July 2022 arXiv papers — page 60

Ehsan Reyhanian, Benedikt Dorschner, Ilya Karlin

In this exploratory study, we apply shock-capturing schemes within the framework of the Particles on Demand kinetic model to simulate compressible flows with mild and strong shock waves and discontinuities. The model is based on the semi-Lagrangian method where the information propagates along the characteristics while a set of shock-capturing concepts such

Jonathan Yu-Meng Li, Tiantian Mao

Data-driven distributionally robust optimization is a recently emerging paradigm aimed at finding a solution that is driven by sample data but is protected against sampling errors. An increasingly popular approach, known as Wasserstein distributionally robust optimization (DRO), achieves this by applying the Wasserstein metric to construct a ball centred at

Nelson Poumaëre, Benoît Pier, Florence Raynal

We investigate the distributions of residence time for in-line chaotic mixers; in particular, we consider the Kenics, the F-mixer and the Multi-level laminating mixer, and also a synthetic model that mimics their behavior and allows exact mathematical calculations. We show that whatever the number of elements of mixer involved, the distribution possesses a $

Alessandra Cipriani, Rajat S. Hazra, Alan Rapoport, Wioletta M. Ruszel

In this paper we study the properties of the centered (norm of the) gradient squared of the discrete Gaussian free field in $U_{\epsilon}=U/\epsilon\cap \mathbb{Z}^d$, $U\subset \mathbb{R}^d$ and $d\geq 2$. The covariance structure of the field is a function of the transfer current matrix and this relates the model to a class of systems (e.g. height-one fiel

Angeles Criado-Alonso, David Aleja, Miguel Romance, Regino Criado

The search for linguistic patterns, stylometry and forensic linguistics have in the theory of complex networks, their structures and associated mathematical tools, allies with which to model and analyze texts. In this paper we present a new model supported by several mathematical structures such as the hypergraphs or the concept of derivative graph to introd

Shenghua Xu, Xinyue Cai, Bin Zhao, Li Zhang

Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark improvements over time, lane detection remains a largely unsolved problem. This is because most of the existing lane detection methods either treat the lane detection as a dense pre

Haili Jiang, Huazhong Tang, Kailiang Wu

This paper designs and analyzes positivity-preserving well-balanced (WB) central discontinuous Galerkin (CDG) schemes for the Euler equations with gravity. A distinctive feature of these schemes is that they not only are WB for a general known stationary hydrostatic solution, but also can preserve the positivity of the fluid density and pressure. The standar

Xin Lyu

An interactive mechanism is an algorithm that stores a data set and answers adaptively chosen queries to it. The mechanism is called differentially private, if any adversary cannot distinguish whether a specific individual is in the data set by interacting with the mechanism. We study composition properties of differential privacy in concurrent compositions.

Florio M. Ciaglia, Fabio Di Nocera, Jürgen Jost, Lorenz Schwachhöfer

We introduce the notion of smooth parametric model of normal positive linear functionals on possibly infinite-dimensional W*-algebras generalizing the notions of parametric models used in classical and quantum information geometry. We then use the Jordan product naturally available in this context in order to define a Riemannian metric tensor on parametric m

Tao Zhang

The travel costs of the players (travelers) in anonymous congestion games depend on their choices of routes and also on the states of the transportation network such as incidents, weather, and road work. In this extended abstract, we consider an incomplete-information environment in which the realizations of the states are unobserved by the travelers. We stu

Reid McIlroy-Young, Jon Kleinberg, Siddhartha Sen, Solon Barocas

An emerging theme in artificial intelligence research is the creation of models to simulate the decisions and behavior of specific people, in domains including game-playing, text generation, and artistic expression. These models go beyond earlier approaches in the way they are tailored to individuals, and the way they are designed for interaction rather than

Suchira Sarkar, Chanda J. Jog

We calculate ab initio the gravitational potential energy per unit area for a gravitationally coupled multi-component galactic disk of stars and gas, which is given as the integration over vertical density distribution, vertical gravitational force, and vertical distance. This is based on the method proposed by Camm for a single-component disk, which we exte

Paola Gentile, Mario Cuoco, Oleksii M. Volkov, Zu-Jian Ying

Research into electronic nanomaterials has recently seen a growing focus into the synthesis of structures with unconventional curved geometries including bent wires in planar systems and three-dimensional architectures obtained by rolling up nanomembranes. The inclusion of these geometries has led to the prediction and observation of a series of novel effect

