May 2024 arXiv papers — page 94

Bartosz Grygielski, Hiren Kakkad, Piotr Kotko

We recently derived a new action for gluodynamics by canonically transforming the Yang-Mills action on light-cone. The transformation elimated triple gluons vertices and replaced the gauge fields with Wilson lines. This greatly reduced the number of diagrams required to compute tree level amplitudes. However, at the quantum level, the action turned out to be

Wentao Ye, Jiaqi Hu, Liyao Li, Haobo Wang

The rapid advancements of Large Language Models (LLMs) tightly associate with the expansion of the training data size. However, the unchecked ultra-large-scale training sets introduce a series of potential risks like data contamination, i.e. the benchmark data is used for training. In this work, we propose a holistic method named Polarized Augment Calibratio

Lijia Ding

We investigate the $p$-essential normality of Hilbert quotient submodules on a relatively compact smooth strongly pseudoconvex domain in a complex manifold satisfying Property (S). For analytic subvarieties that have compact singularities and transversely intersect the strongly pseudoconvex boundary, we prove that the corresponding Bergman-Sobolev quotient s

Yiqing Xu, Jiayuan Mao, Yilun Du, Tomas Lozáno-Pérez

This paper studies the challenge of developing robots capable of understanding under-specified instructions for creating functional object arrangements, such as "set up a dining table for two"; previous arrangement approaches have focused on much more explicit instructions, such as "put object A on the table." We introduce a framework, SetItUp, for learning

Julianna Bor, Peter G Harrison

Join-the-Shortest-Queue (JSQ) is the scheduling policy of choice for many network providers, cloud servers and traffic management systems, where individual queues are served under processor sharing (PS) queueing discipline. A numerical solution for the response time distribution in two parallel PS queues with JSQ scheduling is derived for the first time. Usi

Garry Goldstein

In this work we present a new method for basis set generation for electronic structure calculations of crystalline solids. This procedure is aimed at applications to Density Functional Theory (DFT). In this construction, Energy Window Augmented Plane Waves (EWAPW), we take advantage of the fact that most DFT calculations use a convergence loop in order to ob

Weisong Dong, Jinling Niu, Nadilamu Nizhamuding

In this paper, we study the Dirichlet problem for Monge-Amp\`ere type equations for $p$-plurisubharmonic functions on Riemannian manifolds. The $a$ $priori$ estimates up to the second order derivatives of solutions are established. The existence of a solution then follows by the continuity method.

Aiman Rauf, SK Firoz Islam

Volkov-Pankratov states are nontopological massive bound states which generally arise across the smooth interface between two adjacent regions of a two-band semimetal, over which a gap parameter changes sign smoothly. In this work, we show that these modes can be engineered even for a generic smooth interface without any sign inversion. We consider threefold

Alexander Kent, Thomas B. Berrett, Yi Yu

Most of the literature on differential privacy considers the item-level case where each user has a single observation, but a growing field of interest is that of user-level privacy where each of the $n$ users holds $T$ observations and wishes to maintain the privacy of their entire collection. In this paper, we derive a general minimax lower bound, which sho

Renchi Yang, Yidu Wu, Xiaoyang Lin, Qichen Wang

Attributed bipartite graphs (ABGs) are an expressive data model for describing the interactions between two sets of heterogeneous nodes that are associated with rich attributes, such as customer-product purchase networks and author-paper authorship graphs. Partitioning the target node set in such graphs into k disjoint clusters (referred to as k-ABGC) finds

Jiayue Liu, Xiao Tang, Freeman Cheng, Roy Yang

3D Gaussian Splatting showcases notable advancements in photo-realistic and real-time novel view synthesis. However, it faces challenges in modeling mirror reflections, which exhibit substantial appearance variations from different viewpoints. To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time r

Joel Kostensalo, Eligio Lisi, Antonio Marrone, Jouni Suhonen

We analyze the $^{115}$In $\beta$-decay energy spectrum through the spectral moment method (SMM), previously introduced in the context of $^{113}$Cd $\beta$ decay. The spectral moments $\mu_n$ are defined as averaged $n^{\rm th}$ powers of the $\beta$ particle energy, characterizing the spectrum normalization ($n=0$) and shape ($n\geq 1$) above a given thres

Jan-Christoph Klie, Juan Haladjian, Marc Kirchner, Rahul Nair

Annotated datasets are an essential ingredient to train, evaluate, compare and productionalize supervised machine learning models. It is therefore imperative that annotations are of high quality. For their creation, good quality management and thereby reliable quality estimates are needed. Then, if quality is insufficient during the annotation process, recti

