August 2022 arXiv papers — page 70

Daichi Tsuna, Yuki Takei, Toshikazu Shigeyama

Recent observations of a large fraction of Type II supernovae show traces of dense circumstellar medium (CSM) very close to the progenitor star. If this CSM is created by eruptive mass loss several months before core-collapse, the eruption itself may be visible as a precursor, helpful as an early warning of a near-future supernova. Using radiation hydrodynam

Ashraf Tantawy

Datasets are essential to apply AI algorithms to Cyber Physical System (CPS) Security. Due to scarcity of real CPS datasets, researchers elected to generate their own datasets using either real or virtualized testbeds. However, unlike other AI domains, a CPS is a complex system with many interfaces that determine its behavior. A dataset that comprises merely

Bing-Yang Lin, Daria Dziubałtowska, Piotr Macek, Andreas Penzkofer

In this paper, we investigate the performance of the Tangle 2.0 consensus protocol in a Byzantine environment. We use an agent-based simulation model that incorporates the main features of the Tangle 2.0 consensus protocol. Our experimental results demonstrate that the Tangle 2.0 protocol is robust to the bait-and-switch attack up to the theoretical upper bo

Xinnian Liang, Jing Li, Shuangzhi Wu, Jiali Zeng

Unsupervised summarization methods have achieved remarkable results by incorporating representations from pre-trained language models. However, existing methods fail to consider efficiency and effectiveness at the same time when the input document is extremely long. To tackle this problem, in this paper, we proposed an efficient Coarse-to-Fine Facet-Aware Ra

David Serrano Blanco

We study the solutions to the Dirac equation for the massive spinor field in the universal covering space of two-dimensional anti-de Sitter space. For certain values of the mass parameter, we impose a suitable set of boundary conditions which make the spatial component of the Dirac operator self-adjoint. Then, we use the transformation properties of the spin

Ioannis Besieris, Peeter Saari

Backflow, or retro-propagation, is a counterintuitive phenomenon where for a forward-propagating wave the energy or probability density locally propagates backward. In this study the energy backflow has been examined in connection with relatively simple causal unidirectional finite-energy solutions of the wave equation which are derived from a factorization

Eduardo Ploerer

The Future Circular Collider in the $\mathrm{e^{+}e^{-}}$ configuration offers the opportunity to significantly improve SM measurements with dedicated runs at the Z-pole, WW threshold, ZH (240 GeV), and $\mathrm{t}\bar{\mathrm{t}}$ threshold. With a factor of approximately $10^{5}$ more statistics at the Z-pole and $10^{4}$ at the WW threshold than at LEP, t

Franziska Greinert, Rainer Müller, Philipp Bitzenbauer, Malte S. Ubben

With the increasing industrial relevance of new quantum technologies, a well educated quantum workforce becomes increasingly crucial. The foreseeable lack of workforce raises important questions. What are the expectations regarding the future relevance of second generation quantum technologies? What are the requirements for the workforce in the coming quantu

Sebastian Scher, Simone Kopeinik, Andreas Trügler, Dominik Kowald

The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and data-driven approaches with dynamical mode

Jonathan S. Kent

Ordinary Deep Learning models require having the dimension of their outputs determined by a human practitioner prior to training and operation. For design tasks, this places a hard limit on the maximum complexity of any designs produced by a neural network, which is disadvantageous if a greater allowance for complexity would result in better designs. In this

Alejandro Mus, Ivan Marti-Vidal, Maciek Wielgus, Georgina Stroud

The Galactic Center (GC) presents one of the highest stellar densities in our Galaxy, making its surroundings an environment potentially rich in radio transients, such as pulsars and different kinds of flaring activity. In this paper, we present the first study of transient activity in the region of the GC based on Atacama Large Millimeter/submillimeter (mm/

Steven Kleinegesse, Andrew R. Lawrence, Hana Chockler

Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data. While most previous approaches to causal discovery have implicitly assumed that no expert domain knowledge is available, practitioners can often provide such domain knowledge from prior experience. Recent work has incorp

Gino Cassella, Peru D'Ornellas, Thomas Hodson, Willian M. H. Natori

Topological insulator phases of non-interacting particles have been generalized from periodic crystals to amorphous lattices, which raises the question whether topologically ordered quantum many-body phases may similarly exist in amorphous systems? Here we construct a soluble chiral amorphous quantum spin liquid by extending the Kitaev honeycomb model to ran

