May 2022 arXiv papers — page 73

Mohan Hasama, Jing Li

In recommender systems, models mostly use a combination of embedding layers and multilayer feedforward neural networks. The high-dimensional sparse original features are downscaled in the embedding layer and then fed into the fully connected network to obtain prediction results. However, the above methods have a rather obvious problem, that is, the features

Aleix Bassolas, Sergio Gómez, Alex Arenas

Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens, and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processin

YuHao Liu, Teng Fu, Jean Barbier, TianQi Hou

We recently showed in [1] the superiority of certain structured coding matrices ensembles (such as partial row-orthogonal) for sparse superposition codes when compared with purely random matrices with i.i.d. entries, both information-theoretically and under practical vector approximate message-passing decoding. Here we generalize this result to binary input

Javier Cembrano, Felix Fischer, David Hannon, Max Klimm

Impartial selection is the selection of an individual from a group based on nominations by other members of the group, in such a way that individuals cannot influence their own chance of selection. For this problem, we give a deterministic mechanism with an additive performance guarantee of $O(n^{(1+\kappa)/2})$ in a setting with $n$ individuals where each i

Jaroslaw Rzepecki, Daniel Bates, Chris Doran

We present a novel method for using Neural Networks (NNs) for finding solutions to a class of Partial Differential Equations (PDEs). Our method builds on recent advances in Neural Radiance Field research (NeRFs) and allows for a NN to converge to a PDE solution much faster than classic Physically Informed Neural Network (PINNs) approaches.

Qinqin Tang, F. Richard Yu, Renchao Xie, Azzedine Boukerche

The Internet of intelligence is conceived as an emerging networking paradigm, which will make intelligence as easy to obtain as information. This paper provides an overview of the Internet of intelligence, focusing on motivations, architecture, enabling technologies, applications, and existing challenges. This can provide a good foundation for those who are

Paolo Felli, Marco Montali, Sarah Winkler

The analysis of complex dynamic systems is a core research topic in formal methods and AI, and combined modelling of systems with data has gained increasing importance in applications such as business process management. In addition, process mining techniques are nowadays used to automatically mine process models from event data, often without correctness gu

Michał Stolarz, Alex Mitrevski, Mohammad Wasil, Paul G. Plöger

Children with Autism Spectrum Disorder find robots easier to communicate with than humans. Thus, robots have been introduced in autism therapies. However, due to the environmental complexity, the used robots often have to be controlled manually. This is a significant drawback of such systems and it is required to make them more autonomous. In particular, the

André Artelt, Stelios Vrachimis, Demetrios Eliades, Marios Polycarpou

Transparency is a major requirement of modern AI based decision making systems deployed in real world. A popular approach for achieving transparency is by means of explanations. A wide variety of different explanations have been proposed for single decision making systems. In practice it is often the case to have a set (i.e. ensemble) of decisions that are u

LHCb collaboration

A search for $CP$ and $P$ violation in charmless four-body $B^{0} \to p \bar p K^{+} \pi^{-}$ decays is performed using triple-product asymmetry observables. It is based on proton-proton collision data collected by the LHCb experiment at centre-of-mass energies of $7$, $8$ and $13$ TeV, corresponding to a total integrated luminosity of $8.4$ fb$^{-1}$. The $

Yonatan Nakar, Dana Ron

We study the dynamics of (synchronous) one-dimensional cellular automata with cyclical boundary conditions that evolve according to the majority rule with radius $ r $. We introduce a notion that we term cell stability with which we express the structure of the possible configurations that could emerge in this setting. Our main finding is that apart from the

Alberto Espuny Díaz, Joseph Hyde

Let $G$ be a graph obtained as the union of some $n$-vertex graph $H_n$ with minimum degree $\delta(H_n)\geq\alpha n$ and a $d$-dimensional random geometric graph $G^d(n,r)$. We investigate under which conditions for $r$ the graph $G$ will a.a.s. contain the $k$-th power of a Hamilton cycle, for any choice of $H_n$. We provide asymptotically optimal conditio

Anton Trusov, Elena Limonova, Dmitry Nikolaev, Vladimir V. Arlazarov

Low-bit quantized neural networks are of great interest in practical applications because they significantly reduce the consumption of both memory and computational resources. Binary neural networks are memory and computationally efficient as they require only one bit per weight and activation and can be computed using Boolean logic and bit count operations.

