November 2022 arXiv papers — page 79

Romain Laugier, Denis Defrère, Benjamin Courtney-Barrer, Felix A. Dannert

Context: NOTT (formerly Hi-5) is a new high-contrast L' band (3.5-4.0 \textmu m) beam combiner for the VLTI with the ambitious goal to be sensitive to young giant exoplanets down to 5 mas separation around nearby stars. The performance of nulling interferometers in these wavelengths is affected both by fundamental noise from the background and by the contrib

Marta Bofill Roig, Cora Burgwinkel, Ursula Garczarek, Franz Koenig

Platform trials gained popularity during the last few years as they increase flexibility compared to multi-arm trials by allowing new experimental arms entering when the trial already started. Using a shared control group in platform trials increases the trial efficiency compared to separate trials. Because of the later entry of some of the experimental trea

Shan Gao, Jian Qu, Zhichun Liu, Weigang Ma

The jumping-droplet condensation, namely the out-of-plane jumping of condensed droplets upon coalescence, has been a promising technical innovation in the fields of energy harvesting, droplet manipulation, thermal management, etc., yet is limited owing to the challenge of enabling a sustainable and programmable control. Here, we characterized the morphologic

Susheel Dharmadhikari, Nandana Menon, Amrita Basak

Process optimization for metal additive manufacturing (AM) is crucial to ensure repeatability, control microstructure, and minimize defects. Despite efforts to address this via the traditional design of experiments and statistical process mapping, there is limited insight on an on-the-fly optimization framework that can be integrated into a metal AM system.

Dian Echevarría Pérez, Onel L. Alcaraz López, Hirley Alves

Two of the main problems to achieve ultra-reliable low-latency communications (URLLC) are related to instantaneous channel state information (I-CSI) acquisition and the coexistence with other service modes such as enhanced mobile broadband (eMBB). The former comes from the non-negligible time required for accurate I-CSI acquisition, while the latter, from th

Honghui Liu, Cosimo Bambi, Jiachen Jiang, Javier A. Garcia

We present an analysis of the relativistic reflection spectra of GX 339-4 during the hard-to-soft transition of its 2021 outburst observed by Insight-HXMT. The strong relativistic reflection signatures in the data suggest a high black hole spin ($a_*>0.86$) and an intermediate disk inclination angle (35-43 deg) of the system. The transition is accompanied by

Jianpeng Chan, Iason Papaioannou, Daniel Straub

We propose a modification of the improved cross entropy (iCE) method to enhance its performance for network reliability assessment. The iCE method performs a transition from the nominal density to the optimal importance sampling (IS) density via a parametric distribution model whose cross entropy with the optimal IS is minimized. The efficiency and accuracy

Jason Terry, Cassandra Hall, Sean Abreau, Sergei Gleyzer

Exoplanets in protoplanetary disks cause localized deviations from Keplerian velocity in channel maps of molecular line emission. Current methods of characterizing these deviations are time consuming, and there is no unified standard approach. We demonstrate that machine learning can quickly and accurately detect the presence of planets. We train our model o

Keshari Nandan, Somnath Bhowmick, Yogesh S. Chauhan, Amit Agarwal

The subthreshold leakage current in transistors has become a critical limiting factor for realizing ultra-low-power transistors. The leakage current is predominantly dictated by the long thermal tail of the charge carriers. We propose a solution to this problem by using narrow bandwidth semiconductors for limiting the thermionic leakage current by filtering

Wei Li, Jonas Koehler, Megha Chakraborty, Claudia Quinteros-Cartaya

Seismic phase picking and magnitude estimation are essential components of real time earthquake monitoring and earthquake early warning systems. Reliable phase picking enables the timely detection of seismic wave arrivals, facilitating rapid earthquake characterization and early warning alerts. Accurate magnitude estimation provides crucial information about

Federico Roccati, Archak Purkayastha, G. Massimo Palma, Francesco Ciccarello

We investigate the behavior of correlations dynamics in a dissipative gain-loss system. First, we consider a setup made of two coupled lossy oscillators, with one of them subject to a local gain. This provides a more realistic platform to implement parity-time (PT) symmetry circumventing the implementation of a pure gain. We show how the qualitative dynamics

Simon M. Koop, Mark A. Peletier, Jacobus W. Portegies, Vlado Menkovski

Neural Stochastic Differential Equations (NSDE) have been trained as both Variational Autoencoders, and as GANs. However, the resulting Stochastic Differential Equations can be hard to interpret or analyse due to the generic nature of the drift and diffusion fields. By restricting our NSDE to be of the form of Langevin dynamics, and training it as a VAE, we

