November 2022 arXiv papers — page 33

Zhipeng Zhong, Fei Zhou, Guoping Qiu

Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). Based on the obse

Gérard G. Letac

Consider the centered Gaussian vector $X$ in $\R^n$ with covariance matrix $ \Sigma.$ Randomize $\Sigma$ such that $ \Sigma^{-1}$ has a Wishart distribution with shape parameter $p>(n-1)/2$ and mean $p\sigma.$ We compute the density $f_{p,\sigma}$ of $X$ as well as the Fisher information $I_p(\sigma)$ of the model $(f_{p,\sigma} )$ when $\sigma $ is the para

Meng-Yuan Li, Peng Ye

As a quantum-informative window into quantum many-body physics, the concept and application of entanglement renormalization group (ERG) have been playing a vital role in the study of novel quantum phases of matter, especially long-range entangled (LRE) states in topologically ordered systems. For instance, by recursively applying local unitaries as well as a

J. Zhao, J. -P. Ebran, L. Heitz, E. Khan

Alpha and cluster decays are analyzed for heavy nuclei located above $^{208}$Pb on the chart of nuclides: $^{216-220}$Rn and $^{220-224}$Ra, that are also candidates for observing the $2 \alpha$ decay mode. A microscopic theoretical approach based on relativistic Energy Density Functionals (EDF), is used to compute axially-symmetric deformation energy surfac

Henri Bollaert, Chris Cornelis

Fuzzy rough sets are well-suited for working with vague, imprecise or uncertain information and have been succesfully applied in real-world classification problems. One of the prominent representatives of this theory is fuzzy-rough nearest neighbours (FRNN), a classification algorithm based on the classical k-nearest neighbours algorithm. The crux of FRNN is

Kazuki Osawa, Shigang Li, Torsten Hoefler

Pipeline parallelism enables efficient training of Large Language Models (LLMs) on large-scale distributed accelerator clusters. Yet, pipeline bubbles during startup and tear-down reduce the utilization of accelerators. Although efficient pipeline schemes with micro-batching and bidirectional pipelines have been proposed to maximize utilization, a significan

M. L. L. Dantas, G. Guiglion, R. Smiljanic, D. Romano

We test a scenario in which radial migration could affect the Li abundance pattern of dwarf stars in the solar neighbourhood. This may confirm that the Li abundance in these stars can not serve as a probe for the Li abundance in the interstellar medium. We use the high-quality data (including Li abundances) from the 6th internal Data Release of the Gaia-ESO

Raphaël Gobat, Chiara D'Eugenio, Daizhong Liu, Gabriel Bartosch Caminha

How much gas and dust is contained in high-redshift quiescent galaxies (QGs) is currently an open question with relatively few and contradictory answers, as well as important implications for our understanding of the nature of star formation quenching processes at cosmic noon. Here we revisit far-infrared (FIR) observations of the REQUIEM-ALMA sample of six

Oliver Watts, Lovisa Wihlborg, Cassia Valentini-Botinhao

We present a neural vocoder designed with low-powered Alternative and Augmentative Communication devices in mind. By combining elements of successful modern vocoders with established ideas from an older generation of technology, our system is able to produce high quality synthetic speech at 48kHz on devices where neural vocoders are otherwise prohibitively c

S. S. Agaev, K. Azizi, H. Sundu

We investigate features of the hidden charm-strange scalar tetraquark $c \overline{c}s\overline{s}$ by calculating its spectral parameters and width, and we compare the obtained results with the mass and width of the resonance $ X(3960)$ discovered recently in the LHCb experiment. We model the tetraquark as a diquark-antidiquark state $X=[cs][\overline{c}\ov

Antonio García Muñoz

Photoelectrons, the fast electrons produced in the photoionization of planetary atmospheres, drive transformations in the atmospheric gas that are often inhibited by energy considerations for thermal electrons. The transformations include excitation and ionization of atoms and molecules, which affect the detectability of these gases and constrain the fractio

G. A. Oakes, L. Peri, L. Cochrane, F. Martins

Silicon offers the enticing opportunity to integrate hybrid quantum-classical computing systems on a single platform. For qubit control and readout, high-frequency signals are required. Therefore, devices that can facilitate its generation are needed. Here, we present a quantum dot-based radiofrequency multiplier operated at cryogenic temperatures. The devic

