March 2023 arXiv papers — page 134

Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai

Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data. However, surrogate data typically deviate from test OOD data. Thus, the performance of OE, when facing unseen OOD data, can be weakened. To address this issue, we propose a novel OE-based approach that makes

Ambareesh Revanur, Debraj Basu, Shradha Agrawal, Dhwanit Agarwal

Edit fidelity is a significant issue in open-world controllable generative image editing. Recently, CLIP-based approaches have traded off simplicity to alleviate these problems by introducing spatial attention in a handpicked layer of a StyleGAN. In this paper, we propose CoralStyleCLIP, which incorporates a multi-layer attention-guided blending strategy in

Yuxin Zhang, Fan Tang, Weiming Dong, Haibin Huang

We present Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, which can fit in most existing arbitrary image style transfer models, e.g., CNN-based, ViT-based, and flow-based methods. As the key component in image style transfer tasks, a suitable style representation is essential to achieve sat

Keisuke Nishimura, Yuichi Sugiyama, Yuki Koike, Masaya Motoda

Fuzzing has contributed to automatically identifying bugs and vulnerabilities in the software testing field. Although it can efficiently generate crashing inputs, these inputs are usually analyzed manually. Several root cause analysis (RCA) techniques have been proposed to automatically analyze the root causes of crashes to mitigate this cost. However, outst

Chiara Bellotti, Andrew Yang

We improve unconditional estimates on $\Delta_k(x)$, the remainder term of the generalised divisor function, for large $k$. In particular, we show that $\Delta_k(x) \ll x^{1 - 1.889k^{-2/3}}$ for all sufficiently large fixed $k$.

Wen Huang, Jianhua Zhang

In this paper, we mainly study the turbulence of Galerkin truncations of 2D Navier-Stokes equation under degenerate stochastic forcing and large-scale. We use a kind of chaotic structure named full-horseshoes to describe it. It is proved that if the stochastic forcing satisfies some kind of hypoelliptic condition, then the system has full-horseshoes.

Jiacheng Wang, Hao Li, Han Liu, Dewei Hu

Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with few and/or sparsely labeled datasets. A

Takanori Anegawa, Norihiro Iizuka, Sunil Kumar Sake, Nicolò Zenoni

Volume complexity in dS$_2$ remains $O(1)$ up to a critical time, after which it suddenly diverges. On the other hand, for the dS$_2$ solution in JT gravity there is a linear dilaton which smoothly grows towards the future infinity. From the dimensional reduction viewpoint, the growth of the dilaton is due to the expansion of the orthogonal sphere in higher-

Jiashun Jin, Zheng Tracy Ke, Paxton Turner, Anru R. Zhang

How to detect a small community in a large network is an interesting problem, including clique detection as a special case, where a naive degree-based $\chi^2$-test was shown to be powerful in the presence of an Erd\H{o}s-Renyi background. Using Sinkhorn's theorem, we show that the signal captured by the $\chi^2$-test may be a modeling artifact, and it may d

I. S. Pichkovskiy, V. E. Zobov

We consider a quantum processor based on five qutrits represented by spins S=1, which is driven by radio fre-quency (RF) pulses selective in transitions between adjacent levels. Numerical simulation of the implementation of the quantum-adiabatic clustering algorithm was performed on the example of partitioning a set of six points into three groups. We find t

Kai Liu, Ziqing Du, Xucheng Wan, Huan Zhou

Target speech extraction (TSE) systems are designed to extract target speech from a multi-talker mixture. The popular training objective for most prior TSE networks is to enhance reconstruction performance of extracted speech waveform. However, it has been reported that a TSE system delivers high reconstruction performance may still suffer low-quality experi

Christopher E. Denniston, Gautam Salhotra, Akseli Kangaslahti, David A. Caron

When robots are deployed in the field for environmental monitoring they typically execute pre-programmed motions, such as lawnmower paths, instead of adaptive methods, such as informative path planning. One reason for this is that adaptive methods are dependent on parameter choices that are both critical to set correctly and difficult for the non-specialist

Yiqun Duan, Xianda Guo, Zheng Zhu

Monocular depth estimation is a challenging task that predicts the pixel-wise depth from a single 2D image. Current methods typically model this problem as a regression or classification task. We propose DiffusionDepth, a new approach that reformulates monocular depth estimation as a denoising diffusion process. It learns an iterative denoising process to `d