Xiaoyu Chen, Xinyuan Zhang

We give a near-linear time sampler for the Gibbs distribution of the ferromagnetic Ising models with edge activities $\boldsymbol{\beta} > 1$ and external fields $\boldsymbol{\lambda}<1$ (or symmetrically, $\boldsymbol{\lambda}>1$) on general graphs with bounded or unbounded maximum degree. Our algorithm is based on the field dynamics given in [CFYZ21]. We p

Sandipan Das, Alireza M. Javid, Prakash Borpatra Gohain, Yonina C. Eldar

We propose a greedy algorithm to select $N$ important features among $P$ input features for a non-linear prediction problem. The features are selected one by one sequentially, in an iterative loss minimization procedure. We use neural networks as predictors in the algorithm to compute the loss and hence, we refer to our method as neural greedy pursuit (NGP).

Zhenrong Shen, Xi Ouyang, Bin Xiao, Jie-Zhi Cheng

Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR. However, it requires large-scale and diverse medical data with high-quality annotations to train such robust and accurate CADs. To alleviate the lim

Shilan Abo, Grzegorz Chimczak, Anna Kowalewska-Kudlaszyk, Jan Perina

We describe a novel type of blockade in a hybrid mode generated by linear coupling of photonic and phononic modes. We refer to this effect as hybrid photon-phonon blockade and show how it can be generated and detected in a driven nonlinear optomechanical superconducting system. Thus, we study boson-number correlations in the photon, phonon, and hybrid modes

Minsu Kim, Walid Saad, Mohammad Mozaffari, Merouane Debbah

In this paper, a green-quantized FL framework, which represents data with a finite precision level in both local training and uplink transmission, is proposed. Here, the finite precision level is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. In the considered FL model, each devic

Michael R. Cox, Hossein A. Kafiabad, Jacques Vanneste

The scattering of three-dimensional inertia-gravity waves by a turbulent geostrophic flow leads to the redistribution of their action through what is approximately a diffusion process in wavevector space. The corresponding diffusivity tensor was obtained by Kafiabad, Savva & Vanneste (2019, J. Fluid Mech., 869, R7) under the assumption of a time-independent

Yaping Chen, Kailiang Wu

This paper presents a highly robust third-order accurate finite volume weighted essentially non-oscillatory (WENO) method for special relativistic hydrodynamics on unstructured triangular meshes. We rigorously prove that the proposed method is physical-constraint-preserving (PCP), namely, always preserves the positivity of the pressure and the rest-mass dens

Hao Li, Yang Nan, Javier Del Ser, Guang Yang

Automatic segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition,

Marcin Jurek, Matthias Katzfuss

We propose an approximation to the forward-filter-backward-sampler (FFBS) algorithm for large-scale spatio-temporal smoothing. FFBS is commonly used in Bayesian statistics when working with linear Gaussian state-space models, but it requires inverting covariance matrices which have the size of the latent state vector. The computational burden associated with

Kritsana Srakaew, Pascal Weckesser, Simon Hollerith, David Wei

Enhancing light-matter coupling at the level of single quanta is essential for numerous applications in quantum science. The cooperative optical response of subwavelength atomic arrays has been found to open new pathways for such strong light-matter couplings, while simultaneously offering access to multiple spatial modes of the light field. Efficient single

Paavo Sattler, Markus Pauly

In repeated Measure Designs with multiple groups, the primary purpose is to compare different groups in various aspects. For several reasons, the number of measurements and therefore the dimension of the observation vectors can depend on the group, making the usage of existing approaches impossible. We develop an approach which can be used not only for a pos

Nicolò Crescini, Samuel Cailleaux, Wiebke Guichard, Cécile Naud

The modern primary voltage standard is based on the AC Josephson effect and the ensuing Shapiro steps, where a microwave tone applied to a Josephson junction yields a constant voltage $hf/2e$ ($h$ is Planck's constant and $e$ the electron charge) determined by only the microwave frequency $f$ and fundamental constants. Duality arguments for current and volta