Gokul Karthik Kumar, Praveen S, Pratyush Kumar, Mitesh M. Khapra

Deep learning based text-to-speech (TTS) systems have been evolving rapidly with advances in model architectures, training methodologies, and generalization across speakers and languages. However, these advances have not been thoroughly investigated for Indian language speech synthesis. Such investigation is computationally expensive given the number and div

Shunyao Wu, Chaitali Chakrabarti, Ahmed Alkhateeb

Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. To address this challenge, this paper leverages mmWave and LiDAR sensory data to provide awareness about the communication environment and proactively predict dynamic link blockages before they occur.

H. V. Souza

The Deep Underground Neutrino Experiment (DUNE) is a next generation long baseline (1300 km) neutrino oscillation experiment. The neutrino beam measurements will be performed by a near detector (ND) and far detector (FD). The far detector will consist of four modules, installed 1,5 km deep underground, based on Liquid Argon Time Projection Chamber (LArTPC) t

Yiyue Hu, Lei Zhang, Nan Mu, Lei Liu

Transformers have achieved remarkable success in medical image analysis owing to their powerful capability to use flexible self-attention mechanism. However, due to lacking intrinsic inductive bias in modeling visual structural information, they generally require a large-scale pre-training schedule, limiting the clinical applications over expensive small-sca

Tania Robens

I will briefly discuss the signatures and discovery prospects of several new physics models containing dark matter candidates at future lepton colliders. In particular, I will discuss the two models that, among other signatures, lead to electroweak gauge bosons and missing energy: the Inert Doublet Model, as well as the THDMa, a two Higgs doublet model with

Andreagiovanni Reina, Raina Zakir, Giulia De Masi, Eliseo Ferrante

Strongly opinionated minorities can have a dramatic impact on the opinion dynamics of a large population. Two factions of inflexible minorities, polarised into two competing opinions, could lead the entire population to persistent indecision. Equivalently, populations can remain undecided when individuals sporadically change their opinion based on individual

Guo Chen, Sen Xing, Zhe Chen, Yi Wang

In this report, we present our champion solutions to five tracks at Ego4D challenge. We leverage our developed InternVideo, a video foundation model, for five Ego4D tasks, including Moment Queries, Natural Language Queries, Future Hand Prediction, State Change Object Detection, and Short-term Object Interaction Anticipation. InternVideo-Ego4D is an effective

Luca Rosafalco, Jacopo Maria De Ponti, Luca Iorio, Raffaele Ardito

A reinforcement learning approach to design optimised graded metamaterials for mechanical energy confinement and amplification is described. Through the proximal policy optimisation algorithm, the reinforcement agent is trained to optimally set the lengths and the spacing of an array of resonators. The design optimisation problem is formalised in a Markov de

Fábio Perez, Ian Ribeiro

Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing applications. However, studies that explore their vulnerabilities emerging from malicious user interaction are scarce. By proposing PromptInject, a prosaic alignment framework for mask-based iterative adversarial prompt compo

L. León Andonayre, M. Skugoreva, A. Toporensky, T. Vargas

We revisit the cyclic Universe scenario in scalar field FRW cosmology and check its applicability for a nonminimally coupled scalar field. We show that for the most popular case of a quartic potential and the standard nonminimal coupling this scenario does work. On the other hand, we identify certain cases where cyclic model fails to work and present corresp

Umesh V Dubey, Subham Sarkar

Kapranov and schechtman gave quiver description of perverse sheaves on real hyperplane arrangements. We used this description to relate the perverse sheaves on Coxeter hyperplane arrangements of type $\mathcal A_n$ for different values of $n$. As a consequence we prove that the simple perverse sheaves whose stalk on open cells are zero are induced from the p

Jonathan W. Z. Lim, Vrizlynn L. L. Thing

Cybercrimes are on the rise, in part due to technological advancements, as well as increased avenues of exploitation. Sophisticated threat actors are leveraging on such advancements to execute their malicious intentions. The increase in cybercrimes is prevalent, and it seems unlikely that they can be easily eradicated. A more serious concern is that the comm

László Csató

The eigenvalue method, suggested by the developer of the extensively used Analytic Hierarchy Process methodology, exhibits right-left asymmetry: the priorities derived from the right eigenvector do not necessarily coincide with the priorities derived from the reciprocal left eigenvector. This paper offers a comprehensive numerical experiment to compare the t