Xiu Yang, Li-Lian Wang, Huiyuan Li, Changtao Sheng

In this paper, we introduce a new family of orthogonal systems, termed as the M\"{u}ntz ball polynomials (MBPs), which are orthogonal with respect to the weight function: $\|x\|^{2\theta+2\mu-2} (1-\|x\|^{2\theta})^{\alpha}$ with the parameters $\alpha>-1, \mu>- 1/2$ and $\theta>0$ in the $d$-dimensional unit ball $x\in {\mathbb B}^d=\big\{x\in\mathbb{R}^d:

Makoto Ohshika, Domenico Lippolis, Akira Shudo

We study the effect of homogeneous noise on the escape rate of strongly chaotic area-preserving maps with a small opening. While in the noiseless dynamics the escape rate analytically depends on the instability of the shortest periodic orbit inside the hole, adding noise overall enhances escape, which, however, exhibits a non-trivial response to the noise am

Junfan Li, Shizhong Liao

In this paper, we improve the regret bound for online kernel selection under bandit feedback. Previous algorithm enjoys a $O((\Vert f\Vert^2_{\mathcal{H}_i}+1)K^{\frac{1}{3}}T^{\frac{2}{3}})$ expected bound for Lipschitz loss functions. We prove two types of regret bounds improving the previous bound. For smooth loss functions, we propose an algorithm with a

Senne Van Loon, Carlos A. R. Sa de Melo

We discuss a two-band model for two-dimensional superconductors with electron and hole bands separated by an energy gap and singlet $d$-wave pairing in each band. This type of model exhibits a V-shaped to U-shaped transition in the density of the states of the superconductor, and was phenomenologically used as a possible interpretation of recent tunneling ex

Hyunho Ahn, Tian Chen, Nawras Alnaasan, Aamir Shafi

Quantization is a popular technique used in Deep Neural Networks (DNN) inference to reduce the size of models and improve the overall numerical performance by exploiting native hardware. This paper attempts to conduct an elaborate performance characterization of the benefits of using quantization techniques -- mainly FP16/INT8 variants with static and dynami

Jieren Deng, Xin Zhou, Hao Tian, Zhihong Pan

Distilling the structured information captured in feature maps has contributed to improved results for object detection tasks, but requires careful selection of baseline architectures and substantial pre-training. Self-distillation addresses these limitations and has recently achieved state-of-the-art performance for object detection despite making several s

Jiaqi Li, Erlin Qiao

Observationally, two main spectral states, i.e., the low/hard state and the high/soft state, are identified in black hole X-ray binaries (BH-XRBs). Meanwhile, the transitions between the two states are often observed. In this paper, we re-investigate the transition luminosities in the framework of the self-similar solution of the advection-dominated accretio

Alberto Nocera, Mona Berciu

Spectral functions are important quantities that contain a wealth of information about the quasiparticles of a system, and that can also be measured experimentally. For systems with electron-phonon coupling, good approximations for the spectral function are available only in the Migdal limit (at Fermi energies much larger than the typical phonon frequency, $

Hai Zhu, Qingyang Zhao, Yuren Wu

Natural language processing models based on neural networks are vulnerable to adversarial examples. These adversarial examples are imperceptible to human readers but can mislead models to make the wrong predictions. In a black-box setting, attacker can fool the model without knowing model's parameters and architecture. Previous works on word-level attacks wi

Danlei Hu, Lu Chen, Hanxi Fang, Ziquan Fang

Spatio-temporal trajectory analytics is at the core of smart mobility solutions, which offers unprecedented information for diversified applications such as urban planning, infrastructure development, and vehicular networks. Trajectory similarity measure, which aims to evaluate the distance between two trajectories, is a fundamental functionality of trajecto

Takumi Aburayama, Naoto Miyoshi

We consider a shot-noise field defined on a stationary determinantal point process on $\mathbb{R}^d$ associated with i.i.d. amplitudes and a bounded response function, for which we investigate the scaling limits as the intensity of the point process goes to infinity. Specifically, we show that the centralized and suitably scaled shot-noise field converges in

Ryan Budney, David Gabai

Let Diff(N) and Homeo(N) denote the smooth and topological group of automorphisms respectively that fix the boundary of the n-manifold N, pointwise. We show that the (n-4)-th homotopy group of Homeo(S^1 \times D^{n-1}) is not finitely-generated for n >= 4 and in particular the topological mapping-class group of S^1\times D^3 is infinitely generated. We apply

