July 2022 arXiv papers — page 140

Georgios Koukis

In this work we discuss the utilization of micro-satellite constellations as effective infrastructures for the communication among ground stations or even among 'smart' devices in IoT scenarios. We design and implement a series of experiments in OMNeT++ (with the OS3 framework) and evaluate their results in different scenarios. Initially, we establish the ne

Jie Zhang, Yihui Zhao, Fergus Shone, Zhenhong Li

Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moment) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction betwee

Yue Qin, Xiaojing Liao

Cybersecurity vulnerability information is often recorded by multiple channels, including government vulnerability repositories, individual-maintained vulnerability-gathering platforms, or vulnerability-disclosure email lists and forums. Integrating vulnerability information from different channels enables comprehensive threat assessment and quick deployment

Tsung-Lin Cheng, Chin-Yuan Hu

In this paper, we propose a new proof of the Jensen formula in 1895. We also derive some formulas similar to those in Pitman and Yor, 2003. Besides, a new formula of the generalized Bernoulli function is also derived. At the end of the paper, the probability density functions of sinh and tanh are studied briefly for general cases.

M. Viviani, L. Girlanda, A. Kievsky, D. Logoteta

We present a theoretical study of the processes d(d,p)3H and d(d,n)3He at energies of interest for energy production and for big-bang nucleosynthesis. We accurately solve the four body scattering problem using the ab-initio hyperspherical harmonic method, starting from nuclear Hamiltonians which include modern two- and three-nucleon interactions, derived in

Vincent Billault, Jerome Bourderionnet, Luc Leviandier, Patrick Feneyrou

Multimode receivers based on spatial or modal diversity are promising architectures to mitigate in real time the atmospheric turbulence effects for free space optical (FSO) communications. In this paper, we evaluate and comment on the dynamical communication performances of a FSO mode diversity receiver, based on a spatial demultiplexer and a silicon photoni

Thibault Vieu, Brian Reville, Felix Aharonian

We critically assess limits on the maximum energy of protons accelerated within superbubbles around massive stellar clusters, considering a number of different scenarios. In particular, we derive under which circumstances acceleration of protons above peta-electronvolt (PeV) energies can be expected. While the external forward shock of the superbubble may ac

Yu Kawano, Michele Cucuzzella, Shuai Feng, Jacquelien M. A. Scherpen

Motivated by current sharing in power networks, we consider a class of output consensus (also called agreement) problems for nonlinear systems, where the consensus value is determined by external disturbances, e.g., power demand. This output consensus problem is solved by a simple distributed output feedback controller if a system is either Krasovskii or shi

Yafet Sanchez Sanchez, Elmar Schrohe

Ground states are a well-known class of Hadamard states in smooth spacetimes. In this paper we show that the ground state of the Klein-Gordon field in a non-smooth ultrastatic spacetime is an adiabatic state. The order of the state depends linearly on the regularity of the metric. We obtain the result by combining microlocal estimates for the causal propagat

Filippo Dell'Oro, Vittorino Pata

The purpose of this work is to produce a family of equations describing the evolution of the temperature in a rigid heat conductor. This is obtained by means of successive approximations of the Fourier law, via memory relaxations and integral perturbations.

T. Chang, I. Holzman, T. Cohen, B. C. Johnson

Superconducting flux qubits are promising candidates for the physical realization of a scalable quantum processor. Indeed, these circuits may have both a small decoherence rate and a large anharmonicity. These properties enable the application of fast quantum gates with high fidelity and reduce scaling limitations due to frequency crowding. The major difficu

Jun Rao, Liang Ding, Shuhan Qi, Meng Fang

Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major drawback: the ever-increasing size of VLP models restricts its deployment to real-world search scenarios (where the high latency is unacceptable). To alleviate this problem, we pres

Qingguo Hong, Limin Ma, Jinchao Xu

In this paper, we propose a new finite element approach to simulate the time-dependent Ginzburg-Landau equations under the temporal gauge, and design an efficient preconditioner for the Newton iteration of the resulting discrete system. The new approach solves the magnetic potential in H(curl) space by the lowest order of the second kind Nedelec element. Thi

Yang Li, Shixin Zhu, Pi Li

The Galois hulls of linear codes are a generalization of the Euclidean and Hermitian hulls of linear codes. In this paper, we study the Galois hulls of (extended) GRS codes and present several new constructions of MDS codes with Galois hulls of arbitrary dimensions via (extended) GRS codes. Two general methods of constructing MDS codes with Galois hulls of a

