November 2022 arXiv papers — page 38

Kohei Yamaguchi, Shintarou Yanagida

This note consists of two parts. The first part (\S 1 and \S 2) is a partial review of the works by van Meer and Stokman (2010), van Meer (2011) and Stokman (2014) which established a bispectral analogue of the Cherednik correspondence between quantum affine Knizhnik-Zamolodchikov equations and the eigenvalue problems of Macdonald type. In this review we foc

Youwei Huang, Sen Fang, Jianwen Li, Bin Hu

Smart contracts on the blockchain offer decentralized financial services but often lack robust security measures, leading to significant economic losses. While substantial research has focused on identifying vulnerabilities in smart contracts, a notable gap remains in evaluating the malicious intent behind their development. To address this, we introduce \te

Danila V. Babukhin, Denis V. Sych

Passive light-source side channel in quantum key distribution (QKD) makes the quantum signals more distinguishable thus provides additional information about the quantum signal to an eavesdropper. The explicit eavesdropping strategies aimed at the passive side channel known to date were limited to the separate measurement of the passive side channel in addit

Zi Xu, Zi-Qi Wang, Jun-Lin Wang, Yu-Hong Dai

In this paper, we consider a class of nonconvex-nonconcave minimax problems, i.e., NC-PL minimax problems, whose objective functions satisfy the Polyak-\L ojasiewicz (PL) condition with respect to the inner variable. We propose a zeroth-order alternating gradient descent ascent (ZO-AGDA) algorithm and a zeroth-order variance reduced alternating gradient desc

J. R. Espinosa, J. -F. Fortin

The tunneling potential method to calculate the action for vacuum decay is an alternative to the Euclidean bounce method that has a number of attractive features. In this paper we extend the formalism to general spacetime dimension $d>2$ and use it to give simple proofs of several results. For Minkowski or Anti de Sitter false vacua, we show that gravity or

Fabian Kröninger, Caroline Lasser, Jiří Vaníček

When the semiclassical Herman-Kluk propagator is used for evaluating quantum-mechanical observables or time-correlation functions, the initial conditions for the guiding trajectories are typically sampled from the Husimi density. Here, we employ this propagator to evolve the wavefunction itself. We investigate two grid-free strategies for the initial samplin

Lucas Kook, Philipp FM Baumann, Oliver Dürr, Beate Sick

Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems tractable. Here,

Bharati Naik

Measurements of the production of muons from heavy-flavour hadron decays at forward rapidity ($2.5 < y < 4.0$) in Pb--Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ and 5.02 TeV with the ALICE detector are presented along with the $R_{\rm AA}$ measurements in different centrality intervals as a function of transverse momentum, $p_{\rm T}$. Results of the measur

Johannes Graspeuntner, Markus Richter, Markus Aichhorn

Motivated by recent experiments and computational results on pyrochlore iridates, we compare single-particle properties of Y2Ir2O7 obtained from single-site dynamical mean-field calculations with results within the TRILEX approximation, where the latter takes non-local correlations into account. Our calculations are all based on ab-initio calculations within

Tobias Schlagenhauf, Tim Scheurenbrand

A common challenge in real world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset.

Michail Chabanov, Samuel D. Tootle, Elias R. Most, Luciano Rezzolla

The amplification of magnetic fields plays an important role in explaining numerous astrophysical phenomena associated with binary neutron-star mergers, such as mass ejection and the powering of short gamma-ray bursts. Magnetic fields in isolated neutron stars are often assumed to be confined to a small region near the stellar surface, while they are normall

Indranil Biswas, Sujoy Chakraborty, Arijit Dey

We consider the moduli space of stable parabolic Higgs bundles of rank $r$ and fixed determinant, and having full flag quasi-parabolic structures over an arbitrary parabolic divisor on a smooth complex projective curve $X$ of genus $g$, with $g\,\geq\, 2$. The group $\Gamma$ of $r$-torsion points of the Jacobian of $X$ acts on this moduli space. We describe

Ilya Peshkov, Héctor Olivares, Evgeniy Romenski

This paper presents a derivation of a first-order reduction and 3+1 decomposition of the teleparallel equivalent of general relativity (TEGR) in the pure-tetrad formulation (no spin connection). Our analysis demonstrates that in vacuum spacetimes, our 3+1 TEGR equations has the principal part of the differential operator equivalent to the one of tetrad refor

