July 2023 arXiv papers — page 125

Victor Chernozhukov, Michael Newey, Whitney K Newey, Rahul Singh

We present machine learning estimators for causal and predictive parameters under covariate shift, where covariate distributions differ between training and target populations. One such parameter is the average effect of a policy that alters the covariate distribution, such as a treatment modifying surrogate covariates used to predict long-term outcomes. Ano

Rupert Mitchell, Robin Menzenbach, Kristian Kersting, Martin Mundt

The results of training a neural network are heavily dependent on the architecture chosen; and even a modification of only its size, however small, typically involves restarting the training process. In contrast to this, we begin training with a small architecture, only increase its capacity as necessary for the problem, and avoid interfering with previous o

Mingze Yuan, Yingda Xia, Xin Chen, Jiawen Yao

Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. In this study, we explore the feasibility of using a deep learning approach on non-contrast CT scans for gastric cancer d

Ovidiu Popescu, Cristina Maria Pacurar

The aim of this paper is to generalize the results on expansive mappings of Yesilkaya and Aydin from \cite{Yesilkaya}. We give some fixed point results for q-expansive mappings in metric spaces and prove some fixed point theorems for this class of mappings. Finally, we present some examples to support the new results.

L. Mashonkina, Yu. Pakhomov, T. Sitnova, A. Smogorzhevskii

Detailed chemical abundances of very metal-poor (VMP, [Fe/H] < -2) stars are important for better understanding the First Stars, early star formation and chemical enrichment of galaxies. Big on-going and coming high-resolution spectroscopic surveys provide a wealth of material that needs to be carefully analysed. For VMP stars, their elemental abundances sho

Sujoy Kumar Nath, Dipak Debnath, Kaushik Chatterjee, Riya Bhowmick

We study the recent outburst of the black hole candidate EXO 1846-031 which went into an outburst in 2019 after almost 34 years in quiescence. We use archival data from Swift/XRT, MAXI/GSC, NICER/XTI and NuSTAR/FPM satellites/instruments to study the evolution of the spectral and temporal properties of the source during the outburst. Low energy (2-10 keV) X-

Jens Markus Melenk, Leszek Demkowicz, Stefan Henneking

In a time-harmonic setting, we show for heterogeneous acoustic and homogeneous electromagnetic wavesguides stability estimates with the stability constant depending linearly on the length $L$ of the waveguide. These stability estimates are used for the analysis of the (ideal) ultraweak (UW) variant of the Discontinuous Petrov Galerkin (DPG) method. For this

San Jiang, Yichen Ma, Qingquan Li, Wanshou Jiang

SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features and the large size of the used codebook

Roland Pulch

We consider a second-order linear system of ordinary differential equations (ODEs) including random variables. A stochastic Galerkin method yields a larger deterministic linear system of ODEs. We apply a model order reduction (MOR) of this high-dimensional linear dynamical system, where its internal energy represents a quadratic quantity of interest. We inve

Casey Kennington

This document chronicles this author's attempt to explore how words come to mean what they do, with a particular focus on child language acquisition and what that means for models of language understanding.\footnote{I say \emph{historical} because I synthesize the ideas based on when I discovered them and how those ideas influenced my later thinking.} I expl

Hsin-Tien Chiang, Kuo-Hsuan Hung, Szu-Wei Fu, Heng-Cheng Kuo

Subjective tests are the gold standard for evaluating speech quality and intelligibility; however, they are time-consuming and expensive. Thus, objective measures that align with human perceptions are crucial. This study evaluates the correlation between commonly used objective measures and subjective speech quality and intelligibility using a Chinese speech

Ashish Singh, Antonio Bevilacqua, Timilehin B. Aderinola, Thach Le Nguyen

Wearable sensors such as Inertial Measurement Units (IMUs) are often used to assess the performance of human exercise. Common approaches use handcrafted features based on domain expertise or automatically extracted features using time series analysis. Multiple sensors are required to achieve high classification accuracy, which is not very practical. These se

Amir Ziaee, Georg Suter

The analysis of building models for usable area, building safety, and energy use requires accurate classification data of spaces and space elements. To reduce input model preparation effort and errors, automated classification of spaces and space elements is desirable. A barrier hindering the utilization of Graph Deep Learning (GDL) methods to space function

Tuc Nguyen-Van, Dung D. Le, The-Anh Ta

In graph representation learning, it is important that the complex geometric structure of the input graph, e.g. hidden relations among nodes, is well captured in embedding space. However, standard Euclidean embedding spaces have a limited capacity in representing graphs of varying structures. A promising candidate for the faithful embedding of data with vary