Eric S. Escobar-Aguilar, Tonatiuh Matos, J. I. Jiménez-Aquino

This work presents an alternative approach to obtain the quantum field equations in curved spacetime, considering that sufficiently small particles follow stochastic trajectories around geodesic. Our proposal is based on a stochastic differential equation in which the noise term experienced by the quantum particles is a consequence of the stochastic backgrou

Shangdi Yu, Julian Shun

Given all pairwise weights (distances) among a set of objects, filtered graphs provide a sparse representation by only keeping an important subset of weights. Such graphs can be passed to graph clustering algorithms to generate hierarchical clusters. In particular, the directed bubble hierarchical tree (DBHT) algorithm on filtered graphs has been shown to pr

Angus Chan, Tianxi Li

The statistical modeling of random networks has been widely used to uncover interaction mechanisms in complex systems and to predict unobserved links in real-world networks. In many applications, network connections are collected via egocentric sampling: a subset of nodes is sampled first, after which all links involving this subset are recorded; all other i

Zhiming Chen, Yong Liu

We propose efficient and parallel algorithms for the implementation of the high-order continuous time Galerkin method for dissipative and wave propagation problems. By using Legendre polynomials as shape functions, we obtain a special structure of the stiffness matrix which allows us to extend the diagonal Pad\'e approximation to solve ordinary differential

Jaume Ros, Margarita Geleta, Jordi Pons, Xavier Giro-i-Nieto

The field of steganography has experienced a surge of interest due to the recent advancements in AI-powered techniques, particularly in the context of multimodal setups that enable the concealment of signals within signals of a different nature. The primary objectives of all steganographic methods are to achieve perceptual transparency, robustness, and large

Toru Takahashi

This paper proposes a fast time-domain boundary element method (TDBEM) to solve three-dimensional transient electromagnetic scattering problems regarding perfectly electric conductors in the classical marching-on-in-time manner. The algorithm of the fast TDBEM is a time-domain variant of the interpolation-based fast multipole method (IFMM), which is similar

Yaqi Sun, Wenchuan Wu, Yi Lin, Hai Huang

The main goal of distribution network (DN) expansion planning is essentially to achieve minimal investment constrained with specified reliability requirements. The reliability-constrained distribution network planning (RcDNP) problem can be cast an instance of mixed-integer linear programming (MILP) which involves ultra-heavy computation burden especially fo

Zhichao Ye, Haiyan Jia, Zhangjun Huang, Chen Shen

The foundry development of integrated photonics has revolutionized today's optical interconnect and datacenters. Over the last decade, we have witnessed the rising of silicon nitride (Si$_3$N$_4$) integrated photonics, which is currently transferring from laboratory research to foundry manufacturing. The development and transition are triggered by the ultima

Jianbo Cui, Dianming Hou, Zhonghua Qiao

Understanding the properties of the stochastic phase field models is crucial to model processes in several practical applications, such as soft matters and phase separation in random environments. To describe such random evolution, this work proposes and studies two mathematical models and their numerical approximations for parabolic stochastic partial diffe

Leandro Arosio, Finnur Larusson

We study the dynamics of a generic automorphism $f$ of a Stein manifold with the density property. Such manifolds include all linear algebraic groups. Even in the special case of $\mathbb C^n$, $n\geq 2$, most of our results are new. We study the Julia set, non-wandering set, and chain-recurrent set of $f$. We show that the closure of the set of saddle perio

Ivan Henao, Jader P. Santos, Raam Uzdin

Quantum Error Mitigation (QEM) enables the extraction of high-quality results from the presently-available noisy quantum computers. In this approach, the effect of the noise on observables of interest can be mitigated using multiple measurements without additional hardware overhead. Unfortunately, current QEM techniques are limited to weak noise or lack scal

Ruochen Jiao, Juyang Bai, Xiangguo Liu, Takami Sato

Predicting the future trajectories of surrounding vehicles based on their history trajectories is a critical task in autonomous driving. However, when small crafted perturbations are introduced to those history trajectories, the resulting anomalous (or adversarial) trajectories can significantly mislead the future trajectory prediction module of the ego vehi

Chia-Min Lin

In this work, I consider an inflation model with a quadratic potential and a negative cosmological constant. An analytical solution of the equation of motion for the inflaton field is found without slow-roll approximation. The result is that the inflation field is rolling at a constant speed. The prediction for cosmological perturbation is calculated.

