December 2023 arXiv papers — page 101

Cole Johnston, Mathias Michielsen, Evan H. Anders, Mathieu Renzo

1D stellar evolution calculations produce uncertain predictions for quantities like the age, core mass, core compactness, and nucleo-synthetic yields; a key source of uncertainty is the modeling of interfaces between regions that are convectively stable and those that are not. Theoretical and numerical work has demonstrated that there should be numerous proc

Akitoshi Hoshiya

We develop an abstract perturbation theory for the orthonormal Strichartz estimates, which were first studied by Frank-Lewin-Lieb-Seiringer. The method used in the proof is based on the duality principle and the smooth perturbation theory by Kato. We also deduce the refined Strichartz estimates for the Schr\"odinger operator in terms of the Besov space. Fina

Sahil Nokhwal, Saurabh Pahune, Ankit Chaudhary

The aim of steganographic algorithms is to identify the appropriate pixel positions in the host or cover image, where bits of sensitive information can be concealed for data encryption. Work is being done to improve the capacity to integrate sensitive information and to maintain the visual appearance of the steganographic image. Consequently, steganography i

I. Jesán Velázquez-Reséndiz, Yuri G. Rubo

We propose a grated microcavity setup to form trapped polariton condensates with parity-time ($\mathcal{PT}$) symmetry and study their polarization dynamics. The pseudo-conservative dynamics of the Stokes vector in proposed configuration is preserved in the presence of polariton-polariton interaction. In the case of weak gain-dissipation inbalance, as compar

Nicolas Crampe, Meri Zaimi

The notion of factorized $A_2$-Leonard pair is introduced. It is defined as a rank 2 Leonard pair, with actions in certain bases corresponding to the root system of the Weyl group $A_2$, and with some additional properties. The functions arising as entries of transition matrices are bivariate orthogonal polynomials (of Tratnik type) with bispectral propertie

C. Feller, A. Pommerol, A. Lethuillier, N. Hänni

Objective: In the framework of the Cometary Physics Laboratory (CoPhyLab) and its sublimation experiments of cometary surface analogues under simulated space conditions, we characterize the properties of intimate mixtures of juniper charcoal and SiO$_2$ chosen as a dust analogue \citep{Lethuillier_2022}. We present the details of these investigations for the

Guannan Chen, Mohammadali Foroozandeh, Chris Budd, Pranav Singh

We develop a fourth-order Magnus expansion based quantum algorithm for the simulation of many-body problems involving two-level quantum systems with time-dependent Hamiltonians, $\mathcal{H}(t)$. A major hurdle in the utilization of the Magnus expansion is the appearance of a commutator term which leads to prohibitively long circuits. We present a technique

Sabbir Ahmed, Md Nahiduzzaman, Tariqul Islam, Faisal Haque Bappy

With the surge in cloud storage adoption, enterprises face challenges managing data duplication and exponential data growth. Deduplication mitigates redundancy, yet maintaining redundancy ensures high availability, incurring storage costs. Balancing these aspects is a significant research concern. We propose FASTEN, a distributed cloud storage scheme ensurin

Francesca Crispo, Angelica Pia Di Feola

We prove a result of existence of regular solutions and a maximum principle for solutions to a parabolic p-Laplacian system with convective term.

Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional st

Jérôme Fournier, Pierre-Olivier Downey, Charles-David Hébert, Maxime Charlebois

In recent years, the $T$-linear scattering rate found at low temperatures, defining the strange metal phase of cuprates, has been a subject of interest. Since a wide range of materials have a scattering rate that obeys the equation $ \hbar / \tau \approx k_B T$, the idea of a universal Planckian limit on the scattering rate has been proposed. However, there

Faisal Haque Bappy, Tariqul Islam, Tarannum Shaila Zaman, Md Sajidul Islam Sajid

Although blockchains have become widely popular for their use in cryptocurrencies, they are now becoming pervasive as more traditional applications adopt blockchain to ensure data security. Despite being a secured network, blockchains have some tradeoffs such as high latency, low throughput, and transaction failures. One of the core problems behind these is

Jiang Zhang, Qiong Wu, Yiming Xu, Cheng Cao

Toxic content detection is crucial for online services to remove inappropriate content that violates community standards. To automate the detection process, prior works have proposed varieties of machine learning (ML) approaches to train Language Models (LMs) for toxic content detection. However, both their accuracy and transferability across datasets are li