A. Maiellaro, F. Romeo, F. Illuminati

We study the properties of one-dimensional topological superconductors under the influence of generic boundary conditions mimicking the coupling with external environments. We identify a general four-parameters classification of the boundary effects and show that particle-hole and reflection symmetries can be broken or preserved by appropriately fixing the b

Dongfei Wang, De-Liang Bao, Qi Zheng, Chang-Tian Wang

Stacking two-dimensional layered materials such as graphene and transitional metal dichalcogenides with nonzero interlayer twist angles has recently become attractive because of the emergence of novel physical properties. Stacking of one-dimensional nanomaterials offers the lateral stacking offset as an additional parameter for modulating the resulting mater

Mattia Cafasso, Giulio Ruzza

We study the finite-temperature deformation of the discrete Bessel point process. We show that its largest particle distribution satisfies a reduction of the 2D Toda equation, as well as a discrete version of the integro-differential Painlev\'e II equation of Amir-Corwin-Quastel, and we compute initial conditions for the Poissonization parameter equal to 0.

Gianluigi Lopardo, Damien Garreau

Complex machine learning algorithms are used more and more often in critical tasks involving text data, leading to the development of interpretability methods. Among local methods, two families have emerged: those computing importance scores for each feature and those extracting simple logical rules. In this paper we show that using different methods can lea

Yuqi Wang, Zhiqiang He, Shenghui Huang, Huabin Du

Intestinal parasitic infections, as a leading causes of morbidity worldwide, still lacks time-saving, high-sensitivity and user-friendly examination method. The development of deep learning technique reveals its broad application potential in biological image. In this paper, we apply several object detectors such as YOLOv5 and variant cascadeRCNNs to automat

Yuki Shirai, Xuan Lin, Alexander Schperberg, Yusuke Tanaka

While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet. We present an efficient motion planning framework for simultaneously solving locomotion (e.g., centroidal dynamics), grasping (e.g., patch contact), and contact (e.g., gait) problems. To acce

Jing Wang, Jiangyun Li, Wei Li, Lingfei Xuan

The contextual information is critical for various computer vision tasks, previous works commonly design plug-and-play modules and structural losses to effectively extract and aggregate the global context. These methods utilize fine-label to optimize the model but ignore that fine-trained features are also precious training resources, which can introduce pre

Adolfo O. Fumega, Jose L. Lado

Layered van der Waals materials have risen as a powerful platform to engineer artificial competing states of matter. Here we show the emergence of multiferroic order in twisted chromium trihalide bilayers, an order fully driven by the moir\'e pattern and absent in aligned multilayers. Using a combination of spin models and ab initio calculations, we show tha

Nived Vilangot Nhalil, Juie Shetye, J. Gerry Doyle

We show signatures of spicules termed Rapid Blue-shifted Excursions (RBEs) in the Si iv 1394 {\AA} emission line using a semi-automated detection approach. We use the H{\alpha} filtergrams obtained by the CRISP imaging spectropolarimeter on the Swedish 1-m Solar Telescope and co-aligned Interface Region Imaging Spectrograph data using the SJI 1400 {\AA} chan

Kai Ploeger, Jan Peters

Dynamic movements are ubiquitous in human motor behavior as they tend to be more efficient and can solve a broader range of skill domains than their quasi-static counterparts. For decades, robotic juggling tasks have been among the most frequently studied dynamic manipulation problems since the required dynamic dexterity can be scaled to arbitrarily high dif

Erik Härkönen, Miika Aittala, Tuomas Kynkäänniemi, Samuli Laine

Time-lapse image sequences offer visually compelling insights into dynamic processes that are too slow to observe in real time. However, playing a long time-lapse sequence back as a video often results in distracting flicker due to random effects, such as weather, as well as cyclic effects, such as the day-night cycle. We introduce the problem of disentangli

Zhongxiang Chang, Zhongbao Zhou

The asynchronous development between the observation capability and the transition capability results in that an original image data (OID) formed by one-time observation cannot be completely transmitted in one transmit chance between the EOS and GS (named as a visible time window, VTW). It needs to segment the OID to several segmented image data (SID) and th