Eckart Grosse, Arnd. R. Junghans

Although most nuclear spectroscopy as well as atomic hyperfine structure data do not deliver accurate information on nuclear axiality the ad-hoc assumption of symmetry about one axis found widespread use in nuclear model calculations. In the theoretical interpretation of nuclear properties as well as in the analysis of experimental data triaxiality was consi

Ranjith Krishnamurthy, Goran Piskachev, Eric Bodden

As an alternative to Java, Kotlin has gained rapid popularity since its introduction and has become the default choice for developing Android apps. However, due to its interoperability with Java, Kotlin programs may contain almost the same security vulnerabilities as their Java counterparts. Hence, we question: to what extent can one use an existing Java sta

Debobroto Das Robin, Javed I. Khan

This work presents P4TE, an in-band traffic monitoring, load-aware packet forwarding, and flow rate controlling mechanism for traffic engineering in fat-tree topology-based data center networks using PISA switches. It achieves sub-RTT reaction time to change in network conditions, improved flow completion time, and balanced link utilization. Unlike the class

Aditya Bhaskar, Mark Walth, Richard H. Rand, Alan T. Zehnder

We study the phenomenon of synchronization in pairs of doubly clamped, mechanically coupled silicon micro-oscillators. A continuous-wave laser beam is used to drive the micro-beams into limit cycle oscillations and to detect the oscillations using interferometry. Devices of different dimensions are used to introduce frequency detuning, and short silicon brid

G. Angloher, S. Banik, G. Benato, A. Bento

The CRESST experiment observes an unexplained excess of events at low energies. In the current CRESST-III data-taking campaign we are operating detector modules with different designs to narrow down the possible explanations. In this work, we show first observations of the ongoing measurement, focusing on the comparison of time, energy and temperature depend

Silvan Mertes, Christina Karle, Tobias Huber, Katharina Weitz

Explanation mechanisms from the field of Counterfactual Thinking are a widely-used paradigm for Explainable Artificial Intelligence (XAI), as they follow a natural way of reasoning that humans are familiar with. However, all common approaches from this field are based on communicating information about features or characteristics that are especially importan

Tenggan Zhang, Chuanhe Liu, Xiaolong Liu, Yuchen Liu

This paper presents our system for the Multi-Task Learning (MTL) Challenge in the 4th Affective Behavior Analysis in-the-wild (ABAW) competition. We explore the research problems of this challenge from three aspects: 1) For obtaining efficient and robust visual feature representations, we propose MAE-based unsupervised representation learning and IResNet/Den

Jayprakash S. Nair, Divya D. Kulkarni, Ajitem Joshi, Sruthy Suresh

Federated Learning (FL) allows for collaboratively aggregating learned information across several computing devices and sharing the same amongst them, thereby tackling issues of privacy and the need of huge bandwidth. FL techniques generally use a central server or cloud for aggregating the models received from the devices. Such centralized FL techniques suf

Vladimir Podolskii, Nikolay V. Proskurin

For $S \subseteq \{0,1\}^n$ a Boolean function $f \colon S \to \{-1,1\}$ is a polynomial threshold function (PTF) of degree $d$ and weight $W$ if there is a polynomial $p$ with integer coefficients of degree $d$ and with sum of absolute coefficients $W$ such that $f(x) = \text{sign}(p(x))$ for all $x \in S$. We study a representation of decision lists as PTF

Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spr

José Cernicharo, Raúl Fuentetaja, Marcelino Agúndez, Ralf I. Kaiser

We report the detection of fulvenallene ($c$-C$_5$H$_4$CCH$_2$) in the direction of TMC-1 with the QUIJOTE line survey. Thirty rotational transitions with $K_a$=0,1,2,3 and $J$=9-15 were detected. The best rotational temperature fitting of the data is 9\,K and a derived column density is (2.7$\pm$0.3)$\times$10$^{12}$ cm$^{-2}$, which is only a factor of 4.4

Xudong Mao, Liujuan Cao, Aurele T. Gnanha, Zhenguo Yang

GAN inversion aims to invert an input image into the latent space of a pre-trained GAN. Despite the recent advances in GAN inversion, there remain challenges to mitigate the tradeoff between distortion and editability, i.e. reconstructing the input image accurately and editing the inverted image with a small visual quality drop. The recently proposed pivotal

