Intelligent Bionic Polarization Orientation Method Using Biological Neuron Model for Harsh Conditions.

We developed an intelligent innovative orientation method to improve the accuracy of polarization compasses in harsh conditions: weak skylight polarization patterns resulting from unfavorable weather conditions (e.g., haze, sandstorms) or locally destroyed skylight polarization conditions caused by occlusions (e.

Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis.

In Alzheimer's disease (AD) diagnosis, joint feature selection for predicting disease labels (classification) and estimating cognitive scores (regression) with neuroimaging data has received increasing attention. In this paper, we propose a model named Shared Manifold regularized Joint Feature Selection (SMJFS) that performs classification and regression in a unified framework for AD diagnosis. For classification, unlike the existing works that build least squares regression models which are insufficient in the ability of extracting discriminative information for classification, we design an objective function that integrates linear discriminant analysis and subspace sparsity regularization for acquiring an informative feature subset.

Intracardiac leakage of cement during artificial femoral head replacement: a case report and review.

Bone cement leakage from the femoral medullary cavity is a rare complication following hip replacement. Currently, there are no reports of bone cement leakage into the heart. Here, we report an 81-year-old female patient with right femoral neck fracture.

BLCA prognostic model creation and validation based on immune gene-metabolic gene combination.

Background: Bladder cancer (BLCA) is a prevalent urinary system malignancy. Understanding the interplay of immunological and metabolic genes in BLCA is crucial for prognosis and treatment.

Methods: Immune/metabolism genes were extracted, their expression profiles analyzed.

Horizontal Federated Density Peaks Clustering.

Density peaks clustering (DPC) is a popular clustering algorithm, which has been studied and favored by many scholars because of its simplicity, fewer parameters, and no iteration. However, in previous improvements of DPC, the issue of privacy data leakage was not considered, and the "Domino" effect caused by the misallocation of noncenters has not been effectively addressed. In view of the above shortcomings, a horizontal federated DPC (HFDPC) is proposed.

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.

Difficult Novel Class Detection in Semisupervised Streaming Data.

Streaming data mining can be applied in many practical applications, such as social media, market analysis, and sensor networks. Most previous efforts assume that all training instances except for the novel class have been completely labeled for novel class detection in streaming data. However, a more realistic situation is that only a few instances in the data stream are labeled.

miR-8485 alleviates the injury of cardiomyocytes through TP53INP1.

MicroRNAs (miRNAs) feature prominently in regulating the progression of chronic heart failure (CHF). This study was performed to investigate the role of miR-8485 in the injury of cardiomyocytes and CHF. It was found that miR-8485 level was markedly reduced in the plasma of CHF patients, compared with the healthy controls.

MRTF-A alleviates myocardial ischemia reperfusion injury by inhibiting the inflammatory response and inducing autophagy.

Myocardin-related transcription factor A (MRTF-A) has an inhibitory effect on myocardial infarction; however, the mechanism is not clear. This study reveals the mechanism by which MRTF-A regulates autophagy to alleviate myocardial infarct-mediated inflammation, and the effect of silent information regulator 1 (SIRT1) on the myocardial protective effect of MRTF-A was also verified. MRTF-A significantly decreased cardiac damage induced by myocardial ischemia.

Are Graph Convolutional Networks With Random Weights Feasible?

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks, are receiving extensive attention for their powerful capability in learning node representations on graphs. There are various extensions, either in sampling and/or node feature aggregation, to further improve GCNs' performance, scalability and applicability in various domains. Still, there is room for further improvements on learning efficiency because performing batch gradient descent using the full dataset for every training iteration, as unavoidable for training (vanilla) GCNs, is not a viable option for large graphs.

OPP-Miner: Order-Preserving Sequential Pattern Mining for Time Series.

Traditional sequential pattern mining methods were designed for symbolic sequence. As a collection of measurements in chronological order, a time series needs to be discretized into symbolic sequences, and then users can apply sequential pattern mining methods to discover interesting patterns in time series. The discretization will not only cause the loss of some important information, which partially destroys the continuity of time series, but also ignore the order relations between time-series values.

NetNMSP: Nonoverlapping maximal sequential pattern mining.

Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information.

Multi-Attribute Discriminative Representation Learning for Prediction of Adverse Drug-Drug Interaction.

Adverse drug-drug interaction (ADDI) is a significant life-threatening issue, posing a leading cause of hospitalizations and deaths in healthcare systems. This paper proposes a unified Multi-Attribute Discriminative Representation Learning (MADRL) model for ADDI prediction. Unlike the existing works that equally treat features of each attribute without discrimination and do not consider the underlying relationship among drugs, we first develop a regularized optimization problem based on CUR matrix decomposition for joint representative drug and discriminative feature selection such that the selected drugs and features can well approximate the original feature spaces and the critical factors discriminative to ADDIs can be properly explored.

Online Causal Feature Selection for Streaming Features.

Recently, causal feature selection (CFS) has attracted considerable attention due to its outstanding interpretability and predictability performance. Such a method primarily includes the Markov blanket (MB) discovery and feature selection based on Granger causality. Representatively, the max-min MB (MMMB) can mine an optimal feature subset, i.

Function and Mechanism of Trimetazidine in Myocardial Infarction-Induced Myocardial Energy Metabolism Disorder Through the SIRT1-AMPK Pathway.

Myocardial energy metabolism (MEM) is an important factor of myocardial injury. Trimetazidine (TMZ) provides protection against myocardial ischemia/reperfusion injury. The current study set out to evaluate the effect and mechanism of TMZ on MEM disorder induced by myocardial infarction (MI).

Top-k Self-Adaptive Contrast Sequential Pattern Mining.

For sequence classification, an important issue is to find discriminative features, where sequential pattern mining (SPM) is often used to find frequent patterns from sequences as features. To improve classification accuracy and pattern interpretability, contrast pattern mining emerges to discover patterns with high-contrast rates between different categories. To date, existing contrast SPM methods face many challenges, including excessive parameter selection and inefficient occurrences counting.

Dual-Representation-Based Autoencoder for Domain Adaptation.

Domain adaptation aims to facilitate the learning task in an unlabeled target domain by leveraging the auxiliary knowledge in a well-labeled source domain from a different distribution. Almost existing autoencoder-based domain adaptation approaches focus on learning domain-invariant representations to reduce the distribution discrepancy between source and target domains. However, there is still a weakness existing in these approaches: the class-discriminative information of the two domains may be damaged while aligning the distributions of the source and target domains, which makes the samples with different classes close to each other, leading to performance degradation.

Feature Interaction for Streaming Feature Selection.

Traditional feature selection methods assume that all data instances and features are known before learning. However, it is not the case in many real-world applications that we are more likely faced with data streams or feature streams or both. Feature streams are defined as features that flow in one by one over time, whereas the number of training examples remains fixed.

Toward Mining Capricious Data Streams: A Generative Approach.

Learning with streaming data has received extensive attention during the past few years. Existing approaches assume that the feature space is fixed or changes by following explicit regularities, limiting their applicability in real-time applications. For example, in a smart healthcare platform, the feature space of the patient data varies when different medical service providers use nonidentical feature sets to describe the patients' symptoms.

NetNCSP: Nonoverlapping closed sequential pattern mining.

Sequential pattern mining (SPM) has been applied in many fields. However, traditional SPM neglects the pattern repetition in sequence. To solve this problem, gap constraint SPM was proposed and can avoid finding too many useless patterns.

Object Detection With Deep Learning: A Review.

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers.