March 2023 arXiv papers — page 107

Robert Mattes, Igor Lesanovsky, Federico Carollo

Time-crystals are nonequilibrium many-body phases in which the state of the system dynamically approaches a limit cycle. While these phases are recently in the focus of intensive research, it is still far from clear whether they can host quantum correlations. In fact, mostly classical correlations have been observed so far and time-crystals appear to be effe

Christian Northe

Entanglement is resolved in conformal field theory (CFT) with respect to conformal families to all orders in the UV cutoff. To leading order, symmetry-resolved entanglement is connected to the quantum dimension of a conformal family, while to all orders it depends on null vectors. Criteria for equipartition between sectors are provided in both cases. This an

Alexander von Boetticher, Roman Walczak, Simon Hooker

We investigate the growth of ion density perturbations in large-amplitude linear laser wakefields via two-dimensional particle-in-cell simulations. Growth rates and wave numbers are found to be consistent with a longitudinal strong-field modulational instability (SFMI). We examine the transverse dependence of the instability for a Gaussian wakefield envelope

Matteo Esposito, Andrea Janes, Terhi Kilamo, Valentina Lenarduzzi

Context. Code understandability is fundamental. Developers need to understand the code they are modifying clearly. A low understandability can increase the amount of coding effort, and misinterpreting code impacts the entire development process. Ideally, developers should write clear and understandable code with the least effort. Aim. Our work investigates w

T. Nagao, H. Kuncarayakti, K. Maeda, T. Moore

We present photometric and spectroscopic observations of the Type Icn supernova (SN) 2021ckj. Spectral modeling of SN 2021ckj reveals that its composition is dominated by oxygen, carbon and iron group elements, and the photospheric velocity at peak is ~10000 km/s. From the light curve (LC) modeling applied to SNe 2021ckj, 2019hgp, and 2021csp, we find that t

A. N. Hiller Blin, V. I. Mokeev

In view of the precise data available on inclusive polarized electron scattering off polarized proton targets in the nucleon resonance excitation region, we compare these results with the coherent sum of resonant contributions to the polarized structure function $g_1$ and virtual photon asymmetry $A_1$. To this goal, we employ the nucleon resonance electroex

Ryuhei Sato, Kazuto Akagi, Shigeyuki Takagi, Kartik Sau

Topological data analysis based on persistent homology has been applied to the molecular dynamics simulation for the fast ion-conducting phase ($\alpha$-phase) of AgI, to show its effectiveness on the ion-migration mechanism analysis.Time-averaged persistence diagrams of $\alpha$-AgI, which quantitatively records the shape and size of the ring structures in

Lalit Kumar

Formulating an appropriate elasto-viscoplastic constitutive equation is challenging, especially for a model describing pre-yielding solid and post-yielding liquid behaviours. Oldroyds 1946 formulation was one of the first models explaining it, however, assumptions of a simple linear elastic and quasi-static deformation before yielding made his model idealist

Anne-Marie Rickmann, Murong Xu, Tom Nuno Wolf, Oksana Kovalenko

The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications. A clinically relevant problem that received less attention is the handling of scans with irregular anatomy, e.g., after organ resection. State-of-the-art segmentation models often lead to organ hallucinations, i.e., false-positiv

Yijin Li, Zhaoyang Huang, Shuo Chen, Xiaoyu Shi

Event cameras provide high temporal precision, low data rates, and high dynamic range visual perception, which are well-suited for optical flow estimation. While data-driven optical flow estimation has obtained great success in RGB cameras, its generalization performance is seriously hindered in event cameras mainly due to the limited and biased training dat

Tanita Klas, Jana Bender, Patrick Mischke, Thomas Niederprüm

We report the engineering of molecular potentials at large interatomic distances. The molecular states are generated by off-resonant optical coupling to a highly excited, long-range Rydberg molecular potential. The coupling produces a potential well in the low-lying molecular potential, which supports a bound state. The depth of the potential well, and thus

Uğurcan Çakal, Maryada, Chenxi Wu, Ilkay Ulusoy

Mixed-signal neuromorphic processors provide extremely low-power operation for edge inference workloads, taking advantage of sparse asynchronous computation within Spiking Neural Networks (SNNs). However, deploying robust applications to these devices is complicated by limited controllability over analog hardware parameters, as well as unintended parameter a