Ning Liao, Bowen Shi, Xiaopeng Zhang, Min Cao

Prompt learning has achieved great success in efficiently exploiting large-scale pre-trained models in natural language processing (NLP). It reformulates the downstream tasks as the generative pre-training ones to achieve consistency, thus improving the performance stably. However, when transferring it to the vision area, current visual prompt learning metho

Tianfu Wang, Jiazhang Wang, Oliver Cossairt, Florian Willomitzer

Eye tracking is an important tool with a wide range of applications in Virtual, Augmented, and Mixed Reality (VR/AR/MR) technologies. State-of-the-art eye tracking methods are either reflection-based and track reflections of sparse point light sources, or image-based and exploit 2D features of the acquired eye image. In this work, we attempt to significantly

Stephanie M. Wu, Briana Joy K. Stephenson

Understanding how and why certain communities bear a disproportionate burden of disease is challenging due to the scarcity of data on these communities. Surveys provide a useful avenue for accessing hard-to-reach populations, as many surveys specifically oversample understudied and vulnerable populations. When survey data is used for analysis, it is importan

Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu

In this paper, we study the problem of temporal video grounding (TVG), which aims to predict the starting/ending time points of moments described by a text sentence within a long untrimmed video. Benefiting from fine-grained 3D visual features, the TVG techniques have achieved remarkable progress in recent years. However, the high complexity of 3D convolutio

Yunyun Wang, Tatsushi Oka, Dan Zhu

Vector autoregression is an essential tool in empirical macroeconomics and finance for understanding the dynamic interdependencies among multivariate time series. In this study, we expand the scope of vector autoregression by incorporating a multivariate distributional regression framework and introducing a distributional impulse response function, providing

Jiepeng Fang, Yixin Lan, Jie Xiao

As one of results in [6], Bridgeland realized the quantum group $\mathbf{U}_v$ via the localization of Ringel-Hall algebra for the two-periodic projective complexes of quiver representations over a finite field. In the present paper, we generalize Lusztig's categorical construction for the nilpotent part $\mathbf{U}_v^+$ to Bridgeland's Hall algebra of Dynki

Emma Lejeune, Peerasait Prachaseree

From healing wounds to maintaining homeostasis in cyclically loaded tissue, living systems have a phenomenal ability to sense, store, and respond to mechanical stimuli. Broadly speaking, there is significant interest in designing engineered systems to recapitulate this incredible functionality. In engineered systems, we have seen significant recent computati

Zhongshu Zhao, Haixia Dong, Wenjun Ying

A second-order accurate kernel-free boundary integral method is presented for Stokes and Navier boundary value problems on three-dimensional irregular domains. It solves equations in the framework of boundary integral equations, whose corresponding discrete forms are well-conditioned and solved by the GMRES method. A notable feature of this approach is that

Qichen Fu, Xingyu Liu, Ran Xu, Juan Carlos Niebles

Accurately estimating 3D hand pose is crucial for understanding how humans interact with the world. Despite remarkable progress, existing methods often struggle to generate plausible hand poses when the hand is heavily occluded or blurred. In videos, the movements of the hand allow us to observe various parts of the hand that may be occluded or blurred in a

Yong Peng, Jing Geng, Yi Fei Niu, Wen Hui Long

Deformed ground state of $^{32}$Mg is investigated using the axially deformed relativistic Hartree-Fock-Bogoliubov (D-RHFB) model with the effective Lagrangian PKA1, which provides coincident description with the experimental measurements. It is illustrated that obvious breaking of the pseudo-spin symmetry (PSS) given by PKA1, being consistent with the exper

Ying Zeng, Yushi Chen, Xue Yang, Qingyun Li

Existing oriented object detection methods commonly use metric AP$_{50}$ to measure the performance of the model. We argue that AP$_{50}$ is inherently unsuitable for oriented object detection due to its large tolerance in angle deviation. Therefore, we advocate using high-precision metric, e.g. AP$_{75}$, to measure the performance of models. In this paper,

Yichang Shou, Jiawei Liu, Hailu Luo

As a revolutionary observation tool in life science, biomedical, and material science, optical microscopy allows imaging of samples with high spatial resolution and a wide field of view. However, conventional microscopy methods are limited to single imaging and cannot accomplish real-time image processing. The edge detection, image enhancement and phase visu

