October 2023 arXiv papers — page 7

Erica Bertolini, Nicola Maggiore

In this paper we consider four dimensional (4D) linearized gravity (LG) with a planar boundary, where the most general boundary conditions are derived following Symanzik's approach. The boundary breaks diffeomorphism invariance and this results in a breaking of the corresponding Ward identity. From this, on the boundary we find two conserved currents which f

Sergei D. Odintsov, Diego Sáez-Chillón Gómez, German S. Sharov

The cosmological evolution within the framework of exponential $F(R)$ gravity is analysed by assuming two forms for dark matter: (a) a standard dust-like fluid and (b) an axion scalar field. As shown in previous literature, an axion-like field oscillates during the cosmological evolution but can play the role of dark matter when approaching the minimum of it

Mohamed R. Shoaib, Heba M. Emara, Jun Zhao

Food security, a global concern, necessitates precise and diverse data-driven solutions to address its multifaceted challenges. This paper explores the integration of AI foundation models across various food security applications, leveraging distinct data types, to overcome the limitations of current deep and machine learning methods. Specifically, we invest

Marvin Verstraete

The purpose of this paper is to develop a deformation theory controlled by pre-Lie algebras with divided powers over a ring of positive characteristic. We show that every differential graded pre-Lie algebra with divided powers comes with operations, called weighted braces, which we use to generalize the classical deformation theory controlled by Lie algebras

Roie Reshef, Anan Kabaha, Olga Seleznova, Dana Drachsler-Cohen

Neural networks are susceptible to privacy attacks. To date, no verifier can reason about the privacy of individuals participating in the training set. We propose a new privacy property, called local differential classification privacy (LDCP), extending local robustness to a differential privacy setting suitable for black-box classifiers. Given a neighborhoo

Y. H. Zheng, C. Wang, J. C. Cao

Traditionally, one can construct a waveguide by introduce defects into surface-wave photonic crystals (SPCs). Here we propose a new structure named closed SPC that can introduce waveguide modes out of photonic bandgap of surface-wave photonic crystal. In this paper, we have comprehensively analyzed dispersion relation, group velocity, normalized transmission

Benedikt Zerulla, Alejandro Luna Díaz, Christof Holzer, Carsten Rockstuhl

Second harmonic generation (SHG) is forbidden in centrosymmetric molecular materials. However, a signal is frequently observed from interfaces where the symmetry is broken. Whereas the effect can be phenomenologically accommodated, an ab initio qualitative and quantitative description has remained elusive, preventing the exploration of fascinating questions

Masahiro Asada, Safumi Suzuki

We analyze the terahertz detection characteristics of resonant tunneling diodes (RTDs) using a set of simple equations that covers three detection modes; (i) direct detection, (ii) amplified detection, and (iii) self-homodyne (coherent) detection. (i) and (ii) are based on the square-law detection, and (iii) is on the homodyne detection with the RTD used as

2 Collaboration, J. R. Batley, G. Kalmus, C. Lazzeroni

The NA48/2 experiment at CERN reports the first observation of the $K^{\pm} \rightarrow \pi^{0} \pi^{0} \mu^{\pm} \nu$ decay based on a sample of 2437 candidates with 15% background contamination collected in 2003--2004. The decay branching ratio in the kinematic region of the squared dilepton mass above $0.03$~GeV$^2/c^4$ is measured to be $(0.65 \pm 0.03)

Juntong Chen

In this paper, we consider robust nonparametric regression using deep neural networks with ReLU activation function. While several existing theoretically justified methods are geared towards robustness against identical heavy-tailed noise distributions, the rise of adversarial attacks has emphasized the importance of safeguarding estimation procedures agains

Binhui Xie, Shuang Li, Qingju Guo, Chi Harold Liu

Active learning, a label-efficient paradigm, empowers models to interactively query an oracle for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem from the sheer volume of point clouds, rendering annotation labor-intensive and cost-prohibitive. This paper presents Annotator, a general and efficient active learning baseline,

Shankara Narayanan, Sikha OK, Raul Benitez

Skin lesion segmentation plays a crucial role in the computer-aided diagnosis of melanoma. Deep Learning models have shown promise in accurately segmenting skin lesions, but their widespread adoption in real-life clinical settings is hindered by their inherent black-box nature. In domains as critical as healthcare, interpretability is not merely a feature bu