Sina Hajimiri, Malik Boudiaf, Ismail Ben Ayed, Jose Dolz

This paper introduces a generalized few-shot segmentation framework with a straightforward training process and an easy-to-optimize inference phase. In particular, we propose a simple yet effective model based on the well-known InfoMax principle, where the Mutual Information (MI) between the learned feature representations and their corresponding predictions

Thomas Jantos, Mohamed Amin Hamdad, Wolfgang Granig, Stephan Weiss

Accurate 6D object pose estimation is an important task for a variety of robotic applications such as grasping or localization. It is a challenging task due to object symmetries, clutter and occlusion, but it becomes more challenging when additional information, such as depth and 3D models, is not provided. We present a transformer-based approach that takes

Anthony D. Forbes

We give direct constructions for 233 group divisible designs with block size five, mostly of type $g^u m^1$, $m > 0$.

Elena Callus, Pieter Kok

We demonstrate that the spin state of solid-state emitters inside micropillar cavities can serve as measure qubits in syndrome measurements. The photons, acting as data qubits, interact with the spin state in the microcavity and the total state of the system evolves conditionally due to the resulting circular birefringence. By performing a quantum non-demoli

Chaojun Chen, Khashayar Namdar, Yujie Wu, Shahob Hosseinpour

Scoliosis is a three-dimensional deformity of the spine, most often diagnosed in childhood. It affects 2-3% of the population, which is approximately seven million people in North America. Currently, the reference standard for assessing scoliosis is based on the manual assignment of Cobb angles at the site of the curvature center. This manual process is time

Kai Koike

We construct a time-asymptotic expansion with pointwise remainder estimates for solutions to 1D compressible Navier--Stokes equations. The leading-order term is the well-known diffusion wave and the higher-order terms are newly introduced family of waves which we call \textit{higher-order diffusion waves}. In particular, these provide accurate description of

Seemant Mishra, Urna Basu

We study the behaviour of a symmetric exclusion process in the presence of non-Markovian stochastic resetting, where the configuration of the system is reset to a step-like profile at power-law waiting times with an exponent $\alpha$. We find that the power-law resetting leads to a rich behaviour for the currents, as well as density profile. We show that, fo

Mengting Jiang, Michel Speetjens, Camilo Rindt, David Smeulders

This study concerns the development of a data-based compact model for the prediction of the fluid temperature evolution in district heating (DH) pipeline networks. This so-called "reduced-order model" (ROM) is obtained from reduction of the conservation law for energy for each pipe segment to a semi-analytical input-output relation between the pipe outlet te

Clément Hardy, Yvain Quéau, David Tschumperlé

The photometric stereo (PS) problem consists in reconstructing the 3D-surface of an object, thanks to a set of photographs taken under different lighting directions. In this paper, we propose a multi-scale architecture for PS which, combined with a new dataset, yields state-of-the-art results. Our proposed architecture is flexible: it permits to consider a v

C. W. Cho, A. Pawbake, N. Aubergier, A. L. Barra

Microwave absorption experiments employing a phase-sensitive external resistive detection are performed for a topical van der Waals antiferromagnet CrSBr. The field dependence of two resonance modes is measured in an applied field parallel to the three principal crystallographic directions, revealing anisotropies and magnetic transitions in this material. To

S. Elouazzani, M. Elhodaibi

Let $\mathcal{B} (X)$ be the algebra of all bounded linear operators on an infinite-dimensional complex Banach space $X$. For an operator $ T \in \mathcal{B} (X)$, $K(T)$ denotes as usual the analytic core of $T$. We determine the form of surjective maps $ \phi $ on $ \mathcal{B} (X)$ satisfying $$ K(\phi(T) \phi(S) + \phi (S) \phi (T)) = K(TS + ST) $$ for a

Tomasz Piotrowski, Rafail Ismayilov, Matthias Frey, Renato L. G. Cavalcante

We introduce the concept of inverse feasibility for linear forward models as a tool to enhance OTA FL algorithms. Inverse feasibility is defined as an upper bound on the condition number of the forward operator as a function of its parameters. We analyze an existing OTA FL model using this definition, identify areas for improvement, and propose a new OTA FL

Quyu Kong, Pio Calderon, Rohit Ram, Olga Boichak

Social media is being increasingly weaponized by state-backed actors to elicit reactions, push narratives and sway public opinion. These are known as Information Operations (IO). The covert nature of IO makes their detection difficult. This is further amplified by missing data due to the user and content removal and privacy requirements. This work advances t