Simon Donaldson

This is an expository article discussing some of the work of Uhlenbeck, focusing mainly on work concerning harmonic maps and Yang-Mills fields.

Sadhitro De, Dhrubaditya Mitra, Rahul Pandit

We carry out a detailed study of dynamic multiscaling in the turbulent nonequilibrium, but statistically steady, state of the stochastically forced one-dimensional Burgers equation. We introduce the concept of $\textit{interval collapse times}$ $\tau_{\rm col}$, the time taken for an interval of length $\ell$, demarcated by a pair of Lagrangian tracers, to c

Jawad Ahmed

Enterprise Networks are growing in scale and complexity, with heterogeneous connected assets needing to be secured in different ways. Nevertheless, virtually all connected assets use the Domain Name System (DNS) for address resolution, and DNS has thus become a convenient vehicle for attackers to covertly perform Command and Control (C&C) communication, data

Michał Suchorowski, Anna Dawid, Michał Tomza

We theoretically investigate the properties of two interacting ultracold highly magnetic atoms trapped in a one-dimensional harmonic potential. The atoms interact via an anisotropic long-range dipole-dipole interaction, which in one dimension effectively can be modeled by the contact interaction. We investigate the interplay of the external magnetic field, t

Ying Zhao

In this article, we study skew constacyclic codes over a class of finite commutative semisimple rings. The automorphism group of $\mathcal{R}=\prod_{i=1}^t F_q$ is determined, and we characterize skew constacyclic codes over ring by linear codes over finite field. We also define homomorphisms which map linear codes over $\mathcal{R}$ to matrix product codes

S. S. Gopalakrishnan, M. Tlidi, M. Taki, K. Panajotov

We demonstrate the existence of breathing dissipative light bullets in a birefringent optical resonator filled with Kerr media. The propagation of light inside the cavity for each polarized component, which is coupled by cross-phase modulation, is described by the coupled Lugiato-Lefever equations. The space-time dynamics of breathing light bullets are descr

Prahllad Deb, Somnath Hazra

We construct a large family of positive-definite kernels $K: \mathbb{D}^n\times \mathbb{D}^n \to \mbox{M} (r, \mathbb C)$, holomorphic in the first variable and anti-holomorphic in the second, that are quasi-invariant with respect to the subgroup $\mbox{M\"ob} \times\cdots\times \mbox{M\"ob}$ ($n$ times) of the bi-holomorphic automorphism group of $\mathbb{D

Hannah Gampe, Christopher Griffin

In this paper, we derive and analyze a continuous of a binary option market with exogenous information. The resulting non-linear system has a discontinuous right hand side, which can be analyzed using zero-dimensional Filippov surfaces. Under general assumptions on purchasing rules, we show that when exogenous information is constant in the binary asset mark

Dario Lucente, Andrea Baldassarri, Andrea Puglisi, Angelo Vulpiani

Data from experiments and theoretical arguments are the two pillars sustaining the job of modelling physical systems through inference. In order to solve the inference problem, the data should satisfy certain conditions that depend also upon the particular questions addressed in a research. Here we focus on the characterization of systems in terms of a dista

Klaus Brümann, Simon Doclo

A popular approach for 3D source localization using multiple microphones is the steered-response power method, where the source position is directly estimated by maximizing a function of three continuous position variables. Instead of directly estimating the source position, in this paper we propose an indirect, distance-based method for 3D source localizati

Yuhan Lin, Han Sun, Ningzhong Liu, Yetong Bian

Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for optical RSIs becomes a huge challenge. However, different from natural scene images (NSIs), the discussion on the optical RSI SOD task still remains scarce. In this paper, we propose

Jonathan Richard Schwarz, Yee Whye Teh

Recent work in Deep Learning has re-imagined the representation of data as functions mapping from a coordinate space to an underlying continuous signal. When such functions are approximated by neural networks this introduces a compelling alternative to the more common multi-dimensional array representation. Recent work on such Implicit Neural Representations

Deepankar Basu, Oscar Orellana

Can cost-reducing technical change lead to a fall in the long run rate of profit if class struggle manages to keep the rate of exploitation constant? In a general circulating capital model, we derive sufficient conditions for cost-reducing technical change to both keep the rate of exploitation constant and lead to a fall in the equilibrium rate of profit. Fu

