May 2024 arXiv papers — page 101

Hang Zhou, Ning Liu

Electroweak scale higgsinos with a nearly degenerate spectrum in supersymmetric models are well-motivated, but generally less constrained at collider experiments as the decay products are often too soft to detect. Initial photon fusions alongside the collision of protons at the Large Hadron Collider (LHC) have drawn attention recently as a way to search for

Qifeng Chen, Rengan Xie, Kai Huang, Qi Wang

Recently, implicit neural representation has been widely used to generate animatable human avatars. However, the materials and geometry of those representations are coupled in the neural network and hard to edit, which hinders their application in traditional graphics engines. We present a framework for acquiring human avatars that are attached with high-res

Afaak Lakouader, Youness Hadouch, Daoud Mezzane, Valentin Laguta

In this work, La0.8Sr0.2MnO3 (LSMO) nanopowders are synthesized using two different methods: Pechini (LSMO-PC) and auto-combustion (LSMO-AC). Nanoparticle sizes, structural, magnetic, and magnetocaloric properties were determined and compared. The X-ray diffraction confirms the coexistence of two phases; rhombohedral symmetry with space group R-3c and orthor

Carmine Anzivino, Vinay Vaibhav, Alessio Zaccone

In spite of the success of the Bethe-Weizs\"acker mass formula in its modern numerical and predictive implementations, the common-knowledge principle that it is electrostatics which, ultimately, favors neutron-rich nuclei still presents unclear aspects. For example, while it is true that the Coulomb interaction promotes the tendency towards neutron-rich nucl

Yoàv Montacute, Glynn Winskel

Spoiler-Duplicator games are used in finite model theory to examine the expressive power of logics. Their strategies have recently been reformulated as coKleisli maps of game comonads over relational structures, providing new results in finite model theory via categorical techniques. We present a novel framework for studying Spoiler-Duplicator games by viewi

Ruoyu Diao, Yu-Hong Dai, Liwei Zhang

This paper is devoted to studying the stability properties of the Karush-Kuhn-Tucker (KKT) solution mapping $S_{\rm KKT}$ for Nash equilibrium problems (NEPs) with canonical perturbations. Firstly, we obtain an exact characterization of the strong regularity of $S_{\rm KKT}$ and a sufficient condition that is easy to verify. Secondly, we propose equivalent c

Yu Huang, Liang Guo, Wanqian Guo, Zhe Tao

In the field of environmental science, it is crucial to have robust evaluation metrics for large language models to ensure their efficacy and accuracy. We propose EnviroExam, a comprehensive evaluation method designed to assess the knowledge of large language models in the field of environmental science. EnviroExam is based on the curricula of top internatio

Gargi Sathe, Aneesh Shamraj, Aditya Surve, Nahush Patil

This paper presents an evaluation of the mathematical capability of ChatGPT across diverse languages like Hindi, Gujarati, and Marathi. ChatGPT, based on GPT-3.5 by OpenAI, has garnered significant attention for its natural language understanding and generation abilities. However, its performance in solving mathematical problems across multiple natural langu

Yezhuo Zhang, Zinan Zhou, Yichao Cao, Guangyu Li

With the rapid growth of the Internet of Things ecosystem, Automatic Modulation Classification (AMC) has become increasingly paramount. However, extended signal lengths offer a bounty of information, yet impede the model's adaptability, introduce more noise interference, extend the training and inference time, and increase storage overhead. To bridge the gap

D. J. Flynn, I. P. Grant, H. M. Quiney

Radiative corrections to electronic structure are characterized by perturbative expansions in $\alpha$ and $Z\alpha$, where $\alpha$ is the fine-structure constant and $Z$ is the nuclear charge. A formulation of the leading-order $\alpha(Z\alpha)$ Uehling contribution to the renormalized vacuum polarization is reported in a form that is convenient for implem

Guibin Zhao, Pengfei Li, Zhibo Zhang, Fusen Guo

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large scene data intensive technology puts a high value on automatic target recognition which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create

D. J. Flynn, I. P. Grant, H. M. Quiney

We outline a general formalism for treating vacuum polarization phenomena within an effective field expansion. The coupling between source charges and virtual fields is examined from the perspectives of electrostatic potentials, induced charge densities and form factors in momentum space. A strategy for the efficient calculation of vacuum polarization potent

Guodong Jiang, Yafis Barlas

The superfluid weight of an isolated flat band in multi-orbital superconductors contains contributions from the band's quantum metric and a lattice geometric term that depends on the orbital positions in the lattice. Since the superfluid weight is a measure of the superconductor's energy fluctuation, it is independent of the lattice geometry, leading to the

