIYN/Hydra spectra of 34 red giant candidate members of NGC 188, which, together with WOCS and Gaia data yield 23 single members, 6 binary members, 4 single nonmembers, and 1 binary nonmember. We report [Fe/H] for 29 members and derive [Fe/H]$_{\rm{NGC188}}$ = +0.064 $\pm$ 0.018 dex ($σ_μ$) (sky spectra yield A(Fe)$_{\odot}$ = 7.520 $\pm$ 0.015 de

Axel Wismueller, Larry Stockmaster, Ali Vosoughi

There is an urgent need for streamlining radiology Quality Assurance (QA) programs to make them better and faster. Here, we present a novel approach, Artificial Intelligence (AI)-Based QUality Assurance by Restricted Investigation of Unequal Scores (AQUARIUS), for re-defining radiology QA, which reduces human effort by up to several orders of magnitude over

Yuqian Chen, Chaoyi Zhang, Tengfei Xue, Yang Song

White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. Whi

Meiby Ortiz-Bouza, Selin Aviyente

Networks are commonly used to model complex systems. The different entities in the system are represented by nodes of the network and their interactions by edges. In most real life systems, the different entities may interact in different ways necessitating the use of multiplex networks where multiple links are used to model the interactions. One of the majo

Yuanming Wang, Tara Murphy, David L. Kaplan, Teresa Klinner-Teo

We report the discovery of a highly circularly polarized, variable, steep-spectrum pulsar in the Australian Square Kilometre Array Pathfinder (ASKAP) Variables and Slow Transients (VAST) survey. The pulsar is located about $1^\circ$ from the center of the Large Magellanic Cloud, and has a significant fractional circular polarization of $\sim$20%. We discover

Dawei Wang, Kirill Serkh, Christina Christara

This paper presents a high-order deferred correction algorithm combined with penalty iteration for solving free and moving boundary problems, using a fourth-order finite difference method. Typically, when free boundary problems are solved on a fixed computational grid, the order of the solution is low due to the discontinuity in the solution at the free boun

Hoi-Kwong Lo, Mattia Montagna, Manfred von Willich

We propose and implement a protocol for a scalable, cost-effective, information-theoretically secure key distribution and management system. The system, called Distributed Symmetric Key Establishment (DSKE), relies on pre-shared random numbers between DSKE clients and a group of Security Hubs. Any group of DSKE clients can use the DSKE protocol to distill fr

Alex Buchel

The holographic duality between cascading gauge theory and type IIB supergravity on warped deformed conifold with fluxes reveals exotic thermal phases with nonzero expectation values of certain operators, persistent to high temperatures. These phases, in the limit of vanishing the strong coupling scale of the cascading gauge theory, would realize thermal ord

Yu-Eop Kang, Sunwoong Yang, Kwanjung Yee

Autoencoder-based reduced-order modeling (ROM) has recently attracted significant attention, owing to its ability to capture underlying nonlinear features. However, two critical drawbacks severely undermine its scalability to various physical applications: entangled and therefore uninterpretable latent variables (LVs) and the blindfold determination of laten

Aleksei Golota

Let $X$ be a complex projective variety. Suppose that the group of birational automorphisms of $X$ contains finite subgroups isomorphic to $(\mathbb{Z}/N\mathbb{Z})^r$ for $r$ fixed and $N$ arbitrarily large. We show that $r$ does not exceed $2\dim(X)$. Moreover, the equality holds if and only if $X$ is birational to an abelian variety. We also show that an

C. Ashall, J. Lu, B. J. Shappee, C. R. Burns

We present early-time photometric and spectroscopic observations of the Type Ia Supernova (SN Ia) 2021aefx. The early time u-band light curve shows an excess flux when compared to normal SNe Ia. We suggest that the early-excess blue flux may be due to a rapid change in spectral velocity in the first few days post explosion, produced by the emission of the Ca

Udai Nagpal, Krishan Nagpal

This paper describes an approach to simultaneously identify clusters and estimate cluster-specific regression parameters from the given data. Such an approach can be useful in learning the relationship between input and output when the regression parameters for estimating output are different in different regions of the input space. Variational Inference (VI

Ruben Jakob

Here we continue the investigation of the M\"obius-invariant Willmore flow (MIWF), starting to move in arbitrary smooth and umbilic-free initial immersions $F_0$ which map some fixed compact torus $\Sigma$ into $\mathbb{R}^n$ respectively $\mathbb{S}^n$. Here we investigate the behaviour of flow lines $\{F_t\}$ of the MIWF in $\mathbb{S}^3$ starting with rel

