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How does the human brain construct cognitive maps for decision-making and inference? Here, we conduct an fMRI study on a navigation task in multidimensional abstract spaces. Using a deep neural network model, we assess learning levels and categorized paths into exploration and exploitation stages. Univariate analyses show higher activation in the bilateral hippocampus and lateral prefrontal cortex during exploration, positively associated with learning level and response accuracy. Conversely, the bilateral orbitofrontal cortex (OFC) and retrosplenial cortex show higher activation during exploitation, negatively associated with learning level and response accuracy. Representational similarity analysis show that the hippocampus, entorhinal cortex, and OFC more accurately represent destinations in exploitation than exploration stages. These findings highlight the collaboration between the medial temporal lobe and prefrontal cortex in learning abstract space structures. The hippocampus may be involved in spatial memory formation and representation, while the OFC integrates sensory information for decision-making in multidimensional abstract spaces. An fMRI study on a navigation task in multidimensional abstract spaces showed that the human hippocampus, entorhinal cortex, and orbitofrontal cortex collaborate in learning abstract space structures. [ABSTRACT FROM AUTHOR]

Automatic summarization is attracting increasing attention as one of the most promising research areas. This technology has been tried in various real-world applications in recent years and achieved a good response. However, the applicability of conventional evaluation metrics cannot keep up with rapidly evolving summarization task formats and ensuing indicator. After recent years of research, automatic summarization task requires not only readability and fluency, but also informativeness and consistency. Diversified application scenarios also bring new challenges both for generative language models and evaluation metrics. In this review, we analysis and specifically focus on the difference between the task format and the evaluation metrics. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated possible mechanism of left atrial appendage (LAA) thrombosis and constructed a model to evaluate the future risk of LAA thrombosis and spontaneous echo contrast (SEC) in non-valvular atrial fibrillation (NVAF) patients. Methods: This retrospective study included 2591 patients diagnosed with NVAF. Patients were divided based on the presence of transesophageal echocardiography (TEE) into a thrombus group, SEC group, and control group. General, biochemical, and echocardiography data of the three groups were analyzed. The variables independently associated with LAA thrombosis and SEC were determined by the logistic regression analysis. A nomogram was constituted based on the regression analysis and the discriminatory ability was analyzed by receiver operating characteristic (ROC) curve. Results: LAA thrombosis and SEC were present in 110 (4.2%) patients and 103 (3.9%) patients, respectively. AF type (OR = 1.857), previous stroke (OR = 1.924), fibrinogen (OR = 1.636), diameters of the left atria (OR = 1.094), left ventricular ejection fraction (OR = 0.938), and LAA maximum caliber (OR = 1.238) resulted as independent risk factors for LAA thrombosis and SEC. The area under curve of the nomogram established by multivariate logistic regression was 0.824. Conclusions; Through the study, 6 independent risk factors related to the LAA thrombosis and SEC were found, and an effective nomogram was constructed to predict the LAA thrombosis and SEC in NVAF patients. [ABSTRACT FROM AUTHOR]

SiamPRN algorithm performs well in visual tracking, but it is easy to drift under occlusion and fast motion scenes because it uses ℓ 1 -smooth loss function to measure the regression location of bounding box. In this paper, we propose a multivariate intersection over union (MIOU) loss in SiamRPN tracking framework. Firstly, MIOU loss includes three geometric factors in regression: the overlap area ratio, the center distance ratio, and the aspect ratio, which can better reflect the coincidence degree of target box and prediction box. Secondly, we improve the definition of aspect ratio loss to avoid gradient explosion, improve the optimization performance of prediction box. Finally, based on SiamPRN tracker, we compared the tracking performance of ℓ 1 -smooth loss, IOU loss, GIOU loss, DIOU loss, and MIOU loss. Experimental results show that the MIOU loss has better target location regression than other loss functions on the OTB2015 and VOT2016 benchmark, especially for the challenges of occlusion, illumination change and fast motion. [ABSTRACT FROM AUTHOR]

Similarity among vectors is basic knowledge required to carry out recommendation and classification in recommender systems, which support personalized recommendation during online interactions. In this paper, we propose a Semi-sparse Algorithm based on Multi-layer Optimization to speed up the Pearson Correlation Coefficient, which is conventionally used in obtaining similarity among sparse vectors. In accelerating the batch of similarity-comparisons within one thread, the semi-sparse algorithm spares out over-reduplicated accesses and judgements on the selected sparse vector by making this vector dense locally. Moreover, a reduce-vector is proposed to restrict using locks on critical resources in the thread-pool, which is wrapped with Pthreads on a multi-core node to improve parallelism. Furthermore, among processes in our framework, a shared zip file is read to cut down messages within the Message Passing Interface package. Evaluation shows that the optimized multi-layer framework achieves a brilliant speedup on three benchmarks, Netflix, MovieLens and MovieLen1600. [ABSTRACT FROM AUTHOR]

The acoustic data remotely measured by microphones are widely used to investigate monitoring and diagnose integrity of ball bearing in rotational machines. Early fault diagnosis is very difficult for acoustic emission. We propose a new method using a cross-correlation of frequency spectrum to classify various faults with fine grit. Principal component analysis (PCA) is used to separate the primary frequency spectrum into main frequency and residual frequency. Different with conventional classification using the PCA eigenvalue, we introduce the general cross-correlation (GCC) of main frequency and residual frequency spectrums between a basic signal vector and monitoring signal. Multi-classification strategy based on binary-tree support vector machine (SVM) is applied to perform faults diagnosis. In order to remove noise interference and increase robustness, a normalization method is proposed during time generation. Experiment results show that PCA-GCC-SVM method is able to diagnose various faults with high sensitivity. [ABSTRACT FROM AUTHOR]