July 2023 arXiv papers — page 141

Feng Liu, Achira Boonrath, Prajit KrisshnaKumar, Elenora M. Botta

The earth's orbit is becoming increasingly crowded with debris that poses significant safety risks to the operation of existing and new spacecraft and satellites. The active tether-net system, which consists of a flexible net with maneuverable corner nodes launched from a small autonomous spacecraft, is a promising solution for capturing and disposing of suc

Qian Chen, Zhuan Ning, Yu Tian, Bin Wang

We investigate the dynamical transition processes of an Einstein-Maxwell-scalar gravitational system between two local ground states and an excited state in the anti-de Sitter spacetime. From the linear perturbation theory, only the excited state possesses a single unstable mode, indicating the dynamical instability. Such an instability is associated with th

Eben Blaisdell, Max I. Kanovich, Stepan L. Kuznetsov, Elaine Pimentel

In a previous work we introduced a non-associative non-commutative logic extended by multimodalities, called subexponentials, licensing local application of structural rules. Here, we further explore this system, considering a classical one-sided multi-succedent classical version of the system, following the exponential-free calculi of Buszkowski's and de Gr

Leonardo A. Dos Santos, Antonio García Munõz, David K. Sing, Mercedes López-Morales

One of the most well-studied exoplanets to date, HD 189733 b, stands out as an archetypal hot Jupiter with many observations and theoretical models aimed at characterizing its atmosphere, interior, host star, and environment. We report here on the results of an extensive campaign to observe atmospheric escape signatures in HD 189733 b using the Hubble Space

Manuel Eduardo Hernández-García, Jorge Velázquez-Castro

Describing reaction rates in stochastic bio-circuits is commonly done by directly introducing the deterministically deduced Hill function into the master equation. However, when fluctuations in enzymatic reaction rates are not neglectable, the Hill function must be derived, considering all the involved stochastic reactions. In this work, we derived the stoch

Kevin Du, Lucas Torroba Hennigen, Niklas Stoehr, Alexander Warstadt

Many popular feature-attribution methods for interpreting deep neural networks rely on computing the gradients of a model's output with respect to its inputs. While these methods can indicate which input features may be important for the model's prediction, they reveal little about the inner workings of the model itself. In this paper, we observe that the gr

Ronald Aznavourian, Guillaume Demesy, Sébastien Guenneau, Julien Marot

We revisit the design of cloaks, without resorting to any geometric transform. Cancellation techniques and anomalous resonances have been applied for this purpose. Instead of a deductive reasoning, we propose a novel mono-objective optimization algorithm, namely a ternary grey wolf algorithm, and we adapt a bi-objective optimization algorithm. Firstly, the p

Carlos Theran, Michael Alvarez, Emmanuel Arzuaga, Heidy Sierra

Given the high availability of data collected by different remote sensing instruments, the data fusion of multi-spectral and hyperspectral images (HSI) is an important topic in remote sensing. In particular, super-resolution as a data fusion application using spatial and spectral domains is highly investigated because its fused images is used to improve the

Philipp Pfeffer, Florian Heyder, Jörg Schumacher

Two hybrid quantum-classical reservoir computing models are presented to reproduce low-order statistical properties of a two-dimensional turbulent Rayleigh-B\'enard convection flow at a Rayleigh number Ra=1e+5 and a Prandtl number Pr=10. These properties comprise the mean vertical profiles of the root mean square velocity and temperature and the turbulent co

Carlo Alberto Antonini, Andrea Cianchi, Giulio Ciraolo, Alberto Farina

We deal with boundary value problems for second-order nonlinear elliptic equations in divergence form, which emerge as Euler-Lagrange equations of integral functionals of the Calculus of Variations built upon possibly anisotropic norms of the gradient of trial functions. Integrands with non polynomial growth are included in our discussion. The $W^{1,2}$-regu

Wei-Wei Yang, Hong-Gang Luo, Yin-Zhong

In a conventional boson system, the ground state can either be an insulator or a superfluid (SF) due to the duality between particle number and phase. This paper reveals that the long-sought Bose metal (BM) state can be realized in an exactly solvable interacting bosonic model, i.e. the Bose-Hatsugai-Kohmoto (BHK) model, which acts as the nontrivial extensio

Abhay Pratap Yadav, Sugyan Parida, Yogesh Chandra Joshi, Santosh Joshi

Luminous blue variables (LBVs) are evolved massive stars close to the Eddington limit, with a distinct spectroscopic and photometric variability having unsteady mass-loss rates. These stars show a considerable change in their surface temperature from quiescent to outbursts phase. The cause of irregular variability and unsteady mass-loss rate is not properly

Kai Murai, Fuminobu Takahashi, Masaki Yamada, Wen Yin

We show that the baryon asymmetry of the Universe cannot be explained by a large initial value before inflation because it inevitably predicts correlated baryon isocurvature perturbations that are already excluded by cosmic microwave background observations. Similar arguments can generally be applied to some models of dark matter.

