Research on technical analysis of basketball match based on data mining

The aim of this paper is to preprocess basketball technology actions, to classify these actionswith data mining technology, to mine association rules among them. The main works are shown below:The common approaches of data mining are discussed, such as preprocessing technology, classification technology, clustering technology and mining rules technology. Both ID3 decision tree classification algorithm association and Apriori association rules algorithm are studied in detail.The paper discusses basketball technology actionsboth on a small scale and a large scale, J48 decision tree classification and Apriori association rules mining algorithm basketball are applied, all these research results should have useful instruction to team

Solving specified-time distributed optimization problem via sampled-data-based algorithm

Despite significant advances on distributed continuous-time optimization of multi-agent networks, there is still lack of an efficient algorithm to achieve the goal of distributed optimization at a pre-specified time. Herein, we design a specified-time distributed optimization algorithm for connected agents with directed topologies to collectively minimize the sum of individual objective functions subject to an equality constraint. With the designed algorithm, the settling time of distributed optimization can be exactly predefined. The specified selection of such a settling time is independent of not only the initial conditions of agents, but also the algorithm parameters and the communication topologies. Furthermore, the proposed algorithm can realize specified-time optimization by exchanging information among neighbours only at discrete sampling instants and thus reduces the communication burden. In addition, the equality constraint is always satisfied during the whole process, which makes the proposed algorithm applicable to online solving distributed optimization problems such as economic dispatch. For the special case of undirected communication topologies, a reduced-order algorithm is also designed. Finally, the effectiveness of the theoretical analysis is justified by numerical simulations

Cyclotron Dynamics of a Kondo Singlet in a Spin-Orbit-Coupled Alkaline-Earth Atomic Gas

We propose a scheme to investigate the interplay between Kondo-exchange interaction and quantum spin Hall effect with ultracold fermionic alkaline-earth atoms trapped in two-dimensional optical lattices using ultracold collision and laser-assisted tunneling. In the strong Kondo-coupling regime, though the loop trajectory of the mobile atom disappears, collective dynamics of an atom pair in two clock states can exhibit an unexpected spin-dependent cyclotron orbit in a plaquette, realizing the quantum spin Hall effect of the Kondo singlet. We demonstrate that the collective cyclotron dynamics of the spin-zero Kondo singlet is governed by an effective Harper-Hofstadter model in addition to second-order diagonal tunneling

Counterfactual Cross-modality Reasoning for Weakly Supervised Video Moment Localization

Video moment localization aims to retrieve the target segment of an untrimmed video according to the natural language query. Weakly supervised methods gains attention recently, as the precise temporal location of the target segment is not always available. However, one of the greatest challenges encountered by the weakly supervised method is implied in the mismatch between the video and language induced by the coarse temporal annotations. To refine the vision-language alignment, recent works contrast the cross-modality similarities driven by reconstructing masked queries between positive and negative video proposals. However, the reconstruction may be influenced by the latent spurious correlation between the unmasked and the masked parts, which distorts the restoring process and further degrades the efficacy of contrastive learning since the masked words are not completely reconstructed from the cross-modality knowledge. In this paper, we discover and mitigate this spurious correlation through a novel proposed counterfactual cross-modality reasoning method. Specifically, we first formulate query reconstruction as an aggregated causal effect of cross-modality and query knowledge. Then by introducing counterfactual cross-modality knowledge into this aggregation, the spurious impact of the unmasked part contributing to the reconstruction is explicitly modeled. Finally, by suppressing the unimodal effect of masked query, we can rectify the reconstructions of video proposals to perform reasonable contrastive learning. Extensive experimental evaluations demonstrate the effectiveness of our proposed method. The code is available at \href{https://github.com/sLdZ0306/CCR}{https://github.com/sLdZ0306/CCR}.Comment: Accepted by ACM MM 202

LHX1 as a potential biomarker regulates EMT induction and cellular behaviors in uterine corpus endometrial carcinoma

Objectives: To investigate the expression of LHX1 and its role as a biomarker in the diagnosis and prognosis of Uterine Corpus Endometrial Carcinoma (UCEC). Methods: The Cancer Genome Atlas (TCGA) database was used to detect the expression level of LHX1 in UCEC cells and tissues, and to find out the effect of LHX1 on prognosis. Co-expressed genes were then identified by Spearman correlation analysis, and the protein-protein interaction network was constructed using Cytoscape software. The R “clusterProfiler” package was used to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A series of in vitro experiments were performed to evaluate LHX1 expression and detect UCEC cell proliferation, invasion, and migration. Western blotting was used to determine the effect of LHX1 on expression levels of Epithelial-Mesenchymal Transition (EMT)-related proteins. Results: LHX1 was upregulated in UCEC tissues and correlated with poor overall survival and disease-specific survival outcomes. Functional enrichment analysis suggested that genes co-expressed with LHX1 were enriched in cell adhesion. The expression of LHX1 was positively correlated with the expression levels of genes related to EMT induction and invasion. LHX1 can enhance the proliferation, migration, and invasion activities of UCEC cells in vitro, and alter the expression levels of EMT-related proteins. Conclusion: LHX1 expression was highly upregulated in UCEC cells and tissues, which was correlated with the prognosis of patients with UCEC. LHX1 may regulate UCEC progression at least in part by modulating EMT induction

Cooperative Control of Multi-Channel Linear Systems with Self-Organizing Private Agents

Cooperative behavior design for multi-agent systems with collective tasks is a critical issue to promote swarm intelligence. This paper investigates cooperative control for a multi-channel system, where each channel is managed by an agent that can communicate with neighbors in a network. Each agent is expected to self-organize a controller based only on local information and local interaction to stabilize the multi-channel system collaboratively. A novel cooperative control strategy is designed for each agent by leveraging a decomposing technique and a fusion approach. Then, a privacy-preserving mechanism is incorporated into this strategy to shield all private information from eavesdropping. Moreover, a fully distributed designing method for the strategy parameters is developed. As a result, agents can self-design and self-perform their controllers with private information preserved. It is proved that the multi-channel system stability can be ensured by the proposed strategy with finite fusion steps during each control interval. In addition, the cost of introducing the privacy-preserving mechanism and the effect of adding more channels on the system performance are quantitatively analyzed, which benefits mechanism design and channel placement. Finally, several comparative simulation examples are provided to demonstrate the effectiveness of the theoretical results