Jan Boroński, Henk Bruin, Przemysław Kucharski

Graph covers are a way to describe continuous maps (and homeomorphisms) of a Cantor set, more generally than e.g.\ Bratteli-Vershik systems. Every continuous map on a zero-dimensional compact set can be expressed by a graph cover (e.g.\ non-minimality or aperiodicty are no restrictions). We give a survey on the construction, properties and some special cases

Shan-Qi Duan, Qing-Wen Wang, Xue-Feng Duan

The application of eigenvalue theory to dual quaternion Hermitian matrices holds significance in the realm of multi-agent formation control. In this paper, we study the Rayleigh quotient iteration (RQI) for solving the right eigenpairs of dual quaternion Hermitian matrices. Combined with dual representation, the RQI algorithm can effectively compute the eige

Weizhu Xu, Qingbin Fan, Peicheng Lin, Jiarong Wang

Compared with conventional grating-based spectrometers, reconstructive spectrometers based on spectrally engineered filtering have the advantage of miniaturization because of the less demand for dispersive optics and free propagation space. However, available reconstructive spectrometers fail to balance the performance on operational bandwidth, spectral dive

Macarena Arenas, Karol Duda

We show that the Cohen--Lyndon property holds for all non-metric small-cancellation quotients. This generalises the analogous result from the metric small-cancellation setting, and answers a question asked by Lyndon in 1966 and by Wall in his 1979 problem list.

Woojun Kim, Yongjae Shin, Jongeui Park, Youngchul Sung

Deep reinforcement learning (RL) has achieved remarkable success in solving complex tasks through its integration with deep neural networks (DNNs) as function approximators. However, the reliance on DNNs has introduced a new challenge called primacy bias, whereby these function approximators tend to prioritize early experiences, leading to overfitting. To mi

Belle Collaboration, M. T. Prim, F. Bernlochner, F. Metzner

We measure the complete set of angular coefficients $J_i$ for exclusive $\bar{B} \to D^* \ell \bar{\nu}_\ell$ decays ($\ell = e, \mu$). Our analysis uses the full $711\,\mathrm{fb}^{-1}$ Belle data set with hadronic tag-side reconstruction. The results allow us to extract the form factors describing the $B \to D^*$ transition and the Cabibbo-Kobayashi-Maskaw

Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig

Non-conjugate Gaussian processes (NCGPs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, and are widely used in practice. However, exact inference in NCGPs is prohibitively expensive for large datasets, thus requiring approximations in practice. The approximation error adversely impacts the reliability of the mode

A Belotto da Silva, A Parusiński, L Rifford

Given a smooth totally nonholonomic distribution on a smooth manifold, we construct a singular distribution capturing essential abnormal lifts which is locally generated by vector fields with controlled divergence. Then, as an application, we prove the Sard Conjecture for rank 3 distribution in dimension 4 and generic distributions of corank 1.

Andrei Yu. Zaitsev

Let $X_1,\dots, X_n,\dots$ be i.i.d.\ $d$-dimensional random vectors with common distribution $F$. Then $S_n = X_1+\dots+X_n$ has distribution $F^n$ (degree is understood in the sense of convolution). Let $$ \rho_{\mathcal{C}_d}(F,G) = \sup_A |F\{A\} - G\{A\}|, $$ where the supremum is taken over all convex subsets of $\mathbb R^d$. Basic result is as follow

Luis A. Anchordoqui, Ignatios Antoniadis

We propose the possibility that compact extra dimensions can obtain large size by higher dimensional inflation, relating the weakness of the actual gravitational force to the size of the observable universe. Solution to the horizon problem implies that the fundamental scale of gravity is smaller than $10^{13}$ GeV which can be realised in a braneworld framew

Aurélien Deya

We exhibit various restrictions about the wellposedness of the Schr{\''o}dinger product $$\cl:z \longmapsto -\imath \int\_0^t e^{\imath s {\cop \partial^2\_x}}\big( z\_s\cdot \Psi\_s\big) ds $$ where $\Psi$ refers to the so-called linear solution of the stochastic Schr{\''o}dinger problem. We focus more specifically on the case where $\Psi$ satisfies \begin{

Santosh Palaskar, Vijay Ekambaram, Arindam Jati, Neelamadhav Gantayat

The efficiency of business processes relies on business key performance indicators (Biz-KPIs), that can be negatively impacted by IT failures. Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data. Forecasting Biz-KPIs in advance can enhance efficiency and revenue through proactive c

