July 2022 arXiv papers — page 152

Ben O'Neill

This paper examines the distribution of order statistics taken from simple-random-sampling without replacement (SRSWOR) from a finite population with values 1,...,N. This distribution is a shifted version of the beta-binomial distribution, parameterised in a particular way. We derive the distribution and show how it relates to the distribution of order stati

Shoji Yokura

A bi-variant theory $\mathbb B(X,Y)$ defined for a pair $(X,Y)$ is a theory satisfying properties similar to those of Fulton--MacPherson's bivariant theory $\mathbb B(X \xrightarrow f Y)$ defined for a morphism $f:X \to Y$. In this paper, using correspondences we construct a bi-variant algebraic cobordism $\Omega^{*,\sharp}(X, Y)$ such that $\Omega^{*,\sharp

Hui Liu, Zhenyu Jin, Yongyuan Xiang, Kaifan Ji

Suppressing the interference of atmospheric turbulence and obtaining observation data with a high spatial resolution is an issue to be solved urgently for ground observations. One way to solve this problem is to perform a statistical reconstruction of short-exposure speckle images. Combining the rapidity of Shift-Add and the accuracy of speckle masking, this

Yuri Shtanov

The scalaron of the metric $f(R)$ gravity can constitute dark matter if its mass is in the range $4\,\text{meV} \lesssim m \lesssim 1\,\text{MeV}$. We give an overview of such $f (R)$ gravity theory minimally coupled to the Standard Model. Similarly to other dark-matter models based on scalar fields, this model has the issue of initial conditions. Firstly, t

Yang Shen, Mingpu Qin, Guang-Ming Zhang

To understand the microscopic mechanism of the charge order observed in the parent compound of the infinite-layer nickelate superconductors, we consider a minimal three-legged model consisting of a two-legged Hubbard ladder for the Ni $3d_{x^2-y^2}$ electrons and a free conduction electron chain from the rare-earths. With highly accurate density matrix renor

P. Gelhausen, M. Fischer, G. Peters

Text-based simulated environments have proven to be a valid testbed for machine learning approaches. The process of affordance extraction can be used to generate possible actions for interaction within such an environment. In this paper the capabilities and challenges for utilizing external knowledge databases (in particular ConceptNet) in the process of aff

{Nurul Huda Mahmood, Gilberto Berardinelli, Emil J. Khatib, Ramin Hashemi

Future industrial applications will encompass compelling new use cases requiring stringent performance guarantees over multiple key performance indicators (KPI) such as reliability, dependability, latency, time synchronization, security, etc. Achieving such stringent and diverse service requirements necessitates the design of a special-purpose Industrial Int

Ignjat Pejic, Rui Wang, Kaitai Liang

A Generative Adversarial Network (GAN) is a deep-learning generative model in the field of Machine Learning (ML) that involves training two Neural Networks (NN) using a sizable data set. In certain fields, such as medicine, the training data may be hospital patient records that are stored across different hospitals. The classic centralized approach would inv

Isabella María Sierra-Ponce, Angela Mireya León-Mecías, Damian Valdés-Santiago

This paper presents and validates a novel lung nodule classification algorithm that uses multifractal features found in X-ray images. The proposed method includes a pre-processing step where two enhancement techniques are applied: histogram equalization and a combination of wavelet decomposition and morphological operations. As a novelty, multifractal featur

Patrick Liebig, Maria Sedykh, Kai Herz, Moritz S. Fabian

CEST suffers from two main problems long acquisitin times or restricted coverage as well as incoherent protocol settings. In this paper we give suggestions on how to optimise your protocol settings fro CEST and present one setting for APT CEST. To increase the coverage while keeping the acquisition time constant we suggest using a spatial temporal Compressed

Jun Wu, Lilu Liu, Yue Wang, Rong Xiong

Current RGB-based 6D object pose estimation methods have achieved noticeable performance on datasets and real world applications. However, predicting 6D pose from single 2D image features is susceptible to disturbance from changing of environment and textureless or resemblant object surfaces. Hence, RGB-based methods generally achieve less competitive result

Kenan Morani, Esra Kaya Ayana, Devrim Unay

The significance of efficient and accurate diagnosis amidst the unique challenges posed by the COVID-19 pandemic underscores the urgency for innovative approaches. In response to these challenges, we propose a transfer learning-based approach using a recently annotated Computed Tomography (CT) image database. While many approaches propose an intensive data p

David Fisher, Sebastian Hurtado

We give a new proof of the finiteness of maximal arithmetic reflection groups. Our proof is novel in that it makes no use of trace formulas or other tools from the theory of automorphic forms and instead relies on the arithmetic Margulis lemma of Fraczyk, Hurtado and Raimbault.

