May 2022 arXiv papers — page 130

Lutz Bornmann, Robin Haunschild, Werner Marx

RPYS is a bibliometric method originally introduced in order to reveal the historical roots of research topics or fields. RPYS does not identify the most highly cited papers of the publication set being studied (as is usually done by bibliometric analyses in research evaluation), but instead it indicates most frequently referenced publications - each within

Kazuhiro Sato, Shun Terasaki

To appropriately select control nodes of a large-scale network system, we propose two control centralities called volumetric and average energy controllability scores. The scores are the unique solutions to convex optimization problems formulated using the controllability Gramian. The uniqueness is proven for stable cases and for unstable cases that include

Ze-Tong Li, Fan-Xu Meng, Han Zeng, Zai-Chen Zhang

Variational quantum eigensolver (VQE), aiming at determining the ground state energy of a quantum system described by a Hamiltonian on noisy intermediate scale quantum (NISQ) devices, is among the most significant applications of variational quantum algorithms (VQAs). However, the accuracy and trainability of the current VQE algorithm are significantly influ

Martin Malmsten, Chris Haffenden, Love Börjeson

This paper explains our work in developing new acoustic models for automated speech recognition (ASR) at KBLab, the infrastructure for data-driven research at the National Library of Sweden (KB). We evaluate different approaches for a viable speech-to-text pipeline for audiovisual resources in Swedish, using the wav2vec 2.0 architecture in combination with s

Swapan Das

The cross section of the photonuclear reaction in the nucleon-resonance region is calculated to search the resonance-nucleon scattering amplitude in the nucleus. It is assumed that the resonance $R$ is produced and decayed in the elementary photon-nucleon $(\gamma N)$ reaction in the nucleus as $\gamma N \to R \to \gamma N$. The resonance interacts with the

Ryojun Ito

In this paper, we consider a modular form of weight 3, which is a product of the Borweins theta series, and express its $L$-values at $s=1$, $2$ and $3$ in terms of special values of Kampé de Fériet hypergeometric functions, which are two-variable generalization of generalized hypergeometric functions.

Nicholas H. Barbara, Timothy R. Bedding, Ben D. Fulcher, Simon J. Murphy

With the availability of large-scale surveys like Kepler and TESS, there is a pressing need for automated methods to classify light curves according to known classes of variable stars. We introduce a new algorithm for classifying light curves that compares 7000 time-series features to find those which most effectively classify a given set of light curves. We

Yash Madhani, Sushane Parthan, Priyanka Bedekar, Gokul NC

Transliteration is very important in the Indian language context due to the usage of multiple scripts and the widespread use of romanized inputs. However, few training and evaluation sets are publicly available. We introduce Aksharantar, the largest publicly available transliteration dataset for Indian languages created by mining from monolingual and paralle

Md Ashiqur Rahman, Manuel A. Florez, Anima Anandkumar, Zachary E. Ross

We propose the generative adversarial neural operator (GANO), a generative model paradigm for learning probabilities on infinite-dimensional function spaces. The natural sciences and engineering are known to have many types of data that are sampled from infinite-dimensional function spaces, where classical finite-dimensional deep generative adversarial netwo

Linas Stripinis, Remigijus Paulavičius

This article considers a box-constrained global optimization problem for Lipschitz-continuous functions with an unknown Lipschitz constant. Motivated by the famous DIRECT (DIviding RECTangles), a new HALRECT (HALving RECTangles) algorithm is introduced. A new deterministic approach combines halving (bisection) with a new multi-point sampling scheme in contra

Raman Arora, Raef Bassily, Cristóbal Guzmán, Michael Menart

We study the problem of $(\epsilon,\delta)$-differentially private learning of linear predictors with convex losses. We provide results for two subclasses of loss functions. The first case is when the loss is smooth and non-negative but not necessarily Lipschitz (such as the squared loss). For this case, we establish an upper bound on the excess population r

Jian Song, Meng Wang

This article is concerned with stochastic control problems for backward doubly stochastic differential equations of mean-field type, where the coefficient functions depend on the joint distribution of the state process and the control process. We obtain the stochastic maximum principle which serves as a necessary condition for an optimal control, and we also

S. Kumano

The J-PARC is a hadron-accelerator facility to provide secondary beams of kaons, pions, neutrinos, muons, and the others together with the primary proton beam for investigating a wide range of science projects. High-energy hadron physics can be studied by using high-momentum beams of unseparated hadrons, which are essentially pions, and also primary protons.

