July 2022 arXiv papers — page 93

Silvia Spezzano, Paola Caselli, Olli Sipilä, Luca Bizzocchi

Isotopologue abundance ratios are important to understand the evolution of astrophysical objects and ultimately the origins of a planetary system like our own. Being nitrogen a fundamental ingredient of pre-biotic material, understanding its chemistry and inheritance is of fundamental importance to understand the formation of the building blocks of life. We

Darwin Quezada-Gaibor, Joaquín Torres-Sospedra, Jari Nurmi, Yevgeni Koucheryavy

Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In

G. Homa, R. Balka, J. Z. Bernád, M. Károly

We provide a countable set of conditions based on elementary symmetric polynomials that are necessary and sufficient for a trace class integral operator to be positive semidefinite, which is an important cornerstone for quantum theory in phase-space representation. We also present a new, efficiently computable algorithm based on Newton's identities. Our test

Shaojie Bai, Dongxia Wang, Tim Muller, Peng Cheng

Weighted Majority Voting (WMV) is a well-known optimal decision rule for collective decision making, given the probability of sources to provide accurate information (trustworthiness). However, in reality, the trustworthiness is not a known quantity to the decision maker - they have to rely on an estimate called trust. A (machine learning) algorithm that com

Sandeep Singh, Vimlesh Kumar, Anirban Ghosh, G. K. Samanata

Entangled photon sources (EPS), the major building block for a variety of quantum communication protocols, are commonly developed by utilizing the spontaneous parametric down-conversion (SPDC) in $\chi^{2}$ nonlinear bulk optical materials. While high nonlinearity and long interaction length have established the superiority of the periodically poled crystals

Marc Frei, Jonghoon Kwon, Seyedali Tabaeiaghdaei, Marc Wyss

Many critical computing applications rely on secure and dependable time which is reliably synchronized across large distributed systems. Today's time synchronization architectures are commonly based on global navigation satellite systems at the considerable risk of being exposed to outages, malfunction, or attacks against availability and accuracy. This pape

Wentao Chen, Yao Lu, Shuaining Zhang, Kuan Zhang

Controllable bosonic systems can provide post-classical computational power with sub-universal quantum computational capability. A network that consists of a number of bosons evolving through beam-splitters and phase-shifters between different modes, has been proposed and applied to demonstrate quantum advantages. While the network has been implemented mostl

Mario Lezcano-Casado

We present the classical coordinate-free formalism for forward and backward mode ad in the real and complex setting. We show how to formally derive the forward and backward formulae for a number of matrix functions starting from basic principles.

Amin Faghih, Magda Rebelo

In this work, a class of non-linear weakly singular fractional integro-differential equations is considered, and we first prove existence, uniqueness, and smoothness properties of the solution under certain assumptions on the given data. We propose a numerical method based on spectral Petrov-Galerkin method that handling to the non-smooth behavior of the sol

Mattia Fumagalli, Marco Boffo, Daqian Shi, Mayukh Bagchi

One of the major barriers to the training of algorithms on knowledge graph schemas, such as vocabularies or ontologies, is the difficulty that scientists have in finding the best input resource to address the target prediction tasks. In addition to this, a key challenge is to determine how to manipulate (and embed) these data, which are often in the form of

Tian-Jun Li, Yongbin Ruan, Weiyi Zhang

We establish a blowing down criterion in the context of birational symplectic geometry in dimension 6.

Robbin Bastiaansen, Peter Ashwin, Anna S. von der Heydt

Climate response metrics are used to quantify the Earth's climate response to anthropogenic changes of atmospheric CO2. Equilibrium Climate Sensitivity (ECS) is one such metric that measures the equilibrium response to CO2 doubling. However, both in their estimation and their usage, such metrics make assumptions on the linearity of climate response, although

Christos Stergiadis, Vasiliki-Despoina Kostaridou, Simeon Veloudis, Dimitrios Kazis

Conventional biometrics have been employed in high security user authentication systems for over 20 years now. However, some of these modalities face low security issues in common practice. Brain wave based user authentication has emerged as a promising alternative method, as it overcomes some of these drawbacks and allows for continuous user authentication.