Mathias Butenschoen, Bernd A. Kniehl

We study the associated production of prompt $J/\psi$ mesons and $W$ or $Z$ bosons within the factorization approach of nonrelativistic QCD (NRQCD) at next-to-leading order in $\alpha_s$, via intermediate color singlet ${^3}S_1^{[1]}$ and ${^3}P_J^{[1]}$ and color octet ${^1S}_0^{[8]}$, ${^3S}_1^{[8]}$ and ${^3P}_J^{[8]}$ states. Requiring for our prediction

Jaume Tarrús Castellà

We consider heavy meson-antimeson pairs and their coupling to quarkonium in the context of nonrelativistic EFTs incorporating the adiabatic expansion. We work out all the leading order couplings of quarkonium to heavy meson-antimeson pairs and obtain their contributions to the masses and widths of quarkonia. We match the new potentials terms to NRQCD. Using

Yin Liu, Sam Davanloo Tajbakhsh

In this paper, we study stochastic optimization of two-level composition of functions without Lipschitz continuous gradient. The smoothness property is generalized by the notion of relative smoothness which provokes the Bregman gradient method. We propose three Stochastic Composition Bregman Gradient algorithms for the three possible relatively smooth compos

Yiping Zhang

This paper considers a family of second-order periodic parabolic equations with highly oscillating potentials, which have been considered many times for the time-varying potentials in stochastic homogenization. Following a standard two-scale expansions illusion, we can guess and succeed in determining the homogenized equation in different cases that the pote

Riccardo Cavuoto, Pietro Lenarda, Diego Misseroni, Marco Paggi

Fracture growth in a material is strongly influenced by the presence of inhomogeneities, which deviate crack trajectories from rectilinearity and deeply affect failure. Increasing crack tortuosity is connected to enhancement of fracture toughness, while often a crack may even be stopped when it impinges a void, which releases the stress concentration. Theref

Joachim Cohen, Alexandru Petrescu, Ross Shillito, Alexandre Blais

Transmon qubits are ubiquitously used in superconducting quantum information processor architectures. Strong drives are required to realize fast, high-fidelity, gates and measurements, including parametrically activated processes. Here, we show that even off-resonant drives, in regimes routinely used in experiments, can cause strong modifications to the stru

Alfredo González Lezcano, Junho Hong, James T. Liu, Leopoldo A. Pando Zayas

We explore the notion of $c$-functions in renormalization group flows between theories in different spacetime dimensions. We discuss functions connecting central charges of the UV and IR fixed point theories on the one hand, and functions which are monotonic along the flow on the other. First, using the geometric properties of the holographic dual RG flows a

Michael Cuntz

We investigate special points on the Grassmannian which correspond to friezes with coefficients in the case of rank two. Using representations of arithmetic matroids we obtain a theorem on subpolygons of specializations of the coordinate ring. As a special case we recover the characterization of subpolygons in classic frieze patterns. Moreover, we observe th

Brendan Owens, Sašo Strle

This paper provides a convenient and practical method to compute the homology and intersection pairing of a branched double cover of the 4-ball. To projections of links in the 3-ball, and to projections of surfaces in the 4-ball into the boundary sphere, we associate a sequence of homology groups, called the disoriented homology. We show that the disoriented

Marco Baldovin, David Guéry-Odelin, Emmanuel Trizac

L\'evy stochastic processes, with noise distributed according to a L\'evy stable distribution, are ubiquitous in science. Focusing on the case of a particle trapped in an external harmonic potential, we address the problem of finding "shortcuts to adiabaticity": after the system is prepared in a given initial stationary state, we search for time-dependent pr

Lvzhou Li, Jingquan Luo, Yongzhen Xu

From the 1970s up to now, Mastermind, a classic two-player game, has attracted plenty of attention, not only from the public as a popular game, but also from the academic community as a scientific issue. Mastermind with n positions and k colors is formally described as: the codemaker privately chooses a secret $s\in [k]^n$, and the coderbreaker want to deter

Henning Bahl, Johannes Braathen, Georg Weiglein

Obtaining precise theoretical predictions for both production and decay processes of heavy new particles is of great importance to constrain the allowed parameter spaces of Beyond-the-Standard-Model (BSM) theories, and to properly assess the sensitivity for discoveries and for discriminating between different possible BSM scenarios. In this context, it is we