Daniel Frolovsky, Sergei V. Ketov, Sultan Saburov

We adapted the Appleby-Battye-Starobinsky model of $F(R)$ gravity towards describing double cosmological inflation and formation of primordial black holes with masses up to $10^{19}$ g in the single-field model. We found that it is possible to get an enhancement of the power spectrum of scalar curvature perturbations to the level beyond the Hawking (black ho

Takayoshi Yamanaka, Ryuji Okazaki, Hiroshi Yaguchi

The layered perovskite Sr$_2$RuO$_4$ is a most intensively studied superconductor, but its pairing mechanism, which is often coupled intimately with magnetic fluctuations in correlated materials, is still an open question. Here we present a systematic evolution of the Seebeck coefficient in Co- and Mn-substituted Sr$_2$RuO$_4$ single crystals, in which ferro

Richard Evan Schwartz

In this paper we will give a short and direct proof that Wolfgang Kuehnel&#39;s 9-vertex triangulation of the complex projective plane really is the complex projective plane. The idea of our proof is to recall the trisection of the complex projective plane into 3 bi-disks and then to see this trisection inside a symmetry-breaking subdivision of the triangula

Mingchen Liu, Robert M. Gray, Arkadev Roy, Kirk A. Ingold

Powerful and efficient optical frequency combs in the mid-infrared (MIR) spectral region are highly desirable for a broad range of applications. Despite extensive efforts utilizing various techniques, MIR frequency comb sources are still lacking power, efficiency, or bandwidth for many applications. Here, we report the generation of an intrinsically locked f

Qianfan Chen, Ivar Martin, Liang Jiang, Dafei Jin

A single electron floating on the surface of a condensed noble-gas liquid or solid can act as a spin qubit with ultralong coherence time, thanks to the extraordinary purity of such systems. Previous studies suggest that the electron spin coherence time on a superfluid helium (He) surface can exceed 100 s. In this paper, we present theoretical studies of the

Jiashuo Jiang, Will Ma, Jiawei Zhang

Prophet inequalities consist of many beautiful statements that establish tight performance ratios between online and offline allocation algorithms. Typically, tightness is established by constructing an algorithmic guarantee and a worst-case instance separately, whose bounds match as a result of some "ingenuity". In this paper, we instead formulate the const

Nasr Ahmed, Ajab A. Alfreedi, Alaa A. Alzulaibani

We explore some cosmological features of the newly suggested 4D Gauss-Bonnet gravity through two different models assuming a varying cosmological constant. Observational constraints, such as the cosmic transit and the flat curvature, have been considered in constructing the models. The cosmology in the current work has been probed using a given scale factor

A. H. Nzokem, V. T. Montshiwa

The paper investigates the rich class of Generalized Tempered Stable distribution, an alternative to Normal distribution and the $α$-Stable distribution for modelling asset return and many physical and economic systems. Firstly, we explore some important properties of the Generalized Tempered Stable (GTS) distribution. The theoretical tools developed are use

Ali Ahmadvand, Negar Arabzadeh, Julia Kiseleva, Patricio Figueroa Sanz

Current interactive systems with natural language interfaces lack the ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences e.g.,&#34;find hiking trails around San Francisco which are accessible with toddlers and have beautiful scenery in summ

Tinkle Chugh

Bayesian optimisation (BO) has been widely used to solve problems with expensive function evaluations. In multi-objective optimisation problems, BO aims to find a set of approximated Pareto optimal solutions. There are typically two ways to build surrogates in multi-objective BO: One surrogate by aggregating objective functions (by using a scalarising functi

Hui Liu, Yibiao Huang, Xuejun Liu, Lei Deng

Accurate and efficient prediction of the molecular properties of drugs is one of the fundamental problems in drug research and development. Recent advancements in representation learning have been shown to greatly improve the performance of molecular property prediction. However, due to limited labeled data, supervised learning-based molecular representation

Mark Reid, James Montgomery, Barry Drake, Avraham Ruderman

This document presents the draft specification for delivering machine learning services over HTTP, developed as part of the Protocols and Structures for Inference project, which concluded in 2013. It presents the motivation for providing machine learning as a service, followed by a description of the essential and optional components of such a service.

