April 2023 arXiv papers — page 144

Vinaya Krishnan MB, Aruna Kumar Nayak, Asrith Krishna Radhakrishnan

Many analyses are performed by the LHC experiments to search for heavy gauge bosons, which appear in several new physics models. The invariant mass reconstruction of heavy gauge bosons is difficult when they decay to $\tau$ leptons due to missing neutrinos in the final state. Machine learning techniques are widely utilized in experimental high-energy physics

Sergio Lucarini, Fionn P. E. Dunne, Emilio Martínez-Pañeda

A novel FFT-based phase-field fracture framework for modelling fatigue crack initiation and propagation at the microscale is presented. A damage driving force is defined based on the stored energy and dislocation density, relating phase-field fracture with microstructural fatigue damage. The formulation is numerically implemented using FFT methods to enable

Robert Charity, Lee Sobotka

The fragmentation of a projectile into a number of pieces can lead to the creation of many resonances in different nuclei. We discuss application of the invariant-mass method to the products from such reactions to find some of the most exotic resonances located furthest beyond the proton drip line. We show examples from fragmentation of a fast $^{13}$O beam

Giuseppe Cosma Brusca

We describe the asymptotic behaviour of the minimal heterogeneous $d$-capacity of a small set, which we assume to be a ball for simplicity, in a fixed bounded open set $\Omega\subseteq \mathbb{R}^d$, with $d\geq2$. Two parameters are involved: $\varepsilon$, the radius of the ball, and $\delta$, the length scale of the heterogeneity of the medium. We prove t

Alexei M. Tsvelik, Saheli Sarkar

The newly discovered kagome metals AV$_3$Sb$_5$ (A = K, Rb, Cs) offer an exciting route to study exotic phases arising due to interplay between electronic correlations and topology. Besides superconductivity, these materials exhibit a charge-density wave (CDW) phase occurring at around 100 K, whose origin still remains elusive. The robust multi-component $2

Ryan Gibara, Josh Kline

Let $(X,d,\mu)$ be a doubling metric measure space. We consider the behaviour of the fractional maximal function $M^\alpha$ for $0\leq \alpha<Q$, where $Q$ is the doubling dimension, acting on functions of bounded mean oscillation (BMO) and vanishing mean oscillation ($VMO$). For $\alpha>0$, we additionally assume that the space is bounded. We show that $M^\

Srijan Chattopadhyay, Swapnaneel Bhattacharyya

Similarity index is an important scientific tool frequently used to determine whether different pairs of entities are similar with respect to some prefixed characteristics. Some standard measures of similarity index include Jaccard index, S{\o}rensen-Dice index, and Simpson's index. Recently, a better index ($\hat{\alpha}$) for the co-occurrence and/or simil

Alex Hagen, Shane Jackson

Detection of effects of the parameters of the synthetic process on the microstructure of materials is an important, yet elusive goal of materials science. We develop a method for detecting effects based on copula theory, high dimensional distribution distances, and permutational statistics to analyze a designed experiment synthesizing plutonium oxide from Pu

Ta Duy Nguyen, Alina Ene, Huy L. Nguyen

In this work, we study the convergence \emph{in high probability} of clipped gradient methods when the noise distribution has heavy tails, ie., with bounded $p$th moments, for some $1<p\le2$. Prior works in this setting follow the same recipe of using concentration inequalities and an inductive argument with union bound to bound the iterates across all itera

Maximilien Le Clei, Pierre Bellec

There is a recent surge in interest for imitation learning, with large human video-game and robotic manipulation datasets being used to train agents on very complex tasks. While deep neuroevolution has recently been shown to match the performance of gradient-based techniques on various reinforcement learning problems, the application of deep neuroevolution t

Kirill Krasnov

Cayley 4-form Phi on an 8-dimensional manifold M is a real differential form of a special algebraic type, which determines a Riemannian metric on M as well as a unit real Weyl spinor. It defines a Spin(7) structure on M, and this Spin(7) structure is integrable if and only if Phi is closed. We introduce the notion of a complex Cayley form. This is a one-para

F. O. de Franca, M. Virgolin, M. Kommenda, M. S. Majumder

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of

Maximilien Le Clei, Pierre Bellec

Modern artificial intelligence works typically train the parameters of fixed-sized deep neural networks using gradient-based optimization techniques. Simple evolutionary algorithms have recently been shown to also be capable of optimizing deep neural network parameters, at times matching the performance of gradient-based techniques, e.g. in reinforcement lea