Sepehr Assadi, Soheil Behnezhad, Sanjeev Khanna, Huan Li

We present a new approach for finding matchings in dense graphs by building on Szemer\'edi's celebrated Regularity Lemma. This allows us to obtain non-trivial albeit slight improvements over longstanding bounds for matchings in streaming and dynamic graphs. In particular, we establish the following results for $n$-vertex graphs: * A deterministic single-pass

Deniz Gunduz, Zhijin Qin, Inaki Estella Aguerri, Harpreet S. Dhillon

Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communi

Xiaofei Huang, Michael Wan, Lingfei Luan, Bethany Tunik

Bilateral postural symmetry plays a key role as a potential risk marker for autism spectrum disorder (ASD) and as a symptom of congenital muscular torticollis (CMT) in infants, but current methods of assessing symmetry require laborious clinical expert assessments. In this paper, we develop a computer vision based infant symmetry assessment system, leveragin

Baptiste Journaux

Habitability of exoplanet's deepest oceans could be limited by the presence of high-pressure ices at their base. New work demonstrates that efficient chemical transport within deep planetary ice mantles is possible through significant salt incorporation within the high-pressure ice.

Dewei Zhang, Sam Davanloo Tajbakhsh

This work considers optimization of composition of functions in a nested form over Riemannian manifolds where each function contains an expectation. This type of problems is gaining popularity in applications such as policy evaluation in reinforcement learning or model customization in meta-learning. The standard Riemannian stochastic gradient methods for no

Priyanka Lamba

The discovery of the Higgs boson raises the question of its "lightness" in mass when the Standard Model is considered as an effective quantum field theory. Supersymmetry is the only currently known symmetry which can protect the Higgs mass while still treating the Higgs as an elementary quantum field. However in the view of null experimental confirmation fro

Valentin Gebhart, Augusto Smerzi

Discarding undesirable measurement results in Bell experiments opens the detection loophole that prevents a conclusive demonstration of nonlocality. As closing the detection loophole represents a major technical challenge for many practical Bell experiments, it is customary to assume the so-called fair sampling assumption (FSA) that, in its original form, st

Zachary Dair, Muhammad Muneeb Saad, Urja Pawar, Samantha Dockray

In healthcare, detecting stress and enabling individuals to monitor their mental health and wellbeing is challenging. Advancements in wearable technology now enable continuous physiological data collection. This data can provide insights into mental health and behavioural states through psychophysiological analysis. However, automated analysis is required to

Martin Franckie

Mid-infrared Quantum Cascade Lasers (QCLs) are compact and efficient sources ideal for molecular spectroscopy applications, such as dual-comb spectroscopy. However, despite over a decade of active developments of QCL frequency comb devices, their bandwidth is limited to around $100$ cm$^{-1}$, severely limiting their application for multi-gas, liquid, and so

Mathias Butenschoen, Bernd A. Kniehl

Working in the nonrelativistic-QCD factorization framework at next-to-leading order in $\alpha_s$, we fit the relevant color octet (CO) long-distance matrix elements (LDMEs) of the $\psi(2S)$ meson, $\langle {\cal O}^{\psi(2S)}({^1S}_0^{[8]})\rangle$, $\langle {\cal O}^{\psi(2S)}({^3S}_1^{[8]})\rangle$, and $\langle {\cal O}^{\psi(2S)}({^3P}_0^{[8]})\rangle$

Wojciech Gajda, Sebastian Petersen

Let $A$ and $B$ be abelian varieties defined over the function field $k(S)$ of a smooth algebraic variety $S/k.$ We establish criteria, in terms of restriction maps to subvarieties of $S,$ for existence of various important classes of $k(S)$-homomorphisms from $A$ to $B,$ e.g., for existence of $k(S)$-isogenies. Our main tools consist of Hilbertianity method

Aritra Sinha, Marek M. Rams, Piotr Czarnik, Jacek Dziarmaga

The Hubbard model is a longstanding problem in the theory of strongly correlated electrons and a very active one in the experiments with ultracold fermionic atoms. Motivated by current and prospective quantum simulations, we apply a two-dimensional tensor network, an infinite projected entangled pair state, evolved in imaginary time by the neighborhood tenso

Tom Z. Jiahao, Kong Yao Chee, M. Ani Hsieh

In this work, we consider the task of improving the accuracy of dynamic models for model predictive control (MPC) in an online setting. Although prediction models can be learned and applied to model-based controllers, these models are often learned offline. In this offline setting, training data is first collected and a prediction model is learned through an