Zi-Qi Tang, Xi-Wen Zhou, Yin-Fu Jin, Zhen-Yu Yin

Two primary types of numerical instabilities often occur in low-order finite element method (FEM) analyses of thermo-hydro-mechanical (THM) phenomena: (1) pressure oscillations arising improper interpolation of pressure and displacement fields; and (2) spatial oscillations induced by nonlinear convection terms in convection-dominated scenarios. In response t

Pakanun Dokyeesun, Sandi Klavžar, Jing Tian

Let ${\rm gp}(G)$ be the general position number of a graph $G$. It is proved that ${\rm gp}(G-x)\leq 2{\rm gp}(G)$ holds for any vertex $x$ of a connected graph $G$ and that if $x$ lies in some ${\rm gp}$-set of $G$, then ${\rm gp}(G) - 1 \le {\rm gp}(G-x)$. Constructions are given which show that ${\rm gp}(G-x)$ can be much larger than ${\rm gp}(G)$ also w

Naeimeh Mohseni, Thomas Morstyn, Corey O'Meara, David Bucher

The formation of energy communities is pivotal for advancing decentralized and sustainable energy management. Within this context, Coalition Structure Generation (CSG) emerges as a promising framework. The complexity of CSG grows rapidly with the number of agents, making classical solvers impractical for even moderate sizes. This suggests CSG as an ideal can

Zhipeng Wan, Anda Cheng, Yinggui Wang, Lei Wang

The widespread adoption of large language models (LLMs) has raised concerns regarding data privacy. This study aims to investigate the potential for privacy invasion through input reconstruction attacks, in which a malicious model provider could potentially recover user inputs from embeddings. We first propose two base methods to reconstruct original texts f

Andrea Tonini, Francesco Regazzoni, Matteo Salvador, Luca Dede'

Cardiocirculatory mathematical models serve as valuable tools for investigating physiological and pathological conditions of the circulatory system. To investigate the clinical condition of an individual, cardiocirculatory models need to be personalized by means of calibration methods. In this study we propose a new calibration method for a lumped-parameter

Yiheng Xiong, Angela Dai

Generating 3D shapes from single RGB images is essential in various applications such as robotics. Current approaches typically target images containing clear and complete visual descriptions of the object, without considering common realistic cases where observations of objects that are largely occluded or truncated. We thus propose a transformer-based auto

Sizhe Li, Yiming Qin, Minghang Zheng, Xin Jin

When editing a video, a piece of attractive background music is indispensable. However, video background music generation tasks face several challenges, for example, the lack of suitable training datasets, and the difficulties in flexibly controlling the music generation process and sequentially aligning the video and music. In this work, we first propose a

Chen Huang, Yiping Jin, Ilija Ilievski, Wenqiang Lei

Human annotation is a time-consuming task that requires a significant amount of effort. To address this issue, interactive data annotation utilizes an annotation model to provide suggestions for humans to approve or correct. However, annotation models trained with limited labeled data are prone to generating incorrect suggestions, leading to extra human corr

Junghun Kim, Ka Hyun Park, Hoyoung Yoon, U Kang

Given an edge-incomplete graph, how can we accurately find the missing links? The link prediction in edge-incomplete graphs aims to discover the missing relations between entities when their relationships are represented as a graph. Edge-incomplete graphs are prevalent in real-world due to practical limitations, such as not checking all users when adding fri

Frederick W Young, Umair Ahmed, Nilanjan Chakraborty

The influence of equivalence ratio on preferential diffusion effects and the resulting changes in the distributions of major species and their reaction rates have been analysed based on 2D simulations of lean ${\mathrm{H_2}}$-air laminar premixed flames, at $\phi=0.4$ and $0.7$. The enhancements of burning rate, flame surface area, and stretch factor increas

Islam M. Tanash, Risto Wichman

This paper proposes a three-dimensional (3D) satellite-terrestrial communication network assisted with reconfigurable intelligent surfaces (RISs). Using stochastic geometry models, we present an original framework to derive tractable yet accurate closed-form expressions for coverage probability and ergodic capacity in the presence of fading. A homogeneous Po

Mindaugas Juodėnas, Nadzeya Khinevich, Gvidas Klyvis, Joel Henzie

We demonstrate a surface lattice resonance (SLR)-based plasmonic nanolaser that leverages bulk production of colloidal nanoparticles and assembly on templates with single particle resolution. SLRs emerge from the hybridization of the plasmonic and photonic modes when nanoparticles are arranged into periodic arrays and this can provide feedback for stimulated