Haofeng Yuan, Peng Jiang, Shiji Song

Set covering problem is an important class of combinatorial optimization problems, which has been widely applied and studied in many fields. In this paper, we propose an improved column generation algorithm with neural prediction (CG-P) for solving graph-based set covering problems. We leverage a graph neural network based neural prediction model to predict

Loïc Bidoux, Pierre Briaud, Maxime Bros, Philippe Gaborit

We propose two main contributions: first, we revisit the encryption scheme Rank Quasi-Cyclic (RQC) by introducing new efficient variations, in particular, a new class of codes, the Augmented Gabidulin codes; second, we propose new attacks against the Rank Support Learning (RSL), the Non-Homogeneous Rank Decoding (NHRSD), and the Non-Homogeneous Rank Support

Jil Klünder, Oliver Karras

Background: Teamwork, coordination, and communication are a prerequisite for the timely completion of a software project. Meetings as a facilitator for coordination and communication are an established medium for information exchange. Analyses of meetings in software projects have shown that certain interactions in these meetings, such as proactive statement

Clodoaldo Grotta-Ragazzo

The two papers in the title contain some equations that are not complete. The missing terms, which are of topological origin, were recently unveiled by Bjorn Gustafsson. In this note we present the equations of Gustafsson in the case of a single vortex in a compact boundaryless surface, and show that many conclusions we have reached with the incomplete equat

Rubén Izquierdo, Álvaro Quintanar, David Fernández Llorca, Iván García Daza

This work presents a novel method for predicting vehicle trajectories in highway scenarios using efficient bird's eye view representations and convolutional neural networks. Vehicle positions, motion histories, road configuration, and vehicle interactions are easily included in the prediction model using basic visual representations. The U-net model has been

Björn Lindqvist, Sina Sharif Mansouri, Jakub Haluška, George Nikolakopoulos

In this article we propose a reactive constrained navigation scheme, with embedded obstacles avoidance for an Unmanned Aerial Vehicle (UAV), for enabling navigation in obstacle-dense environments. The proposed navigation architecture is based on Nonlinear Model Predictive Control (NMPC), and utilizes an on-board 2D LiDAR to detect obstacles and translate onl

Zhikai Li, Qingyi Gu

Vision Transformers (ViTs) have achieved state-of-the-art performance on various computer vision applications. However, these models have considerable storage and computational overheads, making their deployment and efficient inference on edge devices challenging. Quantization is a promising approach to reducing model complexity, and the dyadic arithmetic pi

Ling Gao, Yuxuan Liang, Jiaqi Yang, Shaoxun Wu

Event cameras have recently gained in popularity as they hold strong potential to complement regular cameras in situations of high dynamics or challenging illumination. An important problem that may benefit from the addition of an event camera is given by Simultaneous Localization And Mapping (SLAM). However, in order to ensure progress on event-inclusive mu

Yuchen Guo, Shuo Yang

Quantum decoherence due to imperfect manipulation of quantum devices is a key issue in the noisy intermediate-scale quantum (NISQ) era. Standard analyses in quantum information and quantum computation use error rates to parameterize quantum noise channels. However, there is no explicit relation between the decoherence effect induced by a noise channel and it

Elvys Linhares Pontes, Mohamed Benjannet, Jose G. Moreno, Antoine Doucet

This paper summarizes the joint participation of the Trading Central Labs and the L3i laboratory of the University of La Rochelle on both sub-tasks of the Shared Task FinSim-4 evaluation campaign. The first sub-task aims to enrich the 'Fortia ESG taxonomy' with new lexicon entries while the second one aims to classify sentences to either 'sustainable' or 'un

Daniel Etiemble

In Carry Propagate Adders, carry propagation is the critical delay. For the 1-digit adders that they use, the most efficient scheme is to generate two intermediate carries: C$_{out0}$ ($C_{in}$=0) and $C_{out1}$($C_{in}$=1). Then multiplex them to produce the correct output according to $C_{in}$. For any radix, the carry output has always a logical value 0 o

Ignacio Erazo, David Goldsman, Yajun Mei

We study properties of confidence intervals (CIs) for the difference of two Bernoulli distributions' success parameters, $p_x - p_y$, in the case where the goal is to obtain a CI of a given half-width while minimizing sampling costs when the observation costs may be different between the two distributions. Assuming that we are provided with preliminary estim

