March 2023 arXiv papers — page 32

Boris Ryabko

We consider the problem of constructing an unconditionally secure cipher with a short key for the case where the probability distribution of encrypted messages is unknown. Note that unconditional security means that an adversary with no computational constraints can obtain only a negligible amount of information ("leakage") about an encrypted message (withou

Matthew Herbst, Andreas Fleischmann, Daniel Hengstler, David Mazibrada

The performance of superconducting devices like qubits, SQUIDs, and particle detectors is often limited by finite coherence times and 1/f noise. Various types of slow fluctuators in the Josephson junctions and the passive parts of these superconducting circuits can be the cause, and devices usually suffer from a combination of different noise sources, which

Vivien Cabannes, Leon Bottou, Yann Lecun, Randall Balestriero

Self-Supervised Learning (SSL) has emerged as the solution of choice to learn transferable representations from unlabeled data. However, SSL requires to build samples that are known to be semantically akin, i.e. positive views. Requiring such knowledge is the main limitation of SSL and is often tackled by ad-hoc strategies e.g. applying known data-augmentati

Gholamreza Fardipour Raki, Mohsen Khakzad, Shehu AbdusSalam

Photomultiplier tube (PMT) and Silicon Photo Multiplier (SiPM) are often used for detecting small number of photons or very weak radiations. A light guide usually connects these photodetectors to the test space. In this article, we investigate the effect of background signals caused by cosmic muons scintillation or interactions with PMT and SiPM, their light

Lisa Gaedke-Merzhäuser, Elias Krainski, Radim Janalik, Håvard Rue

Bayesian inference tasks continue to pose a computational challenge. This especially holds for spatial-temporal modeling where high-dimensional latent parameter spaces are ubiquitous. The methodology of integrated nested Laplace approximations (INLA) provides a framework for performing Bayesian inference applicable to a large subclass of additive Bayesian hi

M. Scalco, A. Livernois, E. Vesperini, M. Libralato

Several observational studies have shown that many Galactic globular clusters (GCs) are characterised by internal rotation. Theoretical studies of the dynamical evolution of rotating clusters have predicted that, during their long-term evolution, these stellar systems should develop a dependence of the rotational velocity around the cluster's centre on the m

Daniel Westerfeld, Maxime Großpietsch, Hannes Kakuschke, Holger Frahm

We study reduced density matrices of the integrable critical RSOS model in a particular topological sector containing the ground state. Similar as in the spin-$1/2$ Heisenberg model it has been observed that correlation functions of this model on short segments can be `factorized': they are completely determined by a single nearest-neighbour two-point functi

Ritwik Mondal, Levente Rózsa, Michael Farle, Peter M. Oppeneer

The dynamics of magnetic moments consist of a precession around the magnetic field direction and a relaxation towards the field to minimize the energy. While the magnetic moment and the angular momentum are conventionally assumed to be parallel to each other, at ultrafast time scales their directions become separated due to inertial effects. The inertial dyn

Ken Yamamoto

We observed the zig-zag motion of small Leidenfrost water droplets (radii less than 0.6 mm) on a hot, flat substrate. To understand this motion, we conducted an experiment using a glass capillary to fix a droplet at its edge and control the droplet height. Thermographic and interferometric observations reveal that the droplets rotated both vertically and azi

E. Brandon de Leon, J. Frauendiener, C. Klein

We present a computational approach to the classical Schottky problem based on Fay's trisecant identity for genus $g\geq 4$. For a given Riemann matrix $\mathbb{B}\in\mathbb{H}^{g}$, the Fay identity establishes linear dependence of secants in the Kummer variety if and only if the Riemann matrix corresponds to a Jacobian variety as shown by Krichever. The th

M. Punch, M. Senniappan, Y. Becherini, G. Kukec Mezek

In the context of atmospheric shower arrays designed for $\gamma$-ray astronomy and in the context of the ALTO project, we present: a study of the impact of heavier nuclei in the cosmic-ray background on the estimated $\gamma$-ray detection performance on the basis of dedicated Monte Carlo simulations, a method to calculate the sensitivity to a point-like so

Alberto Baldrati, Lorenzo Agnolucci, Marco Bertini, Alberto Del Bimbo

Composed Image Retrieval (CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption that describes the difference between the two images. The high effort and cost required for labeling datasets for CIR hamper the widespread usage of existing methods, as they rely on supervised learning. In this work, we propos