Martin Pijnenburg, Giulia Cusin, Cyril Pitrou, Jean-Philippe Uzan

Quantum Rutherford scattering and scattering of classical waves off Coulomb-like potentials have similar formal structures and can be studied using the same mathematical techniques. In both contexts, the long-range nature of the interaction leads to a divergent total cross-section, which has been interpreted and regularized in various ways in the past litera

Jannis Hessenauer, Ksenia Weber, Julia Benedikter, Timo Gissibl

We demonstrate laser-written concave hemispherical structures produced on the endfacets of optical fibers that serve as mirror substrates for tunable open-access microcavities. We achieve finesse values of up to 250, and a mostly constant performance across the entire stability range. This enables cavity operation also close to the stability limit, where a p

Wissam Ghantous

In 2014, Darmon and Rotger defined the Garrett-Rankin triple product $p$-adic $L$- function and related it to the image of certain diagonal cycles under the $p$-adic Abel- Jacobi map. We introduce a new $p$-adic triple symbol based on this $p$-adic $L$- function and show that it satisfies symmetry relations, when permuting the three input modular forms. We a

Juliane Haug, Fabian Wunder

We investigate the single off-shell scalar box integral with massless internal lines in dimensional regularization. A special emphasis is given to higher orders in the dimensional regularization parameter epsilon, its branch cut structure, and kinematic limits. Common representations of the box integral introduce superficial branch cuts, which we eliminate t

Xudong Zhu, Yuqian Chen, Zheng Liu, Yulei Han

We numerically study the general valley polarization and anomalous Hall effect in van der Waals (vdW) heterostructures based on monolayer jacutingaite family materials Pt$_{2}$AX$_{3}$ (A = Hg, Cd, Zn; X = S, Se, Te). We perform a systematic study on the atomic, electronic, and topological properties of vdW heterostructures composed of monolayer Pt$_{2}$AX$_

Gang Li, Heliang Zheng, Chaoyue Wang, Chang Li

Text-guided diffusion models have shown superior performance in image/video generation and editing. While few explorations have been performed in 3D scenarios. In this paper, we discuss three fundamental and interesting problems on this topic. First, we equip text-guided diffusion models to achieve 3D-consistent generation. Specifically, we integrate a NeRF-

Sangita Kumari, Bhaswati Bhattacharyya, Devojyoti Kansabanik, Jayanta Roy

Results from 11 years of radio timing for eclipsing black widow millisecond pulsar (MSP) binary, J1544+4937, is presented in this paper. We report a phase-connected timing model for this MSP, using observations with the Giant Metrewave Radio Telescope (GMRT) at multiple frequencies and with Green Bank Telescope (GBT). This is the longest-duration timing stud

Nishan C. Jayarama, Viktor Svensson

Circuit complexity has been used as a tool to study various properties in condensed matter systems, in particular as a way to probe the phase diagram. However, compared with measures based on entanglement, complexity has been found lacking. We show that when imposing penalty factors punishing non-locality, it becomes a much stronger probe of the phase diagra

Marlène Careil, Stéphane Lathuilière, Camille Couprie, Jakob Verbeek

Generative image models have been extensively studied in recent years. In the unconditional setting, they model the marginal distribution from unlabelled images. To allow for more control, image synthesis can be conditioned on semantic segmentation maps that instruct the generator the position of objects in the image. While these two tasks are intimately rel

Elena Agliari, Linda Albanese, Francesco Alemanno, Andrea Alessandrelli

We consider dense, associative neural-networks trained by a teacher (i.e., with supervision) and we investigate their computational capabilities analytically, via statistical-mechanics of spin glasses, and numerically, via Monte Carlo simulations. In particular, we obtain a phase diagram summarizing their performance as a function of the control parameters s

Marta Zagorowska, Efe C. Balta, Varsha Behrunani, Alisa Rupenyan

Ensuring safety in industrial control systems usually involves imposing constraints at the design stage of the control algorithm. Enforcing constraints is challenging if the underlying functional form is unknown. The challenge can be addressed by using surrogate models, such as Gaussian processes, which provide confidence intervals used to find solutions tha

Gábor Braun, Alejandro Carderera, Cyrille W. Combettes, Hamed Hassani

The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for function minimization. These algorithms are especially useful in convex optimization when linear optimization is cheaper than projections. The selection of the material has