Yan Lin, Huaiyu Wan, Jilin Hu, Shengnan Guo

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solut

Peter Foukal

The slopes of the linear relations between sunspot and white light (WL) facular areas at the onset of sunspot Cycles 12-21 correlate well with the amplitudes of those cycles between 1878-1980 (Brown and Evans, 1980). We use continuum images from the SOHO Michelson Doppler Imager and SDO Heliospheric Magnetic Imager to show that the relation holds also for Cy

Ralf Borndörfer, Fabian Danecker, Martin Weiser

The algorithmic efficiency of Newton-based methods for Free Flight Trajectory Optimization is heavily influenced by the size of the domain of convergence. We provide numerical evidence that the convergence radius is much larger in practice than what the theoretical worst case bounds suggest. The algorithm can be further improved by a convergence-enhancing do

Walid Bendada, Théo Bontempelli, Mathieu Morlon, Benjamin Chapus

This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer. Track Mix automatically generates "mix" playlists inspired by initial music tracks, allowing users to discover music similar to their favorite content. To generate these mixes, we consider a Transformer model trained on millions

Abel Lacabanne, Daniel Tubbenhauer, Pedro Vaz

We give explicit formulas for the asymptotic growth rate of the number of summands in tensor powers in certain monoidal categories with finitely many indecomposable objects, and related structures.

Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal

For any two point sets $A,B \subset \mathbb{R}^d$ of size up to $n$, the Chamfer distance from $A$ to $B$ is defined as $\text{CH}(A,B)=\sum_{a \in A} \min_{b \in B} d_X(a,b)$, where $d_X$ is the underlying distance measure (e.g., the Euclidean or Manhattan distance). The Chamfer distance is a popular measure of dissimilarity between point clouds, used in ma

Aryo Pradipta Gema, Pasquale Minervini, Luke Daines, Tom Hope

Adapting pretrained language models to novel domains, such as clinical applications, traditionally involves retraining their entire set of parameters. Parameter-Efficient Fine-Tuning (PEFT) techniques for fine-tuning language models significantly reduce computational requirements by selectively fine-tuning small subsets of parameters. In this study, we propo

Heliudson Bernardo, Robert Brandenberger, Aline Favero

We consider a cosmic string moving through a gas of superfluid dark matter (SFDM) particles and analyze how it affects the dark matter distribution. We look at two different cases: first, a cosmic string passing through an already condensed region, and second, through a region that is not yet condensed. In the former, the string induces a weak shock in the s

Alexander Engelmann, Michael Kaupmann, Timm Faulwasser

This note discusses an essentially decentralized interior point method, which is well suited for optimization problems arising in energy networks. Advantages of the proposed method are guaranteed and fast local convergence also for problems with non-convex constraints. Moreover, our method exhibits a small communication footprint and it achieves a comparably

Ludovica Schaerf, Carina Popovici, Eric Postma

In recent years, Transformers, initially developed for language, have been successfully applied to visual tasks. Vision Transformers have been shown to push the state-of-the-art in a wide range of tasks, including image classification, object detection, and semantic segmentation. While ample research has shown promising results in art attribution and art aut

Claudio Corianò, Stefano Lionetti, Matteo Maria Maglio

We analyze the parity-odd correlators $\langle JJO\rangle_{odd}$, $\langle JJT\rangle_{odd}$, $\langle TTO\rangle_{odd}$ and $\langle TTT\rangle_{odd}$ in momentum space, constrained by conformal Ward identities, extending our former investigation of the parity-odd chiral anomaly vertex. We investigate how the presence of parity-odd trace anomalies affect su

Rudolf Tange

Let $k$ be an algebraically closed field of characteristic $p>0$, let G=GL_n be the general linear group over $k$, let g=gl_n be its Lie algebra and let $D_s$ be subalgebra of the divided power algebra of g^* spanned by the divided power monomials with exponents $<p^s$. We give a basis for the $G$-invariants in $D_s$ up to degree $n$ and show that these are

