July 2023 arXiv papers — page 86

Sebastian Forster, Antonis Skarlatos, Tijn de Vos

In this paper, we study algorithms for special cases of energy games, a class of turn-based games on graphs that show up in the quantitative analysis of reactive systems. In an energy game, the vertices of a weighted directed graph belong either to Alice or to Bob. A token is moved to a next vertex by the player controlling its current location, and its ener

Daniel Nicholls, Jack Wells, Alex W. Robinson, Amirafshar Moshtaghpour

Subsampling and fast scanning in the scanning transmission electron microscope is problematic due to scan coil hysteresis - the mismatch between the actual and assumed location of the electron probe beam as a function of the history of the scan. Hysteresis limits the resolution of the microscope and can induce artefacts in our images, particularly during fly

Talía L. M. Lezama, Yevgeny Bar Lev, Lea F. Santos

We provide bounds on temporal fluctuations around the infinite-time average of out-of-time-ordered and time-ordered correlators of many-body quantum systems without energy gap degeneracies. For physical initial states, our bounds predict the exponential decay of the temporal fluctuations as a function of the system size. We numerically verify this prediction

Érica Z. Fornaroli, Mykola Khrypchenko

In the first part of the paper we describe $\varphi$-derivations of the incidence algebra $I(X,K)$ of a locally finite poset $X$ over a field $K$, where $\varphi$ is an arbitrary automorphism of $I(X,K)$. We show that they admit decompositions similar to that of usual derivations of $I(X,K)$. In particular, the quotient of the space of $\varphi$-derivations

Guanhua Su, Shuling Xiang, Jiachang Bi, Fugang Qi

In the nitrogen-doped lutetium hydride (Lu-H-N) system, the presence of Lu-N chemical bonds plays a key role in the emergence of possible room-temperature superconductivity at near ambient pressure. However, due to the synthesis of single-crystalline LuN being a big challenge, the understanding of LuN is insufficient thus far. Here, we report on the epitaxia

Borui Zhao, Quan Cui, Renjie Song, Jiajun Liang

Knowledge distillation transfers knowledge from a large model to a small one via task and distillation losses. In this paper, we observe a trade-off between task and distillation losses, i.e., introducing distillation loss limits the convergence of task loss. We believe that the trade-off results from the insufficient optimization of distillation loss. The r

David Kessler, Nadav M. Shnerb

In the long run, the eventual extinction of any biological population is an inevitable outcome. While extensive research has focused on the average time it takes for a population to go extinct under various circumstances, there has been limited exploration of the distributions of extinction times and the likelihood of significant fluctuations. Recently, Hath

Hao Chen, Yonghan Dong, Zheming Lu, Yunlong Yu

Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples. In this paper, we propose a dense affinity matching (DAM) framework to exploit the support-query interaction by densely capturing both the pixel-to-pixel and pixel-to-patch relations in each support-query pair with the bidirectional 3D convolutions. Different fro

Ahmad Naser Eddin, Jacopo Bono, David Aparício, Hugo Ferreira

Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics in order to harness their full potential in downstream tasks. Previous approaches for graph representation learning have focused on either sampling k-hop neighborhoods, akin to bre

Dhimiter D. Canko, Nikolaos Syrrakos

We discuss the recent progress that has been made towards the computation of three-loop non-planar master integrals relevant to next-to-next-to-next-to-leading-order (N$^3$LO) corrections to processes such as H+jet production at the LHC. We describe the analytic structure of these integrals, as well as several technical issues regarding their analytic comput

Nunzio Dimola, Miroslav Kuchta, Kent-Andre Mardal, Paolo Zunino

In the context of micro-circulation, the coexistence of two distinct length scales - the vascular radius and the tissue/organ scale - with a substantial difference in magnitude, poses significant challenges. To handle slender inclusions and simplify the geometry involved, a technique called topological dimensionality reduction is employed, which suppresses m

Chao Li, Zijie Guo, Qiuting He, Hao Xu

Utilizing long-range dependency, a concept extensively studied in homogeneous graphs, remains underexplored in heterogeneous graphs, especially on large ones, posing two significant challenges: Reducing computational costs while maximizing effective information utilization in the presence of heterogeneity, and overcoming the over-smoothing issue in graph neu