Lukas Heidemann, David Reutter, Jamie Vicary

The theory of associative $n$-categories has recently been proposed as a strictly associative and unital approach to higher category theory. As a foundation for a proof assistant, this is potentially attractive, since it has the potential to allow simple formal proofs of complex high-dimensional algebraic phenomena. However, the theory relies on an implicit

Martin Redmann

Solving optimal stopping problems by backward induction in high dimensions is often very complex since the computation of conditional expectations is required. Typically, such computations are based on regression, a method that suffers from the curse of dimensionality. Therefore, the objective of this paper is to establish dimension reduction schemes for lar

Ankita Phutela, Preeti Bhumla, Manjari Jain, Saswata Bhattacharya

Topological Insulators (TIs) are unique materials where insulating bulk hosts linearly dispersing surface states protected by the Time-Reversal Symmetry (TRS). These states lead to dissipationless current flow, which makes this class of materials highly promising for spintronic applications. Here, we predict new TIs via high-throughput screening by employing

Pavel E. Kashargin, Sergey V. Sushkov

We consider neutron star configurations in the scalar-tensor theory of gravity with the coupling between the kinetic term of a scalar field and the Einstein tensor (such the model is a subclass of Horndeski gravity). Neutron stars in this model were studied earlier for the special case with a vanishing ``bare'' cosmological constant, $\Lambda_0=0$, and a van

Chunyuan Shi, Dapeng Yang, Jingdong Zhao, Li Jiang

Dexterous prosthetic hands have better grasp performance than traditional ones. However, patients still find it difficult to use these hands without a suitable control system. A new hybrid myoelectric control system, termed i-MYO, is presented and evaluated to solve this problem. The core component of the i-MYO is a novel grasp-type switching interface based

Maycol Falla Luza, Rudy Rosas

We study germs of holomorphic distributions with "separated variables'. In codimension one, a well know example of this kind of distribution is given by the canonical contact structure on $\mathbb{P}^{2m+1}$ . Another example is the Darboux distribution, which gives the normal local form of any contact structure. Given a germ $D$ of holomorphic distribution

Naoto Dainobu, Yoshinosuke Hirakawa, Hideki Matsumura

A prime number $p$ is said to be irregular if it divides the class number of the $p$-th cyclotomic field $\mathbb{Q}(\zeta_{p}) = \mathbb{Q}(\mathbb{G}_m[p])$. In this paper, we study its elliptic analogue for the division fields of an elliptic curve. More precisely, for a prime number $p \geq 5$ and a positive integer $n$, we study the $p$-divisibility of t

Engin Büyükaşık, Christian Lomp, Haydar Baran Yurtsever

It is well known that a ring $R$ is right Kasch if each simple right $R$-module embeds in a projective right $R$-module. In this paper we study the dual notion and call a ring $R$ right dual Kasch if each simple right $R$-module is a homomorphic image of an injective right $R$-module. We prove that $R$ is right dual Kasch if and only if every finitely genera

Giovanni Apruzzese, Pavel Laskov, Aliya Tastemirova

Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD) in the recent years. A substantial research effort has been invested in the development of specialized algorithms for CTD tasks. From the operational perspective, however, the progress of ML-based CTD is hindered by the difficulty in obtaining the large sets of labelled da

Penghui Wei, Xuanhua Yang, Shaoguo Liu, Liang Wang

This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR). We propose CREATER, a CTR-driven advertising text generation approach, to generate ad texts based on high-quality user reviews. To incorporate CTR objective, our model learns from o

Nadica Miljković, Jaka Sodnik

We present a method to automatically calculate time to fixate (TTF) from the eye-tracker data in subjects with neurological impairment using a driving simulator. TTF presents the time interval for a person to notice the stimulus from its first occurrence. Precisely, we measured the time since the children started to cross the street until the drivers directe

D. R. Bowler, T. Miyazaki, A. Nakata, L. Truflandier

CONQUEST is a DFT code which was designed from the beginning to enable extremely large-scale calculations on massively parallel platforms, implementing both exact and linear scaling solvers for the ground state. It uses local basis sets (both pseudo-atomic orbitals, PAOs, and systematically convergent B-splines) and sparse matrix storage and operations to en