Yogesh Langhe, Katrin Skerl, Adrien Bartoli

The aim of this work is to implement a simple freehand ultrasound (US) probe calibration technique. This will enable us to visualize US image data during surgical procedures using augmented reality. The performance of the system was evaluated with different experiments using two different pose estimation techniques. A near-millimeter accuracy can be achieved

Yiming Gao

Optimal transport has gained much attention in image processing field, such as computer vision, image interpolation and medical image registration. Recently, Bredies et al. (ESAIM:M2AN 54:2351-2382, 2020) and Schmitzer et al. (IEEE T MED IMAGING 39:1626-1635, 2019) established the framework of optimal transport regularization for dynamic inverse problems. In

Arnaud Mayeux

We introduce multi-centered dilatations of rings, schemes and algebraic spaces, a basic algebraic concept. Dilatations of schemes endowed with a structure (e.g. monoid, group or Lie algebra) are in favorable cases schemes endowed with the same structure. As applications, we use our new formalism to contribute to the understanding of mono-centered dilatations

Chunyu Qiang, Peng Yang, Hao Che, Ying Zhang

Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesized speech of a target speaker's timbre. In most previous methods, the synthesized fine-grained prosody features often represent the source speaker's average style, similar to the one-to-many problem(i.e., multiple prosody variations correspond to the

Nicolas Bousquet, Valentin Gledel, Jonathan Narboni, Théo Pierron

We consider spanning trees of $n$ points in convex position whose edges are pairwise non-crossing. Applying a flip to such a tree consists in adding an edge and removing another so that the result is still a non-crossing spanning tree. Given two trees, we investigate the minimum number of flips required to transform one into the other. The naive $2n-\Omega(1

Xiangwen Deng, Yingshuang Zou, Yuanhao Cai, Chendong Zhao

Style transfer of 3D faces has gained more and more attention. However, previous methods mainly use images of artistic faces for style transfer while ignoring arbitrary style images such as abstract paintings. To solve this problem, we propose a novel method, namely Face-guided Dual Style Transfer (FDST). To begin with, FDST employs a 3D decoupling module to

Marcel Wild

Consider a hypergraph (=set system) $\mathbb{H}$ whose $h$ hyperedges are subsets of a set with w elements. We show that the $R$ minimal hitting sets of $\mathbb{H}$ can be enumerated in polynomial total time $O(Rh^2 w^2)$.

James Parkinson, Hendrik Van Maldeghem

An automorphism of a spherical building is called domestic if it maps no chamber to an opposite chamber. In this paper we classify domestic automorphisms of spherical buildings of classical type.

Rahul Singh, Abhinek Shukla, Dootika Vats

Stochastic gradient descent (SGD) is an estimation tool for large data employed in machine learning and statistics. Due to the Markovian nature of the SGD process, inference is a challenging problem. An underlying asymptotic normality of the averaged SGD (ASGD) estimator allows for the construction of a batch-means estimator of the asymptotic covariance matr

Krzysztof Burnecki, Zbigniew Palmowski, Marek Teuerle, Aleksandra Wilkowska

In this paper, we generalise the results presented in the literature for the ruin probability for the insurer--reinsurer model under a pro-rata reinsurance contract. We consider claim amounts that are described by a phase-type distribution that includes exponential, mixture of exponential, Erlang, and mixture of Erlang distributions. We derive the ruin proba

Yukai Ju, Jun Chen, Shimin Zhang, Shulin He

This paper introduces the Unbeatable Team's submission to the ICASSP 2023 Deep Noise Suppression (DNS) Challenge. We expand our previous work, TEA-PSE, to its upgraded version -- TEA-PSE 3.0. Specifically, TEA-PSE 3.0 incorporates a residual LSTM after squeezed temporal convolution network (S-TCN) to enhance sequence modeling capabilities. Additionally, the

Ziqian Tang

The scattering of test fields by sandwich waves has been studied extensively. It has been found that for a variety of test fields, the energy of the scattered waves is amplified. In this paper, Scattering of test gravitational waves by sandwich waves are calculated by solving gravitational perturbations in the sandwich wave background. Dependence of their en