Yicheng Wu, Zhonghua Wu, Hengcan Shi, Bjoern Picker

New lesion segmentation is essential to estimate the disease progression and therapeutic effects during multiple sclerosis (MS) clinical treatments. However, the expensive data acquisition and expert annotation restrict the feasibility of applying large-scale deep learning models. Since single-time-point samples with all-lesion labels are relatively easy to

Athira Sasidharan, Titus K Mathew

In this paper we consider dissipative effects in $\Lambda$CDM model, i.e., we consider a universe with cosmological constant having viscous matter. We assume the most general form for bulk viscous coefficient, $\zeta=\zeta_{0}+\zeta_{1}\frac{\dot{a}}{a}+\zeta_{2}\frac{\ddot{a}}{\dot{a}}$ and obtained various constrains for $\zeta$'s . We also studied the bac

Michael Sheinman Orenstrakh, Oscar Karnalim, Carlos Anibal Suarez, Michael Liut

Due to the recent improvements and wide availability of Large Language Models (LLMs), they have posed a serious threat to academic integrity in education. Modern LLM-generated text detectors attempt to combat the problem by offering educators with services to assess whether some text is LLM-generated. In this work, we have collected 124 submissions from comp

Guoqiang Zhang, J. P. Lewis, W. Bastiaan Kleijn

Recently, various methods have been proposed to address the inconsistency issue of DDIM inversion to enable image editing, such as EDICT [36] and Null-text inversion [22]. However, the above methods introduce considerable computational overhead. In this paper, we propose a new technique, named \emph{bi-directional integration approximation} (BDIA), to perfor

Jeong Woo Han, Pavlo Sai, Dmytro But, Ece Uykur

Strong circularly polarized excitation opens up the possibility to generate and control effective magnetic fields in solid state systems, e.g., via the optical inverse Faraday effect or the phonon inverse Faraday effect. While these effects rely on material properties that can be tailored only to a limited degree, plasmonic resonances can be fully controlled

Nathan E. Sanders, Alex Ulinich, Bruce Schneier

Political polling is a multi-billion dollar industry with outsized influence on the societal trajectory of the United States and nations around the world. However, it has been challenged by factors that stress its cost, availability, and accuracy. At the same time, artificial intelligence (AI) chatbots have become compelling stand-ins for human behavior, pow

Pavel Rukin, Deborah Prezzi, Carlo Andrea Rozzi

Excited state normal modes analysis is systematically applied to investigate and compare relaxation and internal conversion dynamics of a free-base porphyrin with a novel functional porphyrin derivative. We discuss strenghts and limitation of the method, and employ it to predict very different dynamical behaviours in the two compounds and to clarify the role

Christian Yeo

Linear regression, firstly introduced for the pricing of American-style options, has since been expanded to include swing options pricing. Swing options price may be viewed as the solution to a Backward Dynamic Programming Principle, which involves a conditional expectation known as the continuation value. The approximation of the continuation value using li

Lorenzo Sebastiani

We investigate the first law of thermodynamics and entropy associated to the apparent horizon of (non-flat) FLRW space-time in different theories of modified gravity and in the presence of a perfect fluid of matter. We pose our attention on those theories which lead to second order differential field equations on FLRW background. In this way, we observe that

Johannes Tölle, Niklas Niemeyer, Johannes Neugebauer

We present a simple and accurate GW implementation based on a combination of a Laplace transformation (LT) and other acceleration techniques used in post-SCF quantum chemistry, namely, natural auxiliary functions and the frozen-core approximation. The LT-GW approach combines three major benefits: (a) a small prefactor for the computational scaling, (b) easy

I-Chun Chern, Zhiruo Wang, Sanjan Das, Bhavuk Sharma

Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarizatio

Reiner Czerwinski

Grover's algorithm can solve NP-complete problems on quantum computers faster than all the known algorithms on classical computers. However, Grover's algorithm still needs exponential time. Due to the BBBV theorem, Grover's algorithm is optimal for searches in the domain of a function, when the function is used as a black box. We analyze the NP-complete set

Qiong Liu, Chehao Wang, Ce Zheng

When multiple users share a common link in direct transmission, packet loss and network collision may occur due to the simultaneous arrival of traffics at the source node. To tackle this problem, users may resort to an indirect path: the packet flows are first relayed through a sidelink to another source node, then transmitted to the destination. This behavi