Klavs Hansen, Ori Licht, Adeliya Kurbanov, Yoni Toker

The time development of the excitation energy of molecules and clusters cooling by emission of thermal vibrational infrared radiation has been studied. The energy distributions and the photon emission rates develop into near-universal functions that can be characterized with only a few parameters, irrespective of the precise vibrational spectra and oscillato

ATLAS Collaboration

The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of $\sqrt s = 13$ TeV $pp$ collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yiel

Valerio Piomponi, Mattia Bernetti, Giovanni Bussi

Post-transcriptional modifications are crucial for RNA function, with roles ranging from the stabilization of functional RNA structures to modulation of RNA--protein interactions. Additionally, artificially modified RNAs have been suggested as optimal oligonucleotides for therapeutic purposes. The impact of chemical modifications on secondary structure has b

Huanle Zhang, Lei Fu, Mi Zhang, Pengfei Hu

Federated learning (FL) is a distributed model training paradigm that preserves clients' data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, comp

Ximing Li, Yuanzhi Jiang, Changchun Li, Yiyuan Wang

Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the recent deep PL learning literature have shown that the deep learning paradigms, e.g., self-training, contrastive learning, or class activate valu

Zheng Chen, Yulun Zhang, Jinjin Gu, Yongbing Zhang

Recently, Transformer architecture has been introduced into image restoration to replace convolution neural network (CNN) with surprising results. Considering the high computational complexity of Transformer with global attention, some methods use the local square window to limit the scope of self-attention. However, these methods lack direct interaction amo

Carsten van de Bruck, Gaspard Poulot, Elsa M. Teixeira

Diverse cosmological and astrophysical observations strongly hint at the presence of dark matter and dark energy in the Universe. One of the main goals of Cosmology is to explain the nature of these two components. It may well be that both dark matter and dark energy have a common origin. In this paper, we develop a model in which the dark sector arises due

Francisco J. Mendez, Miguel A. Mendez, Antonio Pasculli

The calibration of an advanced constitutive law for soil is a challenging task. This work describes GA-cal, a Fortran software for automatically calibrating constitutive laws using Genetic Algorithms (GA) optimization. The proposed approach sets the calibration problem as a regression, and the GA optimization is used to adjust the model parameters so that a

P. Jiang, K. Götzen, R. Kliemt, F. Nerling

Deep machine learning methods have been studied for the software trigger of the future PANDA experiment at FAIR, using Monte Carlo simulated data from the GEANT-based detector simulation framework PandaRoot. Ten physics channels that cover the main physics topics, including electromagnetic, exotic, charmonium, open charm, and baryonic reaction channels, have

Emil Gorm Nielsen, You Zhou

The individual studies of the anisotropic flow vector, flow angle and flow magnitude fluctuations with multi-particle correlations provide insight into the initial conditions and properties of the quark-gluon plasma (QGP) created in high-energy heavy-ion collisions. Recent measurements of these fluctuations have been available and the comparison to hydrodyna

Shima Rezaie, David M. J. Smeulders, Azahara Luna-Triguero

Sorbent materials are a promising alternative to advance hydrogen storage technologies. The general disadvantage is the relatively weak solid-gas interaction and adsorption energy, providing low gravimetric and volumetric capacities and extreme operational conditions. Here we propose Au-doped carbon nanotubes (CNTs) as an efficient alternative for reversible

Siyi Li, Mingrui Zhang, Matthew D. Piggott

Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms. This work proposes a surrogate model for the representation of wind turbine wakes based on a state-of-the-art graph representation learning method termed a graph neural network. The proposed end-to-end de

A. Vidal-García, A. Plat, E. Curtis-Lake, A. Feltre

We present the addition of nebular emission from the narrow-line regions (NLR) surrounding active galactic nuclei (AGN) to BEAGLE (BayEsian Analysis of GaLaxy sEds). Using a set of idealised spectra, we fit to a set of observables (emission-line ratios and fluxes) and test the retrieval of different physical parameters. We find that fitting to standard diagn