B. S. Koribalski

Science results from pilot surveys with the full 36-antenna Australian Square Kilometer Array Pathfinder (ASKAP) have increased strongly over the last few years. This trend is likely to continue with full surveys scheduled to commence later this year. Thanks to novel Phased Array Feeds each ASKAP pointing covers around 30 square degr, making it a fast survey

Michal Jablonowski

For immersed surfaces in the four-space, we have a generating set of the Swenton--Hughes--Kim--Miller spatial moves that relate singular banded diagrams of ambient isotopic immersions of those surfaces. We also have Yoshikawa--Kamada--Kawauchi--Kim--Lee planar moves that relate marked graph diagrams of ambient isotopic immersions of those surfaces. One can a

Hiddo Algera, Hanae Inami, Pascal Oesch, Laura Sommovigo

Cosmic dust is an essential component shaping both the evolution of galaxies and their observational signatures. How quickly dust builds up in the early Universe remains an open question that requires deep observations at (sub-)millimeter wavelengths to resolve. Here we use Atacama Large Millimeter Array observations of 45 galaxies from the Reionization Era

Bjørnulf Brekke, Roman Malyshev, Ingeborg-Helene Svenum, Sverre M. Selbach

The antiferromagnetic semiconductor CuFeS$_2$ belongs to a magnetic symmetry class that is of interest for spintronics applications. In addition, its crystal lattice is compatible with Si, making it possible to integrate it with non-magnetic semiconducting structures. Therefore, we investigate this material by finding the effective $\boldsymbol{k}\cdot\bolds

Manuel Brack, Patrick Schramowski, Björn Deiseroth, Kristian Kersting

Bootstrapping from pre-trained language models has been proven to be an efficient approach for building vision-language models (VLM) for tasks such as image captioning or visual question answering. However, outputs of these models rarely align with user's rationales for specific answers. In order to improve this alignment and reinforce commonsense reasons, w

David Berger, Farid Mohamed

We derive a necessary and sufficient condition for stochastic processes to have almost periodic finite dimensional distributions; in particular, we obtain characterizations for infinitely divisible processes to be almost periodic in terms of their characteristic triplets. Furthermore, we derive conditions when the process $(X_t)_{t\in\R}$ defined by the stoc

Yining Wang, Mingzhe Chen, Tao Luo, Walid Saad

In this paper, a semantic communication framework is proposed for textual data transmission. In the studied model, a base station (BS) extracts the semantic information from textual data, and transmits it to each user. The semantic information is modeled by a knowledge graph (KG) that consists of a set of semantic triples. After receiving the semantic inform

Martino Bernasconi, Alberto Marchesi, Francesco Trovò

Designing efficient algorithms to find Nash equilibrium (NE) refinements in sequential games is of paramount importance in practice. Indeed, it is well known that the NE has several weaknesses, since it may prescribe to play sub-optimal actions in those parts of the game that are never reached at the equilibrium. NE refinements, such as the extensive-form pe

John Molloy, John McDermid

Autonomous Systems (AS) are increasingly proposed, or used, in Safety Critical (SC) applications. Many such systems make use of sophisticated sensor suites and processing to provide scene understanding which informs the AS' decision-making. The sensor processing typically makes use of Machine Learning (ML) and has to work in challenging environments, further

Duo Zhang, Hangrui Bi, Fu-Zhi Dai, Wanrun Jiang

Machine learning assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation. With the accumulation of high-quality electronic structure data, a model that can be pretrained on all available data and finetuned on downstream tasks with a small additional effort would bring the field to a new stage.