Jeppe R. Andersen, Andreas Maier, Daniel Maître

We demonstrate that cell resampling can eliminate the bulk of negative event weights in large event samples of high multiplicity processes without discernible loss of accuracy in the predicted observables. The application of cell resampling to much larger data sets and higher multiplicity processes such as vector boson production with up to five jets has bee

Kiran Kumar Ashish Bhyravabhottla, WonSook Lee

Title: Comparison between layer-to-layer network training and conventional network training using Deep Convolutional Neural Networks Abstract: Convolutional neural networks (CNNs) are widely used in various applications due to their effectiveness in extracting features from data. However, the performance of a CNN heavily depends on its architecture and train

Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto

Representing a manifold of very high-dimensional data with generative models has been shown to be computationally efficient in practice. However, this requires that the data manifold admits a global parameterization. In order to represent manifolds of arbitrary topology, we propose to learn a mixture model of variational autoencoders. Here, every encoder-dec

Benjamin Earp-Lynch, Bernadette Faye, Eva G. Goedhart, Ingrid Vukusic

Let $t$ be any imaginary quadratic integer with $|t|\geq 100$. We prove that the inequality \[ |F_t(X,Y)| = | X^4 - t X^3 Y - 6 X^2 Y^2 + t X Y^3 + Y^4 | \leq 1 \] has only trivial solutions $(x,y)$ in integers of the same imaginary quadratic number field as $t$. Moreover, we prove results on the inequalities $|F_t(X,Y)| \leq C|t|$ and $|F_t(X,Y)| \leq |t|^{

M. Damasso, D. Locci, S. Benatti, A. Maggio

For more than two years, we monitored with the HARPS-N spectrograph the 400 Myr-old star HD\,63433, which hosts two close-in (orbital periods $P_b\sim7.1$ and $P_c\sim20.5$ days) sub-Neptunes detected by the TESS space telescope, and it was announced in 2020. Using radial velocities and additional TESS photometry, we aim to provide the first measurement of t

Akash Choudhary, Sankalp Nambiar, Holger Stark

Microswimmer suspensions in Newtonian fluids exhibit unusual macroscale properties, such as a superfluidic behavior, which can be harnessed to perform work at microscopic scales. Since most biological fluids are non-Newtonian, here we study the rheology of a microswimmer suspension in a weakly viscoelastic shear flow. At the individual level, we find that th

Emma Sofie Skarstein, Sara Martino, Stefanie Muff

Measurement error (ME) and missing values in covariates are often unavoidable in disciplines that deal with data, and both problems have separately received considerable attention during the past decades. However, while most researchers are familiar with methods for treating missing data, accounting for ME in covariates of regression models is less common. I

Theo Diamandis, Guillermo Angeris

Public blockchains group submitted transactions into batches, called blocks. A natural question is how to determine which transactions are included in these batches. In this note, we show a gap between the welfare of so-called `fair' ordering, namely first-in-first-out (an ideal that a number of blockchain protocols strive to achieve), where the first transa

Suchetana Sadhukhan, Satish Bhagwatrao Aher, Pon Harshavardhanan, Dharma Raj

This study investigates the hourly concentrations of Ground-level Ozone (O3), Nitric Oxide (NO), Nitrogen Dioxide (NO2), and Oxides of Nitrogen (NOx) in ambient air, along with the various meteorological parameters viz., ambient temperature, relative humidity, wind speed, and solar radiation over one year, from August 2020 to July 2021 for an industrial and

Daniel Gunlycke, C. Stephen Hellberg, John P. T. Stenger

We present a cascaded variational quantum eigensolver algorithm that only requires the execution of a set of quantum circuits once rather than at every iteration during the parameter optimization process, thereby increasing the computational throughput. This algorithm uses a quantum processing unit to probe the needed probability mass functions and a classic

W. S. Cunningham, Y. Zhang, S. L. Thomas, O. El-Atwani

The formation of helium cavities in coarse-grained materials produces hardening proportional to the number density and size of the cavities and due to the interaction of dislocations with intragranular helium defects. In nanostructured metals containing a high density of interfacial sinks, preferential cavity formation on the grain boundaries instead produce

Rolf Larsson

The notion of confidence distributions is applied to inference about the parameter in a simple autoregressive model, allowing the parameter to take the value one. This makes it possible to compare to asymptotic approximations in both the stationary and the non stationary cases at the same time. The main point, however, is to compare to a Bayesian analysis of

Kevin Clark, Priyank Jaini

The excellent generative capabilities of text-to-image diffusion models suggest they learn informative representations of image-text data. However, what knowledge their representations capture is not fully understood, and they have not been thoroughly explored on downstream tasks. We investigate diffusion models by proposing a method for evaluating them as z