Mikhail Kovalev, Olivier R. Hainaut, Xuefei Chen, Zhanwen Han

We present the detection of false positive double-lined spectroscopic binaries candidates (SB2) using medium-resolution survey (MRS) spectra from the one time-domain field of LAMOST data release 10 (DR10). The secondary component in all these binaries has near zero radial velocity and solar-like spectral lines. Highly likely this is light from the semi-trans

Guy Kornowski, Ohad Shamir

We study the complexity of producing $(\delta,\epsilon)$-stationary points of Lipschitz objectives which are possibly neither smooth nor convex, using only noisy function evaluations. Recent works proposed several stochastic zero-order algorithms that solve this task, all of which suffer from a dimension-dependence of $\Omega(d^{3/2})$ where $d$ is the dimen

Roman Andriushchenko, Ezio Bartocci, Milan Ceska, Francesco Pontiggia

Probabilistic hyperproperties specify quantitative relations between the probabilities of reaching different target sets of states from different initial sets of states. This class of behavioral properties is suitable for capturing important security, privacy, and system-level requirements. We propose a new approach to solve the controller synthesis problem

Melchior Wirth

We prove that certain closable derivations on the GNS Hilbert space associated with a non-tracial weight on a von Neumann algebra give rise to GNS-symmetric semigroups of contractive completely positive maps on the von Neumann algebra.

Kamil Erdayandi, Lucas C. Cordeiro, Mustafa A. Mustafa

This paper proposes a privacy-preserving and accountable billing (PA-Bill) protocol for trading in peer-to-peer energy markets, addressing situations where there may be discrepancies between the volume of energy committed and delivered. Such discrepancies can lead to challenges in providing both privacy and accountability while maintaining accurate billing.

David V. Nguyen, Shayan Doroudi, Daniel A. Epstein

Our preliminary experiment examined a potential pain point with ASSIST, California's database of articulation agreements. That pain point is cross-referencing multiple articulation agreements to manually develop an optimal academic plan. Optimal is defined as the minimal set of community college courses that satisfy all transfer requirements for the multiple

Julien Grange, Mathieu Lehaut

We carry on the study of the synthesis problem on data words for fragments of first order logic, and delineate precisely the border between decidability and undecidability.

Javad Ebrahimizadeh, Evgenii Vinogradov, Guy A. E. Vandenbosch

This paper presents a quasi-deterministic ray tracing (QD-RT) method for analyzing the propagation of electromagnetic waves in street canyons. The method uses a statistical bistatic distribution to model the Radar Cross Section (RCS) of various irregular objects such as cars and pedestrians, instead of relying on exact values as in a deterministic propagatio

Petri Varvia, Svetlana Saarela, Matti Maltamo, Petteri Packalen

The ICESat-2, launched in 2018, carries the ATLAS instrument, which is a photon-counting spaceborne lidar that provides strip samples over the terrain. While primarily designed for snow and ice monitoring, there has been a great interest in using ICESat-2 to predict forest above-ground biomass density (AGBD). As ICESat-2 is on a polar orbit, it provides good

Marion Wiese, Axel-Frederik Brand, André van Hoorn

Context. Own experiences and faulty decisions can be an important source of information for software architects. The experiences and mistakes of other architects can also be valuable information sources. Goal. Under the assumption that the knowledge about faulty decisions, i.e., mistakes, regarding software architecture is not shared adequately in practice,

Bhavya Soni, Sheeba Shafaq, Poonam Mehta

In the context of two flavour neutrino oscillations, it is understood that the $2\times 2$ mixing matrix is parameterized by one angle and a Majorana phase. However, this phase does not impact the oscillation probabilities in vacuum or in matter with constant density. Interestingly, the Majorana phase becomes relevant when we describe neutrino oscillations a

Simon Raedler, Juergen Mangler, Stefanie Rinderle-Ma

Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identification and integration of various data sources, the required definition of semantic connections between data attributes, and

David Rodríguez-Martínez, Kentaro Uno, Kenta Sawa, Masahiro Uda

The exploration of the lunar poles and the collection of samples from the martian surface are characterized by shorter time windows demanding increased autonomy and speeds. Autonomous mobile robots must intrinsically cope with a wider range of disturbances. Faster off-road navigation has been explored for terrestrial applications but the combined effects of