Chien-Sheng Yang, Carlson Lin, I-Kang Fu

Mitigating climate change and its impacts is one of the sustainable development goals (SDGs) required by United Nations for an urgent action. Increasing carbon emissions due to human activities is the root cause to climate change. Telecommunication networks that provide service connectivity to mobile users contribute great amount of carbon emissions by consu

Pingbo Chen, Gongqi Wang, Bicong Ye, Jinhua Wang

Superconducting diode effect (SDE) with nonreciprocal supercurrent transport has attracted intense attention recently, not only for its intriguing physics, but also for its great application potential in superconducting circuits. It is revealed in this work that planar Josephson junctions (JJs) based on type-II Weyl semimetal (WSM) MoTe$_2$ can exhibit a pro

A. I. Agafonov

In the bound states in the continuum (BIC) the binding energy is positive, and the mass of a composite particle is greater than the total mass of its constituents. In this work the BIC state is studied for the electron-proton system with using the ladder Bethe-Salpeter equation. We demonstrate that there are two momentum space regions in which the electromag

Junjie Ni, Yijin Li, Zhaoyang Huang, Hongsheng Li

Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensi

Andre Belotto da Silva, Octave Curmi, Guillaume Rond

Based on a recently developed rank Theorem for Eisenstein power series, we provide new proofs of the following two results of W. Pawlucki: I) The non regular locus of a complex or real analytic map is an analytic set. II) The set of semianalytic or Nash points of a subanalytic set X is a subanalytic set, whose complement has codimension two in X.

Hong Wang, Xiaoyu Zhu

We demonstrate that level crossings at the Fermi energy serve as robust indicators for higher-order topology in two-dimensional superconductors of symmetry class D. These crossings occur when the boundary condition in one direction is continuously varied from periodic to open, revealing the topological distinction between opposite edges. The associated Major

Geumbyeol Hwang, Sunwon Hong, Seunghyun Lee, Sungwoo Park

For realistic talking head generation, creating natural head motion while maintaining accurate lip synchronization is essential. To fulfill this challenging task, we propose DisCoHead, a novel method to disentangle and control head pose and facial expressions without supervision. DisCoHead uses a single geometric transformation as a bottleneck to isolate and

Guilherme D. da Fonseca

In the convex covering problem, we are given a convex polygon with holes $P$ and the goal is to cover $P$ using a small number of convex polygons that lie inside $P$. In this paper, we solve the problem using the following strategy. We find a big collection of large (often maximal) convex polygons inside $P$ and then solve several set cover problems to find

Ziemowit Kostana

We introduce and study a family of axioms that closely follows the pattern of parametrized diamonds, studied by Moore, Hru\v{s}\'ak, and D\v{z}amonja in [13]. However, our approach appeals to model theoretic / forcing theoretic notions, rather than pure combinatorics. The main goal of the paper is to exhibit a surprising, close connection between seemingly v

Kwok Kwan Wong

We proposed a proof of the Riemann hypothesis. The proof is based on the Nyman-Beurling-Baez-Duarte condition. By proving existence of the solution for a system of inequalities, we can show that there is a sequence, which act as the coefficient of Beurling's sequence, can approximate the constant vector in a weighted Hilbert space.

Han Zheng, Xufang Luo, Pengfei Wei, Xuan Song

Conventional reinforcement learning (RL) needs an environment to collect fresh data, which is impractical when online interactions are costly. Offline RL provides an alternative solution by directly learning from the previously collected dataset. However, it will yield unsatisfactory performance if the quality of the offline datasets is poor. In this paper,

Hailong Li, Hua Jiang, Qing-Feng Sun, X. C. Xie

Energy dissipation is of fundamental interest and crucial importance in quantum systems. However, whether energy dissipation can emerge inside topological systems remains a question, especially when charge transport is topologically protected and quantized. As a hallmark, we propose a microscopic picture that illustrates energy dissipation in the quantum Hal

Ondřej F. K. Kalenda, Matias Raja

We investigate the question whether the (I)-envelope of any subset of a dual to a Banach space $X$ may be described as the closed convex hull in a suitable topology. If $X$ contains no copy of $\ell^1$ then the weak topology generated by functionals of the first Baire class in the weak$^*$ topology works. On the other hand, if $X$ contains a complemented cop