Motasem ElGamel, Andrew Mugler

Cells employ control strategies to maintain a stable size. Dividing at a target size (the `sizer' strategy) is thought to produce the tightest size distribution. However, this result follows from phenomenological models that ignore the molecular mechanisms required to implement the strategy. Here we investigate a simple mechanistic model for exponentially gr

Mohamed Abdel-Aty, Zijin Wang, Ou Zheng, Amr Abdelraouf

The application of Computer Vision (CV) techniques massively stimulates microscopic traffic safety analysis from the perspective of traffic conflicts and near misses, which is usually measured using Surrogate Safety Measures (SSM). However, as video processing and traffic safety modeling are two separate research domains and few research have focused on syst

Siteng Huang, Biao Gong, Yutong Feng, Min Zhang

Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relying on learning the joint representation of seen compositions, these methods ignore the explicit modeling of the state and object, thus limiting the exploitation of pre-trained know

Horatiu Nastase, Jacob Sonnenschein

In this paper we parallel the construction of Tong of a gauge theory for shallow water, by writing a gauge theory for the Euler fluid in 2+1 dimensions. We then extend it to an Euler fluid coupled to electromagnetic background. We argue that the gauge theory formulation provides a topological argument for the quantization of 2+1 dimensional Euler Hopfion sol

Anna Braghetto, Enzo Orlandini, Marco Baiesi

Explainable and interpretable unsupervised machine learning helps understand the underlying structure of data. We introduce an ensemble analysis of machine learning models to consolidate their interpretation. Its application shows that restricted Boltzmann machines compress consistently into a few bits the information stored in a sequence of five amino acids

P. Candia da Silva, D. Karamitros, T. McKelvey, A. Pilaftsis

We present a class of leptogenesis models where the light neutrinos acquire their observed mass through a symmetry-motivated construction. We consider an extension of the Standard Model, which includes three singlet neutrinos which have mass splittings comparable to their decay widths. We show that this tri-resonant structure leads to an appreciable increase

Francois Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner, Yih-Fang Huang

Online federated learning (FL) enables geographically distributed devices to learn a global shared model from locally available streaming data. Most online FL literature considers a best-case scenario regarding the participating clients and the communication channels. However, these assumptions are often not met in real-world applications. Asynchronous setti

Mario Martínez-Zarzuela, Javier González-Alonso, Míriam Antón-Rodríguez, Francisco J. Díaz-Pernas

Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient gros

Stuti Pathak, Thomas M. McDonald, Seppe Sels, Rudi Penne

The processing, storage and transmission of large-scale point clouds is an ongoing challenge in the computer vision community which hinders progress in the application of 3D models to real-world settings, such as autonomous driving, virtual reality and remote sensing. We propose a novel, one-shot point cloud simplification method which preserves both the sal

Guangzhi Tang, Ali Safa, Kevin Shidqi, Paul Detterer

Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate solution for energy-efficient edge computing. Yet, with the growing complexity of SNN algorithms, it isn't easy to properly benchmark and optimize their computational cost without hardware in the loop. Although digital neuromorphic processors have been widely adopted to b

Sayeh Gholipour Picha, Dawood AL Chanti, Alice Caplier

The success of deep learning models depends on the size and quality of the dataset to solve certain tasks. Here, we explore how far generated data can aid real data in improving the performance of Neural Networks. In this work, we consider facial expression recognition since it requires challenging local data generation at the level of local regions such as

Kelong Zhao, shuhuang Xiang

We present a novel barycentric interpolation algorithm designed for analytic functions $f\in\mathcal{A}(E)$ defined on the complex plane. The algorithm, which encompasses both polynomial and rational interpolation, is tailored to handle singularities near $E$. Our method is applicable to regions $E$ bounded by piecewise smooth Jordan curves, and it imposes n

Krzysztof Janowicz

This chapter presents some of the fundamental assumptions and principles that could form the philosophical foundation of GeoAI and spatial data science. Instead of reviewing the well-established characteristics of spatial data (analysis), including interaction, neighborhoods, and autocorrelation, the chapter highlights themes such as sustainability, bias in

Eli Friedman, Assaf Lehr, Alexey Gruzdev, Vladimir Loginov

The use of synthetic data for training computer vision algorithms has become increasingly popular due to its cost-effectiveness, scalability, and ability to provide accurate multi-modality labels. Although recent studies have demonstrated impressive results when training networks solely on synthetic data, there remains a performance gap between synthetic and

