February 2024 arXiv papers — page 85

Thomas Boettcher

Heavy-flavor hadron production, in particular bottom hadron production, is difficult to study in deep-inelastic scattering (DIS) experiments due to small production rates and branching fractions. To overcome these limitations, a method for identifying heavy-flavor DIS events based on event topology is proposed. Based on a heavy-flavor jet tagging strategy de

Alan S. Cornell, Sheldon R. Herbst, Anele M. Ncube, Hajar Noshad

To expand on the burgeoning research on physics-informed neural networks (PINNs) and their ability to solve the eigenvalue problems in black hole (BH) perturbation theory, we implement a supervised learning approach to solve the Regge-Wheeler and Teukolsky equations, the equations of gravitational perturbations of Schwarzschild and Kerr BHs, respectively. To

Mike Nkongolo, Mahmut Tokmak

The aim of this study is to propose and evaluate an advanced ransomware detection and classification method that combines a Stacked Autoencoder (SAE) for precise feature selection with a Long Short Term Memory (LSTM) classifier to enhance ransomware stratification accuracy. The proposed approach involves thorough pre processing of the UGRansome dataset and t

Shengxin Tu, Chun Li, Bryan E. Shepherd

Clustered data are common in practice. Clustering arises when subjects are measured repeatedly, or subjects are nested in groups (e.g., households, schools). It is often of interest to evaluate the correlation between two variables with clustered data. There are three commonly used Pearson correlation coefficients (total, between-, and within-cluster), which

Vansh Ramani, Tarak Karmakar

Prediction of solubility has been a complex and challenging physiochemical problem that has tremendous implications in the chemical and pharmaceutical industry. Recent advancements in machine learning methods have provided great scope for predicting the reliable solubility of a large number of molecular systems. However, most of these methods rely on using p

Simon Zhang, Cheng Xin, Tamal K. Dey

A hypergraph consists of a set of nodes along with a collection of subsets of the nodes called hyperedges. Higher-order link prediction is the task of predicting the existence of a missing hyperedge in a hypergraph. A hyperedge representation learned for higher order link prediction is fully expressive when it does not lose distinguishing power up to an isom

Vijay Keswani, Anay Mehrotra, L. Elisa Celis

In many predictive contexts (e.g., credit lending), true outcomes are only observed for samples that were positively classified in the past. These past observations, in turn, form training datasets for classifiers that make future predictions. However, such training datasets lack information about the outcomes of samples that were (incorrectly) negatively cl

Randall Balestriero, Yann LeCun

Input space reconstruction is an attractive representation learning paradigm. Despite interpretability of the reconstruction and generation, we identify a misalignment between learning by reconstruction, and learning for perception. We show that the former allocates a model's capacity towards a subspace of the data explaining the observed variance--a subspac

Zhen Zhong, Donald B. Rubin

Rerandomization utilizes modern computing ability to improve covariate balance while adhering to the randomization principle originally advocated by RA Fisher. Affinely invariant rerandomization has the ``Equal Percent Variance Reducing'' (EPVR) property. When dealing with covariates of varying importance and/or mixed types, the conditionally EPVR property i

Karoline Wiesner, Samuel Bien, Matthew C. Wilson

A critical issue for society today is the emergence and decline of democracy worldwide. It is unclear, however, how democratic features, such as elections and civil liberties, influence this change. Democracy indices, which are the standard tool to study this question, are based on the a priori assumption that improvement in any individual feature strengthen

B. J. K. Kleijn, S. Rizzelli

Asymptotic properties of random graph sequences, like occurrence of a giant component or full connectivity in Erd\H{o}s-R\'enyi graphs, are usually derived with very specific choices for defining parameters. The question arises to which extent those parameters choices may be perturbed, without losing the asymptotic property. Writing $(P_n)$ and $(Q_n)$ for t

Sunny Rai, Khushang Jilesh Zaveri, Shreya Havaldar, Soumna Nema

Shame and pride are social emotions expressed across cultures to motivate and regulate people's thoughts, feelings, and behaviors. In this paper, we introduce the first cross-cultural dataset of over 10k shame/pride-related expressions, with underlying social expectations from ~5.4K Bollywood and Hollywood movies. We examine how and why shame and pride are e

Muhammad Ahsan Khan Ameur Yagoub

The classification of maximal left algebras of quaternion Toeplitz matrices is a harder problem that has received little attention up to now. In this paper, we introduce certain families of maximal left algebras of Toeplitz matrices with entries from an algebra of quaternions that cover various classes of the left algebras of quaternion Toeplitz matrices.