Jiaxing Gong, Qi Li, Jing Wang

We investigate the dynamic behavior of optical vortices, or phase singularities, in random wavefields and demonstrate the direct experimental observation of the anomalous diffusion of optical vortices. The observed subdiffusion of optical vortices show excellent agreement with the fractional Brownian motion, a Gaussian process. Paradoxically, the vortex disp

Mengfei Ye

I propose a novel dual-attention model(DAM) for aspect-level sentiment classification. Many methods have been proposed, such as support vector machines for artificial design features, long short-term memory networks based on attention mechanisms, and graph neural networks based on dependency parsing. While these methods all have decent performance, I think t

Takuya Tsutsui, Atsushi Nishizawa

Axions are considered as a candidate of dark matter. The axions form coherent clouds which delay and amplify gravitational waves (GWs) at a resonant frequency. That is, the coherent axion clouds produce secondary GWs following a primary wave. All GWs from compact binary mergers detected so far propagate in the Milky Way halo composed of dark matter so that s

Xulong Zhang, Haobin Tang, Jianzong Wang, Ning Cheng

Because of predicting all the target tokens in parallel, the non-autoregressive models greatly improve the decoding efficiency of speech recognition compared with traditional autoregressive models. In this work, we present dynamic alignment Mask CTC, introducing two methods: (1) Aligned Cross Entropy (AXE), finding the monotonic alignment that minimizes the

Xiangzhi Cao

In this paper, we mainly study Liouville theorem of V T harmonic map from complete noncompact manifold into horoball in Cartan-Hardmard manifold. To this aim, we will establish gradient estimates under some condition on and V and T.

Junhao Zhang, Junjie Tang, Juncheng Jin, Zehui Qu

Traffic flow forecasting is challenging due to the intricate spatio-temporal correlations in traffic flow data. Existing Transformer-based methods usually treat traffic flow forecasting as multivariate time series (MTS) forecasting. However, too many sensors can cause a vector with a dimension greater than 800, which is difficult to process without informati

Mitia Duerinckx, Antoine Gloria, Matthias Ruf

Consider the wave equation with heterogeneous coefficients in the homogenization regime. At large times, the wave interacts in a nontrivial way with the heterogeneities, giving rise to effective dispersive effects. The main achievement of the present work is a new ansatz for the long-time two-scale expansion inspired by spectral analysis. Based on this spect

Haobin Tang, Xulong Zhang, Jianzong Wang, Ning Cheng

Recent expressive text to speech (TTS) models focus on synthesizing emotional speech, but some fine-grained styles such as intonation are neglected. In this paper, we propose QI-TTS which aims to better transfer and control intonation to further deliver the speaker's questioning intention while transferring emotion from reference speech. We propose a multi-s

Wei Jin

This paper initiates the investigation of the family of $(G,s)$-geodesic-transitive digraphs with $s\geq 2$. We first give a global analysis by providing a reduction result. Let $\Gamma$ be such a digraph and let $N$ be a normal subgroup of $G$ maximal with respect to having at least $3$ orbits. Then the quotient digraph $\Gamma_N$ is $(G/N,s')$-geodesic-tra

Simin Wu, Yilong Xie

The significant splitting of $\Lambda$ and $\bar{\Lambda}$ polarization measured in STAR's Au+Au 7.7GeV collisions seems to be huge and unable to be described satisfactorily by any single mechanism, thus we revisit and combine there different mechanisms together on the basis of our PICR hydrodynamic model, to explain the experimental data. The three mechanis

Takumi Harada, Hiroyuki Sakai

Prediction of image memorability has attracted interest in various fields. Consequently, the prediction accuracy of convolutional neural network (CNN) models has been approaching the empirical upper bound estimated based on human consistency. However, identifying which feature representations embedded in CNN models are responsible for the high memorability p

Liang Wang, Nan Yang, Furu Wei

This paper introduces a simple yet effective query expansion approach, denoted as query2doc, to improve both sparse and dense retrieval systems. The proposed method first generates pseudo-documents by few-shot prompting large language models (LLMs), and then expands the query with generated pseudo-documents. LLMs are trained on web-scale text corpora and are

Hui Tang, Yao Lu, Qi Xuan

Despite the popularization of deep neural networks (DNNs) in many fields, it is still challenging to deploy state-of-the-art models to resource-constrained devices due to high computational overhead. Model pruning provides a feasible solution to the aforementioned challenges. However, the interpretation of existing pruning criteria is always overlooked. To c