Hadi Hashamipour, Muhammad Goharipour, K. Azizi, S. V. Goloskokov

We present a new determination of the generalized parton distributions (GPDs) with their uncertainties at zero skewness, $ \xi =0 $, through a simultaneous analysis of all available experimental data of the nucleon electromagnetic form factors (FFs), nucleon charge and magnetic radii, proton axial FFs (AFFs) and wide-angle Compton scattering (WACS) cross sec

Nodoka Yamanaka

CP violating interactions are required to realize the matter abundance of our Universe. It is however known that the standard model of particle physics does not contain sufficient CP violation. The electric dipole moment (EDM) of atomic systems is a very sensitive experimental probe of CP violation beyond the standard model, and it is very actively studied i

Jiahao Chang, Shuo Wang, Haiming Xu, Zehui Chen

Transformer-based detectors (DETRs) are becoming popular for their simple framework, but the large model size and heavy time consumption hinder their deployment in the real world. While knowledge distillation (KD) can be an appealing technique to compress giant detectors into small ones for comparable detection performance and low inference cost. Since DETRs

A. Agreda, T. Wu, A. Hereu, M. Treguer-Delapierre

The nanostructures of natural species offer beautiful visual appearances with saturated and iridescent colors and the question arises whether we can reproduce or even create new appearances with man-made metasurfaces. However, harnessing the specular and diffuse light scattered by disordered metasurfaces to create attractive and prescribed visual effects is

Shiro Takagi

We empirically investigate how pre-training on data of different modalities, such as language and vision, affects fine-tuning of Transformer-based models to Mujoco offline reinforcement learning tasks. Analysis of the internal representation reveals that the pre-trained Transformers acquire largely different representations before and after pre-training, but

Christopher Carr, Peng Wang, Shenglin Wang

While multi-robot systems have been broadly researched and deployed, their success is built chiefly upon the dependency on network infrastructures, whether wired or wireless. Aiming at the first steps toward de-coupling the application of multi-robot systems from the reliance on network infrastructures, this paper proposes a human-friendly verbal communicati

Yiyang Shen, Rongwei Yu, Peng Wu, Haoran Xie

LiDAR and camera, as two different sensors, supply geometric (point clouds) and semantic (RGB images) information of 3D scenes. However, it is still challenging for existing methods to fuse data from the two cross sensors, making them complementary for quality 3D object detection (3OD). We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discre

George Robert Smith, Daniel Waldram

We continue the analysis of the geometry of generic Minkowski $\mathcal{N} = 1$, $D = 4$ flux compactifications in M-theory using exceptional generalised geometry, including the calculation of the infinitesimal moduli spaces. The backgrounds can be classified into two classes: type-0 and type-3. For type-0, we review how the moduli arise from standard de Rha

Manuel Ribeiro

Development of air pollution models for large regions is a priority for population-based epidemiologic studies. The rapid development of big data information systems and machine learning algorithms have opened new grounds for refinements of current model frameworks. This commentary overviews recent contributions and outlines extensions from geostatistics and

Allen G Hart

We establish conditions under which a continuous time reservoir computer, such as a leaky integrator echo state network, admits a generalised synchronisation $f$ between between the source dynamics and reservoir dynamics. We show that multiple generalised synchronisations can exist simultaneously, and connect this to the multi-Echo-State-Property (multi-ESP)

S. I. Alekhin, S. A. Kulagin, R. Petti

We report the results of a new global QCD analysis including deep-inelastic scattering data off $^1$H, $^2$H, $^3$H, and $^3$He targets. Nuclear corrections are treated in terms of a nuclear convolution approach with off-shell bound nucleons. The off-shell (OS) corrections responsible for the modification of the structure functions (SFs) of bound nucleons ar

Ningyi Xie, Xinwei Lee, Dongsheng Cai, Yoshiyuki Saito

The Quantum approximate optimization algorithm (QAOA) is a quantum-classical hybrid algorithm aiming to produce approximate solutions for combinatorial optimization problems. In the QAOA, the quantum part prepares a quantum parameterized state that encodes the solution, where the parameters are optimized by a classical optimizer. However, it is difficult to

Annika Junker, Keno Pape, Julia Timmermann, Ansgar Trächtler

We present a method to obtain a data-driven Koopman operator-based model that adapts itself during operation and can be straightforwardly used for the controller and observer design. The adaptive model is able to accurately describe different state-space regions and additionally consider unpredictable system changes that occur during operation. Furthermore,