Lukas Kirschner, Ezekiel Soremekun, Rahul Gopinath, Andreas Zeller

Often times, input data may ostensibly conform to a given input format, but cannot be parsed by a conforming program, for instance, due to human error or data corruption. In such cases, a data engineer is tasked with input repair, i.e., she has to manually repair the corrupt data such that it follows a given format, and hence can be processed by the conformi

Kevin Nguyen, Peter West

We compute the conserved charges associated with the asymptotic symmetries of massless particles by examining their free theory in Minkowski spacetime. We give a procedure to systematically deduce the fall off of the massless fields at spatial infinity and show that it has a universal behaviour when expressed in tangent space. We do this for generic massless

Binrui Shen, Qiang Niu, Shengxin Zhu

Graph matching aims to find correspondences between two graphs. This paper integrates several well-known graph matching algorithms into a framework: the constrained gradient method. The primary difference among these algorithms lies in tuning a step size parameter and constraining operators. By leveraging these insights, we propose an adaptive step size para

Swaroop Mishra, Elnaz Nouri

Controlling the text generated by language models and customizing the content has been a long-standing challenge. Existing prompting techniques proposed in pursuit of providing control are task-specific and lack generality; this provides overwhelming choices for non-expert users to find a suitable method for their task. The effort associated with those techn

Junan Pan, Zhihao Zhao

This paper proposes a deep autoencoder model based on Pytorch. This algorithm introduces the idea of Pytorch into the auto-encoder, and randomly clears the input weights connected to the hidden layer neurons with a certain probability, so as to achieve the effect of sparse network, which is similar to the starting point of the sparse auto-encoder. The new al

Emadaldin Mozafari-Majd, Visa Koivunen

In this paper, we address the problem of conducting statistical inference in settings involving large-scale data that may be high-dimensional and contaminated by outliers. The high volume and dimensionality of the data require distributed processing and storage solutions. We propose a two-stage distributed and robust statistical inference procedures coping w

Amin Nassiri-Rad, Kosar Asadi, Hassan Firouzjahi

We study the Brownian motion of a field where there are boundaries in the inflationary field space. Both the field and the boundary undergo Brownian motions with the amplitudes of the noises determined by the Hubble expansion rate of the corresponding dS spacetime. This setup mimics models of inflation in which curvature perturbation is induced from inhomoge

Junlee Kim, Eun-Joo Kim, YoungJun Kim, JungKeun Ahn

We report on the simulation results for the angular resolution of an electromagnetic (EM) sampling calorimeter with photons in the range of 100~MeV to 2~GeV. The simulation model of the EM calorimeter consists of alternating layers of a 1-mm-thick lead plate and a 5-mm-thick plastic scintillator plate. The scintillator plates are alternately segmented into h

Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen

Large language models have demonstrated the ability to generate both natural language and programming language text. Such models open up the possibility of multi-language code generation: could code generation models generalize knowledge from one language to another? Although contemporary code generation models can generate semantically correct Python code,

Peidi Xu, Faezeh Moshfeghifar, Torkan Gholamalizadeh, Michael Bachmann Nielsen

Accurate geometry representation is essential in developing finite element models. Although generally good, deep-learning segmentation approaches with only few data have difficulties in accurately segmenting fine features, e.g., gaps and thin structures. Subsequently, segmented geometries need labor-intensive manual modifications to reach a quality where the

Josef Valvoda, Ryan Cotterell, Simone Teufel

Every legal case sets a precedent by developing the law in one of the following two ways. It either expands its scope, in which case it sets positive precedent, or it narrows it, in which case it sets negative precedent. Legal outcome prediction, the prediction of positive outcome, is an increasingly popular task in AI. In contrast, we turn our focus to nega

Muhammad Muzammel, Mohd Zuki Yusoff, Mohamad Naufal Mohamad Saad, Faryal Sheikh

Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other road users. These possible blind-spot collisions can be identified early using vision-based object detection approaches. Yet, the existing s

Tobias Hartung, Karl Jansen, Chiara Sarti

Lattice field theory is a very powerful tool to study Feynman's path integral non-perturbatively. However, it usually requires Euclidean background metrics to be well-defined. On the other hand, a recently developed regularization scheme based on Fourier integral operator $\zeta$-functions can treat Feynman's path integral non-pertubatively in Lorentzian bac

Harish Chandra Rajpoot

This paper encompasses the mathematical derivations of the analytic and generalized formula and recurrence relations to find out the radii of n umber of circles inscribed or packed in the plane region bounded by circular arcs (including sectors, semi and quarter circles) & the straight lines. The values of radii obtained using analytic formula and recurrence

Fakhri Momeni, Stefan Dietze, Philipp Mayr, Kristin Biesenbender

Open Access (OA) facilitates access to articles. But, authors or funders often must pay the publishing costs preventing authors who do not receive financial support from participating in OA publishing and citation advantage for OA articles. OA may exacerbate existing inequalities in the publication system rather than overcome them. To investigate this, we st