Takayuki Miyadera, Ryo Takakura

For a given target system and apparatus described by quantum theory, the so-called quantum no-programming theorem indicates that a family of states called programs in the apparatus with a fixed unitary operation on the total system programs distinct unitary dynamics to the target system only if the initial programs are orthogonal to each other. The current s

Prisca Piccirilli, Sabine Schulte im Walde

Research on metaphorical language has shown ties between abstractness and emotionality with regard to metaphoricity; prior work is however limited to the word and sentence levels, and up to date there is no empirical study establishing the extent to which this is also true on the discourse level. This paper explores which textual and perceptual features huma

Ping Zhang, Wen-Du Li, Wu-Sheng Dai

Some probability distributions have moments, and some do not. For example, the normal distribution has power moments of arbitrary order, but the Cauchy distribution does not have power moments. In this paper, by analogy with the renormalization method in quantum field theory, we suggest a renormalization scheme to remove the divergence in divergent moments.

Ines Messadi, Markus Horst Becker, Kai Bleeke, Leander Jehl

Byzantine fault-tolerant agreement (BFT) in a partially synchronous system usually requires 3f + 1 nodes to tolerate f faulty replicas. Due to their high throughput and finality property BFT algorithms build the core of recent permissioned blockchains. As a complex and resource-demanding infrastructure, multiple cloud providers have started offering Blockcha

Weidong Cao, Changqing Wang, Weijian Chen, Song Hu

Harnessing parity-time (PT) symmetry with balanced gain and loss profiles has created a variety of opportunities in electronics from wireless energy transfer to telemetry sensing and topological defect engineering. However, existing implementations often employ ad-hoc approaches at low operating frequencies and are unable to accommodate large-scale integrati

Shengbing Deng, Xingliang Tian

By using a suitable transform related to Sobolev inequality, we investigate the sharp constants and optimizers in radial space for the following weighted Caffarelli-Kohn-Nirenberg-type inequalities: \begin{equation*} \int_{\mathbb{R}^N}|x|^{\alpha}|\Delta u|^2 dx \geq S^{rad}(N,\alpha)\left(\int_{\mathbb{R}^N}|x|^{-\alpha}|u|^{p^*_{\alpha}} dx\right)^{\frac{

Junshu Jiang, Thomas Dierckx, Duxiang Xiao, Wim Schoutens

Market makers play an essential role in financial markets. A successful market maker should control inventory and adverse selection risks and provide liquidity to the market. As an important methodology in control problems, Reinforcement Learning enjoys the advantage of data-driven and less rigid assumptions, receiving great attention in the market-making fi

Gabriele Lagani, Davide Bacciu, Claudio Gallicchio, Fabrizio Falchi

Features extracted from Deep Neural Networks (DNNs) have proven to be very effective in the context of Content Based Image Retrieval (CBIR). In recent work, biologically inspired \textit{Hebbian} learning algorithms have shown promises for DNN training. In this contribution, we study the performance of such algorithms in the development of feature extractors

Jinkyu Lee, Sanghyeon Bae, Woo Chang Kim, Yongjae Lee

A stagewise decomposition algorithm called value function gradient learning (VFGL) is proposed for large-scale multistage stochastic convex programs. VFGL finds the parameter values that best fit the gradient of the value function within a given parametric family. Widely used decomposition algorithms for multistage stochastic programming, such as stochastic

P. Colangelo, F. De Fazio, F. Loparco, N. Losacco

We analyze the form factors parametrizing the $B_c \to J/\psi, \eta_c$ matrix elements of the operators in a generalized low-energy $b \to c$ semileptonic Hamiltonian. We consider an expansion in nonrelativistic QCD, classifying the heavy quark spin symmetry breaking terms and expressing the form factors in terms of universal functions in a selected kinemati

Hilda Azimi, Ashkan Ebadi, Jessy Song, Pengcheng Xi

Besides vaccination, as an effective way to mitigate the further spread of COVID-19, fast and accurate screening of individuals to test for the disease is yet necessary to ensure public health safety. We propose COVID-Net UV, an end-to-end hybrid spatio-temporal deep neural network architecture, to detect COVID-19 infection from lung point-of-care ultrasound

Riasat Ali, Rimsha Babar, Muhammad Asgher

This work analyzes the Einstein-Gauss-Bonnet gravity of charged black hole solutions through Newman-Janis approach. The Hawking temperature for corresponding black hole is also computed. The solution depends upon rotation parameter $a$, black hole mass, charge and horizon. Moreover, the graphical behavior of temperature via event horizon to analyze the stabi