Anurag Pritam, Ritu Gupta, Prakash Chandra Mondal

Memory has always been a building block element for information technology. Emerging technologies such as artificial intelligence, big data, the internet of things, etc., require a novel kind of memory technology that can be energy efficient and have an exception data retention period. Among several existing memory technologies, resistive random-access memor

Daniel Zhang, Nestor Maslej, Erik Brynjolfsson, John Etchemendy

Welcome to the fifth edition of the AI Index Report! The latest edition includes data from a broad set of academic, private, and nonprofit organizations as well as more self-collected data and original analysis than any previous editions, including an expanded technical performance chapter, a new survey of robotics researchers around the world, data on globa

Yi-An Chen, Jien-De Sui, Tian-Sheuan Chang

Gait phase detection with convolution neural network provides accurate classification but demands high computational cost, which inhibits real time low power on-sensor processing. This paper presents a segmentation based gait phase detection with a width and depth downscaled U-Net like model that only needs 0.5KB model size and 67K operations per second with

Kuo-Wei Chang, Tian-Sheuan Chang

Hardware accelerator for convolution neural network (CNNs) enables real time applications of artificial intelligence technology. However, most of the accelerators only support dense CNN computations or suffers complex control to support fine grained sparse networks. To solve above problem, this paper presents an efficient CNN accelerator with 1-D vector broa

Kuo-Wei Chang, Tian-Sheuan Chang

Hardware accelerators for convolution neural networks (CNNs) enable real-time applications of artificial intelligence technology. However, most of the existing designs suffer from low hardware utilization or high area cost due to complex dataflow. This paper proposes a hardware efficient vectorwise CNN accelerator that adopts a 3$\times$3 filter optimized sy

Justus Mattern, Benjamin Weggenmann, Florian Kerschbaum

As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private mechanisms to perturb word embeddings, thus replacing individual words in a sentence. While these methods represent very imp

Kuo-Wei Chang, Tian-Sheuan Chang

Hardware acceleration for dilated and transposed convolution enables real time execution of related tasks like segmentation, but current designs are specific for these convolutional types or suffer from complex control for reconfigurable designs. This paper presents a design that decomposes input or weight for dilated and transposed convolutions respectively

Chih-Chyau Yang, Tian-Sheuan Chang

With the popularity of the deep neural network (DNN), hardware accelerators are demanded for real time execution. However, lengthy design process and fast evolving DNN models make hardware evaluation hard to meet the time to market need. This paper proposes a pre-RTL DNN hardware evaluator that supports conventional layer-by-layer processing as well as the f

Hsiao-Shan Huang, Tian-Sheuan Chang, Jhih-Yi Wu

There is a great interest in many approaches towards blockchain in providing a solution to record transactions in a decentralized way. However, there are some limitations when storing large files or documents on the blockchain. In order to meet the requirements of storing relatively large data, a decentralized storage medium is produced. IPFS is a distribute

Razieh Faghihpirayesh, Davood Karimi, Deniz Erdogmus, Ali Gholipour

Fetal brain segmentation is an important first step for slice-level motion correction and slice-to-volume reconstruction in fetal MRI. Fast and accurate segmentation of the fetal brain on fetal MRI is required to achieve real-time fetal head pose estimation and motion tracking for slice re-acquisition and steering. To address this critical unmet need, in thi

Shoukun Sun, Min Xian, Aleksandar Vakanski, Hossny Ghanem

Robust self-training (RST) can augment the adversarial robustness of image classification models without significantly sacrificing models&#39; generalizability. However, RST and other state-of-the-art defense approaches failed to preserve the generalizability and reproduce their good adversarial robustness on small medical image sets. In this work, we propos

Inês P. Machado, Esther Puyol-Antón, Kerstin Hammernik, Gastão Cruz

Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this paper, we present a fully-automated,

Kuo-Wei Chang, Hsu-Tung Shih, Tian-Sheuan Chang, Shang-Hong Tsai

Memory bandwidth has become the real-time bottleneck of current deep learning accelerators (DLA), particularly for high definition (HD) object detection. Under resource constraints, this paper proposes a low memory traffic DLA chip with joint hardware and software optimization. To maximize hardware utilization under memory bandwidth, we morph and fuse the ob

Tzu-Hsuan Chen, Tian Sheuan Chang

Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes advantages of the uneven range distribution of different LiDAR laser beams to propose a range aware instance segmentation network, RangeSeg. RangeSeg uses a shared encoder backbone w

