October 2023 arXiv papers — page 106

Sergey Goryainov, Dmitry Panasenko

This paper lies in the context of the studies of eigenfunctions of graphs having minimum cardinality of support. One of the tools is the weight-distribution bound, a lower bound on the cardinality of support of an eigenfunction of a distance-regular graph corresponding to a non-principal eigenvalue. The tightness of the weight-distribution bound was previous

Carolina Camassa

In the rapidly evolving field of crypto assets, white papers are essential documents for investor guidance, and are now subject to unprecedented content requirements under the European Union's Markets in Crypto-Assets Regulation (MiCAR). Natural Language Processing (NLP) can serve as a powerful tool for both analyzing these documents and assisting in regulat

R. Alonso, S. Hoyer, M. Deleuil, A. E. Simon

HD 139139 (a.k.a. 'The Random Transiter') is a star that exhibited enigmatic transit-like features with no apparent periodicity in K2 data. The shallow depth of the events ($\sim$200 ppm -- equivalent to transiting objects with radii of $\sim$1.5 R$_\oplus$ in front of a Sun-like star), and their non-periodicity, constitutes a challenge for the photometric f

Šárka Hudecová, Marie Hušková, Simos G. Meintanis

We propose a goodness-of-fit test for a class of count time series models with covariates which includes the Poisson autoregressive model with covariates (PARX) as a special case. The test criteria are derived from a specific characterization for the conditional probability generating function and the test statistic is formulated as a $L_2$ weighting norm of

Guillermo Encinas-Lago, Antonio Albanese, Vincenzo Sciancalepore, Marco Di Renzo

The advent of reconfigurable intelligent surfaces(RISs) brings along significant improvements for wireless technology on the verge of beyond-fifth-generation networks (B5G).The proven flexibility in influencing the propagation environment opens up the possibility of programmatically altering the wireless channel to the advantage of network designers, enablin

Tomer Wullach, Shlomo E. Chazan

The challenges facing speech recognition systems, such as variations in pronunciations, adverse audio conditions, and the scarcity of labeled data, emphasize the necessity for a post-processing step that corrects recurring errors. Previous research has shown the advantages of employing dedicated error correction models, yet training such models requires larg

Petar Jovanovski, Andrew Golightly, Umberto Picchini

We develop a Bayesian inference method for discretely-observed stochastic differential equations (SDEs). Inference is challenging for most SDEs, due to the analytical intractability of the likelihood function. Nevertheless, forward simulation via numerical methods is straightforward, motivating the use of approximate Bayesian computation (ABC). We propose a

Kristen C. Dage, Arash Bahramian, Clancy W. James, Arunav Kundu

We use multiband archival HST observations to measure the photometric and structural parameters of the M81 globular cluster that hosts the Fast Radio Burst FRB 20200120E. Our best-fitting King model has an effective radius $r_h = 3.06$ pc with a moderate King model concentration of $c = 53$, and an inferred core radius of 0.81 pc. We revisit the exact astrom

Vladimir Korchagin, Artem Lutsenko, Roman Tkachenko, Giovanni Carraro

Detailed analysis of kinematics of the Milky Way disk in the solar neighborhood based on the GAIA DR3 catalog reveals the existence of peculiarities in the stellar velocity distribution perpendicular to the galactic plane. We study the influence of resonances -- the outer Lindblad resonance and the outer vertical Lindblad resonance -- of a rotating bar with

Gilles Dowek, Benjamin Werner

We present constructive arithmetic in Deduction modulo with rewrite rules only.

Marlène Careil, Matthew J. Muckley, Jakob Verbeek, Stéphane Lathuilière

Image codecs are typically optimized to trade-off bitrate \vs distortion metrics. At low bitrates, this leads to compression artefacts which are easily perceptible, even when training with perceptual or adversarial losses. To improve image quality and remove dependency on the bitrate, we propose to decode with iterative diffusion models. We condition the dec

Marija Tepegjozova, Benjamin F. Meyer, Anja Rammig, Christian S. Zang

In light of climate change's impacts on forests, including extreme drought and late-frost, leading to vitality decline and regional forest die-back, we assess univariate drought and late-frost risks and perform a joint risk analysis in Bavaria, Germany, from 1952 to 2020. Utilizing a vast dataset with 26 bioclimatic and topographic variables, we employ vine