Katie Marsden

We study the energy-critical nonlinear Schr\"{o}dinger equation with randomised initial data in dimensions $d>6$. We prove that the Cauchy problem is almost surely globally well-posed with scattering for randomised super-critical initial data in $H^s(\mathbb{R}^d)$ whenever $s>\max\{\frac{4d-1}{3(2d-1)},\frac{d^2+6d-4}{(2d-1)(d+2)}\}$. The randomisation is b

Madeline Chantry Schiappa, Naman Biyani, Prudvi Kamtam, Shruti Vyas

We have seen a great progress in video action recognition in recent years. There are several models based on convolutional neural network (CNN) and some recent transformer based approaches which provide top performance on existing benchmarks. In this work, we perform a large-scale robustness analysis of these existing models for video action recognition. We

Fatimah Al Saleh, Tigran Bakaryan, Diogo A. Gomes, Ricardo Ribeiro

Here, we examine the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary mean-field games (MFG). In the first part of this paper, we present the Wardrop and the first-order MFG models on networks. Then, we show how to reformulate the MFG problem into a Wardrop problem and prove that the MFG solution is the Wardr

Yaguan Qian, Yuqi Wang, Bin Wang, Zhaoquan Gu

Recent studies show deep neural networks (DNNs) are extremely vulnerable to the elaborately designed adversarial examples. Adversarial learning with those adversarial examples has been proved as one of the most effective methods to defend against such an attack. At present, most existing adversarial examples generation methods are based on first-order gradie

Namwoo Lee, Hyunsu Kim, Gayoung Lee, Sungjoo Yoo

Recent studies have shown remarkable progress in GANs based on implicit neural representation (INR) - an MLP that produces an RGB value given its (x, y) coordinate. They represent an image as a continuous version of the underlying 2D signal instead of a 2D array of pixels, which opens new horizons for GAN applications (e.g., zero-shot super-resolution, image

Geon Park, Jaehong Yoon, Haiyang Zhang, Xing Zhang

Neural network quantization aims to transform high-precision weights and activations of a given neural network into low-precision weights/activations for reduced memory usage and computation, while preserving the performance of the original model. However, extreme quantization (1-bit weight/1-bit activations) of compactly-designed backbone architectures (e.g

Hampus Gummesson Svensson, Esben Jannik Bjerrum, Christian Tyrchan, Ola Engkvist

Recent developments in artificial intelligence and automation support a new drug design paradigm: autonomous drug design. Under this paradigm, generative models can provide suggestions on thousands of molecules with specific properties, and automated laboratories can potentially make, test and analyze molecules with minimal human supervision. However, since

Leonardo García-Heveling, Elefterios Soultanis

We give an example of a spacetime with a continuous metric which is globally hyperbolic and exhibits causal bubbling. The metric moreover splits orthogonally into a timelike and a spacelike part. We discuss our example in the context of energy conditions and the recently introduced synthetic timelike curvature-dimension (TCD) condition. In particular we obse

Yaojia Zheng, Zhouwu Liu, Rong Mo, Ziyi Chen

Accurate automated analysis of electroencephalography (EEG) would largely help clinicians effectively monitor and diagnose patients with various brain diseases. Compared to supervised learning with labelled disease EEG data which can train a model to analyze specific diseases but would fail to monitor previously unseen statuses, anomaly detection based on on

André Ferreira, Jianning Li, Kelsey L. Pomykala, Jens Kleesiek

With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortun

Peru d'Ornellas, Ryan Barnett, Derek K. K. Lee

A central property of Chern insulators is the robustness of the topological phase and edge states to impurities in the system. Despite this, Chern number cannot be straightforwardly calculated in the presence of disorder. Recently, work has been done to propose a local analog of the Chern number, called local markers, that can be used to characterise disorde

Chunzhi Gu, Jun Yu, Chao Zhang

Generative model-based motion prediction techniques have recently realized predicting controlled human motions, such as predicting multiple upper human body motions with similar lower-body motions. However, to achieve this, the state-of-the-art methods require either subsequently learning mapping functions to seek similar motions or training the model repeti

Ruobing Xie, Zhijie Qiu, Bo Zhang, Leyu Lin

Contrastive learning (CL) has shown its power in recommendation. However, most CL-based recommendation models build their CL tasks merely focusing on the user's aspects, ignoring the rich diverse information in items. In this work, we propose a novel Multi-granularity item-based contrastive learning (MicRec) framework for the matching stage (i.e., candidate