Mingyuan Zhang, Xinying Guo, Liang Pan, Zhongang Cai

3D human motion generation is crucial for creative industry. Recent advances rely on generative models with domain knowledge for text-driven motion generation, leading to substantial progress in capturing common motions. However, the performance on more diverse motions remains unsatisfactory. In this work, we propose ReMoDiffuse, a diffusion-model-based moti

Hokuto Konno, Jin Miyazawa, Masaki Taniguchi

We develop a version of Seiberg--Witten Floer cohomology/homotopy type for a spin$^c$ 4-manifold with boundary and with an involution which reverses the spin$^c$ structure, as well as a version of Floer cohomology/homotopy type for oriented links with non-zero determinant. This framework generalizes the previous work of the authors regarding Floer homotopy t

Yabo Zhang, Zihao Wang, Jun Hao Liew, Jingjia Huang

In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs. Due to the lack of dense annotations, existing text-supervised methods can only learn to group an image into semantic regions via pixel-insensitive feedback. As a result, their grouped results are coarse and often contain small spurious regions,

Juan Ignacio Bastero, Fernando Karaseur, Sofia Judith Garofalo, Alejandro Gangui

There are vast educational research works that highlight the serious difficulties that students present in learning astronomical subjects, as well as the prevalence of a traditional education distanced from the observational and experiential, thus accentuating the difficulties detected. We argue that progressive teaching with a topocentric and contextualized

Zhiqi Huang

We define an S function as the sum of the asymptotic error terms of digamma function of an arithmetic series, $S(a) \equiv \sum_{n=1}^\infty \left[\ln\frac{n}{a} - \frac{a}{2n}-\psi\left(\frac{n}{a}\right)\right]$, and show a few properties of it. Using the S function, we construct a real and positive $\phi$ function. Riemann hypothesis holds if $\tilde{\phi

Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov

In this paper, we propose a variance reduction approach for Markov chains based on additive control variates and the minimization of an appropriate estimate for the asymptotic variance. We focus on the particular case when control variates are represented as deep neural networks. We derive the optimal convergence rate of the asymptotic variance under various

Giacomo Zara, Subhankar Roy, Paolo Rota, Elisa Ricci

Open-set Unsupervised Video Domain Adaptation (OUVDA) deals with the task of adapting an action recognition model from a labelled source domain to an unlabelled target domain that contains "target-private" categories, which are present in the target but absent in the source. In this work we deviate from the prior work of training a specialized open-set class

Albertus J. Malan, Lukas Rausche, Felix Strehle, Sören Hohmann

In this paper, we present finite-dimensional port-Hamiltonian system (PHS) models of a gas pipeline and a network comprising several pipelines for the purpose of control design and stability analysis. Starting from the partial differential Euler equations describing the dynamical flow of gas in a pipeline, the method of lines is employed to obtain a lumped-p

Jordan W. Suchow, Necdet Gürkan

Generative A.I. models have emerged as versatile tools across diverse industries, with applications in privacy-preserving data sharing, computational art, personalization of products and services, and immersive entertainment. Here, we introduce a new privacy concern in the adoption and use of generative A.I. models: that of coincidental generation, where a g

Fabian Stiehle, Ingo Weber

For the enactment of inter-organizational business processes, blockchain can guarantee the enforcement of process models and the integrity of execution traces. However, existing solutions come with downsides regarding throughput scalability, latency, and suboptimal tradeoffs between confidentiality and transparency. To address these issues, we propose to cha

Mingxiao Li, Rui Jin, Liyao Xiang, Kaiming Shen

The traditional methods for data compression are typically based on the symbol-level statistics, with the information source modeled as a long sequence of i.i.d. random variables or a stochastic process, thus establishing the fundamental limit as entropy for lossless compression and as mutual information for lossy compression. However, the source (including

Paweł Piwek

We extend Wilkes' results on the profinite rigidity of SFSs to the setting of central extensions of 2-orbifold groups with higher-rank centre. We prove that both rigid and non-rigid phenomena arise in this setting and that the non-rigid phenomena are transient in the sense that if $\widehat{G}_1 \cong \widehat{G}_2$, then $G_1 \times \mathbb{Z} \cong G_2 \ti

Milou van Rijnbach, Giuliano Gustavino, Phil Allport, Igancio Asensi

MALTA is part of the Depleted Monolithic Active Pixel sensors designed in Tower 180nm CMOS imaging technology. A custom telescope with six MALTA planes has been developed for test beam campaigns at SPS, CERN, with the ability to host several devices under test. The telescope system has a dedicated custom readout, online monitoring integrated into DAQ with re