Shu-Hung Kuo, Tian-Sheuan Chang

Computing-in-memory (CIM) has attracted significant attentions in recent years due to its massive parallelism and low power consumption. However, current CIM designs suffer from large area overhead of small CIM macros and bad programmablity for model execution. This paper proposes a programmable CIM processor with a single large sized CIM macro instead of mu

D. Keller

We propose to measure neutron and deuteron transversity TMDs. The quark transversity distributions of the nucleon are decoupled from the deuteron gluon transversity in the $Q^2$ evolution due to the chiral-odd property in the transversely-polarized target. The gluon transversity TMD only exists for targets of spin greater or equal to 1 and does not mix with

Anoumou Attiogbe, Mouahmed Moustapha Fall, Tobias Weth

We consider the geometric evolution problem of entire graphs moving by fractional mean curvature. For this, we study the associated nonlocal quasilinear evolution equation satisfied by the family of graph functions. We establish, using an analytic semigroup approach, short time existence, uniqueness and optimal Hölder regularity in time and space of classica

Xichao Zhang, Jing Xia, Xiaoxi Liu

The competition between external drives and interparticle interactions are important for the static and dynamic properties of topological quasiparticles in confined geometries. Here, we study the dynamics of particlelike skyrmions under a current-induced compression, where skyrmions are stabilized in a confined space and form a skyrmion solid. We find that a

Filipo Sharevski, Amy Devine, Emma Pieroni, Peter Jacnim

Warning users about misinformation on social media is not a simple usability task. Soft moderation has to balance between debunking falsehoods and avoiding moderation bias while preserving the social media consumption flow. Platforms thus employ minimally distinguishable warning tags with generic text under a suspected misinformation content. This approach r

Joseph Eremondi, Ronald Garcia, Éric Tanter

Gradual dependent types can help with the incremental adoption of dependently typed code by providing a principled semantics for imprecise types and proofs, where some parts have been omitted. Current theories of gradual dependent types, though, lack a central feature of type theory: propositional equality. Lennon-Bertrand et al. show that, when the reflexiv

Juncheng Tong, Chunyan Wang

The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good processing quality and r

Hrishikesh D Vinod

We show that the abstract and conclusion of Hansen&#39;s {\it Econometrica} paper, \cite{Hansen22}, entitled a modern Gauss-Markov theorem (MGMT), obscures a material fact, which in turn can confuse students. The MGMT places ordinary least squares (OLS) back on a high pedestal by bringing in the Cramer-Rao efficiency bound. We explain why linearity and unbia

Nicolas Charon, Laurent Younes

This paper reviews several Riemannian metrics and evolution equations in the context of diffeomorphic shape analysis. After a short review of of various approaches at building Riemannian spaces of shapes, with a special focus on the foundations of the large deformation diffeomorphic metric mapping algorithm, the attention is turned to elastic metrics, and to

Edward W. Staley, Jared Markowitz

One of the most fundamental design choices in neural networks is layer width: it affects the capacity of what a network can learn and determines the complexity of the solution. This latter property is often exploited when introducing information bottlenecks, forcing a network to learn compressed representations. However, such an architecture decision is typi

Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi, Danica J. Sutherland

Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more images with only image-level labels. Most existing SWSSS algorithms extract pixel-level pseudo-labels from an image classifier - a very difficult task to do well, hence requiring com

Maede Zolanvari, Zebo Yang, Khaled Khan, Raj Jain

Despite AI&#39;s significant growth, its &#34;black box&#34; nature creates challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in IoT high-risk applications, such as critical industrial infrastructures, medical systems, and financial applications, etc. Explainable AI (XAI) has emerged to help with this problem. However,

Maede Zolanvari, Ali Ghubaish, Raj Jain

When dealing with the Internet of Things (IoT), especially industrial IoT (IIoT), two manifest challenges leap to mind. First is the massive amount of data streaming to and from IoT devices, and second is the fast pace at which these systems must operate. Distributed computing in the form of edge/cloud structure is a popular technique to overcome these two c

Hamed Zamani, Fernando Diaz, Mostafa Dehghani, Donald Metzler

Although information access systems have long supported people in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to include task-driven machines, such as machine learning models. In this way, the core principles of indexing, representation, retrieval, and ranking can be applied and extended to subs

Quang-Trung Luu, Olivier Brun, Rachid El-Azouzi, Francesco De Pellegrini

Datacenter networks routinely support the data transfers of distributed computing frameworks in the form of coflows, i.e., sets of concurrent flows related to a common task. The vast majority of the literature has focused on the problem of scheduling coflows for completion time minimization, i.e., to maximize the average rate at which coflows are dispatched