Mariano Pascale, Georgia T. Papadakis

We introduce an analytical framework for near-field radiative heat transfer in bulk plasmonic and polar media. Considering material dispersion, we derive a closed-form expression for the radiative thermal conductance, which disentangles the role of optical loss from other material dispersion characteristics, such as the spectral width of the Reststrahlen ban

Tuomas Hytönen, Tuomas Oikari, Jaakko Sinko

Let $T$ be a non-degenerate Calder\'on-Zygmund operator and let $b:\mathbb{R}^d\to\mathbb{C}$ be locally integrable. Let $1<p\leq q<\infty$ and let $\mu^p\in A_p$ and $\lambda^q\in A_q,$ where $A_{p}$ denotes the usual class of Muckenhoupt weights. We show that \begin{align*} \|[b,T]\|_{L^p_{\mu}\to L^q_{\lambda}}\sim \|b\|_{\operatorname{BMO}_{\nu}^{\alpha}

Giacomo Como, Fabio Fagnani, Anton V. Proskurnikov

Consider discrete-time linear distributed averaging dynamics, whereby agents in a network start with uncorrelated and unbiased noisy measurements of a common underlying parameter (state of the world) and iteratively update their estimates following a non-Bayesian rule. Specifically, let every agent update her estimate to a convex combination of her own curre

Andrei Gaidash, Anton Kozubov, Alexei Kiselev, George Miroshnichenko

We introduce algebraic approach for superoperators that might be useful tool for investigation of quantum (bosonic) multi-mode systems and its dynamics. In order to demonstrate potential of proposed method we consider multi-mode Liouvillian superoperator that describes relaxation dynamics of a quantum system (including thermalization and intermode coupling).

Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha

Spiking Neural Networks (SNNs) have recently emerged as a new generation of low-power deep neural networks, which is suitable to be implemented on low-power mobile/edge devices. As such devices have limited memory storage, neural pruning on SNNs has been widely explored in recent years. Most existing SNN pruning works focus on shallow SNNs (2~6 layers), howe

Xuhong Li, Haoyi Xiong, Yi Liu, Dingfu Zhou

While fine-tuning pre-trained networks has become a popular way to train image segmentation models, such backbone networks for image segmentation are frequently pre-trained using image classification source datasets, e.g., ImageNet. Though image classification datasets could provide the backbone networks with rich visual features and discriminative ability,

Mark Baum

These are notes that I compiled while studying the equations of long-range groundwater flow for my first paper. By "long-range," I mean horizontal distances that are significantly greater than the vertical thickness of the aquifer, in addition to some other assumptions discussed below. None of this material constitutes original development of important new e

Pekka Lahti, Juha-Pekka Pellonpää

We search for a possible mathematical formulation of some of the key ideas of the relational interpretation of quantum mechanics and study their consequences. We also briefly overview some proposals of relational quantum mechanics for an axiomatic reconstruction of the Hilbert space formulation of quantum mechanics.

Alicia Nieto-Reyes

The sea surface elevations are generally stated as Gaussian processes in the literature. To show the inaccuracy of this statement, an empirical study of the buoys in the US coast at a random day is performed, which results in rejecting the null hypothesis of Gaussianity in over 80$\%$ of the cases. The analysis pursued relates to a recent one by the author i

Philippe Gaucher

A flow is a directed space structure on a homotopy type. It is already known that the underlying homotopy type of the realization of a precubical set as a flow is homotopy equivalent to the realization of the precubical set as a topological space. This realization depends on the non-canonical choice of a q-cofibrant replacement. We construct a new realizatio

Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is highly prevalent and requires clinical specialists to diagnose. It is known that an individual's viewing behavior, reflected in their eye movements, is directly related to attentional mechanisms and higher-order cognitive processes. We therefore explore whether ADHD can

SuBeen Lee, WonJun Moon, Jae-Pil Heo

Recognizing discriminative details such as eyes and beaks is important for distinguishing fine-grained classes since they have similar overall appearances. In this regard, we introduce Task Discrepancy Maximization (TDM), a simple module for fine-grained few-shot classification. Our objective is to localize the class-wise discriminative regions by highlighti