Kexin Zhu, Xulong Zhang, Jianzong Wang, Ning Cheng

Using deep learning methods to classify EEG signals can accurately identify people's emotions. However, existing studies have rarely considered the application of the information in another domain's representations to feature selection in the time-frequency domain. We propose a classification network of EEG signals based on the cross-domain feature fusion me

Yoshihisa Kitazawa

The slow rolling inflation is dual to the random walk of conformal zero-mode. The 2 dimensional Fokker-Planck theory predicts the slow roll parameters of 4d inflation theory. The O(N) enhancements of the two point functions, N is the e-folding number, suppress the slow roll parameters by the same magnitude. Under the gaussian approximation, FP equation boils

Mukul Bhutani, J. Zico Kolter

Predicting how distributions over discrete variables vary over time is a common task in time series forecasting. But whereas most approaches focus on merely predicting the distribution at subsequent time steps, a crucial piece of information in many settings is to determine how this probability mass flows between the different elements over time. We propose

Tao Yang, Lisheng Wang

The Koos grading scale is a classification system for vestibular schwannoma (VS) used to characterize the tumor and its effects on adjacent brain structures. The Koos classification captures many of the characteristics of treatment deci-sions and is often used to determine treatment plans. Although both contrast-enhanced T1 (ceT1) scanning and high-resolutio

Cheng-Der Fuh, Chu-Lan Michael Kao, Tianxiao Pang

To characterize the Kullback-Leibler divergence and Fisher information in general parametrized hidden Markov models, in this paper, we first show that the log likelihood and its derivatives can be represented as an additive functional of a Markovian iterated function system, and then provide explicit characterizations of these two quantities through this rep

Tianwei Liang

We develop a theory of perfect algebraic spaces that extend the so-called perfect schemes to the setting of algebraic spaces. We prove several desired properties of perfect algebraic spaces. This extends some previous results of perfect schemes, including the recent one developed by Bertapelle et al. in arXiv:1611.02060. Moreover, our theory extends the prev

D. I. Novikov, A. O. Mihalchenko

This paper proposes a new approach to separate the $\mu$ spectral distortions of the cosmic microwave background from foregrounds with poorly defined spectral shapes. The idea is based on finding the optimal response to the observed signal. This response is weakly sensitive to foregrounds with parameters that are within some certain limits of their possible

Ziyue Zhu, Zhao Zhang, Zheng Lin, Xing Sun

Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. Mining a co-representation is essential for locating co-salient objects. Unfortunately, the current Co-SOD method does not pay enough attention that the information not related to the co-salient object is included in the co-representation. Such irreleva

Kaidi Cao, Jiaxuan You, Jiaju Liu, Jure Leskovec

AutoML has demonstrated remarkable success in finding an effective neural architecture for a given machine learning task defined by a specific dataset and an evaluation metric. However, most present AutoML techniques consider each task independently from scratch, which requires exploring many architectures, leading to high computational cost. Here we propose

Tong Hua, Tao Li, Ling Pei

Invariant Extended Kalman Filter (IEKF) has been successfully applied in Visual-inertial Odometry (VIO) as an advanced achievement of Kalman filter, showing great potential in sensor fusion. In this paper, we propose partial IEKF (PIEKF), which only incorporates rotation-velocity state into the Lie group structure and apply it for Visual-Inertial-Wheel Odome

Ganghui Ru, Xulong Zhang, Jianzong Wang, Ning Cheng

Music genre classification has been widely studied in past few years for its various applications in music information retrieval. Previous works tend to perform unsatisfactorily, since those methods only use audio content or jointly use audio content and lyrics content inefficiently. In addition, as genres normally co-occur in a music track, it is desirable

Kaidi Cao, Jiaxuan You, Jure Leskovec

A key assumption in multi-task learning is that at the inference time the multi-task model only has access to a given data point but not to the data point's labels from other tasks. This presents an opportunity to extend multi-task learning to utilize data point's labels from other auxiliary tasks, and this way improves performance on the new task. Here we i

Pei Guo, Yisheng Xiao, Juntao Li, Min Zhang

Non-autoregressive neural machine translation (NAT) models are proposed to accelerate the inference process while maintaining relatively high performance. However, existing NAT models are difficult to achieve the desired efficiency-quality trade-off. For one thing, fully NAT models with efficient inference perform inferior to their autoregressive counterpart