Paul C Bressloff

In this paper we consider a multiparticle version of a recent probabilistic framework for studying diffusion-mediated surface reactions. The basic idea of the probabilistic approach is to consider the joint probability density or generalized propagator for particle position and the so-called boundary local time. The latter characterizes the amount of time th

Paul C Bressloff

A probabilistic framework for studying single-particle diffusion in partially absorbing media has recently been developed in terms of an encounter-based approach. The latter computes the joint probability density (generalized propagator) for particle position $\X_t$ and a Brownian functional ${\mathcal U}_t$ that specifies the amount of time the particle is

Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright

Child welfare agencies across the United States are turning to data-driven predictive technologies (commonly called predictive analytics) which use government administrative data to assist workers' decision-making. While some prior work has explored impacted stakeholders' concerns with current uses of data-driven predictive risk models (PRMs), less work has

Ron Shnapp, François-Gaël Michalec, Markus Holzner

This work focuses on the formation of mating aggregates in zooplankton. In particular, sexual encounters are behaviourally supported by males actively swimming in search for females, and approaching them for mating once they are found. While the random search leads to a diffusive flux of individuals, the approaching for encounter supports attraction. Thus, w

Sam Blakeman, Denis Mareschal

Deep Reinforcement Learning (RL) involves the use of Deep Neural Networks (DNNs) to make sequential decisions in order to maximize reward. For many tasks the resulting sequence of actions produced by a Deep RL policy can be long and difficult to understand for humans. A crucial component of human explanations is selectivity, whereby only key decisions and ca

Christoph Schultheiss, Peter Bühlmann

We present a new method for causal discovery in linear structural equation models. We propose a simple ``trick'' based on statistical testing in linear models that can distinguish between ancestors and non-ancestors of any given variable. Naturally, this can then be extended to estimating the causal order among all variables. We provide explicit error contro

Justin Hellermann, Qinzhuan Qian, Ankit Shah

Data augmentation is a key regularization method to support the forecast and classification performance of highly parameterized models in computer vision. In the time series domain however, regularization in terms of augmentation is not equally common even though these methods have proven to mitigate effects from small sample size or non-stationarity. In thi

Domagoj Bradač

We prove a generalization of Tur\'{a}n's theorem proposed by Balogh and Lidick\'{y}.

Abeba Birhane, David J. T. Sumpter

When mathematical modelling is applied to capture a complex system, multiple models are often created that characterize different aspects of that system. Often, a model at one level will produce a prediction which is contradictory at another level but both models are accepted because they are both useful. Rather than aiming to build a single unified model of

S. Fagioli, A. Kaufmann, E. Radici

In the present work we deal with the existence of solutions for optimal control problems associated to transport equations. The behaviour of a population of individuals will be influenced by the presence of a population of control agents whose role is to lead the dynamics of the individuals towards a specific goal. The dynamics of the population of individua

Vitor Greati, João Marcos

The characterizing properties of a proof-theoretical presentation of a given logic may hang on the choice of proof formalism, on the shape of the logical rules and of the sequents manipulated by a given proof system, on the underlying notion of consequence, and even on the expressiveness of its linguistic resources and on the logical framework into which it

Yi Ling, Meng-He Wu

We investigate the shadow cast by a sort of new regular black holes which are characterized by an asymptotically Minkowski core and the Sub-Planckian curvature. Firstly, we extend the metric with spherical symmetry to the one of rotating Kerr-like black holes and derive the null geodesics with circular orbit near the horizon of the black hole, and then plot

Philippe Saint-Pierre, Romain Demeulemeester, Nadège Costa, Nicolas Savy

This chapter presents the main lines of agent based modeling in the field of medical research. The general diagram consists of a cohort of patients (virtual or real) whose evolution is observed by means of so-called evolution models. Scenarios can then be explored by varying the parameters of the different models. This chapter presents techniques for virtual

Marina Manso Jimeno, John Thomas Vaughan, Sairam Geethanath

Presently, Magnetic Resonance Imaging (MRI) magnets must deliver excellent magnetic field (B0) uniformity to achieve optimum image quality. Long magnets can satisfy the homogeneity requirements but require considerable superconducting material. These designs result in large, heavy, costly, and unstable systems that aggravate as field strength increases. This