Ramansh Sharma, Varun Shankar

We present a novel deep operator network (DeepONet) architecture for operator learning, the ensemble DeepONet, that allows for enriching the trunk network of a single DeepONet with multiple distinct trunk networks. This trunk enrichment allows for greater expressivity and generalization capabilities over a range of operator learning problems. We also present

Hanaa Zitane, Delfim F. M. Torres

This work deals with the finite time stability of generalized proportional fractional systems with time delay. First, based on the generalized proportional Gr\"onwall inequality, we derive an explicit criterion that enables the system trajectories to stay within a priori given sets during a pre-specified time interval, in terms of the Mittag-Leffler function

Leonarc Michelle Santos, Vince Angelo A. Chavez, Denny Lane B. Sombillo

We probed the pole structure of the $P_\psi^{N}(4312)^{+}$ using a trained deep neural network. The training dataset was generated using uniformized independent S-matrix poles to ensure that the obtained interpretation is as model-independent as possible. To prevent possible ambiguity in the interpretation of the pole structure, we included the contribution

Jaewon Son, Jaehun Park, Kwangsu Kim

Video summarization aims to generate a concise representation of a video, capturing its essential content and key moments while reducing its overall length. Although several methods employ attention mechanisms to handle long-term dependencies, they often fail to capture the visual significance inherent in frames. To address this limitation, we propose a CNN-

Tom Roth, Inigo Jauregi Unanue, Alsharif Abuadbba, Massimo Piccardi

Text classifiers are vulnerable to adversarial examples -- correctly-classified examples that are deliberately transformed to be misclassified while satisfying acceptability constraints. The conventional approach to finding adversarial examples is to define and solve a combinatorial optimisation problem over a space of allowable transformations. While effect

Sushmita Sarker, Prithul Sarker, Gunner Stone, Ryan Gorman

Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future

Donghyeon Kim

In this paper, we develop a theory of diminished multiplier ideals on singular varieties which was introduced by Hacon, and developed by Lehmann. We prove a result regarding the termination of certain type of flips with scaling of an ample divisor if the Cartier index is bounded, and if $\kappa_{\sigma}(K_X+\Delta)\ge \dim X-1$ holds. The proof uses a theory

Tianyun Lin, Yongkang Ju, Haoyuan Zhong, Xiangyu Zeng

Topological Dirac nodal-line semimetals host topologically nontrivial electronic structure with nodal-line crossings around the Fermi level, which could affect the photocarrier dynamics and lead to novel relaxation mechanisms. Herein, by using time- and angle-resolved photoemission spectroscopy, we reveal the previously-inaccessible linear dispersions of the

Xian Liao, Sagbo Marcel Zodji

We first show local-in-time well-posedness of the compressible Navier-Stokes equations, assuming striated regularity while no other smoothness or smallness conditions on the initial density. With these local-in-time solutions served as blocks, for \textit{less} regular initial data where the vacuum is permitted, the global-in-time well-posedness follows from

J. D. Turner, B. W. Stappers, E. Carli, E. D. Barr

We present the description and initial results of the TRAPUM (TRAnsients And PUlsars with MeerKAT) search for pulsars associated with supernova remnants (SNRs), pulsar wind nebulae and unidentified TeV emission. The list of sources to be targeted includes a large number of well-known candidate pulsar locations but also new candidate SNRs identified using a r

Kevin Schultz, Christopher A. Watson, Andrew J. Murphy, Timothy M. Sweeney

Qubit noise spectroscopy (QNS) is a valuable tool for both the characterization of a qubit's environment and as a precursor to more effective qubit control to improve qubit fidelities. Existing approaches to QNS are what the classical spectrum estimation literature would call "non-parametric" approaches, in that a series of probe sequences are used to estima

Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera, Muhammad Imran

During times of crisis, social media platforms play a crucial role in facilitating communication and coordinating resources. In the midst of chaos and uncertainty, communities often rely on these platforms to share urgent pleas for help, extend support, and organize relief efforts. However, the overwhelming volume of conversations during such periods can esc

Ke Wang, Yifei Ge, Tapas Baug

Filamentary structure is important for the ISM and star formation. Galactic distribution of filaments may regulate the star formation rate in the Milky Way. However, interstellar filaments are intrinsically complex, making it difficult to study quantitatively. Here, we focus on linear filaments, the simplest morphology that can be treated as building blocks

Yanlei Yin, Lihua Wang, Dinh Thai Hoang, Wenbo Wang

In the process industry, long-term and efficient optimization of production lines requires real-time monitoring and analysis of operational states to fine-tune production line parameters. However, complexity in operational logic and intricate coupling of production process parameters make it difficult to develop an accurate mathematical model for the entire