Alireza HosseiniArani, Manuel Schilling, Benjamin Tennstedt, Alexey Kupriyanov

Cold atom interferometry (CAI)-based quantum accelerometers are very promising for future satellite gravity missions thanks to their strength in providing long-term stable and precise measurements of non-gravitational accelerations. However, their limitations due to the low measurement rate and the existence of ambiguities in the raw sensor measurements call

Udi Aharon, Revital Marbel, Ran Dubin, Amit Dvir

Web applications and APIs face constant threats from malicious actors seeking to exploit vulnerabilities for illicit gains. To defend against these threats, it is essential to have anomaly detection systems that can identify a variety of malicious behaviors. However, a significant challenge in this area is the limited availability of training data. Existing

Yifan Yang, Zhihao Cui, Qianyi Zhang, Jingtai Liu

6D object pose estimation holds essential roles in various fields, particularly in the grasping of industrial workpieces. Given challenges like rust, high reflectivity, and absent textures, this paper introduces a point cloud based pose estimation framework (PS6D). PS6D centers on slender and multi-symmetric objects. It extracts multi-scale features through

Florian Luca, Makoko Campbell Manape

In this paper, we show that if $(U_n)_{n\ge 1}$ is any nondegenerate linearly recurrent sequence of integers whose general term is up to sign not a polynomial in $n$, then the inequality $\phi(|U_n|)\ge |U_{\phi(n)}|$ holds on a set of positive integers $n$ of density $1$, where $\phi$ is the Euler function. In fact, we show that the set of $n\le x$ for whic

Hendrik Damm, Tabea M. G. Pakull, Bahadır Eryılmaz, Helmut Becker

This study aims to leverage state of the art language models to automate generating the "Brief Hospital Course" and "Discharge Instructions" sections of Discharge Summaries from the MIMIC-IV dataset, reducing clinicians' administrative workload. We investigate how automation can improve documentation accuracy, alleviate clinician burnout, and enhance operati

Hugo A. Camargo, Kyoung-Bum Huh, Viktor Jahnke, Hyun-Sik Jeong

We explore spread and spectral complexity in quantum systems that exhibit a transition from integrability to chaos, namely the mixed-field Ising model and the next-to-nearest-neighbor deformation of the Heisenberg XXZ spin chain. We corroborate the observation that the presence of a peak in spread complexity before its saturation, is a characteristic feature

Jian Wang, Yong Wang

This paper aims to provide an explicit computation of the noncommutative residue density associated with equivariant twisted Bismut Laplacian with torsion on compact manifolds with (or without) boundary. We prove the equivariant twisted Kastler-Kalau-Walze type theorems with torsion on compact manifolds with boundary.

Xingyu Miao, Haoran Duan, Varun Ojha, Jun Song

In this work, we propose a novel Trajectory Score Matching (TSM) method that aims to solve the pseudo ground truth inconsistency problem caused by the accumulated error in Interval Score Matching (ISM) when using the Denoising Diffusion Implicit Models (DDIM) inversion process. Unlike ISM which adopts the inversion process of DDIM to calculate on a single pa

Xiangdong Ye, Jiaqi Yu

For a dynamical system $(X,T)$, $d\in\mathbb{N}$ and distinct non-constant integral polynomials $p_1,\ldots, p_d$ vanishing at $0$, the notion of regionally proximal relation along $C=\{p_1,\ldots,p_d\}$ (denoted by $RP_C^{[d]}(X,T)$) is introduced. It turns out that for a minimal system, $RP_C^{[d]}(X,T)=\Delta$ implies that $X$ is an almost one-to-one exte

Fabrizio Russo, Anna Rapberger, Francesca Toni

Causal discovery amounts to unearthing causal relationships amongst features in data. It is a crucial companion to causal inference, necessary to build scientific knowledge without resorting to expensive or impossible randomised control trials. In this paper, we explore how reasoning with symbolic representations can support causal discovery. Specifically, w

Lizhou Liu, Jiaqi An, Yafei Ren, Yingtao Zhang

We theoretically find that the second-order topological insulator, i.e., corner states, can be engineered by coupling two copies of two-dimensional $\mathbb{Z}_2$ topological insulators with opposite spin-helicities. As concrete examples, we utilize Kane-Mele models (i.e., graphene with intrinsic spin-orbit coupling) to realize the corner states by setting t