Hiroyasu Katsuno, Yuki Kimura, Tomoya Yamazaki, Ichigaku Takigawa

We study a machine learning (ML) technique for refining images acquired during in situ observation using liquid-cell transmission electron microscopy (LC-TEM). Our model is constructed using a U-Net architecture and a ResNet encoder. For training our ML model, we prepared an original image dataset that contained pairs of images of samples acquired with and w

Yun Wang, Ping He

In this study, we investigate how the baryonic effects vary with scale and local density environment mainly by utilizing a novel statistic, the environment-dependent wavelet power spectrum (env-WPS). With four state-of-the-art cosmological simulation suites, EAGLE, SIMBA, Illustris, and IllustrisTNG, we compare the env-WPS of the total matter density field b

Jeff Nijsse, Alan Litchfield

The widespread use of GitHub among software developers as a communal platform for coordinating software development has led to an abundant supply of publicly accessible data. Ever since the inception of Bitcoin, blockchain teams have incorporated the concept of open source code as a fundamental principle, thus making the majority of blockchain-based projects

T. Makino, S. Yusa, D. Oka, T. Fukumura

We determined the complex dielectric functions of $\varepsilon$-Ga$_2$O$_3$ using optical transmittance and reflectance spectroscopies at temperatures from 10 K to room temperature. The measured dielectric-function spectra reveal distinct structures at a bandgap energy. We fitted a model dielectric function based on the electronic energy-band structure to th

Jinheng Zhang, Haoyue Tang, Jintao Wang, Sastry Kompella

In this work, we consider a status update system with a sensor and a receiver. The status update information is sampled by the sensor and then forwarded to the receiver through a channel with non-stationary delay distribution. The data freshness at the receiver is quantified by the Age-of-Information (AoI). The goal is to design an online sampling strategy t

Jatin Arora, Sumit Agrawal, Pawan Goyal, Sayan Pathak

This paper presents a deep learning based approach to extract product comparison information out of user reviews on various e-commerce websites. Any comparative product review has three major entities of information: the names of the products being compared, the user opinion (predicate) and the feature or aspect under comparison. All these informing entities

Elvis Pillinen, Atri Halder, Ari T. Friberg, Tero Setälä

The concepts of geometric phase and wave-particle duality are interlinked to several fundamental phenomena in quantum physics, but their mutual relationship still forms an uncharted open problem. Here we address this question by studying the geometric phase of a photon in double-slit interference. We especially discover a general complementarity relation for

Manush Manju, Subhabrata Majumdar

We introduce the notion of a Bayesian analysis motivated `reliability' that gives a truer distinction of cusp-core and other halo-parameters (like mass-concentration) in an ensemble of observed galaxies. Our approach goes beyond the standard statistical techniques of parameter estimation and model fitting. We create hundreds of thousands of realistic mock SP

Ruxue Wen, Hangjie Yuan, Dong Ni, Wenbo Xiao

In medical image segmentation, domain generalization poses a significant challenge due to domain shifts caused by variations in data acquisition devices and other factors. These shifts are particularly pronounced in the most common scenario, which involves only single-source domain data due to privacy concerns. To address this, we draw inspiration from the s

Iñaki Echeverría-Huarte, Alexandre Nicolas

Robust agent-based models for pedestrian dynamics, which can predict the motion of pedestrians in various situations without specific adjustment of the model or its parameters, are highly desirable. But the modeller's task is challenging, in part because it mingles different types of processes (cognitive and mechanical ones) and different levels of descripti

Alireza Ataei

In this work, we study convection-diffusion equations in the cases of bounded drifts and drifts induced by the gradient of a potential. We define a new notion of solution and prove its existence and uniqueness. Furthermore, we show the conservation of mass, the convergence to the initial data, and the strong maximum principle.

Fuyuan Hu, Jian Zhang, Fan Lyu, Linyan Li

Few-shot class-incremental learning (FSCIL) aims to build machine learning model that can continually learn new concepts from a few data samples, without forgetting knowledge of old classes. The challenges of FSCIL lies in the limited data of new classes, which not only lead to significant overfitting issues but also exacerbates the notorious catastrophic fo

Tommaso Taddei, Xuejun Xu, Lei Zhang

We present a component-based model order reduction procedure to efficiently and accurately solve parameterized incompressible flows governed by the Navier-Stokes equations. Our approach leverages a non-overlapping optimization-based domain decomposition technique to determine the control variable that minimizes jumps across the interfaces between sub-domains