Gissel Velarde, Anindya Sudhir, Sanjay Deshmane, Anuj Deshmunkh

This paper evaluates XGboost's performance given different dataset sizes and class distributions, from perfectly balanced to highly imbalanced. XGBoost has been selected for evaluation, as it stands out in several benchmarks due to its detection performance and speed. After introducing the problem of fraud detection, the paper reviews evaluation metrics for

Xuan Zuo, Zhi-Yuan Fan, Hang Qian, Rui-Chang Shen

We show how to entangle two cavity-magnon polaritons (CMPs) formed by two strongly coupled microwave cavity and magnon modes. This is realized by introducing vibration phonons, via magnetostriction, into the system that are dispersively coupled to the magnon mode. Stationary entanglement between two CMPs can be achieved when they are respectively resonant wi

David Wu, Sebastian Jaimungal

The objectives of option hedging/trading extend beyond mere protection against downside risks, with a desire to seek gains also driving agent's strategies. In this study, we showcase the potential of robust risk-aware reinforcement learning (RL) in mitigating the risks associated with path-dependent financial derivatives. We accomplish this by leveraging a p

Samuel Bernard-Bernardet, David Dumas, Benjamin Apffel

Historically, the observation of half-spin particles was one of the most surprising features of quantum mechanics. They are often described as "objects that do not come back to their initial state after one turn but do after two turns". There are macroscopic implementations using constraints such as clamping a belt or ribbon that purport to show similar beha

Felix Fuentes-Hurtado, Jean-Baptiste Sibarita, Virgile Viasnoff

Microscopy images often suffer from high levels of noise, which can hinder further analysis and interpretation. Content-aware image restoration (CARE) methods have been proposed to address this issue, but they often require large amounts of training data and suffer from over-fitting. To overcome these challenges, we propose a novel framework for few-shot mic

Hiroki Sawada, Wataru Ohata, Jun Tani

The current study investigated possible human-robot kinaesthetic interaction using a variational recurrent neural network model, called PV-RNN, which is based on the free energy principle. Our prior robotic studies using PV-RNN showed that the nature of interactions between top-down expectation and bottom-up inference is strongly affected by a parameter, cal

Abdus Salam Khazi, Sebastian Pineda Arango, Josif Grabocka

Automatically optimizing the hyperparameters of Machine Learning algorithms is one of the primary open questions in AI. Existing work in Hyperparameter Optimization (HPO) trains surrogate models for approximating the response surface of hyperparameters as a regression task. In contrast, we hypothesize that the optimal strategy for training surrogates is to p

Molly G. Hayes, Morgan G. I. Langille, Hong Gu

By creating networks of biochemical pathways, communities of micro-organisms are able to modulate the properties of their environment and even the metabolic processes within their hosts. Next-generation high-throughput sequencing has led to a new frontier in microbial ecology, promising the ability to leverage the microbiome to make crucial advancements in t

Hannes Köhler

As a predictor's quality is often assessed by means of its risk, it is natural to regard risk consistency as a desirable property of learning methods, and many such methods have indeed been shown to be risk consistent. The first aim of this paper is to establish the close connection between risk consistency and $L_p$-consistency for a considerably wider clas

Xiuwei Xu, Ziwei Wang, Jie Zhou, Jiwen Lu

In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis. We empirically observe that sparse convolution operation causes larger quantization errors than standard convolution. However, conventional network quantization methods directly binarize the weights and activations in sparse convolution, resultin

Haley Wahl, Joanna Rankin, Arun Venkataraman, Timothy Olszanski

This paper continues our study of radio pulsar emission-beam configurations with the primary intent of extending study to the lowest possible frequencies. Here we focus on a group of 133 more recently discovered pulsars, most of which were included in the (100-200 MHz) LOFAR High Band Survey, observed with Arecibo at 1.4 GHz and 327 MHz, and some observed at

Ana Paula Jeakel, Jonas Pinheiro da Silva, Hermano Velten

We review the status of $f(R,T)$ cosmological models, where $T$ is the trace of the energy momentum tensor $T^{\mu\nu}$. We start focusing on the modified Friedmann equations for the minimally coupled gravitational Lagrangian of the type $f(R,T)=R +\alpha e^{\beta T} + \gamma_{n} T^{n}$. We show that in such a minimally coupled case there exists a useful con