Justin Diamond, Markus Lill

Understanding the macroscopic characteristics of biological complexes demands precision and specificity in statistical ensemble modeling. One of the primary challenges in this domain lies in sampling from particular subsets of the state-space, driven either by existing structural knowledge or specific areas of interest within the state-space. We propose a me

Shipra Sharma, Balwinder Sodhi

The ease of using a Large Language Model (LLM) to answer a wide variety of queries and their high availability has resulted in LLMs getting integrated into various applications. LLM-based recommenders are now routinely used by students as well as professional software programmers for code generation and testing. Though LLM-based technology has proven useful,

Likang Wu, Zhaopeng Qiu, Zhi Zheng, Hengshu Zhu

Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in various domains. However, their potential for behavior graph understanding in job recommendations remains largely unexplored. This paper focuses on unveiling the capability of large language models in understanding behavior grap

Yuan Zhong

In the study of three-dimensional flat holography, the BMS field theory manifests the infinite-dimensional BMS$_3$ symmetry, a powerful tool in elucidating numerous universal phenomena. This paper explores a certain low-temperature limit of the BMS field theory. The primary focus lies in the calculation of the thermal correction to the R\'enyi entropy of the

Alexander Rothkopf, Jan Nordström

Taking insight from the theory of general relativity, where space and time are treated on the same footing, we develop a novel geometric variational discretization for second order initial value problems (IVPs). By discretizing the dynamics along a world-line parameter, instead of physical time directly, we retain manifest translation symmetry and conservati

Kahkasha Parveen, Srimanta Pakhira

The global rate of anthropogenic CO2 emission is rising, which urges the development of efficient carbon capture and storage (CCS) technologies. Among the various CO2 capture methods, adsorption by the linkers of the Metal-Organic Frameworks (MOFs) materials has received more interest as excellent CO2 adsorbents because of their important role in understandi

Steffan Christ Sølvsten, Jaco van de Pol

The BDD package Adiar manipulates Binary Decision Diagrams (BDDs) in external memory. This enables handling big BDDs, but the performance suffers when dealing with moderate-sized BDDs. This is mostly due to initializing expensive external memory data structures, even if their contents can fit entirely inside internal memory. The contents of these auxiliary d

M. Agundez, N. Marcelino, B. Tercero, I. Jimenez-Serra

We report new observations of molecular anions with the Yebes 40m and IRAM 30m telescopes toward the cold dense clouds TMC-1 CP, Lupus-1A, L1527, L483, L1495B, and L1544. We detected for the first time C3N- and C5N- in Lupus-1A and C4H- and C6H- in L483. In addition, we report new lines of C6H- toward the six targeted sources, of C4H- toward TMC-1 CP, Lupus-

Nicola Apollonio, Daniela De Canditiis, Giovanni Franzina, Paola Stolfi

In this paper we provide explicit upper bounds on some distances between the (law of the) output of a random Gaussian NN and (the law of) a random Gaussian vector. Our results concern both shallow random Gaussian neural networks with univariate output and fully connected and deep random Gaussian neural networks, with a rather general activation function. The

F. Ait Hellal, J. Puibasset, C. Andreazza-Vignolle, P. Andreazza

Stable and metastable metallic nanoparticles exhibit unique properties compared to the bulk, with potentially important applications for catalysis. This is in particular the case for the AgPt alloy that can exhibit the ordered L1$_1$ structure (alternation of pure Ag and Pt (111) planes) in nanometer size particles. However, for such small systems, the inter

Raziye Kubra Kumrular, Thomas Blumensath

X-ray interaction with matter is an energy-dependent process that is contingent on the atomic structure of the constituent material elements. The most advanced models to capture this relationship currently rely on Monte Carlo (MC) simulations. Whilst these very accurate models, in many problems in spectral X-ray imaging, such as data compression, noise remov

Imene Tarakli, Georgios Angelopoulos, Mehdi Hellou, Camille Vindolet

Nowadays, robots are expected to interact more physically, cognitively, and socially with people. They should adapt to unpredictable contexts alongside individuals with various behaviours. For this reason, personalisation is a valuable attribute for social robots as it allows them to act according to a specific user's needs and preferences and achieve natura

Tobias Våge Henriksen, Sonja Hohloch, Nikolay N. Martynchuk

This survey gives a short and comprehensive introduction to a class of finite-dimensional integrable systems known as hypersemitoric systems, recently introduced by Hohloch and Palmer in connection with the solution of the problem how to extend Hamiltonian circle actions on symplectic 4-manifolds to integrable systems with `nice' singularities. The quadratic

