A unified framework to control estimation error in reinforcement learning.

In reinforcement learning, accurate estimation of the Q-value is crucial for acquiring an optimal policy. However, current successful Actor-Critic methods still suffer from underestimation bias. Additionally, there exists a significant estimation bias, regardless of the method used in the critic initialization phase.

Intensive Ambulance-Delivered Blood-Pressure Reduction in Hyperacute Stroke.

Background: Treatment of acute stroke, before a distinction can be made between ischemic and hemorrhagic types, is challenging. Whether very early blood-pressure control in the ambulance improves outcomes among patients with undifferentiated acute stroke is uncertain.

Methods: We randomly assigned patients with suspected acute stroke that caused a motor deficit and with elevated systolic blood pressure (≥150 mm Hg), who were assessed in the ambulance within 2 hours after the onset of symptoms, to receive immediate treatment to lower the systolic blood pressure (target range, 130 to 140 mm Hg) (intervention group) or usual blood-pressure management (usual-care group).

Graph Regulation Network for Point Cloud Segmentation.

In point cloud, some regions typically exist nodes from multiple categories, i.e., these regions have both homophilic and heterophilic nodes.

Efficacy and Safety of Tirofiban Before Stenting for Symptomatic Intracranial Atherosclerotic Stenosis: A Randomized Clinical Trial.

Background And Objectives: Acute stent thrombosis (AST) is not uncommon and even catastrophic during intracranial stenting angioplasty in patients with symptomatic high-grade intracranial atherosclerotic stenosis (ICAS). The purpose of this study was to investigate whether adjuvant intravenous tirofiban before stenting could reduce the risk of AST and periprocedural ischemic stroke in patients receiving stent angioplasty for symptomatic ICAS.

Methods: A prospective, multicenter, open-label, randomized clinical trial was conducted from September 9, 2020, to February 18, 2022, at 10 medical centers in China.

Improving Image Contrastive Clustering Through Self-Learning Pairwise Constraints.

In this article, a new unsupervised contrastive clustering (CC) model is introduced, namely, image CC with self-learning pairwise constraints (ICC-SPC). This model is designed to integrate pairwise constraints into the CC process, enhancing the latent representation learning and improving clustering results for image data. The incorporation of pairwise constraints helps reduce the impact of false negatives and false positives in contrastive learning, while maintaining robust cluster discrimination.

Exploring the role of edge distribution in graph convolutional networks.

Graph Convolutional Networks (GCNs) have shown remarkable performance in processing graph-structured data by leveraging neighborhood information for node representation learning. While most GCN models assume strong homophily within the networks they handle, some models can also handle heterophilous graphs. However, the selection of neighbors participating in the node representation learning process can significantly impact these models' performance.

K -Relations-Based Consensus Clustering With Entropy-Norm Regularizers.

Consensus clustering is to find a high quality and robust partition that is in agreement with multiple existing base clusterings. However, its computational cost is often very expensive and the quality of the final clustering is easily affected by uncertain consensus relations between clusters. In order to solve these problems, we develop a new k -type algorithm, called k -relations-based consensus clustering with double entropy-norm regularizers (KRCC-DE).

Long and Short-Range Dependency Graph Structure Learning Framework on Point Cloud.

Graph convolutional neural networks can effectively process geometric data and thus have been successfully used in point cloud data representation. However, existing graph-based methods usually adopt the K-nearest neighbor (KNN) algorithm to construct graphs, which may not be optimal for point cloud analysis tasks, owning to the solution of KNN is independent of network training. In this paper, we propose a novel graph structure learning convolutional neural network (GSLCN) for multiple point cloud analysis tasks.

Adaptive Prototype Interaction Network for Few-Shot Knowledge Graph Completion.

Few-shot knowledge graph completion (FKGC), which aims to infer new triples for a relation using only a few reference triples of the relation, has attracted much attention in recent years. Most existing FKGC methods learn a transferable embedding space, where entity pairs belonging to the same relations are close to each other. In real-world knowledge graphs (KGs), however, some relations may involve multiple semantics, and their entity pairs are not always close due to having different meanings.

RSS-Bagging: Improving Generalization Through the Fisher Information of Training Data.

The bagging method has received much application and attention in recent years due to its good performance and simple framework. It has facilitated the advanced random forest method and accuracy-diversity ensemble theory. Bagging is an ensemble method based on simple random sampling (SRS) method with replacement.

Evaluating Classification Model Against Bayes Error Rate.

For a classification task, we usually select an appropriate classifier via model selection. How to evaluate whether the chosen classifier is optimal? One can answer this question via Bayes error rate (BER). Unfortunately, estimating BER is a fundamental conundrum.

Nitrogen Preference of Dominant Species during Hailuogou Glacier Retreat Succession on the Eastern Tibetan Plateau.