Vaclav Kosar, Antonín Hoskovec, Milan Šulc, Radek Bartyzal

We introduce GLAMI-1M: the largest multilingual image-text classification dataset and benchmark. The dataset contains images of fashion products with item descriptions, each in 1 of 13 languages. Categorization into 191 classes has high-quality annotations: all 100k images in the test set and 75% of the 1M training set were human-labeled. The paper presents

Mikel K. Ngueajio, Gloria Washington

Speech is the fundamental means of communication between humans. The advent of AI and sophisticated speech technologies have led to the rapid proliferation of human-to-computer-based interactions, fueled primarily by Automatic Speech Recognition (ASR) systems. ASR systems normally take human speech in the form of audio and convert it into words, but for some

Jiawei Jiang, Dayan Pan, Houxing Ren, Xiaohan Jiang

Trajectory Representation Learning (TRL) is a powerful tool for spatial-temporal data analysis and management. TRL aims to convert complicated raw trajectories into low-dimensional representation vectors, which can be applied to various downstream tasks, such as trajectory classification, clustering, and similarity computation. Existing TRL works usually tre

Yichao Zhang, Yang Zhou

We first obtain the dimension formulas for the spaces of holomorphic modular forms with character for the Fricke group $\Gamma_0^+(N)$, then that for $\Gamma_0^*(N)$ with all Atkin-Lehner involutions added in a particular case.

Shengwen Gan, Shaoming Guo, Hong Wang

Let $\gamma: [-1, 1]\to \mathbb{R}^n$ be a smooth curve that is non-degenerate. Take $m\le n$ and a Borel set $E\subset [0, 1]^n$. We prove that the orthogonal projection of $E$ to the $m$-th order tangent space of $\gamma$ at $\theta\in [-1, 1]$ has Hausdorff dimension $\min\{m, \dim(E)\}$ for almost every $\theta\in [-1, 1]$.

Christopher Carr, Shenglin Wang, Peng Wang, Liangxiu Han

Security and safety are of paramount importance to human-robot interaction, either for autonomous robots or human-robot collaborative manufacturing. The intertwined relationship between security and safety has imposed new challenges on the emerging digital twin systems of various types of robots. To be specific, the attack of either the cyber-physical system

Antonino De Martino, Stefano Pinton

The Fueter mapping theorem gives a constructive way to extend holomorphic functions of one complex variable to monogenic functions, i.e., null solutions of the generalized Cauchy-Riemann operator in $\mathbb{R}^4$, denoted by $\mathcal{D}$. This theorem is divided in two steps. In the first step a holomorphic function is extended to a slice hyperholomorphic

Egor Danilov, Aleksandra Ćiprijanović, Brian Nord

The study of quasar light curves poses two problems: inference of the power spectrum and interpolation of an irregularly sampled time series. A baseline approach to these tasks is to interpolate a time series with a Damped Random Walk (DRW) model, in which the spectrum is inferred using Maximum Likelihood Estimation (MLE). However, the DRW model does not des

Parvathy Sobha, Nita R Patne

Energy consumption and energy ananlytics has gained increased focus and consideration in industrial applications especially process lines to upgrade their performance and efficiency in the competitive world. A competent analytics method will be highly advantageous to provide the correct direction of energy saving for an industry. Energy analytics method was

Chenran Xu, Tobias Boltz, Akira Mochihashi, Andrea Santamaria Garcia

We have evaluated the data-efficient Bayesian optimization method for the specific task of injection tuning in a circular accelerator. In this paper, we describe the implementation of this method at the Karlsruhe Research Accelerator with up to nine tuning parameters, including the determination of the associated hyperparameters. We show that the Bayesian op

Marius Faiß

Insects are an integral part of our ecosystem. These often small and evasive animals have a big impact on their surroundings, providing a large part of the present biodiversity and pollination duties, forming the foundation of the food chain and many biological and ecological processes. Due to factors of human influence, population numbers and biodiversity h

Federico Crudu, Michael C. Knaus, Giovanni Mellace, Joeri Smits

Many econometrics textbooks imply that under mean independence of the regressors and the error term, the OLS parameters have a causal interpretation. We show that even when this assumption is satisfied, OLS might identify a pseudo-parameter that does not have a causal interpretation. Even assuming that the linear model is "structural" creates some ambiguity

Ioseph Buchbinder, Evgeny Ivanov, Nikita Zaigraev

In recent papers arXiv:2109.07639 [hep-th] and arXiv:2202.08196 [hep-th], we constructed free off-shell $\mathcal{N}=2$ supersymmetric higher spin gauge theories in harmonic superspace and their cubic couplings to hypermultiplet. The present report is a brief review of the results obtained.