Tung-Lam Duong, Van-Duc Le, Tien-Cuong Bui, Hai-Thien To

Although the smart camera parking system concept has existed for decades, a few approaches have fully addressed the system's scalability and reliability. As the cornerstone of a smart parking system is the ability to detect occupancy, traditional methods use the classification backbone to predict spots from a manual labeled grid. This is time-consuming and l

Finn-Frederik Stiewe, Tristan Winkel, Tobias Kleinke, Tobias Tubandt

In our study, we determine the alignment of magnetic domains in a CoFeB layer using THz radiation. We generate THz-pulses by fs-laser-pulses in magnetized CoFeB/Pt heterostructures, based on spin currents. An LT-GaAs Auston switch detects the radiation phase-sensitively and allows to determine the magnetization alignment. Our scanning technique with motorize

Jin Huang, Bosong Huang, Weihao Yu, Jing Xiao

Origin-Destination (OD) matrices record directional flow data between pairs of OD regions. The intricate spatiotemporal dependency in the matrices makes the OD matrix forecasting (ODMF) problem not only intractable but also non-trivial. However, most of the related methods are designed for very short sequence time series forecasting in specific application s

Quentin Leroy, Olivier Buisson, Alexis Joly

Supervised representation learning with deep networks tends to overfit the training classes and the generalization to novel classes is a challenging question. It is common to evaluate a learned embedding on held-out images of the same training classes. In real applications however, data comes from new sources and novel classes are likely to arise. We hypothe

Federico Izzo, Olof Runborg, Richard Tsai

We present convergence theory for corrected quadrature rules on uniform Cartesian grids for functions with a point singularity. We begin by deriving an error estimate for the punctured trapezoidal rule, and then derive error expansions. We define the corrected trapezoidal rules, based on the punctured trapezoidal rule, where the weights for the nodes close t

Dimitri Leemans, Klara Stokes

Triality is a classical notion in geometry that arose in the context of the Lie groups of type $D_4$. Another notion of triality, Wilson triality, appears in the context of reflexible maps. We build a bridge between these two notions, showing how to construct an incidence geometry with a triality from a map that admits a Wilson triality. We also extend a res

Angela Giraldi

A precision luminosity measurement is essential for LHC cross-section measurements to determine fundamental parameters of the standard model and constrain or discover beyond-the-standard-model phenomena. The luminosity of the CMS detector has been measured at the LHC Interaction Point 5 using proton-proton collisions at $\sqrt{s}=13$ TeV during the Run 2 dat

Alkida Balliu, Mohsen Ghaffari, Fabian Kuhn, Dennis Olivetti

The node-averaged complexity of a distributed algorithm running on a graph $G=(V,E)$ is the average over the times at which the nodes $V$ of $G$ finish their computation and commit to their outputs. We study the node-averaged complexity for some distributed symmetry breaking problems and provide the following results (among others): - The randomized node-ave

L. K. Dewangan, L. E. Pirogov, N. K. Bhadari, A. K. Maity

Numerous research studies on dust and molecular filaments have been conducted in star-forming sites, but only a limited number of studies have focused on ionized filaments. To observationally study this aspect, we present an analysis of multi-wavelength data of an area of $\sim$74.6 arcmin $\times$ 55 arcmin around l = 345.5 degree. Using the 843 MHz continu

Malcolm Woolfson

In Blind Source Separation (BSS), one estimates sources from data mixtures where the mixing coefficients are unknown. In the particular case of Sparse Component Analysis (SCA), each underlying source exists for only a finite amount of time when other sources are negligible. In this paper, one approach to SCA is presented where the data are represented using

Angela Giraldi

After the Higgs boson discovery, a main focus at the CERN LHC has been the measurement of its properties. Observing the Higgs boson associated production with a top quark-antiquark pair ($\mathrm{t\bar{t}H}$) is particularly interesting because it provides tree-level access to measuring the Higgs boson-top quark Yukawa coupling. However, the production of a

Xinzhong Er, Shude Mao

The number of identified Fast Radio Bursts (FRBs) is increasing rapidly with current and future facilities. Strongly lensed FRBs are expected to be found as well, which can provide precise time delays and thus have rich applications in cosmology and fundamental physics. However, the radio signal of lensed FRBs will be deflected by plasma in lens galaxies in