Simone Giombi, Arkady A. Tseytlin

The Wilson loop operator in the $U(N)_k \times U(N)_{-k}$ ABJM theory at large $N$ and fixed level $k$ has a dual description in terms of a wrapped M2 brane in the M-theory background AdS$_4 \times S^7/\mathbb Z_k$. We consider the localization result for the $1\over 2$-BPS circular Wilson loop expectation value $W$ in this regime, and compare it to the pred

Alice Barbara Tumpach

This paper is devoted to the study of unparameterized simple curves in the plane. We propose diverse canonical parameterizations of a 2D-curve. For instance, the arc-length parameterization is canonical, but we consider other natural parameterizations like the parameterization proportionnal to the curvature of the curve. Both aforementionned parameterization

Lourenco Beirão Da Veiga, Yi Liu, Lorenzo Mascotto, Alessandro Russo

We introduce a nonconforming virtual element method for the Poisson equation on domains with curved boundary and internal interfaces. We prove arbitrary order optimal convergence in the energy and $L^2$ norms, and validate the theoretical results with numerical experiments. Compared to existing nodal virtual elements on curved domains, the proposed scheme ha

Jeffrey Shallit, Anatoly Zavyalov

We consider the implementation of the transduction of automatic sequences, and their generalizations, in the Walnut software for solving decision problems in combinatorics on words. We provide a number of applications, including (a) representations of n! as a sum of three squares (b) overlap-free Dyck words and (c) sums of Fibonacci representations. We also

Ran Wei, Anthony D. McDonald, Alfredo Garcia, Gustav Markkula

In this paper we introduce a general estimation methodology for learning a model of human perception and control in a sensorimotor control task based upon a finite set of demonstrations. The model's structure consists of i the agent's internal representation of how the environment and associated observations evolve as a result of control actions and ii the a

Alfred Curry, Richard Booth, James E. Owen, Subhanjoy Mohanty

In this work, we develop a rocky planet interior model and use it to investigate the evolution of catastrophically evaporating rocky exoplanets. These planets, detected through the dust tails produced by evaporative outflows from their molten surfaces, can be entirely destroyed in a fraction of their host star's lifetime. To allow for the major decrease in m

Midia Reshadi, David Gregg

Sparse tensor computing is a core computational part of numerous applications in areas such as data science, graph processing, and scientific computing. Sparse tensors offer the potential of skipping unnecessary computations caused by zero values. In this paper, we propose a new strategy for extending row-wise product sparse tensor accelerators. We propose a

Reut Kedem Orange, Nadav Opatovski, Dafei Xiao, Boris Ferdman

Diffractive optical elements (DOEs) have a wide range of applications in optics and photonics, thanks to their capability to perform complex wavefront shaping in a compact form. However, widespread applicability of DOEs is still limited, because existing fabrication methods are cumbersome and expensive. Here, we present a simple and cost-effective fabricatio

Nayara Fonseca, Veronica Guidetti, Will Trojak

A novel comparison is presented of the effect of optimiser choice on the accuracy of physics-informed neural networks (PINNs). To give insight into why some optimisers are better, a new approach is proposed that tracks the training trajectory curvature and can be evaluated on the fly at a low computational cost. The linear advection equation is studied for s

Felicitas Hörmann, Hannes Bartz

The sum-rank metric is a hybrid between the Hamming metric and the rank metric and suitable for error correction in multishot network coding and distributed storage as well as for the design of quantum-resistant cryptosystems. In this work, we consider the construction and decoding of folded linearized Reed-Solomon (FLRS) codes, which are shown to be maximum

Grzegorz Adamski, Małgorzata Bednarska-Bzdȩga, Václav Blažej

Online Ramsey game is played between Builder and Painter on an infinite board $K_{\mathbb N}$. In every round Builder selects an edge, then Painter colors it red or blue. Both know target graphs $H_1$ and $H_2$. Builder aims to create either a red copy of $H_1$ or a blue copy of $H_2$ in $K_{\mathbb N}$ as soon as possible, and Painter tries to prevent it. T

Robin G. C. Maack, Jonas Lukasczyk, Julien Tierny, Hans Hagen

This paper presents a well-scaling parallel algorithm for the computation of Morse-Smale (MS) segmentations, including the region separators and region boundaries. The segmentation of the domain into ascending and descending manifolds, solely defined on the vertices, improves the computational time using path compression and fully segments the border region.