L. F. Prudente, D. R. Souza

We propose a modified BFGS algorithm for multiobjective optimization problems with global convergence, even in the absence of convexity assumptions on the objective functions. Furthermore, we establish the superlinear convergence of the method under usual conditions. Our approach employs Wolfe step sizes and ensures that the Hessian approximations are update

Paula M. Weber, Tim Drevelow, Jing Qi, Matthias Bode

The spin structure of a Mn triple layer grown pseudomorphically on surfaces is studied using spin-polarized scanning tunneling microscopy (SP-STM) and density functional theory (DFT). In SP-STM images a c$(4 \times 2)$ super structure is found. The magnetic origin of this contrast is verified by contrast reversal and using the c$(2 \times 2)$ AFM state of th

Roman Schnabel

About 40 years ago, the neutrino was ruled out as the dark matter particle based on several arguments. Here I use the well-established concept of quantum uncertainties of position and momentum to describe the decoupling of neutrinos from the primordial plasma, which took place about half a second after the Big Bang. In this way I show that the main arguments

Rebekka Hubert, Artem Sokolov, Stefan Riezler

End-to-end automatic speech translation (AST) relies on data that combines audio inputs with text translation outputs. Previous work used existing large parallel corpora of transcriptions and translations in a knowledge distillation (KD) setup to distill a neural machine translation (NMT) into an AST student model. While KD allows using larger pretrained mod

Małgorzata Pietras, Robert Falewicz, Marek Siarkowski, Anna Kepa

We present an analysis of a flare on the Wolf 359 star based on simultaneous observations of TESS and XMM-Newton. A stellar flare with energy comparable to an X-class solar flare is analyzed on this star for the first time. The main goal of the study was to determine whether the same physical processes drive and occur in stellar flares as in the solar flares

Rongke Liu, Dong Wang, Yizhi Ren, Zhen Wang

Model inversion attacks (MIAs) aim to recover private data from inaccessible training sets of deep learning models, posing a privacy threat. MIAs primarily focus on the white-box scenario where attackers have full access to the model's structure and parameters. However, practical applications are usually in black-box scenarios or label-only scenarios, i.e.,

Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Be

Santiago Núñez-Corrales

Existing abstract models of quantum computation make reference to circuit elements, much in contrast to their classical counterparts. Circuits, as a model of computation, substantially limit algorithmic expression and obscure high-level connections between problems and quantum resources. It is argued here that new models are needed to achieve high-level algo

Lasse Matthias Reinpold, Lukas Peter Wagner, Felix Gehlhoff, Malte Ramonat

To achieve a highly agile and flexible production, it is envisioned that industrial production systems gradually become more decentralized, interconnected, and intelligent. Within this vision, production assets collaborate with each other, exhibiting a high degree of autonomy. Furthermore, knowledge about individual production assets is readily available thr

Tal Ben-Nun, Lukas Gianinazzi, Torsten Hoefler, Yishai Oltchik

Execution graphs of parallel loop programs exhibit a nested, repeating structure. We show how such graphs that are the result of nested repetition can be represented by succinct parametric structures. This parametric graph template representation allows us to reason about the execution graph of a parallel program at a cost that only depends on the program si

Jiaxin Du, Mei Li, Xue Zhang, Bin Xi

Steady-flow dynamics of ferromagnetic 180$^{\circ}$ domain walls (180DWs) in long and narrow spin valves (LNSVs) with interfacial Dzyaloshinskii-Moriya interaction (IDMI) under spin currents with Slonczewski $g-$factor are examined. Depending on the magnetization orientation of polarizers (pinned layers of LNSVs), dynamics of 180DWs in free layers of LNSVs a

Divya Rawat, Aksa Thomas, Ajay Soni

Ultra-thin 2D materials have shown complete paradigm shift of understanding of physical and electronic properties because of confinement effects, symmetry breaking and novel phenomena at nanoscale. Bulk 2H-TaS2 undergoes an incommensurate charge density wave (I-CDW) transition temperature, TI-CDW - 76 K, however, onset of CDW in atomically thin layers is not

Gabriele Trivigno, Gabriele Berton, Juan Aragon, Barbara Caputo

Visual Place recognition is commonly addressed as an image retrieval problem. However, retrieval methods are impractical to scale to large datasets, densely sampled from city-wide maps, since their dimension impact negatively on the inference time. Using approximate nearest neighbour search for retrieval helps to mitigate this issue, at the cost of a perform

Nathaniel Berger, Miriam Exel, Matthias Huck, Stefan Riezler

Supervised learning in Neural Machine Translation (NMT) typically follows a teacher forcing paradigm where reference tokens constitute the conditioning context in the model's prediction, instead of its own previous predictions. In order to alleviate this lack of exploration in the space of translations, we present a simple extension of standard maximum likel

Aral Hekimoglu, Michael Schmidt, Alvaro Marcos-Ramiro

We propose a novel semi-supervised active learning (SSAL) framework for monocular 3D object detection with LiDAR guidance (MonoLiG), which leverages all modalities of collected data during model development. We utilize LiDAR to guide the data selection and training of monocular 3D detectors without introducing any overhead in the inference phase. During trai

Aral Hekimoglu, Adrian Brucker, Alper Kagan Kayali, Michael Schmidt

Curating an informative and representative dataset is essential for enhancing the performance of 2D object detectors. We present a novel active learning sampling strategy that addresses both the informativeness and diversity of the selections. Our strategy integrates uncertainty and diversity-based selection principles into a joint selection objective by mea

R. L. C. Vink

We study dissipation as a function of sample thickness in solids under global oscillatory shear applied to the top layer of the sample. Two types of damping mechanism are considered: Langevin and Dissipative Particle Dynamics (DPD). In the regime of low driving frequency, and under strain-controlled conditions, we observe that for Langevin damping, dissipati

Arnab Mukherjee, Souvik Majumdar, Anup Kumar Kolya, Saborni Nandi

Within a modern democratic nation, elections play a significant role in the nation's functioning. However, with the existing infrastructure for conducting elections using Electronic Voting Systems (EVMs), many loopholes exist, which illegitimate entities might leverage to cast false votes or even tamper with the EVMs after the voting session is complete. The

Lauren Nicole DeLong, Ramon Fernández Mir, Zonglin Ji, Fiona Niamh Coulter Smith

Biomedical datasets are often modeled as knowledge graphs (KGs) because they capture the multi-relational, heterogeneous, and dynamic natures of biomedical systems. KG completion (KGC), can, therefore, help researchers make predictions to inform tasks like drug repositioning. While previous approaches for KGC were either rule-based or embedding-based, hybrid

Carlota Andrés, Liliana Apolinário, Néstor Armesto, André Cordeiro

While experimental studies on jet quenching have achieved a large sophistication, the theoretical description of this phenomenon still misses some important points. One of them is the interplay of vacuum-like emissions, usually formulated in momentum space, with the medium induced ones that demand an interplay with a space-time picture of the medium and thus

Alexandru Chirvasitu

The bounded localization $\beta_b$ of a locally convex topology $\beta$ is defined as the finest locally convex topology agreeing with $\beta$ on all bounded sets. We show that the strict topology on the multiplier algebra of a bornological pro-$C^*$-algebras equals its own localization, generalizing the analogous result due to Taylor for multiplier algebras

M. C. Gordillo, J. M Alcaraz-Pelegrina

The properties of fully-heavy arrangements including a number of quarks between 5 and 12 were calculated within the framework of a constituent quark model by using a diffusion Monte Carlo technique. We considered only clusters in which all the quarks had the same mass, and whose number of particles and antiparticles were adequate to produce color singlets. A

Payaswini Saikia, David M. Russell, Saarah F. Pirbhoy, M. C. Baglio

We present a detailed study of optical data from the 2012 outburst of the candidate black hole X-ray binary Swift J1910.2-0546 using the Faulkes Telescope and Las Cumbres Observatory (LCO). We analyse the peculiar spectral state changes of Swift J1910.2-0546 in different energy bands, and characterise how the optical and UV emission correlates with the unusu

Prabhupada Dixit, Md. Nasim

We present a study on the longitudinal flow decorrelation in heavy-ion collisions at the RHIC Beam Energy Scan (BES) energies ($\sqrt{s_{NN}}$ = 11.5 to 200 GeV in Au+Au collisions) using the AMPT model. We measure the second and third order factorization ratios ($r_{2}$ and $r_{3}$) across BES energies, finding $r_{2}$ weakly dependent on collision energy w

Juha-Pekka Pellonpää, Erkka Haapasalo, Roope Uola

We present a barycentric decomposition for quantum instruments whose output space is finite-dimensional and input space is separable. As a special case, we obtain a barycentric decomposition for channels between such spaces and for normalized positive-operator-valued measures in separable Hilbert spaces. This extends the known results by Ali and Chiribella e

Vladyslav Bivziuk, Vitalii Slynko

We have proved new estimates for the coherent control errors of quantum circuits used in quantum computing. These estimates essentially take into account the commutator properties of the Hamiltonians and are based on the formulas of the commutator calculus.