D. Bazeia, M. J. B. Ferreira, B. F. de Oliveira

This work deals with the time evolution of the Hamming distance density for the public goods game. We consider distinct possibilities for this game, which are exactly described by a function called $q$-exponential, that represents a deformation of the usual exponential function parametrized by $q$, suggesting that the system belongs to the class of weakly-ch

Daniel Caurant, Gilles Wallez, Odile Majérus, Gauthier Roisine

The original electronic structure of the Pb$^{2+}$ ion explains its particular structural role in crystalline phases and silicate glasses. After a description of the structural role of lead in crystalline phases (PbO, silicates), this chapter presents a summary of the main structural studies carried out on glasses from the SiO$_2$-PbO, SiO$_2$-PbO-R$_2$O (R:

Joseph Hirsh, Joan Millès

In this corrigendum, we explain and correct a mistake in our article ''Curved Koszul duality theory''. Our definitions of morphisms between semi-augmented properads and between curved coproperads have to be modified.

Junjie Dong, Mudi Jiang, Lianyu Hu, Zengyou He

Sequence classification has numerous applications in various fields. Despite extensive studies in the last decades, many challenges still exist, particularly in pattern-based methods. Existing pattern-based methods measure the discriminative power of each feature individually during the mining process, leading to the result of missing some combinations of fe

Julia Pöschl

This work contributes to efforts on autonomously detecting a vegetation-occluded target by airborne observers. It investigates and enhances previous work on a Particle Swarm Optimization (PSO) strategy for Airborne Optical Sectioning (AOS) drone swarms. First, it identifies two issues with that method and proposes to resolve them by a leader stabilization fo

Nikhil Danny Babu, Girish S. Setlur

An unconventional bosonization approach that employs a modified Fermi-Bose correspondence is used to obtain the tunneling density of states (TDOS) of fractional quantum Hall (FQHE) edges in the vicinity of a point contact. The chiral Luttinger liquid model is generally used to describe FQHE edge excitations. We introduce a bosonization procedure to study edg

Chong Li, Shaonan Wang, Yunhao Zhang, Jiajun Zhang

Transformer-based models, even though achieving super-human performance on several downstream tasks, are often regarded as a black box and used as a whole. It is still unclear what mechanisms they have learned, especially their core module: multi-head attention. Inspired by functional specialization in the human brain, which helps to efficiently handle multi

Cen Wang, Guang Zeng, Xinyu Wen, Yuhui He

Stochasticity plays a significant role in the low-power operation of a biological neural network. In an artificial neural network (ANN), stochasticity also contributes to critical functions such as the uncertainty quantification (UQ) for estimating the probability for the correctness of prediction. This UQ is vital for cutting-edge applications, including me

Nikolai Dokuchaev

The paper studies spectral representation as well as predictability and recoverability problems for non-vanishing discrete time signals from $\ell_\infty$, i.e. for bounded discrete time signals, including signals that do not vanish at $\pm\infty$. The extends the notions of transfer functions, the spectrum gaps, bandlimitness, and filters, on these general

Kamila Zaman, Alberto Marchisio, Muhammad Abdullah Hanif, Muhammad Shafique

Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing.

Nicolas Curien, Lucile Laulin

We give a short proof of the recurrence of the two-dimensional elephant random walk in the diffusive regime. This was recently established by Shuo Qin, but our proof only uses very rough comparison with the standard plane random walk. We hope that the method can be useful for other applications.

Luisa Fiorot, Teresa Monteiro Fernandes

The aim of this note is threefold. The first is to obtain a simple characterization of relative constructible sheaves when the parameter space is projective. The second is to study the relative Fourier-Mukai for relative constructible sheaves and for relative regular holonomic $\mathcal D$-modules and prove they induce relative equivalences of categories. Th

Tristan Beolet, Alice Adenis, Erik Huneker, Maxime Louis

The development of closed-loop systems for glycemia control in type I diabetes relies heavily on simulated patients. Improving the performances and adaptability of these close-loops raises the risk of over-fitting the simulator. This may have dire consequences, especially in unusual cases which were not faithfully-if at all-captured by the simulator. To addr

Laya Rafiee Sevyeri, Ivaxi Sheth, Farhood Farahnak, Samira Ebrahimi Kahou

Advancements in deep learning techniques have given a boost to the performance of anomaly detection. However, real-world and safety-critical applications demand a level of transparency and reasoning beyond accuracy. The task of anomaly detection (AD) focuses on finding whether a given sample follows the learned distribution. Existing methods lack the ability