Debarati Bhaumik, Diptish Dey

Multilevel models using logistic regression (MLogRM) and random forest models (RFM) are increasingly deployed in industry for the purpose of binary classification. The European Commission's proposed Artificial Intelligence Act (AIA) necessitates, under certain conditions, that application of such models is fair, transparent, and ethical, which consequently i

S. Wallner

COMPASS is a multi-purpose fixed-target experiment at CERN's M2 beam line aimed at studying the structure and spectrum of hadrons. It has collected the so far world's largest data set on diffractive production of the $K^-\pi^-\pi^+$ final state, which in principle gives access to all strange mesons. Based on this data set, we performed an elaborate partial-w

Xiasheng Ma, Shengcai Liu, Wenjing Hong

It has been widely observed that there exists no universal best Multi-objective Evolutionary Algorithm (MOEA) dominating all other MOEAs on all possible Multi-objective Optimization Problems (MOPs). In this work, we advocate using the Parallel Algorithm Portfolio (PAP), which runs multiple MOEAs independently in parallel and gets the best out of them, to com

Mokhtar Adda-Bedia, Eytan Katzav

During the last century, two independent theories using the concept of dimensional reduction have been developed independently. The first, known as F\"oppl-von K\`arm\`an theory, uses Riemannian geometry and continuum mechanics to study the shaping of thin elastic structures which could become as complex as crumpled paper. The second one, known as Kaluza-Kle

Yiwei Guo, Chenpeng Du, Xie Chen, Kai Yu

Although current neural text-to-speech (TTS) models are able to generate high-quality speech, intensity controllable emotional TTS is still a challenging task. Most existing methods need external optimizations for intensity calculation, leading to suboptimal results or degraded quality. In this paper, we propose EmoDiff, a diffusion-based TTS model where emo

Chunyu Qiang, Peng Yang, Hao Che, Jinba Xiao

Conversion of Chinese Grapheme-to-Phoneme (G2P) plays an important role in Mandarin Chinese Text-To-Speech (TTS) systems, where one of the biggest challenges is the task of polyphone disambiguation. Most of the previous polyphone disambiguation models are trained on manually annotated datasets, and publicly available datasets for polyphone disambiguation are

Vladimir Georgiev, Yuan Li

We consider the half-wave equation $iu_t=Du-|u|u$ in two dimensions. For the initial data $u_0(x)\in H^{s}(\mathbb{R}^2)$, $s\in\left(\frac{3}{4},1\right)$, we obtain the non-radial ground state mass blow-up solutions with the blow-up speed $\|D^{\frac{1}{2}}u(t)\|_{L^2}\sim\frac{1}{|t|}$ as $t\to 0^-$.

S. Vaidya, C. Traub, F. Romano, G. Herdrich

Operating satellites in Very Low Earth Orbit (VLEO) benefits the already expanding New Space industry in applications including Earth Observation and beyond. However, long-term operations at such low altitudes require propulsion systems to compensate for the large aerodynamic drag forces. When using conventional propulsion systems, the amount of storable pro

Changhua Li, Yanxia Zhang, Chenzhou Cui, Dongwei Fan

The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. The template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and SDSS DR16 galaxy

Birendra Chhotaray, Gaurava K. Jaisawal, Neeraj Kumari, Sachindra Naik

We report results obtained from the optical and X-ray studies of the Be/X-ray binary 1A 0535+262/HD 245770 during the 2020 October giant X-ray outburst, using the 1.2 m telescope at Mount Abu Infrared observatory and AstroSat, respectively. The peak flux of the outburst was recorded to be around 11 Crab in the 15-50 keV range, the highest ever observed from

Ke Xiao, Chen-Xu Wu, Rui Ma

Endocytosis is an essential biological process for the trafficking of macromolecules (cargo) and membrane proteins in cells. In yeast cells, this involves the invagination of a tubular structure on the membrane and the formation of endocytic vesicles. Bin/Amphiphysin/Rvs (BAR) proteins holding a crescent-shape are generally assumed to be the active player to