Chuan-Ming She, Yi-Zheng Fan, Liying Kang, Yaoping Hou

An $r$-uniform hypergraph is linear if every two edges intersect in at most one vertex. The $r$-expansion $F^{r}$ of a graph $F$ is the $r$-uniform hypergraph obtained from $F$ by enlarging each edge of $F$ with a vertex subset of size $r-2$ disjoint from the vertex set of $F$ such that distinct edges are enlarged by disjoint subsets. Let $ex_{r}^{lin}(n,F^{

Odysseas Bakas, Francesco Di Plinio, Ioannis Parissis, Luz Roncal

This article deals with maximal operators on ${\mathbb R}^n$ formed by taking arbitrary rotations of tensor products of a $d$-dimensional H\"ormander--Mihlin multiplier with the identity in $n-d$ coordinates, in the particular codimension 1 case $d=n-1$. These maximal operators are naturally connected to differentiation problems and maximally modulated singu

Peter A. Clarkson, Kerstin Jordaan, Ana Loureiro

We discuss polynomials orthogonal with respect to a semi-classical generalised higher order Freud weight \[\omega(x;t,\lambda)=|x|^{2\lambda+1}\exp\left(tx^2-x^{2m}\right),\qquad x\in\mathbb{R},\] with parameters $\lambda > -1$, $t\in\mathbb{R}$ and $m=2,3,\dots$\ . The sequence of generalised higher order Freud weights for $m=2,3,\dots$, forms a hierarchy o

Jacob Shams, Ben Nassi, Ikuya Morikawa, Toshiya Shimizu

In recent years, various watermarking methods were suggested to detect computer vision models obtained illegitimately from their owners, however they fail to demonstrate satisfactory robustness against model extraction attacks. In this paper, we present an adaptive framework to watermark a protected model, leveraging the unique behavior present in the model

Daniel J. Bosworth, Frederic Hummel, Peter Schmelcher

We show that the recently observed class of long-range ion-Rydberg molecules can be divided into two families of states, which are characterised by their unique electronic structures resulting from the ion-induced admixture of quantum defect-split Rydberg $n$P states with different low-field seeking high-$l$ states. We predict that in both cases these diatom

Shuai Zhao, Qing Zhou, Si-Ran Zhao, Xin-Yu Xu

Hardy's paradox provides an all-versus-nothing fashion to directly certify that quantum mechanics cannot be completely described by local realistic theory. However, when considering potential imperfections in experiments, like imperfect entanglement source and low detection efficiency, the original Hardy's paradox may induce a rather small Hardy violation an

Daniel Balzani, Maximilian Köhler, Timo Neumeier, Malte A. Peter

This paper presents computationally feasible rank-one relaxation algorithms for the efficient simulation of a time-incremental damage model with nonconvex incremental stress potentials in multiple spatial dimensions. While the standard model suffers from numerical issues due to the lack of convexity, the relaxation by rank-one convexification prevents non-ex

Matteo Bresciani, Mattia Dalla Brida, Leonardo Giusti, Michele Pepe

We present our progresses in the use of the non-perturbative renormalization framework based on considering QCD at finite temperature with shifted and twisted (for quarks only) boundary conditions in the compact direction. We report our final results in the application of this method for the non-perturbative renormalization of the flavor-singlet local vector

Jian-Jie Qiu, Yong Zhang, Jun-ichi Nakashima, Jiang-Shui Zhang

Context. IRAS 19312+1950 is an isolated infrared source that exhibits a characteristic quasi-point-symmetric morphology in the near- and mid-infrared images and is also very bright in molecular radio lines. Because of its unique observational characteristics, various observational studies have been conducted and several hypotheses have been proposed regardin

Marc Bostelmann, Steffen Wilksen, Frederik Lohof, Christopher Gies

We consider photonic arrays made from quantum emitters in optically coupled microcavities as a platform for entanglement generation. These offer a large degree of tunability with the possibility of site-selective optical excitation. Coherent pumping is considered to drive transitions between vacuum and entangled target states both in a time-dependent manner,

Yiqiao Jin, Xiting Wang, Yaru Hao, Yizhou Sun

In this paper, we move towards combining large parametric models with non-parametric prototypical networks. We propose prototypical fine-tuning, a novel prototypical framework for fine-tuning pretrained language models (LM), which automatically learns a bias to improve predictive performance for varying data sizes, especially low-resource settings. Our proto