Chang Hou, Nan Deng, Bernd R. Noack

In this study, we propose a novel data-driven reduced-order model for complex dynamics, including nonlinear, multi-attractor, multi-frequency, and multiscale behaviours. The starting point is a fully automatable cluster-based network model (CNM) (Li et al. J. Fluid Mech. vol.906, 2021, A21) which kinematically coarse-grains the state with clusters and dynami

Manon Reusens, Philipp Borchert, Margot Mieskes, Jochen De Weerdt

This paper investigates the transferability of debiasing techniques across different languages within multilingual models. We examine the applicability of these techniques in English, French, German, and Dutch. Using multilingual BERT (mBERT), we demonstrate that cross-lingual transfer of debiasing techniques is not only feasible but also yields promising re

Daniyar Shamkanov

We consider an extension of the modal logic of transitive closure K+ with some inifinitary derivations and present a sequent calculus for this extension, which allows non-well-founded proofs. For the given calculus, we obtain the cut-elimination theorem following the lines of so called continuous cut elimination. Our consideration also covers ordinary proofs

Xinxin Yan, Zhideng Zhou, Xiaohan Cheng, Xiaolei Yang

The accuracy of solving partial differential equations (PDEs) on coarse grids is greatly affected by the choice of discretization schemes. In this work, we propose to learn time integration schemes based on neural networks which satisfy three distinct sets of mathematical constraints, i.e., unconstrained, semi-constrained with the root condition, and fully-c

Yuhong Deng, Xueqian Wang, Lipeng chen

Goal-conditioned rearrangement of deformable objects (e.g. straightening a rope and folding a cloth) is one of the most common deformable manipulation tasks, where the robot needs to rearrange a deformable object into a prescribed goal configuration with only visual observations. These tasks are typically confronted with two main challenges: the high dimensi

Haichou Li, Xingsi Pu, Hongyu Wang

In this paper, we obtain the Gehring-Hayman type theorem on smoothly bounded pseudoconvex domains of finite type in $\mathbb{C}^2$. As an application, we provide a quantitative comparison between global and local Kobayashi distances near a boundary point for these domains.

Flavien Hirigoyen, Axel Crocherie, Jérôme Vaillant, Yvon Cazaux

This paper presents a new FDTD-based optical simulation model dedicated to describe the optical performances of CMOS image sensors taking into account diffraction effects. Following market trend and industrialization constraints, CMOS image sensors must be easily embedded into even smaller packages, which are now equipped with auto-focus and short-term comin

Lorenzo Sillari, Adriano Tomassini

We study the spaces of $(d + d^c)$-harmonic forms and $(d + d^\Lambda)$-harmonic forms, the natural generalization of the spaces of Bott-Chern harmonic forms, resp. symplectic harmonic forms from complex, resp. symplectic, manifolds to almost Hermitian manifolds. With the same techniques, we also prove that Bott-Chern and Aeppli numbers of compact complex su

Yuzo Maruyama

The original Hotelling-Solomons inequality indicates that an upper bound of |mean - median|/(standard deviation) is 1. In this note, we find a new bound depending on the sample size, which is strictly smaller than 1.

A. Crocherie, Jérôme Vaillant, F. Hirigoyen

In this paper, we present the results of rigorous electromagnetic broadband simulations applied to CMOS image sensors as well as experimental measurements. We firstly compare the results of 1D, 2D, and 3D broadband simulations in the visible range (380nm-720nm) of a 1.75$\mu$m CMOS image sensor, emphasizing the limitations of 1D and 2D simulations and the ne

Kavisha Vidanapathirana, Shin-Fang Chng, Xueqian Li, Simon Lucey

The test-time optimization of scene flow - using a coordinate network as a neural prior - has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art performance. We observe, however, that although coordinate networks capture general motions by implicitly regularizing the scene flow predictions to be spatially smooth, the neural p

Kaixing Yang, Xukun Zhou, Xulong Tang, Ran Diao

Dance and music are closely related forms of expression, with mutual retrieval between dance videos and music being a fundamental task in various fields like education, art, and sports. However, existing methods often suffer from unnatural generation effects or fail to fully explore the correlation between music and dance. To overcome these challenges, we pr

Matteo Zecchin, Sangwoo Park, Osvaldo Simeone

In many real-world problems, predictions are leveraged to monitor and control cyber-physical systems, demanding guarantees on the satisfaction of reliability and safety requirements. However, predictions are inherently uncertain, and managing prediction uncertainty presents significant challenges in environments characterized by complex dynamics and forking