Dieter Debrauwer, Irène Gijbels, Klaus Herrmann

Quantiles, expectiles and extremiles can be seen as concepts defined via an optimization problem, where this optimization problem is driven by two important ingredients: the loss function as well as a distributional weight function. This leads to the formulation of a general class of functionals that contains next to the above concepts many interesting quant

Guanyu Ye, Maolin Dai, Bowen Liu, Yifan Ma

For the first time, we introduce a rapid wavelength-swept, passively mode-locked fiber laser in an all-polarization-maintaining and all-fiber configuration. Achieving an exceptional wavelength sweep rate of up to 19 kHz through external modulation of the LD driver pump current, this laser offers a high sweep rate, simple cavity design, cost-effectiveness, an

Udi Aharon, Ran Dubin, Amit Dvir, Chen Hajaj

Application Programming Interface (API) Injection attacks refer to the unauthorized or malicious use of APIs, which are often exploited to gain access to sensitive data or manipulate online systems for illicit purposes. Identifying actors that deceitfully utilize an API poses a demanding problem. Although there have been notable advancements and contribution

Ming-Tien Tsai, Chia-Hsian Tsai

For the high-dimensional covariance estimation problem, when $\lim_{n\to \infty}p/n=c \in (0,1)$ the orthogonally equivariant estimator of the population covariance matrix proposed by Tsai and Tsai (2024b) enjoys some optimal properties. Under some regularity conditions, they showed that their novel estimators of eigenvalues are consistent with the eigenvalu

Suman Rath, Neel Kanth Kundu, Subham Sahoo

Quantum key distribution (QKD) has often been hailed as a reliable technology for secure communication in cyber-physical microgrids. Even though unauthorized key measurements are not possible in QKD, attempts to read them can disturb quantum states leading to mutations in the transmitted value. Further, inaccurate quantum keys can lead to erroneous decryptio

Yikyung Kim, Jay-Yoon Lee

Measuring a machine's understanding of human language often involves assessing its reasoning skills, i.e. logical process of deriving answers to questions. While recent language models have shown remarkable proficiency in text based tasks, their efficacy in complex reasoning problems involving heterogeneous information such as text, tables, and numbers remai

Arjun Pitchanathan, Albert Cohen, Oleksandr Zinenko, Tobias Grosser

A wide range of symbolic analysis and optimization problems can be formalized using polyhedra. Sub-classes of polyhedra, also known as sub-polyhedral domains, are sought for their lower space and time complexity. We introduce the Strided Difference Bound Matrix (SDBM) domain, which represents a sweet spot in the context of optimizing compilers. Its expressiv

Ashmi Banerjee, Tunar Mahmudov, Wolfgang Wörndl

The importance of promoting sustainable and environmentally responsible practices is becoming increasingly recognized in all domains, including tourism. The impact of tourism extends beyond its immediate stakeholders and affects passive participants such as the environment, local businesses, and residents. City trips, in particular, offer significant opportu

Eitan Waks

This study presents an integrated approach for advancing functional Near-Infrared Spectroscopy (fNIRS) neuroimaging through the synthesis of data and application of machine learning models. By addressing the scarcity of high-quality neuroimaging datasets, this work harnesses Monte Carlo simulations and parametric head models to generate a comprehensive synth

Milto Hadjikyriakou, Rita Giuliano

This paper investigates the asymptotic behavior of the extremes of a sequence of generalized Oppenheim random variables. Particularly, we establish conditions under which some normalized extremes of sequences arising from Oppenheim expansions belong to the maximum domain of attraction of the Frechet distribution. Additionally, we identify conditions under wh

Christian Rathgeb, Mathias Ibsen, Denise Hartmann, Simon Hradetzky

The fairness of biometric systems, in particular facial recognition, is often analysed for larger demographic groups, e.g. female vs. male or black vs. white. In contrast to this, minority groups are commonly ignored. This paper investigates the performance of facial recognition algorithms on individuals with Down syndrome, a common chromosomal abnormality t

Luca Caldera, Chiara Masci, Andrea Cappozzo, Marco Forlani

Evaluating hospitals' performance and its relation to patients' characteristics is of utmost importance to ensure timely, effective, and optimal treatment. Such a matter is particularly relevant in areas and situations where the healthcare system must contend with an unexpected surge in hospitalizations, such as for heart failure patients in the Lombardy reg