Zs. Kovari, K. G. Strassmeier, L. Kriskovics, K. Olah

We are conducting a comprehensive investigation of V815 Her using photometric and spectroscopic data to understand the origin of the activity and what influences it in the short and long term. Using TESS photometry we performed light curve modeling in order to derive astrophysical and orbital parameters for the eclipsing binary subsystem V815 Her B. Using ar

Phil Uttley, Julien Malzac

Black hole X-ray binaries in their hard and hard-intermediate states display hard and soft time lags between broadband noise variations (high-energy emission lagging low-energy and vice versa), which could be used to constrain the geometry of the disk and Comptonising corona in these systems. Comptonisation and reverberation lag models, which are based on li

Samuel Burns, Matthew Woodward

Jumping and hopping locomotion are efficient means of traversing unstructured rugged terrain with the former being the focus of roboticists; a focus that has recently been changing. This focus has led to significant performance and understanding in jumping robots but with limited practical applications as they require significant time between jumps to store

Tiago Gomes, Carlos M. Correia, Lisa Bardou, Sylvain Cetre

The amount of Adaptive Optics (AO) telemetry generated by VIS/NIR ground-based observatories is ever greater, leading to a growing need for a standardised data exchange format to support performance analysis and AO research and development activities that involve large-scale telemetry mining, processing, and curation. This paper introduces the Adaptive Optic

Van Minh Nguyen, Nasheen Nur, William Stern, Thomas Mercer

The COVID-19 pandemic has escalated mental health crises worldwide, with social isolation and economic instability contributing to a rise in suicidal behavior. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression, Post-Traumatic Stress Disorder (PTSD), Attention-Deficit/Hyperactivity Disorder

Jiachen Liu, Fan Lai, Ding Ding, Yiwen Zhang

In recent years, collaborative learning (CL) has emerged as a promising approach for machine learning (ML) and data science across distributed edge devices. As the deployment of CL jobs increases, they inevitably contend for limited resources. However, efficient resource scheduling in this context is challenging because of the ephemeral nature and resource h

Michelangelo Cavina

In this work we prove formulas of quasi-additivity for the capacity associated to kernels of radial type in the setting of the boundary of a tree structure and in the setting of compact Ahlfors-regular spaces. We also define a notion of harmonic extension, to one additional variable, of a function defined over a compact Ahlfors-regular space, and we prove a

D. Yu. Vodolazov

The concept of nonlinear kinetic inductance sensor (NKIS) of electromagnetic radiation is proposed. The idea is based on divergency of kinetic inductance $L_k \sim dq/dI$ ($\hbar q$ is a momentum of superconducting electrons, $I$ is a supercurrent) of hybrid superconductor/normal metal (SN) bridge at current $I^*<I_{dep}$ ($I_{dep}$ is a depairing current of

Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen

Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem. However, traditional approaches such as nested sampling are computationally expensive, thus sparking an interest in solutions based on machine learning (ML). In this ongoing work,

Fabian Belmonte, Giuseppe De Nittis

The main goal of this work is to provide a description of the {trace per unit volume} in terms of the {Dixmier trace} (regularized by the resolvent of the harmonic oscillator) for a large class of two-dimensional \emph{magnetic operators} perturbed by (homogeneous) {potentials}. One of the payoffs of this result is the possibility of reinterpreting the {dens

Han Wang, Zuxun Xiong, Liqun Zhao, Antonis Papachristodoulou

Neural network controllers have shown potential in achieving superior performance in feedback control systems. Although a neural network can be trained efficiently using deep and reinforcement learning methods, providing formal guarantees for the closed-loop properties is challenging. The main difficulty comes from the nonlinear activation functions. One pop

B. Marcote

The transient sky is composed of diverse phenomena that exhibits dramatic changes on short timescales. These events range from sub-second bursts to weeks and month timescale variability from compact systems. Several challenges need to be addressed by any facility that aims to observe such events: a fast re-positioning scheme to trace the first moments of eve

Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda, Antonio Agudo

Previous works on Human Pose and Shape Estimation (HPSE) from RGB images can be broadly categorized into two main groups: parametric and non-parametric approaches. Parametric techniques leverage a low-dimensional statistical body model for realistic results, whereas recent non-parametric methods achieve higher precision by directly regressing the 3D coordina