Mattia Walschaers

Quantum correlations and Wigner negativity are two important signatures of nonclassicality in continuous-variable quantum systems. In this work, we investigate how both are intertwined in the context of the conditional generation of Wigner negativity. It was previously shown that when Alice and Bob share a Gaussian state, Bob can perform some measurement on

János Barát, Dániel Gerbner, Anastasia Halfpap

A set $S$ of vertices in a hypergraph is \textit{strongly independent} if every hyperedge shares at most one vertex with $S$. We prove a sharp result for the number of maximal strongly independent sets in a $3$-uniform hypergraph analogous to the Moon-Moser theorem. Given an $r$-uniform hypergraph ${\mathcal H}$ and a non-empty set $A$ of non-negative intege

Xiang-Kun Dong, Feng-Kun Guo, Alexey Nefediev, Jaume Tarrús Castellà

We compute the chromopolarizabilities of the fully heavy baryons $\Omega_{QQQ'}$ ($Q,Q'=b,c$) in the framework of potential nonrelativistic quantum chromodynamics. At leading order, the fully heavy hadrons are considered as ground chromo-Coulombic bound states. We find that the chromopolarizability $\beta_\Omega$ of a fully heavy baryon $QQQ$ is 2.6 times th

Vasileios Sitokonstantinou, Alkiviadis Koukos, Ilias Tsoumas, Nikolaos S. Bartsotas

Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at

Amol Salunkhe, Georgios Georgalis, Abani Patra, Varun Chandola

Complete computation of turbulent combustion flow involves two separate steps: mapping reaction kinetics to low-dimensional manifolds and looking-up this approximate manifold during CFD run-time to estimate the thermo-chemical state variables. In our previous work, we showed that using a deep architecture to learn the two steps jointly, instead of separately

Lorenzo Cappello, Oscar Hernan Madrid Padilla

This paper introduces a novel Bayesian approach to detect changes in the variance of a Gaussian sequence model, focusing on quantifying the uncertainty in the change point locations and providing a scalable algorithm for inference. Such a measure of uncertainty is necessary when change point methods are deployed in sensitive applications, for example, when o

Huy-Dung Nguyen, Michaël Clément, Vincent Planche, Boris Mansencal

Alzheimer's disease and Frontotemporal dementia are common forms of neurodegenerative dementia. Behavioral alterations and cognitive impairments are found in the clinical courses of both diseases and their differential diagnosis is sometimes difficult for physicians. Therefore, an accurate tool dedicated to this diagnostic challenge can be valuable in clinic

Dimitris Panagopoulos, Giannis Petousakis, Aniketh Ramesh, Tianshu Ruan

This paper presents a Mixed-Initiative (MI) framework for addressing the problem of control authority transfer between a remote human operator and an AI agent when cooperatively controlling a mobile robot. Our Hierarchical Expert-guided Mixed-Initiative Control Switcher (HierEMICS) leverages information on the human operator's state and intent. The control s

J. Rausch, V. Guzey, M. Klasen

We numerically study for the first time the nonlinear GLR-MQ evolution equations for nuclear parton distribution function (nPDFs) to next-to-leading order accuracy and quantify the impact of gluon recombination at small $x$. Using the nCTEQ15 nPDFs as input, we confirm the importance of the nonlinear corrections for small $x \lesssim 10^{-3}$, whose magnitud

Nishu Kumari

Fix an integer $t \geq 2$ and a primitive $t^{\text{th}}$ root of unity $\omega$. We consider the specialized skew hook Schur polynomial $\text{hs}_{\lambda/\mu}(X,\omega X,\dots,\omega^{t-1}X/Y,\omega Y,\dots,\omega^{t-1}Y)$, where $\omega^k X=(\omega^k x_1, \dots, \omega^k x_n)$, $\omega^k Y=(\omega^k y_1, \dots, \omega^k y_m)$ for $0 \leq k \leq t-1$. We

Stefan Duling, Joachim Saur, George Clark, Frederic Allegrini

On June 7th, 2021 the Juno spacecraft visited Ganymede and provided the first in situ observations since Galileo's last flyby in 2000. The measurements obtained along a one-dimensional trajectory can be brought into global context with the help of three-dimensional magnetospheric models. Here we apply the magnetohydrodynamic model of Duling et al. (2014) to

Junbo Zhang, Guofan Fan, Guanghan Wang, Zhengyuan Su

Learning descriptive 3D features is crucial for understanding 3D scenes with diverse objects and complex structures. However, it is usually unknown whether important geometric attributes and scene context obtain enough emphasis in an end-to-end trained 3D scene understanding network. To guide 3D feature learning toward important geometric attributes and scen