Zhijie Zhong, Zhiwen Yu, Xing Xi, Yue Xu

Despite the prevalence of reconstruction-based deep learning methods, time series anomaly detection remains a tremendous challenge. Existing approaches often struggle with limited temporal contexts, insufficient representation of normal patterns, and flawed evaluation metrics, all of which hinder their effectiveness in detecting anomalous behavior. To addres

José Leites, Vitor Cerqueira, Carlos Soares

Most forecasting methods use recent past observations (lags) to model the future values of univariate time series. Selecting an adequate number of lags is important for training accurate forecasting models. Several approaches and heuristics have been devised to solve this task. However, there is no consensus about what the best approach is. Besides, lag sele

Chengcheng Feng, Mu He, Qiuyu Tian, Haojie Yin

As deep learning technology continues to advance, image generation models, especially models like Stable Diffusion, are finding increasingly widespread application in visual arts creation. However, these models often face challenges such as overfitting, lack of stability in generated results, and difficulties in accurately capturing the features desired by c

Devesh Nandal, Yves Sibony, Sophie Tsiatsiou

We present an analysis of the chemical compositions in high-redshift galaxies, with a focus on the nitrogen-enhanced galaxies GN-z11 and CEERS-1019. We use stellar models of massive stars with initial masses ranging from 9 to 120 Msol across various metallicities to deduce the chemical abundances of stellar ejecta for a few light elements (H, He, C, N, O). O

Xiran Zhang, Sameh Abdulah, Jian Cao, Hatem Ltaief

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n^3) and

T. Mishenina, M. Pignatari, I. Usenko, C. Soubiran

The oldest stars in the Milky Way are metal-poor with [Fe/H] < -- 1.0, displaying peculiar elemental abundances compared to solar values. The relative variations in the chemical compositions among stars is also increasing with decreasing stellar metallicity, allowing for the pure signature of unique nucleosynthesis processes to be revealed. In this work, we

Yujia Chen, Cuiyun Gao, Zezhou Yang, Hongyu Zhang

In the field of code intelligence, effectively modeling long-range code poses a significant challenge. Existing pre-trained language models (PLMs) such as UniXcoder have achieved remarkable success, but they still face difficulties with long code inputs. This is mainly due to their limited capacity to maintain contextual continuity and memorize the key infor

Yingjin Bi

In this paper, we study multiplication formula of $F$-polynomial of representations of Hernandez and Leclerc's quivers with potentials. Since the truncated $q$-characters of some real simple modules over a quantum affine group $U_q(\widehat{\mathfrak{g}})$ can be expressed in terms of such $F$-polynomials, one can describe the product of two simple modules o

Fan Zhong

When a system is brought to a metastable state, nuclei of the equilibrium phase form and grow. This is the well-known nucleation and growth of first-order phase transitions. Near a critical point of a continuous phase transition, critical phenomena such as critical opalescence characterized by universal scaling emerge. These two sets of behavior are so compl

Baran Koseoglu, Luca Traverso, Mohammed Topiwalla, Egor Kraev

Output thresholding is the technique to search for the best threshold to be used during inference for any classifiers that can produce probability estimates on train and testing datasets. It is particularly useful in high imbalance classification problems where the default threshold is not able to refer to imbalance in class distributions and fail to give th

A. B. Arbuzov, A. A. Nikitenko

The possibility of using spin connection components as basic quantization variables of a conformal version of General Relativity is studied. The considered model contains gravitational degrees of freedom and a scalar dilaton field. The standard tetrad formalism is applied. Properties of spin connections in this model are analyzed. Secondary quantization of t

Jie-Fang Zhang, Mei-zhen Jin, Meng-yang Zhang

An analytical method for constructing various coherent localized solutions with short-lived characteristics is proposed based on a novel self-mapping transformation of the (2+1) dimensional KdV equation. The highlight of this method is that it allows one to generate a class of basic two--dimensional rogue waves excited on zero-background for this equation, w

He Sun, Rongke Liu, Bin Dai

A novel SC decoding method of polar codes is proposed in $d$-deletion channels, where a new pruning strategy is designed to reduce decoding complexity. Considering the difference of the scenario weight distributions, pruning thresholds for each node are designed separately according to a uniform constraint on the pruning error probability, which further redu

Yunlong Zhao, Xiaoheng Deng, Yijing Liu, Xinjun Pei

Model stealing (MS) involves querying and observing the output of a machine learning model to steal its capabilities. The quality of queried data is crucial, yet obtaining a large amount of real data for MS is often challenging. Recent works have reduced reliance on real data by using generative models. However, when high-dimensional query data is required,