Eitan Kosman, Dotan Di Castro

We propose a concise representation of videos that encode perceptually meaningful features into graphs. With this representation, we aim to leverage the large amount of redundancies in videos and save computations. First, we construct superpixel-based graph representations of videos by considering superpixels as graph nodes and create spatial and temporal co

Sergey Alyaev, Jan Tveranger, Kristian Fossum, Ahmed H. Elsheikh

Quantitative workflows utilizing real-time data to constrain ahead-of-bit uncertainty have the potential to improve geosteering significantly. Fast updates based on real-time data are essential when drilling in complex reservoirs with high uncertainties in pre-drill models. However, practical assimilation of real-time data requires effective geological model

Andrea Pimpinella, Federico Di Giusto, Alessandro Redondi, Luisa Venturini

The dramatic growth in cellular traffic volume requires cellular network operators to develop strategies to carefully dimension and manage the available network resources. Forecasting traffic volumes is a fundamental building block for any proactive management strategy and is therefore of great interest in such a context. Differently from what found in the l

Ronan Fablet, Quentin Febvre, Bertrand Chapron

Due to the irregular space-time sampling of sea surface observations, the reconstruction of sea surface dynamics is a challenging inverse problem. While satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents, the associated sampling pattern prevents from retrie

Philippe Gaucher

This note explores the link between the q-model structure of flows and the Ilias model structure of topologically enriched small categories. Both have weak equivalences which induce equivalences of fundamental (semi)categories. The Ilias model structure cannot be left-lifted along the left adjoint adding identity maps. The minimal model structure on flows ha

Mareike Fischer, Tom Niklas Hamann, Kristina Wicke

Phylogenetic networks which are, as opposed to trees, suitable to describe processes like hybridization and horizontal gene transfer, play a substantial role in evolutionary research. However, while non-treelike events need to be taken into account, they are relatively rare, which implies that biologically relevant networks are often assumed to be similar to

Alexander Dicke, Ivan Veselic

It is shown that the restriction of a polynomial to a sphere satisfies a Logvinenko-Sereda-Kovrijkine type inequality (a specific type of uncertainty relation). This implies a spectral inequality for the Laplace-Beltrami operator, which, in turn, yields observability and null-controllability with explicit estimates on the control costs for the spherical heat

Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray

In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural Language Query (NLQ), Moment Query (MQ), Object State Change Classification (OSCC), and PNR Localization (PNR). Especially, we exploit the recently released Ego4D dataset \cite{grauman2021ego4d} to pioneer Egoc

Alexandros Kontogiannis, Scott V. Elgersma, Andrew J. Sederman, Matthew P. Juniper

We formulate and solve a generalized inverse Navier-Stokes problem for the joint velocity field reconstruction and boundary segmentation of noisy flow velocity images. To regularize the problem we use a Bayesian framework with Gaussian random fields. This allows us to estimate the uncertainties of the unknowns by approximating their posterior covariance with

David J. Prömel, David Scheffels

The existence of weak solutions is established for stochastic Volterra equations with time-inhomogeneous coefficients allowing for general kernels in the drift and convolutional or bounded kernels in the diffusion term. The presented approach is based on a newly formulated local martingale problem associated to stochastic Volterra equations.

Philippe Gaucher

The notion of reparametrization category is incorrectly axiomatized and it must be adjusted. It is proved that for a general reparametrization category $\mathcal{P}$, the tensor product of $\mathcal{P}$-spaces yields a biclosed semimonoidal structure. It is also described some kind of objectwise braiding for $\mathcal{G}$-spaces.

Tsung-Lin Cheng, Chin-Yuan Hu

In this paper, we obtain the upper and lower bounds for two inequalities related to the range statistics. The first one is concerning the one-variable case and the second one is about the bivariate case.