Kounianhua Du, Jizheng Chen, Jianghao Lin, Menghui Zhu

Recommender models play a vital role in various industrial scenarios, while often faced with the catastrophic forgetting problem caused by the fast shifting data distribution. To alleviate this problem, a common approach is to reuse knowledge from the historical data. However, preserving the vast and fast-accumulating data is hard, which causes dramatic stor

Arcadia John Fegebank, Sergei M. Kuzenko

There are several approaches to formulate gauge-invariant models for massive integer-spin fields in $d$ dimensions including the following: (i) in terms of symmetric tensor fields $\phi_{\mu_1 \dots \mu_k} $, with $k = s, s-1, \dots , 0$, restricted to be double traceless for $k\geq 4$; and (ii) in terms of a quartet of $traceful$ symmetric tensor fields $\p

Takahiro Shindo, Yui Tatsumi, Taiju Watanabe, Hiroshi Watanabe

Scalable image coding for both humans and machines is a technique that has gained a lot of attention recently. This technology enables the hierarchical decoding of images for human vision and image recognition models. It is a highly effective method when images need to serve both purposes. However, no research has yet incorporated the post-processing commonl

Hanbo Xiao, Han Gao, Min Li, Fanqiang Chen

Over the years, great efforts have been devoted in introducing a sizable and tunable band gap in graphene for its potential application in next-generation electronic devices. The primary challenge in modulating this gap has been the absence of a direct method for observing changes of the band gap in momentum space. In this study, we employ advanced spatial-

Pratik Sahu, Jatin Kumar Bidika, Bubunu Biswal, S. Satpathy

We demonstrate the formation of orbital and spin Hall effects (OHE/SHE) in the 1T phase of non-magnetic transition metal dichalcogenides. With the aid of density functional theory calculations and model Hamiltonian studies on MX$_2$ (M = Pt, Pd and X = S, Se, and Te), we show an intrinsic orbital Hall conductivity ($\sim 10^3 \hbar /e\ \Omega^{-1}cm^{-1}$) ,

Zijian Feng, Hanzhang Zhou, Zixiao Zhu, Junlang Qian

Prompts play a crucial role in guiding the responses of Large Language Models (LLMs). However, the intricate role of individual tokens in prompts, known as input saliency, in shaping the responses remains largely underexplored. Existing saliency methods either misalign with LLM generation objectives or rely heavily on linearity assumptions, leading to potent

Mark Smith

Flavour changing neutral currents are suppressed in the Standard Model, making them a prime avenue to search for new physics. Although several measurements of the decay $B^{0}\to{}K^{\ast{}0}\mu^{+}\mu^{-}$ have shown deviations from the Standard Model expectations, long-distance contributions may be imitating new physics. Here, the first amplitude analysis

Soh Kumabe, Yuichi Yoshida

In cooperative game theory, the primary focus is the equitable allocation of payoffs or costs among agents. However, in the practical applications of cooperative games, accurately representing games is challenging. In such cases, using an allocation method sensitive to small perturbations in the game can lead to various problems, including dissatisfaction am

Christos Charalambous

Understanding the social determinants of preventive behavior is vital for epidemic modelling and effective policy making. Traditional models emphasize imitation or rational trade-offs, but recent evidence highlights the role of social norms. We develop a behavioral epidemic model of seasonal disease on multilayer networks, where vaccination decisions combine

Paul Raux, Christophe Goupil, Gatien Verley

In the linear regime, Onsager's response matrix provides the coupling between heat and charge currents crossing a section of thermoelectric materials of infinitesimal thickness. Integrating this response over the finite thickness of a one-dimensional Thermoelectric Converter (TEC) leads to quadratic heat-force characteristics (Joule's law) and linear current

Johanna Barzen, Frank Leymann

Nowadays, predominant asymmetric cryptographic schemes are considered to be secure because discrete logarithms are believed to be hard to be computed. The algorithm of Shor can effectively compute discrete logarithms, i.e. it can brake such asymmetric schemes. But the algorithm of Shor is a quantum algorithm and at the time this algorithm has been invented,

Wenguo Li, Xinling Guo, Xu Jiao, Tiancheng Huang

Vertical Federated Learning (VFL), which has a broad range of real-world applications, has received much attention in both academia and industry. Enterprises aspire to exploit more valuable features of the same users from diverse departments to boost their model prediction skills. VFL addresses this demand and concurrently secures individual parties from exp