Shivani Verma, Nisanth N. Nair

Alchemical free energy calculations are widely used in predicting pKa, and binding free energy calculations in biomolecular systems. These calculations are carried out using either Free Energy Perturbation (FEP) or Thermodynamic Integration (TI). Numerous efforts have been made to improve the accuracy and efficiency of such calculations, especially by boosti

François Berteloot, Maxence Brévard

In the context of holomorphic families of endomorphisms of $\mathbb P^k$, we prove that stability in the sense of [BBD18] is equivalent to a summability condition for the post-critical mass and to the convergence of a suitably defined ramification current. This allows us to both simplify the approach of [BBD18] and better relate stability to post-critical no

Marcos S. Ferreira

In this paper we use orthonormal basis for the Hardy space $H^{2}(\mathbb{T})$, formed by rational functions, to characterize complex symmetric Toeplitz operators on $H^{2}(\mathbb{T})$. As a result, we get examples of these operators whose symbols are non-trigonometric functions.

Peter Krizan

The Belle II detector at the SuperKEKB accelerator complex is covering a wide range of exciting physics topics. To achieve the project's research goals, a substantial increase of the data sample to 50~ab$^{-1}$ is needed, and for that, the luminosity has to reach the ambitious goal of $6 \times 10^{35}$ cm$^{-2}$ s$^{-1}$. The progress towards the design lum

Yu-Bo Li, Ning-Liu Wei

In this paper, by using the theory of circulant matrices we study some matrices over finite fields which involve the quadratic character and trinomial coefficients.

Tobias Kalmbach, Marcel Hoffmann, Nicolas Lell, Ansgar Scherp

Due to the large amount of daily scientific publications, it is impossible to manually review each one. Therefore, an automatic extraction of key information is desirable. In this paper, we examine STEREO, a tool for extracting statistics from scientific papers using regular expressions. By adapting an existing regular expression inclusion algorithm for our

Agustina Czenky

We give lower bounds for the rank of a symmetric fusion category in characteristic $p\geq 5$ in terms of $p$. We prove that the second Adams operation $\psi_2$ is not the identity for any non-trivial symmetric fusion category, and that symmetric fusion categories satisfying $\psi_2^a=\psi_2^{a-1}$ for some positive integer $a$ are super Tannakian. As an appl

Benno Käch, Dirk Krücker, Isabell Melzer-Pellmann, Moritz Scham

Fast data generation based on Machine Learning has become a major research topic in particle physics. This is mainly because the Monte Carlo simulation approach is computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation with complex correlations

Henrik Eklund

The contrasts and magnitude of observable signatures of small-scale features degrade as angular resolution decreases. High-cadence time-series of synthetic observable maps at 1.25 mm were produced from 3D magnetohydrodynamic Bifrost simulations of the solar atmosphere and degraded to the angular resolution corresponding to observational data with the Atacama

Milan Vojnovic, Kaifang Zhou

We consider a discrete-time voter model process on a set of nodes, each being in one of two states, either 0 or 1. In each time step, each node adopts the state of a randomly sampled neighbor according to sampling probabilities, referred to as node interaction parameters. We study the maximum likelihood estimation of the node interaction parameters from obse

Leo Micklem, Gabriel D. Weymouth, Blair Thornton

The optimal stiffness for soft swimming robots depends on swimming speed, which means no single stiffness can maximise efficiency in all swimming conditions. Tunable stiffness would produce an increased range of high-efficiency swimming speeds for robots with flexible propulsors and enable soft control surfaces for steering underwater vehicles. We propose an

Guy Avni, Ismael Jecker, Djordje Zikelic

Two-player zero-sum "graph games" are a central model, which proceeds as follows. A token is placed on a vertex of a graph, and the two players move it to produce an infinite "play", which determines the winner or payoff of the game. Traditionally, the players alternate turns in moving the token. In "bidding games", however, the players have budgets and in e

Elena Raycheva, Jared B. Garrison, Christian Schaffner, Gabriela Hug

In this paper, we use a formulation of the generation expansion planning problem with hourly temporal resolution, to investigate the impact of the availability of different sources of flexibility on a carbon-neutral central European power system (with a focus on Switzerland) for the year 2040. We assess the role of flexible generation, load shifting and impo