Z. Pan, P. S. Barry, T. Cecil, C. Albert

This work presents a suite of measurement techniques for characterizing the dielectric loss tangent across a wide frequency range from $\sim$1 GHz to 150 GHz using the same test chip. In the first method, we fit data from a microwave resonator at different temperatures to a model that captures the two-level system (TLS) response to extract and characterize b

Julian Aron Prenner, Romain Robbes

Recently, we can notice a transition to data-driven techniques in Automated Program Repair (APR), in particular towards deep neural networks. This entails training on hundreds of thousands or even millions of non-executable code fragments. We would like to bring more attention to an aspect of code often neglected in Neural Program Repair (NPR), namely its ex

Shizhen Chang, Michael Kopp, Pedram Ghamisi, Bo Du

Change detection, an essential application for high-resolution remote sensing images, aims to monitor and analyze changes in the land surface over time. Due to the rapid increase in the quantity of high-resolution remote sensing data and the complexity of texture features, several quantitative deep learning-based methods have been proposed. These methods out

Till Kahlke, Alexander K. Hartmann

The maximum-weight matching problem and the behavior of its energy landscape is numerically investigated. We apply a perturbation method adapted from the analysis of spin glasses. This gives inside into the complexity of the energy landscape of different ensembles. Erd\"os-Renyi graphs and ring graphs with randomly added edges are considered and two types of

Felicia Lucke, Daniël Paulusma, Bernard Ries

The (Perfect) Matching Cut problem is to decide if a graph $G$ has a (perfect) matching cut, i.e., a (perfect) matching that is also an edge cut of $G$. Both Matching Cut and Perfect Matching Cut are known to be NP-complete. A perfect matching cut is also a matching cut with maximum number of edges. To increase our understanding of the relationship between t

Dingke Tang, Dehan Kong, Linbo Wang

In many observational studies, researchers are often interested in studying the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data such as the lasso assume the associations between the exposures and the outcome are sparse. These methods, however, do not estimate the causal effects in the presence of unmeasured co

Honglin Xiong, Sheng Wang, Yitao Zhu, Zihao Zhao

The recent progress of large language models (LLMs), including ChatGPT and GPT-4, in comprehending and responding to human instructions has been remarkable. Nevertheless, these models typically perform better in English and have not been explicitly trained for the medical domain, resulting in suboptimal precision in diagnoses, drug recommendations, and other

Maximilien Le Clei

Our society is increasingly fond of computational tools. This phenomenon has greatly increased over the past decade following, among other factors, the emergence of a new Artificial Intelligence paradigm. Specifically, the coupling of two algorithmic techniques, Deep Neural Networks and Stochastic Gradient Descent, thrusted by an exponentially increasing com

Nimish Pujara, Jean-Luc Thiffeault

Particles such as microplastics and phytoplankton suspended in the water column in the natural environment are often subject to the action of surface gravity waves. By modelling such anisotropic particles as small spheroids that slowly settle (or rise) in a wavy environment, we consider how the particle shape and buoyancy couple to the background wave-driven

Abhishek Paudel, Gregory J. Stein

We present a novel approach for fast and reliable policy selection for navigation in partial maps. Leveraging the recent learning-augmented model-based Learning over Subgoals Planning (LSP) abstraction to plan, our robot reuses data collected during navigation to evaluate how well other alternative policies could have performed via a procedure we call offlin

Florian Stadtmann, Henrik Gusdal Wassertheurer, Adil Rasheed

Digital Twins bring several benefits for planning, operation, and maintenance of remote offshore assets. In this work, we explain the digital twin concept and the capability level scale in the context of wind energy. Furthermore, we demonstrate a standalone digital twin, a descriptive digital twin, and a prescriptive digital twin of an operational floating o

Hoyoung Song

If S is a smooth compact surface in $\mathbb{R}^{3}$ with strictly positive second fundamental form, and $E_S$ is the corresponding extension operator, then we prove that for all $p > 3$, $\left\|E_S f\right\|_{L^p\left(\mathbb{R}^3\right)} \leq C(p, S)\|f\|_{L^{\infty}(S)}.$ The proof of restriction conjecture in $\mathbb{R}^{3}$ implies that Kakeya set con

Shizhen Chang, Pedram Ghamisi

In recent years, advanced research has focused on the direct learning and analysis of remote sensing images using natural language processing (NLP) techniques. The ability to accurately describe changes occurring in multi-temporal remote sensing images is becoming increasingly important for geospatial understanding and land planning. Unlike natural image cha