Miaoyu Li, Ying Fu, Yulun Zhang

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications. Unfortunately, though witnessing the development of deep learning in HSI denoising area, existing convolution-based methods face the trade-off between computational efficiency and capability to model non-local characteristics of HSI. In this paper, w

Alessandro Nobile

Let X be a projective, equidimensional, singular scheme over an algebraically closed field. Then the existence of a geometric smoothing (i.e. a family of deformations of X over a smooth base curve whose generic fibre is smooth) implies the existence of a formal smoothing as defined by Tziolas. In this paper we address the reverse question giving sufficient c

Muhammad Zaid Hameed, Beat Buesser

Standard adversarial training approaches suffer from robust overfitting where the robust accuracy decreases when models are adversarially trained for too long. The origin of this problem is still unclear and conflicting explanations have been reported, i.e., memorization effects induced by large loss data or because of small loss data and growing differences

Jian-Tong Hou, Peng Wang, Hong Guo, Hao-Ran Chang

The unified form of longitudinal optical conductivities (LOCs) in the tilted Weyl fermions for arbitrary spatial dimensionality are analytically calculated and expressed in terms of the joint density of state. The results are valid for both undoped and doped cases, both parallel and perpendicular components, and all the tilted phases. In addition, they repro

Marco van Hulten, Christoph Heinze, Jörg Schwinger, Jerry Tjiputra

In this report we set forth a simulation method for long-term simulations of NorESM, the Norwegian Earth System Model. In this the sediment is repeatedly decoupled and coupled to the ocean model (BLOM/iHAMOCC), a process called burst coupling. Through this, the ocean (seawater and sediment) is brought into an approximate steady state. We show that just the m

Jingwang Ling, Zhibo Wang, Feng Xu

By supervising camera rays between a scene and multi-view image planes, NeRF reconstructs a neural scene representation for the task of novel view synthesis. On the other hand, shadow rays between the light source and the scene have yet to be considered. Therefore, we propose a novel shadow ray supervision scheme that optimizes both the samples along the ray

Bojan Žunkovič

Positive unlabeled learning is a binary classification problem with positive and unlabeled data. It is common in domains where negative labels are costly or impossible to obtain, e.g., medicine and personalized advertising. Most approaches to positive unlabeled learning apply to specific data types (e.g., images, categorical data) and can not generate new po

Maciej Jankowski, Valentina Belova, Yuriy Chushkin, Federico Zontone

The Complex System and Biomedical Sciences (CBS) group at the European Synchrotron Radiation Facility (ESRF) in Grenoble is dedicated to the study of a broad family of materials and systems, including soft and hard condensed matter, nanomaterials, and biological materials. The main experimental methods used for this purpose are X-ray diffraction, reflectivit

Paul Mücksch

We study the combinatorics of modular flats of oriented matroids and the topological consequences for their Salvetti complexes. We show that the natural map to the localized Salvetti complex at a modular flat of corank one is what we call a poset quasi-fibration -- a notion derived from Quillen's fundamental Theorem B from algebraic $K$-theory. As a direct c

Benedikt M. Plank, Kevin Schewior

We revisit the Stochastic Score Classification (SSC) problem introduced by Gkenosis et al. (ESA 2018): We are given $n$ tests. Each test $j$ can be conducted at cost $c_j$, and it succeeds independently with probability $p_j$. Further, a partition of the (integer) interval $\{0,\dots,n\}$ into $B$ smaller intervals is known. The goal is to conduct tests so a

Mark Roelands, Christopher Michael Schwanke

Using the notion of order convergent nets, we develop an order-theoretic approach to differentiable functions on Archimedean complex $\Phi$-algebras. Most notably, we improve the Cauchy-Hadamard formulas for universally complete complex vector lattices given by both authors in a previous paper in order to prove that analytic functions are holomorphic in this

Patrik Schönfeldt, Sunke Schlüters, Keno Oltmanns

MTRESS is a tool that facilitates the creation of models of residential energy supply systems by providing a template with meaningful presets. This model can then be used to linearly optimise the operation of the energy system. Version~3.0 enables multiple locations belonging to one energy system to be defined. Furthermore, it adds hydrogen as an energy carr

