Twotier -- A Layered Analysis of Backbone Members in a Moderate Sized Community Sports Organization

Backbone members are recognized as essential parts of an organization, yet their role and mechanisms of functioning in networks are not fully understood. In this paper, we propose a new framework called Twotier to analyze the evolution of community sports organizations (CSOs) and the role of backbone members. Tier-one establishes a dynamic user interaction network based on grouping relationships, and weighted k-shell decomposition is used to select backbone members. We perform community detection and capture the evolution of two separate sub-networks: one formed by backbone members and the other formed by other members. In Tier-two, the sub-networks are abstracted, revealing a core-periphery structure in the organization where backbone members serve as bridges connecting all parts of the network. Our findings suggest that relying on backbone members can keep newcomers actively involved in rewarding activities, while non-rewarding activities solidify relations between backbone members

Cognitive Mirage: A Review of Hallucinations in Large Language Models

As large language models continue to develop in the field of AI, text generation systems are susceptible to a worrisome phenomenon known as hallucination. In this study, we summarize recent compelling insights into hallucinations in LLMs. We present a novel taxonomy of hallucinations from various text generation tasks, thus provide theoretical insights, detection methods and improvement approaches. Based on this, future research directions are proposed. Our contribution are threefold: (1) We provide a detailed and complete taxonomy for hallucinations appearing in text generation tasks; (2) We provide theoretical analyses of hallucinations in LLMs and provide existing detection and improvement methods; (3) We propose several research directions that can be developed in the future. As hallucinations garner significant attention from the community, we will maintain updates on relevant research progress.Comment: work in progress; 21 page

Understanding the power-law nature of participation in community sports organizations

The improvement of living standards and awareness of chronic diseases have increased the importance of community sports organizations in promoting the physical activity levels of the public. However, limited understanding of human behavior in this context often leads to suboptimal resource utilization. In this study, we analyzed the participation behavior of 2,956 members with a time span of 6 years in a community sports organization. Our study reveals that, at the population level, the participation frequency in activities adheres to a power-law distribution. To understand the underlying mechanisms driving crowd participation, we introduce a novel behavioral model called HFBI (Habit-Formation and Behavioral Inertia), demonstrating a robust fit to the observed power-law distribution. The habit formation mechanism indicates that individuals who are more engaged are more likely to maintain participation, while the behavioral inertia mechanism suggests that individuals' willingness to participate in activities diminishes with their absences from activities. At the individual level, our analysis reveals a burst-quiet participation pattern, with bursts often commencing with incentive activities. We also find a power-law distribution in the intervals between individual participations. Our research offers valuable insights into the complex dynamics of human participation in community sports activity and provides a theoretical foundation to inform intervention design. Furthermore, the flexibility of our model enables its application to other data exhibiting power-law properties, broadening its potential impact beyond the realm of community sports

Dipolar Dynamics for Interacting Ultracold Fermions in a Trapped Optical Lattice

Using the time-dependent density matrix renormalization group method, we calculate transport properties of an interacting Fermi gas in an optical lattice with a confining trap after a sudden displacement of the trap center. In the regime of attractive interactions, the dipolar motion after the displacement can be classified into underdamped oscillations and overdamped relaxations, depending on the interaction strength. These numerical calculations are consistent with experimental results. In the regime of repulsive interactions, we predict a revival of the oscillations of the center of mass when the interaction strength is increased. This unique feature can be considered as a dynamical signature for the emergence of a Mott plateau for an interacting trapped Fermi gas in an optical lattice.Comment: 5 pages, 5 figure

Trapped Ultracold Bosons in Periodically Modulated Lattices

Motivated by the recent rapid development of the field of quantum gases in optical lattices, we present a comprehensive study of the spectrum of ultracold atoms in a one-dimensional optical lattice subjected to a periodic lattice modulation. Using the time-dependent density-matrix renormalization group method, we study the dynamical response due to lattice modulations in different quantum phases of the system with varying density. For the Mott insulating state, we identify several excitation processes, which provide important information about the density profile of the gases. For the superfluid, the dynamical response can be well described in a local density approximation. This simplification can be valuable in understanding the strong-correlated superfluid in a slow-varying harmonic potential. All these spectroscopic features of an inhomogeneous system can be used as a test for the validity of the Bose-Hubbard model in a parabolic trapping potential.Comment: 8 pages, 6 figure

Population-Based Evolutionary Gaming for Unsupervised Person Re-identification

Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. However, limited by the lack of diversity of discriminant information, a single network has difficulty learning sufficient discrimination ability by itself under unsupervised conditions. To address this limit, we develop a population-based evolutionary gaming (PEG) framework in which a population of diverse neural networks is trained concurrently through selection, reproduction, mutation, and population mutual learning iteratively. Specifically, the selection of networks to preserve is modeled as a cooperative game and solved by the best-response dynamics, then the reproduction and mutation are implemented by cloning and fluctuating hyper-parameters of networks to learn more diversity, and population mutual learning improves the discrimination of networks by knowledge distillation from each other within the population. In addition, we propose a cross-reference scatter (CRS) to approximately evaluate re-ID models without labeled samples and adopt it as the criterion of network selection in PEG. CRS measures a model's performance by indirectly estimating the accuracy of its predicted pseudo-labels according to the cohesion and separation of the feature space. Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples; (2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning.Comment: Accepted in IJC

Local spin fluctuations in iron-based superconductors: 77Se and 87Rb NMR measurements of Tl0.47Rb0.34Fe1.63Se2

We report nuclear magnetic resonance (NMR) studies of the intercalated iron selenide superconductor (Tl, Rb)$_{y}$Fe$_{2-x}$Se$_2$ ($T_c = 32$ K). Single-crystal measurements up to 480 K on both $^{77}$Se and $^{87}$Rb nuclei show a superconducting phase with no magnetic order. The Knight shifts $K$ and relaxation rates $1/T_1T$ increase very strongly with temperature above $T_c$, before flattening at 400 K. The quadratic $T$-dependence and perfect proportionality of both $K$ and $1/T_1T$ data demonstrate their origin in paramagnetic moments. A minimal model for this pseudogap-like response is not a missing density of states but two additive contributions from the itinerant electronic and local magnetic components, a framework unifying the $K$ and $1/T_1 T$ data in many iron-based superconductors

Elevated diversity of the supply chain boosts global food system resilience

Food supply shock is defined as a drastic shortage in food supply, which would likely threaten the achievement of Sustainable Development Goals 2: zero hunger. Traditionally, highly-connected global food supply system was deemed to help overcome shortages easily in response to food supply shock. However, recent studies suggested that overconnected trade networks potentially increase exposure to external shocks and amplify shocks. Here, we develop an empirical–statistical method to quantitatively and meticulously measure the diversity of international food supply chain. Our results show that boosting a country’s food supply chain diversity will increase the resistance of the country to food shocks. The global diversity of food supply chain increased gradually during 1986–2021; correspondingly, the intensity of food shocks decreased, the recovery speed after a shock increased. The food supply chain diversity in high-income countries is significantly higher than that in other countries, although it has improved greatly in the least developed regions, like Africa and Middle East. International emergencies and geopolitical events like the Russia–Ukraine conflict could potentially threaten global food security and impact low-income countries the most. Our study provides a reference for measuring resilience of national food system, thus helping managers or policymakers mitigate the risk of food supply shocks