Tianya Li, Yongpeng Wu, Junyuan Gao, Wenjun Zhang

This paper investigates asynchronous multiple-input multiple-output (MIMO) massive unsourced random access (URA) in an orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels, with the presence of both timing and carrier frequency offsets (TO and CFO) and non-negligible codeword collisions. The proposed coding framew

Lukas Nulens, Davi A. D. Chaves, Omar J. Y. Harb, Jeroen E. Scheerder

The energy landscape of multiply connected superconducting structures is ruled by fluxoid quantization due to the implied single-valuedness of the complex wave function. The transitions and interaction between these energy states, each defined by a specific phase winding number, are governed by classical and/or quantum phase slips. Understanding these events

Alvin Heng, Alexandre H. Thiery, Harold Soh

Out-of-distribution (OOD) detection is a critical task in machine learning that seeks to identify abnormal samples. Traditionally, unsupervised methods utilize a deep generative model for OOD detection. However, such approaches require a new model to be trained for each inlier dataset. This paper explores whether a single model can perform OOD detection acro

Siyu Lou, Yuntian Chen, Xiaodan Liang, Liang Lin

In this study, we propose an axiomatic system to define and quantify the precise memorization and in-context reasoning effects used by the large language model (LLM) for language generation. These effects are formulated as non-linear interactions between tokens/words encoded by the LLM. Specifically, the axiomatic system enables us to categorize the memoriza

Bo Lan, Xiu-Ping Fan, Ru-Min Wang

With the accurate measurements of $\chi _{cJ}(J=0,1,2)$ charmonium decays, we explore $\chi _{cJ}\to \mathcal{B}_{8}\bar{\mathcal{B}}_{8}$ and $\mathcal{B}_{10}\bar{\mathcal{B}}_{10}$ decays based on the SU(3) flavor symmetry model, where $\mathcal{B}_{8}$ and $\mathcal{B}_{10}$ are light octet and decuplet baryons, respectively. The decay amplitude relation

Anand Pillay

We adapt the notion of a (relatively) definable subset of Aut(M) when M is a saturated model to the case Aut(M/A) when M is atomic and strongly omega-homogeneous over A. We discuss the existence and uniqueness of invariant measures on the Boolean algebra of definable subsets of Aut(M/A). For example when Th(M) is stable we have existence and uniqueness. We a

Eduard Poesina, Cornelia Caragea, Radu Tudor Ionescu

Natural language inference (NLI), the task of recognizing the entailment relationship in sentence pairs, is an actively studied topic serving as a proxy for natural language understanding. Despite the relevance of the task in building conversational agents and improving text classification, machine translation and other NLP tasks, to the best of our knowledg

Alejandro de la Cruz, Sergio Pastrana

Crypters are pieces of software whose main goal is to transform a target binary so it can avoid detection from Anti Viruses (AVs from now on) applications. They work similar to packers, by taking a malware binary and applying a series of modifications, obfuscations and encryptions to output a binary that evades one or more AVs. The goal is to remain fully un

Francisco de la Hoz, Sergei Iakunin

The simplest case is the reconnection of a pair of antiparallel line vortices, e.g., condensation trails of an aircraft. The vortices first undergo long wave deformation (Crow waves), and then reconnect to form coherent structures. Although the behavior of the vortices before and after the reconnection can be clearly observed, what happens during the reconne

Qingchen Yu, Zifan Zheng, Shichao Song, Zhiyu Li

The continuous advancement of large language models (LLMs) has brought increasing attention to the critical issue of developing fair and reliable methods for evaluating their performance. Particularly, the emergence of cheating phenomena, such as test set leakage and prompt format overfitting, poses significant challenges to the reliable evaluation of LLMs.

Gabriel Istrate, Cosmin Bonchiş, Victor Bogdan

We study the power of (competitive) algorithms with predictions in a multiagent setting. To this goal, we introduce a multiagent version of the ski-rental problem. In this problem agents can collaborate by pooling resources to get a group license for some asset. If the license price is not met then agents have to rent the asset individually for the day at a

Shantanu Ghosh, Clare B. Poynton, Shyam Visweswaran, Kayhan Batmanghelich

The lack of large and diverse training data on Computer-Aided Diagnosis (CAD) in breast cancer detection has been one of the concerns that impedes the adoption of the system. Recently, pre-training with large-scale image text datasets via Vision-Language models (VLM) (\eg CLIP) partially addresses the issue of robustness and data efficiency in computer visio

Fabio Benatti, Giovanni Nichele

We investigate the divisibility properties of the tensor products $\Lambda^{(1)}_t\otimes\Lambda^{(2)}_t$ of open quantum dynamics $\Lambda^{(1,2)}_t$ with time-dependent generators. These dynamical maps emerge from a compound open system $S_1+S_2$ that interacts with its own environment in such a way that memory effects remain when the environment is traced