Zhijian Xu, Ailing Zeng, Qiang Xu

In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis. Unlike existing models that directly process raw time-domain data, FITS operates on the principle that time series can be manipulated through interpolation in the complex frequency domain. By discarding high-frequency components with negligible impact on time series

Yvain Bruned, Pablo Linares

We provide an algebraic framework to describe renormalization in regularity structures based on multi-indices for a large class of semi-linear stochastic PDEs. This framework is ``top-down", in the sense that we postulate the form of the counterterm and use the renormalized equation to build a canonical smooth model for it. The core of the construction is a

Kris Youakim, Karin Lind, Iryna Kushniruk

The gravitational interactions between the Milky Way and in-falling satellites offer a wealth of information about the formation and evolution of our Galaxy. In this paper, we explore the high-dimensionality of the GALAH DR3 plus Gaia eDR3 data set to identify new tidally stripped candidate stars of the nearby star cluster Omega Centauri ($\omega\,\mathrm{Ce

Keqin Liu, Qizhen Jia

In this paper, we consider a general observation model for restless multi-armed bandit problems. The operation of the player is based on the past observation history that is limited (partial) and error-prone due to resource constraints or environmental or intrinsic noises. By establishing a general probabilistic model for dynamics of the observation process,

Martin Groenewegen

In this invited review I discuss the calibration and applications of the period-luminosity relation of classical Cepheid and RR Lyrae stars. After a brief introduction, starting with results from Hipparcos and discussing some post-Hipparcos era developments, I focus on recent results using Gaia Data Release 3 data. I present an overview of the most recent pe

Cheng-Jun Xia, Hao-Miao Jin, Ting-Ting Sun

By combining RMF models and equivparticle models with density-dependent quark masses, we construct explicitly ``a quark Fermi Sea'' and ``a baryonic Fermi surface'' to model the quarkyonic phase, where baryons with momentums ranging from zero to Fermi momentums are included. The properties of nuclear matter, quark matter, and quarkyonic matter are then inves

Võ Ti\'ên Phong, Héctor Sainz-Cruz, Eugene J. Mele, Francisco Guinea

Inspired by recent experimental discoveries of superconductivity in chirally-stacked and twisted multilayer graphene, we study models of $f$-wave superconductivity on the honeycomb lattice with arbitrary numbers of layers. These models respect a mirror symmetry that allows classification of the bands by a mirror-projected winding number $\nu_\pm$. For odd nu

Narongrid Seesawad, Piyalitt Ittichaiwong, Thapanun Sudhawiyangkul, Phattarapong Sawangjai

Patch classification models based on deep learning have been utilized in whole-slide images (WSI) of H&E-stained tissue samples to assist pathologists in grading follicular lymphoma patients. However, these approaches still require pathologists to manually identify centroblast cells and provide refined labels for optimal performance. To address this, we prop

A. M. Zenkin, A. A. Peregudin, A. A. Bobtsov

This paper considers a wide class of smooth continuous dynamic nonlinear systems (control objects) with a measurable vector of state. The problem is to find a special function (Lyapunov function), which in the framework of the second Lyapunov method guarantees asymptotic stability for the above described class of nonlinear systems. It is well known that the

Justin Tabbett, Karen L. Aplin

The full instrument response of a superminiaturised CsI(Tl)-PiN photodiode radioactivity detector, intended for deployment on a meteorological radiosonde, has been modelled by combining a physics-based model of the sensor with the detector circuit response, obtained via an LTspice simulation. The model uses the incident energy of a gamma ray as an input, and

Zeping Sui, Shefeng Yan, Hongming Zhang, Sumei Sun

In orthogonal time sequency multiplexing (OTSM) modulation, the information symbols are conveyed in the delay-sequency domain upon exploiting the inverse Walsh Hadamard transform (IWHT). It has been shown that OTSM is capable of attaining a bit error ratio (BER) similar to that of orthogonal time-frequency space (OTFS) modulation at a lower complexity, since

Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel, Shaopeng Wei

Retrieval augmentation enables large language models to take advantage of external knowledge, for example on tasks like question answering and data imputation. However, the performance of such retrieval-augmented models is limited by the data quality of their underlying retrieval corpus. In this paper, we propose an algorithm based on multilinear extension f

Junyi Guo, Xia Han, Hao Wang

In this paper, we study a continuous-time exploratory mean-variance (EMV) problem under the framework of reinforcement learning (RL), and the Choquet regularizers are used to measure the level of exploration. By applying the classical Bellman principle of optimality, the Hamilton-Jacobi-Bellman equation of the EMV problem is derived and solved explicitly via