Tomoaki Murata, Tomohiro Fujita, Tsutomu Kobayashi

We study the homogeneous and anisotropic dynamics of pseudoscalar inflation coupled to an SU($N$) gauge field. To see how the initially anisotropic universe is isotropized in such an inflation model, we derive the equations to obtain axisymmetric SU($N$) gauge field configurations in Bianchi type-I geometry and discuss a method to identify their isotropic su

Chunhui Li, Mingquan Zhou, Zehua Liu, Yuhe Zhang

The curve skeleton is an important shape descriptor that has been utilized in various applications in computer graphics, machine vision, and artificial intelligence. In this study, the endpoint-based part-aware curve skeleton (EPCS) extraction method for low-quality point clouds is proposed. The novel random center shift (RCS) method is first proposed for de

Richard Bubel, Dilian Gurov, Reiner Hähnle, Marco Scaletta

Contracts specifying a procedure's behavior in terms of pre- and postconditions are essential for scalable software verification, but cannot express any constraints on the events occurring during execution of the procedure. This necessitates to annotate code with intermediate assertions, preventing full specification abstraction. We propose a logic over symb

Margarita Akhmejanova, Ilya Bogdanov, Grigory Chelnokov

The paper deals with an algorithmic problem concerning combinatorial game theory. Here we introduce and analyze a continuous generalization of Chip Game from the work of Duraj, Gutowski and Kozik. The general Chip game was introduced by Aslam and Dhagat to model on-line type problems on hypergraph coloring.

Tali Kaufman, David Mass

Recent works have shown that expansion of pseudorandom sets is of great importance. However, all current works on pseudorandom sets are limited only to product (or approximate product) spaces, where Fourier Analysis methods could be applied. In this work we ask the natural question whether pseudorandom sets are relevant in domains where Fourier Analysis meth

Xinlu Li, Meng Zhu, Yaoyuan Wang, Fanxing Zheng

Recently, van der Waals (vdW) magnetic heterostructures have received increasing research attention in spintronics. However, the lack of room-temperature magnetic order of vdW material has largely impedes its development in practical spintronics devices. Inspired by the recently discovered vdW ferromagnet Fe3GaTe2, which has been shown to have magnetic order

Ville A. J. Pyykkönen, Sebastiano Peotta, Päivi Törmä

We study non-equilibrium transport through a superconducting flat-band lattice in a two-terminal setup with the Schwinger-Keldysh method. We find that quasiparticle transport is suppressed and coherent pair transport dominates. For superconducting leads, the AC supercurrent overcomes the DC current which relies on multiple Andreev reflections. With normal-no

Tali Kaufman, David Mass

In recent years, high dimensional expanders have been found to have a variety of applications in theoretical computer science, such as efficient CSPs approximations, improved sampling and list-decoding algorithms, and more. Within that, an important high dimensional expansion notion is \emph{cosystolic expansion}, which has found applications in the construc

Bin Gao, Nguyen Thanh Son, Tatjana Stykel

Numerous problems in optics, quantum physics, stability analysis, and control of dynamical systems can be brought to an optimization problem with matrix variable subjected to the symplecticity constraint. As this constraint nicely forms a so-called symplectic Stiefel manifold, Riemannian optimization is preferred, because one can borrow ideas from unconstrai

Offry Hayon, Stefan Münger, Ilan Shimshoni, Ayellet Tal

Archaeology is an intriguing domain for computer vision. It suffers not only from shortage in (labeled) data, but also from highly-challenging data, which is often extremely abraded and damaged. This paper proposes a novel semi-supervised model for classification and retrieval of images of archaeological artifacts. This model utilizes unique data that exists

Stavros Sykiotis, Christoforos Menos-Aikateriniadis, Anastasios Doulamis, Nikolaos Doulamis

Power sector decarbonization plays a vital role in the upcoming energy transition towards a more sustainable future. Decentralized energy resources, such as Electric Vehicles (EV) and solar photovoltaic systems (PV), are continuously integrated in residential power systems, increasing the risk of bottlenecks in power distribution networks. This paper aims to

Adrián Pérez Herrero, Paulo Félix Lamas, Jesús María Rodríguez Presedo

This work aims at providing a new model for time series classification based on learning from just one example. We assume that time series can be well characterized as a parametric random process, a sort of Hidden semi-Markov Model representing a sequence of regression models with variable duration. We introduce a parametric stochastic model for time series

E. Maiewski, H. Malova, V. Popov, D. Sokoloff

We study the relation between stellar dynamo-wave propagation and the structure of the stellar magnetic field. Modeling dynamo waves by the well-known Parker migratory dynamo, we vary the intensity of dynamo drivers in order to obtain activity-wave propagation toward the Equator (as in the solar-activity cycle) or towards the Poles. We match the magnetic fie