Edouard Duriez, Miguel Charlotte, Catherine Azzaro-Pantel, Joseph Morlier

Selecting the optimal material for a part designed through topology optimization is a complex problem. The shape and properties of the Pareto front plays an important role in this selection. In this paper we show that the compliance-volume fraction Pareto fronts of some topology optimization problems in linear elasticity share some useful properties. These p

Hannes Mareen, Dante Vanden Bussche, Glenn Van Wallendael, Luisa Verdoliva

Manipulated images are a threat to consumers worldwide, when they are used to spread disinformation. Therefore, Comprint enables forgery detection by utilizing JPEG-compression fingerprints. This paper evaluates the impact of the training set on Comprint's performance. Most interestingly, we found that including images compressed with low quality factors dur

Ivan Silajev, Niels Victor, Phillip Mortimer

This paper presents an application of the LayoutLMv3 model for semantic table detection on financial documents from the IIIT-AR-13K dataset. The motivation behind this paper's experiment was that LayoutLMv3's official paper had no results for table detection using semantic information. We concluded that our approach did not improve the model's table detectio

Sarang Kahvazadeh, Hamzeh Khalili, Rasoul Nikbakht Silab, Bahador Bakhshi

5G realizes an impactful convergence, where Network Functions Virtualization (NFV) and cloud-native models become fundamental for profiting from the unprecedented capacity offered at the 5G Radio Access Network (RAN). For providing scalability and automation management over resources in 5G infrastructure, cloud-native and Platform as a service (PaaS) are pro

Miguel Angel Pérez-Cutiño, Juan Sebastián Valverde, José Miguel Díaz-Báñez

Concentrated solar power (CSP) is one of the growing technologies that is leading the process of changing from fossil fuels to renewable energies. The sophistication and size of the systems require an increase in maintenance tasks to ensure reliability, availability, maintainability and safety. Currently, automatic fault detection in CSP plants using Parabol

Léo Poirier, Wolfgang Steiner

A set of words, also called a language, is letter-balanced if the number of occurrences of each letter only depends on the length of the word, up to a constant. Similarly, a language is factor-balanced if the difference of the number of occurrences of any given factor in words of the same length is bounded. The most prominent example of a letter-balanced but

Linda Boudjemila, Alexander Bobyl, Vadim Davydov, Vladislav Malyshkin

A new type of moving average is developed. Whereas a regular moving average (e.g. of price) has a built-in internal time scale (time-window, exponential weight, etc.), the moving average developed in this paper has the weight as the product of a polynomial by window factor. The polynomial is the square of a wavefunction obtained from an eigenproblem correspo

Liang Zeng, Attila Lengyel, Nergis Tömen, Jan van Gemert

In this work, we leverage estimated depth to boost self-supervised contrastive learning for segmentation of urban scenes, where unlabeled videos are readily available for training self-supervised depth estimation. We argue that the semantics of a coherent group of pixels in 3D space is self-contained and invariant to the contexts in which they appear. We gro

Nathan Morsa

Electronic shot counters allow armourers to perform preventive and predictive maintenance based on quantitative measurements, improving reliability, reducing the frequency of accidents, and reducing maintenance costs. To answer a market pressure for both low lead time to market and increased customisation, we aim to solve the shot detection and shot counting

Tongqi Wen, Anwen Liu, Rui Wang, Linfeng Zhang

Ti exhibits complex plastic deformation controlled by active dislocation and twinning systems. Understandings on dislocation cores and twin interfaces are currently not complete or quantitative, despite extensive experimental and simulation studies. Here, we determine all the core and twin interface properties in both HCP and BCC Ti using a Deep Potential (D

Dmytro Fedorchenko, Shlomi Alani

We used Monte Carlo simulations to study release of radium-224 daughter nuclei from the seed used for Diffusing Alpha-Emitters Radiation Therapy (DART). Calculated desorption probabilities for polonium-216 (15%) and lead-212 (12%) show that they make a significant contribution to total release from the seed. We also showed that the dose to tissue from decays

Anirban Mukherjee, Punyabrata Pradhan

We study dynamic correlations for current and mass, as well as the associated power spectra, in the one-dimensional conserved Manna sandpile. We show that, in the thermodynamic limit, the variance of cumulative bond current up to time $T$ grows subdiffusively as $T^{1/2-\mu}$ with the exponent $\mu \ge 0$ depending on the density regimes considered and, like

Alexei Vazquez

The shape of empirical distributions with heavy tails is a recurrent matter of debate. There are claims of a power laws and the associated scale invariance. There are plenty of challengers as well, the lognormal and stretched exponential among others. Here I point out that, with regard to summation invariance, all what matters is they are subexponential dist

