July 2023 arXiv papers — page 160

Etera R. Livine

The spread of the wave-function, or quantum uncertainty, is a key notion in quantum mechanics. At leading order, it is characterized by the quadratic moments of the position and momentum operators. These evolve and fluctuate independently from the position and momentum expectation values. They are extra degrees of quantum mechanics compared to classical mech

Tan Van Vu, Tomotaka Kuwahara, Keiji Saito

Understanding the ultimate rate at which information propagates is a pivotal issue in nonequilibrium physics. Nevertheless, the task of elucidating the propagation speed inherent in quantum bosonic systems presents challenges due to the unbounded nature of their interactions. In this study, we tackle the problem of macroscopic particle transport in a long-ra

Ahmed Magbool, Vaibhav Kumar, Mark F. Flanagan

Users in millimeter-wave (mmWave) systems often exhibit diverse channel strengths, which can negatively impact user fairness in resource allocation. Moreover, exact channel state information (CSI) may not be available at the transmitter, rendering suboptimal resource allocation. In this paper, we address these issues within the context of energy efficiency m

Wen-Fan Feng, Tan Liu, Jie-Wen Chen, Yan Wang

Spinning neutron stars (NSs) can emit continuous gravitational waves (GWs) that carry a wealth of information about the compact object. If such a signal is detected, it will provide us with new insight into the physical properties of matter under extreme conditions. Future space-based GW detectors, such as LISA and TianQin, can potentially detect some double

Michael Mesarcik, Albert-Jan Boonstra, Marco Iacobelli, Elena Ranguelova

As radio telescopes increase in sensitivity and flexibility, so do their complexity and data-rates. For this reason automated system health management approaches are becoming increasingly critical to ensure nominal telescope operations. We propose a new machine learning anomaly detection framework for classifying both commonly occurring anomalies in radio te

Sanchayan Bhowal, Somabha Mukherjee

In this paper, we derive results about the limiting distribution of the empirical magnetization vector and the maximum likelihood (ML) estimates of the natural parameters in the tensor Curie-Weiss Potts model. Our results reveal surprisingly new phase transition phenomena including the existence of a smooth curve in the interior of the parameter plane on whi

Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken

Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path space. Our work culminates in the development of the \emph{Controlled Monte Carlo Diffusion} sampler (CMCD) for Bayesian computation, a score-based annealing technique that crucially

Fiona McCarthy, J. Colin Hill

We use the full-mission $\textit{Planck}$ PR4 data to construct maps of the thermal Sunyaev$--$Zel'dovich effect (Compton-$y$ parameter) in our Universe. To do so, we implement a custom needlet internal linear combination (NILC) pipeline in a Python package, $\texttt{pyilc}$, which we make publicly available. We publicly release our Compton-$y$ maps, which w

Amit Patel, Primoz Skraba

This paper introduces and develops M\"obius homology, a homology theory for representations of finite posets into abelian categories. Although the connection between poset topology and M\"obius functions is classical, we go further by establishing a direct connection between poset topology and M\"obius inversions. In particular, we show that M\"obius homolog

Dominik Strutz, Andrew Curtis

This paper introduces variational design methods that are novel to Geophysics, and discusses their benefits and limitations in the context of geophysical applications and more established design methods. Variational methods rely on functional approximations to probability distributions and model-data relationships. They can be used to design experiments that

Chengyu Shao, Sujie Lin, Lili Yang

With the breakthrough in PeV gamma-ray astronomy brought by the LHAASO experiment, the high-energy sky is getting richer than before. Lately, LHAASO Collaboration reported the observation of a gamma-ray diffuse emission with energy up to the PeV level from both the inner and outer Galactic plane. In these spectra, there is one bump that is hard to explain by

Marco Pegoraro, Sanketh Vedula, Aviv A. Rosenberg, Irene Tallini

Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features. QR is limited by the assumption that the target distribution is univariate and defined on an Euclidean domain. Although the notion of quantiles was recently extended to multi-variate distributions, QR for m

Marie Korsaga, Benoit Famaey, Jonathan Freundlich, Lorenzo Posti

Observed scaling relations in galaxies between baryons and dark matter global properties are key to shed light on the process of galaxy formation and on the nature of dark matter. Here, we study the scaling relation between the neutral hydrogen (HI) and dark matter mass in isolated rotationally-supported disk galaxies at low redshift. We first show that stat