Marcel Grimmer, Haoyu Zhang, Raghavendra Ramachandra, Kiran Raja

The vast progress in synthetic image synthesis enables the generation of facial images in high resolution and photorealism. In biometric applications, the main motivation for using synthetic data is to solve the shortage of publicly-available biometric data while reducing privacy risks when processing such sensitive information. These advantages are exploite

David Cubero

An exact series expansion of the average velocity of cold atoms in dissipative optical lattices under probe driving, based on the amplitudes of the excited atomic density waves, is derived from the semiclassical equations for the phase space densities of the Zeeman ground-state sublevels. This expansion permits the identification of the precise contribution

Hsin-Chuang Chou

This paper introduces a notion of decompositions of integral varifolds into countably many integral varifolds, and the existence of such decomposition of integral varifolds whose first variation is representable by integration is established. Furthermore, this result can be generalized by replacing the class of integral varifolds by some classes of rectifiab

Maksim Golyadkin, Vitaliy Pozdnyakov, Leonid Zhukov, Ilya Makarov

Modern industrial facilities generate large volumes of raw sensor data during the production process. This data is used to monitor and control the processes and can be analyzed to detect and predict process abnormalities. Typically, the data has to be annotated by experts in order to be used in predictive modeling. However, manual annotation of large amounts

Edan Lerner, Eran Bouchbinder

Disordered elastic networks provide a framework for describing a wide variety of physical systems, ranging from amorphous solids, through polymeric fibrous materials to confluent cell tissues. In many cases, such networks feature two widely separated rigidity scales and are nearly floppy, yet they undergo a dramatic stiffening transition when driven to suffi

Payongkit Lakhan, Nannapas Banluesombatkul, Natchaya Sricom, Korn Surapat

Brain biometrics based on electroencephalography (EEG) have been used increasingly for personal identification. Traditional machine learning techniques as well as modern day deep learning methods have been applied with promising results. In this paper we present EEG-BBNet, a hybrid network which integrates convolutional neural networks (CNN) with graph convo

Kenichi Shimizu

If $\mathcal{M}$ is a finite abelian category and $\mathbf{T}$ is a linear right exact monad on $\mathcal{M}$, then the category $\mathbf{T}\mbox{-mod}$ of $\mathbf{T}$-modules is a finite abelian category. We give an explicit formula of the Nakayama functor of $\mathbf{T}\mbox{-mod}$ under the assumption that the underlying functor of the monad $\mathbf{T}$

Fetullah Atas, Grzegorz Cielniak, Lars Grimstad

This paper introduces a new method for robot motion planning and navigation in uneven environments through a surfel representation of underlying point clouds. The proposed method addresses the shortcomings of state-of-the-art navigation methods by incorporating both kinematic and physical constraints of a robot with standard motion planning algorithms (e.g.,

Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton

In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct is defined as the smallest element of learning. The second challenge is to predict the impact of learning one

Hai Pham, Guoxin Wang, Yijuan Lu, Dinei Florencio

Despite several successes in document understanding, the practical task for long document understanding is largely under-explored due to several challenges in computation and how to efficiently absorb long multimodal input. Most current transformer-based approaches only deal with short documents and employ solely textual information for attention due to its

Shujie Yang, Binchi Zhang, Shangbin Feng, Zhaoxuan Tan

Graph anomaly detection on attributed networks has become a prevalent research topic due to its broad applications in many influential domains. In real-world scenarios, nodes and edges in attributed networks usually display distinct heterogeneity, i.e. attributes of different types of nodes show great variety, different types of relations represent diverse m

S. Restuccia, G. M. Gibson, L. Cronin, M. J. Padgett

Quantifying the optical chirality of a sample requires the precise measurement of the rotation of the plane of linear polarisation of the transmitted light. Central to this notion is that the sample needs to be exposed to light of a defined polarisation state. We show that by using a polarisation-entangled photon source we can measure optical activity whilst

Rasmus Adler, Michael Klaes

The European Machinery Directive and related harmonized standards do consider that software is used to generate safety-relevant behavior of the machinery but do not consider all kinds of software. In particular, software based on machine learning (ML) are not considered for the realization of safety-relevant behavior. This limits the introduction of suitable