Alexandre Marthe, Aurélien Garivier, Claire Vernade

What are the functionals of the reward that can be computed and optimized exactly in Markov Decision Processes?In the finite-horizon, undiscounted setting, Dynamic Programming (DP) can only handle these operations efficiently for certain classes of statistics. We summarize the characterization of these classes for policy evaluation, and give a new answer for

A. Demir, M. M. A. Shames, O. N. Gerek, S. Ergin

The application of ionizing radiation for diagnostic imaging is common around the globe. However, the process of imaging, itself, remains to be a relatively hazardous operation. Therefore, it is preferable to use as low a dose of ionizing radiation as possible, particularly in computed tomography (CT) imaging systems, where multiple x-ray operations are perf

Jiaozi Wang, Jonas Richter, Mats H. Lamann, Robin Steinigeweg

We study statistical properties of matrix elements of observables written in the energy eigenbasis and truncated to small microcanonical windows. We present numerical evidence indicating that for all few body operators in chaotic many-body systems, truncated below certain energy scale, collective statistical properties of matrix elements exhibit emergent uni

Shengwen Xie, Xuezhi Zhao

The Whitney disks play a central role in defining Heegaard Floer homology of a $3$-dimensional manifold. We use Nielsen theory to a simple criterion to the existence of Whitney disks, connecting two given intersections.

Aleksandra Huczek, Andrzej Wiśnicki

Let $D\subset \mathbb{R}^{n}$ be a bounded convex domain and $F:D\rightarrow D$ a $1$-Lipschitz mapping with respect to the Hilbert metric $d$ on $D$ satisfying condition $d(sx+(1-s)y,sz+(1-s)w)\leq \max \{d(x,z),d(y,w) \}$. We show that if $F$ does not have fixed points, then the convex hull of the accumulation points (in the norm topology) of the family $\

K. Trachenko

Our understanding of the three basic states of matter (solids, liquids and gases) is based on temperature and pressure phase diagrams with three phase transition lines: solid-gas, liquid-gas and solid-liquid lines. There are analytical expressions $P(T)$ for the first two lines derived on a purely general-theoretical thermodynamic basis. In contrast, there e

Haifa Alrdahi, Riza Batista-Navarro

Learning chess strategies has been investigated widely, with most studies focussing on learning from previous games using search algorithms. Chess textbooks encapsulate grandmaster knowledge, explain playing strategies and require a smaller search space compared to traditional chess agents. This paper examines chess textbooks as a new knowledge source for en

Fang Sun, Shengwen Xie, Xuezhi Zhao

In this work, we present a generalization of extended persistent homology to filtrations of graded sub-groups by defining relative homology in this setting. Our work provides a more comprehensive and flexible approach to get an algebraic invariant overcoming the limitations of the standard approach. The main contribution of our work is the development of a s

Seunghun Lee, Jaewon Chu, Sihyeon Kim, Juyeon Ko

Bayesian optimization is a powerful method for optimizing black-box functions with limited function evaluations. Recent works have shown that optimization in a latent space through deep generative models such as variational autoencoders leads to effective and efficient Bayesian optimization for structured or discrete data. However, as the optimization does n

Christoph Aistleitner, Lorenz Frühwirth, Joscha Prochno

It is a classical observation that lacunary function systems exhibit many properties which are typical for systems of independent random variables. However, it had already been observed by Erd\H{o}s and Fortet in the 1950s that probability theory's limit theorems may fail for lacunary sums $\sum f(n_k x)$ if the sequence $(n_k)_{k \geq 1}$ has a strong arith

Yutong Quan, Xintong Wu, Wanlin Deng, Luyao Zhang

Blockchain technology is leading a revolutionary transformation across diverse industries, with effective governance being critical for the success and sustainability of blockchain projects. Community forums, pivotal in engaging decentralized autonomous organizations (DAOs), significantly impact blockchain governance decisions. Concurrently, Natural Language

Tao Yang, Tianyuan Shi, Fanqi Wan, Xiaojun Quan

Recent advances in large language models (LLMs), such as ChatGPT, have showcased remarkable zero-shot performance across various NLP tasks. However, the potential of LLMs in personality detection, which involves identifying an individual's personality from their written texts, remains largely unexplored. Drawing inspiration from Psychological Questionnaires,

Stéphane Goria

The 20th century saw the emergence of an intermediate level of consideration, situated between the tactical and strategic levels: the operational level. This level of scale is that of a large area of operations, that is to say coordinated engagements bringing together forces belonging to different corps over a considerable geographical distance. In this arti