Michele Panariello, Massimiliano Todisco, Nicholas Evans

For the most popular x-vector-based approaches to speaker anonymisation, the bulk of the anonymisation can stem from vocoding rather than from the core anonymisation function which is used to substitute an original speaker x-vector with that of a fictitious pseudo-speaker. This phenomenon can impede the design of better anonymisation systems since there is a

Mariem Abdellatif, Peter Kuchling, Barbara Rüdiger, Irene Ventura

In this article, we represent the Wasserstein metric of order $p$, where $p\in [1,\infty)$, in terms of the comonotonicity copula, for the case of probability measures on $\R^d$, by revisiting existing results. In 1973, Vallender established the link between the $1$-Wasserstein metric and the corresponding distribution functions for $d=1$. In 1956 Giorgio da

Stavros Orfanoudakis, Georgios Chalkiadakis

The increasing number of Distributed Energy Resources (DERs) in the emerging Smart Grid, has created an imminent need for intelligent multiagent frameworks able to utilize these assets efficiently. In this paper, we propose a novel DER aggregation framework, encompassing a multiagent architecture and various types of mechanisms for the effective management a

Renxing Wan, Wenyuan Yang

This paper studies the locally uniform exponential growth and product set growth for a finitely generated group $G$ acting properly on a finite product of hyperbolic spaces. Under the assumption of coarsely dense orbits or shadowing property on factors, we prove that any finitely generated non-virtually abelian subgroup has uniform exponential growth. These

Mahyar Pourjabar, Ahmed AlKatheeri, Manuele Rusci, Agata Barcis

Relative localization is a crucial functional block of any robotic swarm. We address it in a fleet of nano-drones characterized by a 10 cm-scale form factor, which makes them highly versatile but also strictly limited in their onboard power envelope. State-of-the-Art solutions leverage Ultra-WideBand (UWB) technology, allowing distance range measurements bet

Khulood D. Alazwary, Ahmad Adnan Qidan, T. E. H. El-Gorashi, Jaafar M. H. Elmirghani

Optical wireless communication (OWC) provides high aggregate data rates in the range of Terabits per second (Tb/s). Specifically, OWC using infrared lasers as transmitters has been considered as a strong candidate in the next generation of wireless communication. Rate splitting (RS) is a transmission scheme derived to improve spectral efficiency in dense wir

Grigalius Taujanskas

We report on the recent construction of a scattering theory for Maxwell potentials on curved spacetimes.

Ahmet Canberk Baykal, Abdul Basit Anees, Duygu Ceylan, Erkut Erdem

Researchers have recently begun exploring the use of StyleGAN-based models for real image editing. One particularly interesting application is using natural language descriptions to guide the editing process. Existing approaches for editing images using language either resort to instance-level latent code optimization or map predefined text prompts to some e

Krystian Kazaniecki, Anton Tselishchev, Michał Wojciechowski

We consider weakly null sequences in the Banach space of functions of bounded variation $\mathrm{BV}(\mathbb{R}^d)$. We prove that for any such sequence $\{f_n\}$ the jump parts of the gradients of functions $f_n$ tend to $0$ strongly as measures. It implies that Dunford--Pettis property for the space $\mathrm{SBV}$ is equivalent to the Dunford--Pettis prope

Qing Jiang, Jiapeng Wang, Dezhi Peng, Chongyu Liu

This paper aims to re-assess scene text recognition (STR) from a data-oriented perspective. We begin by revisiting the six commonly used benchmarks in STR and observe a trend of performance saturation, whereby only 2.91% of the benchmark images cannot be accurately recognized by an ensemble of 13 representative models. While these results are impressive and

K. B. Alkalaev, A. O. Kanoda, V. S. Khiteev

We develop the relation between gravitational Wilson line networks, defined as a particular product of Wilson line operators averaged over the cap states, and conformal correlators in the context of the AdS$_2$/CFT$_1$ correspondence. The $n$-point $sl(2, \mathbb{R})$ comb channel global conformal block in CFT$_1$ is explicitly calculated by means of the ext