Martin Bladt, Clara Brimnes Gardner

In this paper we introduce a bivariate distribution on $\mathbb{R}_{+} \times \mathbb{N}$ arising from a single underlying Markov jump process. The marginal distributions are phase-type and discrete phase-type distributed, respectively, which allow for flexible behavior for modeling purposes. We show that the distribution is dense in the class of distributio

Carsten W. Scherer

We develop a novel convex parametrization of integral quadratic constraints with a terminal cost for subdifferentials of convex functions, involving general O'Shea-Zames-Falb multipliers. We show the benefit of our results for the reduction of conservatism of existing techniques, and sketch applications to the analysis of optimization algorithms or the stabi

Philip B. Stark

Risk-limiting audits (RLAs) guarantee a high probability of correcting incorrect reported outcomes before the outcomes are certified. The most efficient use ballot-level comparison, comparing the voting system's interpretation of individual ballot cards sampled at random (cast-vote records, CVRs) from a trustworthy paper trail to a human interpretation of th

Tommaso Bevilacqua, Simone Scacchi

The Virtual Element Method (VEM) is a new family of numerical methods for the approximation of partial differential equations, where the geometry of the polytopal mesh elements can be very general. The aim of this article is to extend the balancing domain decomposition by constraints (BDDC) preconditioner to the solution of the saddle-point linear system ari

Guillermo García-Pérez, Elsi-Mari Borrelli, Matea Leahy, Joonas Malmi

The rapid progress in quantum computing witnessed in recent years has sparked widespread interest in developing scalable quantum information theoretic methods to work with large quantum systems. For instance, several approaches have been proposed to bypass tomographic state reconstruction, and yet retain to a certain extent the capability to estimate multipl

Philipp Eck, Yuan Fang, Domenico Di Sante, Giorgio Sangiovanni

We present a recipe for an electronic 2D higher order topological insulator (HOTI) on the triangular lattice that can be realized in a large family of materials. The essential ingredient is mirror symmetry breaking, which allows for a finite quadrupole moment and trivial $\mathbb{Z}_2$ index. The competition between spin-orbit coupling and the symmetry break

Subhadip Bisal, Debottam Das, Swapan Majhi, Subhadip Mitra

The leading order production of an SM singlet-like scalar has primarily been realized through the gluon fusion process by mixing with the $SU(2)_L$ scalar doublet of the model. The dominant part of the physical state, i.e., the singlet component, does not have any role in its direct production. Focusing on such a state with a mass smaller than the SM-like Hi

Ayala Glick-Magid, Doron Gazit

A multipole decomposition of a cross-section is a useful tool to simplify the analysis of reactions due to their symmetry properties. By using a new approach to decompose antisymmetric tensor-type interactions within the multipole analysis, we introduce a general mathematical formalism for working with tensor couplings. This allows us to present a general te

Xiaogang Xu, Yitong Yu, Nianjuan Jiang, Jiangbo Lu

To facilitate video denoising research, we construct a compelling dataset, namely, "Practical Video Denoising Dataset" (PVDD), containing 200 noisy-clean dynamic video pairs in both sRGB and RAW format. Compared with existing datasets consisting of limited motion information, PVDD covers dynamic scenes with varying and natural motion. Different from datasets

María Lorente, Francisco J. Martín-Reyes, Israel P. Rivera-Ríos

We recall that $w\in C_{p}^{+}$ if there exist $\varepsilon>0$ and $C>0$ such that for any $a<b<c$ with $c-b<b-a$ and any measurable set $E\subset(a,b)$, the following holds \[ \int_{E}w\leq C\left(\frac{|E|}{(c-b)}\right)^{\varepsilon}\int_{\mathbb{R}}\left(M^{+}\chi_{(a,c)}\right)^{p}w<\infty. \] This condition was introduced by Riveros and de la Torre as

ATLAS Collaboration

The inclusive top-quark pair ($t\bar{t}$) production cross-section $\sigma_{t\bar{t}}$ is measured in proton-proton collisions at a centre-of-mass energy $\sqrt{s}=5.02$ TeV, using 257 pb$^{-1}$ of data collected in 2017 by the ATLAS experiment at the LHC. The $t\bar{t}$ cross-section is measured in both the dilepton and single-lepton final states of the $t\

Flaviano Della Pia, Andrea Zen, Dario Alfè, Angelos Michaelides

Ice is one of the most important and interesting molecular crystals exhibiting a rich and evolving phase diagram. Recent discoveries mean that there are now twenty distinct polymorphs; a structural diversity that arises from a delicate interplay of hydrogen bonding and van der Waals dispersion forces. This wealth of structures provides a stern test of electr

Milan Jović, Lovro Šubelj, Tea Golob, Matej Makarovič

Terrorist attacks not only harm citizens but also shift their attention, which has long-lasting impacts on public opinion and government policies. Yet measuring the changes in public attention beyond media coverage has been methodologically challenging. Here we approach this problem by starting from Wikipedia's r\'epertoire of 5.8 million articles and a samp

A. G. Grozin

A package for drawing publication-quality Feynman diagrams written in GLE is described.