Weiran Pan, Wei Wei, Feida Zhu

Fine-grained entity typing (FET) aims to assign proper semantic types to entity mentions according to their context, which is a fundamental task in various entity-leveraging applications. Current FET systems usually establish on large-scale weakly-supervised/distantly annotation data, which may contain abundant noise and thus severely hinder the performance

Takahiro Orito, Yoshihito Kuno, Ikuo Ichinose

Quantum information spreading and scrambling in many-body systems attract interests these days. Tripartite mutual information (TMI) based on operator-based entanglement entropy (EE) is an efficient tool for measuring them. In this paper, we study random spin chains that exhibit phase transitions accompanying nontrivial change in topological properties. In th

Jonathan Kunjummen, Daniel Carney, Jacob M. Taylor

Atom interferometers provide a powerful means of realizing quantum coherent systems with increasingly macroscopic extent in space and time. These systems provide an opportunity for a variety of novel tests of fundamental physics, including ultralight dark matter searches and tests of modifications of gravity, using long drop times, microgravity. However, as

Gilberto Aguilar-Pérez, Ana A. Avilez-López, Miguel Cruz

We explore the dynamical properties of a cosmological model that includes viscous effects in the dark matter sector of the fluid equations in a flat Friedmann-Lemaitre-Robertson-Walker (FLRW) spacetime. The bulk viscous effects are described by a non linear extension of the full Israel-Stewart model. We allow the interchange of energy in the dark sector by m

Bo Feng, Chang Hu, Tingfei Li, Yuekai Song

An improved PV-reduction method for one-loop integrals with auxiliary vector $R$ has been proposed in \cite{Feng:2021enk,Hu:2021nia}. It has also been shown that the new method is a self-completed method in \cite{Feng:2022uqp}. Analytic reduction coefficients can be easily produced by recursion relations in this method, where the Gram determinant appears in

Beidi Zhao, Boxin Du, Zhe Xu, Liangyue Li

Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph might not be optimal in terms of specific downstream tasks, because of information scarcity, noise, adversarial attacks, or discrepancies between the distribution in graph topology

Yue Wu, Yibo Liu, Maoguo Gong, Peiran Gong

Multi-view point cloud registration is fundamental in 3D reconstruction. Since there are close connections between point clouds captured from different viewpoints, registration performance can be enhanced if these connections be harnessed properly. Therefore, this paper models the registration problem as multi-task optimization, and proposes a novel bi-chann

N. T. Duy, P. N. Thu, D. T. Huong

There are two sources that help to explain the $\text{R}_\text{K}$, $\text{R}_{\text{K}^*}$ anomalies in the MF331 model. The first is non-LFUV couplings of the new neutral gauge boson $\text{Z}^{\prime}$ with leptons, $\text{g}^{\text{Z}^\prime}(e)\neq \text{g}^{\text{Z}^\prime}(\mu,\tau)$, which causes the $\text{R}_\text{K}$, $\text{R}_{\text{K}^*}$ anoma

Wanting Lyu, Yue Xiu, Yang Zhao, Chadi Assi

In this paper, we investigate an outdoor and indoor wireless communication network with the assistance of a novel relay-aided double-sided reconfigurable intelligent surface (RIS). A scheduling problem is considered at the outdoor access point (AP) to minimize the sum of age of information (AoI). To serve the indoor users and further enhance the wireless lin

James Levitt, Per-Gunnar Martinsson

A randomized algorithm for computing a compressed representation of a given rank-structured matrix $A \in \mathbb{R}^{N\times N}$ is presented. The algorithm interacts with $A$ only through its action on vectors. Specifically, it draws two tall thin matrices $\Omega,\,\Psi \in \mathbb{R}^{N\times s}$ from a suitable distribution, and then reconstructs $A$ fr

Daniel Dilley, Alvin Gonzales, Mark Byrd

Quantum state manipulation of two-qubits on the local systems by special unitaries induces special orthogonal rotations on the Bloch spheres. An exact formula is given for determining the local unitaries for some given rotation on the Bloch sphere. The solution allows for easy manipulation of two-qubit quantum states with a single definition that is programm