Anis Bourou, Thomas Boyer, Kévin Daupin, Véronique Dubreuil

For the past few years, deep generative models have increasingly been used in biological research for a variety of tasks. Recently, they have proven to be valuable for uncovering subtle cell phenotypic differences that are not directly discernible to the human eye. However, current methods employed to achieve this goal mainly rely on Generative Adversarial N

Rosco Hunter, Łukasz Dudziak, Mohamed S. Abdelfattah, Abhinav Mehrotra

Text-to-image diffusion models have demonstrated unprecedented capabilities for flexible and realistic image synthesis. Nevertheless, these models rely on a time-consuming sampling procedure, which has motivated attempts to reduce their latency. When improving efficiency, researchers often use the original diffusion model to train an additional network desig

Christoph Aistleitner, Manuel Hauke, Agamemnon Zafeiropoulos

We disprove a folklore conjecture stating that a sequence in $[0,1]$ with exponential gap distribution must necessarily be uniformly distributed.

Piyush Arora, Pratik Mazumder

Deep learning models are known to suffer from the problem of bias, and researchers have been exploring methods to address this issue. However, most of these methods require prior knowledge of the bias and are not always practical. In this paper, we focus on a more practical setting with no prior information about the bias. Generally, in this setting, there a

Anup Shakya, Abisha Thapa Magar, Somdeb Sarkhel, Deepak Venugopal

The standard approach to verify representations learned by Deep Neural Networks is to use them in specific tasks such as classification or regression, and measure their performance based on accuracy in such tasks. However, in many cases, we would want to verify more complex properties of a learned representation. To do this, we propose a framework based on a

Brendon G. Anderson, Jingqi Li, Somayeh Sojoudi, Murat Arcak

We consider evolutionary dynamics for population games in which players have a continuum of strategies at their disposal. Models in this setting amount to infinite-dimensional differential equations evolving on the manifold of probability measures. We generalize dissipativity theory for evolutionary games from finite to infinite strategy sets that are compac

S. Salgado

We propose an extension of the formalism developed by Stelle-West and Grignani-Nardelli to the case of FDAs. We first consider the case of FDAs carrying one $p$-form extension and no non-trivial cohomology. We show that it is possible to define large gauge transformations as a direct extension of the large transformations induced by their Lie subalgebras and

N. Aucar Boidi, A. P. Kampf, K. Hallberg

We study the non-degenerate one dimensional two-orbital Hubbard model with interorbital Coulomb interaction. By means of the density-matrix renormalization group technique, we calculate the local single-particle density of states and the optical conductivity at zero temperature. We find that a finite interorbital Coulomb repulsion $V$ generates a new class o

William O. Balmer, L. Pueyo, S. Lacour, J. J. Wang

Young, low-mass Brown Dwarfs orbiting early-type stars, with low mass ratios ($q\lesssim0.01$), appear intrinsically rare and present a formation dilemma: could a handful of these objects be the highest mass outcomes of ``planetary" formation channels (bottom up within a protoplanetary disk), or are they more representative of the lowest mass ``failed binari

Rajgowrav Cheenikundil, Massimiliano d'Aquino, Riccardo Hertel

Modern three-dimensional nanofabrication methods make it possible to generate arbitrarily shaped nanomagnets, including periodic networks of interconnected magnetic nanowires. Structurally similar to optical or acoustic metamaterials, these arrays could represent magnetic variants of such artificial materials. Using micromagnetic simulations, we investigate

Aldan Creo, Manuel Lama, Juan C. Vidal

This paper presents novel prompting techniques to improve the performance of automatic summarization systems for scientific articles. Scientific article summarization is highly challenging due to the length and complexity of these documents. We conceive, implement, and evaluate prompting techniques that provide additional contextual information to guide summ

Lukas Bentkamp, Michael Wilczek

Turbulent flows in three dimensions are characterized by the transport of energy from large to small scales through the energy cascade. Since the small scales are the result of the nonlinear dynamics across the scales, they are often thought of as universal and independent of the large scales. However, as famously remarked by Landau, sufficiently slow variat

Alina Chertock, Michael Herty, Arsen S. Iskhakov, Safa Janajra

In this paper, we develop new high-order numerical methods for hyperbolic systems of nonlinear partial differential equations (PDEs) with uncertainties. The new approach is realized in the semi-discrete finite-volume framework and is based on fifth-order weighted essentially non-oscillatory (WENO) interpolations in (multidimensional) random space combined wi