Roman Schnabel

Squeezed states of the optical field were theoretically described in the early 1970s and first observed in the mid 1980s. The measured photon number of a squeezed state is correlated with the measured photon numbers of all other squeezed states of the same ensemble, providing sub-Poissonian statistics. Today all gravitational-wave observatories use squeezed

Andrew Caines, Luca Benedetto, Shiva Taslimipoor, Christopher Davis

The recent release of very large language models such as PaLM and GPT-4 has made an unprecedented impact in the popular media and public consciousness, giving rise to a mixture of excitement and fear as to their capabilities and potential uses, and shining a light on natural language processing research which had not previously received so much attention. Th

Grigorii Verkhogliadov, Ross Haroldson, Dmitry Gets, Anvar A. Zakhidov

Mixed halide perovskites undergo phase segregation, manifested as spectral red-shifting of photoluminescence spectra under illumination. In the iodine-bromide mixed perovskites, the origin of the low-energy luminescence is related to iodine-enriched domains formation. Such domains create favorable bands for the induced carrier funneling into them. Despite th

Antonio Amariti, Andrea Zanetti

We evaluate the superconformal index of 4d $\mathcal{N}=4$ SYM with gauge algebra $so(2N_c+1)$ in the Cardy-like limit. We then study the relation with the results obtained for the S-dual $usp(2N_c)$, discussing the fate of S-duality in different regions of charges. We find that S-duality is preserved thanks to a non-trivial integral identity that relates th

Alejandro Perez, Sami Viollet

The black hole information puzzle can be resolved if two conditions are met. Firstly, if the information of what falls inside a black hole remains encoded in degrees of freedom that persist after the black hole completely evaporates. These degrees of freedom should be capable of purifying the information. Secondly, if these purifying degrees of freedom do no

Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi

Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cannot capture the non-linear relations well or conventional deep learning models (e.g., CNN and LSTM) that do not

Begoña Cano, María Jesús Moreta

In a previous paper, a technique was suggested to avoid order reduction with any explicit exponential Runge-Kutta method when integrating initial boundary value nonlinear problems with time-dependent boundary conditions. In this paper, we significantly simplify the full discretization formulas to be applied under conditions which are nearly always satisfied

Yaolei Qi, Yuting He, Xiaoming Qi, Yuan Zhang

Accurate segmentation of topological tubular structures, such as blood vessels and roads, is crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However, many factors complicate the task, including thin local structures and variable global morphologies. In this work, we note the specificity of tubular structures and use this know

Máté Benjámin Vizi, Gábor Orosz, Dénes Takács, Gábor Stépán

The steering control of an autonomous unicycle is considered. The underlying dynamical model of a single rolling wheel is discussed regarding the steady state motions and their stability. The unicycle model is introduced as the simplest possible extension of the rolling wheel where the location of the center of gravity is controlled. With the help of the App

Rebecca Potts, Rick Hackney, Georgios Leontidis

Predicting emissions for gas turbines is critical for monitoring harmful pollutants being released into the atmosphere. In this study, we evaluate the performance of machine learning models for predicting emissions for gas turbines. We compare an existing predictive emissions model, a first principles-based Chemical Kinetics model, against two machine learni

Meiling Jin, Qing Wang, Ying Liu, Qunfei Zheng

This paper examines the micro-parameters of superconductors. It studies the modulations from weak van der Waals interaction to strong covalence bonding of superconductors. In particular, we studied layered black phosphorus (BP) as a function of pressure. These results reveal a rich scenario of phase transitions and related quantum phenomena, which show that

Julien Zylberman, Fabrice Debbasch

A new approximate Quantum State Preparation (QSP) method is introduced, called the Walsh Series Loader (WSL). The WSL approximates quantum states defined by real-valued functions of single real variables with a depth independent of the number $n$ of qubits. Two approaches are presented: the first one approximates the target quantum state by a Walsh Series tr

Maoteng Zheng, Nengcheng Chen, Junfeng Zhu, Xiaoru Zeng

We propose a distributed bundle adjustment (DBA) method using the exact Levenberg-Marquardt (LM) algorithm for super large-scale datasets. Most of the existing methods partition the global map to small ones and conduct bundle adjustment in the submaps. In order to fit the parallel framework, they use approximate solutions instead of the LM algorithm. However