Luis C. García-Lirola, G. Grelier

We study several properties and applications of the ultrapower $M_{\mathcal U}$ of a metric space $M$. We prove that the Lipschitz-free space $\mathcal F(M_{\mathcal U})$ is finitely representable in $\mathcal F(M)$. We also characterize the metric spaces that are finitely Lipschitz representable in a Banach space as those that biLipschitz embed into an ultr

Zhigang Bao, Jiang Hu, Xiaocong Xu, Xiaozhuo Zhang

A fundamental concept in multivariate statistics, sample correlation matrix, is often used to infer the correlation/dependence structure among random variables, when the population mean and covariance are unknown. A natural block extension of it, {\it sample block correlation matrix}, is proposed to take on the same role, when random variables are generalize

Pengfei Wang, Hyukjoon Kwon, Chun-Yang Luan, Wentao Chen

Measurement-induced state disturbance is a major challenge in obtaining quantum statistics at multiple time points. We propose a method to extract dynamic information from a quantum system at intermediate time points, namely snapshotting quantum dynamics. To this end, we apply classical post-processing after performing the ancilla-assisted measurements to ca

Christopher Bamford, Minqi Jiang, Mikayel Samvelyan, Tim Rocktäschel

Progress in reinforcement learning (RL) research is often driven by the design of new, challenging environments -- a costly undertaking requiring skills orthogonal to that of a typical machine learning researcher. The complexity of environment development has only increased with the rise of procedural-content generation (PCG) as the prevailing paradigm for p

Matthias Rottmann, Marco Reese

In this work, we for the first time present a method for detecting label errors in image datasets with semantic segmentation, i.e., pixel-wise class labels. Annotation acquisition for semantic segmentation datasets is time-consuming and requires plenty of human labor. In particular, review processes are time consuming and label errors can easily be overlooke

Hansen Feng, Lizhi Wang, Yuzhi Wang, Hua Huang

Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream. However, the limited data volume and complicated noise distribution have constituted a learnability bottleneck for paired real data, which limits the denoising performance of learning-based methods.

Zhijie Sun, Dezhi Han, Dun Li, Xiangsheng Wang

Medical data involves a large amount of personal information and is highly privacy sensitive. In the age of big data, the increasing informatization of healthcare makes it vital that medical information is stored securely and accurately. However, current medical information is subject to the risk of privacy leakage and difficult to share. To address these is

Boeun Kim, Hyung Jin Chang, Jungho Kim, Jin Young Choi

We propose a new transformer model for the task of unsupervised learning of skeleton motion sequences. The existing transformer model utilized for unsupervised skeleton-based action learning is learned the instantaneous velocity of each joint from adjacent frames without global motion information. Thus, the model has difficulties in learning the attention gl

Jonathan Brugger, Christoph Dittel, Andreas Buchleitner

We port the concept of non-Markovian quantum dynamics to the many-particle realm, by a suitable decomposition of the many-particle Hilbert space. We show how the specific structure of many-particle states determines the observability of non-Markovianity by single- or many-particle observables, and discuss a realization in a readily implementable few-particle

Anirudh Chandrasekaran, Joseph J. Betouras

We present a practical method to detect, diagnose and engineer higher order Van Hove singularities in multiband systems, with no restrictions on the number of bands and hopping terms. The method allows us to directly compute the Taylor expansion of the dispersion of any band at arbitrary points in momentum space, using a generalised extension of the Feynman

Liang Wu, Alberto Bemporad

This paper proposes a construction-free algorithm for solving linear MPC problems based on autoregressive with exogenous terms (ARX) input-output models. The solution algorithm relies on a coordinate-descent augmented Lagrangian (CDAL) method previously proposed by the authors, which we adapt here to exploit the special structure of ARX-based MPC. The CDAL-A

Shaokang Cai, Dezhi Han, Zibin Zheng, Dun Li

The rapid development of artificial intelligence (AI) technology has enabled large-scale AI applications to land in the market and practice. However, while AI technology has brought many conveniences to people in the productization process, it has also exposed many security issues. Especially, attacks against online learning vulnerabilities of chatbots occur

Enrico Bernieri, Gheorghe Stratan, Sara Bacchini, Liviu Mircea

In 1980 Kowal and Drake found that in December 1612 and January 1613 Galileo observed the planet Neptune. At that time, according to these authors, Galileo was able to measure angular separations with an accuracy of about 10 seconds of arc. However, as noticed by Kowal and Drake, the position of Neptune reported by Galileo is wrong with respect to the positi