Tarek Saier, Youxiang Dong, Michael Färber

In the wake of information overload in academia, methodologies and systems for search, recommendation, and prediction to aid researchers in identifying relevant research are actively studied and developed. Existing work, however, is limited in terms of granularity, focusing only on the level of papers or a single type of artifact, such as data sets. To enabl

Tapas Singha

We study the mean first passage time of a one-dimensional active fluctuating membrane that is stochastically returned to the same flat initial condition at a finite rate. We start with a Fokker Planck equation to describe the evolution of the membrane coupled with an Ornstein-Uhlenbeck type of active noise. Using the method of characteristics, we solve the e

Giovanni Masciocchi, Thomas J. Kools, Pingzhi Li, Adrien A. D. Petrillo

Synthetic ferrimagnets are an attractive materials class for spintronics as they provide access to all-optical switching of magnetization and, at the same time, allow for ultrafast domain wall motion at angular momentum compensation. In this work, we systematically study the effects of strain on the perpendicular magnetic anisotropy and magnetization compens

Milan Bhan, Nina Achache, Victor Legrand, Annabelle Blangero

Attention mechanisms have played a crucial role in the development of complex architectures such as Transformers in natural language processing. However, Transformers remain hard to interpret and are considered as black-boxes. This paper aims to assess how attention coefficients from Transformers can help in providing interpretability. A new attention-based

Kaelin Cook-Powell, David Jensen, Eric Larson, Hannah Larson

We generalize the Embedding Theorem of Eisenbud-Harris from classical Brill-Noether theory to the setting of Hurwitz-Brill-Noether theory. More precisely, in classical Brill-Noether theory, the embedding theorem states that a general linear series of degree d and rank r on a general curve of genus g is an embedding if r is at least 3. If \(f \colon C \to \ma

Denis G. Artiukhin, Ian H. Godtliebsen, Gunnar Schmitz, Ove Christiansen

We present a new program implementation of the gaussian process regression adaptive density-guided approach [J. Chem. Phys. 153 (2020) 064105] in the MidasCpp program. A number of technical and methodological improvements made allowed us to extend this approach towards calculations of larger molecular systems than those accessible previously and maintain the

Alberto Villani, Giovanni Cortigiani, Bernardo Brogi, Nicole D'Aurizio

Metaverse is an immersive shared space that remote users can access through virtual and augmented reality interfaces, enabling their avatars to interact with each other and the surrounding. Although digital objects can be manipulated, physical objects cannot be touched, grasped, or moved within the metaverse due to the lack of a suitable interface. This work

Haoyan Chen, Yi Zhang

In general, the energy spectrum of a non-Hermitian system turns out to be complex, which is not so satisfactory since the time evolution of eigenstates with complex eigenvalues is either exponentially growing or decaying. Here we provide a sufficient and necessary condition of the real spectrum under open boundary conditions for one-dimensional non-Hermitian

Andrea Aiello

One of the most puzzling consequences of interpreting quantum mechanics in terms of concepts borrowed from classical physics, is the so-called wave-particle duality. Usually, wave-particle duality is illustrated in terms of complementarity between path distinguishability and fringe visibility in interference experiments. In this work, we instead propose a ne

Ioana Ciuclea, Alice Barbara Tumpach, Cornelia Vizman

The gauge invariant elastic metric on the shape space of surfaces involves the mean curvature and the normal deformation, i.e. the sum and the difference of the principal curvatures $\kappa_1,\kappa_2$. The proposed gauge invariant elastic metrics on the space of surfaces decorated with curves involve, in addition, the geodesic and normal curvatures $\kappa_

Y. Selmani, H. Labrim, A. Jabar, L. Bahmad

Using the density functional theory (DFT) in combination with Boltzmann transport theory, the influence of Mg concentrations (x) doping on the thermoelectric properties of Hg1-xMgxSe ternary alloys was systematically investigated. The generalized gradient approximations of Perdew-Burke-Ernzerhof (GGA-PBE) have been used to illustrate the exchange correlation

Lin Qiao, Gavin A. L. Coleman, Thomas J. Haworth

We demonstrate that planet formation via pebble accretion is sensitive to external photoevaporation of the outer disc. In pebble accretion, planets grow by accreting from a flux of solids (pebbles) that radially drift inwards from the pebble production front. If external photoevaporation truncates the outer disc fast enough, it can shorten the time before th

Moustafa Rahal, Benoit Denis, Kamran Keykhosravi, Musa Furkan Keskin

The technology of reconfigurable intelligent surfaces (RIS) has been showing promising potential in a variety of applications relying on Beyond-5G networks. Reconfigurable intelligent surface (RIS) can indeed provide fine channel flexibility to improve communication quality of service (QoS) or restore localization capabilities in challenging operating condit