Alessandro D'Ortenzio, Costanzo Manes, Umut Orguner

Target tracking represents a state estimation problem recurrent in many practical scenarios like air traffic control, autonomous vehicles, marine radar surveillance and so on. In a Bayesian perspective, when phenomena like clutter are present, the vast majority of the existing tracking algorithms have to deal with association hypotheses which can grow in the

Benno Käch, Dirk Krücker, Isabell Melzer-Pellmann

Data generation based on Machine Learning has become a major research topic in particle physics. This is due to the current Monte Carlo simulation approach being computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation, but arguably more difficult

Per Erik Strandberg

There is a growing body of knowledge in the computer science, software engineering, software testing and software test automation disciplines. However, there is a challenge for researchers to evaluate their research findings, innovations and tools due to lack of realistic data. This paper presents the Westermo test results data set, more than one million ver

Philippe Weitz, Masi Valkonen, Leslie Solorzano, Circe Carr

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established bioma

Yuanhang Ning, Zheng Cai, Linhua Jiang, Xiaojing Lin

We present a study of seven spectroscopically confirmed (Ly$\alpha$ emitting) galaxies at redshift $z\simeq6$ using the $JWST$/NIRCam imaging data. These galaxies, with a wide range of Ly$\alpha$ luminosities, were recently observed in a series of NIRCam broad- and medium-bands. We constrain the rest-frame UV/optical continua and measure the H$\alpha$ line e

Paul Cousin, Aude Maignan

Graph-Rewriting Automata (GRA) are an extension of Cellular Automata to a dynamic structure using local graph-rewriting rules. This work introduces linear algebra based tools that allow for a practical investigation of their behavior in deeply extended time scales. A natural subset of GRA is explored in different ways thereby demonstrating the benefits of th

Daniel J Graham

The paper reviews methods that seek to draw causal inference from observational data and demonstrates how they can be applied to empirical problems in engineering research. It presents a framework for causal identification based on the concept of potential outcomes and reviews core contemporary methods that can be used to estimate causal quantities. The pape

Tim Räz

The interpretability of ML models is important, but it is not clear what it amounts to. So far, most philosophers have discussed the lack of interpretability of black-box models such as neural networks, and methods such as explainable AI that aim to make these models more transparent. The goal of this paper is to clarify the nature of interpretability by foc

Josef Seitz

We comment on approaches to the Cosmological Constant Problem in 4+1 dimensions, with the Standard Model fields confined to a 3-brane. We show that requiring maximal 4D symmetry only on this 3-brane (and not on all other 3-branes that foliate the 4+1-dimensional space) leads to a wide class of de Sitter solutions on the brane, with the Hubble constant source

Sayan Mondal, Saurabh Basu

We study the topological properties of a Haldane model on a band deformed dice lattice, which has three atoms per unit cell (call them as A, B and C) and the spectrum comprises of three bands, including a flat band. The bands are systematically deformed with an aim to study the evolution of topology and the transport properties. The deformations are induced

R. S. Beckmann, R. J. Smethurst, B. D. Simmons, A. Coil

Here we use the Horizon-AGN simulation to test whether the spins of SMBHs in merger-free galaxies are higher. We select samples using an observationally motivated bulge-to-total mass ratio of < 0.1, along with two simulation motivated thresholds selecting galaxies which have not undergone a galaxy merger since z = 2, and those SMBHs with < 10% of their mass

Rotem Shalev-Arkushin, Amit Moryossef, Ohad Fried

Translating spoken languages into Sign languages is necessary for open communication between the hearing and hearing-impaired communities. To achieve this goal, we propose the first method for animating a text written in HamNoSys, a lexical Sign language notation, into signed pose sequences. As HamNoSys is universal, our proposed method offers a generic solu

Eva Murphy, Whitney Huang, Julie Bessac, Jiali Wang

Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to climate change research. It is therefore crucial to accurately estimate the joint probability distribution of wind speed and direction. In this work we develop a conditional approach

William O'Mullane, Frossie Economou, Kian-Tat Lim, Fritz Mueller

Starting from a description of the Rubin Observatory Data Management System Architecture, and drawing on our experience with and involvement in a range of other projects including Gaia, SDSS, UKIRT, and JCMT, we derive a series of generic design patterns and lessons learned.