Oleh Fedkevych, Jonathan R. Gaunt

Multi-parton distributions in a proton, the nonperturbative quantities needed to make predictions for multiple scattering rates, are poorly constrained from theory and data and must be modelled. All Monte Carlo event generators that simulate multiple parton interactions (e.g. Pythia) contain such a model of multi-parton PDFs. One important theoretical constr

S. Sajad Dabiri, H. Cheraghchi

We present a method for simulating any non-interacting and time-periodic tight-binding Hamiltonian in Fourier space using electric circuits made of inductors and capacitors. We first map the time-periodic Hamiltonian to a Floquet Hamiltonian, which converts the time dimension into a Floquet dimension. In electric circuits, this Floquet dimension is simulated

Josef Valvoda, Naomi Saphra, Jonathan Rawski, Adina Williams

Recombining known primitive concepts into larger novel combinations is a quintessentially human cognitive capability. Whether large neural models in NLP can acquire this ability while learning from data is an open question. In this paper, we investigate this problem from the perspective of formal languages. We use deterministic finite-state transducers to ma

Luca Chiantini

In the space of sextic forms in 4 variables with a decomposition of length 18 we determine and describe a closed subvariety which contains all non-identifiable sextics. The description of the subvariety is geometric, but one can derive from that an algorithm which can guarantee that a given form is identifiable.

Nandita Pattnaik, Shujun Li, Jason R. C. Nurse

The security and privacy of smart home systems, particularly from a home user's perspective, have been a very active research area in recent years. However, via a meta-review of 52 review papers covering related topics (published between 2000 and 2021), this paper shows a lack of a more recent literature review on user perspectives of smart home security and

Amichay Vardi

The entanglement between two weakly coupled bosonic Josephson junctions is studied in relation to the classical mixed phasespace structure of the system, containing symmetry-related regular islands separated by chaos. The symmetry-resolved entanglement spectrum and bi-partite entanglement entropy of the system's energy eigenstates are calculated and compared

Dan Navon, Alex M. Bronstein

Vision-Transformers are widely used in various vision tasks. Meanwhile, there is another line of works starting with the MLP-mixer trying to achieve similar performance using mlp-based architectures. Interestingly, until now those mlp-based architectures have not been adapted for NLP tasks. Additionally, until now, mlp-based architectures have failed to achi

Gabriel Van Zandycke, Vladimir Somers, Maxime Istasse, Carlo Del Don

With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues. This paper introduces DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks for automated sport understanding. The main purpose of this

S. Sajad Dabiri, H. Cheraghchi, A. Sadeghi

We study the topology of the Floquet states and time-averaged optical conductivity of the lattice model of a thin topological insulator subject to a circularly polarized light using the extended Kubo formalism. Two driving regimes, the off-resonant and on-resonant, and two models for the occupation of the Floquet states, the ideal and mean-energy occupation,

Jiwon Ju, Hiraku Saito, Takashi Kurumaji, Max Hirschberger

We have investigated magnetic structures of the centrosymmetrric skyrmion material Gd2PdSi3 by means of polarized neutron scattering near zero field with an isotope-160Gd-enriched single crystal. In a previous study, magnetic structures in Gd2PdSi3 at low temperatures were studied by resonant X-ray scattering measurements [T. Kurumaji et al. Science 365, 914

Xun-Wei Xu, Jie-Qiao Liao, Hui Jing, Le-Man Kuang

Phase transition from the over-damping to under-damping states is a ubiquitous phenomenon in physical systems. However, what kind of symmetry is broken associated with this phase transition remains unclear. Here, we discover that this phase transition is determined by an anti-parity-time (anti-$\mathcal{PT}$) symmetry hidden in a single damping linear resona

Jordan Serres

We control the behavior of the Poincar{\'e} constant along the Polchinski renormalization flow using a dynamic version of $\Gamma$-calculus. We also treat the case of higher order eigenvalues. Our method generalizes a method introduced by B. Klartag and E. Putterman to analyze the evolution of log-concave distributions along the heat flow. Furthermore, we ap

Alexander V. Petrunin, Steffen Bochenek, Walter Richtering, Andrea Scotti

Micro- and nanogels are widely used to stabilise emulsions and simultaneously implement their responsiveness to the external stimuli. One of the factors that improves the emulsion stability is the nanogel softness. Here, we study how the softest nanogels that can be synthesised with precipitation polymerisation of N-isopropylacrylamide (NIPAM), the ultra-low