William S. Moses, Ivan R. Ivanov, Jens Domke, Toshio Endo

While parallelism remains the main source of performance, architectural implementations and programming models change with each new hardware generation, often leading to costly application re-engineering. Most tools for performance portability require manual and costly application porting to yet another programming model. We propose an alternative approach t

Jichao Zhang, Jingjing Chen, Hao Tang, Enver Sangineto

This paper proposes a gaze correction and animation method for high-resolution, unconstrained portrait images, which can be trained without the gaze angle and the head pose annotations. Common gaze-correction methods usually require annotating training data with precise gaze, and head pose information. Solving this problem using an unsupervised method remain

Michael Heider, Helena Stegherr, Richard Nordsieck, Jörg Hähner

In socio-technical settings, operators are increasingly assisted by decision support systems. By employing these, important properties of socio-technical systems such as self-adaptation and self-optimization are expected to improve further. To be accepted by and engage efficiently with operators, decision support systems need to be able to provide explanatio

Görkay Aydemir, Adil Kaan Akan, Fatma Güney

Predicting future locations of agents in the scene is an important problem in self-driving. In recent years, there has been a significant progress in representing the scene and the agents in it. The interactions of agents with the scene and with each other are typically modeled with a Graph Neural Network. However, the graph structure is mostly static and fa

Julio A. Placed, Jared Strader, Henry Carrillo, Nikolay Atanasov

Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientifi

Esther Villar-Rodriguez, Eneko Osaba, Izaskun Oregi

Despite being considered as the next frontier in computation, Quantum Computing is still in an early stage of development. Indeed, current commercial quantum computers suffer from some critical restraints, such as noisy processes and a limited amount of qubits, among others, that affect the performance of quantum algorithms. Despite these limitations, resear

K. Uldall Kristiansen, P. Szmolyan

In this paper, we revisit the classical linear turning point problem for the second order differential equation $\epsilon^2 x'' +\mu(t)x=0$ with $\mu(0)=0,\,\mu'(0)\ne 0$ for $0<\epsilon\ll 1$. Written as a first order system, $t=0$ therefore corresponds to a turning point connecting hyperbolic and elliptic regimes. Our main result is that we provide an alte

Chengwei Pan, Gangming Zhao, Junjie Fang, Baolian Qi

Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to much time and resource consumption. Weakly supervised learning (WSL), which leverages coarse-grained labels to accomplish fine-grained tasks, has the potential to solve this problem.

Mitake Miyazaki, Takashi Yanagisawa

We have performed a variational Monte Carlo simulation to study the ground state of a two-dimensional Hubbard model on a square lattice in the strong coupling region. The energy gain of possible inhomogeneous electron states are computed as a function of $U$ when the hole density $\epsilon=1/8$ and next nearest-neighbor hopping $t'/t=-0.30$. The bond-centere

Giovanni Cistaro, Mikhail Malakhov, Juan José Esteve-Paredes, Alejandro José Uría-Álvarez

In this manuscript we present a theoretical framework and its numerical implementation to simulate the out-of-equilibrium electron dynamics induced by the interaction of ultrashort laser pulses in condensed-matter systems. Our approach is based on evolving in real-time the density matrix of the system in reciprocal space. It considers excitonic and non-pertu

D. Serim, M. M. Serim, A. Baykal

In this study, we examine the long term torque noise fluctuations of persistent Xray binaries Her X-1, Vela X-1, GX 301-2, CEN X-3, 4U 1538-53, OAO 1657-415 and 4U 1626-67 using the historical pulse frequency measurements provided by CGRO/BATSE and Fermi/GBM. We find that known disk-fed sources exhibit $1/\omega^{2}$ red noise component in their power densit

J. Mckenna, V. Glenis, C. Kilsby

Flows in rivers can be strongly affected by obstacles to flow or artificial structures such as bridges, weirs and dams. This is especially true during floods, where significant backwater effects or diversion of flow out of bank can result. However, within contemporary industry practice, linear features such as bridges are often modelled using coarse approxim