Cong Ma, Reese Pathak, Martin J. Wainwright

We study the covariate shift problem in the context of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We focus on two natural families of covariate shift problems defined using the likelihood ratios between the source and target distributions. When the likelihood ratios are uniformly bounded, we prove that the kernel ridge regressio

Fernando A. Pizaña, Roberto A. Sussman, Juan Carlos Hidalgo

Gravitational entropy is an elusive concept. Various theoretical proposals have been presented, initially based on Penrose's Weyl Curvature Hypothesis, and variations of it. A more recent proposal by Clifton, Ellis, and Tavakol (CET) considered a novel approach by defining such entropy from a Gibbs equation constructed from an effective stress-energy ten

Hendrik Meer, Olena Gomonay, Christin Schmitt, Rafael Ramos

We demonstrate how shape-induced strain can be used to control antiferromagnetic order in NiO/Pt thin films. For rectangular elements patterned along the easy and hard magnetocrystalline anisotropy axes of our film, we observe different domain structures and we identify magnetoelastic interactions that are distinct for different domain configurations. We rep

Yun Wang, Lu-Yao Jiang, Jia Ren

Gamma-ray bursts (GRBs) are divided into short gamma-ray bursts (SGRBs) and long gamma-ray bursts (LGRBs) based on the bimodal distribution of their durations. LGRBs and SGRBs are typically characterized by different statistical characteristics. Nevertheless, there are some samples that challenge such a framework, such as GRB 060614, a long-duration burst wi

Huanhuan Tian, Martin Z. Bazant

Nonvolatile resistive-switching (RS) memories promise to revolutionize hardware architectures with in-memory computing. Recently, ion-interclation materials have attracted increasing attention as potential RS materials for their ion-modulated electronic conductivity. In this Letter, we propose RS by multiphase polarization (MP) of ion-intercalated thin films

Jiaqi Luo, Zepeng Wen, Hongmei Kang, Zhouwang Yang

Automatically determining knot number and positions is a fundamental and challenging problem in B-spline approximation. In this paper, the knot placement is abstracted as a mapping from initial knots to the optimal knots. We innovatively introduce a deep neural network solver to approximate the mapping. The neural network is composed of several subnetworks.

Hua Zheng, Wei Xie

Built on our previous study on green simulation assisted policy gradient (GS-PG) focusing on trajectory-based reuse, in this paper, we consider infinite-horizon Markov Decision Processes and create a new importance sampling based policy gradient optimization approach to support dynamic decision making. The existing GS-PG method was designed to learn from com

Jeong-gi Kwak, Yuanming Li, Dongsik Yoon, David Han

Recently, synthesizing personalized characters from a single user-given portrait has received remarkable attention as a drastic popularization of social media and the metaverse. The input image is not always in frontal view, thus it is important to acquire or predict canonical view for 3D modeling or other applications. Although the progress of generative mo

Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, Ashok Cutkosky

Differential Privacy (DP) provides a formal framework for training machine learning models with individual example level privacy. In the field of deep learning, Differentially Private Stochastic Gradient Descent (DP-SGD) has emerged as a popular private training algorithm. Unfortunately, the computational cost of training large-scale models with DP-SGD is su

Tao Gong, Inigo Liberal, Benjamin Spreng, Miguel Camacho

Vacuum fluctuation-induced interactions between macroscopic metallic objects result in an attractive force between them, a phenomenon known as the Casimir effect. This force is the result of both plasmonic and photonic modes. For very thin films, field penetration through the films will modify the allowed modes. Here, we investigate the Casimir interaction b

Rafael L. H. Freire, Felipe Crasto de Lima, Adalberto Fazzio

Two-dimensional transition metal dichalcogenides (MX$_2$) vacancy formation energetics is extensively investigated. Within an ab-initio approach we study the MX$_2$ systems, with M=Mo, W, Ni, Pd and Pt, and X=S, Se, and Te. Here we classify that chalcogen vacancies are always energetic favorable over the transition metal ones. However, for late transition me

Yiming Huo

Recent years have witnessed an exponential growth of the commercial space industry, including rocket launch, satellite network deployment, private space travel, and even extraterrestrial colonization. Several trends are predicted in this unprecedented transition to an era of space-enabled broadband access.