Andrew Buchanan, Ivan Dimitrov, Olivia Grace, Charles Paquette

Let $\mathbb{K}$ denote an algebraically closed field and $A$ a free product of finitely many semisimple associative $\mathbb{K}$-algebras. We associate to $A$ a finite acyclic quiver $\Gamma$ and show that the category of finite dimensional $A$-modules is equivalent to a full subcategory of the category ${\rm rep}(\Gamma)$ of finite dimensional representati

Elena Di Lavore, Paweł Sobociński

Monoidal width was recently introduced by the authors as a measure of the complexity of decomposing morphisms in monoidal categories. We have shown that in a monoidal category of cospans of graphs, monoidal width and its variants can be used to capture tree width, path width and branch width. In this paper we study monoidal width in a category of matrices, a

Henrik Wilming

I show that if a finite-dimensional density matrix has strictly smaller von Neumann entropy than a second one of the same dimension (and the rank is not bigger), then sufficiently (but finitely) many tensor-copies of the first density matrix majorize a density matrix whose single-body marginals are all exactly equal to the second density matrix. This implies

Abdulqader Mustafa, Cenap Ozel, Alexander Pigazzini, Richard Pincak

This paper has two goals; the first is to generalize results for the existence and nonexistence of warped product submanifolds of almost contact manifolds, accordingly a self-contained reference of such submanifolds is offered to save efforts of potential research. Most of the results of this paper are general and decisive enough to generalize both discovere

Dian Yu, Jianjun Gao, Weiping Wu, Zizhuo Wang

Prediction markets are long known for prediction accuracy. This study systematically explores the fundamental properties of prediction markets, addressing questions about their information aggregation process and the factors contributing to their remarkable efficacy. We propose a novel multivariate utility (MU) based mechanism that unifies several existing a

Tal Mizrahi, Jose Yallouz

On 24 February 2022 Russia invaded Ukraine, starting one of the largest military conflicts in Europe in recent years. In this paper we present preliminary findings about the impact of the conflict on the Internet performance in Ukraine and in Russia, introducing an ironically asymmetric picture: the Internet performance in Ukraine has significantly degraded,

Yangming Lai, Luca Venturino, Emanuele Grossi, Wei Yi

In this paper, we consider a distributed multiple-input multiple-output (MIMO) radar which radiates waveforms with non-ideal cross- and auto-correlation functions and derive a novel subspace-based procedure to detect and localize multiple prospective targets. The proposed solution solves a sequence of composite binary hypothesis testing problems by resorting

Mustapha Hamad, Michèle Wigger, Mireille Sarkiss

The main contribution of this paper is a strong converse result for $K$-hop distributed hypothesis testing against independence with multiple (intermediate) decision centers under a Markov condition. Our result shows that the set of type-II error exponents that can simultaneously be achieved at all the terminals does not depend on the maximum permissible typ

Martin Kronbichler, Dmytro Sashko, Peter Munch

This work investigates a variant of the conjugate gradient (CG) method and embeds it into the context of high-order finite-element schemes with fast matrix-free operator evaluation and cheap preconditioners like the matrix diagonal. Relying on a data-dependency analysis and appropriate enumeration of degrees of freedom, we interleave the vector updates and i

Yongchang Wu, Zhengxia Zou, Zhenwei Shi

Novel view synthesis of remote sensing scenes is of great significance for scene visualization, human-computer interaction, and various downstream applications. Despite the recent advances in computer graphics and photogrammetry technology, generating novel views is still challenging particularly for remote sensing images due to its high complexity, view spa

Yannick S. Klaß, Juliane Doster, Maximilian Bückle, Rémy Braive

We present a method for the in-situ determination of Young's modulus of a nanomechanical string resonator subjected to tensile stress. It relies on measuring a large number of harmonic eigenmodes and allows to access Young's modulus even for the case of a stress-dominated frequency response. We use the proposed framework to obtain the Young's modulus of four

Encieh Erfani, Tadeo D. Gomez-Aguilar, Juan Carlos Hidalgo

We study the merger history of primordial black holes (PBHs) in a scenario where they represent the dominant dark matter component of a typical dwarf galaxies' core. We investigate the possibility of a sequence of collisions resulting in a hierarchical merger of black holes and look at the final mass spectrum in such {\it clusters}, which initially present a