Minghao Wu, Alham Fikri Aji

As large language models (LLMs) continue to advance, accurately and comprehensively evaluating their performance becomes increasingly challenging. Ranking the relative performance of LLMs based on Elo ratings, according to human judgment, is gaining more popularity. However, the extent to which humans and LLMs are capable evaluators remains uncertain. This s

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The $D^+_s\to K^+K^-\mu^+\nu_\mu$ decay is studied based on 7.33 fb$^{-1}$ of $e^+e^-$ collision data collected with the BESIII detector at center-of-mass energies in the range from 4.128 to 4.226 GeV. The absolute branching fraction is measured as ${\mathcal B}(D^+_s\to \phi \mu^+\nu_\mu) = (2.25\pm 0.09 \pm 0.07) \times10^{-2}$, the most precise measuremen

Luca Nenna, Paul Pegon

We investigate the convergence rate of multi-marginal optimal transport costs that are regularized with the Boltzmann-Shannon entropy, as the noise parameter $\varepsilon$ tends to $0$. We establish lower and upper bounds on the difference with the unregularized cost of the form $C\varepsilon\log(1/\varepsilon)+O(\varepsilon)$ for some explicit dimensional c

Sebastian P. Horvath, Christopher M. Phenicie, Salim Ourari, Mehmet T. Uysal

Engineering the local density of states with nanophotonic structures is a powerful tool to control light-matter interactions via the Purcell effect. At optical frequencies, control over the electric field density of states is typically used to couple to and manipulate electric dipole transitions. However, it is also possible to engineer the magnetic density

Guanghui Yang, Zhijiang Shao, Rui Wang, Zuhua Xu

A novel human-machine interactive training method for dynamic load change operation in air separation processes (ASPs) is proposed. A shadow operator (SO) is developed in this method to train ASP operators through industrial model predictive control (IMPC) and Bloom's taxonomy. First, a nonlinear two-layer IMPC machine algorithm is developed for dynamic load

Shun Uchino

How dissipation affects transport is an important theme in quantum science. Here we theoretically investigate an impact of a single-particle loss in mesoscopic transport, which has been an issue in experiments of ultracold atomic gases. By explicitly analyzing quantum point contact and quantum dot systems, we obtain a cumulant generating function on the part

Javlon Rayimbaev, Konstantinos F. Dialektopoulos, Furkat Sarikulov, Ahmadjon Abdujabbarov

Testing gravity theories and their parameters using observations is an important issue in relativistic astrophysics. In this context, we investigate the motion of test particles and their harmonic oscillations in the spacetime of non-rotating hairy black holes (BHs) in Hordeski gravity, together with astrophysical applications of quasiperiodic oscillations (

Silviu Balanescu, Mircea Cimpoeas

Let $K$ be a field and $S=K[x_1,\ldots,x_n]$, the ring of polynomials in $n$ variables, over $K$. Using the fact that the Hilbert depth is an upper bound for the Stanley depth of a quotient of squarefree monomial ideals $0\subset I\subsetneq J\subset S$, we prove several combinatorial inequalities which involve the coefficients of the polynomial $f(t)=(1+t+\

Yijie Deng, Lei Han, Tianpeng Lin, Lin Li

With the rise of Extended Reality (XR) technology, there is a growing need for real-time light field reconstruction from sparse view inputs. Existing methods can be classified into offline techniques, which can generate high-quality novel views but at the cost of long inference/training time, and online methods, which either lack generalizability or produce

Vladimir Dorovskyy, Valentin Melnik, Anatolii Brazhenko

The purpose of this work is to demonstrate the effectiveness of ground-based support for space missions, primarily PSP, using large Ukrainian decameter radio telescopes. Another goal of the work is to carry out cross calibration of the radiometers onboard spacecraft using the calibrated data of the ground-based radio telescopes. One of the most common method

Hongzhan Yu, Chiaki Hirayama, Chenning Yu, Sylvia Herbert

There are two major challenges for scaling up robot navigation around dynamic obstacles: the complex interaction dynamics of the obstacles can be hard to model analytically, and the complexity of planning and control grows exponentially in the number of obstacles. Data-driven and learning-based methods are thus particularly valuable in this context. However,

L. R. Dai, E. Oset

We develop a model to reproduce the mass distributions of pairs of mesons in the Cabibbo-suppressed $D^+_s \to K^+ \pi^+ \pi^-$ decay. The largest contributions to the process comes from the $D^+_s \to K^+ \rho^0$ and $D^+_s \to K^{*0} \pi^+$ decay modes, but the $D^+_s \to K^*_0(1430) \pi^+$ and $D^+_s \to K^+ f_0(1370)$ modes also play a moderate role and

Mukhtar Karazym

We use the Lusternik-Schnirelman theory to prove the existence of a nondecreasing sequence of variational eigenvalues for the subelliptic $p$-Laplacian subject to the Dirichlet boundary condition.