A. M. Mukhamedzhanov, R. J. deBoer, B. F. Irgaziev, L. D. Blokhintsev

The $^{12}{\rm C}(\alpha,\gamma)^{16}$O reaction, determining the survival of carbon in red giants, is of interest for nuclear reaction theory and nuclear astrophysics. A specific feature of the $^{16}$O nuclear structure is the presence of two subthreshold bound states, (6.92 MeV, 2$^+$) and (7.12 MeV, 1$^-$), that dominate the behavior of the low-energy $S

Pedro Marote, Marie Martin, Anne Bonhomme, Pierre Lantéri

The reverse engineering of a complex mixture, regardless of its nature, has become significant today. Being able to quickly assess the potential toxicity of new commercial products in relation to the environment presents a genuine analytical challenge. The development of digital tools (databases, chemometrics, machine learning, etc.) and analytical technique

Gilles Dowek, Alexandre Miquel

We show how to express intuitionistic Zermelo set theory in deduction modulo (i.e. by replacing its axioms by rewrite rules) in such a way that the corresponding notion of proof enjoys the normalization property. To do so, we first rephrase set theory as a theory of pointed graphs (following a paradigm due to P. Aczel) by interpreting set-theoretic equality

Gerhard Jung, Giulio Biroli, Ludovic Berthier

We develop a transferable machine learning model which predicts structural relaxation from amorphous supercooled liquid structures. The trained networks are able to predict dynamic heterogeneity across a broad range of temperatures and time scales with excellent accuracy and transferability. We use the network transferability to predict dynamic heterogeneity

Yingjie Zhou, Yaodong Chen, Kaiyue Bi, Lian Xiong

With the rapid development of artificial intelligence (AI), digital humans have attracted more and more attention and are expected to achieve a wide range of applications in several industries. Then, most of the existing digital humans still rely on manual modeling by designers, which is a cumbersome process and has a long development cycle. Therefore, facin

Gaichao Li, Jinsong Chen, John E. Hopcroft, Kun He

Graph pooling methods have been widely used on downsampling graphs, achieving impressive results on multiple graph-level tasks like graph classification and graph generation. An important line called node dropping pooling aims at exploiting learnable scoring functions to drop nodes with comparatively lower significance scores. However, existing node dropping

Qingqing Zhao, Peizhuo Li, Wang Yifan, Olga Sorkine-Hornung

Creating believable motions for various characters has long been a goal in computer graphics. Current learning-based motion synthesis methods depend on extensive motion datasets, which are often challenging, if not impossible, to obtain. On the other hand, pose data is more accessible, since static posed characters are easier to create and can even be extrac

Gilles Dowek, Alexandre Miquel

G{\"o}del's second incompleteness theorem forbids to prove, in a given theory U, the consistency of many theories-in particular, of the theory U itself-as well as it forbids to prove the normalization property for these theories, since this property implies their consistency. When we cannot prove in a theory U the consistency of a theory T , we can try to pr

V. O. Galkin, E. M. Savchenko

Masses of the ground, orbitally and radially excited states of the asymmetric fully heavy tetraquarks, composed of charm (c) and bottom (b) quarks and antiquarks are calculated in the relativistic diquark-antidiquark picture. The relativistic quark model based on the quasipotential approach and quantum chromodynamics is used to construct the quasipotentials

Nuo Chen, Zinan Zheng, Ning Wu, Ming Gong

Existing research predominantly focuses on developing powerful language learning models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in preserving efficacy in a multilingual context. To bridge this gap, this paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. Firstly, by utilizing tr

Yuni Iwamasa, Yusuke Kobayashi, Kenjiro Takazawa

The problem of finding a maximum $2$-matching without short cycles has received significant attention due to its relevance to the Hamilton cycle problem. This problem is generalized to finding a maximum $t$-matching which excludes specified complete $t$-partite subgraphs, where $t$ is a fixed positive integer. The polynomial solvability of this generalized p

Zhoufei Liu, Jiping Huang

Recent studies have identified plasma as a topological material. Yet, these researches often depict plasma as a fluid governed by electromagnetic fields, i.e., a classical wave system. Indeed, plasma transport can be characterized by a unique diffusion process distinguished by its collective behaviors. In this work, we adopt a simplified diffusion-migration