Zihan Lin, Jincheng Yu, Lipu Zhou, Xudong Zhang

Localization and navigation are basic robotic tasks requiring an accurate and up-to-date map to finish these tasks, with crowdsourced data to detect map changes posing an appealing solution. Collecting and processing crowdsourced data requires low-cost sensors and algorithms, but existing methods rely on expensive sensors or computationally expensive algorit

Vadim Bobylev, Anisa Bajkova, Yury Mishurov

The presence of the Radcliffe wave is shown both in the positions and in the vertical velocities of masers and radio stars belonging to the Local Arm. This gives the impression that the structure of the Radcliffe wave is not a wave in the full sense of the word. It is more like a local high-amplitude burst, rapidly fading away. Moreover, this structure has t

Wenjia Zhang, Haoran Xu, Haoyi Niu, Peng Cheng

Offline imitation learning (IL) is a powerful method to solve decision-making problems from expert demonstrations without reward labels. Existing offline IL methods suffer from severe performance degeneration under limited expert data. Including a learned dynamics model can potentially improve the state-action space coverage of expert data, however, it also

Claus O. W. Trost, Stanislav Zak, Sebastian Schaffer, Christian Saringer

As the need for miniaturized structural and functional materials has increased,the need for precise materials characterizaton has also expanded. Nanoindentation is a popular method that can be used to measure material mechanical behavior which enables high-throughput experiments and, in some cases, can also provide images of the indented area through scannin

Takumi Muto, Toshiki Maruyama, Toshitaka Tatsumi

Possible coexistence of kaon condensation and hyperons in highly dense matter [the ($Y+K$) phase] is investigated on the basis of the relativistic mean-field theory combined with the effective chiral Lagrangian. Two coupling schemes for the $s$-wave kaon-baryon interaction are compared regarding the onset density of kaon condensation in the hyperon-mixed mat

William St-Arnaud, Margarida Carvalho, Golnoosh Farnadi

In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompatible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access

Ping Tang, Ken-ichi Uchida, Gerrit E. W. Bauer

The \textquotedblleft ferron\textquotedblright\ excitations of the electric-dipolar order carry energy as well as electric dipoles. Here we predict a nonlocal ferron drag effect in a ferroelectric on top of a metallic film: An electric current in the conductor generates a heat current in the ferroelectric by long-range charge-dipole interactions. The non-loc

Dongtai He

We construct a hyperbolic fibered slice knot with right-veering monodromy, giving a negative answer to the question posed by Hubbard-Kawamuro-Kose-Martin-Plamenevskaya-Raoux-Truong-Turner.

Jürgen Seiler, André Kaup

This paper introduces a framework for distributed parallel image signal extrapolation. Since high-quality image signal processing often comes along with a high computational complexity, a parallel execution is desirable. The proposed framework allows for the application of existing image signal extrapolation algorithms without the need to modify them for a p

Muqiao Yang, Joseph Konan, David Bick, Anurag Kumar

While deep learning based speech enhancement systems have made rapid progress in improving the quality of speech signals, they can still produce outputs that contain artifacts and can sound unnatural. We propose a novel approach to speech enhancement aimed at improving perceptual quality and naturalness of enhanced signals by optimizing for key characteristi

Sedigheh Deldar, Zahra Asmaee

Inspired by direct and indirect maximal center gauge methods which identify vortices in lattice calculations, and by using the connection formalism, we show that under some appropriate gauge transformations, vortices and chains appear in the continuum limit of QCD vacuum.

Kuldeep Verma, Jakob L. Rørsted, Aldo M. Serenelli, Víctor Aguirre Børsen-Koch

Current stellar model predictions of adiabatic oscillation frequencies differ significantly from the corresponding observed frequencies due to the non-adiabatic and poorly understood near-surface layers of stars. However, certain combinations of frequencies -- known as frequency ratios -- are largely unaffected by the uncertain physical processes as they are

Sihun Baek, Jihong Park, Praneeth Vepakomma, Ramesh Raskar

This article seeks for a distributed learning solution for the visual transformer (ViT) architectures. Compared to convolutional neural network (CNN) architectures, ViTs often have larger model sizes, and are computationally expensive, making federated learning (FL) ill-suited. Split learning (SL) can detour this problem by splitting a model and communicatin