Shugang Hao, Lingjie Duan

After upgrading to 5G, a network operator still faces congestion when providing the ubiquitous wireless service to the crowd. To meet users' ever-increasing demand, some other operators (e.g., Fon) have been developing another crowdsourced WiFi network to combine many users' home WiFi access points and provide enlarged WiFi coverage to them. While th

T. M. Saita, P. L. Natti, E. R. Cirilo, N. M. L. Romeiro

This study presents numerical simulations of faecal coliforms dynamics in Luruaco lake, located in Atlántico Department, Colombia. The velocity field is obtained through a two-dimensional horizontal (2DH) model of Navier-Stokes equations system. The transport equation of faecal coliforms concentration is provided from a convective-diffusive-reactive equation

Yuhang Li, Shikuang Deng, Xin Dong, Shi Gu

Spiking Neural Network (SNN), originating from the neural behavior in biology, has been recognized as one of the next-generation neural networks. Conventionally, SNNs can be obtained by converting from pre-trained Artificial Neural Networks (ANNs) by replacing the non-linear activation with spiking neurons without changing the parameters. In this work, we ar

V. Apinyan, T. K. Kopec

In this paper, the effects of the external transverse magnetic field $B$ (perpendicular to the surface of the layers) on the electronic and excitonic properties are studied in the AA-stacked bilayer graphene (BLG). The effects of the Coulomb interactions and excitonic pairing have been taken into account and analyzed in detail within the bilayer Hubbard mode

Zohreh Hajiakhondi-Meybodi, Ming Hou, Arash Mohammadi

Ultra-Wideband (UWB) is one of the key technologies empowering the Internet of Thing (IoT) concept to perform reliable, energy-efficient, and highly accurate monitoring, screening, and localization in indoor environments. Performance of UWB-based localization systems, however, can significantly degrade because of Non Line of Sight (NLoS) connections between

Yufeng Shi, Shuhuang Chen, Xinge You, Qinmu Peng

Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneous modalities (i.e., hashing-based cross-modal medical data retrieval), provides a new view to promot computeraided diagnosis. Nevertheless, there remains a barrier to boost medica

Shuyuan Wu, Danyang Huang, Hansheng Wang

We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, diffe

Haoteng Tang, Xiyao Fu, Lei Guo, Yalin Wang

MRI-based modeling of brain networks has been widely used to understand functional and structural interactions and connections among brain regions, and factors that affect them, such as brain development and disease. Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since b

D. K. K. Vamsi, C. Bishal Chhetri, D. Bhanu Prakash, Seshasainath Ch.

In this study, we have formulated and analyzed a non-linear compartmental model (SEIR) for the dynamics of COVID-19 with reference to immigration from urban to rural population in Indian scenario. We have captured the effect of the immigration as two separate factors contributing in the rural compartments of the model. We have first established the positivit

Eric Ngondiep

This paper considers a two-step fourth-order modified explicit Euler/Crank-Nicolson numerical method for solving the time-variable fractional mobile-immobile advection-dispersion model subjects to suitable initial and boundary conditions. Both stability and error estimates of the new approach are deeply analyzed in the $L^{\infty}(0,T;L^{2})$-norm. The theor

Lindsay Weinberg

This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions into machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, feminist studies, critical race and ethnic studies, legal studies, anthropology, and science and technology studies. It bridges epistemic divid

Shilan Jin, Rui Tuo, Akash Tiwari, Satish Bukkapatnam

This work is concerned with providing a principled decision process for stopping or tool-changing in a surface finishing process. The decision process is supposed to work for products of non-flat geometry. The solution is based on conducting hypothesis testing on the bearing area curves from two consecutive stages of a surface finishing process. In each stag

Francesco Guzzi, George Kourousias, Fulvio Billè, Roberto Pugliese

Computational methods are driving high impact microscopy techniques such as ptychography. However, the design and implementation of new algorithms is often a laborious process, as many parts of the code are written in close-to-the-hardware programming constructs to speed up the reconstruction. In this paper, we present SciComPty, a new ptychography software

Yogesh Dandekar, Barak Kol, Lior Lederer, Subhajit Mazumdar

The micro-canonical phase-space volume for the three-body problem is an elementary quantity of intrinsic interest, and within the flux-based statistical theory, it sets the scale of the disintegration time. While the bare phase-volume diverges, we show that a regularized version can be defined by subtracting a reference phase-volume, which is associated with