Sander Barendse

We propose an $\ell_1$-penalized estimator for high-dimensional models of Expected Shortfall (ES). The estimator is obtained as the solution to a least-squares problem for an auxiliary dependent variable, which is defined as a transformation of the dependent variable and a pre-estimated tail quantile. Leveraging a sparsity condition, we derive a nonasymptoti

Onkar Kamlakar Kale, Girja S Tripathi

We study the constructible Witt theory of \'etale sheaves of $\Lambda$-modules on a scheme $X$ for coefficient rings $\Lambda$ having finite characteristic not equal to 2 and prime to the residue characteristics of the scheme $X$. Our construction is based on the recent advances by Cisinski and D\'eglise on six-functor formalism for derived categories of \'e

Magnus Malmström, Isaac Skog, Daniel Axehill, Fredrik Gustafsson

The uncertainty in the prediction calculated using the delta method for an overparameterized (parametric) black-box model is shown to be larger or equal to the uncertainty in the prediction of a canonical (minimal) model. Equality holds if the additional parameters of the overparameterized model do not add flexibility to the model. As a conclusion, for an ov

Eugenio Moro, Francesco Linsalata, Maurizio Magarini, Umberto Spagnolini

Vehicular communications integrated with the Radio Access Network (RAN) are envisioned as a breakthrough application for the 6th generation (6G) cellular systems. However, traditional RANs lack the flexibility to enable sophisticated control mechanisms that are demanded by the strict performance requirements of the vehicle-to-everything (V2X) environment. In

Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey

We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks and cover a diverse set of domains includ

Changhong Fu, Liangliang Yao, Haobo Zuo, Guangze Zheng

Domain adaptation (DA) has demonstrated significant promise for real-time nighttime unmanned aerial vehicle (UAV) tracking. However, the state-of-the-art (SOTA) DA still lacks the potential object with accurate pixel-level location and boundary to generate the high-quality target domain training sample. This key issue constrains the transfer learning of the

C. Coelho, M. Fernanda P. Costa, L. L. Ferrás

This work introduces Neural Chronos Ordinary Differential Equations (Neural CODE), a deep neural network architecture that fits a continuous-time ODE dynamics for predicting the chronology of a system both forward and backward in time. To train the model, we solve the ODE as an initial value problem and a final value problem, similar to Neural ODEs. We also

Nicola Manca, Gaia Tarsi, Alexei Kalaboukhov, Francesco Bisio

EuTiO3 (ETO) is a well-known complex oxide mainly investigated for its magnetic properties and its incipient ferro-electricity. In this work, we demonstrate the realization of suspended micro-mechanical structures, such as cantilevers and micro-bridges, from 100 nm-thick single-crystal epitaxial ETO films deposited on top of SrTiO3(100) substrates. By combin

Meng Kang, Tao Liu, C. T. Chan, Meng Xiao

Bound states in the continuum (BICs) have attracted attention in photonics owing to their interesting properties. For example, BICs can effectively confine light in a counter-intuitive way and the far-field radiation of photonic structures that exhibit BICs manifests fascinating topological characteristics. Early research into photonic BICs was primarily foc

S. W. Hancock, S. Zahedpour, A. Goffin, H. M. Milchberg

We demonstrate the controlled spatiotemporal transfer of transverse orbital angular momentum (OAM) to electromagnetic waves: the spatiotemporal torquing of light. This is a radically different situation than OAM transfer to longitudinal, spatially-defined OAM light by stationary or slowly varying refractive index structures such as phase plates or air turbul

Davide Pastorello, Enrico Blanzieri

This paper focuses on the construction of a general parametric model that can be implemented executing multiple swap tests over few qubits and applying a suitable measurement protocol. The model turns out to be equivalent to a two-layer feedforward neural network which can be realized combining small quantum modules. The advantages and the perspectives of th

Hany Khalifa, Kirill Petrovnin, Riku Jäntti, Gheorghe Sorin Paraoanu

In quantum illumination (QI) the non-classical correlations between continuous variable (CV) entangled modes of radiation are exploited to detect the presence of a target embedded in thermal noise. The extreme environment where QI outperforms its optimal classical counterpart suggests that applications in the microwave domain would benefit the most from this