Yutaka Okabe, Komajiro Niizeki, Yoshiaki Araki

Smith et al discovered an aperiodic monotile of 13-sided shape in 2023. It is called the `Smith hat' and consists of 8 kites. We deal with the statistical physics of the lattice of the kites, which we call the `Smith-kite lattice'. We studied the Ising model on the aperiodic Smith-kite lattice and the dual Smith-kite lattice using Monte Carlo simulations. We

Franz Zotter, Stefan Riedel, Lukas Gölles, Matthias Frank

Can uncorrelated surrounding sound sources be used to generate extended diffuse sound fields? By definition, targets are a constant sound pressure level, a vanishing average sound intensity, uncorrelated sound waves arriving isotropically from all directions. Does this require specific sources and geometries for surrounding 2D and 3D source layouts? As metho

Lukas Kölsch

APN functions play a big role as primitives in symmetric cryptography as building blocks that yield optimal resistance to differential attacks. In this note, we consider a recent extension of a biprojective APN family by G\"olo\u{g}lu defined on $\mathbb{F}_{2^{2m}}$. We show that this generalization yields functions equivalent to G\"olo\u{g}lu's original fa

Jesús A. De Loera, Laura Escobar, Nathan Kaplan, Chengyang Wang

We study the problem of counting lattice points of a polytope that are weighted by an Ehrhart quasi-polynomial of a family of parametric polytopes. As applications one can compute integrals and maximum values of such quasi-polynomials, as well as obtain new identities in representation theory. These topics have been of great interest to Mich\`ele Vergne sinc

I. Brevik, S. Pal, Y. Li, A. Gholamhosseinian

We present a concise review of selected parts of axion electrodynamics and its application to Casimir physics. We present the general formalism including the boundary conditions at a dielectric surface, derive the dispersion relation in the case where the axion parameter has a constant spatial derivative in the direction normal to the conducting plates, and

M. Boström, A. Gholamhosseinian, S. Pal, Y. Li

From the late 1960s and onwards the groups of Barry Ninham and Adrian Parsegian, and their many collaborators, made a number of important contributions to theory and experiment of intermolecular forces. In particular, they explored the semi-classical theory: Maxwell's equations and Planck quantization of light $\rightarrow$ Lifshitz and Casimir interactions.

Zhenghang Yuan, Zhitong Xiong, Lichao Mou, Xiao Xiang Zhu

An in-depth comprehension of global land cover is essential in Earth observation, forming the foundation for a multitude of applications. Although remote sensing technology has advanced rapidly, leading to a proliferation of satellite imagery, the inherent complexity of these images often makes them difficult for non-expert users to understand. Natural langu

Abuzar B. M. Adam, Mohammed A. M. Elhassan, Elhadj Moustapha Diallo

In this work, we conduct a comparative study on two deep unfolding mechanisms to efficiently perform power control in the next generation wireless networks. The power control problem is formulated as energy efficiency over multiple interference links. The problem is nonconvex. We employ fractional programming transformation to design two solutions for the pr

Jiateng Liu, Pengfei Yu, Yuji Zhang, Sha Li

The dynamic nature of real-world information necessitates efficient knowledge editing (KE) in large language models (LLMs) for knowledge updating. However, current KE approaches, which typically operate on (subject, relation, object) triples, ignore the contextual information and the relation among different knowledge. Such editing methods could thus encount

Deepak Prasad, Mayur Pimpude, Alankar Alankar

In this work a Large Language Model (LLM) based workflow is presented that utilizes OpenAI ChatGPT model GPT-3.5-turbo-1106 and Google Gemini Pro model to create summary of text, data and images from research articles. It is demonstrated that by using a series of processing, the key information can be arranged in tabular form and knowledge graphs to capture

Rachmad Vidya Wicaksana Putra, Muhammad Shafique

Embedded AI systems are expected to incur low power/energy consumption for solving machine learning tasks, as these systems are usually power constrained (e.g., object recognition task in autonomous mobile agents with portable batteries). These requirements can be fulfilled by Spiking Neural Networks (SNNs), since their bio-inspired spike-based operations of