Tobias Grafke, Alessandro Laio

Stochastic systems often exhibit multiple viable metastable states that are long-lived. Over very long timescales, fluctuations may push the system to transition between them, drastically changing its macroscopic configuration. In realistic systems, these transitions can happen via multiple physical mechanisms, corresponding to multiple distinct transition c

Y. Litman, F. P. Bonafé, A. Akkoush, H. Appel

Tip-enhanced Raman scattering (TERS) has emerged as a powerful tool to obtain subnanometer spatial resolution fingerprints of atomic motion. Theoretical calculations that can simulate the Raman scattering process and provide an unambiguous interpretation of TERS images often rely on crude approximations of the local electric field. In this work, we present a

Peter Kroll, Kornelija Passek-Kumerički

We investigate exclusive electroproduction of $\pi^-\Delta^{++}$ within the handbag approach in which the helicity amplitudes factorize into generalized parton distributions (GPDs) and hard partonic subprocesses. We define the $p-\Delta$ transversity GPDs while the helicity non-flip GPDs are taken from the literature. For the numerical estimates of observabl

Dennis Höning, Tilman Spohn

A balanced ratio of ocean to land is believed to be essential for an Earth-like biosphere and one may conjecture that plate-tectonics planets should be similar in geological properties. After all, the volume of continental crust evolves towards an equilibrium between production and erosion. If the interior thermal states of Earth-sized exoplanets are similar

Aleš Drápal, Ian M. Wanless

Let $\mathbb{F}$ be a finite field of odd order and $a,b\in\mathbb{F}\setminus\{0,1\}$ be such that $\chi(a) = \chi(b)$ and $\chi(1-a)=\chi(1-b)$, where $\chi$ is the extended quadratic character. Let $Q_{a,b}$ be the quasigroup upon $\mathbb{F}$ defined by $(x,y)\mapsto x+a(y-x)$ if $\chi(y-x) \ge 0$, and $(x,y)\mapsto x+b(y-x)$ if $\chi(y-x) = -1$. We show

Marianna Chatzakou, Serena Federico, Boguslaw Zegarlinski

In this work we give a sufficient condition under which the global Poincar\'{e} inequality on Carnot groups holds true for a large family of probability measures absolutely continuous with respect to the Lebesgue measure. The density of such probability measure is given in terms of homogeneous quasi-norm on the group. We provide examples to which our conditi

Thibaut Arnoulx de Pirey, Leticia F. Cugliandolo, Vivien Lecomte, Frédéric van Wijland

Path integrals are a ubiquitous tool in theoretical physics. However, their use is sometimes hindered by the lack of control on various manipulations -- such as performing a change of the integration path -- one would like to carry out in the light-hearted fashion that physicists enjoy. Similar issues arise in the field of stochastic calculus, which we revie

Pengpeng Zeng, Haonan Zhang, Lianli Gao, Xiangpeng Li

Generating consecutive descriptions for videos, i.e., Video Captioning, requires taking full advantage of visual representation along with the generation process. Existing video captioning methods focus on making an exploration of spatial-temporal representations and their relationships to produce inferences. However, such methods only exploit the superficia

M. H. Bien, P. V. Danchev, M. Ramezan-Nassab, T. N. Son

Let $F$ be a field with at least three elements and $G$ a locally finite group. This paper aims to show that if either $F$ is algebraically closed or the characteristic of $F$ is positive, then an element in the group algebra $FG$ is a product of unipotent elements if, and only if, it? lies in the first derived subgroup of the unit group of $FG$. In addition

Gunnar G. Peng, Ory Schnitzer

We develop a reduced model for the slow unsteady dynamics of an isotropic chemically active particle near the threshold for spontaneous motion. Building on the steady theory developed in part I of this series, we match a weakly nonlinear expansion valid on the particle scale with a leading-order approximation in a larger-scale unsteady remote region, where t

Konpal Shaukat Ali, Marwa Chafii

A large network employing integrated sensing and communication (ISAC) where a single transmit signal by the base station (BS) serves both the radar and communication modes is studied. We consider bistatic detection at a passive radar and monostatic detection at the transmitting BS. The radar-mode performance is significantly more vulnerable than the communic

Audie Warren

In this paper we prove an incidence bound for points and cubic curves over prime fields. The methods generalise those used by Mohammadi, Pham, and Warren (2021).