M. Yu. Kuznetsov, N. A. Petrov, I. A. Plokhikh, V. V. Sotnikov

We report the reconstruction of the mass component spectra of cosmic rays (protons, helium, carbon, silicon and iron) and their mean mass composition, at energies from 1.4 to 100 PeV. The results are derived from the archival data of the extensive air shower experiment KASCADE. We use a novel machine learning technique developed specifically for this reconst

Gowtham S Seenivasaharagavan, Milan Korda, Hassan Arbabi, Igor Mezić

Any deterministic autonomous dynamical system may be globally linearized by its' Koopman operator. This object is typically infinite-dimensional and can be approximated by the so-called Dynamic Mode Decomposition (DMD). In DMD, the central idea is to preserve a fundamental property of the Koopman operator: linearity. This work augments DMD by preserving addi

Joey Braspenning, Joop Schaye, Matthieu Schaller, Ian G. McCarthy

Galaxy clusters are important probes for both cosmology and galaxy formation physics. We test the cosmological, hydrodynamical FLAMINGO simulations by comparing to observations of the gaseous properties of clusters measured from X-ray observations. FLAMINGO contains unprecedented numbers of massive galaxy groups ($>10^6$) and clusters ($>10^5$) and includes

Nadav Tamir, Ilan Bessudo, Boping Chen, Hely Raiko

We train several neural networks and boosted decision trees to discriminate fully-hadronic boosted di-$\tau$ topologies against background QCD jets, using calorimeter and tracking information. Boosted di-$\tau$ topologies consisting of a pair of highly collimated $\tau$-leptons, arise from the decay of a highly energetic Standard Model Higgs or Z boson or fr

Luca Capizzi, Andrei Rotaru

We consider a quantum junction described by a 1+1-dimensional boundary conformal field theory (BCFT). Our analysis focuses on correlations emerging at finite temperature, achieved through the computation of entanglement measures. Our approach relies on characterizing correlation functions of twist fields using BCFT techniques. We provide non-perturbative pre

Songchi Zhou, Sheng Yu

Objective: To develop a high-throughput biomedical relation extraction system that takes advantage of the large language models'(LLMs) reading comprehension ability and biomedical world knowledge in a scalable and evidential manner. Methods: We formulate the relation extraction task as binary classifications for large language models. Specifically, LLMs make

Sela Fried, Toufik Mansour

Generalizing the notion of staircase words, introduced by Knopfmacher et.\ al, we define staircase graph words. These are functions $w$ from the vertex set $V$ of a graph into the set $\{1,2,\ldots,k\}$, such that $|w(x)-w(y)|\leq 1$, for every adjacent $x,y\in V$. We find the explicit generating functions for the number of staircase graph words for the grid

Ian S. Winter, Timofey Frolov

In this work we derive conditions that predict the existence of two-phase periodic-pattern grain boundary structures that are stable against coarsening. While previous research has established that elastic effects can lead to phase pattern formation on crystal surfaces, the possibility of stable grain boundary structures composed of alternating grain boundar

Xiao Han

In this paper, we prove that the Fourier entropy of an $n$-dimensional boolean function $f$ can be upper-bounded by $O(I(f)+ \sum\limits_{k\in[n]}I_k(f)\log \frac{1}{I_k(f)})$, where $I(f)$ is its total influence and $I_k(f)$ is the influence of the $k$-th coordinate. The proof is elementary and uses iterative bounds on moments of Fourier coefficients over d

Laura Magrini, Thomas Bensby, Anna Brucalassi, Sofia Randich

The High-Resolution Multi-Object Spectrograph (HRMOS) is a facility instrument that we plan to propose for the Very Large Telescope (VLT) of the European Southern Observatory (ESO), following the initial presentation at the VLT 2030 workshop held at ESO in June 2019. HRMOS provides a combination of capabilities that are essential to carry out breakthrough sc

Li-Tong Deng, Yong-Xiong Li, Shuai Zhai

Let $f$ be a positive definite integral quadratic form in $d$ variables. In the present paper, we establish a direct link between the genus representation number of $f$ and the order of higher even $K$-groups of the ring of integers of real quadratic fields, provided $f$ is diagonal and $d \equiv 1 \mod 4$, by applying the Siegel mass formula. When $d=3$, we