Eran Zvuloni, Jesse Read, Antônio H. Ribeiro, Antonio Luiz P. Ribeiro

Objective: Machine learning techniques have been used extensively for 12-lead electrocardiogram (ECG) analysis. For physiological time series, deep learning (DL) superiority to feature engineering (FE) approaches based on domain knowledge is still an open question. Moreover, it remains unclear whether combining DL with FE may improve performance. Methods: We

Zekun Li, Zhengyang Geng, Zhao Kang, Wenyu Chen

Reference-based line-art colorization is a challenging task in computer vision. The color, texture, and shading are rendered based on an abstract sketch, which heavily relies on the precise long-range dependency modeling between the sketch and reference. Popular techniques to bridge the cross-modal information and model the long-range dependency employ the a

L. Chantry, V. Cayatte, C. Sauty, N. Vlahakis

Recent observations of supermassive black holes have brought us new information on their magnetospheres. In this study we attempt a theoretical modelling of the coupling of black holes with their jets and discs, via three innovations. First, we propose a semi-analytical MHD description of a steady relativistic inflow-outflow structure characteristic to the e

Elmir R. Bigushev, Oleg N. German

In this paper we study the three-dimensional analogue of the relation between the irrationality exponent of a real number and the growth of its regular continued fraction partial quotients. As a multidimensional generalisation of continued fractions, we consider Klein polyhedra.

George Georgiou, Georgios Itsios, Dimitrios Zoakos

We construct and thoroughly study a new integrable example of the AdS/CFT correspondence with Schr\"{o}dinger symmetry. On the gravity side, the supergravity solution depends on two parameters and is obtained by marginally deforming the internal space of the Schr\"{o}dinger background through a series of TsT transformations. On the field theory side, we iden

Benjamin Merlin Bumpus, Zoltan A. Kocsis, Jade Edenstar Master, Emilio Minichiello

We introduce structured decompositions, category-theoretic structures which simultaneously generalize notions from graph theory (including treewidth, layered treewidth, co-treewidth, graph decomposition width, tree independence number, hypergraph treewidth and H-treewidth), geometric group theory (specifically Bass-Serre theory), and dynamical systems (e.g.

M. Renger, S. Pogorzalek, F. Fesquet, K. Honasoge

We study nonclassical correlations in propagating two-mode squeezed microwave states in the presence of noise. We focus on two different types of correlations, namely, quantum entanglement and quantum discord. Quantum discord has various intriguing fundamental properties which require experimental verification, such as the asymptotic robustness to environmen

Tim Uhlemann, Alexander Span, Sebastian Dörner, Stephan ten Brink

Pulse shaping for coherent optical fiber communication has been an active area of research for the past decade. Most of the early schemes are based on classic Nyquist pulse shaping that was originally intended for linear channels. The best known classic scheme, the split digital back-propagation (DBP), uses joint pre-distortion and post equalization and henc

Zhengxi Liu, Qiao Tian, Chenxu Hu, Xudong Liu

Some recent studies have demonstrated the feasibility of single-stage neural text-to-speech, which does not need to generate mel-spectrograms but generates the raw waveforms directly from the text. Single-stage text-to-speech often faces two problems: a) the one-to-many mapping problem due to multiple speech variations and b) insufficiency of high frequency

Hao Chen, Liqing Xu

Private information retrieval (PIR) schemes (with or without colluding servers) have been proposed for realistic coded distributed data storage systems. Star product PIR schemes with colluding servers for general coded distributed storage system were constructed over general finite fields by R. Freij-Hollanti, O. W. Gnilke, C. Hollanti and A. Karpuk in 2017.

Thobeka Nombebe, James Allison, Leonard Santana, Jaco Visagie

In this paper we investigate the performance of a variety of estimation techniques for the scale and shape parameter of the Lomax distribution. These methods include traditional methods such as the maximum likelihood estimator and the method of moments estimator. A version of the maximum likelihood estimator adjusted for bias is also included. Furthermore, a

Qiang Li, Zhaoliang Yao, Jingjing Wang, Ye Tian

Assessing the blurriness of an object image is fundamentally important to improve the performance for object recognition and retrieval. The main challenge lies in the lack of abundant images with reliable labels and effective learning strategies. Current datasets are labeled with limited and confused quality levels. To overcome this limitation, we propose to

Damien Dablain, Bartosz Krawczyk, Nitesh Chawla

Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. ML models inform decisions in criminal justice, the extension of credit in banking, and the hiring practices of corporations. This posits the requirement of model fairness, which holds that automated decisions should be equit