Murat Polat, Kiran Meena

In this paper, first, we recall the notion of Clairaut Riemannian map (CRM) ${F}$ using a geodesic curve on the base manifold and give the Ricci equation. We also show that if base manifold of CRM is space form then leaves of $(ker{F}_\ast)^\perp$ become space forms and symmetric as well. Secondly, we define Clairaut semi-invariant Riemannian map (CSIRM) fro

Jing Geng, Yi Fei Niu, Wen Hui Long

Using the axially deformed relativistic Hartree-Fock-Bogoliubov (D-RHFB) model, we explore the mechanism behind the parity inversion and halo occurrence in $^{11}$Be, which are well reproduced by the RHF Lagrangian PKA1. It is illustrated that evidently enhanced deformation effects by the $\pi$-pseudo-vector and $\rho$-tensor couplings in PKA1 are crucial fo

Mengdi Chu, Keyu Zong, Xin Shu, Jiangtao Gong

The development of Autonomous Vehicle (AV) has created a novel job, the safety driver, recruited from experienced drivers to supervise and operate AV in numerous driving missions. Safety drivers usually work with non-perfect AV in high-risk real-world traffic environments for road testing tasks. However, this group of workers is under-explored in the HCI com

Junheng Li, Quan Nguyen

This paper presents a novel approach for controlling humanoid robots to push heavy objects. The approach combines kinodynamics-based pose optimization and loco-manipulation model predictive control (MPC). The proposed pose optimization considers the object-robot dynamics model, robot kinematic constraints, and object parameters to plan the optimal pushing po

V. Zakharov, E. Shulga, P. Garg, T. Hemmick

Time Projection Chambers (TPC)s are excellent tracking detectors for high multiplicity events and can intrinsically be high-rate, but are limited by the ions created in their avalanche stage. GEMs and Micromegas can reduce IBF through their geometry and E-field ratios, but these can lead to gain fluctuations and still leave IBF as the dominant source of spac

Min Zeng, Haimiao Mo, Zhiming Liang, Hua Wang

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, the

Yui Hayashi, Shun Katakami, Shigeo Kuwamoto, Kenji Nagata

In this paper, we propose a method for estimating model parameters using Small-Angle Scattering (SAS) data based on the Bayesian inference. Conventional SAS data analyses involve processes of manual parameter adjustment by analysts or optimization using gradient methods. These analysis processes tend to involve heuristic approaches and may lead to local solu

Su Bonan

I present a quick and sound method for the robustness verification of a sort of quantum classifiers who are Linear Sound. Since quantum machine learning has been put into practice in relevant fields and Linear Sound Property, LSP is a pervasive property, the method could be universally applied. I implemented my method with a Quantum Convolutional Neural Netw

Yong Chen, Jinqiao Duan, Hongjun Gao, Xingyu Guo

We study the stochastic Camassa-Holm equation with pure jump noise. We prove that if the initial condition of the solution is a solitary wave solution of the unperturbed equation, the solution decomposes into the sum of a randomly modulated solitary wave and a small remainder. Moreover, we derive the equations for the modulation parameters and show that the

Geunhyeok Yu, Minwoo Jeon, Hyoseok Hwang

The susceptibility of deep neural networks (DNNs) to adversarial examples has prompted an increase in the deployment of adversarial attacks. Image-agnostic universal adversarial perturbations (UAPs) are much more threatening, but many limitations exist to implementing UAPs in real-world scenarios where only binary decisions are returned. In this research, we

Aysan Razzaghi, Arun Ramachandran

Collision of two dispersed drops in the matrix of suspending liquid is the first step toward coalescence. However, to quantify the rate of coalescence, the configuration of the collision should be definable and the force that induces the collision should be measurable. We present a strategy to use the hydrodynamic force in a six-port microfluidic channel to

Yulin Cai

We study two kinds of push-forwards of $\delta$-forms and define the pull-backs of $\delta$-forms. As a generalization of Gubler-K\"unnemann, we prove the projection formula and the tropical Poincar\'e-Lelong formula. As an application, we follow the idea of Gubler-K\"unnemann and generalize the notion of $\delta$-forms on algebraic varieties, this allows us