Maria R. Kodenko, Yuriy A. Vasilev, Nicholas S. Kulberg, Andrey V. Samorodov

Background and objective: CTA is a gold standard of preoperative diagnosis of abdominal aorta and typically used for geometric-only characteristic extraction. We assume that a model describing the dynamic behavior of the contrast agent in the vessel can be developed from the data of routine CTA studies, allowing the procedure to be investigated and optimized

Tao Liu, Bingyang Wang, JiangTao Zhao, Maik Kahnt

Ptychography has become prominent at synchrotron facilities worldwide for characterizing biological and material specimens' topological structures and properties at the nanometer or atomic scale, due to its lens - less, highly quantitative phase imaging. Its high - resolution imaging depends on accurate lateral scan position info, a large overlap ratio, and

Zhaolong Ning, Tengfeng Li, Yu Wu, Xiaojie Wang

With the continuous development of Intelligent Reflecting Surfaces (IRSs) and Unmanned Aerial Vehicles (UAVs), their combination has become foundational technologies to complement the terrestrial network by providing communication enhancement services for large-scale users. This article provides a comprehensive overview of IRS-assisted UAV communications for

Jonathon Cottom, Qiong Cai, Emilia Olsson

An ever present challenge for Li-ion batteries is the formation of metallic dendrites on cycling that dramatically reduces cycle life and leads to the untimely failure of the cell. In this work we investigate the modes of Li-cluster formation on pristine and defective graphene. Firstly, we demonstrate that on a defect free surface the cluster formation is im

Wei Zhao, Yijun Wang, Tianyu He, Lianying Yin

The creation of lifelike speech-driven 3D facial animation requires a natural and precise synchronization between audio input and facial expressions. However, existing works still fail to render shapes with flexible head poses and natural facial details (e.g., wrinkles). This limitation is mainly due to two aspects: 1) Collecting training set with detailed 3

Minquan Cheng, Kai Wan, Petros Elia, Giuseppe Caire

This paper studies a multiaccess coded caching (MACC) where the connectivity topology between the users and the caches can be described by a class of combinatorial designs. Our model includes as special cases several MACC topologies considered in previous works. The considered MACC network includes a server containing $N$ files, $\Gamma$ cache nodes and $K$

Yanir Maymon, Israel Nelken, Boaz Rafaely

Speaker localization for binaural microphone arrays has been widely studied for applications such as speech communication, video conferencing, and robot audition. Many methods developed for this task, including the direct path dominance (DPD) test, share common stages in their processing, which include transformation using the short-time Fourier transform (S

Manoj Changat, Jeny Jacob, Lekshmi Kamal K. Sheela, Iztok Peterin

A walk $W=w_1w_2\dots w_k$, $k\geq 2$, is called a toll walk if $w_1\neq w_k$ and $w_2$ and $w_{k-1}$ are the only neighbors of $w_1$ and $w_k$, respectively, on $W$ in a graph $G$. A toll walk interval $T(u,v)$, $u,v\in V(G)$, contains all the vertices that belong to a toll walk between $u$ and $v$. The toll walk intervals yield a toll walk transit function

Fei Cheng, Masayuki Asahara, Ichiro Kobayashi, Sadao Kurohashi

Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i.e. event, time, and document creation time (DCT). It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information. 2) Existing models with independent classifiers for each TLINK category (E2E,

Yi-Huang Shen, Guangjun Zhu

In this article, we study the powers of the generalized binomial edge ideal $\mathcal{J}_{K_m,P_n}$ of a path graph $P_n$. We explicitly compute their regularities and determine the limit of their depths. We also show that these ordinary powers coincide with their symbolic powers. Additionally, we study the Rees algebra and the special fiber ring of $\mathca

Gang Zhang, Junnan Chen, Guohuan Gao, Jianmin Li

3D object detection in point clouds is important for autonomous driving systems. A primary challenge in 3D object detection stems from the sparse distribution of points within the 3D scene. Existing high-performance methods typically employ 3D sparse convolutional neural networks with small kernels to extract features. To reduce computational costs, these me

Erwan Weckenmann, Thyago Monteiro, Uiara Celine de Moura, Francesco Da Ros

The flatness of electro-optic frequency combs (EOFCs) generated in a single silicon ring resonator modulator (RRM) is optimized by employing harmonic superposition of the radio-frequency driving signal. A differential evolution algorithm is employed in conjunction with a simplified model of the RRM for offline optimization of the amplitudes and phases of har