Jerry Lacmou Zeutouo, Vianney Kengne Tchendji, Jean Frederic Myoupo

This paper presents a parallel solution based on the coarse-grained multicomputer (CGM) model using the four-splitting technique to solve the optimal binary search tree problem. The well-known sequential algorithm of Knuth solves this problem in $\mathcal{O}\left(n^2\right)$ time and space, where $n$ is the number of keys used to build the optimal binary sea

M. Ehret, L. Volpe, J. I. Apiñaniz, P. Puyuelo-Valdes

We present experimental results for the controlled mitigation of electromagnetic pulses (EMP) produced in interactions of the 1PW high-power 30fs Ti:Sa laser VEGA-3 with matter. This study aims at the band of very high frequencies (VHF), notably hundreds of MHz, comprising the fundamental cavity modes of the rectangular VEGA-3 vacuum chamber. We demonstrate

Miguel A. Benitez-Rathgeb, Diogo Boito, André H. Hoang, Matthias Jamin

In a recent work by some of us it was shown that the long-standing discrepancy between the QCD perturbation series for the inclusive hadronic tau decay rate computed in the CIPT and FOPT expansion approaches can be understood from the fact that CIPT has an infrared (IR) sensitivity that it not compatible with the standard form of the operator production expa

Damien Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh Chawla

Deep learning models tend to memorize training data, which hurts their ability to generalize to under-represented classes. We empirically study a convolutional neural network's internal representation of imbalanced image data and measure the generalization gap between a model's feature embeddings in the training and test sets, showing that the gap is wider f

Awet Haileslassie Gebrehiwot, Patrik Vacek, David Hurych, Karel Zimmermann

Automatic pseudo-labeling is a powerful tool to tap into large amounts of sequential unlabeled data. It is specially appealing in safety-critical applications of autonomous driving, where performance requirements are extreme, datasets are large, and manual labeling is very challenging. We propose to leverage sequences of point clouds to boost the pseudolabel

Jinwei Lin

Monitoring and streaming is one of the most important applications for the real time cameras. The research of this has provided a novel design idea that uses the FFmpeg and Tkinter, combining with the libraries: OpenCV and PIL to develop a simple but fast streaming toolkit MultiSteam that can achieve the function of visible monitoring streaming for multiple

Bertram Bitsch, Aaron David Schneider, Laura Kreidberg

Atmospheric abundances are thought to constrain the planet formation pathway, because different species evaporate at different temperatures leaving distinct signatures in the accreted atmosphere. The planetary C/O ratio is thought to constrain the planet formation pathway, because of the condensation sequence of H$_2$O, CO$_2$, CH$_4$, and CO, resulting in a

Jussi T. S. Heikkila

The Journal of Economic Literature codes classification system (JEL) published by the American Economic Association (AEA) is the de facto standard classification system for research literature in economics. The JEL classification system is used to classify articles, dissertations, books, book reviews, and working papers in EconLit, a database maintained by t

Shaoru Wang, Zeming Li, Jin Gao, Liang Li

Self-supervised learning (SSL) has achieved promising downstream performance. However, when facing various resource budgets in real-world applications, it costs a huge computation burden to pretrain multiple networks of various sizes one by one. In this paper, we propose Discriminative-SSL-based Slimmable Pretrained Networks (DSPNet), which can be trained at

Eddie Aamari, Clément Berenfeld, Clément Levrard

We study the estimation of the reach, an ubiquitous regularity parameter in manifold estimation and geometric data analysis. Given an i.i.d. sample over an unknown $d$-dimensional $\mathcal{C}^k$-smooth submanifold of $\mathbb{R}^D$, we provide optimal nonasymptotic bounds for the estimation of its reach. We build upon a formulation of the reach in terms of

Stefan Schiffer

In this work, a new approach to obtain a solenoidal Lipschitz truncation is presented. More precisely, the goal of the truncation is to modify a function $u \in W^{1,p}(\mathbb{R}^3,\mathbb{R}^3)$ that satisfies the additional constraint $\mathrm{div}~ u=0$, such that its modification $\tilde{u}$ is in $W^{1,\infty}(\mathbb{R}^3,\mathbb{R}^3)$ and still is d

Salman Ahmadi-Asl, Maame Gyamfua Asante-Mensah, Andrzej Cichocki, Anh-Huy Phan

This paper proposes a general framework to use the cross tensor approximation or tensor ColUmn-Row (CUR) approximation for reconstructing incomplete images and videos. The key importance of the new algorithms is their simplicity and ease of implementation with low computational complexity. For the case of data tensors with 1) structural missing components or