Felix T Petersma, Len Thomas, Aaron M Thode, Danielle Harris

Passive acoustic monitoring is a promising method for surveying wildlife populations that are easier to detect acoustically than visually. When animal vocalisations can be uniquely identified on an array of sensors, the potential exists to estimate population density through acoustic spatial capture-recapture (ASCR). However, sound classification is imperfec

N. J. Adams, R. A. A. Bowler, M. J. Jarvis, R. G. Varadaraj

We present measurements of the rest-frame ultraviolet luminosity function at redshifts $z=3$, $z=4$ and $z=5$, using 96894, 38655 and 7571 sources respectively to map the transition between AGN and galaxy-dominated ultraviolet emission shortly after the epoch of reionization. Sources are selected using a comprehensive photometric redshift approach, using $10

Evan Tilley, Alexander Liebeskind, Rafael Asensio

Manufacturers of modern electronic devices are constantly attempting to implement additional features into ever-increasingly complex and performance demanding systems. This race has been historically driven by improvements in the processor's clock speed, but as power consumption and real estate concerns in the embedded space pose an growing challenge, multit

Aaron Berk, Simone Brugiapaglia, Babhru Joshi, Yaniv Plan

In Bora et al. (2017), a mathematical framework was developed for compressed sensing guarantees in the setting where the measurement matrix is Gaussian and the signal structure is the range of a generative neural network (GNN). The problem of compressed sensing with GNNs has since been extensively analyzed when the measurement matrix and/or network weights f

Li Zhang, Jiachen Lu, Sixiao Zheng, Xinxuan Zhao

The emergence of vision transformers (ViTs) in image classification has shifted the methodologies for visual representation learning. In particular, ViTs learn visual representation at full receptive field per layer across all the image patches, in comparison to the increasing receptive fields of CNNs across layers and other alternatives (e.g., large kernels

Yiming Li, Zhaozhong Chen, Zhouyi Hu, David M. Benton

We experimentally demonstrate the enhanced atmospheric turbulence resiliency in a 137.8 Gbit/s/mode mode-division multiplexing free-space optical communication link through the application of a successive interference cancellation digital signal processing algorithm. The turbulence resiliency is further enhanced through redundant receive channels in the mode

A. Marinucci, F. Muleri, M. Dovčiak, S. Bianchi

We report on the first observation of a radio-quiet Active Galactic Nucleus (AGN) using polarized X-rays: the Seyfert 1.9 galaxy MCG-05-23-16. This source was pointed with the Imaging X-ray Polarimetry Explorer (IXPE) starting on May 14, 2022 for a net observing time of 486 ks, simultaneously with XMM-Newton (58 ks) and NuSTAR (83 ks). A polarization degree

Shiva P. Pudasaini

The landslide velocity plays a dominant role in estimating impact force and devastated area. Here, based on Pudasaini and Krautblatter (2022), I develop a novel extended landslide velocity model that includes the force induced by the hydraulic pressure gradient which was neglected by all the existing analytical landslide velocity models. By a rigorous conver

Shervin Ardeshir, Navid Azizan

The superior performance of some of today's state-of-the-art deep learning models is to some extent owed to extensive (self-)supervised contrastive pretraining on large-scale datasets. In contrastive learning, the network is presented with pairs of positive (similar) and negative (dissimilar) datapoints and is trained to find an embedding vector for each dat

Hisham Galal, Mohammad Mannan, Amr Youssef

Public key infrastructure (PKI) is a certificate-based technology that helps in authenticating systems identities. HTTPS/TLS relies mainly on PKI to minimize fraud over the Internet. Nowadays, websites utilize CDNs to improve user experience, performance, and resilience against cyber attacks. However, combining HTTPS/TLS with CDNs has raised new security cha

Ahmed Gaber

In this paper, we study the reciprocal sums of the Jacobsthal numbers. We establish many results on the infinite sum and alternating infinite sum of the reciprocals of Jacobsthal numbers and square Jacobsthal numbers.