Vladimir Koltchinskii

Let $f:{\mathbb R}_+\mapsto {\mathbb R}$ be a smooth function with $f(0)=0.$ A problem of estimation of a functional $\tau_f(\Sigma):= {\rm tr}(f(\Sigma))$ of unknown covariance operator $\Sigma$ in a separable Hilbert space ${\mathbb H}$ based on i.i.d. mean zero Gaussian observations $X_1,\dots, X_n$ with values in ${\mathbb H}$ and covariance operator $\S

Georg Wolschin

During recombination, the cosmic background radiation is disturbed, in particular, by Lyman-alpha emissions from neutral hydrogen. It is proposed to account for the subsequent time-dependent partial thermalization of the Lyman-alpha energy content in an analytically solvable nonlinear diffusion model. The amplitude of the partially thermalized and redshifted

Junhyun Park, Seonghyeok Jang, Hyojae Park, Seongjun Bae

Flexible continuum manipulators are valued for minimally invasive surgery, offering access to confined spaces through nonlinear paths. However, cable-driven manipulators face control difficulties due to hysteresis from cabling effects such as friction, elongation, and coupling. These effects are difficult to model due to nonlinearity and the difficulties bec

Sepanta Zeighami, Cyrus Shahabi

While extremely useful (e.g., for COVID-19 forecasting and policy-making, urban mobility analysis and marketing, and obtaining business insights), location data collected from mobile devices often contain data from a biased population subset, with some communities over or underrepresented in the collected datasets. As a result, aggregate statistics calculate

Xinyu Zhang, Wenjie Qiu, Yi-Chen Li, Lei Yuan

Developing policies that can adjust to non-stationary environments is essential for real-world reinforcement learning applications. However, learning such adaptable policies in offline settings, with only a limited set of pre-collected trajectories, presents significant challenges. A key difficulty arises because the limited offline data makes it hard for th

Ge Chen, Baoyi Chen, Jiaxing Zhao

The static properties and dynamic evolution of bottomonium states in a hot QCD medium are investigated through the Schr\"odinger equation with complex heavy quark potentials, which are presented recently in lattice QCD study and with three different extractions. This approach builds a direct connection between the in-medium heavy quark potentials from the la

Gregory J. Galloway, Eric Ling

In this paper, we study rigidity aspects of Penrose's singularity theorem. Specifically, we aim to answer the following question: if a spacetime satisfies the hypotheses of Penrose's singularity theorem except with weakly trapped surfaces instead of trapped surfaces, then what can be said about the global spacetime structure if the spacetime is null geodesic

Jin Gao, Hanyong Xu, Luc Dao

Reaching consensus in urban planning is a complex process often hindered by prolonged negotiations, trade-offs, power dynamics, and competing stakeholder interests, resulting in inefficiencies and inequities. Advances in large language models (LLMs), with their increasing capabilities in knowledge transfer, reasoning, and planning, have enabled the developme

Charles L. Bérubé, Jean-Luc Gagnon

Accurately interpreting induced polarization (IP) data that reflects the inherent anisotropy of the Earth's crust requires anisotropic IP models. The Generalized Effective Medium Theory of Induced Polarization (GEMTIP) model effectively simulates the IP signatures of rocks containing polarizable minerals. A pivotal element of the GEMTIP model is calculating

Richard Monge, Tom Delord, Carlos A. Meriles

Color centers in wide-bandgap semiconductors feature metastable charge states that can be interconverted with the help of optical excitation at select wavelengths. The distinct fluorescence and spin properties in each of these states have been exploited to show storage of classical information in three dimensions, but the memory capacity of these platforms h

Aamir Hamid Dar

A novel addition to the family of integral transforms, the quadratic phase Fourier transform (QPFT) embodies a variety of signal processing tools, including the Fourier transform (FT), fractional Fourier transform (FRFT), linear canonical transform (LCT), and special affine Fourier transforms. Due to its additional degrees of freedom, QPFT performs better in

Indranil Biswas, Sorin Dumitrescu

We prove that on the product of two elliptic curves a generic nonsingular turbulent foliation does not admit any transversely projective structure.