Zhian Liu, Maomao Li, Yong Zhang, Cairong Wang

We present a novel paradigm for high-fidelity face swapping that faithfully preserves the desired subtle geometry and texture details. We rethink face swapping from the perspective of fine-grained face editing, \textit{i.e., ``editing for swapping'' (E4S)}, and propose a framework that is based on the explicit disentanglement of the shape and texture of faci

Elena Agliari, Linda Albanese, Francesco Alemanno, Andrea Alessandrelli

We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, and numerically, via Monte Carlo simulations. In particular, we obtain a phase diagram summarizing their performance as a function of the control parameters such as the quality and

Marek Tyburec, Michal Kočvara, Martin Kružík

Weight optimization of frame structures with continuous cross-section parametrization is a challenging non-convex problem that has traditionally been solved by local optimization techniques. Here, we exploit its inherent semi-algebraic structure and adopt the Lasserre hierarchy of relaxations to compute the global minimizers. While this hierarchy generates a

Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent CS Lee

Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attr

Mazen Ali, Matthias Kabel

Recent advances in quantum computing and their increased availability has led to a growing interest in possible applications. Among those is the solution of partial differential equations (PDEs) for, e.g., material or flow simulation. Currently, the most promising route to useful deployment of quantum processors in the short to near term are so-called hybrid

A. G. Pazukhin, I. I. Zinchenko, E. A. Trofimova, C. Henkel

With the 30-m IRAM radio telescope, we observed several massive star forming regions at wavelengths of 3-4 and 2 mm. The temperature of the gas in the sources was estimated from the lines of CH$_{3}$CCH and from the transitions of the NH$_3$ molecule obtained during observations at the 100-m radio telescope in Effelsberg. As a result, a correlation between t

Florimond Houssiau, Vincent Schellekens, Antoine Chatalic, Shreyas Kumar Annamraju

Differential privacy is the standard privacy definition for performing analyses over sensitive data. Yet, its privacy budget bounds the number of tasks an analyst can perform with reasonable accuracy, which makes it challenging to deploy in practice. This can be alleviated by private sketching, where the dataset is compressed into a single noisy sketch vecto

Marco Loog, Tom Viering

Plotting a learner's generalization performance against the training set size results in a so-called learning curve. This tool, providing insight in the behavior of the learner, is also practically valuable for model selection, predicting the effect of more training data, and reducing the computational complexity of training. We set out to make the (ideal) l

Hajar Asgari, Nicoletta Risi, Giacomo Indiveri

Artificial vision systems of autonomous agents face very difficult challenges, as their vision sensors are required to transmit vast amounts of information to the processing stages, and to process it in real-time. One first approach to reduce data transmission is to use event-based vision sensors, whose pixels produce events only when there are changes in th

Massimiliano Alessandro, Christian Gleissner, Julia Kotonski

A semi-projective representation is a homomorphism of a finite group into the group of semi-projective transformations of a finite dimensional vector space over a field. Schur's concept of a representation group for projective representations is extended to semi-projective representations under the assumption that the field is algebraically closed. A compute

Kyungmoon Lee, Sungyeon Kim, Suha Kwak

Domain generalization is the task of learning models that generalize to unseen target domains. We propose a simple yet effective method for domain generalization, named cross-domain ensemble distillation (XDED), that learns domain-invariant features while encouraging the model to converge to flat minima, which recently turned out to be a sufficient condition

C. A. Jothishwaran, Abhishek Chakraborty, Vishvendra Singh Poonia, Pantelimon Stanica

We propose a quantum algorithm (in the form of a quantum oracle) that estimates the closeness of a given Boolean function to one that satisfies the ``strict avalanche criterion'' (SAC). This algorithm requires $n$ queries of the Boolean function oracle, where $n$ is the number of input variables, this is fewer than the queries required by the classical algor

Elia Bruè, Michele Coti Zelati, Elio Marconi

Let $H\in C^1\cap W^{2,p}$ be an autonomous, non-constant Hamiltonian on a compact $2$-dimensional manifold, generating an incompressible velocity field $b=\nabla^\perp H$. We give sharp upper bounds on the enhanced dissipation rate of $b$ in terms of the properties of the period $T(h)$ of the close orbits $\{H=h\}$. Specifically, if $0<\nu\ll 1$ is the diff