Lala Shakti Swarup Ray, Bo Zhou, Lars Krupp, Sungho Suh

Accurate camera calibration is crucial for various computer vision applications. However, measuring calibration accuracy in the real world is challenging due to the lack of datasets with ground truth to evaluate them. In this paper, we present SynthCal, a synthetic camera calibration benchmarking pipeline that generates images of calibration patterns to meas

S. Fagioli, E. Radici, L. Romagnoli

We investigated existence of global weak solutions for a system of chemotaxis type with nonlinear degenerate diffusion, arising in modelling Multiple Sclerosis disease. The model consists of three equations describing the evolution of macrophages ($m$), cytokine ($c$) and apoptotic oligodendrocytes ($d$). The main novelty in our work is the presence of a non

Sheng-Lun Xie, Ce Cai, Yun-Wei Yu, Shao-Lin Xiong

Magnetar is a neutron star with an ultrahigh magnetic field ($\sim 10^{14}-10^{15}$ G). The magnetar SGR J1935+2154 is not only one of the most active magnetars detected so far, but also the unique confirmed source of fast radio bursts (FRBs). Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is dedicated to monitor gamma-ray

Carl M. Bender, Christos Karapoulitidis, S. P. Klevansky

The Dyson-Schwinger (DS) equations for a quantum field theory in $D$-dimensional space-time are an infinite sequence of coupled integro-differential equations that are satisfied exactly by the Green's functions of the field theory. This sequence of equations is underdetermined because if the infinite sequence of DS equations is truncated to a finite sequence

Richard A. N. Brooks, Kyle A. Oman, Carlos S. Frenk

The number density of extragalactic 21-cm radio sources as a function of their spectral line-widths -- the HI width function (HIWF) -- is a tracer of the dark matter halo mass function. The ALFALFA 21-cm survey measured the HIWF in northern and southern Galactic fields finding a systematically higher number density in the north; an asymmetry which is in tens

Dongyang Yu, Yunshi Xie, Wangpeng An, Li Zhang

We introduce a novel one-stage end-to-end multi-person 2D pose estimation algorithm, known as Joint Coordinate Regression and Association (JCRA), that produces human pose joints and associations without requiring any post-processing. The proposed algorithm is fast, accurate, effective, and simple. The one-stage end-to-end network architecture significantly i

Lala Shakti Swarup Ray, Daniel Geißler, Bo Zhou, Paul Lukowicz

In this work, we propose a novel single-end morphing capacitive sensing method for shape tracking, FxC, by combining Folding origami structures and Capacitive sensing to detect the morphing structural motions using state-of-the-art sensing circuits and deep learning. It was observed through embedding areas of origami structures with conductive materials as s

Delong Chen, Jianfeng Liu, Wenliang Dai, Baoyuan Wang

Recent research has demonstrated that the multi-task fine-tuning of multi-modal Large Language Models (LLMs) using an assortment of annotated downstream vision-language datasets significantly enhances their performance. Yet, during this process, a side effect, which we termed as the "multi-modal alignment tax", surfaces. This side effect negatively impacts t

Anoop Mutneja, Bhanu Prasad Bhowmik, Smarajit Karmakar

Enhancing the mechanical properties of amorphous solids is crucial for material design, with microalloying being a common but not well-understood method. Using extensive molecular dynamics simulations, we investigate the effect of impurity particles on the yielding transition of amorphous solids in the context of brittle-ductile transition with microalloying

Antonio Lei, Katharina Müller

Let $p$ and $q$ be distinct prime numbers, with $q\equiv 1\pmod{12}$. Let $N$ be a positive integer that is coprime to $pq$. We prove a formula relating the Hasse--Weil zeta function of the modular curve $X_0(qN)_{\mathbb{F}_q}$ to the Ihara zeta function of the $p$-isogeny graphs of supersingular elliptic curves defined over $\overline{\mathbb{F}_q}$ equipp

Alessandro Zito, Dylan Greaves, Jacopo Soriano, Lee Richardson

North star metrics and online experimentation play a central role in how technology companies improve their products. In many practical settings, however, evaluating experiments based on the north star metric directly can be difficult. The two most significant issues are 1) low sensitivity of the north star metric and 2) differences between the short-term an

Weitao Chen, Gabriel Lemarie, Jiangbin Gong

Long-range hoppings in quantum disordered systems are known to yield quantum multifractality, whose features can go beyond the characteristic properties associated with an Anderson transition. Indeed, critical dynamics of long-range quantum systems can exhibit anomalous dynamical behaviours distinct from those at the Anderson transition in finite dimensions.