Andrew McDonald, Pang-Ning Tan, Lifeng Luo

Normalizing flows, a popular class of deep generative models, often fail to represent extreme phenomena observed in real-world processes. In particular, existing normalizing flow architectures struggle to model multivariate extremes, characterized by heavy-tailed marginal distributions and asymmetric tail dependence among variables. In light of this shortcom

Maximilian Mueller, Robin Greif, Frank Jenko, Nils Thuerey

We investigate uncertainty estimation and multimodality via the non-deterministic predictions of Bayesian neural networks (BNNs) in fluid simulations. To this end, we deploy BNNs in three challenging experimental test-cases of increasing complexity: We show that BNNs, when used as surrogate models for steady-state fluid flow predictions, provide accurate phy

Ashley Nunes, Chung Yi See, Lucas Woodley, Nicole A. Divers

Government efforts to address child poverty commonly encompass economic assistance programs that bolster household income. The Child Tax Credit (CTC) is the most prominent example of this. Introduced by the United States Congress in 1997, the program endeavors to help working parents via income stabilization. Our work examines the extent to which the CTC has

Vojtěch Kulvait, Josef Málek, K. R. Rajagopal

In this paper we study the deformation of a body with a notch subject to an anti-plane state of stress within the context of a new class of elastic models. These models stem as approximations of constitutive response functions for an elastic body that is defined within the context of an implicit constitutive relation between the stress and the deformation gr

Rahul Sarkar

The unique prime factorization theorem is used to show the existence of a function on a countable set $\mathcal{X}$ so that the sum aggregator function is injective on all multisets of $\mathcal{X}$ of finite size.

Andrea Pizzo, Angel Lozano, Sundeep Rangan, Thomas Marzetta

We provide a deterministic channel model for a scenario where wireless connectivity is established through a reflection from a planar smooth surface of an infinite extent. The developed model is rigorously built upon the physics of wave propagation, and is as precise as tight are the unboundedness and smoothness assumptions on the surface. This model allows

Rylan Schaeffer, Gabrielle Kaili-May Liu, Yilun Du, Scott Linderman

Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents. Here, we attack learning under all three conditions (unsupervised, streaming, non-stationary) in the context of clustering, also known as mixture modeling. We introduce a novel clusteri

J. Morgan, B. W. J. McNeil

A method to temporally tailor the properties of X-ray radiation carrying Orbital Angular Momentum (OAM) is presented. In simulations, an electron beam is prepared with a temporally modulated micro-bunching structure which, when radiating at the second harmonic in a helical undulator, generates OAM light with a corresponding temporally modulated intensity. Th

Mathieu Goutay

Innovation in the physical layer of communication systems has traditionally been achieved by breaking down the transceivers into sets of processing blocks, each optimized independently based on mathematical models. Conversely, deep learning (DL)-based systems are able to handle increasingly complex tasks for which no tractable models are available. This thes

Jonathan T. Willman, Kien Nguyen-Cong, Ashley S. Williams, Anatoly B. Belonoshko

A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed for simulations of carbon at extreme pressures (up to 5 TPa) and temperatures (up to 20,000 K). This was achieved using a large database of experimentally relevant quantum molecular dynamics (QMD) data, training the SNAP potential using a robust machine learn

Simon Thébaud, Tom Berlijn, Lucas Lindsay

Lowest-order quantum perturbation theory (Fermi&#39;s golden rule) for phonon-disorder scattering has been used to predict thermal conductivities in several semiconducting alloys with surprising success given its underlying hypothesis of weak and dilute disorder. In this paper, we explain how this is possible by focusing on the case of maximally mass-disorde

Mounika Marreddy, Subba Reddy Oota, Lakshmi Sireesha Vakada, Venkata Charan Chinni

Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc. However, the performance is achieved by testing and reporting on resource-rich languages like English. Applying GCN for multi-task text classification is an unexplored area. Moreover, training a GCN or ado

S. Duarte Puertas, J. M. Vilchez, J. Iglesias-Páramo, M. Mollá

In this work we study the stellar mass -- metallicity relation (MZR) of an extended sample of star-forming galaxies in the local Universe and its possible dependence with the star formation rate (SFR). A sample of $\sim$195000 Sloan Digital Sky Survey (SDSS) star-forming galaxies has been selected up to z=0.22 with the aim of analysing the behaviour of the r

Jaime E. Pineda, Jorma Harju, Paola Caselli, Olli Sipilä

Spectral lines of ammonia, NH$_3$, are useful probes of the physical conditions in dense molecular cloud cores. In addition to advantages in spectroscopy, ammonia has also been suggested to be resistant to freezing onto grain surfaces, which should make it a superior tool for studying the interior parts of cold, dense cores. Here we present high-resolution N

Mikhail Lifshits, Arman Tadevosian

We consider the asymptotic behavior of the expectation of the maximum for a special assignment process with constant or i.i.d. coefficients. We show how it depends on the coefficients&#39; distribution.