Kenta Koshihara, Kazuya Yuasa

We study the energy extraction from and charging to a finite-dimensional quantum system by general quantum operations. We prove that the changes in energy induced by unital quantum operations are limited by the ergotropy/charging bound for unitary quantum operations. This implies that, in order to break the ergotropy/charging bound for unitary quantum operat

Umais Zahid, Qinghai Guo, Karl Friston, Zafeirios Fountas

Predictive coding (PC) accounts of perception now form one of the dominant computational theories of the brain, where they prescribe a general algorithm for inference and learning over hierarchical latent probabilistic models. Despite this, they have enjoyed little export to the broader field of machine learning, where comparative generative modelling techni

Shima Nabiee, Nader Bagherzadeh

Market financial forecasting is a trending area in deep learning. Deep learning models are capable of tackling the classic challenges in stock market data, such as its extremely complicated dynamics as well as long-term temporal correlation. To capture the temporal relationship among these time series, recurrent neural networks are employed. However, it is d

Jordi Piñero, Ricard Solé, Artemy Kolchinsky

Harvesting free energy from the environment is essential for the operation of many biological and artificial systems. We investigate the maximum rate of harvesting achievable by optimizing a set of reactions in a Markovian system, possibly given topological, kinetic, and thermodynamic constraints. We show that the maximum harvesting rate can be expressed as

Kai Zhang, Fuhai Su, Dayong Liu, Wenjun Wang

We investigate the nonequilibrium photocarrier dynamics of WTe2 under pressure using the optical pump-probe spectroscopy. The pressure dependences of the electronic relaxation manifest anomalous changes around 0.8, 3.5, and 6 GPa, indicating the abruptions in the electron-phonon interactions. In addition, the coherent phonon oscillations originating from she

Sijing Liu, Zhiyu Tan, Yi Zhang

We investigate discontinuous Galerkin methods for an elliptic optimal control problem with a general state equation and pointwise state constraints on general polygonal domains. We show that discontinuous Galerkin methods for general second-order elliptic boundary value problems can be used to solve the elliptic optimal control problems with pointwise state

Benar Fux Svaiter

In this note we review the iteration-complexity of a relaxed Hybrid-Proximal Extragradient Method under the large step condition. We also derive some useful proprieties of this method.

Stefano Massei, Francesco Tudisco

We consider the problem of attaining either the maximal increase or reduction of the robustness of a complex network by means of a bounded modification of a subset of the edge weights. We propose two novel strategies combining Krylov subspace approximations with a greedy scheme and an interior point method employing either the Hessian or its approximation co

Lin Zhang, Xin Li, Dongliang He, Errui Ding

It is widely agreed that reference-based super-resolution (RefSR) achieves superior results by referring to similar high quality images, compared to single image super-resolution (SISR). Intuitively, the more references, the better performance. However, previous RefSR methods have all focused on single-reference image training, while multiple reference image

Joseph Cho, So Young Kim, Dami Lee, Wonjoo Lee

We solve the Bj\"orling problem for zero mean curvature surfaces in the three-dimensional light cone. As an application, we construct and classify all rotational zero mean curvature surfaces.

Xiaoxiang Han, Yang Chen, Qiaohong Liu, Yiman Liu

Cardiac cine magnetic resonance imaging (MRI) is one of the important means to assess cardiac functions and vascular abnormalities. Mitigating artifacts arising during image reconstruction and accelerating cardiac cine MRI acquisition to obtain high-quality images is important. A novel end-to-end deep learning network is developed to improve cardiac cine MRI

Y. Tian, R. Zhou, Z. -R. Liu, Y. Liu

In the known field of topological photonics, what remains less so is the breakdown effect of topological phases deteriorated by perturbation. In this paper, we investigate the variance on topological invariants for a periodic Kekul{\'e} medium perturbed in unit cells, which was a gyromagnetic photonic crystal holding topological phases induced by \emph{synch

Hamid Khodadadi Koodiani, Elahe Jafari, Arsalan Majlesi, Mohammad Shahin

Modeling parameters are essential to the fidelity of nonlinear models of concrete structures subjected to earthquake ground motions, especially when simulating seismic events strong enough to cause collapse. This paper addresses two of the most significant barriers to improving nonlinear modeling provisions in seismic evaluation standards using experimental