Sam Ellis, Octavio E. Martinez Manzanera, Vasileios Baltatzis, Ibrahim Nawaz

GANs are able to model accurately the distribution of complex, high-dimensional datasets, e.g. images. This makes high-quality GANs useful for unsupervised anomaly detection in medical imaging. However, differences in training datasets such as output image dimensionality and appearance of semantically meaningful features mean that GAN models from the natural

Maxime Henriquet, Michel Peyret, Stéphane Dominguez, Giovanni Barreca

The Quaternary geodynamics of the Central Mediterranean region is controlled by the migration of narrow orogenic belts within the slow Nubia-Eurasia plate convergence. As testified by the occurrence of major volcanic and seismic events, the Eastern Sicilian Margin is presently one of the most active regions. Using a Permanent-Scatterer approach, we process S

Patrick Fuhlert, Anne Ernst, Esther Dietrich, Fabian Westhaeusser

Deep neural networks for survival prediction outper-form classical approaches in discrimination, which is the ordering of patients according to their time-of-event. Conversely, classical approaches like the Cox Proportional Hazards model display much better calibration, the correct temporal prediction of events of the underlying distribution. Especially in t

Yuliang Wang, Xiaolai Li, Shuai Ren, Hadush Tedros Alem

Spherical-cap-shaped interfacial nanobubbles (NBs) forming on hydrophobic surfaces in aqueous solutions have extensively been studied both from a fundamental point of view and due to their relevance for various practical applications. In this study, the nucleation mechanism of spontaneously generated NBs at solid-liquid interfaces of immersed nanostructured

Refael Hassin, Jiesen Wang

We study a make-to-order system with a finite set of customers. Production is stochastic with a nonlinear dependence between the ordered quantity and the production rate. Customers may have to queue until their turn arrives, and therefore their order decisions interact. Specifically, while being served, customers are aware of the queue length and choose one

Julien Garaud, Anatolii Korneev, Albert Samoilenka, Alexander Molochkov

We demonstrate that superconductors with broken inversion symmetry support a family of stable, spatially localized configurations of the self-knotted magnetic field. These solutions, that we term ``toroflux,'' are the superconducting counterparts of the Chandrasekhar-Kendall states (spheromaks) that appear in highly conducting, force-free astrophysical and n

Paul Breiding, Mateusz Michałek, Leonid Monin, Simon Telen

Approximating periodic solutions to the coupled Duffing equations amounts to solving a system of polynomial equations. The number of complex solutions measures the algebraic complexity of this approximation problem. Using the theory of Khovanskii bases, we show that this number is given by the volume of a certain polytope. We also show how to compute all sol

Giuseppe Carbone, Cosimo Mandriota, Nicola Menga

We present a novel theory of the adhesive contact of linear viscoelastic materials against rigid substrates moving at constant velocity. Despite the non-conservative behavior of the system, the closure equation of the contact problem can be rigorously formulated in the form of a local energy balance. In the case of adhesiveless contacts, this is equivalent t

Chiara Bernardini, Annalisa Cesaroni

We consider second-order ergodic Mean-Field Games systems in the whole space $\mathbb{R}^N$ with coercive potential and aggregating nonlocal coupling, defined in terms of a Riesz interaction kernel. These MFG systems describe Nash equilibria of games with a large population of indistinguishable rational players attracted toward regions where the population i

Rita Sevastjanova, Eren Cakmak, Shauli Ravfogel, Ryan Cotterell

Neural language models are widely used; however, their model parameters often need to be adapted to the specific domains and tasks of an application, which is time- and resource-consuming. Thus, adapters have recently been introduced as a lightweight alternative for model adaptation. They consist of a small set of task-specific parameters with a reduced trai

François Desbouvries, Yohan Petetin, Achille Salaün

Hidden Markov Chains (HMC) and Recurrent Neural Networks (RNN) are two well known tools for predicting time series. Even though these solutions were developed independently in distinct communities, they share some similarities when considered as probabilistic structures. So in this paper we first consider HMC and RNN as generative models, and we embed both s