Linian Wang, Jianghong Liu, Huibin Zhang, Leye Wang

Accurate day-ahead electricity price forecasting is essential for residential welfare, yet current methods often fall short in forecast accuracy. We observe that commonly used time series models struggle to utilize the prior correlation between price and demand-supply, which, we found, can contribute a lot to a reliable electricity price forecaster. Leveragi

Hanxiang Bao, Mingxin Wang, Shaowen Yao

This paper studies qualitative properties of solutions of nonlocal infectious SIR epidemic models (1.3)-(1.5), with the homogeneous Neumann boundary conditions, Dirichlet boundary conditions and free boundary, respectively. We first use the upper and lower solutions method and the Lyapunov function method to prove the global asymptotically stabilities of the

Ermo Hua, Biqing Qi, Kaiyan Zhang, Kai Tian

Supervised Fine-Tuning (SFT) and Preference Optimization (PO) are key processes for aligning Language Models (LMs) with human preferences post pre-training. While SFT excels in efficiency and PO in effectiveness, they are often combined sequentially without integrating their optimization objectives. This approach ignores the opportunities to bridge their par

Osamu Seto, Yo Toda

Baryon acoustic oscillation (BAO) is one of the important standard rulers in cosmology. The results of the latest BAO measurements by Dark Energy Spectroscopic Instrument (DESI) survey have been reported. Cosmology with the varying electron mass model and the early dark energy models are regarded as interesting models to resolve the Hubble tension. We presen

Wei Ju, Yifan Wang, Yifang Qin, Zhengyang Mao

In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to its prohibitive cost and time-intensive nature. To address this challenge, self-supervised learning (SSL) on graphs has gained increasing attention and has made significant progress

Jin-Hwi Park, Chanhwi Jeong, Junoh Lee, Hae-Gon Jeon

Dense depth maps have been used as a key element of visual perception tasks. There have been tremendous efforts to enhance the depth quality, ranging from optimization-based to learning-based methods. Despite the remarkable progress for a long time, their applicability in the real world is limited due to systematic measurement biases such as density, sensing

Anna Miriam Benini, Vasiliki Evdoridou, Núria Fagella, Philip J. Rippon

We prove sharp results about recurrent behaviour of orbits of forward compositions of inner functions, inspired by fundamental results about iterates of inner functions, and give examples to illustrate behaviours that cannot occur in the simpler case of iteration. A result of Fern\'andez, Meli\'an and Pestana gives a precise version of the classical Poincar\

Andrew Rueda, Elena Álvarez Mellado, Constantine Lignos

Modern named entity recognition systems have steadily improved performance in the age of larger and more powerful neural models. However, over the past several years, the state-of-the-art has seemingly hit another plateau on the benchmark CoNLL-03 English dataset. In this paper, we perform a deep dive into the test outputs of the highest-performing NER model

Zhiyi Li, Pengcheng Hou, Youjin Deng, Kun Chen

The spin Coulomb drag effect, arising from the exchange of momentum between electrons of opposite spins, plays a crucial role in the spin transport of interacting electron systems and can be characterized by the exchange-correlation (XC) kernel in the spin channel $K_{\rm XC}^-(q,\omega)$. Using the state-of-the-art Variational Diagrammatic Monte Carlo appro

Bing Xue, Juha Tuomela, Katsuyuki Haneda, Clemens Icheln

This paper introduces a novel approach to experimentally characterize effective human skin permittivity at sub-Terahertz (sub-THz) frequencies, specifically from $140$~to $210$~GHz, utilizing a quasi-optical measurement system. To ensure accurate measurement of the reflection coefficients of human skin, a planar, rigid, and thick reference plate with a low-l

Chunxia Qin, Zhenrong Zhang, Pengfei Hu, Chenyu Liu

Table structure recognition (TSR) aims to parse the inherent structure of a table from its input image. The `"split-and-merge" paradigm is a pivotal approach to parse table structure, where the table separation line detection is crucial. However, challenges such as wireless and deformed tables make it demanding. In this paper, we adhere to the "split-and-mer

Jiaxin Sun, Hongmei Yao, Shao-Ming Fei, Zhaobing Fan

The detection and estimation of quantum entanglement are the essential issues in the theory of quantum entanglement. We construct matrices based on the realignment of density matrices and the vectorization of the reduced density matrices, from which a family of separability criteria are presented for both bipartite and multipartite systems. Moreover, new low