Yongqiang Mao, Xian Sun, Xingliang Huang, Kaiqiang Chen

Building extraction and height estimation are two important basic tasks in remote sensing image interpretation, which are widely used in urban planning, real-world 3D construction, and other fields. Most of the existing research regards the two tasks as independent studies. Therefore the height information cannot be fully used to improve the accuracy of buil

Zhihang Yuan, Lin Niu, Jiawei Liu, Wenyu Liu

Large-scale language models (LLMs) have demonstrated impressive performance, but their deployment presents challenges due to their significant memory usage. This issue can be alleviated through quantization. In this paper, we identify that the challenge in quantizing activations in LLMs arises from varying ranges across channels, rather than solely the prese

Eduard P. Kontar, A. Gordon Emslie, Galina G. Motorina, Brian R. Dennis

Solar flares are known to be prolific electron accelerators, yet identifying the mechanism(s) for such efficient electron acceleration in solar flare (and similar astrophysical settings) presents a major challenge. This is due in part to a lack of observational constraints related to conditions in the primary acceleration region itself. Accelerated electrons

Sundaram Thangavelu

Any bounded linear operator $ T $ on $ L^2(\mathbb{R}^n) $ gives rise to the operator $ S= B \circ T \circ B^\ast $ on the Fock space $ \mathcal{F}(\C^n) $ where $ B $ is the Bargmann transform. In this article we identify those $ S $ which correspond to Fourier multipliers and pseudo-differential operators on $ L^2(\mathbb{R}^n)$ and study their boundedness

Jyrki Kauppinen, Pekka Malmi

In our previous paper "No experimental evidence for the significant anthropogenic climate change" we had a reference to this paper. Thus, we have presented a new theory: how Henry's Law regulates the concentration of CO$_2$ in the atmosphere. This theory uses a physically perfect unit impulse response to convolve signals. By comparing the theory and the pres

Andrea Ferigo, Giovanni Iacca

During the first part of life, the brain develops while it learns through a process called synaptogenesis. The neurons, growing and interacting with each other, create synapses. However, eventually the brain prunes those synapses. While previous work focused on learning and pruning independently, in this work we propose a biologically plausible model that, t

Rui Xu, Yong Luo, Bo Du

Cross-domain pulmonary nodule detection suffers from performance degradation due to a large shift of data distributions between the source and target domain. Besides, considering the high cost of medical data annotation, it is often assumed that the target images are unlabeled. Existing approaches have made much progress for this unsupervised domain adaptati

Zheyu Wang, Wei Zhang, Lingfei Wang, Tsz Fung Poon

Materials with AV$_3$Sb$_5$ (A=K, Rb, Cs) stoichiometry are recently discovered kagome superconductors with the electronic structure featuring a Dirac band, van Hove singularities and flat bands. These systems undergo anomalous charge-density-wave (CDW) transitions at $T_{\rm CDW}$~80-100 K, resulting in the reconstruction of the Fermi surface from the prist

Wen Shen, Lei Cheng, Yuxiao Yang, Mingjie Li

In this paper, we explain the inference logic of large language models (LLMs) as a set of symbolic concepts. Many recent studies have discovered that traditional DNNs usually encode sparse symbolic concepts. However, because an LLM has much more parameters than traditional DNNs, whether the LLM also encodes sparse symbolic concepts is still an open problem.

Wenxing Zhang, Waqas Ahmed, Imtiaz Khan, Tianjun Li

The axino, the supersymmetric partner of axion, is a well-motivated warm/hot dark matter candidate, and provides a natural solution to the relic density problem for the bino-like neutralino if it is the lightest supersymmetric particle (LSP). With the Generalized Minimal Supergravity, we study such kind of the viable parameter space where the bino-like neutr

Cheng Deng, Fan Xu, Jiaxing Ding, Luoyi Fu

Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks. Most existing works embed graph data in the Euclidean space, while recent works extend the embedding models to hyperbolic or spherical spaces to achieve better performance on graphs with complex structures, such as hierarchical or ring structures. Fus

Qinyue Zheng, Arun Venkitaraman, Simona Petravic, Pascal Frossard

Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electrode set of electroencephalogram (EEG) measurements could be inconvenient for every day use. In this paper, we propose a novel knowledge distillation approach to transfer the knowle

Antje Dabeler, Emilie Mai Elkiær, Maria Gerasimova, Tim de Laat

Let $G$ be a locally compact group with an open subgroup $H$ with the Kunze-Stein property, and let $\pi$ be a unitary representation of $H$. We show that the representation $\widetilde{\pi}$ of $G$ induced from $\pi$ is an $L^{p+}$-representation if and only if $\pi$ is an $L^{p+}$-representation. We deduce the following consequence for a large natural clas

Thomas Witdouck

It is proven that every non-abelian right-angled Artin group has the $R_\infty$-property and bounds are given on the $R_\infty$-nilpotency index. In case the graph is transposition-free, which is true for almost all graphs, it is shown that the $R_\infty$-nilpotency index is equal to 2.