Thomas G. Bisbas, Stefanie Walch, Thorsten Naab, Natalia Lahén

We present [CII] synthetic observations of smoothed particle hydrodynamics (SPH) simulations of a dwarf galaxy merger. The merging process varies the star-formation rate by more than three orders of magnitude. Several star clusters are formed, the feedback of which disperses and unbinds the dense gas through expanding HII regions and supernova (SN) explosion

Lioba Heimbach, Eric Schertenleib, Roger Wattenhofer

Trade execution on Decentralized Exchanges (DEXes) is automatic and does not require individual buy and sell orders to be matched. Instead, liquidity aggregated in pools from individual liquidity providers enables trading between cryptocurrencies. The largest DEX measured by trading volume, Uniswap V3, promises a DEX design optimized for capital efficiency.

Tal Mizrahi, Jose Yallouz

The conflict in Ukraine, starting in February 2022, began the largest refugee crisis in decades, with millions of Ukrainian refugees crossing the border to neighboring countries and millions of others forced to move within the country. In this paper we present an insight into how Internet measurements can be used to analyze the refugee crisis. Based on preli

Àlex Giménez-Romero, Federico Vazquez, Cristóbal López, Manuel A. Matías

Emerging marine infectious diseases pose a substantial threat to marine ecosystems and the conservation of their biodiversity. Compartmental models of epidemic transmission in marine sessile organisms, available only recently, are based on non-spatial descriptions in which space is homogenised and parasite mobility is not explicitly accounted for. However, i

Ruth Angus, Adrian M. Price-Whelan, Joel C. Zinn, Megan Bedell

Precise Gaia measurements of positions, parallaxes, and proper motions provide an opportunity to calculate 3D positions and 2D velocities (i.e. 5D phase-space) of Milky Way stars. Where available, spectroscopic radial velocity (RV) measurements provide full 6D phase-space information, however there are now and will remain many stars without RV measurements.

O. Cohen, J. D. Alvarado-Gomez, J. J. Drake, L. M. Harbach

We simulate the space environment around AU Microscopii b and the interaction between the magnetized stellar wind with a planetary atmospheric outflow for ambient stellar wind conditions and Coronal Mass Ejection (CME) conditions. We also calculate synthetic Ly$\alpha$ absorption due to neutral hydrogen in the ambient and the escaping planetary atmosphere af

Maurice Mignotte, Paul Voutier

We provide a technique to obtain explicit bounds for problems that can be reduced to linear forms in three complex logarithms of algebraic numbers. This technique can produce bounds significantly better than general results on lower bounds for linear forms in logarithms. We give worked examples to demonstrate both the use of our technique and the improvement

Roberto Taverna, Roberto Turolla, Fabio Muleri, Jeremy Heyl

We report on the first detection of linearly polarized x-ray emission from an ultra-magnetized neutron star with the Imaging X-ray Polarimetry Explorer (IXPE). The IXPE 35 observations of the anomalous x-ray pulsar 4U 0142+61 reveal a linear polarization degree of $(12\pm 1)\%$ throughout the IXPE 2--8 keV band. We detect a substantial variation of the polar

Tian Zhou, Ziqing Ma, Xue wang, Qingsong Wen

Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information. We found, however, that there is still great room for improvement in how to preserve historical information in neural networks while avoi

Manuel Santos Gutiérrez, Valerio Lucarini

The perturbation theory of operator semigroups is used to derive response formulas for a variety of combinations of acting forcings and reference background dynamics. In the case of background stochastic dynamics, we decompose the response formulas using the Koopman operator generator eigenfunctions and the corresponding eigenvalues, thus providing a functio

Yu Min, Yupeng Wang

Let $\calO_K$ be a complete discrete valuation ring of mixed characteristic $(0,p)$ with a perfect residue field. In this paper, for a semi-stable $p$-adic formal scheme $\frakX$ over $\calO_K$ with rigid generic fibre $X$ and canonical log structure $\calM_{\frakX} = \calO_{\frakX}\cap\calO_X^{\times}$, we study Hodge--Tate crystals over the absolute logari

Keigo Fukumura, Mauro Dadina, Gabriele Matzeu, Francesco Tombesi

We aim to explore spectral signatures of the predicted multi-ion UFOs in the broadband X-ray spectra of active galactic nuclei (AGNs) by exploiting an accretion disk wind model in the context of a simple magnetohydrodynamic (MHD) framework. We are focused primarily on examining the spectral dependences on a number of key properties; (1) ionizing luminosity r