Arul Selvam Periyasamy, Vladimir Tsaturyan, Sven Behnke

Object pose estimation is a long-standing problem in computer vision. Recently, attention-based vision transformer models have achieved state-of-the-art results in many computer vision applications. Exploiting the permutation-invariant nature of the attention mechanism, a family of vision transformer models formulate multi-object pose estimation as a set pre

Aaron Cao, Vishwanatha M. Rao, Kejia Liu, Xinrui Liu

Subcortical segmentation remains challenging despite its important applications in quantitative structural analysis of brain MRI scans. The most accurate method, manual segmentation, is highly labor intensive, so automated tools like FreeSurfer have been adopted to handle this task. However, these traditional pipelines are slow and inefficient for processing

Yoshimasa Hidaka, Masaru Hongo, Mikhail Stephanov, Ho-Ung Yee

We study the relaxation dynamics of the spin polarization of baryons (nucleon and $\Lambda$-baryon), in a thermal pion gas as a simple model of the hadronic phase of the QCD plasma produced in relativistic heavy-ion collisions. For this purpose, we formulate the quantum kinetic theory for the spin density matrix of baryons in the leading order of the gradien

Quentin Dubroff, Benjamin Gunby, Bhargav Narayanan, Sam Spiro

We study how many copies of a graph $F$ that another graph $G$ with a given number of cliques is guaranteed to have. For example, one of our main results states that for all $t\ge 2$, if $G$ is an $n$ vertex graph with $kn^{3/2}$ triangles and $k$ is sufficiently large in terms of $t$, then $G$ contains at least \[\Omega(\min\{k^t n^{3/2},k^{\frac{2t^2}{3t-1

Marvin Dippell, David Kern

We present a framework for the reduction of various geometric structures extending the classical coisotropic Poisson reduction. For this we introduce constraint manifolds and constraint vector bundles. A constraint Serre-Swan theorem is proven, identifying constraint vector bundles with certain finitely generated projective modules, and a Cartan calculus for

Carole Porrier, Alain Goupil, Alexandre Blondin Massé

We study a graph-theoretic problem in the Penrose P2-graphs which are the dual graphs of Penrose tilings by kites and darts. Using substitutions, local isomorphism and other properties of Penrose tilings, we construct a family of arbitrarily large induced subtrees of Penrose graphs with the largest possible number of leaves for a given number $n$ of vertices

Sounav Sengupta, Hezi Gildor, Yosef Ashkenazy

The Gulf of Eilat (Gulf of Aqaba) is a semi-enclosed basin situated at the northern end of the Red Sea, renowned for its exceptional marine ecosystem. To evaluate the response of the Gulf to climate variations, we analyzed various factors including temperature down to 700 m, surface air temperature, and heat fluxes. We find that the sea temperature is rising

Paul Worm, Qisi Wang, Motoharu Kitatani, Izabela Biało

Infinite-layer nickelates show high-temperature superconductivity, and the experimental phase diagram agrees well with the one simulated within the dynamical vertex approximation (D$\Gamma$A). Here, we compare the spin-fluctuation spectrum behind these calculations to resonant inelastic X-ray scattering experiments. The overall agreement is good. This indepe

Alexander Katsevich

We study the accuracy of reconstruction of a family of functions $f_\epsilon(x)$, $x\in\mathbb R^2$, $\epsilon\to0$, from their discrete Radon transform data sampled with step size $O(\epsilon)$. For each $\epsilon>0$ sufficiently small, the function $f_\epsilon$ has a jump across a rough boundary $\mathcal S_\epsilon$, which is modeled by an $O(\epsilon)$-s

Irving Dai, Abhishek Mallick, Ian Zemke

Gompf showed that for $K$ in a certain family of double-twist knots, the swallow-follow operation makes $1/n$-surgery on $K \# -K$ into a cork boundary. We derive a general Floer-theoretic condition on $K$ under which this is the case. Our formalism allows us to produce many further examples of corks, partially answering a question of Gompf. Unlike Gompf's m

Mihailo Stojnic

We consider the memorization capabilities of multilayered \emph{sign} perceptrons neural networks (SPNNs). A recent rigorous upper-bounding capacity characterization, obtained in \cite{Stojnictcmspnncaprdt23} utilizing the Random Duality Theory (RDT), demonstrated that adding neurons in a network configuration may indeed be very beneficial. Moreover, for par