Ruitao Zhang, Xueying Han, Ijaz Gul, Shiyao Zhai

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) thera

Yang Liu, Ersi Zhang, Lulu Xu, Chufan Xiao

Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions. However, training these segmentation models requires a large number of manually annot

Ankan Mullick, Abhilash Nandy, Manav Nitin Kapadnis, Sohan Patnaik

A law practitioner has to go through a lot of long legal case proceedings. To understand the motivation behind the actions of different parties/individuals in a legal case, it is essential that the parts of the document that express an intent corresponding to the case be clearly understood. In this paper, we introduce a dataset of 93 legal documents, belongi

Ian McInerney

Iterative methods play an important role in science and engineering applications, with uses ranging from linear system solvers in finite element methods to optimization solvers in model predictive control. Recently, a new computational strategy for iterative methods called ARCHITECT was proposed by Li et al. in [1] that uses the redundant number representati

Minyue Fu

This paper addresses two important estimation problems for linear systems, namely system identification and model-free state estimation. Our focus is on ARMAX models with unknown parameters. We first provide a reinforcement learning algorithm for system identification with guaranteed consistency. This algorithm is then used to provide a novel solution to mod

Clarissa Hoffman, Jingheng Cheng, Daoyun Ji, Y. Dabaghian

Our current understanding of brain rhythms is based on quantifying their instantaneous or time-averaged characteristics. What remains unexplored, is the actual structure of the waves -- their shapes and patterns over finite timescales. To address this, we used two independent approaches to link wave forms to their physiological functions: the first is based

Michael D. Sorochan Armstrong, Jesper Løve Hinrich, A. Paulina de la Mata, James J. Harynuk

Reliable analysis of comprehensive two-dimensional gas chromatography - time-of-flight mass spectrometry (GC$\times$GC-TOFMS) data is considered to be a major bottleneck for its widespread application. For multiple samples, GC$\times$GC-TOFMS data for specific chromatographic regions manifests as a 4th order tensor of I mass spectral acquisitions, J mass cha

David J. Fernández C., Daniel O-Campa

In this paper we construct the generalized coherent states for an electron in monolayer or bilayer graphene placed in an external magnetic field. At first we define an appropriate set of ladder operators acting on the eigenfunctions for each Hamiltonian, afterwards we derive the generalized coherent states through several definitions and analyze the commutat

Philip Gordon Judge

As the powerhouse of our solar system, the Sun's electromagnetic planetary influences appear contradictory. On the one hand, the Sun for aeons emitted radiation which was "just right" for life to evolve in our terrestrial Goldilocks zone, even for such complex organisms as ourselves. On the other, in the dawn of Earth's existence the Sun was

Yang Pan, Shutong Li, Mahfujur Rahaman, Ilya Milekhin

Twisted 2D bilayer materials are created by artificial stacking of two monolayer crystal networks of 2D materials with a desired twisting angle $θ$. The material forms a moiré superlattice due to the periodicity of both top and bottom layer crystal structure. The optical properties are modified by lattice reconstruction and phonon renormalization, which make

Xiao Lin, Ce Liu

We propose a new notion of credibility for Bayesian persuasion problems. A disclosure policy is credible if the sender cannot profit from tampering with her messages while keeping the message distribution unchanged. We show that the credibility of a disclosure policy is equivalent to a cyclical monotonicity condition on its induced distribution over states a

Tien-Ju Yang, Yonghui Xiao, Giovanni Motta, Françoise Beaufays

This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost. The proposed Online Model Compression (OMC) provides a framework that stores model parameters in a compressed format and decompresses them only when needed. We use quantization as the compression m

Deepak Ravikumar, Kaushik Roy

Out-of-Distribution (OoD) inputs are examples that do not belong to the true underlying distribution of the dataset. Research has shown that deep neural nets make confident mispredictions on OoD inputs. Therefore, it is critical to identify OoD inputs for safe and reliable deployment of deep neural nets. Often a threshold is applied on a similarity score to

Michael Khanevsky

Let $Σ$ be a compact surface equipped with an area form. There is an long standing open question by Katok, which, in particular, asks whether every entropy-zero Hamiltonian diffeomorphism of a surface lies in the $C^0$-closure of the set of integrable diffeomorphisms. A natural generalization of this question is to ask to what extent one family of `simple&#3