Marko Mlikota

Many economic environments involve units linked by a network. I develop an econometric framework that derives the dynamics of cross-sectional variables from the lagged innovation transmission along bilateral links and that can accommodate general patterns of how higher-order network effects accumulate over time. The proposed NVAR rationalizes the Spatial Aut

Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski

While there has been progress in developing non-vacuous generalization bounds for deep neural networks, these bounds tend to be uninformative about why deep learning works. In this paper, we develop a compression approach based on quantizing neural network parameters in a linear subspace, profoundly improving on previous results to provide state-of-the-art g

Ranbir Sharma, H K Jassal

Determination of cosmological parameters is a major goal in cosmology at present. The availability of improved data sets necessitates the development of novel statistical tools to interpret the inference from a cosmological model. In this paper, we combine the Principal Component Analysis (PCA) and Markov Chain Monte Carlo (MCMC) method to infer the paramete

Toni Mäkelä, André Hoang, Katerina Lipka, Sven-Olaf Moch

Single-differential cross section predictions for top quark pair production are presented at next-to-leading order, using running top quark mass renormalization schemes. The evolution of the mass of the top quark is performed in the MSR scheme $m_{\mathrm{t}}^{\textrm{MSR}}(\mu)$ for renormalization scales $\mu$ below the $\overline{\textrm{MS}}$ top quark m

Soroosh Tayebi Arasteh, Peter Isfort, Marwin Saehn, Gustav Mueller-Franzes

Due to the rapid advancements in recent years, medical image analysis is largely dominated by deep learning (DL). However, building powerful and robust DL models requires training with large multi-party datasets. While multiple stakeholders have provided publicly available datasets, the ways in which these data are labeled vary widely. For Instance, an insti

Federico Vaccari

This paper studies the organization of communication between biased senders and a receiver. Senders can misreport their private information at a cost. Efficiency is achieved by clearing information asymmetries without incurring costs. Results show that only one communication protocol is efficient, robust to collusion, and free from unnecessary complexities.

Ruxandra Bondarescu, Helena Ubach, Oleg Bulashenko, Andrew P. Lundgren

Massive objects located between Earth and a compact binary merger can act as a magnifying glass improving the sensitivity of gravitational wave detectors to distant events. Depending on the parameters of the system, a point mass lens between the detector and the source can either lead to a smooth frequency-dependent amplification of the gravitational wave si

Nikolay Kivel

We study relativistic corrections in exclusive $S$-wave charmonium decays into proton-antiproton final state. We calculate the NRQCD corrections to the dominant decay amplitude, which depend on the nucleon twist-3 light-cone distribution amplitudes only. It is shown that in this case the collinear factorisation is also valid beyond the leading-order approxim

David J. E. Marsh

I demonstrate a simple example of how the time series obtained from searches for ultralight bosonic dark matter (DM), such as the axion, can be used to determine whether it is in a coherent or incoherent quantum state. The example is essentially trivial, but I hope that explicitly addressing it provokes experimental exploration. In the standard coherent stat

Gabor Toth

We analyse a generalisation of the Galam model of binary opinion dynamics in which iterative discussions take place in local groups of individuals and study the effects of random deviations from the group majority. The probability of a deviation or flip depends on the magnitude of the majority. Depending on the values of the flip parameters which give the pr

Musa Furkan Keskin, Carina Marcus, Olof Eriksson, Henk Wymeersch

Phase noise (PN) can become a major bottleneck for integrated sensing and communications (ISAC) systems towards 6G wireless networks. In this paper, we consider an OFDM ISAC system with oscillator imperfections and investigate the impact of PN on monostatic sensing performance by performing a misspecified Cram\'er-Rao bound (MCRB) analysis. Simulations are c

M. Raquel Serial, Stefan Benders, Perrine Rotzetter, Daniel L. Brummerloh

Controlling the temperature distribution inside catalytic fixed bed reactors is crucial for yield optimization and process stability. Yet, in situ temperature measurements with spatial and temporal resolution are still challenging. In this work, we perform temperature measurements in a cylindrical fixed bed reactor by combining the capabilities of real-time