Gwilherm Lesné, Yann Gousseau, Saïd Ladjal, Alasdair Newson

Recent advances in the field of generative models and in particular generative adversarial networks (GANs) have lead to substantial progress for controlled image editing, especially compared with the pre-deep learning era. Despite their powerful ability to apply realistic modifications to an image, these methods often lack properties like disentanglement (th

Mikhail Kulyabin, Aleksei Zhdanov, Anastasia Nikiforova, Andrey Stepichev

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstruc

Alireza Talebian

We investigate a cosmological model wherein a waterfall symmetry breaking occurs during the radiation-dominated era. The model comprises a complex waterfall field, an axion field, and the gauge field (dark photon) generated through a tachyonic instability due to the Chern-Simons interaction. Prior to symmetry breaking, the total energy density incorporates a

Assaf Rinot

In this survey, we collect necessary conditions for the successor of a singular cardinal to be Jonsson.

Robert Klement, Thomas Rivinius, Douglas R. Gies, Dietrich Baade

Rapid rotation and nonradial pulsations enable Be stars to build decretion disks, where the characteristic line emission forms. A major but unconstrained fraction of Be stars owe their rapid rotation to mass and angular-momentum transfer in a binary. The faint, stripped companions can be helium-burning subdwarf OB-type stars (sdOBs), white dwarfs (WDs), or n

Tianyi Chen, Qidi Wang, Zhen Dong, Liwei Shen

Program synthesis aims to automatically generate an executable program that conforms to the given specification. Recent advancements have demonstrated that deep neural methodologies and large-scale pretrained language models are highly proficient in capturing program semantics. For robot programming, prior works have facilitated program synthesis by incorpor

Georg Schnabel, Daniel Lopez Aldama, Roberto Capote

The ENDF-6 format, widely used worldwide for storing and disseminating nuclear data, is managed by the Cross Sections Evaluation Working Group (CSEWG) and fully documented in the ENDF-6 formats manual. This manual employs a combination of formal and natural language, introducing the possibility of ambiguity in certain parts of the format specification. To el

Huan Yan, Yong Li

Intelligent transportation systems are vital for modern traffic management and optimization, greatly improving traffic efficiency and safety. With the rapid development of generative artificial intelligence (Generative AI) technologies in areas like image generation and natural language processing, generative AI has also played a crucial role in addressing k

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

Using $e^+e^-$ collision data collected with the BESIII detector operating at the BEPCII collider, the Born cross sections of $e^+e^-\to \Lambda_{c}^+ \bar{\Lambda}_{c}(2595)^- + \rm{c.c.}$ and $e^+e^-\to \Lambda_{c}^+ \bar{\Lambda}_{c}(2625)^- + \rm{c.c.}$ are measured for the first time at center-of-mass energies of $\sqrt{s}=4918.0$ and 4950.9 MeV. Non-ze

Mikael Granvik, Kevin J. Walsh

Numerical modeling has long suggested that gravitationally-bound (or so-called rubble-pile) near-Earth asteroids (NEAs) can be destroyed by tidal forces during close and slow encounters with terrestrial planets. However, tidal disruptions of NEAs have never been directly observed nor have they been directly attributed to any families of NEAs. Here we show po

Benjamin Ertel, Udo Seifert

A central task in stochastic thermodynamics is the estimation of entropy production for partially accessible Markov networks. We establish an effective transition-based description for such networks with transitions that are not distinguishable and therefore blurred for an external observer. We demonstrate that, in contrast to a description based on fully re

Henryk Fukś

In commemoration of the fifth anniversary since Nino Boccara's departure, this article offers some personal recollections and provides insight into his life and accomplishments. Detailed bibliography of his works is included together with commentary highlighting his major achievements.

Neel Patel, David Wajc

Numerous recent papers have studied the tension between thickening and clearing a market in (uncertain, online) long-time horizon Markovian settings. In particular, (Aouad and Sarita{\c{c}} EC'20, Collina et al. WINE'20, Kessel et al. EC'22) studied what the latter referred to as the Stationary Prophet Inequality Problem, due to its similarity to the classic

Mihailo Stojnic

We study the capacity of \emph{sign} perceptrons neural networks (SPNN) and particularly focus on 1-hidden layer \emph{treelike committee machine} (TCM) architectures. Similarly to what happens in the case of a single perceptron neuron, it turns out that, in a statistical sense, the capacity of a corresponding multilayered network architecture consisting of