Kounianhua Du, Jizheng Chen, Jianghao Lin, Yunjia Xi

Recommender systems play important roles in various applications such as e-commerce, social media, etc. Conventional recommendation methods usually model the collaborative signals within the tabular representation space. Despite the personalization modeling and the efficiency, the latent semantic dependencies are omitted. Methods that introduce semantics int

Emilio Franco, Robert Hanson, Johannes Horn, André Oliveira

We study Arinkin's Poincar\'e sheaf $\mathcal{P}_C$ on the singular locus of $\overline{\mathsf{Jac}}_C$, the compactified Jacobian of rank one torsion-free sheaves on an integral nodal projective curve $C$. Each stratum of the singular locus $\mathsf{Sing}(\overline{\mathsf{Jac}}_C)$ is indexed by a partial normalisation $\Sigma \to C$. We prove that the Po

Ann-Kathrin Raab, Marvin Schmoll, Emma R. Simpson, Melvin Redon

We present a novel, interferometric, two-color, high-order harmonic generation setup, based on a turn-key Ytterbium-doped femtosecond laser source and its second harmonic. Each interferometer arm contains a spatial light modulator, with individual capabilities to manipulate the spatial beam profiles and to stabilize the relative delay between the fundamental

Hajime Sotani

The crust region is a tiny fraction of neutron stars, but it has a variety of physical properties and plays an important role in astronomical observations. One of the properties characterizing the crust is the elasticity. In this review, with the approach of asteroseismology, we systematically examine neutron star oscillations excited by crust elasticity, ad

Vladimir Rovenski

Many contact metric manifolds are critical points of curvature functionals restricted to spaces of associated metrics. The Godbillon-Vey functional has never been considered in a variational context in contact geometry. Recently we extended this functional from foliations to arbitrary plane fields on a 3-dimensional manifold, so, the following question arise

Qi Li, Liang Peng, Zhiyuan Wu, Pengda Ye

In order to solve the problem of stable jumping of micro robot, we design a special mechanism: elastic passive joint (EPJ). EPJ can assist in achieving smooth jumping through the opening-closing process when the robot jumps. First, we introduce the composition and operation principle of EPJ, and perform a dynamic modeling of the robot's jumping process. Then

Jooyong Park, Jungwoo Lee, Euncheol Choi, Younggun Cho

In urban environments for delivery robots, particularly in areas such as campuses and towns, many custom features defy standard road semantic categorizations. Addressing this challenge, our paper introduces a method leveraging Salient Object Detection (SOD) to extract these unique features, employing them as pivotal factors for enhanced robot loop closure an

Ming-Long Wang, Yin-Hui Peng, Ji-Hai Liao, Xiao-Bao Yang

Room-temperature superconductivity represents a significant scientific milestone, with the initial report of LK-99, a copper-substituted lead apatite $\mathrm{Pb}_{10-x}\mathrm{Cu}_{x}(\mathrm{PO}_{4})_{6}\mathrm{O}$, offering a potential breakthrough. However, other researchers have encountered numerous challenges in replicating the original experimental re

Yueyue Jiang, Jing Zhong, Songmei Qin, Tong Tang

We investigated the stellar mass function and the binary fraction of 114 nearby open clusters (OCs) using the high-precision photometric data from Gaia Data Release 3 (Gaia DR3). We estimated the mass of member stars by using a ridge line (RL) that is better in line with the observed color-magnitude diagram (CMD), thus obtaining more accurate stellar mass an

Xiyu Wang, Yufei Wang, Satoshi Tsutsui, Weisi Lin

Diffusion-based models for story visualization have shown promise in generating content-coherent images for storytelling tasks. However, how to effectively integrate new characters into existing narratives while maintaining character consistency remains an open problem, particularly with limited data. Two major limitations hinder the progress: (1) the absenc

Mengjie Lyu, Min Wang, Ran Wang

Let $\{X(t)\}_{t\geqslant0}$ be the generalized fractional Brownian motion introduced by Pang and Taqqu (2019): \begin{align*} \{X(t)\}_{t\ge0}\overset{d}{=}&\left\{ \int_{\mathbb R} \left((t-u)_+^{\alpha}-(-u)_+^{\alpha} \right) |u|^{-\gamma/2} B(du) \right\}_{t\ge0}, \end{align*} where $ \gamma\in [0,1), \ \ \alpha\in \left(-\frac12+\frac{\gamma}{2}, \ \fr