Jürgen Seiler, André Kaup

In the recent years, multi-core processor designs have found their way into many computing devices. To exploit the capabilities of such devices in the best possible way, signal processing algorithms have to be adapted to an operation in parallel tasks. In this contribution an optimized processing order is proposed for Frequency Selective Extrapolation, a pow

Peipei Liu, Hong Li, Zuoguang Wang, Jie Liu

Extracting cybersecurity entities such as attackers and vulnerabilities from unstructured network texts is an important part of security analysis. However, the sparsity of intelligence data resulted from the higher frequency variations and the randomness of cybersecurity entity names makes it difficult for current methods to perform well in extracting securi

Jürgen Seiler, André Kaup

Although wireless and IP-based access to video content gives a new degree of freedom to the viewers, the risk of severe block losses caused by transmission errors is always present. The purpose of this paper is to present a new method for concealing block losses in erroneously received video sequences. For this, a motion compensated data set is generated aro

ATLAS Collaboration

This article presents a search for new resonances decaying into a $Z$ or $W$ boson and a 125 GeV Higgs boson $h$, and it targets the $\nu\bar{\nu}b\bar{b}$, $\ell^+\ell^-b\bar{b}$, or $\ell^{\pm}{\nu}b\bar{b}$ final states, where $\ell=e$ or $\mu$, in proton-proton collisions at $\sqrt{s}=13$ TeV. The data used correspond to a total integrated luminosity of

Nobuo Iida, Hokuto Konno, Masaki Taniguchi

We give a generalized Thurston--Bennequin-type inequality for links in $S^3$ using a Bauer--Furuta-type invariant for 4-manifolds with contact boundary. As a special case, we also give an adjunction inequality for smoothly embedded orientable surfaces with negative intersection in a closed oriented smooth 4-manifold whose non-equivariant Bauer--Furuta invari

Yangfan Xie, Feng Wang, Hui Deng, Ying Mei

The Square Kilometre Array (SKA) is the largest radio interferometer under construction in the world. The high accuracy, wide-field and large size imaging significantly challenge the construction of the Science Data Processor (SDP) of SKA. We propose a hybrid imaging method based on improved W-Stacking and snapshots. The w range is reduced by fitting the sna

Sai Nithin Reddy Kantareddy, Rahul Bhattacharya, Sanjay E. Sarma, Ian Mathews

Billions of everyday objects could become part of the Internet of Things (IoT) by augmentation with low-cost, long-range, maintenance-free wireless sensors. Radio Frequency Identification (RFID) is a low-cost wireless technology that could enable this vision, but it is constrained by short communication range and lack of sufficient energy available to power

Indranil Chakraborty, Soumya Bhattacharya, Sumanta Chakraborty

Gravitational wave memory is studied in the context of a certain class of braneworld wormholes. Unlike other wormhole geometries, this novel class of wormholes do not require any exotic matter fields for its traversability. First, we study geodesics in this wormhole spacetime, in the presence of a gravitational wave pulse. The resulting evolution of the geod

Yeonsoo Park, Soohyun Bae

When adapting Simultaneous Mapping and Localization (SLAM) to real-world applications, such as autonomous vehicles, drones, and augmented reality devices, its memory footprint and computing cost are the two main factors limiting the performance and the range of applications. In sparse feature based SLAM algorithms, one efficient way for this problem is to li

Jacopo Nanni, Andrea Giovannini, Enrico Lenzi, Simone Rusticelli

In the context of Radioastronomic applications where the Analog Radio-over-Fiber technology is used for the antenna downlink, detrimental nonlinearity effects arise because of the interference between the forward signal generated by the laser and the Rayleigh backscattered one which is re-forwarded by the laser itself toward the photodetector. The adoption o

Haonan Hu, Yan Jiang, Jiliang Zhang, Yanan Zheng

The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in user equipments (UEs) to the edge to reduce the processing latency. However, it incorporates the task transmission latency, which may become the bottleneck of latency requirements. Data compression (DC) has been con

Yuchu Liu, David Issa Mattos, Jan Bosch, Helena Holmström Olsson

Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating software changes. In the automotive domain, running randomised field experiments is not always desired, possible, or even ethical. In the face of such limitations, we develop a framework BOAT (Bayesian causal modelling for ObvservAtional Testing), utilising obs