O. Ya. Yakovlev, A. F. Valeev, G. G. Valyavin, A. V. Tavrov

Here we present eight new candidates for exoplanets detected by the transit method at the Special Astrophysical Observatory of the Russian Academy of Sciences. Photometric observations were performed with a 50-cm robotic telescope during the second half of 2020. We detected transits with depths of $\Delta m = 0.056-0.173^m$ and periods $P = 18.8^h-8.3^d$ in

Matthew Cleaveland, Insup Lee, George J. Pappas, Lars Lindemann

Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction to time series data leads to conservative prediction regions. In fact, to obtain prediction regions over $T$ time steps with confidence $1-\delta$, {previous works require that eac

Sudarshan S Harithas, Gurkirat Singh, Aneesh Chavan, Sarthak Sharma

We focus on the problem of LiDAR point cloud based loop detection (or Finding) and closure (LDC) in a multi-agent setting. State-of-the-art (SOTA) techniques directly generate learned embeddings of a given point cloud, require large data transfers, and are not robust to wide variations in 6 Degrees-of-Freedom (DOF) viewpoint. Moreover, absence of strong prio

Seungju Lee, Mona Wang, Watson Jia, Qiang Wu

Internet censors often rely on information in the first few packets of a connection to censor unwanted traffic. With the rise of the QUIC transport protocol, prior work has suggested the method of using QUIC connection migration to conceal the first few handshake packets using a different network path (e.g., an encrypted proxy channel). However, the use of c

M. H. Freedman, M. B. Hastings

Quantum information is about the entanglement of states. To this starting point we add parameters whereby a single state becomes a non-vanishing section of a bundle. We consider through examples the possible entanglement patterns of sections.

Benjamin Ertel, Jann van der Meer, Udo Seifert

In single-molecule experiments, the dynamics of molecular motors are often observed indirectly by measuring the trajectory of an attached bead in a motor-bead assay. In this work, we propose a method to extract the step size and stalling force for a molecular motor without relying on external control parameters. We discuss this method for a generic hybrid mo

Ed Bennett, Jack Holligan, Deog Ki Hong, Ho Hsiao

We review the current status of the long-term programme of numerical investigation of $Sp(2N)$ gauge theories with and without fermionic matter content. We start by introducing the phenomenological as well as theoretical motivations for this research programme, which are related to composite Higgs models, models of partial top compositeness, dark matter mode

Sebastian Ulbricht, Johannes Dickmann, Andrey Surzhykov

We theoretically investigate the propagation of light in the presence of a homogeneous gravitational field. To model this, we derive the solutions of the wave equation in Rindler spacetime, which account for gravitational redshift and light deflection. The developed theoretical framework is used to explore the propagation of plane light waves in a horizontal

Maja Groll, Julius Bürger, Ioannis Caltzidis, Klaus D. Jöns

Most properties of solid materials are defined by their internal electric field and charge density distributions which so far are difficult to measure with high spatial resolution. Especially for 2D materials, the atomic electric fields influence the optoelectronic properties. In this study, the atomic-scale electric field and charge density distribution of

Gabriel Barrenechea, Emmanuil Georgoulis, Tristan Pryer, Andreas Veeser

This work proposes a nonlinear finite element method whose nodal values preserve bounds known for the exact solution. The discrete problem involves a nonlinear projection operator mapping arbitrary nodal values into bound-preserving ones and seeks the numerical solution in the range of this projection. As the projection is not injective, a stabilisation base

C. Cabezas, J. R. Pardo, M. Agundez, B. Tercero

We report on the detection of a series of six lines in the ultra-deep Q-band integration toward IRC+10216 carried out with the Yebes 40m telescope, which are in harmonic relation with integer quantum numbers J from 12 to 18. After a detailed analysis of all possible carriers, guided by high-level quantum chemical calculations, we conclude that the lines belo