Veronika Solopova, Adrian Gruszczynski, Eiad Rostom, Fritz Cremer

Written reflective practice is a regular exercise pre-service teachers perform during their higher education. Usually, their lecturers are expected to provide individual feedback, which can be a challenging task to perform on a regular basis. In this paper, we present the first open-source automated feedback tool based on didactic theory and implemented as a

Uddipta Kar, Elisha Cho-Hao Lu, Akhilesh Kr. Singh, P. V. Sreenivasa Reddy

The identification of distinct charge transport features, deriving from nontrivial bulk band and surface states, has been a challenging subject in the field of topological systems. In topological Dirac and Weyl semimetals, nontrivial conical bands with Fermi-arc surface states give rise to negative longitudinal magnetoresistance due to chiral anomaly effect

Giuseppe Desolda, Andrea Esposito, Florian Müller, Sebastian Feger

Manufacturing tools like 3D printers have become accessible to the wider society, making the promise of digital fabrication for everyone seemingly reachable. While the actual manufacturing process is largely automated today, users still require knowledge of complex design applications to produce ready-designed objects and adapt them to their needs or design

Daniel Jakubczyk, Gennadiy Derkachov, Kwasi Nyandey, Sima Alikhanzadeh-Arani

Chromatic dispersion and thermal coefficients of 6 hygroscopic liquids: ethylene glycol, diethylene glycol, triethylene glycol, tetraethylene glycol, propylene glycol (propane-1,2-diol), and glycerol were measured in the range from 390 to 1070 nm for temperatures from 1 to 45degC. A modified Abbe refractometer was utilised. Special care was taken to avoid co

Md. Rabiul Islam Khan, Shadman Shahriar, Shaikh Farhan Rafid

Sequence Alignment is the process of aligning biological sequences in order to identify similarities between multiple sequences. In this paper, a Quantum Algorithm for finding the optimal alignment between DNA sequences has been demonstrated which works by mapping the sequence alignment problem into a path-searching problem through a 2D graph. The transition

Andrea Panteghini

The spectral decomposition of a symmetric, second-order tensor is widely adopted in many fields of Computational Mechanics. As an example, in elasto-plasticity under large strain and rotations, given the Cauchy deformation tensor, it is a fundamental step to compute the logarithmic strain tensor. Recently, this approach has been also adopted in small-strain

Gayane Vardoyan, Emily van Milligen, Saikat Guha, Stephanie Wehner

We consider the problem of multi-path entanglement distribution to a pair of nodes in a quantum network consisting of devices with non-deterministic entanglement swapping capabilities. Multi-path entanglement distribution enables a network to establish end-to-end entangled links across any number of available paths with pre-established link-level entanglemen

Kento Sasaki, Takashi Taniguchi, Kensuke Kobayashi

There has been growing interest in studying hexagonal boron nitride (hBN) for quantum technologies. Here, we investigate nitrogen isotope effects on boron vacancy (V$_\text{B}$) defects, one of the candidates for quantum sensors, in $^{15}$N isotopically enriched hBN synthesized using a metathesis reaction. The Raman shifts are scaled with the reduced mass,

Daniele Vilone, Eugenia Polizzi

In the last decades an increasing deal of research has investigated the phenomenon of opinion misperception in human communities and, more recently, in social media. Opinion misperception is the wrong evaluation by community's members of the real distribution of opinions or beliefs about a given topic. In this work we explore the mechanisms giving rise to op

Pronita Chettri, Bhakta Kunwar, Gurukrishna, Suraj Mangavati

A methodical inquiry of the outcome of oxygen plasma exposure in low bandwidth compounds belonging to the perovskite family Pr1-xSrxMnO3 manganites where x = 0.5, has been presented in this communication by comparing the structural and transport properties of the untreated and plasma treated samples. It is witnessed that the high-temperature transmission is

Tony J. Puthenpurakal

Let $k$ be an infinite field of characteristic $p > 0$ and let $R = k[Y_1,\ldots, Y_d]$ (or $R = k[[Y_1,\ldots, Y_d]]$). Let $F \colon \text{Mod}(R) \rightarrow \text{Mod}(R)$ be the Frobenius functor and let $\mathcal{M}$ be a $F_R$-finite module (in the sense of Lyubeznik \cite{Lyu-2}). We show that if $r \geq 1$ then the Koszul homology modules $H_i(Y_1,\