Tiancheng Zhao, Tianqi Zhang, Mingwei Zhu, Haozhan Shen

Vision-Language Pretraining (VLP) models have recently successfully facilitated many cross-modal downstream tasks. Most existing works evaluated their systems by comparing the fine-tuned downstream task performance. However, only average downstream task accuracy provides little information about the pros and cons of each VLP method, let alone provides insigh

Peter Henderson, Mark S. Krass, Lucia Zheng, Neel Guha

One concern with the rise of large language models lies with their potential for significant harm, particularly from pretraining on biased, obscene, copyrighted, and private information. Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach

Jake Weiner, Andreas T. Ernst, Xiaodong Li, Yuan Sun

Solving large-scale Mixed Integer Programs (MIP) can be difficult without advanced algorithms such as decomposition based techniques. Even if a decomposition technique might be appropriate, there are still many possible decompositions for any large MIP and it may not be obvious which will be the most effective. This paper presents a comprehensive analysis of

Matthias Loipersberger, Fionn D. Malone, Alicia R. Welden, Robert M. Parrish

The computation of interaction energies on noisy intermediate-scale quantum (NISQ) computers appears to be challenging with straightforward application of existing quantum algorithms. For example, use of the standard supermolecular method with the variational quantum eigensolver (VQE) would require extremely precise resolution of the total energies of the fr

Jing Hao Chai, Hui Khoon Ng

Fault-tolerant quantum computing based on surface codes has emerged as a popular route to large-scale quantum computers capable of accurate computation even in the presence of noise. Its popularity is, in part, because the fault-tolerance or accuracy threshold for surface codes is believed to be less stringent than competing schemes. This threshold is the no

Yuki Takashima, Shota Horiguchi, Shinji Watanabe, Paola García

In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model. Conventional approaches require extra parameters of the same size as the model for optimization, and it is difficult to apply these approaches to end-to-end ASR models because they have a huge amo

Zhuoxiao Li, Haiyang Jiang, Yinqiang Zheng

Polarized color photography provides both visual textures and object surficial information in one single snapshot. However, the use of the directional polarizing filter array causes extremely lower photon count and SNR compared to conventional color imaging. Thus, the feature essentially leads to unpleasant noisy images and destroys polarization analysis per

T. Mizuno, K. Hayashi, J. Metzger, I. V. Moskalenko

In studying the interstellar medium (ISM) and Galactic cosmic rays (CRs), uncertainty of the interstellar gas density has always been an issue. To overcome this difficulty, we used a component decomposition of the 21-cm HI line emission and used the resulting gas maps in an analysis of $\gamma$-ray data obtained by the Fermi Large Area Telescope (LAT) for th

Bernard Chazelle, Kritkorn Karntikoon

We establish sufficient conditions for the quick relaxation to kinetic equilibrium in the classic Vicsek-Cucker-Smale model of bird flocking. The convergence time is polynomial in the number of birds as long as the number of flocks remains bounded. This new result relies on two key ingredients: exploiting the convex geometry of embedded averaging systems; an

Tetsutaro Higaki, Kohei Kamada, Kentaro Nishimura

The Chiral Soliton Lattice (CSL) is a lattice structure composed of domain walls aligned in parallel at equal intervals, which is energetically stable in the presence of a background magnetic field and a finite (baryon) chemical potential due to the topological term originated from the chiral anomaly. We study its formation from the vacuum state, with descri

Minoru Eto, Muneto Nitta

The chiral soliton lattice is an array of topological solitons realized as ground states of QCD at finite density under strong magnetic fields or rapid rotation, and chiral magnets with an easy-plane anisotropy. In such cases, topological solitons have negative energy due to topological terms originating from the chiral magnetic or vortical effect and the Dz

Li Ma, Xiaoyu Li, Jing Liao, Xuan Wang

Implicit radiance functions emerged as a powerful scene representation for reconstructing and rendering photo-realistic views of a 3D scene. These representations, however, suffer from poor editability. On the other hand, explicit representations such as polygonal meshes allow easy editing but are not as suitable for reconstructing accurate details in dynami

F. A. Bayocboc,, K. V. Kheruntsyan

We study the breathing (monopole) oscillations and their damping in a harmonically trapped one-dimensional (1D) Bose gas in the quasicondensate regime using a finite-temperature classical field approach. By characterising the oscillations via the dynamics of the density profile's rms width over long time, we find that the rms width displays beating of two di