Joel Clay, Sofia Wyetzner, Alex Gaudio, Boxi Xia

Simulation is an important step in robotics for creating control policies and testing various physical parameters. Soft robotics is a field that presents unique physical challenges for simulating its subjects due to the nonlinearity of deformable material components along with other innovative, and often complex, physical properties. Because of the computati

M. J. Rickard, R. Hainich, W. -R. Hamann, L. M. Oskinova

Massive stars are among the main cosmic engines driving the evolution of star-forming galaxies. Their powerful ionising radiation and stellar winds inject a large amount of energy in the interstellar medium. Furthermore, mass-loss ($\dot{M}$) through radiatively driven winds plays a key role in the evolution of massive stars. Even so, the wind mass-loss pres

Hualian Sheng, Sijia Cai, Na Zhao, Bing Deng

Since Intersection-over-Union (IoU) based optimization maintains the consistency of the final IoU prediction metric and losses, it has been widely used in both regression and classification branches of single-stage 2D object detectors. Recently, several 3D object detection methods adopt IoU-based optimization and directly replace the 2D IoU with 3D IoU. Howe

John Joseph O'Hare, Allen Fairchild, Umran Ali

In the digital society's evolving landscape, open-source tooling and generative AI are pivotal in transforming global collaboration. These technologies promise to dismantle traditional barriers of accessibility, language, and governance, fostering an inclusive digital ecosystem. However, the journey towards a fully integrated digital society faces significan

K. Kovlakas, T. Fragos, D. Schaerer, A. Mesinger

While there is now a consensus that X-ray binaries (XRBs) are the dominant X-ray sources in the early Universe and play a significant role during the epoch of heating of the intergalactic medium (IGM), recent studies report contradicting results regarding their contribution in the nebular emission of local Universe galaxies. Ultraluminous X-ray sources (ULXs

Amanda Fernandes, Miguel Carrión, Ruth Domínguez, Rodrigo Ramos

The insertion of renewable sources in power systems may cause a decrease in the system's equivalent inertia, which result in the instability of the power system. On the other hand, energy storage systems have proven to be an effective tool to increase the flexibility in the operation of energy systems, which may favor integrating renewable energy sources. In

Matthijs Meijers, Denis Ruchnewitz, Marta Łuksza, Michael Lässig

The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, as well as antigenic changes that reduce the cross-immunity induced by previous infections or vaccinations. How this functional variation shapes the global evolutionary dynamics has re

Lysander Christakis, Jason S. Rosenberg, Ravin Raj, Sungjae Chi

Synthetic quantum systems with interacting constituents play an important role in quantum information processing and in elucidating fundamental phenomena in many-body physics. Following impressive advances in cooling and trapping techniques, ensembles of ultracold polar molecules have emerged as a promising synthetic system that combines several advantageous

Antoine D. Meyer, David A. van Dyk, Hyungsuk Tak, Aneta Siemiginowska

Cosmological parameters encoding our understanding of the expansion history of the Universe can be constrained by the accurate estimation of time delays arising in gravitationally lensed systems. We propose TD-CARMA, a Bayesian method to estimate cosmological time delays by modelling the observed and irregularly sampled light curves as realizations of a Cont

Arunava Mukherjee, Reinhard Prix, Karl Wette

Scorpius X-1 (Sco X-1) has long been considered one of the most promising targets for detecting continuous gravitational waves with ground-based detectors. Observational searches for Sco X-1 have achieved substantial sensitivity improvements in recent years, to the point of starting to rule out emission at the torque-balance limit in the low-frequency range

Jian Liu, Rui Bo, Siyuan Wang

Enhancing existing transmission lines is a useful tool to combat transmission congestion and guarantee transmission security with increasing demand and boosting the renewable energy source. This study concerns the selection of lines whose capacity should be expanded and by how much from the perspective of independent system operator (ISO) to minimize the sys

Christopher Borger, Andreas Kretschmer, Benjamin Nill

In this paper we study the novel notion of thin polytopes: lattice polytopes whose local $h^*$-polynomials vanish. The local $h^*$-polynomial is an important invariant in modern Ehrhart theory. Its definition goes back to Stanley with fundamental results achieved by Karu, Borisov & Mavlyutov, Schepers, and Katz & Stapledon. The study of thin simplices was or

Giorgio Corani, Dario Azzimonti, Nicolò Rubattu

Forecast reconciliation is an important research topic. Yet, there is currently neither formal framework nor practical method for the probabilistic reconciliation of count time series. In this paper we propose a definition of coherency and reconciled probabilistic forecast which applies to both real-valued and count variables and a novel method for probabili