Jie Zhou, Mingshen Sun, John Criswell

Rust is one of the most promising systems programming languages to fundamentally solve the memory safety issues that have plagued low-level software for over forty years. However, to accommodate the scenarios where Rust's type rules might be too restrictive for certain systems programming and where programmers opt for performance over security checks, Rust o

Georgii D. Ulev, Gennady A. Ovsyannikov, Karen Y. Constantinian, Anton V. Shadrin

The present study focuses on experimental investigations of spin current across the interface of an iridate/manganite heterostructure (SrIrO3/La0.7Sr0.3MnO3) consisting of oxide epitaxial films with nanometer thickness. Pure spin current is induced by microwave irradiation in the GHz frequency band, specifically under conditions of ferromagnetic resonance. T

Yafei Wang, Hongwei Hou, Wenjin Wang, Xinping Yi

In this paper, we consider symbol-level precoding (SLP) in channel-coded multiuser multi-input single-output (MISO) systems. It is observed that the received SLP signals do not always follow Gaussian distribution, rendering the conventional soft demodulation with the Gaussian assumption unsuitable for the coded SLP systems. It, therefore, calls for novel sof

Roberto Di Cosmo, Stefano Zacchiroli

Software Heritage is the largest public archive of software source code and associated development history, as captured by modern version control systems. As of July 2023, it has archived more than 16 billion unique source code files coming from more than 250 million collaborative development projects. In this chapter, we describe the Software Heritage ecosy

Aadit Deshpande, Shreya Goyal, Prateek Nagwanshi, Avinash Tripathy

With the recent advances in social media, the use of NLP techniques in social media data analysis has become an emerging research direction. Business organizations can particularly benefit from such an analysis of social media discourse, providing an external perspective on consumer behavior. Some of the NLP applications such as intent detection, sentiment c

Qiaomei Liu, Dong Wu, Tianyi Wu, Shanshan Han

Utilizing ultrafast light-matter interaction to manipulate electronic states of quantum materials is an emerging area of research in condensed matter physics. It has significant implications for the development of future ultrafast electronic devices. However, the ability to induce long-lasting metastable electronic states in a fully reversible manner is a lo

Kuan Tian, Yonghang Guan, Jinxi Xiang, Jun Zhang

Under certain circumstances, advanced neural video codecs can surpass the most complex traditional codecs in their rate-distortion (RD) performance. One of the main reasons for the high performance of existing neural video codecs is the use of the entropy model, which can provide more accurate probability distribution estimations for compressing the latents.

Seungbeom Chin, Marcin Karczewski, Yong-Su Kim

Non-destructive heralded entanglement with photons is a valuable resource for quantum information processing. However, they generally entail ancillary particles and modes that amplify the circuit intricacy. To address this challenge, a recent work (\href{https://www.nature.com/articles/s41534-024-00845-6}{npj Quantum Information 10, 67 (2024)}) introduced a

Dharmateja Adapa, Virendra Singh Shekhawat, Avinash Gautam, Sudeept Mohan

Large indoor spaces have complex layouts making them difficult to navigate. Indoor spaces in hospitals, universities, shopping complexes, etc., carry multi-modal information in the form of text and symbols. Hence, it is difficult for Blind and Visually Impaired (BVI) people to independently navigate such spaces. Indoor environments are usually GPS-denied; th

Muhammad Shalihan, Zhiqiang Cao, Khattiya Pongsirijinda, Lin Guo

Localization of objects is vital for robot-object interaction. Light Detection and Ranging (LiDAR) application in robotics is an emerging and widely used object localization technique due to its accurate distance measurement, long-range, wide field of view, and robustness in different conditions. However, LiDAR is unable to identify the objects when they are

Vane Jacky, Batkam Mbatchou, Frédéric Patras, Calvin Tcheka

Motivated by various developments in algebraic combinatorics and its applications, we investigate here the fine structure of a fundamental but little known theorem, the Gerstenhaber and Schack cohomology comparison theorem.The theorem classically asserts that there is a cochain equivalence between the usual singular cochain complex of a simplicial complex an