Wonyoung Shin, Jonghun Park, Taekang Woo, Yongwoo Cho

Understanding vision and language representations of product content is vital for search and recommendation applications in e-commerce. As a backbone for online shopping platforms and inspired by the recent success in representation learning research, we propose a contrastive learning framework that aligns language and visual models using unlabeled raw produ

Jade Powell, Ling Sun, Katinka Gereb, Paul D. Lasky

Transient noise glitches in gravitational-wave detector data limit the sensitivity of searches and contaminate detected signals. In this Paper, we show how glitches can be simulated using generative adversarial networks. We produce hundreds of synthetic images for the 22 most common types of glitches seen in the LIGO, KAGRA, and Virgo detectors. The artifici

Johannes Hermle, Giorgio Martini

Ad platforms require reliable measurement of advertising returns: what increase in performance (such as clicks or conversions) can an advertiser expect in return for additional budget on the platform? Even from the perspective of the platform, accurately measuring advertising returns is hard. Selection and omitted variable biases make estimates from observat

Beáta Bényi, Toshiki Matsusaka

The central binomial series at negative integers are expressed as a linear combination of values of certain two polynomials. We show that one of the polynomials is a special value of the bivariate Eulerian polynomial and the other polynomial is related to the antidiagonal sum of poly-Bernoulli numbers. As an application, we prove Stephan's observation from 2

Bashdar Rahman Pirot, Nzar Rauf Abdullah, Ari Karim Ahmed

We investigate thermal transport in a serial asymmetric double quantum dot (DQD) coupled to two electron reservoirs with different temperatures. The inter- and intra-Coulomb interactions are taken into account in a Coulomb blockade DQD where the electron sequential tunneling via four different master equation approaches is considered. In the absence of Coulo

Fuchuan Lei, Zhichao Ye, Krishna Twayana, Yan Gao

Optical hyperparametric oscillation based on the third-order nonlinearity is one of the most significant mechanisms to generate coherent electromagnetic radiation and produce quantum states of light. Advances in dispersion-engineered high-$Q$ microresonators allow for generating signal waves far from the pump and decrease the oscillation power threshold to s

Kevin Tracy, Taylor A. Howell, Zachary Manchester

Collision detection plays an important role in simulation, control, and learning for robotic systems. However, no existing method is differentiable with respect to the configurations of the objects, greatly limiting the sort of algorithms that can be built on top of collision detection. In this work, we propose a set of differentiable collision detection alg

Shifei Luo

This paper first strictly proved that the growth of the second moment of a large class of Gaussian processes is not greater than power function and the covariance matrix is strictly positive definite. Under these two conditions, the maximum likelihood estimators of the mean and variance of such classes of drift Gaussian process have strong consistency under

Francesco Corti, Rahim Entezari, Sara Hooker, Davide Bacciu

We study the impact of different pruning techniques on the representation learned by deep neural networks trained with contrastive loss functions. Our work finds that at high sparsity levels, contrastive learning results in a higher number of misclassified examples relative to models trained with traditional cross-entropy loss. To understand this pronounced

Nzar Rauf Abdullah, Botan Jawdat Abdullah, Hunar Omar Rashid, Chi-Shung Tang

We consider electronic and optical properties of group III-Nitride monolayers using first-principle calculations. The group III-Nitride monolayers have flat hexagonal structures with almost zero planar buckling, $\Delta$. By tuning the $\Delta$, the strong $\sigma\text{-}\sigma$ bond through sp$^2$ hybridization of a flat form of these monolayers can be chan

Fang Da

Safety guarantees in motion planning for autonomous driving typically involve certifying the trajectory to be collision-free under any motion of the uncontrollable participants in the environment, such as the human-driven vehicles on the road. As a result they usually employ a conservative bound on the behavior of such participants, such as reachability anal

Antoine Comeau-Lapointe, Chantal David, Matilde Lalin, Wanlin Li

We investigate in this paper the vanishing at $s=1$ of the twisted $L$-functions of elliptic curves $E$ defined over the rational function field $\mathbb{F}_q(t)$ (where $\mathbb{F}_q$ is a finite field of $q$ elements and characteristic $\geq 5$) for twists by Dirichlet characters of prime order $\ell \geq 3$, from both a theoretical and numerical point of