Sanjar Shaymatov, Mirzabek Alloqulov, Bobomurat Ahmedov, Anzhong Wang

In this paper, we study the magnetic reconnection process of energy extraction from a rapidly rotating Kerr-Newman-modified-gravity (MOG) black hole by investigating the combined effect of black hole charge and the MOG parameter. We explore the energy efficiency of energy extraction and power by applying the new energy extraction mechanism proposed by Comiss

Masaya Amo, Antonia M. Frassino, Robie A. Hennigar

We propose novel thermodynamic inequalities that apply to stationary asymptotically Anti-de Sitter (AdS) black holes. These inequalities incorporate the thermodynamic volume and refine the reverse isoperimetric inequality. To assess the validity of our conjectures, we apply them to a wide range of analytical black hole solutions, observing compelling evidenc

Balint Varga

This letter presents an extended analysis and a novel upper bound of the subclass of Linear Quadratic Near Potential Differential Games (LQ NPDG). LQ NPDGs are a subclass of potential differential games, for which a distance between an LQ exact potential differential game and the LQ NPDG. LQ NPDGs exhibit a unique characteristic: the smaller the distance fro

Galina L. Klimchitskaya, Constantine C. Korikov, Vladimir M. Mostepanenko, Oleg Yu. Tsybin

We consider the nonequilibrium dispersion force acting on nanoparticles on the source side of gapped graphene sheet. Nanoparticles are kept at the environmental temperature, whereas the graphene sheet may be either cooler or hotter than the environment. Calculation of the dispersion force as a function of separation at different values of the mass-gap parame

José Morano, Guilherme Aresta, Dmitrii Lachinov, Julia Mai

Deep learning has become a valuable tool for the automation of certain medical image segmentation tasks, significantly relieving the workload of medical specialists. Some of these tasks require segmentation to be performed on a subset of the input dimensions, the most common case being 3D-to-2D. However, the performance of existing methods is strongly condit

Christian Jauch, Timo Leitritz, Marco F. Huber

Manual assembly workers face increasing complexity in their work. Human-centered assistance systems could help, but object recognition as an enabling technology hinders sophisticated human-centered design of these systems. At the same time, activity recognition based on hand poses suffers from poor pose estimation in complex usage scenarios, such as wearing

Jenny Poulton, Age Tjalma, Lotte Slim, Pieter Rein ten Wolde

One major challenge for living cells is the measurement and prediction of signals corrupted by noise. In general, cells need to make decisions based on their compressed representation of noisy, time-varying signals. Strategies for signal noise mitigation are often tackled using Wiener filtering theory, but this theory cannot account for systems that have lim

Emilie Chouzenoux, Victor Elvira

Time-series datasets are central in machine learning with applications in numerous fields of science and engineering, such as biomedicine, Earth observation, and network analysis. Extensive research exists on state-space models (SSMs), which are powerful mathematical tools that allow for probabilistic and interpretable learning on time series. Learning the m

Kirill A. Bronnikov, Arkady A. Popov, Sergey G. Rubin

We discuss the way of solving the hierarchy problem. We show that starting at the Planck scale, the three energy scales -- inflationary, electroweak and the cosmological ones can be restored. The formation of small parameters is proposed that leads to a successful solution of the problem. The tools involved in the process are $f(R)$ gravity and inhomogeneous

Johannes Köhler, Matthas A. Müller, Frank Allgöwer

This article provides an overview of model predictive control (MPC) frameworks for dynamic operation of nonlinear constrained systems. Dynamic operation is often an integral part of the control objective, ranging from tracking of reference signals to the general economic operation of a plant under online changing time-varying operating conditions. We focus o

Johannes Jakubik, Daniel Weber, Patrick Hemmer, Michael Vössing

Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues th

Eduardo Marin-Bujedo, Matthieu Génévriez

The autoionization of core-excited Rydberg states is theoretically studied for a broad range of principal and angular-momentum quantum numbers $n$ and $\ell$ in alkaline-earth-metal atoms. We combined two theoretical methods to calculate accurate autoionization rates for $n=10-65$ and $\ell=0-45$ over the 100 orders of magnitude that they span. The strong in