Biao Yi, Sishuo Chen, Yiming Li, Tong Li

Backdoor attacks pose an increasingly severe security threat to Deep Neural Networks (DNNs) during their development stage. In response, backdoor sample purification has emerged as a promising defense mechanism, aiming to eliminate backdoor triggers while preserving the integrity of the clean content in the samples. However, existing approaches have been pre

Ruitao Chen, Liwei Wang

Reinforcement learning from human feedback (RLHF) has contributed to performance improvements in large language models. To tackle its reliance on substantial amounts of human-labeled data, a successful approach is multi-task representation learning, which involves learning a high-quality, low-dimensional representation from a wide range of source tasks. In t

Yingguang Yang, Qi Wu, Buyun He, Hao Peng

Recent advancements in social bot detection have been driven by the adoption of Graph Neural Networks. The social graph, constructed from social network interactions, contains benign and bot accounts that influence each other. However, previous graph-based detection methods that follow the transductive message-passing paradigm may not fully utilize hidden gr

Fraser Binns, Subhankar Dey

Martin showed that link Floer homology detects braid axes. In this paper we extend this result to give a topological characterisation of links which are almost braided from the point of view of link Floer homology. The result is inspired by work of Baldwin-Sivek and Li-Ye on nearly fibered knots. Applications include that Khovanov homology detects the Whiteh

Xinhui Wang, Xu Guo, Xiaoli Li

In this paper, we construct and analyze new first- and second-order implicit-explicit (IMEX) schemes for the unsteady Navier-Stokes-Darcy model to describe the coupled free flow-porous media system, which is based on the scalar auxiliary variable (SAV) approach in time and finite element method in space. The constructed schemes are linear, only require solvi

Claudiu Creanga, Liviu P. Dinu

This paper outlines the approach of the ISDS-NLP team in the SemEval 2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF). For Subtask 1 we obtained a weighted F1 score of 0.43 and placed 12 in the leaderboard. We investigate two distinct approaches: Masked Language Modeling (MLM) and Causal Language Modeling (CLM). For MLM, we emp

Torsten Hoefler, Alexandru Calotoiu, Anurag Dipankar, Thomas Schulthess

We discuss the computational challenges and requirements for high-resolution climate simulations using the Icosahedral Nonhydrostatic Weather and Climate Model (ICON). We define a detailed requirements model for ICON which emphasizes the need for specialized supercomputers to accurately predict climate change impacts and extreme weather events. Based on the

Guohui Zheng, Songfen Liu, Zongyu Yang, Rui Ma

A neural network model, EFITNN, has been developed capable of real-time magnetic equilibrium reconstruction based on HL-3 tokamak magnetic measurement signals. The model processes inputs from 68 channels of magnetic measurement data gathered from 1159 HL-3 experimental discharges, including plasma current, loop voltage, and the poloidal magnetic fields measu

Wenxia Wu, Yunnan Li

The complex representation rings of finite groups are the fundamental class of fusion rings, categorified by the corresponding fusion categories of complex representations. The category of $\mathbb{Z}_+$-modules of finite rank over such a representation ring is also semisimple. In this paper, we classify the irreducible based modules of rank up to 5 over the

Anthony Hughes, Xingyi Song

Evidence-based medicine is the practice of making medical decisions that adhere to the latest, and best known evidence at that time. Currently, the best evidence is often found in the form of documents, such as randomized control trials, meta-analyses and systematic reviews. This research focuses on aligning medical claims made on social media platforms with

Chenchen Liu, Wenjun Jiang, Xiaojun Yuan

In this paper, we propose a learning-based block-wise planar channel estimator (LBPCE) with high accuracy and low complexity to estimate the time-varying frequency-selective channel of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system. First, we establish a block-wise planar channel model (BPCM) to characterize

Dinesh Kumar, Soumyajeet Das

In this paper, we have discussed the dynamics of composite entire functions in terms of relationship between bungee set, escaping set and filled-in Julia set. We have established some relation between the dynamics of composition of entire functions and the functions taken for composition. We have shown that the union of the bungee set of two entire functions

Shayan Roofeh, Vahid Karimipour

In $d=2j+1$ dimensions, the Landau-Streater quantum channel is defined on the basis of spin $j$ representation of the $su(2)$ algebra. Only for $j=1$, this channel is equivalent to the Werner-Holevo channel and enjoys covariance properties with respect to the group $SU(3)$. We extend this class of channels to higher dimensions in a way which is based on the