Jamie Hathaway, Abdelaziz Shaarawy, Cansu Akdeniz, Ali Aflakian

Disassembly of electric vehicle batteries is a critical stage in recovery, recycling and re-use of high-value battery materials, but is complicated by limited standardisation, design complexity, compounded by uncertainty and safety issues from varying end-of-life condition. Telerobotics presents an avenue for semi-autonomous robotic disassembly that addresse

Chenyang Qi, Xin Yang, Ka Leong Cheng, Ying-Cong Chen

Contemporary image rescaling aims at embedding a high-resolution (HR) image into a low-resolution (LR) thumbnail image that contains embedded information for HR image reconstruction. Unlike traditional image super-resolution, this enables high-fidelity HR image restoration faithful to the original one, given the embedded information in the LR thumbnail. Howe

Yunwei Ren, Mo Zhou, Rong Ge

Depth separation -- why a deeper network is more powerful than a shallower one -- has been a major problem in deep learning theory. Previous results often focus on representation power. For example, arXiv:1904.06984 constructed a function that is easy to approximate using a 3-layer network but not approximable by any 2-layer network. In this paper, we show t

Abel Goedegebuure, Indika Kumara, Stefan Driessen, Dario Di Nucci

Data mesh is an emerging domain-driven decentralized data architecture that aims to minimize or avoid operational bottlenecks associated with centralized, monolithic data architectures in enterprises. The topic has picked the practitioners' interest, and there is considerable gray literature on it. At the same time, we observe a lack of academic attempts at

Yi C. Huang, Fuping Shi

We establish in this paper some weighted Hardy and Rellich type inequalities on the half line in the framework of equalities, extending recent results proved by Machihara-Ozawa-Wadade and Bez-Machihara-Ozawa. In particular, the one-dimensional classical Rellich inequality is proved in the framework of equality.

Eike Müller, Francesca Calore, Pierluca Carenza, Christopher Eckner

We investigate the characteristics of the gamma-ray signal following the decay of MeV-scale Axion-Like Particles (ALPs) coupled to photons which are produced in a Supernova (SN) explosion. This analysis is the first to include the production of heavier ALPs through the photon coalescence process, enlarging the mass range of ALPs that could be observed in thi

Yi Wang, Zun Wang

Recently, it was hypothesized that some supermassive black holes (SMBHs) may couple to the cosmic expansion. The mass of these SMBHs increase as the cubic power of the cosmic scale factor, leaving the energy density of the SMBHs unchanged when the universe expands. However, following general principles of general relativity, namely, locality or junction cond

Thanh Huy Nguyen, Sophie Ricci, Andrea Piacentini, Ehouarn Simon

Flood simulation and forecast capability have been greatly improved thanks to advances in data assimilation (DA) strategies incorporating various types of observations; many are derived from spatial Earth Observation. This paper focuses on the assimilation of 2D flood observations derived from Synthetic Aperture Radar (SAR) images acquired during a flood eve

Eray Guven, Caner Goztepe, Mehmet Akif Durmaz, Semiha Tedik Basaran

Connected autonomous vehicles (CAV) constitute an important application of future-oriented traffic management .A vehicular system dominated by fully autonomous vehicles requires a robust and efficient vehicle-to-everything (V2X) infrastructure that will provide sturdy connection of vehicles in both short and long distances for a large number of devices, requ

Haipeng An, Shuailiang Ge, Jia Liu

Ultralight axions and dark photons are well-motivated dark matter candidates. Inside the plasma, once the mass of ultralight dark matter candidates equals the plasma frequency, they can resonantly convert into electromagnetic waves, due to the coupling between the ultralight dark matter particles and the standard model photons. The converted electromagnetic

Gonzalo Ferrer, Dmitrii Iarosh, Anastasiia Kornilova

Modern depth sensors can generate a huge number of 3D points in few seconds to be latter processed by Localization and Mapping algorithms. Ideally, these algorithms should handle efficiently large sizes of Point Clouds under the assumption that using more points implies more information available. The Eigen Factors (EF) is a new algorithm that solves SLAM by

Zhuoling Li, Chuanrui Zhang, Wei-Chiu Ma, Yipin Zhou

In recent years, transformer-based detectors have demonstrated remarkable performance in 2D visual perception tasks. However, their performance in multi-view 3D object detection remains inferior to the state-of-the-art (SOTA) of convolutional neural network based detectors. In this work, we investigate this issue from the perspective of bird's-eye-view (BEV)