Frédéric Menous, Jean-Christophe Novelli, Jean-Yves Thibon

We prove that the Catalan Lie idempotent $D_n(a,b)$, introduced in [Menous {\it et al.}, Adv. Appl. Math. 51 (2013), 177] can be refined by introducing $n$ independent parameters $a_0,\ldots,a_{n-1}$ and that the coefficient of each monomial is itself a Lie idempotent in the descent algebra. These new idempotents are multiplicity-free sums of subsets of the

Giacomo Borraccini, Vittorio Gatto, Andrea D'Amico, Stefano Straullu

Relying on a two-measurement characterization phase, a gain profile model for dual-stage EDFAs is presented and validated in full spectral load condition. It precisely reproduces the EDFA dynamics varying the target gain and tilts parameters as shown experimentally on two commercial items from different vendors.

Vanessa López-Barquero, Paolo Desiati

After more than a century of discovering cosmic rays, a comprehensive description of their origin, propagation, and composition still eludes us. One of the difficulties is that these particles interact with magnetic fields; therefore, their directional information is distorted as they travel. In addition, as cosmic rays (CRs) propagate in the Galaxy, they ca

Lorenz Eberhardt, Sridip Pal

We compute the worldsheet sphere partition function of string theory on global AdS$_3$ with pure NS-NS flux. Because of an unfixed M\"obius symmetry on the worldsheet, there is a cancellation of infinities and only a part of the answer is unambiguous. We show that it precisely reproduces the holographic Weyl anomaly and the ambiguous terms correspond to the

Kasra Farain, Daniel Bonn

Dry granular materials consist of a vast ensemble of discrete solid particles, interacting through complex frictional forces at the contact points. The particles are so large that these systems are believed to be completely athermal. Here, we arrest the dynamics of a flowing granular material in a steady-state flow configuration, enabling an isolated examina

Shan Li, Ming Huang, Yongfeng Song, Bo Lan

This paper presents theoretical and numerical models for the backscattering of 2D Rayleigh waves in single-phase, untextured polycrystalline materials with statistically equiaxed grains. The theoretical model, based on our prior inclusion-induced Rayleigh wave scattering model and the independent scattering approximation, considers single scattering of Rayle

Xi Jia, Alexander Thorley, Alberto Gomez, Wenqi Lu

U-Net style networks are commonly utilized in unsupervised image registration to predict dense displacement fields, which for high-resolution volumetric image data is a resource-intensive and time-consuming task. To tackle this challenge, we first propose Fourier-Net, which replaces the costly U-Net style expansive path with a parameter-free model-driven dec

S. Dichiara, D. Tsang, E. Troja, D. Neill

GRB 230307A is an extremely bright long duration GRB with an observed gamma-ray fluence of $\gtrsim$3$\times$10$^{-3}$ erg cm$^{-2}$ (10-1000 keV), second only to GRB 221009A. Despite its long duration, it is possibly associated with a kilonova, thus resembling the case of GRB 211211A. In analogy with GRB 211211A, we distinguish three phases in the prompt ga

Zeming Wu, Baochi Fu, Shujun Zhao, Runsheng Liu

By varying the intrinsic initial geometry, the p/d/$^3$He+Au collisions at the Relativistic Heavy Ion Collider (RHIC) provide a unique opportunity to understand the collective behavior and probe the possible sub-nucleon fluctuations in small systems. In this paper, we employ the hybrid model iEBE-VISHNU with Trento initial conditions to study the collective

Meenu Upadhyay, Kai Töpfer, Markus Meuwly

The decomposition and chemical dynamics for vibrationally excited syn-CH$_3$CHOO is followed based on statistically significant numbers of molecular dynamics simulations. Using a neural network-based reactive potential energy surface, transfer learned to the CASPT2 level of theory, the final total kinetic energy release and rotational state distributions of

Yecheng Jing, Jian-Jun Dong, Yu-Yu Zhang, Zi-Xiang Hu

By utilizing biorthogonal bases, we develop a comprehensive framework for studying biorthogonal dynamical quantum phase transitions in non-Hermitian systems. With the help of the previously overlooked associated state, we define the automatically normalized biorthogonal Loschmidt echo. This approach is capable of handling arbitrary non-Hermitian systems with