Zheng Zhang, Xiaolei Zhang, Yaolei Qi, Guanyu Yang

Coronary artery segmentation on coronary-computed tomography angiography (CCTA) images is crucial for clinical use. Due to the expertise-required and labor-intensive annotation process, there is a growing demand for the relevant label-efficient learning algorithms. To this end, we propose partial vessels annotation (PVA) based on the challenges of coronary a

Arkadiusz Kosior, Servaas Kokkelmans, Maciej Lewenstein, Jakub Zakrzewski

Polarons, which arise from the self-trapping interaction between electrons and lattice distortions in a solid, have been known and extensively investigated for nearly a century. Nevertheless, the study of polarons continues to be an active and evolving field, with ongoing advancements in both fundamental understanding and practical applications. Here, we pre

Yexin Liu, Weiming Zhang, Guoyang Zhao, Jinjing Zhu

The ability to scene understanding in adverse visual conditions, e.g., nighttime, has sparked active research for RGB-Thermal (RGB-T) semantic segmentation. However, it is essentially hampered by two critical problems: 1) the day-night gap of RGB images is larger than that of thermal images, and 2) the class-wise performance of RGB images at night is not con

Yang Huang, Timothy C. Beers, Hai-Bo Yuan, Ke-Feng Tan

We present precise photometric estimates of stellar parameters, including effective temperature, metallicity, luminosity classification, distance, and stellar age, for nearly 26 million stars using the methodology developed in the first paper of this series, based on the stellar colors from the Stellar Abundances and Galactic Evolution Survey (SAGES) DR1 and

Hassan Jalil Hadi, Irshad ullah, Sheetal Harris

Traditional hard drives consisting of spinning magnetic media platters are becoming things of the past as with the emergence of the latest digital technologies and electronic equipment, the demand for faster, lighter, and more reliable alternate storage solutions is imperative. To attain these requirements, flash storage technologies like Solid State Drive (

Julien Siebert, Daniel Seifert, Patricia Kelbert, Michael Kläs

Generating context specific data quality deficits is necessary to experimentally assess data quality of data-driven (artificial intelligence (AI) or machine learning (ML)) applications. In this paper we present badgers, an extensible open-source Python library to generate data quality deficits (outliers, imbalanced data, drift, etc.) for different modalities

Zhongwei Yang, Can Huang, Xiaocheng Guo, Riku Jarvinen

Kelvin-Helmholtz (KH) vortices are widely observed in astrophysics and heliophysics, including at Jovian and terrestrial magnetopauses, the Martian sheath-ionosphere boundary, the heliopause, and within stellar accretion disks. These vortices play a critical role in transporting mass, momentum, and energy across boundary layers. Magnetized planets such as Ea

Jacob H. Robertson, Riccardo Senese, Fabian H. L. Essler

The presence of long-lived oscillations in the expectation values of local observables after quantum quenches has recently attracted considerable attention in relation to weak ergodicity breaking. Here we focus on an alternative mechanism that gives rise to such oscillations in a class of systems that support kinematically protected gapped excitations at zer

Andrei Comăneci

We investigate location problems whose optimum lies in the tropical convex hull of the input points. Firstly, we study geodesically star-convex sets under the asymmetric tropical distance and introduce the class of tropically quasiconvex functions whose sub-level sets have this shape. The latter are related to monotonic functions. Then we show that location

Horst Alzer, Man Kam Kwong

We present several new inequalities for trigonometric sums. Among others, we show that the inequality $$ \sum_{k=1}^n (n-k+1)(n-k+2)k\sin(kx) > \frac{2}{9} \sin(x) \bigl( 1+2\cos(x) \bigr)^2 $$ holds for all $n\geq 1$ and $x\in (0, 2\pi/3)$. The constant factor $2/9$ is sharp. This refines the classical Szeg\"o-Schweitzer inequality which states that the sin

Michiya Mori

We prove that the distance from an $n\times n$ complex matrix $M$ to the set of nilpotents is at least $\frac{1}{2}\sec\frac{\pi}{n+2}$ if there is a nonzero projection $P$ such that $PMP=M$ and $M^*M\geq P$. In the particular case where $M$ equals $P$, this verifies a conjecture by G.W. MacDonald in 1995. We also confirm a related conjecture in D.A. Herrero

Dimosthenis Kontogiorgos

This paper presents an overview of robot failure detection work from HRI and adjacent fields using failures as an opportunity to examine robot explanation behaviours. As humanoid robots remain experimental tools in the early 2020s, interactions with robots are situated overwhelmingly in controlled environments, typically studying various interactional phenom