Amir Yazdani, Roya Sabbagh Novin, Andrew Merryweather, Tucker Hermans

Ergonomics and human comfort are essential concerns in physical human-robot interaction applications. Defining an accurate and easy-to-use ergonomic assessment model stands as an important step in providing feedback for postural correction to improve operator health and comfort. Common practical methods in the area suffer from inaccurate ergonomics models in

F. D. Moyano, P. Eggenberger, G. Meynet, C. Gehan

Thanks to asteroseismology, constraints on the core rotation rate are available for hundreds of low- and intermediate-mass stars in evolved phases. Current physical processes tested in stellar evolution models cannot reproduce the evolution of these core rotation rates. We investigate the efficiency of the internal angular momentum redistribution in red gian

Alexander Leon, Juan E. Gomez, Freddy R. Perez

The interaction between carbon nanostructures like quantum dots and radiation can generate different effects inside the nanomaterial, with the use of computational methods such effects can be predicted and optimize the material allowing a desired output. In this work, a theoretical model for pristine graphene quantum dots is studied, allowing to explain the

Qi Yan, Xian'an Jin

Recently, we introduced the twist polynomials of delta-matroids and gave a characterization of even normal binary delta-matroids whose twist polynomials have only one term and posed a problem: what would happen for odd binary delta-matroids? In this paper, we show that a normal binary delta-matroid whose twist polynomials have only one term if and only if ea

Omar Eidous

Although there is an extensive literature on the upper bound for cumulative standard normal distribution, there are relatively not sharp for all values of the interested argument x. The aim of this paper is to establish a sharp upper bound for standard normal distribution function, in the sense that its maximum absolute difference from phi(x) is less than fo

Arash Vahabpour, Tianyi Wang, Qiujing Lu, Omead Pooladzandi

Imitation learning is the task of replicating expert policy from demonstrations, without access to a reward function. This task becomes particularly challenging when the expert exhibits a mixture of behaviors. Prior work has introduced latent variables to model variations of the expert policy. However, our experiments show that the existing works do not exhi

Sankaran Panchapagesan, Arun Narayanan, Turaj Zakizadeh Shabestary, Shuai Shao

Acoustic Echo Cancellation (AEC) is essential for accurate recognition of queries spoken to a smart speaker that is playing out audio. Previous work has shown that a neural AEC model operating on log-mel spectral features (denoted "logmel" hereafter) can greatly improve Automatic Speech Recognition (ASR) accuracy when optimized with an auxiliary loss

Pankaj Kumar, Valery M. Nakariakov, Judith T. Karpen, C. Richard DeVore

We report a decaying kink oscillation of a flux rope during a confined eruptive flare, observed off the solar limb by SDO/AIA, that lacked a detectable white-light coronal mass ejection. The erupting flux rope underwent kinking, rotation, and apparent leg-leg interaction during the event. The oscillations were observed simultaneously in multiple AIA channels

Oliver Boodram, Craig O. Heinke

The unexplained excess gamma-ray emission from the Milky Way's Galactic Center has puzzled astronomers for nearly a decade. Two theories strive to explain the origin of this excess: self-annihilating dark matter particles or an unresolved population of radio millisecond pulsars. We examine the plausibility of a pulsar origin for the GeV excess using N-bo

Soheil Habibian, Dylan P Losey

Recent works explore collaboration between humans and teams of robots. These approaches make sense if the human is already working with the robot team; but how should robots encourage nearby humans to join their teams in the first place? Inspired by behavioral economics, we recognize that humans care about more than just team efficiency -- humans also have b

Kasturi Barkataki, Eleni Panagiotou

Measuring the entanglement complexity of collections of open curves in 3-space has been an intractable, yet pressing mathematical problem, relevant to a plethora of physical systems, such as in polymers and biopolymers. In this manuscript, we give a novel definition of the Jones polynomial that generalizes the classic Jones polynomial to collections of open

Minghao Shen, Chaozhe R. He, Tamas Molnar, A. Harvey Bell

This paper focuses on energy-efficient longitudinal controller design for a connected automated truck that travels in mixed traffic consisting of connected and non-connected vehicles. The truck has access to information about connected vehicles beyond line of sight using vehicle-to-vehicle (V2V) communication. A novel connected cruise control design is propo