Abramo Agosti, Pierluigi Colli, Michel Frémond

In this paper we derive a new model for visco-elasticity with large deformations where the independent variables are the stretch and the rotation tensors which intervene with second gradients terms accounting for physical properties in the principle of virtual power. Another basic feature of our model is that there is conditional compatibility, entering the

Abhijeet Pendyala, Justin Dettmer, Tobias Glasmachers, Asma Atamna

We present ContainerGym, a benchmark for reinforcement learning inspired by a real-world industrial resource allocation task. The proposed benchmark encodes a range of challenges commonly encountered in real-world sequential decision making problems, such as uncertainty. It can be configured to instantiate problems of varying degrees of difficulty, e.g., in

Jonatan A. González, Julia Wrobel, Simon Vandekar, Paula Moraga

Multiplex immunofluorescence (mIF) imaging technology facilitates the study of the tumour microenvironment in cancer patients. Due to the capabilities of this emerging bioimaging technique, it is possible to statistically analyse, for example, the co-varying location and functions of multiple different types of immune cells. Complex spatial relationships bet

Y. Y. Wu, L. Mu, X. Zhang, D. Z. Dai

The van der Waals (vdW) semiconductor black phosphorus has been widely studied, especially after the discovery of phosphorene. On the contrary, its sister compound violet phosphorus, also a vdW semiconductor, has been rarely studied. Here we report the pressure-induced superconductivity in violet phosphorus up to $\sim$40 GPa. The superconductivity emerges a

Kai Cui, Lars Baumgärtner, Burak Yilmaz, Mengguang Li

Both data ferrying with disruption-tolerant networking (DTN) and mobile cellular base stations constitute important techniques for UAV-aided communication in situations of crises where standard communication infrastructure is unavailable. For optimal use of a limited number of UAVs, we propose providing both DTN and a cellular base station on each UAV. Here,

A. G. Ferrari, M. Ballardini, F. Finelli, D. Paoletti

We study the cosmological effects of a Galileon term in scalar-tensor theories of gravity. The subset of scalar-tensor theories considered are characterized by a non-minimal coupling $F(\sigma) R$, a kinetic term with arbitrary sign $Z (\partial \sigma)^2$ with $Z = \pm 1$, a potential $V(\sigma)$, and a Galileon term $G_3(\sigma, (\partial \sigma)^2) \squar

David Pride, Matteo Cancellieri, Petr Knoth

In this paper, we present CORE-GPT, a novel question-answering platform that combines GPT-based language models and more than 32 million full-text open access scientific articles from CORE. We first demonstrate that GPT3.5 and GPT4 cannot be relied upon to provide references or citations for generated text. We then introduce CORE-GPT which delivers evidence-

Brian Skinner

These are lecture notes compiled for a short lecture series at the 2023 Condensed Matter Summer School at the University of Minnesota. They are designed to be conversational and fun, and not to take the place of review articles that do a serious job of stating things precisely and citing literature thoroughly. The goal of the notes is to introduce some centr

Arkadiusz Kosior, Markus Heyl

Over the past decade, dynamical quantum phase transitions (DQPTs) have emerged as a paradigm shift in understanding nonequilibrium quantum many-body systems. However, the challenge lies in identifying order parameters that effectively characterize the associated dynamic phases. In this study, we investigate the behavior of vortex singularities in the phase o

Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo

Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution. However, from a privacy perspective, using GANs as a proxy for data sharing is not a safe solution, as they tend to embed near-duplicates of real samples in the latent space. Recent works, inspired by k-anonymity principles,

Johannes Blümlein, Abilio De Freitas, Peter Marquard, Narayan Rana

The quarkonic contributions to the three-loop heavy-quark form factors for vector, axial-vector, scalar and pseudoscalar currents are described by closed form difference equations for the expansion coefficients in the limit of small virtualities $q^2/m^2$. A part of the contributions can be solved analytically and expressed in terms of harmonic and cyclotomi

Nicole Dalia Cilia, Claudio De Stefano, Francesco Fontanella, Sabato Marco Siniscalchi

Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In partic

Andreas Opedal, Ran Zmigrod, Tim Vieira, Ryan Cotterell

This paper provides a reference description, in the form of a deduction system, of Earley's (1970) context-free parsing algorithm with various speed-ups. Our presentation includes a known worst-case runtime improvement from Earley's $O (N^3|G||R|)$, which is unworkable for the large grammars that arise in natural language processing, to $O (N^3|G|)$, which m