Maria V. Kulikova, Gennady Yu. Kulikov

Recent research in nonlinear filtering and signal processing has suggested an efficient derivative-free Extended Kalman filter (EKF) designed for discrete-time stochastic systems. Such approach, however, has failed to address the estimation problem for continuous-discrete models. In this paper, we develop a novel continuous-discrete derivative-free EKF metho

Carolin Kreisbeck, Hidde Schönberger

This work revolves around properties and applications of functions whose nonlocal gradient, or more precisely, finite-horizon fractional gradient, vanishes. Surprisingly, in contrast to the classical local theory, we show that this class forms an infinite-dimensional vector space. Our main result characterizes the functions with zero nonlocal gradient in ter

Xinlei Yu, Xinyang Li, Ruiquan Ge, Shibin Wu

Intracerebral Hemorrhage (ICH) is the deadliest subtype of stroke, necessitating timely and accurate prognostic evaluation to reduce mortality and disability. However, the multi-factorial nature and complexity of ICH make methods based solely on computed tomography (CT) image features inadequate. Despite the capacity of cross-modal networks to fuse additiona

Ayman R. Mohammed, Ahmad Abu Sleem, Mohammad A. M. Abdel-Aal

The literature on master production scheduling for product mix problems under the Theory of Constraints (TOC) was considered by many previous studies. Most studies assume a static resources availability. In this study, the raw materials supplied to the manufacturer is considered as dynamic depending on the results of the problem. Thus, an integer linear heur

Juliette Marrie, Michael Arbel, Julien Mairal, Diane Larlus

Large pretrained visual models exhibit remarkable generalization across diverse recognition tasks. Yet, real-world applications often demand compact models tailored to specific problems. Variants of knowledge distillation have been devised for such a purpose, enabling task-specific compact models (the students) to learn from a generic large pretrained one (t

Xiangyu Ivy Wang, Yun-Wei Yu, Jia Ren, Jun Yang

The second brightest gamma-ray burst, GRB 230307A (with a duration $T_{90}$ ~ 40 s), exhibited characteristics indicative of a magnetar engine during the prompt emission phase. Notably, a suspected kilonova was identified in its follow-up optical and infrared observations. Here we propose that the origin of GRB 230307A is a neutron star-white dwarf (NS-WD) m

Yulong Shi, Mingwei Sun, Yongshuai Wang, Zengqiang Chen

Vision transformers have achieved encouraging progress in various computer vision tasks. A common belief is that this is attributed to the capability of self-attention in modeling the global dependencies among feature tokens. However, self-attention still faces several challenges in dense prediction tasks, including high computational complexity and absence

Kamal Taha

This survey paper presents a comprehensive analysis of crime prediction methodologies, exploring the various techniques and technologies utilized in this area. The paper covers the statistical methods, machine learning algorithms, and deep learning techniques employed to analyze crime data, while also examining their effectiveness and limitations. We propose

Yu Wang, Amin Javari, Janani Balaji, Walid Shalaby

Session-based recommender systems (SBRSs) predict users' next interacted items based on their historical activities. While most SBRSs capture purchasing intentions locally within each session, capturing items' global information across different sessions is crucial in characterizing their general properties. Previous works capture this cross-session informat

Anxhelo Diko, Danilo Avola, Marco Cascio, Luigi Cinque

Vision Transformer (ViT) self-attention mechanism is characterized by feature collapse in deeper layers, resulting in the vanishing of low-level visual features. However, such features can be helpful to accurately represent and identify elements within an image and increase the accuracy and robustness of vision-based recognition systems. Following this ratio

Sayantan Ghosh, Raja Solanki, P. K. Sahoo

In this article, we offer the dynamical system analysis of the DBI (Dirac-Born-Infeld) scalar field in a modified $f(Q)$ gravity context. We have taken a polynomial form of modified gravity and used two different kinds of scalar potential, i.e., polynomial and exponential, and found a closed autonomous dynamical system of equations. We have analyzed the fixe

Kars Knook, Sheehan Olver, Ioannis P. A. Papadopoulos

We show, in one dimension, that an $hp$-Finite Element Method ($hp$-FEM) discretisation can be solved in optimal complexity because the discretisation has a special sparsity structure that ensures that the reverse Cholesky factorisation (Cholesky starting from the bottom right instead of the top left) remains sparse. Moreover, computing and inverting the fac

Jean-Christophe Pain

A particular case of degenerate Clebsch-Gordan coefficient can be expressed with three binomial coefficients. Such a formula, which may be obtained using the standard ladder operator procedure, can also be derived from the Racah-Shimpuku formula or from expressions of Clebsch-Gordan coefficients in terms of $_3F_2$ hypergeometric functions. The O'Hara intere

Husein Zolkepli, Aisyah Razak, Kamarul Adha, Ariff Nazhan

Our contribution introduces a groundbreaking multimodal large language model designed to comprehend multi-images, multi-audio, and multi-images-multi-audio within a single multiturn session. Leveraging state-of-the-art models, we utilize the SigLIP encoder for visual inputs and the Whisper Encoder for audio inputs. Notably, this multimodal large language mod