Zhicheng Zhang, Yasumasa Fujisaki

In this paper, we explore the discrete time sparse feedback control for a linear invariant system, where the proposed optimal feedback controller enjoys input sparsity by using a dynamic linear compensator, i.e., the components of feedback control signal having the smallest possible nonzero values. The resulting augmented dynamics ensures closed-loop stabili

Bartholomaeaus Jagielski, Uwe Wenzel, Thomas Sunn Pedersen, Andre Melzer

We have set up a diagnostic magnet (D-Mag) laboratory for a wide range of applications in plasma physics. It consists of a superconducting magnet for field strengths of up to 5.9 T. The main purpose is to provide an experimental environment for the development of plasma diagnostics for nuclear fusion studies and the investigation of dusty plasmas in strong m

Yi-Cheng Zhao, Chun-Meng Tang, Liang Tang

In this article, we study the mass spectrum of the low-lying triply heavy hybrid baryon, which consists of three valence heavy quarks in a color octet and one valence gluon, with spin-parity $J^P=(\frac{1}{2})^+$ via QCD sum rules. This is the first study on the triply heavy hybrid baryons in the framework of QCD sum rules. After performing the QCD sum rule

Huaqing Guan, Shaosong Huang, Fuyang Tian, Chenyang Lu

We present a proof-of-principle demonstration of a universal law for the element Mn, which greatly enhances vacancy diffusion through an anomalous Friedel Oscillation effect in a series of Ni-based concentrated solid-solution alloys, regardless of the type of atom involved. The antiferromagnetic element Mn possesses a unique half-filled 3d electron structure

Luca Giorgetti

Conformal Quantum Field Theories (CFT) in 1 or 1+1 spacetime dimensions (respectively called chiral and full CFTs) admit several "axiomatic" (mathematically rigorous and model-independent) formulations. In this note, we deal with the von Neumann algebraic formulation due to Haag and Kastler, mainly restricted to the chiral CFT setting. Irrespectively of the

Daniel Ackerberg, Garth Frazer, Kyoo il Kim, Yao Luo

We revisit identification based on timing and information set assumptions in structural models, which have been used in the context of production functions, demand equations, and hedonic pricing models (e.g. Olley and Pakes (1996), Blundell and Bond (2000)). First, we demonstrate a general under-identification problem using these assumptions in a simple vers

XinXin Qi, Hao Sun

We discuss the possibility of unifying dark matter physics and inflation in the $Z_5$ model of the two-component dark matter. Inflation driven by the two-component dark matter fields can be divided into two cases, singlet dark matter inflation and mixed dark matter inflation, where both two-component play the role of inflaton in the latter case. For dark mat

Yang Lu, Pinxin Qian, Gang Huang, Hanzi Wang

Personalized Federated Learning (PFL) aims to learn personalized models for each client based on the knowledge across all clients in a privacy-preserving manner. Existing PFL methods generally assume that the underlying global data across all clients are uniformly distributed without considering the long-tail distribution. The joint problem of data heterogen

Fumiaki Sato, Ryo Hachiuma, Taiki Sekii

This study investigates unsupervised anomaly action recognition, which identifies video-level abnormal-human-behavior events in an unsupervised manner without abnormal samples, and simultaneously addresses three limitations in the conventional skeleton-based approaches: target domain-dependent DNN training, robustness against skeleton errors, and a lack of n

Ran Yi, Haoyuan Tian, Zhihao Gu, Yu-Kun Lai

Image aesthetics assessment (IAA) is a challenging task due to its highly subjective nature. Most of the current studies rely on large-scale datasets (e.g., AVA and AADB) to learn a general model for all kinds of photography images. However, little light has been shed on measuring the aesthetic quality of artistic images, and the existing datasets only conta

Alice Barbara Tumpach

For $s >\frac{3}{2}$, the group of Sobolev class s diffeomorphisms of the circle is a smooth manifold modeled on the space of Sobolev class s sections of the tangent bundle of the circle. It is a topological group in the sense that multiplication given by the composition of applications is well-defined and continuous, the inverse is continuous, left translat

Christoph J. Börner, Ingo Hoffmann, John H. Stiebel

Models for spin systems known from statistical physics are applied by analogy in econometrics in the form of agent-based models. Researchers suggest that the state variable temperature $T$ corresponds to volatility $\sigma$ in capital market theory problems. To the best of our knowledge, this has not yet been theoretically derived, for example, for an ideal