Sebastian Schuster, Tal Linzen

Understanding longer narratives or participating in conversations requires tracking of discourse entities that have been mentioned. Indefinite noun phrases (NPs), such as 'a dog', frequently introduce discourse entities but this behavior is modulated by sentential operators such as negation. For example, 'a dog' in 'Arthur doesn't own

Théo Jules, Laura Michel, Adèle Douin, Frédéric Lechenault

Traditional memory writing operations proceed one bit at a time, where e.g. an individual magnetic domain is force-flipped by a localized external field. One way to increase material storage capacity would be to write several bits at a time in the bulk of the material. However, the manipulation of bits is commonly done through quasi-static operations. While

Oscar Sandoval Carlos

In military organizations, Cyber Threat Intelligence (CTI) supports cyberspace operations by providing the commander with essential information about the adversary, their capabilities and objectives as they operate through cyberspace. This paper, combines CTI with the MITRE ATT&CK framework in order to establish an adversary profile. In addition, it identifi

Levi Burner, Anton Mitrokhin, Cornelia Fermüller, Yiannis Aloimonos

A new event camera dataset, EVIMO2, is introduced that improves on the popular EVIMO dataset by providing more data, from better cameras, in more complex scenarios. As with its predecessor, EVIMO2 provides labels in the form of per-pixel ground truth depth and segmentation as well as camera and object poses. All sequences use data from physical cameras and m

Elizabeth S. Allman, Hector Baños, Jonathan D. Mitchell, John A. Rhodes

Inference of species networks from genomic data under the Network Multispecies Coalescent Model is currently severely limited by heavy computational demands. It also remains unclear how complicated networks can be for consistent inference to be possible. As a step toward inferring a general species network, this work considers its tree of blobs, in which non

Meng Chen, Dao Zhou, Frede Blaabjerg

The active and reactive power controllers of grid-forming converters are traditionally designed separately, which relies on the assumption of loop decoupling. This paper proposes a full-state feedback control for the power loops of grid-forming converters. First, the power loops are modeled considering their natural coupling, which, therefore, can apply to a

Poorna Banerjee Dasgupta

Image blurring refers to the degradation of an image wherein the image's overall sharpness decreases. Image blurring is caused by several factors. Additionally, during the image acquisition process, noise may get added to the image. Such a noisy and blurred image can be represented as the image resulting from the convolution of the original image with th

Derong Xu, Yun Luo, Yue Hao

In this paper, we present the effects of linear transverse-longitudinal coupling on beam size at Interaction Point (IP) of a collider with local crab crossing scheme, when time dependent transverse deflection (crab kicks) and dispersive orbit intertwine near IP. The analytic propagation formula and the closed orbit form of the crab dispersion and momentum di

Michael L. Katz, Camilla Danielski, Nikolaos Karnesis, Valeriya Korol

The Laser Interferometer Space Antenna (LISA) will detect and characterize $\sim10^4$ Galactic Binaries consisting predominantly of two White Dwarfs (WD). An interesting prospect within this population is a third object--another WD star, a Circumbinary Exoplanet (CBP), or a Brown Dwarf (BD)--in orbit about the inner WD pair. We present the first fully Bayesi

William N Anderson

The Farrington-Manning method is a common method for evaluating equivalence and non-inferiority of independent proportions. It is implemented in various software, in particular SAS$^\circledR$ PROC FREQ, and the R$^\circledR$ function farrington.manning(), which is part of the DescrTab2 package. The equations for the estimated proportions can create numerica

Danila Rozhevskii, Jie Zhu, Boyuan Zhao

In the last few years, automated recommendation systems have been a major focus in the music field, where companies such as Spotify, Amazon, and Apple are competing in the ability to generate the most personalized music suggestions for their users. One of the challenges developers still fail to tackle is taking into account the psychological and emotional as

Raul Quiroga-Barranco

Let $\mathrm{D}^\mathrm{I}_{n \times n}$ be the Cartan domain of type I which consists of the complex $n \times n$ matrices $Z$ that satisfy $Z^*Z < I_n$. For a symbol $a \in L^\infty(\mathrm{D}^\mathrm{I}_{n \times n})$ we consider three radial-like type conditions: 1) left (right) $\mathrm{U}(n)$-invariant symbols, which can be defined by the condition $a(