Li Li, Jean-Pierre S. El Rami, Adrian Taylor, James Hailing Rao

Autonomous cyber agents may be developed by applying reinforcement and deep reinforcement learning (RL/DRL), where agents are trained in a representative environment. The training environment must simulate with high-fidelity the network Cyber Operations (CyOp) that the agent aims to explore. Given the complexity of net-work CyOps, a good simulator is difficu

Xuan Xu, Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna

Generative AI has received substantial attention in recent years due to its ability to synthesize data that closely resembles the original data source. While Generative Adversarial Networks (GANs) have provided innovative approaches for histopathological image analysis, they suffer from limitations such as mode collapse and overfitting in discriminator. Rece

Prashin Sharma, Benjamin Kraske, Joseph Kim, Zakariya Laouar

Unmanned aircraft systems (UAS) are being increasingly adopted for various applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit failure and/or battery degradation. The

Andreas Deutschmann-Olek, Katharina Schrom, Nikolaus Würkner, Jörg Schmiedmayer

In this paper, we investigate the manipulation of quasi-1D Bose gases that are trapped in a highly elongated potential by optimal control methods. The effective meanfield dynamics of the gas can be described by a one-dimensional non-polynomial Schr\"odinger equation. We extend the indirect optimal control method for the Gross-Pitaevskii equation by Winckel a

Arul Shankar, Artane Siad, Ashvin A. Swaminathan

In this article, we combine Bhargava's geometry-of-numbers methods with the dynamical point-counting methods of Eskin--McMullen and Benoist--Oh to develop a new technique for counting integral points on symmetric varieties lying within fundamental domains for coregular representations. As applications, we study the distribution of the $2$-torsion subgroup of

Mandana Mirbakhsh, Zahra Zahed, Sepideh Mashayekhi, Monire Jafari

Crocus sativus is a triploid sterile plant with red stigmas belonging to the family of Iridaceae, and sub-family Crocoideae. Crocin, picrocrocin, and safranal are three major carotenoid derivatives that are responsible for the color, taste, and specific aroma of Crocus. Saffron flowers are harvested manually and used as spice, dye, or medicinal applications.

A. G. M. Pietrow, R. Hoppe, M. Bergemann, F. Calvo

Solar oxygen abundance measurements based on the O I near-infrared triplet have been a much-debated subject for several decades since non-local thermodynamic equilibrium (NLTE) calculations with 3D radiation-hydrodynamics model atmospheres introduced a large change to the 1D LTE modelling. In this work, we aim to test solar line formation across the solar di

Nuria Corral, Marcelo E. Hernandes, Maria Elenice R. Hernandes

In this work we describe dicritical foliations in $(\mathbb{C}^2,0)$ at a triple point of the resolution dual graph of an analytic plane branch $\mathcal{C}$ using its semiroots. In particular, we obtain a constructive method to present a one-parameter family $\mathcal{C}_{u}$ of separatrices for such foliations. As a by-product we relate the contact order b

Jeffrey Lu, Ivan Rodriguez

The current trend in developing machine learning models for reading comprehension and logical reasoning tasks is focused on improving the models' abilities to understand and utilize logical rules. This work focuses on providing a novel loss function and accompanying model architecture that has more interpretable components than some other models by represent

Dženan Lapandić, Christos K. Verginis, Dimos V. Dimarogonas, Bo Wahlberg

In this paper we propose a novel distributed model predictive control (DMPC) based algorithm with a trajectory predictor for a scenario of landing of unmanned aerial vehicles (UAVs) on a moving unmanned surface vehicle (USV). The algorithm is executing DMPC with exchange of trajectories between the agents at a sufficient rate. In the case of loss of communic

René Côté, Rémi N. Duchesne, Gautier D. Duchesne, Olivier Trépanier

In Weyl semimetals with broken inversion and time-reversal symmetries, the Maxwell equations are modified by the presence of the axion terms $\mathbf{b} $ and $b_{0}$ where, in the simplest case of a two-node Weyl semimetal, $% 2\hslash \mathbf{b}$ is the vector that connects two Weyl nodes in momentum space and $2\hslash b_{0}$ is the separation in energy o

Xiaohong Li, Rony Keppens, Yuhao Zhou

Flux emergence is responsible for various solar eruptions. Combining observation and simulations, we investigate the influence of flux emergence at one footpoint of an arcade on coronal rain as well as induced eruptions. The emergence changes the pressure in the loops, and the internal coronal rain all moves to the other side. The emerging flux reconnects wi