Jiangrui Tan, Mei Zhang

This paper is concerned with the characterizations of fixed points of the generating function of branching processes with countably infinitely many types. We assume each particle of type $i$ can only give offspring of type $j\geq i$, whose number only depends on $j-i$. We prove that, for these processes, there are at least countably infinitely many fixed poi

Thoa Thieu, Roderick Melnik

In this chapter, we consider probabilistic drift-diffusion models and Bayesian inference frameworks to address this issue, assisting better social human decision-making. We provide details of the models, as well as representative numerical examples, and discuss the decision-making process with a representative example of the escape route decision-making phen

Roberto Montemanni, Mauro Dell'Amico

Drones are currently seen as a viable way for improving the distribution of parcels in urban and rural environments, while working in coordination with traditional vehicles like trucks. In this paper we consider the parallel drone scheduling vehicle routing problem, where the service of a set of customers requiring a delivery is split between a fleet of truc

Sebastian Kubis, Włodzimierz Wójcik, David Alvarez Castillo, Noemi Zabari

The recent observation of the object HESS J1731-347 suggests the existence of a very light and very compact neutron star being a challenge for commonly used equation of state for dense matter. In this work we present a relativistic mean field model enriched with meson crossing terms among isovector and isoscalar mesons. Such interactions particularly dominat

Sushanta Kumar Sahu, Ananda S. Chowdhury

Parkinson disease is the second most common neurodegenerative disorder, as reported by the World Health Organization. In this paper, we propose a direct three-Class PD classification using two different modalities, namely, MRI and DTI. The three classes used for classification are PD, Scans Without Evidence of Dopamine Deficit and Healthy Control. We use whi

David Abella, Juan Carlos González-Avella, Maxi San Miguel, José J. Ramasco

The so-called Granovetter-Watts model was introduced to capture a situation in which the adoption of new ideas or technologies requires a certain redundancy in the social environment of each agent to take effect. This model has become a paradigm for complex contagion. Here we investigate a symmetric version of the model: agents may be in two states that can

Ulisses Barres de Almeida, Martin Tluczykont

Ground-based gamma-ray astronomy is a powerful tool to study cosmic-ray physics, providing a diagnostic of the high-energy processes at work in the most extreme astrophysical accelerators of the universe. Ground-based gamma-ray detectors apply a number of experimental techniques to measure the products of air showers induced by the primary gamma-rays over a

Mattia Giovanni Campana, Franca Delmastro, Elena Pagani

Disease detection from smartphone data represents an open research challenge in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are an important case study in this area and their early detection is a potential real instrument to counteract the pandemic situation. The efficacy of this solution mainly depends on the performances of AI a

Yuting Lu, Lingtong Min, Binglu Wang, Le Zheng

Remote sensing image super-resolution (RSISR) plays a vital role in enhancing spatial detials and improving the quality of satellite imagery. Recently, Transformer-based models have shown competitive performance in RSISR. To mitigate the quadratic computational complexity resulting from global self-attention, various methods constrain attention to a local wi

Andrey Kuzmin, Markus Nagel, Mart van Baalen, Arash Behboodi

Neural network pruning and quantization techniques are almost as old as neural networks themselves. However, to date only ad-hoc comparisons between the two have been published. In this paper, we set out to answer the question on which is better: neural network quantization or pruning? By answering this question, we hope to inform design decisions made on ne

Daniel Balzani, Alexander Heinlein, Axel Klawonn, Jascha Knepper

A computational framework is presented to numerically simulate the effects of antihypertensive drugs, in particular calcium channel blockers, on the mechanical response of arterial walls. A stretch-dependent smooth muscle model by Uhlmann and Balzani is modified to describe the interaction of pharmacological drugs and the inhibition of smooth muscle activati

Bingshuai Liu, Longyue Wang, Chenyang Lyu, Yong Zhang

One challenge in text-to-image (T2I) generation is the inadvertent reflection of culture gaps present in the training data, which signifies the disparity in generated image quality when the cultural elements of the input text are rarely collected in the training set. Although various T2I models have shown impressive but arbitrary examples, there is no benchm

Emily K Pass, Jennifer G Winters, David Charbonneau, Aurelia Balkanski

Previous studies of the exoplanet LTT 1445Ac concluded that the light curve from the Transiting Exoplanet Survey Satellite (TESS) was consistent with both grazing and non-grazing geometries. As a result, the radius and hence density of the planet remained unknown. To resolve this ambiguity, we observed the LTT 1445 system for six spacecraft orbits of the Hub