Junlong Li, Fan Zhou, Shichao Sun, Yikai Zhang

As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluation. Yet, these preferences merely reflect broad tendencies, resulting in less explainable and controllable models with potential safety risks. In this work, we dissect the preferenc

Yuzhuang Xu, Xu Han, Zonghan Yang, Shuo Wang

Model quantification uses low bit-width values to represent the weight matrices of existing models to be quantized, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs. However, current quantization methods suffer severe performance degradation when the bit-width is extremely reduced, and thus

Xingliang Lou, Wenchao Xia, Kai-Kit Wong, Haitao Zhao

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active

X. Chen, W. Wang

Wavelet analysis, in addition to power density spectra, is another method to study the quasi-periodic signals in the light curves, but has been rarely used in black hole X-ray transients. We performed wavelet analysis of X-ray timing features and quasi-periodic oscillations (QPOs) based on NICER observations of the black hole candidate MAXI J1535-571 in this

Silvia Novo, Germán Aneiros, Philippe Vieu

This paper focuses on a semiparametric regression model in which the response variable is explained by the sum of two components. One of them is parametric (linear), the corresponding explanatory variable is measured with additive error and its dimension is finite ($p$). The other component models, in a nonparametric way, the effect of a functional variable

Panagiotis Giadikiaroglou, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou

Exploring the capabilities of Large Language Models (LLMs) in puzzle solving unveils critical insights into their potential and challenges in AI, marking a significant step towards understanding their applicability in complex reasoning tasks. This survey leverages a unique taxonomy -- dividing puzzles into rule-based and rule-less categories -- to critically

Mohammad Ali Tareq, Piyush Tripathi, Nurhayati Md Issa, Mahdi H. Miraz

Although blockchain technology was first introduced in 2008 and materialised in 2009, the early usage of blockchain were mainly limited to financial technologies, particularly cryptocurrencies. Later, blockchain became a widespread emerging technology, utilised in multifaceted sectors and applications. In fact, various new and innovative application of block

Hossein Moghimianavval, Baharan Meghdadi, Tasmine Clement, Man I Wu

Numerous tools have been recently developed to predict disease phenotypes using single-cell RNA sequencing (RNA-seq) data. CloudPred is an end-to-end differentiable learning algorithm coupled with a biologically informed mixture model, originally tested on lupus data. This study extends CloudPred's applications to breast cancer disease phenotype prediction t

Merryn D. Constable, Hubert P. H. Shum, Stephen Clark

When technical requirements are high, and patient outcomes are critical, opportunities for monitoring and improving surgical skills via objective motion analysis feedback may be particularly beneficial. This narrative review synthesises work on technical and non-technical surgical skills, collaborative task performance, and pose estimation to illustrate new

Tomáš Jelínek, Jonáš Šerých, Jiří Matas

Optical flow is a useful input for various applications, including 3D reconstruction, pose estimation, tracking, and structure-from-motion. Despite its utility, the field of dense long-term tracking, especially over wide baselines, has not been extensively explored. This paper extends the concept of combining multiple optical flows over logarithmically space

Ahmet Kazan, Mustafa Altin, Nurettin Cenk Turgay

In this paper, we study the Gauss map of rotational hypersurfaces in 4-dimensional Lorentz-Minkowski space concerning the linear second order differential operators $L_1$ and $L_2$, where $L_1$ is usually called as the Cheng-Yau operator. We obtain some classifications of rotational hypersurfaces which have $L_k$-harmonic Gauss map, $L_k$-pointwise 1-type Ga

Zhipeng Zhang, Ruizhi Yang, Shoushan Zhang, Liqiao Yin

In this paper we proposed a joint reconstruction of \gray events using both extensive air array (EAS) and Imaging air Cherenkov Telescope array (IACT). We considered eight Cherenkov telescopes to be built on the LHAASO (Large High Altitude Air Shower Observatory) site and investigate the improvement in differential sensitivity when combining the information

Fatih Aslan, George Iosifidis, Jose A. Ayala-Romero, Andres Garcia-Saavedra

O-RAN systems and their deployment in virtualized general-purpose computing platforms (O-Cloud) constitute a paradigm shift expected to bring unprecedented performance gains. However, these architectures raise new implementation challenges and threaten to worsen the already-high energy consumption of mobile networks. This paper presents first a series of exp