Shin Miyahara, Isao Maruyama

We present a theory of the realization of a ferromagnetic Haldane state in a spin-2 bilinear-biquadratic spin system on an orthogonal-dimer chain. The coexistence of a ferromagnetic state and a Haldane state is due to the rigorous correspondence between the eigenstates of a spin-2 model and a spin-1/2 Heisenberg model; i.e., "eigensystem embedding." Numerica

Arghya Sil, Asim Kumar Ghosh

Existence of nontrivial topological phases in a tight binding Haldane-like model on the depleted Lieb lattice is reported. This two-band model is formulated by considering the nearest-neighbor, next-nearest-neighbor and next-next-nearest-neighbor hopping terms along with complex phase which breaks the time reversal symmetry of this semi-metallic system. Topo

Weixiao Zhou, Gengyao Li, Xianfu Cheng, Xinnian Liang

Dialogue summarization involves a wide range of scenarios and domains. However, existing methods generally only apply to specific scenarios or domains. In this study, we propose a new pre-trained model specifically designed for multi-scenario multi-domain dialogue summarization. It adopts a multi-stage pre-training strategy to reduce the gap between the pre-

Berkane M., Desrier A., Lévêque C., Taïeb R.

We investigate signatures of anisotropy on the dynamics of time-resolved near-threshold molecular photoemission, through simulations on a one-dimension asymmetric model molecule. More precisely, we study the relationship between the fundamental Wigner delays that fully characterizes the dynamics of one-photon ionization, and the delays inferred from two-colo

Shijia Song, Handong Li

Bank crisis is challenging to define but can be manifested through bank contagion. This study presents a comprehensive framework grounded in nonlinear time series analysis to identify potential early warning signals (EWS) for impending phase transitions in bank systems, with the goal of anticipating severe bank crisis. In contrast to traditional analyses of

A Hirsch-Passicos, C L C Lacoste, F André, Y Elskens

The quality of the proton beam produced by Target Normal Sheath Acceleration (TNSA) with high power lasers can be significantly improved with the use of helical coils. While they showed promising results in terms of focusing, their performances in terms of the of cutoff energy and bunching stay limited due to the dispersive nature of helical coils. A new sch

Attila Nagy, Csaba Tóth

In this paper we deal with the following problem: how does the structure of a finite semigroup $S$ depend on the probability that two elements selected at random from $S$, with replacement, define the same inner right translation of $S$. We solve a subcase of this problem. As the main result of the paper, we show how to construct not necessarily finite media

Hadar Mulian, Segev Shlomov, Lior Limonad, Alessia Noccaro

Motor skills, especially fine motor skills like handwriting, play an essential role in academic pursuits and everyday life. Traditional methods to teach these skills, although effective, can be time-consuming and inconsistent. With the rise of advanced technologies like robotics and artificial intelligence, there is increasing interest in automating such tea

Angela Montanaro, Enrico Maria Rigoni, Francesca Giusti, Luisa Barba

In the cuprates, the opening of a d-wave superconducting (SC) gap is accompanied by a redistribution of spectral weight at energies two orders of magnitude larger than this gap. This indicates the importance to the pairing mechanism of on-site electronic excitations, such as orbital transitions or charge transfer excitations. Here, we resort to a three-pulse

Daniel Zhang, Toby Cubitt

We introduce a generalisation of quantum error correction, relaxing the requirement that a code should identify and correct a set of physical errors on the Hilbert space of a quantum computer exactly, instead allowing recovery up to a pre-specified admissible set of errors on the code space. We call these quantum error transmuting codes. They are of particul

Loris Guichard, Clara Simmat, Margot Dupeux, Stéphane Sockeel

Introduction: Nottingham grading system is a major prognostic factor for invasive breast carcinoma (IBC). Its determination requires the evaluation of the mitotic score (MS) which is subject to low intra- and inter-observer reproducibility. The MS shall be performed in the most proliferative area of the tumor, which determination is hard but critical. Artifi

Pavankumar Ganjimala, Subrahmanyam Mula

The high computation complexity of nonlinear adaptive filtering algorithms poses significant challenges at the hardware implementation level. In order to tackle the computational complexity problem, this paper proposes a novel block-oriented functional link adaptive filter (BO-FLAF) to model memoryless nonlinear systems. Through theoretical complexity analys

Andrey Zhitnikov, Ori Sztyglic, Vadim Indelman

Continuous POMDPs with general belief-dependent rewards are notoriously difficult to solve online. In this paper, we present a complete provable theory of adaptive multilevel simplification for the setting of a given externally constructed belief tree and MCTS that constructs the belief tree on the fly using an exploration technique. Our theory allows to acc