Jeonghee Rho, Aravind P. Ravi, Jonathan D. Slavin, Heechan Cha

We present Spitzer, WISE, and Herschel observations of the young supernova remnant (SNR) N132D in the LMC, including 3-40 microns Spitzer IRS mapping, 12 microns WISE and 70, 100, 160, 250, 350, and 500 microns Herschel images. The high-velocity lines of [Ne II] at 12.8 microns, [Ne III] at 15.5 microns, and [O IV] 26 microns reveal infrared ejecta concentra

Salvador A. Ochoa-Oregon, David F. Rentería-Estrada, Roger J. Hernández-Pinto, German F. R. Sborlini

In certain situations, such as one-particle inclusive processes, it is possible to model the hadronization through Fragmentation Functions (FFs), which are universal non-perturbative functions extracted from experimental data through advanced fitting techniques. Constraining the parameters of such fits is crucial to reduce the uncertainties, and provide reli

M. Grandadam, J. P. F. LeBlanc

We study the frequency and temperature dependence of the optical conductivity in the weakly coupled two-dimensional Hubbard model using a renormalized perturbative expansion. The perturbative expansion is based on the skeleton series for the current-current correlation function with a dressed Green`s function and the results are obtained directly on the real

Noboru Isobe, Mizuho Okumura

This paper presents a mathematical analysis of ODE-Net, a continuum model of deep neural networks (DNNs). In recent years, Machine Learning researchers have introduced ideas of replacing the deep structure of DNNs with ODEs as a continuum limit. These studies regard the "learning" of ODE-Net as the minimization of a "loss" constrained by a parametric ODE. Al

Susan E. Martonosi, Martin Gonzalez, Nicolas Oshiro

NBA team managers and owners try to acquire high-performing players. An important consideration in these decisions is how well the new players will perform in combination with their teammates. Our objective is to identify elite five-person lineups, which we define as those having a positive plus-minus per minute (PMM). Using individual player order statistic

Rie Kamikubo, Kyungjun Lee, Hernisa Kacorri

To ensure that AI-infused systems work for disabled people, we need to bring accessibility datasets sourced from this community in the development lifecycle. However, there are many ethical and privacy concerns limiting greater data inclusion, making such datasets not readily available. We present a pair of studies where 13 blind participants engage in data

Aidan Walk, Charles-Antoine Claveau, Michael Bottom, Mark Chun

Geometric optical distortion is a significant contributor to the astrometric error budget in large telescopes using adaptive optics. To increase astrometric precision, optical distortion calibration is necessary. We investigate using smartphone OLED screens as astrometric calibrators. Smartphones are low cost, have stable illumination, and can be quickly rec

Bernhard Klemt, Victor El-Homsy, Martin Nurizzo, Pierre Hamonic

For semiconductor spin qubits, complementary-metal-oxide-semiconductor (CMOS) technology is the ideal candidate for reliable and scalable fabrication. Making the direct leap from academic fabrication to qubits fabricated fully by industrial CMOS standards is difficult without intermediate solutions. With a flexible back-end-of-line (BEOL) new functionalities

Philipp Roser, Matthew T. Scoggins

The trajectories of the pilot-wave formulation of quantum mechanics and hence its empirical predictions may be recovered via the dynamics of a density function on the configuration space of a system, without reference to a physical wave function. We label such formulations `CSD frameworks.' But this result only holds if a particular, apparently ad hoc condit

Karim Guirguis, Johannes Meier, George Eskandar, Matthias Kayser

Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These concerns arise from the need for huge amounts of data to train deep neural networks. A promise of Generalized Few-shot Object Detection (G-FSOD), a learning paradigm in AI, is to alleviate the need for collecting abundant training samples of novel class

Peiyi Yang, Jie Fan, Zice Wei, Haoqian Li

Voice Personal Assistants (VPA) have become a common household appliance. As one of the leading platforms for VPA technology, Amazon created Alexa and designed Amazon Kids for children to safely enjoy the rich functionalities of VPA and for parents to monitor their kids' activities through the Parent Dashboard. Although this ecosystem is in place, the usage

Joon Kwon, Yijun Wan, Bruno Ziliotto

Blackwell's approachability (Blackwell, 1954, 1956) is a very general online learning framework where a Decision Maker obtains vector-valued outcomes, and aims at the convergence of the average outcome to a given ``target'' set. Blackwell gave a sufficient condition for the decision maker having a strategy guaranteeing such a convergence against an adversari