M. Kanafani, X. Fléchard, O. Naviliat-Cuncic, G. D. Chung

The half-life of $^{6}$He has been measured using a low energy radioactive beam implanted in a YAP scintillator and recording decay events in a 4$\pi$ geometry. Events were time-stamped with a digital data acquisition system enabling a reliable control of dead-time effects and detector gain variations. The result, $T_{1/2} = (807.25 \pm 0.16_{\rm stat} \pm 0

Rafael L. Delgado, Antonio Dobado, Domènec Espriu

Some effective field theories exhibit dynamical resonances that, when properly included, mitigate their bad behaviour at high energies. Unitarization of the partial wave amplitudes is the preferred method to unveil such resonances. Interpreting the Einstein-Hilbert theory in the spirit of effective Lagrangians, we implement the Inverse Amplitude Method and u

Tamer Tlas

Yang-Mills is reformulated in terms of the logarithmic derivative of the holonomies. The classical equations of motion are recovered, and the path integral is rewritten in two ways, both of which are of the form of a Gaussian satisfying a quadratic constraint.

V. Bozza

The Pre-Big Bang cosmology inspired generations of cosmologists in attempts to cure the initial Big Bang singularity using a fundamental length scale as proposed by String Theory. The existence of a phase of collapse/inflation with increasing curvature followed by a cosmic bounce has been proposed as an alternative to standard inflation in the solution of th

Omid Nejati Manzari, Amin Boudesh, Shahriar B. Shokouhi

Traffic sign detection is a vital task in the visual system of self-driving cars and the automated driving system. Recently, novel Transformer-based models have achieved encouraging results for various computer vision tasks. We still observed that vanilla ViT could not yield satisfactory results in traffic sign detection because the overall size of the datas

Suneghyeon Cho, Sanghyun Hong, Kookjin Lee, Noseong Park

Recent work by Xia et al. leveraged the continuous-limit of the classical momentum accelerated gradient descent and proposed heavy-ball neural ODEs. While this model offers computational efficiency and high utility over vanilla neural ODEs, this approach often causes the overshooting of internal dynamics, leading to unstable training of a model. Prior work a

Gerardo Campos-Villalobos, Giuliana Giunta, Susana Marín-Aguilar, Marjolein Dijkstra

Spherically-symmetric atom-centered descriptors of atomic environments have been widely used for constructing potential or free energy surfaces of atomistic and colloidal systems and to characterize local structures using machine learning techniques. However, when particle shapes are non-spherical, as in the case of rods and ellipsoids, standard spherically-

Aly Sabri Abdalla, Vuk Marojevic

This paper defines the problem of optimizing the downlink multi-user multiple input, single output (MU-MISO) sum-rate for ground users served by an aerial reconfigurable intelligent surface (ARIS) that acts as a relay to the terrestrial base station. The deep deterministic policy gradient (DDPG) is proposed to calculate the optimal active beamforming matrix

Yuxi Liu, Zhenhao Zhang, Antonio Jimeno Yepes, Flora D. Salim

Building models for health prediction based on Electronic Health Records (EHR) has become an active research area. EHR patient journey data consists of patient time-ordered clinical events/visits from patients. Most existing studies focus on modeling long-term dependencies between visits, without explicitly taking short-term correlations between consecutive

Zhen Rong

In this paper, we characterize mixing unilateral backward shifts on barrelled and ultrabarrelled topological sequence spaces respectively, which extend several well-known results in the existing literature. We present some nontrivial examples to show the validity of our results.

Peter Coppens, Panagiotis Patrinos

Control of linear dynamics with multiplicative noise naturally introduces robustness against dynamical uncertainty. Moreover, many physical systems are subject to multiplicative disturbances. In this work we show how these dynamics can be identified from state trajectories. The least-squares scheme enables exploitation of prior information and comes with pra

Daniel Bogdoll, Jonas Rauch, J. Marius Zöllner

Autonomous driving is a key technology towards a brighter, more sustainable future. To enable such a future, it is necessary to utilize autonomous vehicles in shared mobility models. However, to evaluate, whether two or more route requests have the potential for a shared ride, is a compute-intensive task, if done by rerouting. In this work, we propose the Dy