A. Ferraguti

Let $K$ be a number field, $f\in K[x]$ and $\alpha\in K$. A recent conjecture of Andrews and Petsche predicts that the dynamical Galois group of the pair $(f,\alpha)$ is abelian if and only if the pair $(f,\alpha)$ is $K^{\text{ab}}$-conjugated to $(g,\beta)$, where $g$ is a power or a Chebyshev map and $\beta$ is $\zeta$ or $\zeta+\zeta^{-1}$, respectively,

L. Cardani, I. Colantoni, A. Cruciani, F. De Dominicis

Radioactivity was recently discovered as a source of decoherence and correlated errors for the real-world implementation of superconducting quantum processors. In this work, we measure levels of radioactivity present in a typical laboratory environment (from muons, neutrons, and gamma's emitted by naturally occurring radioactive isotopes) and in the most com

Thomas Gehrmann, Tiziano Peraro, Lorenzo Tancredi

We compute the two-loop corrections to the helicity amplitudes for the coupling of a massive vector boson to a massless quark-antiquark pair and a gluon, accounting for vector and axial-vector couplings of the vector boson and distinguishing isospin non-singlet and singlet contributions. A new four-dimensional basis for the decomposition of the amplitudes in

Mathias B. M. Svendsen, Beatriz Olmos

When an emitter ensemble interacts with the electromagnetic field, dipole-dipole interactions are induced between the emitters. The magnitude and shape of these interactions are fully determined by the specific form of the electromagnetic field modes. If the emitters are placed in the vicinity of a nanophotonic waveguide, such as a cylindrical nanofiber, the

Pengfei Li, Gang Liu, Lin Tan, Jinying Liao

Medical image visual question answering (VQA) is a task to answer clinical questions, given a radiographic image, which is a challenging problem that requires a model to integrate both vision and language information. To solve medical VQA problems with a limited number of training data, pretrain-finetune paradigm is widely used to improve the model generaliz

Ennio Gozzi

In this paper we show that a method of quantization proposed few years ago (ann.of phys. (314),2005, 24) is equivalent to studying the system for values of the action close to zero. In this paper we also study the behaviour when the action gets very very large which could be the regime where dark energy and dark matter are invoked. Surprisingly the behaviour

Dongbin Wang, Jiaqi Nan, Lijun Yang, Aimee S. Morgans

Predictions of thermoacoustic instabilities in annular combustors are essential but difficult. Axial variations of flow and thermal parameters increase the cost of numerical simulations and restrict the application of analytical solutions. This work aims to find approximate analytical solutions for the acoustic field in annular ducts with linear gradients of

Ori Noked, Amir Levi, Shirly Someck, Ortal Amber-Vitos

Objective: Optogenetic manipulations of excitable cells enable activating or silencing specific types of neurons. By expressing two types of exogenous proteins, a single neuron can be depolarized using light of one wavelength and hyperpolarized with another. However, routing two distinct wavelengths into the same brain locality typically requires bulky optic

Philippe Baratta, Julien Bel, Sylvain Gouyou Beauchamps, Carmelita Carbone

We validate the COVMOS method introduced in Baratta et al. (2019) allowing for the fast simulation of catalogues of different cosmological field tracers (e.g. dark matter particles, halos, galaxies, etc.). The power spectrum and one-point probability distribution function of the underlying tracer density field are set as inputs of the method and are arbitrar

Alexander Tarnavsky Eitan, Shirly Someck, Mario Zajac, Eran Socher

In the intact brain, neural activity can be recorded using sensing electrodes and manipulated using light stimulation. Silicon probes with integrated electrodes and micro-LEDs enable the detection and control of neural activity using a single implanted device. Miniaturized solutions for recordings from small freely moving animals are commercially available,

Lisa W. K. Goh, Adrià Gómez-Valent, Valeria Pettorino, Martin Kilbinger

We consider coupled dark energy (CDE) cosmologies, where dark matter particles feel a force stronger than gravity, due to the fifth force mediated by a scalar field which plays the role of dark energy. We perform for the first time a tomographic analysis of coupled dark energy, where the coupling strength is parametrized and constrained in different redshift