Daniel Areán, Blaise Goutéraux, Eric Mefford, Filippo Sottovia

We study the linear response of relativistic superfluids with a non-zero superfluid velocity. For sufficiently large superflow, an instability develops via the crossing of a pole of the retarded Green's functions to the upper half complex frequency plane. We show that this is caused by a local thermodynamic instability, i.e. when an eigenvalue of the static

Shanon Vuglar, Julio Gea-Banacloche

We introduce a family of quantized field states that can perform exact (entanglement- and error-free) rotations of a two-level atom starting from a specific state on the Bloch sphere. We discuss the similarities and differences between these states and the recently-introduced "transcoherent states." Our field states have the property that they are left uncha

Yukinao Akamatsu, Shimpei Endo, Keisuke Fujii, Masaru Hongo

We formulate the induced potential in a finite temperature cold atomic medium between two heavy impurities, or polarons, which is shown to be \textit{complex-valued} in general. The imaginary part of the complex-valued potential describes a decoherence effect, and thus, the resulting Schr\"odinger equation for the two polarons acquires a non-Hermitian term.

Eugenio Chisari, Nick Heppert, Tim Welschehold, Wolfram Burgard

Reliable object grasping is a crucial capability for autonomous robots. However, many existing grasping approaches focus on general clutter removal without explicitly modeling objects and thus only relying on the visible local geometry. We introduce CenterGrasp, a novel framework that combines object awareness and holistic grasping. CenterGrasp learns a gene

Xuwen Chen, Justin Holmer

We consider the quantum many-body dynamics at the weak-coupling scaling. We derive rigorously the quantum Boltzmann equation, which contains the classical hard sphere model and, effectively, the inverse power law model, from the many-body dynamics assuming a physical and optimal regularity bound. The regularity bound we find, on the one hand, is satisfied by

A. N. Grekov, M. A Pasynkov, . S. S. Peliushenko

The work analyzes existing methods for increasing the accuracy of determining weekend navigation parameters of unmanned underwater vehicles. It is proposed to retrofit the navigation system with an additional hydrodynamic tilt unit, which will increase accuracy of coordinate determination. A prototype of the proposed system was made, and algorithmic software

Wenlong Li, Jiahong Chen

This paper explores the evolution of China's Personal Information Protection Law (PIPL) and situates it within the context of global data protection development. It draws inspiration from the theory of 'Brussels Effect' and provides a critical account of its application in non-Western jurisdictions, taking China as a prime example. Our objective is not to pr

Saumen Chaudhuri, Amrita Bhattacharya, A. K. Das, G. P. Das

Density functional theory based calculations have been performed for solving the phonon Boltzmann transport equation to investigate the thermal transport properties of monolayer (ML) ZnO under in-plane isotropic biaxial tensile strain. The in-plane lattice thermal conductivity ($\kappa_{\text{L}}$) of ML-ZnO increases dramatically in response to the biaxial

Kenjiro Inoue, Mitsuo Yoshida

The online advertising industry continues to grow and accounts for over 40% of global advertising spending. Online display advertising consists of images and text, and advertisers maximize sales revenue by contacting consumers through advertisements and encouraging them to make purchases. In today's society, where products are becoming more homogenized and n

Yujun Chen, Xin Tan, Zhizhong Zhang, Yanyun Qu

As the exorbitant expense of labeling autopilot datasets and the growing trend of utilizing unlabeled data, semi-supervised segmentation on point clouds becomes increasingly imperative. Intuitively, finding out more ``unspoken words'' (i.e., latent instance information) beyond the label itself should be helpful to improve performance. In this paper, we disco

Tomáš Krajňák, Veronika Stará, Pavel Procházka, Jakub Planer

The interface between a metal electrode and an organic semiconductor (OS) layer has a defining role in the properties of the resulting device. To obtain a desired performance, interlayers are introduced to modify the adhesion and growth of OS and enhance the efficiency of charge transport through the interface. However, the employed interlayers face common c

Gianluca Rizzo, Marco Ajmone Marsan, Christian Esposito, Biagio Boi

Internet of Things (IoT) devices often come with batteries of limited capacity that are not easily replaceable or rechargeable, and that constrain significantly the sensing, computing, and communication tasks that they can perform. The Simultaneous Wireless Information and Power Transfer (SWIPT) paradigm addresses this issue by delivering power wirelessly to