P. T. Rahna, Zhen-Ya Zheng, Ana L. Chies-Santos, Zheng Cai

The Ly$\alpha$ emission is an important tracer of neutral gas in a circum-galactic medium (CGM) around high-z QSOs. The origin of Lya emission around QSOs is still under debate which has significant implications for galaxy formation and evolution. In this paper, we study Ly$\alpha$ nebulae around two high redshift QSOs, SDSS J141935.58+525710.7 at $z=3.218$

Albert Wu, Michelle Guo, C. Karen Liu

To fully utilize the versatility of a multi-fingered dexterous robotic hand for executing diverse object grasps, one must consider the rich physical constraints introduced by hand-object interaction and object geometry. We propose an integrative approach of combining a generative model and a bilevel optimization (BO) to plan diverse grasp configurations on n

Wencai Liu, Kang Lyu

In this paper, we introduce a new family of functions to construct Schr\"odinger operators with embedded eigenvalues. This particularly allows us to construct discrete Schr\"odinger operators with arbitrary prescribed sets of eigenvalues.

Mingkun Yang, Minghui Liao, Pu Lu, Jing Wang

Existing text recognition methods usually need large-scale training data. Most of them rely on synthetic training data due to the lack of annotated real images. However, there is a domain gap between the synthetic data and real data, which limits the performance of the text recognition models. Recent self-supervised text recognition methods attempted to util

Zhongkai Li, Haihua Wei

For $\lambda\ge0$, a $C^2$ function $f$ defined on the unit disk ${{\mathbb D}}$ is said to be $\lambda$-analytic if $D_{\bar{z}}f=0$, where $D_{\bar{z}}$ is the (complex) Dunkl operator given by $D_{\bar{z}}f=\partial_{\bar{z}}f-\lambda(f(z)-f(\bar{z}))/(z-\bar{z})$. The aim of the paper is to study several problems on the associated Bergman spaces $A^{p}_{

Minh Cao, Ramin Ramezani

Recent advancements in computer graphics technology allow more realistic ren-dering of car driving environments. They have enabled self-driving car simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large amounts of synthetic data that can complement the existing real-world dataset in training autonomous car perception. Furthermore, sin

Sangeetha P S, Sukanta Nayak

Uncertainties plays an important character in almost every problem which generally not considered and ideal cases are studied. As such, the consequences cost more and need model prediction. In view of these, this paper investigates nanofluid heat transfer problem under a variable magnetic field with uncertain bounded parameters and analyzed effect of uncerta

Minh Cao, Brett Bailey, Wenhao Zhang, Solana Fernandez

A low-cost, accurate device to measure and record knee range of motion (ROM) is of the essential need to improve confidence in at-home rehabilitation. It is to reduce hospital stay duration and overall medical cost after Total Knee Arthroplasty (TKA) procedures. The shift in Medicare funding from pay-as-you-go to the Bundled Payments for Care Improvement (BP

Rishab Mitra, Arpit Narechania, Alex Endert, John Stasko

Natural language (NL) toolkits enable visualization developers, who may not have a background in natural language processing (NLP), to create natural language interfaces (NLIs) for end-users to flexibly specify and interact with visualizations. However, these toolkits currently only support one-off utterances, with minimal capability to facilitate a multi-tu

Cheng Li, Yangxin Liu

Vision Transformers have achieved state-of-the-art performance in many visual tasks. Due to the quadratic computational and memory complexities of self-attention, recent works either apply attention only to low-resolution inputs or restrict the receptive field to a small local region. To overcome these limitations, we propose key-only attention, which exclud

Feiliang Ren, Yongkang Liu, Bochao Li, Shilei Liu

Although existing machine reading comprehension models are making rapid progress on many datasets, they are far from robust. In this paper, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over sensitivity, over stability and generalization. Specifically, we first use a natural la

Qingwen Zhang, Mingkai Tang, Ruoyu Geng, Feiyi Chen

Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused through transformers for better driving performance. These inputs are normally further interpreted as high-le

Guo-Jian Wang, Cheng Cheng, Yin-Zhe Ma, Jun-Qing Xia

In this work, we propose using the mixture density network (MDN) to estimate cosmological parameters. We test the MDN method by constraining parameters of the $\Lambda$CDM and $w$CDM models using Type Ia supernovae and the power spectra of the cosmic microwave background. We find that the MDN method can achieve the same level of accuracy as the Markov Chain