Zhening Huang, Weiwei Chen, Abir Al-Tabbaa, Ioannis Brilakis

Crack detection plays a key role in automated pavement inspection. Although a large number of algorithms have been developed in recent years to further boost performance, there are still remaining challenges in practice, due to the complexity of pavement images. To further accelerate the development and identify the remaining challenges, this paper conducts

Hengyi Wang, Changjae Oh

We present a refinement framework to boost the performance of pre-trained semi-supervised video object segmentation (VOS) models. Our work is based on scale inconsistency, which is motivated by the observation that existing VOS models generate inconsistent predictions from input frames with different sizes. We use the scale inconsistency as a clue to devise

A. Martinez Torres, Brenda B. Malabarba, Xiu-Lei Ren, K. P. Khemchandani

In this talk we present a summary of our latest results on the investigation of three-body systems with explicit/hidden charm and with explicit/hidden strangeness. To be more concrete, in case of explicit strangeness quantum number, we pay attention on the $K D D $ and $K D\bar D^*$ systems, where, in the former, a charm $+2$, isospin $1/2$ and strangeness $

Shunran Li, Zhenghong Dai, Linda Li, Nitin P. Padture

Understanding thermal transport at the micro- to nanoscale is crucially important for a wide range of technologies ranging from device thermal management and protection systems to thermal-energy regulation and harvesting. In the past decades, non-contact optical methods such as time-domain and frequency-domain thermoreflectance (TDTR and FDTR) have emerged a

Marcelo Gleiser

Most amino acids and sugars molecules occur in mirror, or chiral, images of each other, knowns as enantiomers. However, life on Earth is mostly homochiral: proteins contain almost exclusively L-amino acids, while only D-sugars appear in RNA and DNA. The mechanism behind this fundamental asymmetry of life remains unknown, despite much progress in the theoreti

Michelle Chalupnik, Hans Melo, Yuri Alexeev, Alexey Galda

The quantum approximate optimization algorithm (QAOA) promises to solve classically intractable computational problems in the area of combinatorial optimization. A growing amount of evidence suggests that the originally proposed form of the QAOA ansatz is not optimal, however. To address this problem, we propose an alternative ansatz, which we call QAOA+, th

Jakub Gajarský, Lars Jaffke, Paloma T. Lima, Jana Novotná

We confirm a conjecture of Gartland and Lokshtanov [arXiv:2007.08761]: if for a hereditary graph class $\mathcal{G}$ there exists a constant $k$ such that no member of $\mathcal{G}$ contains a $k$-creature as an induced subgraph or a $k$-skinny-ladder as an induced minor, then there exists a polynomial $p$ such that every $G \in \mathcal{G}$ contains at most

Satyajit Patra, Jean-Benoît Claude, Jérôme Wenger

Zero-mode waveguide (ZMW) nanoapertures are widely used to monitor single molecules beyond the range accessible to normal microscopes. However, several aspects of the ZMW influence on the photophysics of fluorophores remain inadequately documented and sometimes controversial. Here, we thoroughly investigate the ZMW influence on the fluorescence of single imm

Henry B. Welles

In this critique, we examine the technical report by Daniel Uribe entitled &#34;P vs. NP.&#34; The paper claims to show an exponential lower bound on the runtime of algorithms that decide CLIQUE. We show that the paper&#39;s proofs fail to generalize to all possible algorithms and that, even on those algorithms to which the proofs do apply, the proofs&#39; a

Ghazal Mirabnahrazam, Da Ma, Cédric Beaulac, Sieun Lee

Dementia of Alzheimer&#39;s Type (DAT) is a complex disorder influenced by numerous factors, but it is unclear how each factor contributes to disease progression. An in-depth examination of these factors may yield an accurate estimate of time-to-conversion to DAT for patients at various disease stages. We used 401 subjects with 63 features from MRI, genetic,