Ari Rappoport

I present a theory of Alzheimer's Disease (AD) that explains its symptoms, pathology, and risk factors. To do this, I introduce a new theory of brain plasticity that elucidates the physiological roles of AD-related agents. New events generate synaptic and branching candidates competing for long-term enhancement. Competition resolution crucially depends on th

Peishan Dai, Yun Shi, Tong Xiong, Xiaoyan Zhou

Diagnosis of major depressive disorder (MDD) primarily relies on the patient's self-reported symptoms and a clinical evaluation. Effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI) analysis can reflect the directionality of connections between brain regions, making it a candidate method to classify MDD. This study u

Zhuang Xiong, Yaoping Hou

In this paper we consider the eigenvalues and the Seidel eigenvalues of a chain graph. An$\dbar$eli\'{c}, da Fonseca, Simi\'{c}, and Du \cite{andelic2020tridiagonal} conjectured that there do not exist non-isomorphic cospectral chain graphs with respect to the adjacency spectrum. Here we disprove this conjecture. Furthermore, by considering the relation betw

M. V. Bastrakova, V. O. Munyaev

An analytical theory to calculate the dissipatively stable concurrence in the system of two coupled flux superconducting qubits in the strong driving field is developed. The conditions for the entanglement state generation and destruction during the formation of the multiphoton transitions regions due to the interference of Landau--Zener--St\"uckelberg--Majo

Ryohei Fukuma, Kei Majima, Yoshinobu Kawahara, Okito Yamashita

Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic oscillatory components (DMs). DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features. However, such kernel-based machine learning algorithms have three limitations: large computational time preventing re

Nguyen Quang Hieu, Dinh Thai Hoang, Diep N. Nguyen, Mohammad Abu Alsheikh

The ability to sense, localize, and estimate the 3D position and orientation of the human body is critical in virtual reality (VR) and extended reality (XR) applications. This becomes more important and challenging with the deployment of VR/XR applications over the next generation of wireless systems such as 5G and beyond. In this paper, we propose a novel f

Lei Lei, Aimin Tang, Xudong Wang

Wireless mesh networks play a critical role in enabling key networking scenarios in beyond-5G (B5G) and 6G networks, including integrated access and backhaul (IAB), multi-hop sidelinks, and V2X. However, it still poses a challenge to deliver scalable per-node throughput via mesh networking, which significantly limits the potential of large-scale deployment o

Shikui Shang

In this paper, we study the value sets of non-permutation polynomial functions over the residue class ring $\mathbb{Z}/m\mathbb{Z}$. When $m=p^r$ is a power of some prime $p$, an upper bound is given for the size of the value set of a polynomial function which is not a permutation. We also show that this upper bound can be achieved by some integral polynomia

Mihran Miroyan, Shiny Weng, Rahul Shah, Lisa Yan

In today's rapidly evolving educational landscape, traditional modes of passive information delivery are giving way to transformative pedagogical approaches that prioritize active student engagement. Within the context of large-scale hybrid classrooms, the challenge lies in fostering meaningful and active interaction between students and course content. This

Yu Hao, Fan Yang, Hao Huang, Shuaihang Yuan

People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards on their own. In this paper, we present a pioneering appr

Rajesh Singh, Anamika Kumari

In order to estimate the population mean in the presence of both non-response and measurement errors that are uncorrelated, the paper presents some novel estimators employing ranked set sampling by utilizing auxiliary information.Up to the first order of approximation, the equations for the bias and mean squared error of the suggested estimators are produced

Ziyue Li, Hao Yan, Chen Zhang, Lijun Sun

Passenger clustering based on trajectory records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical structure of the passenger trip information, including multiple trips within each passenger and multi-dimensional information about each trip. Furthermore, existing approaches rely

Maoxiang Sun, Weilong Ding, Tianpu Zhang, Zijian Liu

As the development of cities, traffic congestion becomes an increasingly pressing issue, and traffic prediction is a classic method to relieve that issue. Traffic prediction is one specific application of spatio-temporal prediction learning, like taxi scheduling, weather prediction, and ship trajectory prediction. Against these problems, classical spatio-tem

Xin-Hua Ma

In the previous work, the traditional flavor-related additive quantum numbers were substituted by the fermion quantum number and the unit electroweak charge, which are, same as the electric charge, conserved in electromagnetic interaction, weak interaction and strong interaction. The empirical selection rules in electroweak interaction were found that they c