Yonglin Li, Jing Zhang, Xiao Teng, Long Lan

The Segment Anything Model (SAM) has gained significant attention for its impressive performance in image segmentation. However, it lacks proficiency in referring video object segmentation (RVOS) due to the need for precise user-interactive prompts and a limited understanding of different modalities, such as language and vision. This paper presents the RefSA

Samuel Tovey, David Zimmer, Christoph Lohrmann, Tobias Merkt

Multi-Agent Reinforcement Learning (MARL) is a promising candidate for realizing efficient control of microscopic particles, of which micro-robots are a subset. However, the microscopic particles' environment presents unique challenges, such as Brownian motion at sufficiently small length-scales. In this work, we explore the role of temperature in the emerge

M. V. Galynskii, Yu. M. Bystritskiy, V. M. Galynsky

In this letter, we propose a new method for measuring the Sachs form factors ratio ($R =μ_p G_E/G_M$) based on the transfer of polarization from the initial proton to the final electron in the elastic $e \vec p \to \vec e p$ process, in the case when the axes of quantization of spins of the target proton at rest and of the scattered electron are parallel, i.

Lea Happel, Axel Voigt

Coordinated cellular movements are key processes in tissue morphogenesis. Using a cell-based modeling approach we study the dynamics of epithelial layers lining surfaces with constant and varying curvature. We demonstrate that extrinsic curvature effects can explain the alignment of cell elongation with the principal directions of curvature. Together with sp

Shashank Kushwaha, Junyan He, Diab Abueidda, Iwona Jasiuk

Biological structural designs in nature, like hoof walls, horns, and antlers, can be used as inspiration for generating structures with excellent mechanical properties. A common theme in these designs is the small percent porosity in the structure ranging from 1 - 5\%. In this work, the sheep horn was used as an inspiration due to its higher toughness when l

Liangquan Zhang

In this paper, we study a kind of optimal control problem for forward-backward stochastic differential equations (FBSDEs for short) of McKean--Vlasov type via the dynamic programming principle (DPP for short) motivated by studying the infinite dimensional Hamilton--Jacobi--Bellman (HJB for short) equation derived from the decoupling field of the FBSDEs posed

Maik Punke, Vidar Skogvoll, Marco Salvalaglio

The phase-field crystal model (PFC) describes crystal structures at diffusive timescales through a periodic order parameter representing the atomic density. One of its main features is that it naturally incorporates elastic and plastic deformation. To correctly interpret numerical simulation results or devise extensions related to the elasticity description,

Hiroyuki Hirayama, Masahiro Ikeda

We consider the Cauchy problem of the system of nonlinear Schrödinger equations with derivative nonlinearlity. This system was introduced by Colin-Colin (2004) as a model of laser-plasma interactions. We study existence of ground state solutions and the global well-posedness of this system by using the variational methods. We also consider the stability of t

Arnaud Allera, Alexandra M. Goryaeva, Paul Lafourcade, Jean-Bernard Maillet

Accurate structural analysis is essential to gain physical knowledge and understanding of atomic-scale processes in materials from atomistic simulations. However, traditional analysis methods often reach their limits when applied to crystalline systems with thermal fluctuations, defect-induced distortions, partial vitrification, etc. In order to enhance the

Hassan Firouzjahi, Haidar Sheikhahmadi

We study the vacuum zero point energy associated to a scalar field with an arbitrary mass and conformal coupling in a dS background. Employing dimensional regularization scheme, we calculate the regularized zero point energy density, pressure and the trace of the energy momentum tensor. It is shown that the classical relation $\langle T \rangle =-4 \langle \

Ruimin Ma, Ruitao Xie, Yanlin Wang, Jintao Meng

Autism spectrum disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and intervention of ASD critical. With the development of the machine learning and neuroimaging technology, extensive research has been conducted on machine classification of ASD based o

Konstantin Sonntag, Sebastian Peitz

We present a new gradient-like dynamical system related to unconstrained convex smooth multiobjective optimization which involves inertial effects and asymptotic vanishing damping. To the best of our knowledge, this system is the first inertial gradient-like system for multiobjective optimization problems including asymptotic vanishing damping, expanding the