Vadim Briaud, Kenji Kadota, Shinji Mukohyama, Alireza Talebian

If dark matter is made of QCD axions, its abundance is determined by the vacuum expectation value acquired by the axion field during inflation. The axion is usually assumed to follow the equilibrium distribution arising from quantum diffusion during inflation. This leads to the so-called stochastic window under which the QCD axion can make up all the dark ma

Paulo F. Bedaque, Hyunwoo Oh

Results obtained with stochastic methods have an inherent uncertainty due to the finite number of samples that can be achieved in practice. In lattice QCD this problem is particularly salient in some observables like, for instance, observables involving one or more baryons and it is the main problem preventing the calculation of nuclear forces from first pri

Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi, Alain Rakotomamonjy

Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling. This can be achieved through either differentially private stochastic gradient descent or a differentially private metric for training models or generators. In this paper, we introduce a novel differentially private generative modeling approach b

Jiangdong Ai, Hui Lei, Bo Ning, Yongtang Shi

Let $G$ be a connected graph and $\mathcal{P}(G)$ a graph parameter. We say that $\mathcal{P}(G)$ is feasible if $\mathcal{P}(G)$ satisfies the following properties: (I) $\mathcal{P}(G)\leq \mathcal{P}(G_{uv})$, if $G_{uv}=G[u\to v]$ for any $u,v$, where $G_{uv}$ is the graph obtained by applying Kelmans operation from $u$ to $v$; (II) $\mathcal{P}(G) <\math

Akashdip Karmakar, Pramit Rej

Hybrid star is the term given to a neutron star with a quark core. Due to a lot of uncertainties in the calculations and compositions of such a high-density system, it is of great interest and a preferred scenario for particle physicists and astrophysicists. To explore some novel aspects within the framework of the $5\mathcal{D}$ Einstein-Gauss-Bonnet(EGB) g

Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao

The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP partitions large routing problems into Travel

Chanyong Jung, Gihyun Kwon, Jong Chul Ye

Recently, patch-wise contrastive learning is drawing attention for the image translation by exploring the semantic correspondence between the input and output images. To further explore the patch-wise topology for high-level semantic understanding, here we exploit the graph neural network to capture the topology-aware features. Specifically, we construct the

R. Krivonos, M. Gilfanov, P. Medvedev, S. Sazonov

The eROSITA X-ray telescope on board the Spectrum-Roentgen-Gamma (SRG) spacecraft observed the field of the UKIDSS Ultra-Deep Survey (UDS) in August-September 2019, during its flight to Sun-Earth L2 point. The resulting eROSITA UDS (or eUDS) survey was thus the first eROSITA X-ray imaging survey, which demonstrated the capability of the telescope to perform

Baoyuan Wu, Shaokui Wei, Mingli Zhu, Meixi Zheng

Adversarial phenomenon has been widely observed in machine learning (ML) systems, especially in those using deep neural networks, describing that ML systems may produce inconsistent and incomprehensible predictions with humans at some particular cases. This phenomenon poses a serious security threat to the practical application of ML systems, and several adv

Jin Li, Qirong Zhang, Shuling Xu, Xinlong Chen

Despite Graph neural networks' significant performance gain over many classic techniques in various graph-related downstream tasks, their successes are restricted in shallow models due to over-smoothness and the difficulties of optimizations among many other issues. In this paper, to alleviate the over-smoothing issue, we propose a soft graph normalization m

Peiqi Duan, Boyu Li, Yixin Yang, Hanyue Lou

Event cameras are emerging imaging technology that offers advantages over conventional frame-based imaging sensors in dynamic range and sensing speed. Complementing the rich texture and color perception of traditional image frames, the hybrid camera system of event and frame-based cameras enables high-performance imaging. With the assistance of event cameras

Saumen Chaudhuri, Amrita Bhattacharya, A. K. Das, G. P. Das

In the calculations of lattice thermal conductivity ($\kappa_{\text{L}}$), vital contributions stemming from four-phonon scattering are often neglected. The significance of four-phonon scattering in the thermal transport properties of monolayer (ML) MoS$_{2}$ has been unraveled using first-principles calculations combined with the Boltzmann transport equatio

Priyanka Jalan, Vibhore Negi, Jean Surdej, Céline Boehm

Gravitational lensing is proven to be one of the most efficient tools for studying the Universe. The spectral confirmation of such sources requires extensive calibration. This paper discusses the spectral extraction technique for the case of multiple source spectra being very near each other. Using the masking technique, we first detect high Signal-to-Noise