Oem Trivedi, Maxim Khlopov, Jackson Levi Said, Rafael C. Nunes

The pursuit of understanding the mysteries surrounding dark energy has sparked significant interest within the field of cosmology. While conventional approaches, such as the cosmological constant, have been extensively explored, alternative theories incorporating scalar field-based models and modified gravity have emerged as intriguing avenues. Among these,

Prohrak Kruengthomya, Dmitry Berdinsky

This paper looks at the class of groups admitting normal forms for which the right multiplication by a group element is computed in linear time on a multi-tape Turing machine. We show that the groups $\mathbb{Z}_2 \wr \mathbb{Z}^2$, $\mathbb{Z}_2 \wr \mathbb{F}_2$ and Thompson's group $F$ have normal forms for which the right multiplication by a group elemen

Yusuke Ide, Akihiro Narimatsu

In this paper, we consider a spectral analysis of the Correlated Random Walk (CRW) on the path. We apply an analytical method for the Quantum Walk to CRW. For the isospectral coin cases, we obtain all of the eigenvalues and the corresponding eigenvectors of the time evolution operator of CRW, and also obtain the limiting distribution.

Gaurav Bhatnagar, Archna Kumari, Michael J. Schlosser

We provide elliptic extensions of elementary identities such as the sum of the first $n$ odd or even numbers, the geometric sum and the sum of the first $n$ cubes. Many such identities, and their $q$-analogues, are indefinite sums, and can be obtained from telescoping. So we used telescoping in our study to find elliptic extensions of these identities. In th

Liyilei Su, Xumin Zuo, Rui Li, Xin Wang

The emergence of deep learning has yielded noteworthy advancements in time series forecasting (TSF). Transformer architectures, in particular, have witnessed broad utilization and adoption in TSF tasks. Transformers have proven to be the most successful solution to extract the semantic correlations among the elements within a long sequence. Various variants

Supriyo Ghosh, Pijush K. Ghosh

We consider equal-mass quantum Toda lattice with balanced loss-gain for two and three particles. The two-particle Toda lattice is integrable and two integrals of motion which are in involution have been found. The bound-state energy and the corresponding eigenfunctions have been obtained numerically for a few low-lying states. The three-particle quantum Toda

Rik Ghosh, Bibhas Chakraborty, Inbal Nahum-Shani, Megan E. Patrick

In a sequential multiple-assignment randomized trial (SMART), a sequence of treatments is given to a patient over multiple stages. In each stage, randomization may be done to allocate patients to different treatment groups. Even though SMART designs are getting popular among clinical researchers, the methodologies for adaptive randomization at different stag

Cameron R. Jones, Benjamin K. Bergen

We evaluated GPT-4 in a public online Turing test. The best-performing GPT-4 prompt passed in 49.7% of games, outperforming ELIZA (22%) and GPT-3.5 (20%), but falling short of the baseline set by human participants (66%). Participants' decisions were based mainly on linguistic style (35%) and socioemotional traits (27%), supporting the idea that intelligence

Ju-Hyung Lee, Chanyoung Park, Soohyun Park, Andreas F. Molisch

This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagation delays in low-Earth orbit (LEO) satellite networks' HO procedures. DHO skips the Measurement Report (MR) in the HO procedure by leveraging its predictive capabilities after being

Matías Gárate, Paola Pinilla, Thomas J. Haworth, Stefano Facchini

The dust in planet-forming disks evolve rapidly through growth and radial drift, and external photoevaporation also contributes to this evolution in massive star-forming regions. We test whether the presence of substructures can explain the survival of the dust component and observed millimeter continuum emission in protoplanetary disks located within massiv

Yure M. I. A. Rodrigues, Matheus F. V. Oliveira, Andre M. C. Souza, Marcelo L. Lyra

The time evolution of a topological Su-Schrieffer-Heeger chain is analyzed through the statistics of speckle patterns. The emergence of topological edge states dramatically affects the dynamical fluctuations of the wavefunction. The intensity statistics is found to be described by a family of noncentral chi-squared distributions, with the noncentrality param

Zhiyuan Wei, Xianhao Zhang, Jing Sun, Zijian Zhang

Blockchain smart contracts have emerged as a transformative force in the digital realm, spawning a diverse range of compelling applications. Since solidity smart contracts across various domains manage trillions of dollars in virtual coins, they become a prime target for attacks. One of the primary challenges is keeping abreast of the latest techniques and t