Tingkai Li, Zihao Zhou, Adam Thelen, David Howey

Accurate battery lifetime prediction is important for preventative maintenance, warranties, and improved cell design and manufacturing. However, manufacturing variability and usage-dependent degradation make life prediction challenging. Here, we investigate new features derived from capacity-voltage data in early life to predict the lifetime of cells cycled

Erick Lavoie

Replicated append-only logs sequentially order messages from the same author such that their ordering can be eventually recovered even with out-of-order and unreliable dissemination of individual messages. They are widely used for implementing replicated services in both clouds and peer-to-peer environments because they provide simple and efficient increment

J. Sommerfeldt, S. Strnat, V. A. Yerokhin, W. Middents

We present a theoretical study of elastic photon scattering by atomic targets. This process is of special interest since various channels from atomic and nuclear physics as well as quantum elctrodynamics (QED) contribute to it. In this work, we focus on Delbr\"uck scattering which proceeds via production of virtual $e^+e^-$ pairs. In particular, we explore w

Ranieri D. Baldi

Radio-loud compact radio sources (CRSs) are characterised by morphological compactness of the jet structure centred on the active nucleus of the galaxy. Most of the local elliptical galaxies are found to host a CRS with nuclear luminosities lower than those of typical quasars, $\lesssim$10$^{42}\, {\rm erg\, s}^{-1}$. Recently, low-luminosity CRSs with a LIN

Martin Langhammer, George Constantinides

This paper introduces the eGPU, a SIMT soft processor designed for FPGAs. Soft processors typically achieve modest operating frequencies, a fraction of the headline performance claimed by modern FPGA families, and obtain correspondingly modest performance results. We propose a GPGPU architecture structured specifically to take advantage of both the soft logi

Gianluca Finocchio, Tatyana Krivobokova

In many applications, particularly in the natural sciences, the available high-dimensional set of features may contain variables that are not correlated with the response under consideration. Such irrelevant features can, in certain cases, hinder both the accurate estimation and meaningful interpretation of the effects of the relevant features on the respons

Sarah V. White

Radio observations allow us to identify a wide range of active galactic nuclei (AGN), which are galaxies that have gas accreting onto the supermassive black-hole at the centre. By observing these sources at multiple radio frequencies, a more-complete picture can be built of black-hole accretion activity. This completeness is aided by radio waves being unaffe

Cong Li, Zhan Sun, Gui-Yuan Zhang

In this article, we study in detail the double-$J/\psi$ yield through $Z$ decay at the next-to-leading-order (NLO) QCD accuracy within the nonrelativistic QCD factorization. At the tree level, the pure QCD diagrams predict a branching ratio of $\mathcal{B}_{Z \to J/\psi+J/\psi} \sim 10^{-12}$; however, the inclusion of the QED diagrams would augment this pre

Shinsuke Kawai, Nobuchika Okada, Qaisar Shafi

Renormalisation group analysis with the present measurements of the top quark mass $m_t = 172.69\pm 0.30$ GeV indicates that the Standard Model (SM) Higgs potential becomes unstable at energy scales $\sim 10^{10}$ GeV. This may be interpreted as hinting at new particles at high energy. The minimal extension of the SM that can avoid this instability while lea

Stephan Wong, Terry A. Loring, Alexander Cerjan

Nonlinear topological insulators have garnered substantial recent attention as they have both enabled the discovery of new physics due to interparticle interactions, and may have applications in photonic devices such as topological lasers and frequency combs. However, due to the local nature of nonlinearities, previous attempts to classify the topology of no

Z. N. Liu, X. Q. Zhao, Y. L. Zhao, S. N. Zhu

In recent years, moir\'e lattice has become a hot topic and inspired the research upsurge of moir\'e lattice. In this work, we propose a method of constructing a multi-composite moir\'e lattice, which is composed of over three periodic component structures. Moreover, we propose the moir\'e lattice-metasurface structure, which can realize the multi-wavelength

Khuram Tariq

Testing the Yukawa couplings of the Higgs boson to quarks and leptons is important to understand the origin of fermion masses. These proceedings will review several measurements of Higgs boson decays to two bottom quarks or two tau leptons, searches for Higgs boson decays to two charm quarks or two muons, as well as direct constraints on the charm-Yukawa cou

Susanna Caroppo, Giordano Da Lozzo, Giuseppe Di Battista

In this paper, we initiate the study of quantum algorithms in the Graph Drawing research area. We focus on two foundational drawing standards: 2-level drawings and book layouts. Concerning $2$-level drawings, we consider the problems of obtaining drawings with the minimum number of crossings, $k$-planar drawings, quasi-planar drawings, and the problem of rem