Victor P. Seidel, Christoph Riedl

Online innovation communities allow for a search for novel solutions within a design space bounded by constraints. Past research has focused on the effect of creative constraints on individual projects, but less is known about how constraints affect learning from repeated design submissions and the effect of the technical constraints that are integral to onl

Yunhao Chen, Yunjie Zhu, Zihui Yan, Jianlu Shen

Despite consistent advancement in powerful deep learning techniques in recent years, large amounts of training data are still necessary for the models to avoid overfitting. Synthetic datasets using generative adversarial networks (GAN) have recently been generated to overcome this problem. Nevertheless, despite advancements, GAN-based methods are usually har

Andrea Cosso, Mattia Martini

In this paper we investigate the approximation of continuous functions on the Wasserstein space by smooth functions, with smoothness meant in the sense of Lions differentiability. In particular, in the case of a Lipschitz function we are able to construct a sequence of infinitely differentiable functions having the same Lipschitz constant as the original fun

George Lazarides, Qaisar Shafi, Amit Tiwari

We explore a variety of composite topological structures that arise from the spontaneous breaking of $SO(10)$ to $SU(3)_c \times U(1)_{em}$ via one of its maximal subgroups $SU(5) \times U(1)_\chi$, $SU(4)_c \times SU(2)_L \times SU(2)_R$, and $SU(5) \times U(1)_X$ (also known as flipped $SU(5)$). They include i) a network of $\mathbb{Z}$ strings which devel

Yoshimasa Uematsu, Takashi Yamagata

This paper proposes novel inferential procedures for discovering the network Granger causality in high-dimensional vector autoregressive models. In particular, we mainly offer two multiple testing procedures designed to control the false discovery rate (FDR). The first procedure is based on the limiting normal distribution of the $t$-statistics with the debi

Z. C. Tu

The Brownian motion of a particle with higher-derivative dynamics (HDD) coupling with a bath consisting of harmonic oscillators is investigated. The Langevin equation and corresponding Fokker-Planck equation for the Brownian motion of the HDD particle are derived. As a case study, we particularly consider a stochastic Pais-Uhlenbeck oscillator. It is found t

M. dal Ponte, B. Santiago, A. Carnero Rosell, L. De Paris

We present a sample of 19,583 ultracool dwarf candidates brighter than z $\leq 23$ selected from the Dark Energy Survey DR2 coadd data matched to VHS DR6, VIKING DR5 and AllWISE covering $\sim$ 4,800 $deg^2$. The ultracool candidates were first pre-selected based on their (i-z), (z-Y), and (Y-J) colours. They were further classified using a method that compa

Nour Skaf, Anthony Boccaletti, Eric Pantin, Philippe Thebault

[abridged] We analyzed mid-infrared high-contrast coronagraphic images of the beta Pictoris system, taking advantage of the NEAR experiment using the VLT/VISIR instrument. The goal of our analysis is to investigate both the detection of the planet beta Pictoris b and of the disk features at mid-IR wavelengths. In addition, by combining several epochs of obse

Přemysl Jedlička, Agata Pilitowska

We study 2-reductive non-involutive non-degenerate set-theoretic solutions of the Yang-Baxter equation. We give a combinatorial construction of any such solution of any (even infinite) size. We also prove that solutions associated to a skew left brace are 2-reductive if and only if the skew left brace is nilpotent of class 2. Moreover, all such skew left bra

Yanick Ricard, Thierry Alboussière

The radial density of planets increases with depth due to compressibility, leading to impacts on their convective dynamics. To account for these effects, including the presence of a quasi-adiabatic temperature profile and entropy sources due to dissipation, the compressibility is expressed through a dissipation number, $\mathcal{D}$, proportional to the plan

Alireza Sharifian, Nuno Cardoso, Pedro Bicudo

We compute the spectra of flux tubes formed between a static quark antiquark pair up to a significant number of excitations and for eight symmetries of the flux tubes, up to $\Delta_u$, using pure $SU(3)$ gauge lattice QCD in 3+1 dimensions. To accomplish this goal, we use a large set of appropriate operators, an anisotropic tadpole improved action, smearing

Élise Fressart, Barbara Verfürth

This work is concerned with the classical wave equation with a high-contrast coefficient in the spatial derivative operator. We first treat the periodic case, where we derive a new limit in the one-dimensional case. The behavior is illustrated numerically and contrasted to the higher-dimensional case. For general unstructured high-contrast coefficients, we p