David Bate, Sylvester Eriksson-Bique, Elefterios Soultanis

The $p$-modulus of curves, test plans, upper gradients, charts, differentials, approximations in energy and density of directions are all concepts associated to the theory of Sobolev functions in metric measure spaces. The purpose of this paper is to give an analogous geometric and ``fragment-wise'' theory for Lipschitz functions and Weaver derivations, wher

Junyi Peng, Marc Delcroix, Tsubasa Ochiai, Oldrich Plchot

Pre-trained self-supervised learning (SSL) models have achieved remarkable success in various speech tasks. However, their potential in target speech extraction (TSE) has not been fully exploited. TSE aims to extract the speech of a target speaker in a mixture guided by enrollment utterances. We exploit pre-trained SSL models for two purposes within a TSE fr

Xili Wang, Kejun Tang, Jiayu Zhai, Xiaoliang Wan

Surrogate modeling is of great practical significance for parametric differential equation systems. In contrast to classical numerical methods, using physics-informed deep learning methods to construct simulators for such systems is a promising direction due to its potential to handle high dimensionality, which requires minimizing a loss over a training set

Qihang Yang, Caimei Yang, Yu Liao, Ziman Zhuang

In several languages, omitting a verb phrase (VP) in double centre-embedded structures creates a grammaticality illusion. Similar illusion also exhibited in Mandarin missing-NP double centre-embedded structures. However, there is no consensus on its very nature. Instead of treating it as grammaticality illusion, we argue that ambiguous interpretations of ver

Yixin Yang, Zheng Li, Qingxiu Dong, Heming Xia

Understanding the deep semantics of images is essential in the era dominated by social media. However, current research works primarily on the superficial description of images, revealing a notable deficiency in the systematic investigation of the inherent deep semantics. In this work, we introduce DEEPEVAL, a comprehensive benchmark to assess Large Multimod

Lei Zhang, Pingwen Zhang, Xiangcheng Zheng

High-index saddle dynamics (HiSD) serves as a competitive instrument in searching the any-index saddle points and constructing the solution landscape of complex systems. The Lagrangian multiplier terms in HiSD ensure the Stiefel manifold constraint, which, however, are dropped in the commonly-used discrete HiSD scheme and are replaced by an additional Gram-S

Pei Wang, Yejie Wang, Muxi Diao, Keqing He

In the deployment of large language models (LLMs), accurate confidence estimation is critical for assessing the credibility of model predictions. However, existing methods often fail to overcome the issue of overconfidence on incorrect answers. In this work, we focus on improving the confidence estimation of large language models. Considering the fragility o

Junyi Peng, Marc Delcroix, Tsubasa Ochiai, Oldrich Plchot

Large-scale pre-trained self-supervised learning (SSL) models have shown remarkable advancements in speech-related tasks. However, the utilization of these models in complex multi-talker scenarios, such as extracting a target speaker in a mixture, is yet to be fully evaluated. In this paper, we introduce target speech extraction (TSE) as a novel downstream t

Deng Liu, Alam Noor A, Zhenzhen Qin, Yang Lei

The distribution of nuclear ground-state spin in the two-body random ensemble (TBRE) is studied by using a general classification neural network (NN) model with the two-body interaction matrix elements as input features and corresponding ground-state spins as labels or output predictions. It seems that quantum many-body system problem exceeds the capability

Tomoki Tokuda

In this article, we compute the mod 2 representarion of the symmetric group of order 2 over the singular cohomology groups of orderd 2-configuration space $C_{2}(T^{d})$ of the $d$-torus $T^{d}$ for $d\geq 1$. As applications of the computation, we determine the Stiefel-Whitney height of $C_{2}(T^{d})$ for any $d$, and determine $\mathbb{F}_{2}[{\Sigma}_{2}]

Y. Lei, J. Qi, Y. Lu, H. Jiang

We investigate nuclear shape coexistence for a wide range of even-even nuclides. By varying general pair condensates, which include Slater determinants as a limit but also allow for arbitrary pairing channels, we frequently find multiple coexisting mimina, and often more than two. This is consistent with recent experimental results. In order to measure gener

Aowen Li, Paul Zeiger, Zuxian He, Mingquan Xu

Phonon dispersion is widely used to elucidate the vibrational properties of materials. As an emerging technique, momentum-resolved vibrational spectroscopy in scanning transmission electron microscopy (STEM) offers an unparalleled approach to explore q-dependent phonon behavior at local structures. In this study, we systematically investigate the phonon disp