Siddhant Agarwal, Shivam Sharma, Preslav Nakov, Tanmoy Chakraborty

Memes have evolved as a prevalent medium for diverse communication, ranging from humour to propaganda. With the rising popularity of image-focused content, there is a growing need to explore its potential harm from different aspects. Previous studies have analyzed memes in closed settings - detecting harm, applying semantic labels, and offering natural langu

Dan Li, Minghui Yan, Zhaolin Teng

A signed graph $\Sigma=(G,\sigma)$ consists of an underlying graph $G=(V,E)$ with a sign function $\sigma:E\rightarrow\{-1,1\}$. Let $A(\Sigma)$ be the adjacency matrix of $\Sigma$ and $\lambda_1(\Sigma)$ denote the largest eigenvalue (index) of $\Sigma$.Define $(K_n,H^-)$ as a signed complete graph whose negative edges induce a subgraph $H$. In this paper,

Ke Liu, Kaijing Ding, Lu Dai, Mark Hansen

In this paper, we employ the long-short-term memory model (LSTM) to predict the real-time go-around probability as an arrival flight is approaching JFK airport and within 10 nm of the landing runway threshold. We further develop methods to examine the causes to go-around occurrences both from a global view and an individual flight perspective. According to o

Agniva Das, Kunnummal Muralidharan

Since the inception of the SARS - CoV - 2 (COVID - 19) novel coronavirus, a lot of time and effort is being allocated to estimate the trajectory and possibly, forecast with a reasonable degree of accuracy, the number of cases, recoveries, and deaths due to the same. The model proposed in this paper is a mindful step in the same direction. The primary model i

Claudiu Creanga, Liviu Petrisor Dinu

We used Data Maps to model and characterize the AuTexTification dataset. This provides insights about the behaviour of individual samples during training across epochs (training dynamics). We characterized the samples across 3 dimensions: confidence, variability and correctness. This shows the presence of 3 regions: easy-to-learn, ambiguous and hard-to-learn

Ke Liu, Mark Hansen

Ground Delay Programs (GDPs) have been widely used to resolve excessive demand-capacity imbalances at arrival airports by shifting foreseen airborne delay to pre-departure ground delay. While offering clear safety and efficiency benefits, GDPs may also create additional delay because of imperfect execution and uncertainty in predicting arrival airport capaci

C. Cui, P. Bortot, M. Ortolani, E. Martínez-Pañeda

A new model is presented to predict hydrogen-assisted fatigue. The model combines a phase field description of fracture and fatigue, stress-assisted hydrogen diffusion, and a toughness degradation formulation with cyclic and hydrogen contributions. Hydrogen-assisted fatigue crack growth predictions exhibit an excellent agreement with experiments over all the

S. Simón-Díaz, N. Britavskiy, N. Castro, G. Holgado

Observations have definitively strengthened the long-standing assertion that binaries are crucial in massive star evolution. While the percentage of spectroscopic binary systems among main-sequence O stars is well-studied, other phases of massive star evolution remain less explored. We aim to estimate the spectroscopic binary fraction in Galactic late O- and

Bo Zhang, Chao Yang

In recent years, the researches about solving partial differential equations (PDEs) based on artificial neural network have attracted considerable attention. In these researches, the neural network models are usually designed depend on human experience or trial and error. Despite the emergence of several model searching methods, these methods primarily conce

Tong Li, Yucong Tang, Guiying Yan

For an $r$-graph $H$, the anti-Ramsey number ${\rm ar}(n,r,H)$ is the minimum number $c$ of colors such that for any edge-coloring of the complete $r$-graph on $n$ vertices with at least $c$ colors, there is a copy of $H$ whose edges have distinct colors. A 2-graph $F$ is doubly edge-$p$-critical if the chromatic number $\chi(F - e)\geq p$ for every edge $e$

Thanh Nguyen, Tung M. Luu, Tri Ton, Chang D. Yoo

Offline reinforcement learning (RL) addresses the challenge of expensive and high-risk data exploration inherent in RL by pre-training policies on vast amounts of offline data, enabling direct deployment or fine-tuning in real-world environments. However, this training paradigm can compromise policy robustness, leading to degraded performance in practical co

Yichen Yan, Xingjian He, Sihan Chen, Shichen Lu

Referring Image Segmentation (RIS) aims to segment an object described in natural language from an image, with the main challenge being a text-to-pixel correlation. Previous methods typically rely on single-modality features, such as vision or language features, to guide the multi-modal fusion process. However, this approach limits the interaction between vi