Oleksii Ivanytskyi, David Blaschke

We introduce a family of equations of state (EoS) for hybrid neutron star (NS) matter that is obtained by a two-zone parabolic interpolation between a soft hadronic EoS at low densities and a stiff quark matter EoS with color superconductivity at high densities within a finite region of baryonic chemical potentials $μ_B^h < μ_B < μ_B^q$. We consider two scen

Hang Zhang, Afshin Abdi, Faramarz Fekri

This paper considers learning of the graphical structure of a $p$-dimensional random vector $X \in R^p$ using both parametric and non-parametric methods. Unlike the previous works which observe $x$ directly, we consider the indirect observation scenario in which samples $y$ are collected via a sensing matrix $A \in R^{d\times p}$, and corrupted with some add

Giuseppe Barbaro, Mehdi Lejmi

We study 4-dimensional second-Chern-Einstein almost-Hermitian manifolds. In the compact case, we observe that under a certain hypothesis the Riemannian dual of the Lee form is a Killing vector field. We use that observation to describe 4-dimensional compact second-Chern-Einstein locally conformally symplectic manifolds and we give some examples of such manif

Matt Gibson-Lopez, Serge Zamarripa

Representation of Euclidean objects in a digital space has been a focus of research for over 30 years. Digital line segments are particularly important as other digital objects depend on their definition (e.g., digital convex objects or digital star-shaped objects). It may be desirable for the digital line segment systems to satisfy some nice properties that

E. R. Garro, D. Minniti, M. Gómez, J. G. Fernández-Trincado

The VISTA Variables in the Via Láctea Extended Survey (VVVX) allows probing previously unexplored regions of the inner Milky Way (MW). We are looking for new candidate globular clusters (GCs), with the aim of completing the census of the MW GC system. We searched and characterised new GCs, using a combination of the near-IR VVVX survey and 2MASS datasets, an

Luke Thomas Peachey

We formulate a uniqueness conjecture for curve shortening flow of proper curves on certain symmetric surfaces and give an example of a non-flat metric on the plane with respect to which curve shortening flow is not unique. That is, with respect to a suitably chosen metric, we construct a non-static solution to curve shortening flow starting from a properly e

Brian García Sarmina

A comparison is made between Exhaustive Search (ES) and Iterative Local Search (ILS). Such comparison was made using the Quantum Approximation Optimization Algorithm (QAOA). QAOA has been extensively researched due to its this potential to be implemented in actual quantum hardware, and its promising future in optimization problems and quantum machine learnin

Viktoria Markova, Matteo Ronchetti, Wolfgang Wein, Oliver Zettinig

Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already available, they all require a good initialization to succeed due to the challenging appearance of ultrasound images and the arbitrary coordinate sy

Esma Dirican Erdal

Let $M$ be a triangulated, oriented, connected compact $3$-manifold with connected non-empty boundary. Such a manifold admits a unique decomposition into $\triangle$-prime $3$-manifolds. In this paper, we show that the adjoint Reidemeister torsion has a multiplicative property on the disk sum decomposition of compact $3$-manifolds without a corrective term.

Thomas J. X. Li, Christian M. Reidys

We develop a framework for computing the homology of weighted simplicial complexes with coefficients in a discrete valuation ring. A weighted simplicial complex, $(X,v)$, introduced by Dawson [Cah. Topol. Géom. Différ. Catég. 31 (1990), pp. 229--243], is a simplicial complex, $X$, together with an integer-valued function, $v$, assigning weights to simplices,

Yuan Gong, Ziyi Chen, Iek-Heng Chu, Peng Chang

Automatic pronunciation assessment is an important technology to help self-directed language learners. While pronunciation quality has multiple aspects including accuracy, fluency, completeness, and prosody, previous efforts typically only model one aspect (e.g., accuracy) at one granularity (e.g., at the phoneme-level). In this work, we explore modeling mul

Alekzander Kosakowski, M. Kilic, W. R. Brown, P. Bergeron

We present the results of a search for deeply-eclipsing white dwarfs in the ZTF Data Release 4. We identify nine deeply-eclipsing white dwarf candidates, four of which we followed up with high-cadence photometry and spectroscopy. Three of these systems show total eclipses in the ZTF data and our follow-up APO 3.5-meter telescope observations. Even though the