Arthur Vereijken

In the context of a chiral hadronic model, we compute the decay ratio of a tensor glueball decaying into a nucleon and antinucleon compared to the decay into 2 pions. Tensor meson dominance is assumed to also hold for the tensor glueball in order to relate the coupling constants of the different decay channels. We find that the decay width to nucleons is sli

Anna Klimova

Log-linear models are widely used to express the association in multivariate frequency data on contingency tables. The paper focuses on the power analysis for testing the goodness-of-fit hypothesis for this model type. Conventionally, for the power-related sample size calculations a deviation from the null hypothesis (effect size) is specified by means of th

Cheng Meng, Alapan Mukhopadhyay

Given ideals $I,J$ of a noetherian local ring $(R, \mathfrak m)$ such that $I+J$ is $\mathfrak m$-primary and a finitely generated $R$-module $M$, we associate an invariant of $(M,R,I,J)$ called the $h$-function. Our results on $h$-functions allow extensions of the theories of Frobenius-Poincar\'e functions and Hilbert-Kunz density functions from the known g

Amitay Kamber, Péter P. Varjú

We show that every element of $\mathrm{SL}_{n}(\mathbb{Z}/q\mathbb{Z})$ can be lifted to an element of $\mathrm{SL}_{n}(\mathbb{Z})$ of norm at most $Cq^2\log q$, while there exists an element such that every lift of it is of norm at least $q^{2+o(1)}$. This should be compared to the recent result that almost every element has a lift of norm bounded by $q^{1

Gabriel Mendonça, Matheus Santos, André Gonçalves, Yan Almeida

The fintech PicPay offers a wide range of financial services to its 30 million monthly active users, with more than 50 thousand items recommended in the PicPay mobile app. In this scenario, promoting specific items that are strategic to the company can be very challenging. In this work, we present a Switching Hybrid Recommender System that combines two algor

Shi-Xian Sun, Si-Yuan Cui, Long-Xing Huang, Yong-Qiang Wang

In this paper, we construct a Dirac star model composed of $|\kappa|$ pairs of spinor fields. The azimuthal harmonic indeces $m$ of these spinor fields are half-integers, and they satisfiy $-(|\kappa|-\frac{1}{2})\leq m \leq |\kappa|-\frac{1}{2}$. When $\kappa=1$, it corresponds to the conventional Dirac star model, formed by two spinor fields with $m=\frac{

Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jun Zhao

As one of the most exciting features of large language models (LLMs), in-context learning is a mixed blessing. While it allows users to fast-prototype a task solver with only a few training examples, the performance is generally sensitive to various configurations of the prompt such as the choice or order of the training examples. In this paper, we for the f

Uwe Bäsel

In this paper, we discuss some problems of elementary plane differential geometry and kinematics. Although the results are not new, the consistent use of complex-valued functions (plane curves) of a real variable (parameter) allows to derive them ab ovo in a particularly simple, uniform and transparent way. A number of examples with figures complete the expl

Yang Wu, Shenglong Hu, Huihui Song, Kaihua Zhang

The traditional definition of co-salient object detection (CoSOD) task is to segment the common salient objects in a group of relevant images. This definition is based on an assumption of group consensus consistency that is not always reasonable in the open-world setting, which results in robustness issue in the model when dealing with irrelevant images in t

Priyanshi Bhasin, Tanmoy Das

Describing systems with non-Hermitian (NH) operators remains a challenge in quantum theory due to instabilities (e.g., exceptional points and decoherence) arising from interactions with the environment. We propose a framework to express the energy states of NH Hamiltonians using a well-defined basis (dub computational basis) derived from a related Hermitian

Natalia Flechas Manrique, Wanqian Bao, Aurelie Herbelot, Uri Hasson

Interpretability methods in NLP aim to provide insights into the semantics underlying specific system architectures. Focusing on word embeddings, we present a supervised-learning method that, for a given domain (e.g., sports, professions), identifies a subset of model features that strongly improve prediction of human similarity judgments. We show this metho

Yu Tian, Lijun Yang, Aimee S. Morgans, Jingxuan Li

This paper investigates a predictive model that considers the impact of stretch on the dynamic response of laminar premixed conical and V- flames; the flame stretch consists of two components: the flame curvature and flow strain. The steady and perturbed flame fronts are determined via the linearized $G$-equation associated with the flame stretch model. Para