Chenyan Zhang, Yifei Chen, Zhenxiong Fan, Yiyu Huang

Recently, diffusion models have gained significant attention as a novel set of deep learning-based generative methods. These models attempt to sample data from a Gaussian distribution that adheres to a target distribution, and have been successfully adapted to the reconstruction of MRI data. However, as an unconditional generative model, the diffusion model

Yifei Chen, Chenyan Zhang, Yifan Ke, Yiyu Huang

Traditional supervised learning methods have historically encountered certain constraints in medical image segmentation due to the challenging collection process, high labeling cost, low signal-to-noise ratio, and complex features characterizing biomedical images. This paper proposes a semi-supervised model, DFCPS, which innovatively incorporates the Fixmatc

Jiameng Hong, Taotao Hu

Many-body localization (MBL) is an intriguing physical phenomenon that arises from the interplay of interaction and disorder, allowing quantum systems to prevent thermalization. In this study, we investigate the MBL properties of the fully frustrated Heisenberg spin-1/2 ladder model with next-nearest-neighbor hopping interaction along the leg direction and c

Shu Yang, Muhammad Asif Ali, Lu Yu, Lijie Hu

The increasing significance of large models and their multi-modal variants in societal information processing has ignited debates on social safety and ethics. However, there exists a paucity of comprehensive analysis for: (i) the interactions between human and artificial intelligence systems, and (ii) understanding and addressing the associated limitations.

Bingqiu Chen, Fei Qin, Guangxing Li

One of the major challenges we face is how to quickly and accurately create the three-dimensional (3D) density distributions of interstellar dust in the Milky Way using extinction and distance measurements of large samples of stars. In this study, we introduce a novel machine-learning approach that utilizes a convolution neural network, specifically a V-net,

Minki Hhan

This paper studies the limitations of the generic approaches to solving cryptographic problems in classical and quantum settings in various models. - In the classical generic group model (GGM), we find simple alternative proofs for the lower bounds of variants of the discrete logarithm (DL) problem: the multiple-instance DL and one-more DL problems (and thei

Maciej Buze

We show that the problem of finding the barycenter in the Hellinger-Kantorovich setting admits a least-cost soft multi-marginal formulation, provided that a one-sided hard marginal constraint is introduced. The constrained approach is then shown to admit a conic multi-marginal reformulation based on defining a single joint multi-marginal perspective cost fun

Balwin Bokor, Wolfgang Seiringer, Klaus Altendorfer

In manufacturing, a bottleneck workstation frequently emerges, complicating production planning and escalating costs. To address this, Drum-Buffer-Rope (DBR) is a widely recognized production planning and control method that focuses on centralizing the bottleneck workstation, thereby improving production system performance. Although DBR is primarily focused

A. Patrón, A. D. Chepelianskii, A. Prados, E. Trizac

We study the relaxation of the Metropolis Monte Carlo algorithm corresponding to a single particle trapped in a one-dimensional confining potential, with even jump distributions that ensure that the dynamics verifies detailed balance. Previous work suggested that, for smooth jump distributions, the fastest relaxation rate is obtained as a result of the compe

Lun Ji, Hang Li, Alexander Ostermann, Chunmei Su

We investigate a filtered Lie-Trotter splitting scheme for the ``good" Boussinesq equation and derive an error estimate for initial data with very low regularity. Through the use of discrete Bourgain spaces, our analysis extends to initial data in $H^{s}$ for $0<s\leq 2$, overcoming the constraint of $s>1/2$ imposed by the bilinear estimate in smooth Sobolev

Wenxuan Wang, Yisi Zhang, Xingjian He, Yichen Yan

Visual grounding (VG) aims at locating the foreground entities that match the given natural language expressions. Previous datasets and methods for classic VG task mainly rely on the prior assumption that the given expression must literally refer to the target object, which greatly impedes the practical deployment of agents in real-world scenarios. Since use

Hans van Haren

The near-equatorial ocean experiences particular dynamics because the Coriolis force is weak. One modelled effect of these dynamics is strong reduction of turbulent mixing in the ocean interior. Unknowns are effects on internal wave breaking and associated turbulent mixing above steeply sloping topography. In this paper, high-resolution temperature observati

Yongluo Cao, Zeya Mi, Rui Zou

For $C^1$ diffeomorphisms with continuous invariant splitting without domination, we prove the existence of (un)stable manifold under the hyperbolicity of invariant measures.