Xiu-Wu Wang, Zhi-Gang Wang

In this work, we tentatively identify the $P_{cs}(4338)$ as the $\bar{D}\Xi_c$ molecular state, and distinguish the isospins of the current operators to explore the $\bar{D}\Xi_c$, $\bar{D}\Lambda_c$, $\bar{D}_s\Xi_c$, $\bar{D}_s\Lambda_c$, $\bar{D}^*\Xi_c$, $\bar{D}^*\Lambda_c$, $\bar{D}^*_s\Xi_c$ and $\bar{D}^*_s\Lambda_c$ molecular states without strange,

Gabriel Wlazłowski, Klejdja Xhani, Marek Tylutki, Nikolaos P. Proukakis

We characterize numerically the dominant dynamical regimes in a superfluid ultracold fermionic Josephson junction. Beyond the coherent Josephson plasma regime, we discuss the onset and physical mechanism of dissipation due to the superflow exceeding a characteristic speed, and provide clear evidence distinguishing its physical mechanism across the weakly- an

Fangwen Shu, Jiaxuan Wang, Alain Pagani, Didier Stricker

This paper presents a visual SLAM system that uses both points and lines for robust camera localization, and simultaneously performs a piece-wise planar reconstruction (PPR) of the environment to provide a structural map in real-time. One of the biggest challenges in parallel tracking and mapping with a monocular camera is to keep the scale consistent when r

Jian Ma, Zhedong Zheng, Hao Fei, Feng Zheng

Voice conversion is to generate a new speech with the source content and a target voice style. In this paper, we focus on one general setting, i.e., non-parallel many-to-many voice conversion, which is close to the real-world scenario. As the name implies, non-parallel many-to-many voice conversion does not require the paired source and reference speeches an

T. Cieślak, P. Kokocki, W. S. Ożański

We consider solutions of the 2D incompressible Euler equation in the form of $M\geq 1$ cocentric logarithmic spirals. We prove the existence of a generic family of spirals that are nonsymmetric in the sense that the angles of the individual spirals are not uniformly distributed over the unit circle. Namely, we show that if $M=2$ or $M\geq 3 $ is an odd integ

Daniel Bogdoll, Meng Zhang, Maximilian Nitsche, J. Marius Zöllner

Great progress has been achieved in the community of autonomous driving in the past few years. As a safety-critical problem, however, anomaly detection is a huge hurdle towards a large-scale deployment of autonomous vehicles in the real world. While many approaches, such as uncertainty estimation or segmentation-based image resynthesis, are extremely promisi

Ji-Cai Liu, Wei-Wei Qi

We study divisibility for the $q$-trinomial coefficients $\tau_0(n,m,q)$, $T_0(n,m,q)$ and $T_1(n,m,q)$, which were first introduced by Andrews and Baxter. In particular, we completely determine $\tau_0(an,bn,q)$, $T_0(an,bn,q)$ and $T_1(an,bn,q)$ modulo the square of the cyclotomic polynomial $\Phi_n(q)$ for $(a,b)=(m,m-1)$.

Sébastien Breteaux, Jérémy Faupin, Jimmy Payet

We consider a spinless, non-relativistic particle bound by an external potential and linearly coupled to a quantized radiation field. The energy $\mathcal{E}(u,f)$ of product states of the form $u\otimes \Psi_f$, where $u$ is a normalized state for the particle and $\Psi_f$ is a coherent state in Fock space for the field, gives the energy of a Klein-Gordon--

Marc Arnaudon, Koléhé Abdoulaye Coulibaly-Pasquier, Laurent Miclo

This note proves that the separation convergence towards the uniform distribution abruptly occurs at times around ln(n)/n for the (time-accelerated by 2) Brownian motion on the sphere with a high dimension n. The arguments are based on a new and elementary perturbative approach for estimating hitting times in a small noise context. The quantitative estimates

Jaume Alonso, Yuri B. Suris, Kangning Wei

We propose a geometric construction of three-dimensional birational maps that preserve two pencils of quadrics. The maps act as compositions of involutions, which, in turn, act along the straight line generators of the quadrics of the first pencil and are defined by the intersections with quadrics of the second pencil. On each quadric of the first pencil, th

Valentina Rizzello, Matteo Nerini, Michael Joham, Bruno Clerckx

In this work, we propose an efficient method for channel state information (CSI) adaptive quantization and feedback in frequency division duplexing (FDD) systems. Existing works mainly focus on the implementation of autoencoder (AE) neural networks (NNs) for CSI compression, and consider straightforward quantization methods, e.g., uniform quantization, which

Shuai Feng, Shi-Yin Shen, Fang-Ting Yuan, Y. Sophia Dai

The SDSS-IV MaNGA survey has measured two-dimensional maps of emission line velocities for a statistically powerful sample of nearby galaxies. The asymmetric features of these kinematics maps reflect the non-rotational component of a galaxy's internal motion of ionized gas. In this study, we present a catalog of kinematic asymmetry measurement of $H\alpha$ v

Louis Dupaigne, Alberto Farina, Troy Petitt

We prove that 0 the only classical solution of the Lane-Emden equation in the half-space which is stable outside a compact set. We also consider weak solutions and the case of general cones.