Adel Ammar, Anis Koubaa, Bilel Benjdira, Omar Najar

In the intricate field of legal studies, the analysis of court decisions is a cornerstone for the effective functioning of the judicial system. The ability to predict court outcomes helps judges during the decision-making process and equips lawyers with invaluable insights, enhancing their strategic approaches to cases. Despite its significance, the domain o

Ahmed Sayeed Faruk, Elena Zheleva

Recommender systems relying on contextual multi-armed bandits continuously improve relevant item recommendations by taking into account the contextual information. The objective of bandit algorithms is to learn the best arm (e.g., best item to recommend) for each user and thus maximize the cumulative rewards from user engagement with the recommendations. The

Simran Bedi, Sanjay Kumar

Harmonic mappings have long intrigued researchers due to their intrinsic connection with minimal surfaces. In this paper, we investigate shearing of two distinct classes of univalent conformal mappings which are convex in horizontal direction with appropriate dilatations. Subsequently, we present a family of minimal surfaces constructed by lifting the harmon

Irina Ignatiouk-Robert

In this paper, we obtain the exact asymptotic behavior of Green functions of homogeneous random walks in $\Z^d$ killed at the first exit from and open cone of $\R^d$. Our approach combines methods of functional equations, integral representations of the Green function and Woess' approach for the case of homogeneous random walks in $\Z^d$.

Simon Machado

Approximate lattices of locally compact groups were first studied in a seminal monograph of Yves Meyer and were subsequently used in the theory of aperiodic order to model objects such as Pisot numbers, quasi-cristals or aperiodic tilings. Meyer studied approximate lattices of Euclidean spaces - now dubbed Meyer sets. A fascinating feature of this theory is

Hideki Okawa

Usage of cutting-edge artificial intelligence will be the baseline at future high energy colliders such as the High Luminosity Large Hadron Collider, to cope with the enormously increasing demand of the computing resources. The rapid development of quantum machine learning could bring in further paradigm-shifting improvement to this challenge. One of the two

He Wang, Chuanbo Liu, Jin Wang

In this paper, we propose a novel quantum classifier utilizing dissipative engineering. Unlike standard quantum circuit models, the classifier consists of a central spin-qubit model. By subjecting the auxiliary qubits to carefully tailored strong dissipations, we establish a one-to-one mapping between classical data and dissipative modes. This mapping enable

Yi-Chi Wang, Thomas J A Slater, Gerard M. Leteba, Candace I Lang

Tailoring nanoparticles composition and morphology is of particular interest for improving their performance for catalysis. A challenge of this approach is that the nanoparticles optimized initial structure often changes during use. Visualizing the three dimensional (3D) structural transformation in situ is therefore critical, but often prohibitively difficu

Daniel Lawson, Sophie Blundell, Martin Ebert, Otto L. Muskens

The development of the next generation of optical phase change technologies for integrated photonic and free-space platforms relies on the availability of materials that can be switched repeatedly over large volumes and with low optical losses. In recent years, the antimony-based chalcogenide phase-change material Sb$_2$Se$_3$ has been identified as particul

Constantinos Kanellopoulos, Peter Rangelow, Boris Jeremic, Ioannis Anastasopoulos

The paper explores the linear and nonlinear dynamic interaction between the reactor and the auxiliary buildings of a Nuclear Power Plant, aiming to evaluate the effect of the auxiliary building on the seismic response of crucial components inside the reactor building. Based on realistic geometrical assumptions, high-fidelity 3D finite element (FE) models of

Simon Hakenes, Tobias Glasmachers

This work addresses the challenge of navigating expansive spaces with sparse rewards through Reinforcement Learning (RL). Using topological maps, we elevate elementary actions to object-oriented macro actions, enabling a simple Deep Q-Network (DQN) agent to solve otherwise practically impossible environments.

Milo Bechtloff Weising

We construct a new family of graded representations $\widetilde{W}_{\lambda}$ indexed by Young diagrams $\lambda$ for the positive elliptic Hall algebra $\mathcal{E}^{+}$ which generalizes the standard $\mathcal{E}^{+}$ action on symmetric functions. These representations have homogeneous bases of eigenvectors for the action of the Macdonald element $P_{0,1}

Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt

Objective: Reconstructing freehand ultrasound in 3D without any external tracker has been a long-standing challenge in ultrasound-assisted procedures. We aim to define new ways of parameterising long-term dependencies, and evaluate the performance. Methods: First, long-term dependency is encoded by transformation positions within a frame sequence. This is ac

Murat Akman, Shirsho Mukherjee

We study the Minkowski problem corresponding to the p-harmonic measures and obtain results previously known for harmonic measures due to Jerison. We show that a class of Borel measures on spheres can be prescribed by p-harmonic measures on convex domains.