Manuel Burger, Gunnar Rätsch, Rita Kuznetsova

Clinicians are increasingly looking towards machine learning to gain insights about patient evolutions. We propose a novel approach named Multi-Modal UMLS Graph Learning (MMUGL) for learning meaningful representations of medical concepts using graph neural networks over knowledge graphs based on the unified medical language system. These representations are

Daniel Fejgin, Simon Doclo

In hearing aid applications, an important objective is to accurately estimate the direction of arrival (DOA) of multiple speakers in noisy and reverberant environments. Recently, we proposed a binaural DOA estimation method, where the DOAs of the speakers are estimated by selecting the directions for which the so-called Hermitian angle spectrum between the e

Qian Li, Yu Zhang, Qi-Quan Li, Qi Sun

In this work, we study the thermal fluctuation, deflection angle and greybody factor of the high-dimensional Schwarzschild black hole in scalar-tensor-vector gravity (STVG). Based on the correction of black hole entropy due to thermal fluctuation, we calculate some thermodynamic quantities associated with the correction of black hole entropy. The influence o

Victor Prokhorenko, Chadni Islam, Muhammad Ali Babar

An Operating System (OS) combines multiple interdependent software packages, which usually have their own independently developed architectures. When a multitude of independent packages are placed together in an OS, an implicit inter-package architecture is formed. For an evolutionary effort, designers/developers of OS can greatly benefit from fully understa

Szymon Urbas, Pierre Lovera, Robert Daly, Alan O'Riordan

High-dimensional spectral data -- routinely generated in dairy production -- are used to predict a range of traits in milk products. Partial least squares (PLS) regression is ubiquitously used for these prediction tasks. However, PLS regression is not typically viewed as arising from a probabilistic model, and parameter uncertainty is rarely quantified. Addi

Adarsh Barik, Suvrit Sra, Jean Honorio

Invex programs are a special kind of non-convex problems which attain global minima at every stationary point. While classical first-order gradient descent methods can solve them, they converge very slowly. In this paper, we propose new first-order algorithms to solve the general class of invex problems. We identify sufficient conditions for convergence of o

Xinpeng Li, Ting Jiang, Haoqiang Fan, Shuaicheng Liu

Image Quality Assessment (IQA) is a challenging task that requires training on massive datasets to achieve accurate predictions. However, due to the lack of IQA data, deep learning-based IQA methods typically rely on pre-trained networks trained on massive datasets as feature extractors to enhance their generalization ability, such as the ResNet network trai

Iqra Aslam, Adina Aniculaesei, Abhishek Buragohain, Daniel Bamal

Last-mile delivery of goods has gained a lot of attraction during the COVID-19 pandemic. However, current package delivery processes often lead to parking in the second lane, which in turn has negative effects on the urban environment in which the deliveries take place, i.e., traffic congestion and safety issues for other road users. To tackle these challeng

Naoki Seto

From prolonged X-ray and optical data of the ultra-compact binary HM Cancri, two groups recently measured the second derivative of its orbital frequency. The space gravitational wave (GW) detector LISA will detect $\sim10^4$ Galactic binaries and their second frequency derivatives will be interesting observational targets for LISA. Here, we forecast the GW s

Cédric Arhancet

We define a notion of nonassociative $\mathrm{L}^p$-space associated to a $\mathrm{JBW}^*$-algebra (Jordan von Neumann algebra) equipped with a normal faithful state $\varphi$. In the particular case of $\mathrm{JW}^*$-algebras underlying von Neumann algebras, we connect these spaces to a complex interpolation theorem of Ricard and Xu on noncommutative $\mat

Tibor Jordán, Soma Villányi

A $d$-dimensional framework is a pair $(G,p)$, where $G=(V,E)$ is a graph and $p$ is a map from $V$ to $\mathbb{R}^d$. The length of an edge $xy\in E$ in $(G,p)$ is the distance between $p(x)$ and $p(y)$. A vertex pair $\{u,v\}$ of $G$ is said to be globally linked in $(G,p)$ if the distance between $p(u)$ and $p(v)$ is equal to the distance between $q(u)$ a

T. Alghamdi, Y. Alanazi, M. Battaglieri, L. Bibrzycki

AI-supported algorithms, particularly generative models, have been successfully used in a variety of different contexts. In this work, we demonstrate for the first time that generative adversarial networks (GANs) can be used in high-energy experimental physics to unfold detector effects from multi-particle final states, while preserving correlations between