Shanshan Zhong, Zhongzhan Huang, Daifeng Li, Wushao Wen

Multimodal recommender systems utilize various types of information to model user preferences and item features, helping users discover items aligned with their interests. The integration of multimodal information mitigates the inherent challenges in recommender systems, e.g., the data sparsity problem and cold-start issues. However, it simultaneously magnif

Elena Bunina

In this paper we give invariants that characterize isotypically equivalent Abelian periodic groups. Also, we describe types of standart tuples of elements in these groups. As the particular case we prove that two Abelian $p$-groups with separable reduced parts are isotypically equivalent if and only if their divisible parts and their basic subgroups are elem

Shu Yang, Muhammad Asif Ali, Cheng-Long Wang, Lijie Hu

Adapting large language models (LLMs) to new domains/tasks and enabling them to be efficient lifelong learners is a pivotal challenge. In this paper, we propose MoRAL, i.e., Mixture-of-Experts augmented Low-Rank Adaptation for Lifelong Learning. MoRAL combines the multi-tasking abilities of MoE with the fine-tuning abilities of LoRA for effective life-long l

Partha Sarathi Banerjee, Rahul Marathe, Sankalpa Ghosh

The field of electron optics exploits the analogy between the movement of electrons or charged quasiparticles, primarily in two-dimensional materials subjected to electric and magnetic (EM) fields and the propagation of electromagnetic waves in a dielectric medium with varied refractive index. We significantly extend this analogy by introducing an electronic

Vibhavasu Pasumarti, Shantanu Desai

We apply the generalized Lomb-Scargle periodogram to 22 years data of solar $^{8}$B neutrino fluxes detected by Super-Kamiokande. The primary motivation of this work was to check if the sinusoidal modulation at a frequency of 9.43/year (with a period of 38 days), which we had found to be marginally significant with the first five years of Super-K data, persi

Wajid M. Shaikh, Rupali S. Jain, B. Surendranath Reddy

In this paper, we develop the python code for generating unit graph $G(\mathbb{Z}_{n}\oplus\mathbb{Z}_{m})$, for any integers $m\ \& \ n$. For any prime $r$, we construct $r$-ary linear codes from the incidence matrix of the unit graph $G(\mathbb{Z}_{n}\oplus\mathbb{Z}_{m})$, where $n \ \& \ m$ are either power of prime or product of power of primes. We also

Avinash Patil, P. S. Momale, C. M. Jadhav

Let F be a finite field and R = M2(F) be 2x2 matrix ring over F. In this paper, we explicitly determine all the idempotents in R. Using these idempotents, we study the idempotent graph of R whose vertex set is the set of non-trivial idempotents in R and two idempotents e, f are adjacent if ef = 0 or fe = 0. It is proved that the idempotent graph of R is conn

Zhi Xu, Hong-Chen Jiang, Yi-Fan Jiang

The role of near-neighbor electron attraction $V$ in strongly correlated systems has been at the forefront of recent research of unconventional superconductivity. However, its implications in the doped Hubbard model on expansive systems remain predominantly unexplored. In this study, we employ the density-matrix renormalization group to examine its effect in

Ying Mo, Jiahao Liu, Jian Yang, Qifan Wang

There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE). Although researchers are exploring the use of few-shot information extraction through in-context learning

Sangkyu Lee, Sungdong Kim, Ashkan Yousefpour, Minjoon Seo

Existing approaches for aligning large language models with human preferences face a trade-off that requires a separate reward model (RM) for on-policy learning. In this paper, we present a novel alignment framework, SELF-JUDGE that (1) does on-policy learning and 2) is parameter efficient, as it does not require an additional RM for evaluating the samples f

Philipp Dijkstal, Weilun Qin, Sergey Tomin

Time-resolved diagnostics at Free-Electron Laser (FEL) facilities, in particular electron beam longitudinal phase space (LPS) and FEL power profile measurements, provide information highly valuable for users, machine development studies, and beam setup. We investigate the slice energy resolution of passive streaker setups, in particular the effect of an ener

Miroslav Grmela

Multiscale thermodynamics is a theory of relations among levels of description. Energy and entropy are its two main ingredients. Their roles in the time evolution describing approach of a level (starting level) to another level involving less details (target level) is examined on several examples, including the level on which macroscopic systems are seen as