Ruiqi Zhang, Christopher Lane, Johannes Nokelainen, Bahadur Singh

Understanding similarities and differences between the cuprate and nickelate superconductors is drawing intense current interest. Competing charge orders have been observed recently in the $undoped$ infinite-layer nickelates in sharp contrast to the $undoped$ cuprates which exhibit robust antiferromagnetic insulating ground states. The microscopic mechanisms

Hang Liu, Jinchen He, Liuming Liu, Peng Sun

We present a lattice QCD study of hidden-charm hexaquarks with quark content $usc\bar{d}\bar{s}\bar{c}$ based on four ensembles of gauge configurations generated by CLQCD Collaboration with pion mass in the range of 220-300MeV. Four operators with quantum numbers $0^{++}, 0^{-+}, 1^{++}$ and $1^{--}$ respectively are constructed to interpolate the hexaquarks

Marissa Ramirez de Chanlatte, Matheus Gadelha, Thibault Groueix, Radomir Mech

We present a fine-tuning method to improve the appearance of 3D geometries reconstructed from single images. We leverage advances in monocular depth estimation to obtain disparity maps and present a novel approach to transforming 2D normalized disparity maps into 3D point clouds by using shape priors to solve an optimization on the relevant camera parameters

Joseph Patterson, Marguerite Epstein-Martin, Josie Enenstein, Jonathan Kemp

We present time-series photometry during eruption of the extremely fast nova V1674 Herculis (Nova Her 2021). The 2021 light curve showed periodic signals at 0.152921(3) d and 501.486(5) s, which we interpret as respectively the orbital and white dwarf spin-periods in the underlying binary. We also detected a sideband signal at the /difference/ frequency betw

Teppei Ogihara

We study maximum-likelihood-type estimation for diffusion processes when the coefficients are nonrandom and observation occurs in nonsynchronous manner. The problem of nonsynchronous observations is important when we consider the analysis of high-frequency data in a financial market. Constructing a quasi-likelihood function to define the estimator, we adapti

Gang-Feng Guo, Xi-Xi Bao, Lei Tan

The localization is one of the active and fundamental research in topology physics. Based on a generalized Su-Schrieffer-Heeger model with the quasiperiodic non-Hermitian emerging at the off-diagonal location, we propose a novel systematic method to analyze the localization behaviors for the bulk and the edge, respectively. For the bulk, it can be found that

Ye Xu

It is assumed that heavy dark matter particles $\phi$ with O(TeV) mass captured by the Sun may decay to relativistic light milli-charged particles (MCPs). These MCPs could be measured by the IceCube detector. The massless hidden photon model was taken for MCPs to interact with nuclei, so that the numbers and fluxes of expected MCPs and neutrinos may be evalu

Mingyuan Luo, Xin Yang, Hongzhang Wang, Liwei Du

Freehand 3D ultrasound (US) has important clinical value due to its low cost and unrestricted field of view. Recently deep learning algorithms have removed its dependence on bulky and expensive external positioning devices. However, improving reconstruction accuracy is still hampered by difficult elevational displacement estimation and large cumulative drift

Zhongyi Shui, Shichuan Zhang, Chenglu Zhu, Bingchuan Wang

Reliable quantitative analysis of immunohistochemical staining images requires accurate and robust cell detection and classification. Recent weakly-supervised methods usually estimate probability density maps for cell recognition. However, in dense cell scenarios, their performance can be limited by pre- and post-processing as it is impossible to find a univ

Yunquan Gao, Suo Tang

We investigate the optimization of the photon polarization to increase the yield of the Breit-Wheeler pair production in arbitrarily polarized plane wave backgrounds. We show that the optimized photon polarization can improve the positron yield by more than $20\%$ compared to the unpolarized case, in the intensity regime of current laser-particle experiments

Nathaniel R. Fried, Thomas J. Longo, Mikhail A. Anisimov

Fluid polyamorphism, the existence of multiple amorphous fluid states in a single-component system, has been observed or predicted in a variety of substances. A remarkable example of this phenomenon is the fluid-fluid phase transition in high-pressure hydrogen between insulating and conducting high-density fluids. This transition is induced by the reversible

Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan

Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models. A common theme in these results is the surprising observation that high-dimensional models can achieve favorable privacy-utility trade-offs. This seemingly contradicts known results on the model-size dependence of differentially private convex l