Augustine Okolie, Johannes Müller, Mirjam Kretzschmar

We adopt a maximum-likelihood framework to estimate parameters of a stochastic susceptible-infected-recovered (SIR) model with contact tracing on a rooted random tree. Given the number of detectees per index case, our estimator allows to determine the degree distribution of the random tree as well as the tracing probability. Since we do not discover all infe

Barbara Brandolini, Florica Corina Cirstea

We prove the uniform boundedness of all solutions for a general class of Dirichlet anisotropic elliptic problems of the form $$-\Delta_{\overrightarrow{p}}u+\Phi_0(u,\nabla u)=\Psi(u,\nabla u) +f $$ on a bounded open subset $\Omega\subset \mathbb R^N$ $(N\geq 2)$, where $ \Delta_{\overrightarrow{p}}u=\sum_{j=1}^N \partial_j (|\partial_j u|^{p_j-2}\partial_j

Ajaya Adhikari, Steven Vethman, Daan Vos, Marc Lenz

Skills-based matching promises mobility of workers between different sectors and occupations in the labor market. In this case, job seekers can look for jobs they do not yet have experience in, but for which they do have relevant skills. Currently, there are multiple occupations with a skewed gender distribution. For skills-based matching, it is unclear if a

Giorgio Gonnella

Prokaryotic organisms usually possess compact genomes, which are particularly suitable to complete sequencing with existing technologies, which led to an escalating accumulation of available genome data. In response to this ever-expanding repository of information, we introduce ProSt, a computational system designed for the batch computation, storage, and in

Qi-Nan Wang, Ding-Kun Lian, Wei Chen

We study the non-strange and strangeonium light hybrid mesons with $J^{PC}=2^{+-}$ by using the method of QCD sum rules. The local hybrid interpolating currents with three Lorentz indices are constructed to couple to such exotic quantum numbers. We calculate the correlation functions up to dimension eight condensates at the leading order of $\alpha_{s}$. In

Martino Salomone Centonze, Ido Kanter, Adriano Barra

We study bi-directional associative neural networks that, exposed to noisy examples of an extensive number of random archetypes, learn the latter (with or without the presence of a teacher) when the supplied information is enough: in this setting, learning is heteroassociative -- involving couples of patterns -- and it is achieved by reverberating the inform

Lennart Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel

Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES). However, we believe that GES may not always be optimal, as it performs a simple deterministic greedy search. In this work, we introduce two novel population-based ensemble selection methods, QO-ES and

Ali Alabbas, Miguel Altamirano Cabrera, Oussama Alyounes, Dzmitry Tsetserukou

The current capabilities of robotic systems make human collaboration necessary to accomplish complex tasks effectively. In this work, we are introducing a framework to ensure safety in a human-robot collaborative environment. The system is composed of a wearable 2-DOF robot, a low-cost and easy-to-install tracking system, and a collision avoidance algorithm

Thayse A. Pacheco, Ronaldo S. Levenhagen, Marcos P. Diaz, Paula R. T. Coelho

We present an update of the grid of detailed atmosphere models and homogeneous synthetic spectra for hot, high-gravity subdwarf stars. High-resolution spectra and synthetic photometry were calculated in the wavelength range 1,000 \r{A} - 10,000 \r{A} using Non-LTE extensively line-blanketed atmosphere structures.

Xiying Du, António Girão, Zach Hunter, Rose McCarty

We prove that there exists a constant $C$ so that, for all $s,k \in \mathbb{N}$, if $G$ has average degree at least $k^{Cs^3}$ and does not contain $K_{s,s}$ as a subgraph then it contains an induced subgraph which is $C_4$-free and has average degree at least $k$. It was known that some function of $s$ and $k$ suffices, but this is the first explicit bound.

Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou

In this paper, we propose an online convex optimization approach with two different levels of adaptivity. On a higher level, our approach is agnostic to the unknown types and curvatures of the online functions, while at a lower level, it can exploit the unknown niceness of the environments and attain problem-dependent guarantees. Specifically, we obtain $\ma

Andreas Zachariae, Julia Widera, Frederik Plahl, Björn Hein

Human transports in hospitals are labor-intensive and primarily performed in beds to save time. This transfer method does not promote the mobility or autonomy of the patient. To relieve the caregivers from this time-consuming task, a mobile robot is developed to autonomously transport humans around the hospital. It provides different transfer modes including

Jacopo Fumagalli, Sukannya Bhattacharya, Marco Peloso, Sébastien Renaux-Petel

We show that, whenever the perturbations of some field are excited during inflation by a physical process on sub-horizon scales, they unavoidably generate, even through gravitational interactions alone, a significant resonant IR cascade of power down to scales that are of the order of the horizon at that time (we denote these scales as near IR). We provide g

Kieran Saunders, George Vogiatzis, Luis Manso

Current, self-supervised depth estimation architectures rely on clear and sunny weather scenes to train deep neural networks. However, in many locations, this assumption is too strong. For example in the UK (2021), 149 days consisted of rain. For these architectures to be effective in real-world applications, we must create models that can generalise to all

H. M. Verhelst, A. Mantzaflaris, M. Möller, J. H. Den Besten

Mesh adaptivity is a technique to provide detail in numerical solutions without the need to refine the mesh over the whole domain. Mesh adaptivity in isogeometric analysis can be driven by Truncated Hierarchical B-splines (THB-splines) which add degrees of freedom locally based on finer B-spline bases. Labeling of elements for refinement is typically done us

Coleridge Faraday, W. A. Horowitz

We present leading hadron suppression predictions in $Pb+Pb$ and $p+Pb$ collisions from a convolved radiative and collisional energy loss model in which partons propagate through a realistic background, and in which the radiative energy loss receives a short pathlength correction. We find that the short pathlength correction is small for $D$ meson $R_{AA}(p_

Christian Herglotz, Simon Grosche, Akarsh Bharadwaj, André Kaup

This paper presents a novel method to estimate the power consumption of distinct active components on an electronic carrier board by using thermal imaging. The components and the board can be made of heterogeneous material such as plastic, coated microchips, and metal bonds or wires, where a special coating for high emissivity is not required. The thermal im

Wenze Liu, Hao Lu, Yuliang Liu, Zhiguo Cao

Conditional spatial queries are recently introduced into DEtection TRansformer (DETR) to accelerate convergence. In DAB-DETR, such queries are modulated by the so-called conditional linear projection at each decoder stage, aiming to search for positions of interest such as the four extremities of the box. Each decoder stage progressively updates the box by p

Yichuan Deng, Zhihang Li, Sridhar Mahadevan, Zhao Song

Large language models (LLMs) have brought about significant transformations in human society. Among the crucial computations in LLMs, the softmax unit holds great importance. Its helps the model generating a probability distribution on potential subsequent words or phrases, considering a series of input words. By utilizing this distribution, the model select

Gokul Jayakrishnan, Vijayanand Banahatti, Sachin Lodha

Serious games are increasingly being used in cybersecurity education to engage and educate users. Several studies with cybersecurity serious games have shown that they are successful in educating users and the users also find them both fun and engaging. Meanwhile, several studies have also reported issues in identifying real life effects of the game and even

Samuele Papa, David M. Knigge, Riccardo Valperga, Nikita Moriakov

Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex class of Cone Beam Geometry CT (CBCT) reconstruction. Recently, deep learning methods have been proposed to overcome these limitations, with methods based on neural fields (NF) sh

Khalid Barkaoui, Francisco J. Pozuelos, Coel Hellier, Barry Smalley

Gas giants transiting bright nearby stars provide crucial insights into planetary system formation and evolution mechanisms. Most of these planets exhibit certain average characteristics, serving as benchmarks for our understanding of planetary systems. However, outliers like the planet we present in this study, WASP-193b, offer unique opportunities to explo

Feng Dong, Shaofei Li, Peng Jiang, Ding Li

Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future APT defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we cond

Hui Ying, Tianjia Shao, He Wang, Yin Yang

In this paper, we focus on the task of 3D shape completion from partial point clouds using deep implicit functions. Existing methods seek to use voxelized basis functions or the ones from a certain family of functions (e.g., Gaussians), which leads to high computational costs or limited shape expressivity. On the contrary, our method employs adaptive local b

Che Liu, Sibo Cheng, Chen Chen, Mengyun Qiao

Medical vision-language models enable co-learning and integrating features from medical imaging and clinical text. However, these models are not easy to train and the latent representation space can be complex. Here we propose a novel way for pre-training and regularising medical vision-language models. The proposed method, named Medical vision-language pre-