Yuwei Cheng, Fan Yao, Xuefeng Liu, Haifeng Xu

This paper studies Learning from Imperfect Human Feedback (LIHF), addressing the potential irrationality or imperfect perception when learning from comparative human feedback. Building on evidences that human's imperfection decays over time (i.e., humans learn to improve), we cast this problem as a concave-utility continuous-action dueling bandit but under a

Jindong Wang, Shuonan Wu

In this paper, we present a robust and efficient multigrid solver based on an exponential-fitting discretization for 2D H(curl) convection-diffusion problems. By leveraging an exponential identity, we characterize the kernel of H(curl) convection-diffusion problems and design a suitable hybrid smoother. This smoother incorporates a lexicographic Gauss-Seidel

Paulina Lewandowska, Ryszard Kukulski

This work presents the problem of learning an unknown von Neumann measurement of dimension $d$ using indefinite causal structures. In the considered scenario, we have access to $N$ copies of the measurement. We use formalism of process matrices to store information about the given measurement, that later will be used to reproduce its best possible approximat

Pradeep Kumar Sahu, Nitin Gupta

The extropy measure, first proposed by Lad, Sanfilippo, and Agro in their (2015) paper in Statistical Science, has attracted considerable attention in recent years. Our study introduces a fresh approach to representing weighted extropy in the framework of percentile ranked set sampling. Furthermore, we provide additional insights such as stochastic orders, c

Ayush Maheshwari, Atul Kumar Singh, Karthika NJ, Krishnakant Bhatt

Lexicon or dictionary generation across domains has the potential for societal impact, as it can potentially enhance information accessibility for a diverse user base while preserving language identity. Prior work in the field primarily focuses on bilingual lexical induction, which deals with word alignments using mapping or corpora-based approaches. However

Amin Esfahani, Hichem Hajaiej, Alessio Pomponio

In this paper, we study the following anisotropic nonlinear Schr\"odinger equation on the plane, \[ \begin{cases} {\rm i}\partial_t \Phi+\partial_{xx} \Phi -D_y^{2s} \Phi +|\Phi|^{p-2}\Phi=0,&\quad (t,x,y)\in\mathbb{R} \times \mathbb{R}^2, \Phi(x,y,0)=\Phi_0(x,y),&\quad (x,y)\in\mathbb{R}^2, \end{cases} \] where $D_y^{2s}=\left(-\partial_{yy}\right)^s$ denot

Yunzhuang Shen, Yuan Sun, Xiaodong Li, Zhiguang Cao

Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and using its dual solution to generate new columns with negative reduced costs. This process continues until the dual values converge to the optimal dual solution to the original problem.

Claudiu Creanga, Liviu P. Dinu

Natural Language Inference (NLI) is foundational for evaluating language understanding in AI. However, progress has plateaued, with models failing on ambiguous examples and exhibiting poor generalization. We argue that this stems from disregarding the subjective nature of meaning, which is intrinsically tied to an individual's \textit{weltanschauung} (which

Ye Yang, Chengyuan Wang, Yun Chen, Jianyi Xv

The quantum eraser effect exemplifies the distinct properties of quantum mechanics that challenge classical intuition and expose the wave-particle duality of light. This effect has been extensively explored in various experiments; most of these investigations use polarisation to distinguish which path information, and less attention has been paid to the phas

Yan Wang, Xiaoning Li, Tien Nguyen, Shaohua Wang

Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are heavy in computational complexity, and quadratically with the length of the input. Toward simplifying the input program of an LLM, the state-of-the-art approach has the strategies to filter the input code tokens based on the attentio

Jesse Friedbaum, Sudarshan Adiga, Ravi Tandon

Counterfactuals, or modified inputs that lead to a different outcome, are an important tool for understanding the logic used by machine learning classifiers and how to change an undesirable classification. Even if a counterfactual changes a classifier's decision, however, it may not affect the true underlying class probabilities, i.e. the counterfactual may

Koustubh Phalak, Swaroop Ghosh

Quantum Machine Learning (QML) is an accelerating field of study that leverages the principles of quantum computing to enhance and innovate within machine learning methodologies. However, Noisy Intermediate-Scale Quantum (NISQ) computers suffer from noise that corrupts the quantum states of the qubits and affects the training and inferencing accuracy. Furthe

Hitoshi Konno

We expose the elliptic quantum groups in the Drinfeld realization associated with both the affine Lie algebra \g and the toroidal algebra \g_tor. There the level-0 and level \not=0 representations appear in a unified way so that one can define the vertex operators as intertwining operators of them. The vertex operators are key for many applications such as a