Arjun K. Rathie, Mykola A. Shpot

The objective of this short note is to provide two closed-form evaluations for the generalized hypergeometric function $_4F_3$ of the argument $\frac1{16}$. This is achieved by means of separating a generalized hypergeometric function $_3F_2$ into even and odd components, together with the use of two known results for $_3F_2(\pm\frac14)$ available in the lit

V. S. Hariharan, B. S. Murty, Gandham Phanikumar

The near-rapid solidification conditions during additive manufacturing can lead to selection of non-equilibrium phases. Sharp interface models via interface response functions have been used earlier to explain the microstructure selection under such solidification conditions. However, most of the sharp interface models assume linear superposition of contribu

Alexandra Dana, Nadav Carmel, Amit Shomer, Ofer Manela

Transferring the absolute depth prediction capabilities of an estimator to a new domain is a task with significant real-world applications. This task is specifically challenging when images from the new domain are collected without ground-truth depth measurements, and possibly with sensors of different intrinsics. To overcome such limitations, a recent zero-

Guillem Domènech, Misao Sasaki

The end state of Hawking evaporation of a black hole is uncertain. Some candidate quantum gravity theories, such as loop quantum gravity and asymptotic safe gravity, hint towards Planck sized remnants. If so, the Universe might be filled with remnants of tiny primordial black holes, which formed with mass $M<10^9\,{\rm g}$. A unique scenario is the case of $

Yasuhiro Ohkura, Suguru Endo, Takahiko Satoh, Rodney Van Meter

In the era of quantum computing without full fault-tolerance, it is essential to suppress noise effects via the quantum error mitigation techniques to enhance the computational power of the quantum devices. One of the most effective noise-agnostic error mitigation schemes is the generalized quantum subspace expansion (GSE) method, which unifies various mitig

Qihan He, Juntao Lv, Zongyan Lv, Tong Wu

In this paper, we consider the existence of nontrivial solutions to the following critical biharmonic problem with a logarithmic term \begin{equation*} \begin{cases} \Delta^2 u=\mu \Delta u+\lambda u+|u|^{2^{**}-2}u+\tau u\log u^2, \ \ x\in\Omega, u|_{\partial \Omega }=\frac{\partial u}{\partial n}|_{\partial\Omega}=0, \end{cases} \end{equation*} where $\mu,

Haihang Gu, Houyi Yu

The odd length in Weyl groups is a new statistic analogous to the classical Coxeter length, and features combinatorial and parity conditions. We establish explicit closed product formulas for the sign-twisted generating functions of the odd length for parabolic quotients of Weyl groups of type $D$. As a consequence, we verify three conjectures of Brenti and

Xiaolin Wen, Kim Siang Yeo, Yong Wang, Ling Cheng

Ethereum has become a popular blockchain with smart contracts for investors nowadays. Due to the decentralization and anonymity of Ethereum, Ponzi schemes have been easily deployed and caused significant losses to investors. However, there are still no explainable and effective methods to help investors easily identify Ponzi schemes and validate whether a sm

Yulia Khoryakova, Alexander Shlapunov

We describe the strong dual space $({\mathcal O} (D))^*$ for the space ${\mathcal O} (D)$ of holomorphic functions of several complex variables over a bounded Lipschitz domain $D$ with connected boundary $\partial D$ (as usual, ${\mathcal O} (D)$ is endowed with the topology of the uniform convergence on the compact subsets of $D$). We identify the dual spac

Kourosh Darvish, Serena Ivaldi, Daniele Pucci

This paper presents a novel approach to solve simultaneously the problems of human activity recognition and whole-body motion and dynamics prediction for real-time applications. Starting from the dynamics of human motion and motor system theory, the notion of mixture of experts from deep learning has been extended to address this problem. In the proposed app

G. R. Boroun, B. Rezaei

We consider the nuclear shadowing in deep-inelastic scattering corresponding to kinematic regions accessible by future experiments at electron-ion colliders. The gluon distribution at small $x$ is obtained using an improved dipole model depended on the impact parameter for atomic nucleus and compared with nCETQ15 parametrization group. The nuclear shadowing