Simon Machado

We provide and motivate in this paper a natural framework for the study of approximate lattices. Namely, we consider approximate lattices in so-called $S$-adic linear groups and define relevant notions of arithmeticity. We also adapt to this framework classical results of the theory of lattices and Meyer sets. Results from this paper will play a role in the

Peng Wen, Junhu Zhang, Haitao Li

Wearing a mask is one of the important measures to prevent infectious diseases. However, it is difficult to detect people's mask-wearing situation in public places with high traffic flow. To address the above problem, this paper proposes a mask-wearing face detection model based on YOLOv5l. Firstly, Multi-Head Attentional Self-Convolution not only improves t

M. Mallorquín, E. Goffo, E. Pallé, N. Lodieu

We report the discovery, mass, and radius determination of TOI-1801 b, a temperate mini-Neptune around a young M dwarf. TOI-1801 b was observed in TESS sectors 22 and 49, and the alert that this was a TESS planet candidate with a period of 21.3 days went out in April 2020. However, ground-based follow-up observations, including seeing-limited photometry in a

Joy Morris, Gabriel Verret

A necessary condition for a Cayley digraph Cay$(R,S)$ to be a regular representation is that there are no non-trivial group automorphisms of $R$ that fix $S$ setwise. A group is DRR-detecting or GRR-detecting if this condition is also sufficient for all Cayley digraphs or graphs on the group, respectively. In this paper, we determine precisely which groups o

Jung-Chao Ban, Yu-Liang Wu

This article investigates the topological pressure of isotropic axial products of Markov subshifts on the $d$-tree. We show that the quantity increases with dimension $d$. To achieve this, we introduce the pattern distribution vectors and the associated transition matrices and partially transplant the large deviation theory to tree-shifts. Additionally, we a

Shalom Eliahou, Eshita Mazumdar

We provide optimal upper bounds on the growth of iterated sumsets $hA=A+\dots+A$ for finite subsets $A$ of abelian semigroups. More precisely, we show that the new upper bounds recently derived from Macaulay's theorem in commutative algebra are best possible, i.e., are actually reached by suitable subsets of suitable abelian semigroups. Our constructions, in

Paweł Czyż, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel

The pointwise mutual information profile, or simply profile, is the distribution of pointwise mutual information for a given pair of random variables. One of its important properties is that its expected value is precisely the mutual information between these random variables. In this paper, we analytically describe the profiles of multivariate normal distri

Mingyang Ren, Xin He, Junhui Wang

Directed acyclic graph (DAG) has been widely employed to represent directional relationships among a set of collected nodes. Yet, the available data in one single study is often limited for accurate DAG reconstruction, whereas heterogeneous data may be collected from multiple relevant studies. It remains an open question how to pool the heterogeneous data to

Hang Guo, Xian Zhang, Shuai Chen, Li Huang

Recently, an extremely-air-stable one-dimensional 7-9-7-AGNR was successfully fabricated. To further reveal its potential application in sub-5-nm field-effect transistors (FETs), there is an urgent need to develop integrated circuits. Here, we report first-principles quantum-transport simulations on the performance limits of n- and p-type sub-5-nm one-dimens

Zhihao Ding, Jieming Shi, Shiqi Shen, Xuequn Shang

Graph-level representation learning is important in a wide range of applications. Existing graph-level models are generally built on i.i.d. assumption for both training and testing graphs. However, in an open world, models can encounter out-of-distribution (OOD) testing graphs that are from different distributions unknown during training. A trustworthy model

ALICE Collaboration

Results on the transverse spherocity dependence of light-flavor particle production ($\pi$, K, p, $\phi$, ${\rm K^{*0}}$, ${\rm K}^{0}_{\rm{S}}$, $\Lambda$, $\Xi$) at midrapidity in high-multiplicity pp collisions at $\sqrt{s} = 13$ TeV were obtained with the ALICE apparatus. The transverse spherocity estimator ($S_{{\rm O}}^{{\it p}_{\rm T}=1}$) categorizes