Sizhe Yang, Yanjie Ze, Huazhe Xu

Visual Reinforcement Learning (RL) agents trained on limited views face significant challenges in generalizing their learned abilities to unseen views. This inherent difficulty is known as the problem of $\textit{view generalization}$. In this work, we systematically categorize this fundamental problem into four distinct and highly challenging scenarios that

Elfarouk Harb

This work introduces \emph{sharding} and \emph{Poissonization} as a unified framework for analyzing prophet inequalities. Sharding involves splitting a random variable into several independent random variables, shards, that collectively mimic the original variable's behavior. We combine this with Poissonization, where these shards are modeled using a Poisson

Hamza Jaffali, Frédéric Holweck, Luke Oeding

The absolute values of polynomial SLOCC invariants (which always vanish on separable states) can be seen as measures of entanglement. We study the case of real 3-qutrit systems and discover a new set of maximally entangled states (from the point of view of maximizing the hyperdeterminant). We also study the basic fundamental invariants and find real 3-qutrit

Cheng Chen, Yong Wang, Lizi Liao, Yueguo Chen

Given a limited labeling budget, active learning (AL) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, AL typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider erroneous instances with their neighborh

G. Guilluy, V. Bourrier, Y. Jaziri, W. Dethier

The population of close-in exoplanets features a desert of hot Neptunes whose origin is uncertain. These planets may have lost their atmosphere, eroding into mini-Neptunes and super-Earths. Direct observations of evaporating atmospheres are essential to derive mass-loss estimates and constrain this scenario. The metastable 1083.3nm HeI triplet represents a p

Mikel Garcia-de-Andoin, Álvaro Saiz, Pedro Pérez-Fernández, Lucas Lamata

Digital-analog quantum computing is a computational paradigm which employs an analog Hamiltonian resource together with single-qubit gates to reach universality. Here, we design a new scheme which employs an arbitrary two-body source Hamiltonian, extending the experimental applicability of this computational paradigm to most quantum platforms. We show that t

Geng Li, Hui Dong

To achieve fast computation, it is crucial to reset the memory to a desired state within a limited time. However, the inherent delay in the system's response often prevents reaching the desired state once the control process is completed in finite time. To address this challenge, we propose a shortcut strategy that incorporates an auxiliary control to guide

Kenta Higuchi, Hisashi Morioka

In this paper, some properties of resonances for multi-dimensional quantum walks are studied. Resonances for quantum walks are defined as eigenvalues of complex translated time evolution operators in the pseudo momentum space. For some typical cases, we show some results of existence or nonexistence of resonances. One is a perturbation of an elastic scatteri

Steven Robertson

For any prime $p$ and real number and $\alpha$, the $p$-adic Littlewood Conjecture due to de Mathan and Teuli\'e asserts that \[\inf_{|m|\ge1}|m|_p\cdot |m|\cdot |\left\langle\alpha m\right\rangle|=0.\] Above, $|m|$ is the usual absolute value, $|m|_p$ is the $p$-adic norm and $\left|\left\langle x\right\rangle\right|$ is the distance from $x\in\mathbb{R}$ t

Abdul Karim Gizzini, Yahia Medjahdi, Ali J. Ghandour, Laurent Clavier

Research into 6G networks has been initiated to support a variety of critical artificial intelligence (AI) assisted applications such as autonomous driving. In such applications, AI-based decisions should be performed in a real-time manner. These decisions include resource allocation, localization, channel estimation, etc. Considering the black-box nature of

Felix Härer

Open and permissionless blockchains are distributed systems with thousands to tens of thousands of nodes, establishing novel platforms for decentralized applications. When realizing such an application, data might be stored and retrieved from one or more blockchains by distributed network nodes without relying on centralized coordination and trusted third pa

Gershon Wolansky

The Choquard equation is a partial differential equation that has gained significant interest and attention in recent decades. It is a nonlinear equation that combines elements of both the Laplace and Schr\"odinger operators, and it arises frequently in the study of numerous physical phenomena, from condensed matter physics to nonlinear optics. In particular

Uladzislau Kapustsin, Utku Kaya, Thomas Richter

We analyze a hybrid method that enriches coarse grid finite element solutions with fine scale fluctuations obtained from a neural network. The idea stems from the Deep Neural Network Multigrid Solver (DNN-MG), (Margenberg et al., J Comput Phys 460:110983, 2022; A neural network multigrid solver for the Navier-Stokes equations) which embeds a neural network i

Dirk Pauly, Rainer Picard

In this short note we show that Hilbert complexes are strongly related to what we shall call annihilating sets of skew-selfadjoint operators. This provides for a new perspective on the classical topic of Hilbert complexes viewed as families of commuting normal operators.