Florian U. Bernlochner, Daniel C. Fry, Stephen B. Menary, Eric Persson

We investigate the asymptotic distribution of the profile likelihood ratio (PLR) when constraining effective field theories (EFTs) and show that Wilks' theorem is often violated, meaning that we should not assume the PLR to follow a $\chi^2$-distribution. We derive the correct asymptotic distributions when either one or two real EFT couplings modulate observ

Koushik Paul, Qian Kong, Xi Chen

The method of adiabatic frequency conversion, in analogy with the two level atomic system, has been put forward recently and verified experimentally to achieve robust frequency mixing processes such as sum and difference frequency generation. Here we present a comparative study of efficient frequency mixing using various techniques of shortcuts to adiabatici

Shankhadeep Mondal

Finding the optimal dual frame and optimal dual pair for signal reconstruction, which can minimize the reconstruction error when erasure occurs during data transmission, is a deep rooted problem from the perspective of frame theory. In this paper, we consider a new measurement for the error operator by taking the average of spectral radius and operator norm

Kanishka Perera, Caterina Sportelli

We study critical growth elliptic problems with jumping nonlinearities. Standard linking arguments based on decompositions of $H^1_0(\Omega)$ into eigenspaces of $- \Delta$ cannot be used to obtain nontrivial solutions to such problems. We show that the associated variational functional admits certain linking structures based on splittings of $H^1_0(\Omega)$

Luca Ballotta, Giacomo Como, Jeff S. Shamma, Luca Schenato

We investigate a novel approach to resilient distributed optimization with quadratic costs in a multi-agent system prone to unexpected events that make some agents misbehave. In contrast to commonly adopted filtering strategies, we draw inspiration from phenomena modeled through the Friedkin-Johnsen dynamics and argue that adding competition to the mix can i

Hongyan Xu, Dadong Wang, Arcot Sowmya

Coronavirus Disease 2019 (COVID-19) has spread globally and become a health crisis faced by humanity since first reported. Radiology imaging technologies such as computer tomography (CT) and chest X-ray imaging (CXR) are effective tools for diagnosing COVID-19. However, in CT and CXR images, the infected area occupies only a small part of the image. Some com

Michael Seifert, Evgeny Krüger, Michael S. Bar, Stefan Merker

We study the dielectric function of CuBr$_\mathrm{x}$I$_{1-\mathrm{x}}$ thin film alloys using spectroscopic ellipsometry in the spectral range between 0.7 eV to 6.4 eV, in combination with first-principles calculations based on density functional theory. Through the comparison of theory and experiment, we attribute features in the dielectric function to ele

Charles Paul Moore, Julien Husson, Arezki Boudaoud, Gabriel Amselem

The capture of a soft spherical particle by a rectangular slit leads to a non-monotonic pressure-flow rate relation at low Reynolds number. In the presence of the trapped particle the flow-induced deformations focus the streamlines and pressure drop to a small region. This increases the resistance to flow by several orders of magnitude as the driving pressur

Yuzhong Zhao, Yuanqiang Cai, Weijia Wu, Weiqiang Wang

Generally pre-training and long-time training computation are necessary for obtaining a good-performance text detector based on deep networks. In this paper, we present a new scene text detection network (called FANet) with a Fast convergence speed and Accurate text localization. The proposed FANet is an end-to-end text detector based on transformer feature

Stefano Villa, Christophe Blanc, Abdallah Daddi-Moussa-Ider, Antonio Stocco

Although the dynamics of colloids in the vicinity of a solid interface has been widely characterized in the past, experimental studies of Brownian diffusion close to an air-water interface are rare and limited to particle-interface gap distances larger than the particle size. At the still unexplored lower distances, the dynamics is expected to be extremely s

Bartosz Kuśmierz, Roman Overko

Rapidly growing distributed ledger technologies (DLTs) have recently received attention among researchers in both industry and academia. While a lot of existing analysis (mainly) of the Bitcoin and Ethereum networks is available, the lack of measurements for other crypto projects is observed. This article addresses questions about tokenomics and wealth distr