Tim Whittaker, Romuald A. Janik, Yaron Oz

Chaos and turbulence are complex physical phenomena, yet a precise definition of the complexity measure that quantifies them is still lacking. In this work we consider the relative complexity of chaos and turbulence from the perspective of deep neural networks. We analyze a set of classification problems, where the network has to distinguish images of fluid

Yu-Tong Cao, Jingya Wang, Baosheng Yu, Dacheng Tao

Active learning has been proposed to reduce data annotation efforts by only manually labelling representative data samples for training. Meanwhile, recent active learning applications have benefited a lot from cloud computing services with not only sufficient computational resources but also crowdsourcing frameworks that include many humans in the active lea

Mahdi Abolghasemi, Richard Bean

In this paper, we examine the concept of the predict and optimise problem with specific reference to the third Technical Challenge of the IEEE Computational Intelligence Society. In this competition, entrants were asked to forecast building energy use and solar generation at six buildings and six solar installations, and then use their forecast to optimize e

Eden Saig, Nir Rosenfeld

Optimizing user engagement is a key goal for modern recommendation systems, but blindly pushing users towards increased consumption risks burn-out, churn, or even addictive habits. To promote digital well-being, most platforms now offer a service that periodically prompts users to take breaks. These, however, must be set up manually, and so may be suboptimal

B. A. Levitan, J. Eid, T. Pereg-Barnea

Experiments have observed superconductivity in atomically-thin metallic layers deposited on semiconducting substrates. As in any superconductor, it is important to determine the structure of the superconducting pairing function in order to reveal the mechanism responsible for superconductivity. To that end, we study the possible superconducting states of two

Mahdi Abolghasemi

Forecasting and optimisation are two major fields of operations research that are widely used in practice. These methods have contributed to each other growth in several ways. However, the nature of the relationship between these two fields and integrating them have not been explored or understood enough. We advocate the integration of these two fields and e

Lifang Pei, Yifei Li

We propose and analyze a structure-preserving parametric finite element method (SP-PFEM) to simulate the motion of closed curves governed by area-conserved generalized mean curvature flow in two dimensions (2D). We first present a variational formulation and rigorously prove that it preserves two fundamental geometric structures of the flows, i.e., (a) the c

Jiajie Yao, Congcong Xie

To evaluate the Hadamard finite-part integrals accurately, a novel interpolatory-type quadrature is proposed in this article. In our approach, numerical divided difference is utilized to represent the high order derivatives of the integrated function, which make it possible to reduced the numerical quadrature into a concise formula based on the cycle index f

Yasaman Abdollahian, Ehsan Tohidi, Martin Kasparick, Li Wang

MIMO technology has enabled spatial multiple access and has provided a higher system spectral efficiency (SE). However, this technology has some drawbacks, such as the high number of RF chains that increases complexity in the system. One of the solutions to this problem can be to employ reconfigurable antennas (RAs) that can support different radiation patte

Yu-Tong Cao, Ye Shi, Baosheng Yu, Jingya Wang

Federated learning enables multiple decentralized clients to learn collaboratively without sharing the local training data. However, the expensive annotation cost to acquire data labels on local clients remains an obstacle in utilizing local data. In this paper, we propose a federated active learning paradigm to efficiently learn a global model with limited

Bo Li, Xiaowei Wu, Chenyang Xu, Ruilong Zhang

Given a connected graph on whose edges we can build roads to connect the nodes, a number of agents hold possibly different perspectives on which edges should be selected by assigning different edge weights. Our task is to build a minimum number of roads so that every agent has a spanning tree in the built subgraph whose weight is the same as a minimum spanni

Nicolas M. Müller, Jochen Jacobs, Jennifer Williams, Konstantin Böttinger

Machine learning is a data-driven field, and the quality of the underlying datasets plays a crucial role in learning success. However, high performance on held-out test data does not necessarily indicate that a model generalizes or learns anything meaningful. This is often due to the existence of machine learning shortcuts - features in the data that are pre

Maximilian Zipfl, Felix Hertlein, Achim Rettinger, Steffen Thoma

Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving. Modeling the surrounding of an autonomous car using semantic relations, i.e., how different traffic participants relate in the context of traffic rule based behaviors, is hardly been considered in previous work. This stems