Christian Capezza, Fabio Centofanti, Antonio Lepore, Alessandra Menafoglio

Modern statistical process monitoring (SPM) applications focus on profile monitoring, i.e., the monitoring of process quality characteristics that can be modeled as profiles, also known as functional data. Despite the large interest in the profile monitoring literature, there is still a lack of software to facilitate its practical application. This article i

Wissam Al Jurdi, Jacques Bou Abdo, Jacques Demerjian, Abdallah Makhoul

Interpreting the performance results of models that attempt to realize user behavior in platforms that employ recommenders is a big challenge that researchers and practitioners continue to face. Although current evaluation tools possess the capacity to provide solid general overview of a system's performance, they still lack consistency and effectiveness in

Stefan More, Jakob Heher, Clemens Walluschek

Trust management systems often use registries to authenticate data, or form trust decisions. Examples are revocation registries and trust status lists. By introducing distributed ledgers (DLs), it is also possible to create decentralized registries. A verifier then queries a node of the respective ledger, e.g., to retrieve trust status information during the

G. Florio, G. Puglisi

By extending the classical Peyrard-Bishop model, we are able to obtain a fully analytical description for the mechanical resistance of DNA under stretching at variable values of temperature, number of base pairs and intrachains and interchains bonds stiffness. In order to compare elasticity and temperature effects, we first analyze the system in the zero tem

Akhtar A. Khan, Jinlu Li, Simeon Reich

In general Banach spaces, the metric projection map lacks the powerful properties it enjoys in Hilbert spaces. There are a few generalized projections that have been proposed in order to resolve many of the deficiencies of the metric projection. However, such notions are predominantly studied in Banach spaces with rich topological structures, such as uniform

Renato Vizuete, Charles Monnoyer de Galland, Julien M. Hendrickx, Paolo Frasca

The resource allocation problem consists of the optimal distribution of a budget between agents in a group. We consider such a problem in the context of open systems, where agents can be replaced at some time instances. These replacements lead to variations in both the budget and the total cost function that hinder the overall network's performance. For a si

Ziyu Li, Rihan Hai, Alessandro Bozzon, Asterios Katsifodimos

Machine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoper

Houjian Yu, Changhyun Choi

Instance segmentation with unseen objects is a challenging problem in unstructured environments. To solve this problem, we propose a robot learning approach to actively interact with novel objects and collect each object's training label for further fine-tuning to improve the segmentation model performance, while avoiding the time-consuming process of manual

Bin Chen, Jian Zhang

To more efficiently address image compressed sensing (CS) problems, we present a novel content-aware scalable network dubbed CASNet which collectively achieves adaptive sampling rate allocation, fine granular scalability and high-quality reconstruction. We first adopt a data-driven saliency detector to evaluate the importances of different image regions and

Kai Ma, Pengcheng Xi, Karim Habashy, Ashkan Ebadi

Building AI models with trustworthiness is important especially in regulated areas such as healthcare. In tackling COVID-19, previous work uses convolutional neural networks as the backbone architecture, which has shown to be prone to over-caution and overconfidence in making decisions, rendering them less trustworthy -- a crucial flaw in the context of medi

Dominik Beck

In this paper, we generalise the formula for the fourth moment of a random determinant to account for entries with asymmetric distribution. We also derive the second moment of a random Gram determinant.

Daniele Secci, Laura Molino, Andrea Zanini

In a desired environmental protection system, groundwater may not be excluded. In addition to the problem of over-exploitation, in total disagreement with the concept of sustainable development, another not negligible issue concerns the groundwater contamination. Mainly, this aspect is due to intensive agricultural activities or industrialized areas. In lite

Nicolás Abadía

This work aims to design a CMOS compatible, low-electrical power consumption modulator assisted by plasmons. For compactness and reduction of the electrical power consumption, electro-absorption based on the Franz-Keldysh effect in Germanium was chosen for modulation. It consists in the change of the absorption coefficient of the material near the band edge

Vasil R. Yordanov, Felipe Isaule

We report a comprehensive study of the ground-state properties of one and two bosonic impurities immersed in small one-dimensional optical lattices loaded with a few interacting bosons. We model the system with a two-component Bose-Hubbard model and solve the problem numerically by means of the exact diagonalization (ED) method. We report binding energies of