Roberto Rossi, Zhen Chen, S. Armagan Tarim

We consider the single-item single-stocking location stochastic inventory system under a fixed ordering cost component. A long-standing problem is that of determining the structure of the optimal control policy when this system is subject to order quantity capacity constraints; to date, only partial characterisations of the optimal policy have been discussed

Giada Franz, Mario B. Schulz

We establish general bounds on the topology of free boundary minimal surfaces obtained via min-max methods in compact, three-dimensional ambient manifolds with mean convex boundary. We prove that the first Betti number is lower semicontinuous along min-max sequences converging in the sense of varifolds to free boundary minimal surfaces. In the orientable cas

Andrew Freeman, Maarten Golterman, Yigal Shamir

We apply dilaton chiral perturbation theory (dChPT) at next-to-leading order to lattice data from the LatKMI collaboration for the eight-flavor SU(3) gauge theory. In previous work, we found that leading-order dChPT does not account for these data, but that a model extension of leading-order dChPT with a varying mass anomalous dimension describes these data

Shi Chen, Yuya Tanizaki

Originating from the topology of the path-integral target space $Y$, solitonic symmetry describes the conservation law of topological solitons and the selection rule of defect operators. As Ref.~\cite{Chen:2022cyw} exemplifies, the conventional treatment of solitonic symmetry as an invertible symmetry based on homotopy groups is inappropriate. In this paper,

Oscar Alberto Juiña Quilachamín, Nicolás Navarro-Guerrero

Tactile sensors have been developed since the early '70s and have greatly improved, but there are still no widely adopted solutions. Various technologies, such as capacitive, piezoelectric, piezoresistive, optical, and magnetic, are used in haptic sensing. However, most sensors are not mechanically robust for many applications and cannot cope well with curve

Ellery Smith, Rahel Paloots, Dimitris Giagkos, Michael Baudis

With the proliferation of research means and computational methodologies, published biomedical literature is growing exponentially in numbers and volume. Cancer cell lines are frequently used models in biological and medical research that are currently applied for a wide range of purposes, from studies of cellular mechanisms to drug development, which has le

Tianye Wang, Haoxuan Yao, Tai Sing Lee, Jiayi Hong

The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of

Marcin Maździarz

Recently, Chuanying Li, Tao Fu, Xule Li, Hao Hu, and Xianghe Peng in [Phys. Rev. B 107, 224106] investigated the mechanical behavior of cubic HfC and TaC under simple shear (SS) and pure shear (PS) using first-principles calculations. Unfortunately, the paper contains some serious and fundamental flaws in the field of continuum mechanics and nanomechanics. T

Avinash Tiwari, Aditya Vijaykumar, Shasvath J. Kapadia, Giacomo Fragione

The motion of the center of mass of a coalescing binary black hole (BBH) in a gravitational potential imprints a line-of-sight acceleration (LOSA) onto the emitted gravitational wave (GW) signal. The acceleration could be sufficiently large in dense stellar environments, such as globular clusters (GCs), to be detectable with next-generation space-based detec

Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami, Kristian Kersting

Visual reasoning is essential for building intelligent agents that understand the world and perform problem-solving beyond perception. Differentiable forward reasoning has been developed to integrate reasoning with gradient-based machine learning paradigms. However, due to the memory intensity, most existing approaches do not bring the best of the expressivi

Jorge Martinez-Gil

Semantic similarity measures are a key component in natural language processing tasks such as document analysis, requirement matching, and user input interpretation. However, the performance of individual measures varies considerably across datasets. To address this, ensemble approaches that combine multiple measures are often employed. This paper presents a

J. L. Alonso, C. Bouthelier-Madre, J. Clemente-Gallardo, D. Martínez-Crespo

We generalize the Hamiltonian picture of General Relativity coupled to classical matter, known as geometrodynamics, to the case where such matter is described by a Quantum Field Theory in Curved Spacetime, but gravity is still described by a classical metric tensor field over a spatial hypersurface and its associated momentum. Thus, in our approach there is

I. Khomchenko, H. Ouerdane, G. Benenti

So far, the efficiency of thermoelectric energy conversion remains low compared to traditional technologies, such as coal or nuclear. This low efficiency can be explained by connecting the thermoelastic properties of the electronic working fluid to its transport properties. Such connection also shows that operating close to electronic phase transitions can b