Niloofar Ranjbar, Saeedeh Momtazi, MohammadMehdi Homayounpour

Currently, there is a significant amount of research being conducted in the field of artificial intelligence to improve the explainability and interpretability of deep learning models. It is found that if end-users understand the reason for the production of some output, it is easier to trust the system. Recommender systems are one example of systems that gr

Yunfan Ye, Renjiao Yi, Zhirui Gao, Chenyang Zhu

We study the problem of reconstructing 3D feature curves of an object from a set of calibrated multi-view images. To do so, we learn a neural implicit field representing the density distribution of 3D edges which we refer to as Neural Edge Field (NEF). Inspired by NeRF, NEF is optimized with a view-based rendering loss where a 2D edge map is rendered at a gi

Ahmad Bazzi, Marwa Chafii

This work presents a novel robust beamforming design dedicated for dual-functional radar and communication (DFRC) base stations (BSs) in the context of integrated sensing and communications (ISAC). The architecture is intended for circumstances with imperfect channel state information (CSI). Our suggested approach demonstrates several tradeoffs for joint rad

Bilal Faye, Mohamed-Djallel Dilmi, Hanane Azzag, Mustapha Lebbah

Normalization is a pre-processing step that converts the data into a more usable representation. As part of the deep neural networks (DNNs), the batch normalization (BN) technique uses normalization to address the problem of internal covariate shift. It can be packaged as general modules, which have been extensively integrated into various DNNs, to stabilize

Xuchu Chen, Yu Pu, Jinpeng Li, Wei-Qiang Zhang

We present our submission to the ICASSP-SPGC-2023 ADReSS-M Challenge Task, which aims to investigate which acoustic features can be generalized and transferred across languages for Alzheimer's Disease (AD) prediction. The challenge consists of two tasks: one is to classify the speech of AD patients and healthy individuals, and the other is to infer Mini Ment

Dominic G. Lewis, Achim Kempf, Nicolas C. Menicucci

Bandlimited approaches to quantum field theory offer the tantalizing possibility of working with fields that are simultaneously both continuous and discrete via the Shannon Sampling Theorem from signal processing. Conflicting assumptions in general relativity and quantum field theory motivate the use of such an appealing analytical tool that could thread the

Jiyong Moon, Seongsik Park

One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images. Therefore, in this paper, we propose a self-supervised simple facial landmark encoding (SimFLE) method that can learn effective encoding of facial landmarks

Hanxun Jin, Enrui Zhang, Horacio D. Espinosa

For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number o

Thummaluru Siddartha Reddy, Sundeep Prabhakar Chepuri, Pierre Borgnat

In this work, we propose a new clustering algorithm to group nodes in networks based on second-order simplices (aka filled triangles) to leverage higher-order network interactions. We define a simplicial conductance function, which on minimizing, yields an optimal partition with a higher density of filled triangles within the set while the density of filled

Jared Espenant, J. Mark Keil, Debajyoti Mondal

A disk graph is an intersection graph of disks in the Euclidean plane, where the disks correspond to the vertices of the graph and a pair of vertices are adjacent if and only if their corresponding disks intersect. The problem of determining the time complexity of computing a maximum clique in a disk graph is a long-standing open question. The problem is kno

Yulong Yang, Mingming Shuai, Haiming Huang, Rui Song

Magnetic ultrathin films grown on heavy metal substrates often exhibit rich spin structures due to the competition between various magnetic interactions such as Heisenberg exchange, Dzyaloshinskii-Moriya interaction and higher-order spin interactions. Here we employ spin-polarized scanning tunneling microscopy to study magnetic nano-skyrmion phase in Fe mono

Zuheng Kang, Yayun He, Jianzong Wang, Junqing Peng

Data-Free Knowledge Distillation (DFKD) has recently attracted growing attention in the academic community, especially with major breakthroughs in computer vision. Despite promising results, the technique has not been well applied to audio and signal processing. Due to the variable duration of audio signals, it has its own unique way of modeling. In this wor

Rahul Mazumder, Haoyue Wang

Coordinate descent algorithms are popular for huge-scale optimization problems due to their low cost per-iteration. Coordinate descent methods apply to problems where the constraint set is separable across coordinates. In this paper, we propose a new variant of the cyclic coordinate descent method that can handle polyhedral constraints provided that the poly