Tu Le, Alan Wang, Yaxing Yao, Yuanyuan Feng

Data collection through the Internet of Things (IoT) devices, or smart devices, in commercial buildings enables possibilities for increased convenience and energy efficiency. However, such benefits face a large perceptual challenge when being implemented in practice, due to the different ways occupants working in the buildings understand and trust in the dat

Joon Hyeop Lee, Mina Pak, Hyunjin Jeong, Sree Oh

We introduce a practical methodology for investigating the star formation and chemical evolution history of a galaxy: age-divided mean stellar populations (ADPs) from full spectrum fitting. In this method, the mass-weighted mean stellar populations and mass fractions (f_mass) of young and old stellar components in a galaxy are separately estimated, which are

Kevin K. Bowden, Marilyn Walker

There has been an increased focus on creating conversational open-domain dialogue systems in the spoken dialogue community. Unlike traditional dialogue systems, these conversational systems cannot assume any specific information need or domain restrictions, i.e., the only inherent goal is to converse with the user on an unknown set of topics. While massive i

Jiayi Pan, Glen Chou, Dmitry Berenson

To make robots accessible to a broad audience, it is critical to endow them with the ability to take universal modes of communication, like commands given in natural language, and extract a concrete desired task specification, defined using a formal language like linear temporal logic (LTL). In this paper, we present a learning-based approach for translating

Hermie Monterde

In this paper, we provide a characterization of fractional revival between twin vertices in a weighted graph with respect to its adjacency, Laplacian and signless Laplacian matrices. As an application, we characterize fractional revival between apexes of double cones.

Wojciech Brzezicki, Filomena Forte, Canio Noce, Mario Cuoco

The phenomenon of negative thermal expansion (NTE) deals with the increase of the lattice parameters and the volume of the unit cell when the material is thermally cooled. The NTE is typically associated with thermal phonons and anomalous spin-lattice coupling at low temperatures. However, the underlying mechanisms in the presence of strong electron correlat

William Golding

In this note, we show nonlinear stability in $L^\infty$ for Lipschitz solutions to genuinely nonlinear, multi-dimensional scalar conservation laws. As an application, we are able to compute explicit algebraic decay rates of the $L^\infty$ norm of perturbations of global-in-time Lipschitz solutions, including perturbations of planar rarefaction waves. Our ana

Hemant K. Mishra, Ludovico Lami, Prabha Mandayam, Mark M. Wilde

The pretty good measurement is a fundamental analytical tool in quantum information theory, giving a method for inferring the classical label that identifies a quantum state chosen probabilistically from an ensemble. Identifying and constructing the pretty good measurement for the class of bosonic Gaussian states is of immediate practical relevance in quantu

Zhe He, Yide Zhang, Xin Tong, Lei Li

Entangled biphoton sources exhibit nonclassical characteristics and have been applied to imaging techniques such as ghost imaging, quantum holography, and quantum optical coherence tomography. The development of wide-field quantum imaging to date has been hindered by low spatial resolutions, speeds, and contrast-to-noise ratios (CNRs). Here, we present quant

Ziheng Qin, Kai Wang, Zangwei Zheng, Jianyang Gu

Data pruning aims to obtain lossless performances with less overall cost. A common approach is to filter out samples that make less contribution to the training. This could lead to gradient expectation bias compared to the original data. To solve this problem, we propose \textbf{InfoBatch}, a novel framework aiming to achieve lossless training acceleration b

Yelleti Vivek, Vadlamani Ravi, Abhay Anand Mane, Laveti Ramesh Naidu

Gaining the trust and confidence of customers is the essence of the growth and success of financial institutions and organizations. Of late, the financial industry is significantly impacted by numerous instances of fraudulent activities. Further, owing to the generation of large voluminous datasets, it is highly essential that underlying framework is scalabl

Julian Lemmel, Radu Grosu

The approximation capability of ANNs and their RNN instantiations, is strongly correlated with the number of parameters packed into these networks. However, the complexity barrier for human understanding, is arguably related to the number of neurons and synapses in the networks, and to the associated nonlinear transformations. In this paper we show that the

Yuxin Zhou

We consider the spherical mixed $p$-spin models and investigate the structure of the Parisi measure at zero temperature. We prove that for the spherical spin models with $n$ components, the Parisi measure at zero temperature is at most $n$-RSB or $n$-FRSB. We also provide a necessary and sufficient computational criterion for the spherical mixed $p$-spin mod