Sergio Burdisso, Esaú Villatoro-Tello, Srikanth Madikeri, Petr Motlicek

We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews. To this end, we use a GCN for modeling non-consecutive and long-distance semantics to classify the transcriptions into depressed or control subjects. The proposed method aims

Haiyan Guo, Bo Zhou

Let $G$ be a connected graph with vertex set $V(G)$. The distance, $d_G(u,v)$, between vertices $u$ and $v$ in $G$ is defined as the length of a shortest path between $u$ and $v$ in $G$. The distance matrix of $G$ is the matrix $D(G)=(d_G(u,v))_{u,v\in V(G)}$. The second largest distance eigenvalue of $G$ is the second largest one in the spectrum of $D(G)$.

Florian Feuchtmayr, Robert Berghaus, Selene Sachero, Gregor Bayer

Color centers in diamond, among them the negatively-charged germanium vacancy (GeV$^-$), are promising candidates for many applications of quantum optics such as a quantum network. For efficient implementation, the optical transitions need to be coupled to a single optical mode. Here, we demonstrate the transfer of a nanodiamond containing a single ingrown G

Marienza Caldarola, Sachiko Kuroyanagi, Savvas Nesseris, Juan Garcia-Bellido

The hyperbolic encounters of two massive objects are characterized by the emission of a gravitational wave burst, with most of the energy released during the closest approach (near the periastron). The detection of such events, different from the well-known inspiral emission, would be an interesting discovery and provide complementary information to observat

Anina Timmermann, Yutong Shan, Ansgar Reiners, Andreas Pack

We introduce ECCOplanets, an open-source Python code that simulates condensation in the protoplanetary disk. Our aim is to analyse how well a simplistic model can reproduce the main characteristics of rocky planet formation. For this purpose, we revisited condensation temperatures ($T_c$) as a means to study disk chemistry, and explored their sensitivity to

Debsoumya Chakraborti, Jaehoon Kim, Hyunwoo Lee, Jaehyeon Seo

It is well-known that every tournament contains a Hamilton path, and every strongly connected tournament contains a Hamilton cycle. This paper establishes transversal generalizations of these classical results. For a collection $\mathbf{T}=\{T_1,\dots,T_m\}$ of not-necessarily distinct tournaments on a common vertex set $V$, an $m$-edge directed graph $\math

Andreas Erlebach, Martin Šípka, Indranil Saha, Petr Nachtigall

Under operating conditions, the dynamics of water and ions confined within protonic aluminosilicate zeolite micropores are responsible for many of their properties, including hydrothermal stability, acidity and catalytic activity. However, due to high computational cost, operando studies of acidic zeolites are currently rare and limited to specific cases and

Koustava Goswami, Srikrishna Karanam, Prateksha Udhayanan, K J Joseph

Recent advances in multimodal learning has resulted in powerful vision-language models, whose representations are generalizable across a variety of downstream tasks. Recently, their generalization ability has been further extended by incorporating trainable prompts, borrowed from the natural language processing literature. While such prompt learning techniqu

Yaswitha Gujju, Atsushi Matsuo, Rudy Raymond

The past decade has witnessed significant advancements in quantum hardware, encompassing improvements in speed, qubit quantity, and quantum volume-a metric defining the maximum size of a quantum circuit effectively implementable on near-term quantum devices. This progress has led to a surge in Quantum Machine Learning (QML) applications on real hardware, aim

Weiran Guo, Guanjun Liu, Ziyuan Zhou, Ling Wang

Deep reinforcement learning (DRL) performance is generally impacted by state-adversarial attacks, a perturbation applied to an agent's observation. Most recent research has concentrated on robust single-agent reinforcement learning (SARL) algorithms against state-adversarial attacks. Still, there has yet to be much work on robust multi-agent reinforcement le

Giacomo Albi, Elisa Calzola, Giacomo Dimarco

Opinion dynamics is an important and very active area of research that delves into the complex processes through which individuals form and modify their opinions within a social context. The ability to comprehend and unravel the mechanisms that drive opinion formation is of great significance for predicting a wide range of social phenomena such as political