Manav Chaudhary, Harshit Gupta, Vasudeva Varma

The proliferation of LLMs in various NLP tasks has sparked debates regarding their reliability, particularly in annotation tasks where biases and hallucinations may arise. In this shared task, we address the challenge of distinguishing annotations made by LLMs from those made by human domain experts in the context of COVID-19 symptom detection from tweets in

Chaokun Chang, Eric Lo, Chunxiao Ye

Machine learning inference pipelines commonly encountered in data science and industries often require real-time responsiveness due to their user-facing nature. However, meeting this requirement becomes particularly challenging when certain input features require aggregating a large volume of data online. Recent literature on interpretable machine learning r

Ying Jin, Pengyang Ling, Xiaoyi Dong, Pan Zhang

Instruction-based image editing focuses on equipping a generative model with the capacity to adhere to human-written instructions for editing images. Current approaches typically comprehend explicit and specific instructions. However, they often exhibit a deficiency in executing active reasoning capacities required to comprehend instructions that are implici

Alberto Boscaggin, Guglielmo Feltrin, Duccio Papini

The paper studies the existence of periodic solutions of a perturbed relativistic Kepler problem of the type \begin{equation*} \dfrac{\mathrm{d}}{\mathrm{d}t}\left(\frac{m\dot{x}}{\sqrt{1-|\dot{x}|^{2}/c^{2}}}\right) = -\alpha\frac{x}{|x|^{3}} + \varepsilon \, \nabla_{x} U(t,x), \qquad x \in \mathbb{R}^d\setminus\{0\}, \end{equation*} with $d=2$ or $d=3$, bi

Md Saiful Islam Sajol, Md Shazid Islam, A S M Jahid Hasan, Md Saydur Rahman

Accurate prediction of wind power is essential for the grid integration of this intermittent renewable source and aiding grid planners in forecasting available wind capacity. Spatial differences lead to discrepancies in climatological data distributions between two geographically dispersed regions, consequently making the prediction task more difficult. Thus

Takashi Sakamori, Kenki Matsuoka, Mitsuhiro Teshigawara, Yasunori Mawatari

The energy levels of quasiparticles in superconductors experience Doppler shifts due to the influence of a finite current flow. The influence of the Doppler shift is particularly pronounced in unconventional superconductors with gap nodes. In the case of a normal/insulator/superconductor junction of $d$-wave superconductors, a zero-bias conductance peak exhi

Zhen-Ke Dou, Chong Lv, Yousef I. Salamin, Nan Zhang

Compact spin-polarized positron accelerators play a major role in promoting significant positron application research, which typically require high acceleration gradients and polarization degree, both of which, however, are still great challenging. Here, we put forward a novel spin-polarized positron acceleration method which employs an ultrarelativistic hig

Fumika Mizoguchi, Hiroshi Tamaru

Nilpotent Lie groups with left-invariant metrics provide non-trivial examples of Ricci solitons. One typical example is given by the class of two-step nilpotent Lie algebras obtained from simple directed graphs. In this paper, however, we focus on the use of quivers to construct nilpotent Lie algebras. A quiver is a directed graph that allows loops and multi

Zhiying Liang, Vahab Jabrayilov, Aleksey Charapko, Abutalib Aghayev

MultiPaxos, while a fundamental Replicated State Machine algorithm, suffers from a dearth of comprehensive guidelines for achieving a complete and correct implementation. This deficiency has hindered MultiPaxos' practical utility and adoption and has resulted in flawed claims about its capabilities. Our paper aims to bridge the gap between MultiPaxos' comple

Zhiying Liang, Vahab Jabrayilov, Aleksey Charapko, Abutalib Aghayev

State Machine Replication (SMR) protocols form the backbone of many distributed systems. Enterprises and startups increasingly build their distributed systems on the cloud due to its many advantages, such as scalability and cost-effectiveness. One of the first technical questions companies face when building a system on the cloud is which programming languag

Mohit Tomar, Abhisek Tiwari, Sriparna Saha

With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this work, we build a diagnosis assistant to assist doctors, which identifies diseases based on patient-doctor inter

Mallika Garg, Debashis Ghosh, Pyari Mohan Pradhan

Transformer model have achieved state-of-the-art results in many applications like NLP, classification, etc. But their exploration in gesture recognition task is still limited. So, we propose a novel GestFormer architecture for dynamic hand gesture recognition. The motivation behind this design is to propose a resource efficient transformer model, since tran