Chi Qiu, Qingqing Wu, Meng Hua, Xinrong guan

Intelligent reflecting surface (IRS) is a promising technology to boost the efficiency of wireless energy transfer (WET) systems. However, for a multiuser WET system, simultaneous multi-beam energy transmission is generally required to achieve the maximum performance, which may not be implemented by using the IRS having only a single set of coefficients. As

Jan M. Lüttgens, Zhuoran Kuang, Nicolas F. Zorn, Tiago Buckup

Strong coupling of excitonic resonances with a cavity gives rise to exciton-polaritons which possess a modified energy landscape compared to the uncoupled emitter. However, due to the femtosecond lifetime of the so-called bright polariton states and transient changes of the cavity reflectivity under excitation, it is challenging to directly measure the polar

Jingqing Zhang, Atri Sharma, Luis Bolanos, Tong Li

Clinicians may rely on medical coding systems such as International Classification of Diseases (ICD) to identify patients with diseases from Electronic Health Records (EHRs). However, due to the lack of detail and specificity as well as a probability of miscoding, recent studies suggest the ICD codes often cannot characterise patients accurately for specific

Benjamin Krumnow, Hugo Jonker, Stefan Karsch

Automated browsers are widely used to study the web at scale. Their premise is that they measure what regular browsers would encounter on the web. In practice, deviations due to detection of automation have been found. To what extent automated browsers can be improved to reduce such deviations has so far not been investigated in detail. In this paper, we inv

Jérémie Guilhot, Cédric Lecouvey, Pierre Tarrago

An oriented graph is said positively multiplicative when its adjacency matrix $A$ embeds in a matrix algebra admitting a basis $\mathsf{B}$ with nonnegative structure constants in which the matrix of the multiplication by $A$ coincides with $A$. The goal of this paper is to present basic properties of this notion and explain, through various simple examples,

Yifan Yu, Junzhi Cui

The atomic volume and surface are important geometric quantities for calculating various macroscopic physical quantities from atomic models. This paper proposes a new analytical method to calculate the atomic volumes and surfaces of a metal cluster. This method adopts metallic radii to describe atom sizes and constructs the overall volume/surface by the mole

Yang Zhou, Zhiwen Yang, Hui Zhang, Eric I-Chao Chang

Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great interest to translate low-dose PET images into full-dose. Previous studies based on deep learning methods usually directly extract hierarchical features for recon

Teddy Cunningham, Konstantin Klemmer, Hongkai Wen, Hakan Ferhatosmanoglu

Synthetic data generation is a fundamental task for many data management and data science applications. Spatial data is of particular interest, and its sensitive nature often leads to privacy concerns. We introduce GeoPointGAN, a novel GAN-based solution for generating synthetic spatial point datasets with high utility and strong individual level privacy gua

Sungjae Bae, SungWoo Youn, Junu Jeong

In the search for axion dark matter, the cavity-based haloscope offers the most sensitive approach to the theoretically interesting models in the microwave region. However, experimental searches have been limited to relatively low masses up to a few tens of $\mu$eV, benefiting from large detection volumes and high quality factors for a given experimental set

Filip Huško, Cedric G. Lacey

Simulations of active galactic nuclei (AGN) jets have thus far been performed almost exclusively using grid-based codes. We present the first results from hydrodynamical tests of AGN jets, and their interaction with the intracluster medium (ICM), using smoothed particle hydrodynamics (SPH) as implemented in the SWIFT code. We launch these jets into a constan

Takaaki Kuwahara, Asato Tsuchiya

We propose a method to represent the path integral over gauge fields as a tensor network. We introduce a trial action with variational parameters and generate gauge field configurations with the weight defined by the trial action. We construct initial tensors with indices labelling these gauge field configurations. We perform the tensor renormalization group

Marco Spaziani Brunella, Marco Bonola, Animesh Trivedi

Since the inception of computing, we have been reliant on CPU-powered architectures. However, today this reliance is challenged by manufacturing limitations (CMOS scaling), performance expectations (stalled clocks, Turing tax), and security concerns (microarchitectural attacks). To re-imagine our computing architecture, in this work we take a more radical bu