Ioannis Maniadis Metaxas, Georgios Tzimiropoulos, Ioannis Patras

Clustering has been a major research topic in the field of machine learning, one to which Deep Learning has recently been applied with significant success. However, an aspect of clustering that is not addressed by existing deep clustering methods, is that of efficiently producing multiple, diverse partitionings for a given dataset. This is particularly impor

Haichao Liu, Kai Chen, Yulin Li, Zhenmin Huang

In this article, we propose an optimization-based integrated behavior planning and motion control scheme, which is an interpretable and adaptable urban autonomous driving solution that complies with complex traffic rules while ensuring driving safety. Inherently, to ensure compliance with traffic rules, an innovative design of potential functions (PFs) is pr

Mitchell Black, Georgios Fainekos, Bardh Hoxha, Danil Prokhorov

We propose a novel class of risk-aware control barrier functions (RA-CBFs) for the control of stochastic safety-critical systems. Leveraging a result from the stochastic level-crossing literature, we deviate from the martingale theory that is currently used in stochastic CBF techniques and prove that a RA-CBF based control synthesis confers a tighter upper b

Francisco Correa, Luis Inzunza, Ian Marquette

A non-Hermitian generalisation of the Marsden--Weinstein reduction method is introduced to construct families of quantum $\mathcal{PT}$-symmetric superintegrable models over an $n$-dimensional sphere $S^n$. The mechanism is illustrated with one- and two-dimensional examples, related to $u(2)$ and $u(3)$ Lie algebras respectively, providing new quantum models

Ryuji Tanimoto

Let $k$ be an algebraically closed field of positive characteristic $p$. In this article, we classify representations of $\mathbb{G}_a \rtimes \mathbb{G}_m$ into ${\rm SL}(3, k)$, and thereby we classify fundamental representations of $\mathbb{G}_a$ into ${\rm SL}(3, k)$.

Binzhou Xia, Junyang Zhang, Zhishuo Zhang, Wenying Zhu

For positive integers $k$ and $n$, the shuffle group $G_{k,kn}$ is generated by the $k!$ permutations of a deck of $kn$ cards performed by cutting the deck into $k$ piles with $n$ cards in each pile, and then perfectly interleaving these cards following a certain permutation of the $k$ piles. For $k=2$, the shuffle group $G_{2,2n}$ was determined by Diaconis

G. Chanfray, H. Hansen, J. Margueron

In this paper we discuss the combined effects on nuclear matter properties of the quark confinement mechanism in nucleon and of the chiral effective potential resulting from the spontaneous breaking of the chiral symmetry in nuclear matter. Based on the Nambu-Jona-Lasinio predictions, it is shown that the chiral potential acquires a specific scalar field cub

Chaitanya Afle, Patrick R. Miles, Silvina Caino-Lores, Collin D. Capano

NASA's Neutron Star Interior Composition Explorer (NICER) observed X-ray emission from the pulsar PSR J0030+0451 in 2018. Riley et al. reported Bayesian parameter measurements of the mass and the star's radius using pulse-profile modeling of the X-ray data. This paper reproduces their result using the open-source software X-PSI and publicly available data wi

Ju Tan, Ming Xu

In this paper, we generalize the notion of cyclic metric to homogeneous Finsler geometry. Firstly, we prove that a homogeneous Finsler space $(G/H, F)$ must be symmetric when it satisfies the naturally reductive and cyclic conditions simultaneously. Then we prove that a Finsler cyclic Lie group which is either flat or nilpotent must have an Abelian Lie algeb

Thuyen Dang, Yuliya Gorb, Silvia Jimenez Bolanos

This paper concerns the rigorous periodic homogenization for a weakly coupled electroelastic system of a nonlinear electrostatic equation with an elastic equation enriched with electrostriction. Such coupling is employed to describe dielectric elastomers or deformable (elastic) dielectrics. It is shown that the effective response of the system consists of a

Janet J. W. Dong, Kathy Q. Ji

The objective of this paper is to prove that the polynomials $\prod_{k=0}^n(1+q^{3k+1})(1+q^{3k+2})$ are symmetric and unimodal for $n\geq 0$ by an analytical method.

Jia Wang, Yi Niu, Tianyi Xu, Mingming Ma

Motivation: Despite significant advances in Third-Generation Sequencing (TGS) technologies, Next-Generation Sequencing (NGS) technologies remain dominant in the current sequencing market. This is due to the lower error rates and richer analytical software of NGS than that of TGS. NGS technologies generate vast amounts of genomic data including short reads, q