Yuan Liu, Le Tian, Xiao Zhou, Jie Zhou

Recent advancements in large vision-language models (LVLMs), such as GPT4-V and LLaVA, have been substantial. LLaVA's modular architecture, in particular, offers a blend of simplicity and efficiency. Recent works mainly focus on introducing more pre-training and instruction tuning data to improve model's performance. This paper delves into the often-neglecte

Shaull Almagor, Neta Dafni, Ishai Salgado

Jumping automata are finite automata that read their input in a non-sequential manner, by allowing a reading head to ``jump'' between positions on the input, consuming a permutation of the input word. We argue that allowing the head to jump should incur some cost. To this end, we propose four quantitative semantics for jumping automata, whereby the jumps of

Mohammad Reza Rezaei, Adji Bousso Dieng

This paper introduces alternators, a novel family of non-Markovian dynamical models for sequences. An alternator features two neural networks: the observation trajectory network (OTN) and the feature trajectory network (FTN). The OTN and the FTN work in conjunction, alternating between outputting samples in the observation space and some feature space, respe

Lu Cheng, Kuan Xu

The ultraspherical spectral method features high accuracy and fast solution. In this article, we determine the sources of error arising from the ultraspherical spectral method and derive its effective condition number, which explains why its backward error is consistent with a numerical method with bounded condition number. In addition, we show the cause for

Mengqi Lei, Xin Wang

Accurate segmentation of polyps in colonoscopy images is essential for early-stage diagnosis and management of colorectal cancer. Despite advancements in deep learning for polyp segmentation, enduring limitations persist. The edges of polyps are typically ambiguous, making them difficult to discern from the background, and the model performance is often comp

Dongjian Qian, Yang Xiao

This paper deals with a transient random walk in Dirichlet environment, or equivalently a linearly edge reinforced random walk, on a Galton-Watson tree. We compute the stationary distribution of the environment seen from the particle of an edge reinforced random walk. We obtain a formula for the speed and give a necessary and sufficient condition for the wal

Harideep Nair, William Leyman, Agastya Sampath, Quinn Jacobson

Neuromorphic architectures mimicking biological neural networks have been proposed as a much more efficient alternative to conventional von Neumann architectures for the exploding compute demands of AI workloads. Recent neuroscience theory on intelligence suggests that Cortical Columns (CCs) are the fundamental compute units in the neocortex and intelligence

Wei Liu, Shengbang Qian

We report the superhumps analysis of seven SU UMa-type dwarf novae based on the observations of Transiting Exoplanet Survey Satellite (TESS). Superhumps are seen during superoutbursts of SU UMa-type dwarf novae. The month-long data sets of TESS are well suited for studying the variation of superhumps. We selected seven non-eclipsing SU UMa-type dwarf novae w

Yu. M. Shabelski, A. G. Shuvaev

The differential elastic cross sections of $^{12}$C - $^{12}$C, $^{16}$O - $^{16}$O and $^{20}$Ne - $^{20}$Ne nuclei scattering are calculated in the complete Glauber theory with the account of the modification due to Coulomb interaction and form factor effects. The role of the Coulomb interaction is shown to be significant mainly in the diffractive minima.

Junqi Wang, Chunhui Zhang, Jiapeng Li, Yuxi Ma

Facing the current debate on whether Large Language Models (LLMs) attain near-human intelligence levels (Mitchell & Krakauer, 2023; Bubeck et al., 2023; Kosinski, 2023; Shiffrin & Mitchell, 2023; Ullman, 2023), the current study introduces a benchmark for evaluating social intelligence, one of the most distinctive aspects of human cognition. We developed a c

Mahnaz Asghari, Ahmad Sheykhi

We explore the generalized $f(R,T)$ modified theory of gravity, where the gravitational Lagrangian is a function of Ricci scalar $R$ and the trace of the energy-momentum tensor $T$. We derive modified field equations to the linear order of perturbations in the context of $f(R,T)$ model. We then investigate the growth of perturbations in the context of $f(R,T

V. A. Dzuba, V. V. Flambaum

Highly charged ions of heavy actinides from uranium to einsteinium are studied theoretically to find optical transitions sensitive to the variation of the fine structure constant. A number of promising transitions have been found in ions with ionisation degree $\sim$~10. All these transitions correspond in single-electron approximation to the $6p$ - $5f$ tra

Jiacheng Sun, Shuanhong Wang, Chi Zhang, Haoran Zhu

We establish a dual version of infinite-dimensional Hom-algebras and Hom-modules by using the Sweedler duality construction. Additionally, linear morphisms between infinite-dimensional Hom-algebras (resp. Hom-modules) and Hom-coalgebras (resp. Hom-comodules) are derived under this construction. As an application, we present a Hom-type binary linearly recursi