Daniel Braun, Kerstin Ebell, Vera Schemann, Laura Pelchmann

This paper presents a novel color scheme designed to address the challenge of visualizing data series with large value ranges, where scale transformation provides limited support. We focus on meteorological data, where the presence of large value ranges is common. We apply our approach to meteorological scatterplots, as one of the most common plots used in t

Santiago Velasco-Forero, Jesús Angulo

This paper analyses both nonlinear activation functions and spatial max-pooling for Deep Convolutional Neural Networks (DCNNs) by means of the algebraic basis of mathematical morphology. Additionally, a general family of activation functions is proposed by considering both max-pooling and nonlinear operators in the context of morphological representations. E

Ming-Jing Zhao, Lin Zhang, Shao-Ming Fei

Variance is a ubiquitous quantity in quantum information theory. Given a basis, we consider the averaged variances of a fixed diagonal observable in a pure state under all possible permutations on the components of the pure state and call it the symmetrized variance. Moreover we work out the analytical expression of the symmetrized variance and find that suc

Waleed Khalid, Manfred Valentan, Albert Doblas, David Flores

Silicon sensors are the go-to technology for high-precision sensors in particle physics. But only recently low-noise silicon sensors with internal amplification became available. The so-called Low Gain Avalanche Detector (LGAD) sensors have been developed for applications in High Energy Physics, but lack two characteristics needed for the measurement of low-

Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while nai

Hubert Baty

In this study, the hyperbolic method is adopted to explore the flow field states of incompressible flow in a four-sided lid-driven square cavity. In particular, we focus on the flow bifurcation obtained at the critical Reynolds number $R_e \simeq 130$. In the hyperbolic method, the diffusive term is transformed into an hyperbolic one by introducing a diffusi

Louis-Alexandre Couston, Joseph Nandaha, Benjamin Favier

We investigate the dynamics of a fluid layer subject to an imposed bottom heat flux and a top monotonically-increasing temperature profile driving horizontal convection. We use direct numerical simulations and consider a large range of flux-based Rayleigh numbers $10^6 \leq Ra_F \leq 10^9$ and imposed top horizontal to bottom vertical heat flux ratios $0 \le

Alessandro Coclite, Marco D. de Tullio, Giuseppe Pascazio, Tiziano Politi

In this paper, the dynamic of inertial capsules into microfluidic bifurcations is studied. The fluid evolution is based on the solution of the BGK -- lattice Boltzmann scheme including a forcing term accounting for immersed geometries. The dynamic-Immersed Boundary forcing strategy is adopted for imposing no-slip boundary conditions on moving deformable or r

Xiangru Li, Si Zeng, Zhu Wang, Bing Du

Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) acquired tens of millions of low-resolution stellar spectra. The large amount of the spectra result in the urgency to explore automatic atmospheric parameter estimation methods. There are lots of LAMOST spectra with low signal-to-noise ratios (SNR), which result in a sharp degradation on the

Junpu Zhang, Liang Li, Siwei Wang, Jiyuan Liu

Clustering is a representative unsupervised method widely applied in multi-modal and multi-view scenarios. Multiple kernel clustering (MKC) aims to group data by integrating complementary information from base kernels. As a representative, late fusion MKC first decomposes the kernels into orthogonal partition matrices, then learns a consensus one from them,

Kensuke Tamura, Hosho Katsura

We introduce a class of spinless fermion models that exhibit quantum many-body scars (QMBS) originating from kinetic constraints in the form of density-assisted hopping. The models can be defined on any lattice in any dimension and allow for spatially varying interactions. We construct a tower of exact eigenstates with finite energy density, and we demonstra