Eni Musta, Tsz Pang Yuen

We consider survival data in the presence of a cure fraction, meaning that some subjects will never experience the event of interest. We assume a mixture cure model consisting of two sub-models: one for the probability of being uncured (incidence) and one for the survival of the uncured subjects (latency). Various approaches, ranging from parametric to nonpa

Stefano Berrone, Chiara Giverso, Denise Grappein, Luigi Preziosi

A new mathematical model and numerical approach are proposed for the simulation of fluid and chemical exchanges between a growing capillary network and the surrounding tissue, in the context of tumor-induced angiogenesis. Thanks to proper modeling assumptions the capillaries are reduced to their centerline: a well posed mathematical model is hence worked out

Ivan Samoylenko, David Aleja, Eva Primo, Karin Alfaro-Bittner

A wealth of evidence shows that real world networks are endowed with the small-world property i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regulari

Alexander Panaetov, Karim Elhadji Daou, Igor Samenko, Evgeny Tetin

Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution (SISR). However, very deep networks can suffer from training difficulty and hardly achieve further performance gain. There are two main trends to solve that problem: improving the network architecture for better propagation of features through larg

Philip Freese, Moritz Hauck, Tim Keil, Daniel Peterseim

This paper presents a novel multi-scale method for elliptic partial differential equations with arbitrarily rough coefficients. In the spirit of numerical homogenization, the method constructs problem-adapted ansatz spaces with uniform algebraic approximation rates. Localized basis functions with the same super-exponential localization properties as the rece

Pengpeng Zeng, Jinkuan Zhu, Jingkuan Song, Lianli Gao

Studies of image captioning are shifting towards a trend of a fully end-to-end paradigm by leveraging powerful visual pre-trained models and transformer-based generation architecture for more flexible model training and faster inference speed. State-of-the-art approaches simply extract isolated concepts or attributes to assist description generation. However

Ashutosh Dash, Masoud Shokri, Luciano Rezzolla, Dirk H. Rischke

We study charge diffusion in relativistic resistive second-order dissipative magnetohydrodynamics. In this theory, charge diffusion is not simply given by the standard Navier-Stokes form of Ohm's law, but by an evolution equation which ensures causality and stability. This, in turn, leads to transient effects in the charge diffusion current, the nature of wh

Yifu Qiu, Shay B. Cohen

Sequential abstractive neural summarizers often do not use the underlying structure in the input article or dependencies between the input sentences. This structure is essential to integrate and consolidate information from different parts of the text. To address this shortcoming, we propose a hierarchy-aware graph neural network (HierGNN) which captures suc

Md. Ashraful Alam, F. Parvin, S. H. Naqib

Here we have used the density functional theory (DFT) with the GGA-PBE approximation to investigate the structural, mechanical, electronic, hardness, thermal, superconductivity and optoelectronic properties under pressure for monoclinic (P21/m) and orthorhombic (Pmn21) structures of MgVH6. We have studied optical properties of P21/m phase at 0 GPa and Pmn21

Hanaa Abumarshoud, Cheng Chen, Iman Tavakkolnia, Harald Haas

The rise of intelligent reflecting surfaces (IRSs) is opening the door for unprecedented capabilities in visible light communication (VLC) systems. By controlling light propagation in indoor environments, it is possible to manipulate the channel conditions to achieve specific key performance indicators. In this paper, we investigate the role that IRSs can pl

Aleksandar Savkov, Francesco Moramarco, Alex Papadopoulos Korfiatis, Mark Perera

Evaluating automatically generated text is generally hard due to the inherently subjective nature of many aspects of the output quality. This difficulty is compounded in automatic consultation note generation by differing opinions between medical experts both about which patient statements should be included in generated notes and about their respective impo

Håkon Hukkelås, Frank Lindseth

Generative Adversarial Networks (GANs) are widely adapted for anonymization of human figures. However, current state-of-the-art limit anonymization to the task of face anonymization. In this paper, we propose a novel anonymization framework (DeepPrivacy2) for realistic anonymization of human figures and faces. We introduce a new large and diverse dataset for

Haijin Huang, Armandas Balcytis, Aditya Dubey, Andreas Boes

In this contribution, we simulate, design, and experimentally demonstrate an integrated optical isolator based on spatiotemporal modulation in the thin-film lithium niobate on insulator waveguide platform. We used two cascaded travelling wave phase modulators for spatiotemporal modulation and a ring resonator as a wavelength filter to suppress the sidebands