Nam Tuan Ly, Atsuhiro Takasu, Phuc Nguyen, Hideaki Takeda

Most of the previous methods for table recognition rely on training datasets containing many richly annotated table images. Detailed table image annotation, e.g., cell or text bounding box annotation, however, is costly and often subjective. In this paper, we propose a weakly supervised model named WSTabNet for table recognition that relies only on HTML (or

Jue Hou, Yunfeng Jiang, Rui-Dong Zhu

Bethe ansatz equations for spin-$s$ Heisenberg spin chain with $s\ge1$ are significantly more difficult to analyze than the spin-$\tfrac{1}{2}$ case, due to the presence of repeated roots. As a result, it is challenging to derive extra conditions for the Bethe roots to be physical and study the related completeness problem. In this paper, we propose the rati

Ruihuang Li, Chenhang He, Yabin Zhang, Shuai Li

Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention. Most of the current efforts leverage low-level image features as extra supervision without explicitly exploiting the high-level semantic information of the objects, which will become ineffective when the foreground objects have similar

Ken Chen, Jia Ren, Zi-Gao Dai

Compact objects (COs) can exist and evolve in an active galactic nuclei (AGN) disk, triggering a series of attractive CO-related multi-messenger events around a supermassive black hole. To better understand the nature of an embedded CO and its surroundings and to investigate CO-related events more accurately, in this paper, we study the specific accretion pr

Hojin Lee, Sangmin Lee, Subhajit Mazumdar

We study the formalism of Kosower-Maybee-O'Connell (KMOC) to extract classical impulse from quantum amplitude in the context of the partial wave expansion of a 2-to-2 elastic scattering. We take two complementary approaches to establish the connection. The first one takes advantage of Clebsch-Gordan relations for the base amplitudes of the partial wave expan

Priyadarshini Kapri, Takuto Kawakami, Mikito Koshino

Using density functional theory (DFT), we investigate mechanical properties of a few 2D metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) having Dirac and flat bands. These porous materials have become a subject of great captivation because of their physical stability, distinctive structural characteristics and large surface to volume ra

Yoshihisa Morita, Yoshitaro Tanaka

We deal with a mass-conserved three-component reaction-diffusion system which is proposed by a model describing the dynamics of wavelike actin polymerization in the macropinocytosis and numerically exhibits dynamical patterns such as annihilation, crossover, and nucleation of pulses (Yochelis-Beta-Giv 2020). In this article we first establish the condition f

Willie Huang

Using Grover algorithm, this work investigates a technique for encoding search phrases used in wildcard searches. The technique involves creating a phase Oracle that loads data into a quantum circuit together with the search terms that have been encoded. The method entails constructing a phase Oracle programmatically using encoded input information and encod

Barak Shoshany, Zipora Stober

For time travel to be consistent with the known laws of physics, the resulting paradoxes must be resolved. It has been suggested that parallel timelines (a.k.a. multiple histories) may provide a resolution. However, so far, a concrete mechanism by which parallel timelines can be created has never been satisfactorily formulated. In this paper we propose such

Jingsen Zhu, Yuchi Huo, Qi Ye, Fujun Luan

In this work, we present I$^2$-SDF, a new method for intrinsic indoor scene reconstruction and editing using differentiable Monte Carlo raytracing on neural signed distance fields (SDFs). Our holistic neural SDF-based framework jointly recovers the underlying shapes, incident radiance and materials from multi-view images. We introduce a novel bubble loss for

Yungi Kwon, Jung-Hoon Jung, Young-Ho Eom

Making the connection between the function and structure of networked systems is one of the fundamental issues in complex systems and network science. Urban traffic flows are related to various problems in cities and can be represented as a network of local traffic flows. To identify an empirical relation between the function and network structure of urban t

Tatsuya Ogawa, Hideki Ishihara

We numerically obtain gravastar solutions as nontopological solitons in a system that consists of a U(1) gauge Higgs model with a complex scalar field and Einstein gravity. The solitonic gravastar solutions are compact enough to have a photon sphere.

Xinxin Yang, Lilun Du

Large-scale multiple testing under static factor models is widely used to detect sparse signals in high-dimensional data. However, static factor models are arguably too stringent because they ignore serial correlation, which seriously distorts error rate control in large-scale inference. In this manuscript, we propose a new multiple testing procedure under d