Yubiao Wang, Qianqian Du, Yun Guo

In the real time formalism of the finite-temperature field theory, we compute the one-loop gluon self-energy in a semi-quark-gluon plasma (QGP) where a background filed ${\cal Q}$ has been introduced for the vector potential, leading to a non-trivial expectation value for the Polyakov loop in the deconfined phase. Explicit results of the gluon self-energies

Bo Xiang, Jinxi Chen, Lei Li

We show that the solid hull of every weakly precompact set of a Banach lattice $E$ is weakly precompact if and only if every order interval in $E$ is weakly precompact, or equivalently, if and only if every disjoint weakly compact set is weakly precompact. Some results on the domination property for weakly precompact positive operators are obtained. Among ot

Laëtitia Farinacci, Lukas M. Veldman, Philip Willke, Sander Otte

Historically, electron spin resonance (ESR) has provided excellent insight into the electronic, magnetic, and chemical structure of samples hosting spin centers. In particular, the hyperfine interaction between the electron and the nuclear spins yields valuable structural information of these centers. In recent years, the combination of ESR and scanning tunn

Jinkyung Kim, Kyungju Noh, Yi Chen, Fabio Donati

Hyperfine interactions between electron and nuclear spins have been widely used in material science, organic chemistry, and structural biology as a sensitive probe to the local chemical environment through spatial identification of nuclear spins. With the nuclear spins identified, the isotropic and anisotropic components of the hyperfine interactions in turn

Min Ren, Yuhao Zhu, Yunlong Wang, Zhenan Sun

Deep learning-based face recognition models are vulnerable to adversarial attacks. To curb these attacks, most defense methods aim to improve the robustness of recognition models against adversarial perturbations. However, the generalization capacities of these methods are quite limited. In practice, they are still vulnerable to unseen adversarial attacks. D

Indrani Banerjee, Subhadip Sau, Soumitra SenGupta

We study the prospect of Bardeen black holes in explaining the observed shadow of Sgr A* and M87*. Bardeen black holes are regular black holes endowed with a magnetic monopole charge that arise in Einstein gravity coupled to non-linear electrodynamics. These black holes are interesting as they can evade the r = 0 curvature singularity arising in general rela

Arnaud Bodin, Pierre Dèbes, Salah Najib

We consider polynomial equations, or systems of polynomial equations, with integer coefficients, modulo prime numbers $p$. We offer an elementary approach based on a counting method. The outcome is a weak form of the Lang-Weil lower bound for the number of solutions modulo $p$, only differing from Lang-Weil by an asymptotic $p^\epsilon$ multiplicative factor

Ali Etemadi, Maryam Farahnak-Ghazani, Hamidreza Arjmandi, Mahtab Mirmohseni

Abnormality detection and localization (ADL) have been studied widely in wireless sensor networks (WSNs) literature, where the sensors use electromagnetic waves for communication. Molecular communication (MC) has been introduced as an alternative approach for ADL in particular areas such as healthcare, being able to tackle the shortcomings of conventional WS

Sheng-Chen Bai, Yi-Cheng Tang, Shi-Ju Ran

Given an image of a white shoe drawn on a blackboard, how are the white pixels deemed (say by human minds) to be informative for recognizing the shoe without any labeling information on the pixels? Here we investigate such a ``white shoe'' recognition problem from the perspective of tensor network (TN) machine learning and quantum entanglement. Utilizing a g

Xuefeng Liu, Fangfang Xia, Rick L. Stevens, Yuxin Chen

While training models and labeling data are resource-intensive, a wealth of pre-trained models and unlabeled data exists. To effectively utilize these resources, we present an approach to actively select pre-trained models while minimizing labeling costs. We frame this as an online contextual active model selection problem: At each round, the learner receive

Alessandro Coclite, Sergio Ranaldo, Giuseppe Pascazio, Marco D. de Tullio

In this work, a dynamic-Immersed--Boundary method combined with a BGK-Lattice--Boltzmann technique is developed and critically discussed. The fluid evolution is obtained on a three-dimensional lattice with 19 reticular velocities (D3Q19 computational molecule) while the immersed body surface is modeled as a collection of Lagrangian points responding to an el

Moshe Sipper

Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. But just how useful is said tuning? While smaller-scale experiments have been previously conducted, herein we carry out a large-scale investigation, specifically, one involving 26 ML alg

Xu Wang, Huan Zhao, Lanning Wei, Quanming Yao

Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classification task by introducing PAS(Pooling Architecture Search). At the same time, we improve it based on the GNN topology design method F2GNN to further design the feature selection an