M. Tiwari, M. Wolfire, M. W. Pound, E. Tarantino

We quantified the effects of stellar feedback in RCW 49 by determining the physical conditions in different regions using the [CII] 158 $\mu$m and [OI] 63 $\mu$m observations from SOFIA, the $^{12}$CO (3-2) observations from APEX and the H$_2$ line observations from Spitzer telescopes. Large maps of RCW 49 were observed with the SOFIA and APEX telescopes, wh

Imen Sayar, Alexandre Bartel, Eric Bodden, Yves Le Traon

Nowadays, an increasing number of applications uses deserialization. This technique, based on rebuilding the instance of objects from serialized byte streams, can be dangerous since it can open the application to attacks such as remote code execution (RCE) if the data to deserialize is originating from an untrusted source. Deserialization vulnerabilities are

David Andrade, Raphael Stuhlmeier

We develop a general framework to describe the cubically nonlinear interaction of a unidirectional degenerate quartet of deep-water gravity waves. Starting from the discretised Zakharov equation, and thus without restriction on spectral bandwidth, we derive a planar Hamiltonian system in terms of the dynamic phase and a modal amplitude. This is characterised

Claudius Gros

The productivity of a common pool of resources may degrade when overly exploited by a number of selfish investors, a situation known as the tragedy of the commons (TOC). Without regulations, agents optimize the size of their individual investments into the commons by balancing incurring costs with the returns received. The resulting Nash equilibrium involves

Dan Navon, Alex M. Bronstein

Hyperparameter tuning is a common technique for improving the performance of neural networks. Most techniques for hyperparameter search involve an iterated process where the model is retrained at every iteration. However, the expected accuracy improvement from every additional search iteration, is still unknown. Calculating the expected improvement can help

Athanase Papadopoulos

I discuss Ren{\'e} Thom's approach to philosophy based on his mathematical background. At the same time, I will highlight his connection with Aristotle, his criticism of the modern view of science as a predictive process, his ideas on mathematical education, his position with respect to the French school of mathematics that was dominent in his time and his r

Ivonne Schwartz, Mark Kirstein

One challenge in the estimation of financial market agent-based models (FABMs) is to infer reliable insights using numerical simulations validated by only a single observed time series. Ergodicity (besides stationarity) is a strong precondition for any estimation, however it has not been systematically explored and is often simply presumed. For finite-sample

Athanase Papadopoulos

The sphericity of the form of the Earth was questioned around the year 1687, primarily, by Isaac Newton who deduced from his theory of universal gravitation that the Earth has the form of a spheroid flattened at the poles and elongated at the equator. In France, somepreeminent geographers were not convinced by Newton's arguments, and about the same period, b

Siddharth Barman, Arindam Khan, Sudarshan Shyam, K. V. N. Sreenivas

We study the fair allocation of indivisible goods among agents with identical, additive valuations but individual budget constraints. Here, the indivisible goods--each with a specific size and value--need to be allocated such that the bundle assigned to each agent is of total size at most the agent's budget. Since envy-free allocations do not necessarily exi

Christine Joblin

Carbon macromolecules are intermediates between small gas-phase species and larger dust structures. I illustrate how observations and dedicated laboratory experiments support this picture.

Finn Behrendt, Debayan Bhattacharya, Julia Krüger, Roland Opfer

Radiographs are a versatile diagnostic tool for the detection and assessment of pathologies, for treatment planning or for navigation and localization purposes in clinical interventions. However, their interpretation and assessment by radiologists can be tedious and error-prone. Thus, a wide variety of deep learning methods have been proposed to support radi

Tao He, Lianli Gao, Jingkuan Song, Yuan-Fang Li

Scene graph generation (SGG) is a fundamental task aimed at detecting visual relations between objects in an image. The prevailing SGG methods require all object classes to be given in the training set. Such a closed setting limits the practical application of SGG. In this paper, we introduce open-vocabulary scene graph generation, a novel, realistic and cha

Isabeau Birindelli, Giulio Galise, Hitoshi Ishii

In this paper we state some sharp maximum principle, i.e. we characterize the geometry of the sets of minima for supersolutions of equations involving the $k$-\emph{th fractional truncated Laplacian} or the $k$-\emph{th fractional eigenvalue} which are fully nonlinear integral operators whose nonlocality is somehow $k$-dimensional.