Stefano Carrazza, Juan M. Cruz-Martinez, Gabriele Palazzo

In this proceedings we demonstrate some advantages of a top-bottom approach in the development of hardware-accelerated code. We start with an autogenerated hardware-agnostic Monte Carlo generator, which is parallelized in the event axis. This allow us to take advantage of the parallelizable nature of Monte Carlo integrals even if we don't have control of the

Yue Wu, Sivan Yakov, Afu Fu, Gilad Yossifon

Electrically powered micro- and nanomotors are promising tools for in-vitro single-cell analysis. In particular, single cells can be trapped, transported and electroporated by a Janus particle (JP) using an externally applied electric field. However, while dielectrophoretic (DEP)-based cargo manipulation can be achieved at high-solution conductivity, electri

Steven Moonen, Bram Vanherle, Joris de Hoog, Taoufik Bourgana

The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of performance and robustness. However, a main drawback is that they require sufficiently large and labeled train

Chen Zhao, Shuming Liu, Karttikeya Mangalam, Bernard Ghanem

Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content. Given limited GPU memory, training TAL end to end (i.e., from videos to predictions) on long videos is a significant challenge. Most methods can only train on pre-extracted features without optimizing them for the localization problem,

Zaiyu Huang, Hanhui Li, Zhenyu Xie, Michael Kampffmeyer

In this paper, we target image-based person-to-person virtual try-on in the presence of diverse poses and large viewpoint variations. Existing methods are restricted in this setting as they estimate garment warping flows mainly based on 2D poses and appearance, which omits the geometric prior of the 3D human body shape. Moreover, current garment warping meth

Jianning Li, André Ferreira, Behrus Puladi, Victor Alves

We present a deep learning-based approach for skull reconstruction for MONAI, which has been pre-trained on the MUG500+ skull dataset. The implementation follows the MONAI contribution guidelines, hence, it can be easily tried out and used, and extended by MONAI users. The primary goal of this paper lies in the investigation of open-sourcing codes and pre-tr

Tianqi Yang, Nantheera Anantrasirichai, Oktay Karakuş, Marco Allinovi

Studies have proved that the number of B-lines in lung ultrasound images has a strong statistical link to the amount of extravascular lung water, which is significant for hemodialysis treatment. Manual inspection of B-lines requires experts and is time-consuming, whilst modelling automation methods is currently problematic because of a lack of ground truth.

Jiawei Shao, Xinjie Zhang, Jun Zhang

With the development of artificial intelligence (AI) techniques and the increasing popularity of camera-equipped devices, many edge video analytics applications are emerging, calling for the deployment of computation-intensive AI models at the network edge. Edge inference is a promising solution to move the computation-intensive workloads from low-end device

P. Sarkanych, M. Krasnytska

Different models are proposed to understand magnetic phase transitions through the prism of competition between the energy and the entropy. One of such models is a $q$-state Potts model with invisible states. This model introduces $r$ invisible states such that if a spin lies in one of them, it does not interact with the rest states. We consider such a model

Ting Lin, Zuowei Shen, Qianxiao Li

We study the approximation of shift-invariant or equivariant functions by deep fully convolutional networks from the dynamical systems perspective. We prove that deep residual fully convolutional networks and their continuous-layer counterpart can achieve universal approximation of these symmetric functions at constant channel width. Moreover, we show that t

Benjamin Hinrichs, Oliver Matte

We consider the renormalized relativistic Nelson model in two spatial dimensions for a finite number of spinless, relativistic quantum mechanical matter particles in interaction with a massive scalar quantized radiation field. We find a Feynman-Kac formula for the corresponding semigroup and discuss some implications such as ergodicity and weighted $L^p$ to

Leonardo Bacciottini, Luciano Lenzini, Enzo Mingozzi, Giuseppe Anastasi

The primary task of a quantum repeater network is to deliver entanglement among end nodes. Most of existing entanglement distribution protocols do not consider purification, which is thus delegated to an upper layer. This is a major drawback since, once an end-to-end entangled connection (or a portion thereof) is established it cannot be purified if its fide

David Afonso Delgado, Tanausú del Pino Alemán, Javier Trujillo Bueno

The Mg II h and k lines are among the strongest in the near-ultraviolet solar spectrum and their line core originates in the upper chromosphere, just below the transition region. Consequently, they have become one of the main targets for investigating the magnetism of the upper solar atmosphere. The recent CLASP2 mission obtained unprecedented spectropolarim