Plant nitrogen (N) uptake preference is a key factor affecting plant nutrient acquisition, vegetation composition and ecosystem function. However, few studies have investigated the contribution of different N sources to plant N strategies, especially during the process of primary succession of a glacial retreat area. By measuring the natural abundance of N isotopes (δN) of dominant plants and soil, we estimated the relative contribution of different N forms (ammonium-NH, nitrate-NO and soluble organic N-DON) and absorption preferences of nine dominant plants of three stages (12, 40 and 120 years old) of the Hailuogou glacier retreat area.

Self-Constrained Spectral Clustering.

As a leading graph clustering technique, spectral clustering is one of the most widely used clustering methods to capture complex clusters in data. Some additional prior information can help it to further reduce the difference between its clustering results and users' expectations. However, it is hard to get the prior information under unsupervised scene to guide the clustering process.

GUIDE: Training Deep Graph Neural Networks via Guided Dropout Over Edges.

Graph neural networks (GNNs) have made great progress in graph-based semi-supervised learning (GSSL). However, most existing GNNs are confronted with the oversmoothing issue that limits their expressive ability. A key factor that leads to this problem is the excessive aggregation of information from other classes when updating the node representation.

Generalization Performance of Pure Accuracy and its Application in Selective Ensemble Learning.

The pure accuracy measure is used to eliminate random consistency from the accuracy measure. Biases to both majority and minority classes in the pure accuracy are lower than that in the accuracy measure. In this paper, we demonstrate that compared with the accuracy measure and F-measure, the pure accuracy measure is class distribution insensitive and discriminative for good classifiers.

Local Causal Discovery in Multiple Manipulated Datasets.

We consider the problem of distinguishing direct causes from direct effects of a target variable of interest from multiple manipulated datasets with unknown manipulated variables and nonidentical data distributions. Recent studies have shown that datasets attained from manipulated experiments (i.e.

AF: An Association-Based Fusion Method for Multi-Modal Classification.

Multi-modal classification (MMC) aims to integrate the complementary information from different modalities to improve classification performance. Existing MMC methods can be grouped into two categories: traditional methods and deep learning-based methods. The traditional methods often implement fusion in a low-level original space.

Deconvolutional neural network for image super-resolution.

This study builds a fully deconvolutional neural network (FDNN) and addresses the problem of single image super-resolution (SISR) by using the FDNN. Although SISR using deep neural networks has been a major research focus, the problem of reconstructing a high resolution (HR) image with an FDNN has received little attention. A few recent approaches toward SISR are to embed deconvolution operations into multilayer feedforward neural networks.

Semi-Supervised Clustering With Constraints of Different Types From Multiple Information Sources.

Semi-supervised clustering is one of important research topics in cluster analysis, which uses pre-given knowledge as constraints to improve the clustering performance. While clustering a data set, people often get prior constraints from different information sources, which may have different representations and contents, to guide clustering process. However, most of existing semi-supervised clustering algorithms are based on single-source constraints and rarely consider to integrate multi-source constraints to enhance the clustering quality.

An Algorithm for Clustering Categorical Data With Set-Valued Features.

In data mining, objects are often represented by a set of features, where each feature of an object has only one value. However, in reality, some features can take on multiple values, for instance, a person with several job titles, hobbies, and email addresses. These features can be referred to as set-valued features and are often treated with dummy features when using existing data mining algorithms to analyze data with set-valued features.

A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks.

Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information.

Two 3D Isostructural Ln(III)-MOFs: Displaying the Slow Magnetic Relaxation and Luminescence Properties in Detection of Nitrobenzene and CrO.

Two new three-dimensional isostructural lanthanide metal-organic frameworks (Ln(III)-MOFs), [LnL(HO)]·3HO·0.75DMF (1-Ln; Ln = Dy(III) and Eu(III) ions, HL = biphenyl-3'-nitro-3,4',5-tricarboxylic acid, DMF = N,N'-dimethylformamide), were synthesized and characterized. The appearance of temperature-dependent out-of-phase (χ″) signal reveals that complex 1-Dy displays slow magnetic relaxation behavior with the energy barrier (ΔU) of 57 K and a pre-exponential factor (τ) of 3.

[Spatial distribution paittern of Populus euphratica and P. pruinosa1 clonal ramets in Ta.rim River Basin, China].

Spatial distribution pattern of Populus euphratica and P. pruinosa clonal ramets at three sites was studied, including natural mixed forest of P. euphratica and P.

Space Structure and Clustering of Categorical Data.

Learning from categorical data plays a fundamental role in such areas as pattern recognition, machine learning, data mining, and knowledge discovery. To effectively discover the group structure inherent in a set of categorical objects, many categorical clustering algorithms have been developed in the literature, among which k -modes-type algorithms are very representative because of their good performance. Nevertheless, there is still much room for improving their clustering performance in comparison with the clustering algorithms for the numeric data.