Jiamin Jiang, Bo Guo

Numerical simulation of multi-phase fluid dynamics in porous media is critical for many energy and environmental applications in Earth's subsurface. Data-driven surrogate modeling provides computationally inexpensive alternatives to high-fidelity numerical simulators. While the commonly used convolutional neural networks (CNNs) are powerful in approximating

Ar Rohim, Apriadi Salim Adam, Arista Romadani

We study the effect of the Lorentz symmetry breaking on the Casimir energy of charged Dirac in the presence of a uniform magnetic field. We use the boundary condition from the MIT bag model to represent the property of the plates. We investigate two cases of the direction of violation, namely, time-like and space-like vector cases. We discuss how the Lorentz

Alexey Gordeev

In this note we describe how Laso\'n's generalization of Alon's Combinatorial Nullstellensatz gives a framework for constructing lower bounds on the Tur\'an number $\operatorname{ex}(n, K^{(r)}_{s_1,\dots,s_r})$ of the complete $r$-partite $r$-uniform hypergraph $K^{(r)}_{s_1,\dots,s_r}$. To illustrate the potential of this method, we give a short and simple

Felix Klingelhoefer, Alantha Newman

The chromatic number of a directed graph is the minimum number of induced acyclic subdigraphs that cover its vertex set, and accordingly, the chromatic number of a tournament is the minimum number of transitive subtournaments that cover its vertex set. The neighborhood of an arc $uv$ in a tournament $T$ is the set of vertices that form a directed triangle wi

Tiantian Feng, Brandon M Booth, Shrikanth Narayanan

Continuously-worn wearable sensors enable researchers to collect copious amounts of rich bio-behavioral time series recordings of real-life activities of daily living, offering unprecedented opportunities to infer novel human behavior patterns during daily routines. Existing approaches to routine discovery through bio-behavioral data rely either on pre-defin

Qian Li, Yu Zhang, Zhi-Wen Lin, Qi-Quan Li

This paper is devoted to studying the weak-field gravitational lensing properties of a 4D ESTGB black hole, which is surrounded by the plasma medium. The effects of the magnetic charges and the three plasma distribution models in the deflection of light around a 4D ESTGB black hole are investigated in detail. We find that the uniform plasma leads to a larger

Oshin Dutta, Tanu Kanvar, Sumeet Agarwal

Edge computing aims to enable edge devices, such as IoT devices, to process data locally instead of relying on the cloud. However, deep learning techniques like computer vision and natural language processing can be computationally expensive and memory-intensive. Creating manual architectures specialized for each device is infeasible due to their varying mem

Aymen Sekhri, Marouane Tliba, Mohamed Amine Kerkouri, Yassine Nasser

Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. Early detection and diagnosis are crucial for successful clinical intervention and management to prevent severe complications, such as loss of mobility. In this paper, we propose an automated approach that employs the Swin Transformer to predict t

Nathaniel Harms, Viktor Zamaraev

We prove a characterization of the structural conditions on matrices of sign-rank 3 and unit disk graphs (UDGs) which permit constant-cost public-coin randomized communication protocols. Therefore, under these conditions, these graphs also admit implicit representations. The sign-rank of a matrix $M \in \{\pm 1\}^{N \times N}$ is the smallest rank of a matri

Yongzhi Wu, Chong Han, Meixia Tao

Terahertz (THz) integrated sensing and communication (ISAC) enables simultaneous data transmission with Terabit-per-second (Tbps) rate and millimeter-level accurate sensing. To realize such a blueprint, ultra-massive antenna arrays with directional beamforming are used to compensate for severe path loss in the THz band. In this paper, the time-frequency-spac

Luca Donati, Marcus Weber

Extracting the kinetic properties of a system whose dynamics depend on the pH of the environment with which it exchanges energy and atoms requires sampling the Grand Canonical Ensemble. As an alternative, we present a novel strategy that requires simulating only the most recurrent Canonical Ensembles that compose the Grand Canonical Ensemble. The simulations

Aline Habib, Ammar El Falou, Charlotte Langlais, Marion Berbineau

The number of train passengers and the demand for high data rates to handle new technologies such as video streaming and IoT technologies are continuously increasing. Therefore the exploration of millimeter waves (mmWave) band is a key technology to meet this demand. However, the high penetration loss makes mmWave very sensitive to blocking, limiting its cov