Jamie Burke, Justin Engelmann, Charlene Hamid, Megan Reid-Schachter

Purpose: To develop an open-source, fully-automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography (OCT) data. Methods: We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes) from 3 clinical studies related to systemic disease. Ground truth segmentations were generated using a clinically validated, se

Xin-Wei Jin, Shi-Jie Shen, Zhan-Ying Yang, Ji Lin

In this paper, we study in detail the nonlinear propagation of magnetic soliton in a ferromagnetic film. The sample is magnetized to saturation by an external field perpendicular to film plane. A new generalized (2+1)-dimensional short-wave asymptotic model is derived. The bilinear-like forms of this equation are constructed, and exact magnetic line soliton

Soham Sen, Manjari Dutta, Sunandan Gangopadhyay

In this work, we consider a gravitational wave interacting with a quantum harmonic oscillator in the transverse-traceless gauge. We take the gravitational wave to be carrying the signatures of both plus and cross polarization at first. We then try to obtain a suitable form of the Lewis invariant using the most general form possible while considering only qua

Debargha Banerjee, Priyanka Majumder

In this article, we are interested in modular forms with non-vanishing central critical values and linear independence of Fourier coefficients of modular forms. The main ingredient is a generalization of a theorem due to VanderKam to modular symbols of higher weights. We prove that for sufficiently large primes $p$, Hecke operators $T_1, T_2, \ldots, T_D$ ac

Hanka Řada, Štěpán Starosta, Vítězslav Kala

We consider expansions of vectors by a general class of multidimensional continued fraction algorithms. If the expansion is eventually periodic, then we describe the possible structure of a matrix corresponding to the repetend, and use it to prove that a number of vectors has an eventually periodic expansion in the Algebraic Jacobi--Perron Algorithm. Further

Giovanni Cinà, Daniel Fernandez-Llaneza, Ludovico Deponte, Nishant Mishra

Feature attribution methods have become a staple method to disentangle the complex behavior of black box models. Despite their success, some scholars have argued that such methods suffer from a serious flaw: they do not allow a reliable interpretation in terms of human concepts. Simply put, visualizing an array of feature contributions is not enough for huma

Xiaoxin Zhang, Martin Brandt, Xiaoye Tong, Xiaowei Tong

Trees play a crucial role in urban environments, offering various ecosystem services that contribute to public health and human well-being. China has initiated a range of urban greening policies over the past decades, however, monitoring their impact on urban tree dynamics at a national scale has proven challenging. In this study, we deployed nano-satellites

Jia-Jia Luo, Han Pu, Xi-Wen Guan

We present analytical results of fundamental properties of one-dimensional (1D) Hubbard model with a repulsive interaction, ranging from fractional excitations to universal thermodynamics, interaction-driven criticality, correlation functions, Contact susceptibilities and quantum cooling. Using the exact solutions of the Bethe Ansatz equations of the Hubbard

Büşra Karadeniz Şen, Camille Plénat, Meral Tosun

This paper is devoted to construct a minimal toric embedded resolution of a rational singularity via jet schemes. The minimality is reached by extending the concept of the profile of a simplicial cone given in 6.

Shukai Chen, Lina Ji, Jie Xiong

We prove a scaling limit theorem for two-type Galton-Waston branching processes with interaction. The limit theorem gives rise to a class of mixed state branching processes with interaction using to simulate the evolution for cell division affected by parasites. Such process can also be obtained by the pathwise unique solution to a stochastic equation system

Bogdan R. Bułka

We simulated the radiative response of the cavity quantum electrodynamics (QED) inductively coupled to the ring pierced by magnetic flux, and analyzed its spectral dependence to get insight into persistent current dynamics. Current fluctuations in the ring induce changes in the microwave resonator: shifting the resonant frequency and changing its damping. We

Raphael Frick, Inna Vogel, Jeong-Eun Choi

This paper describes the second-placed approach developed by the Fraunhofer SIT team in the CLEF-2023 CheckThat! lab Task 1B for English. Given a text snippet from a political debate, the aim of this task is to determine whether it should be assessed for check-worthiness. Detecting check-worthy statements aims to facilitate manual fact-checking efforts by pr

David Machado, Roberto Mulet, Federico Ricci-Tersenghi

We study the stochastic relaxation dynamics of the Ising p-spin model on a random graph, a well-known model with glassy dynamics at low temperatures. We introduce and discuss a new closure scheme for the master equation governing the continuous-time relaxation of the system, that translates into a set of differential equations for the evolution of local prob