Temporal Recurrent Networks for Online Action Detection

Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed. However, important real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, Temporal Recurrent Network (TRN), to model greater temporal context of a video frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS'14. The results show that TRN significantly outperforms the state-of-the-art

Functional maturation of immature β cells: A roadblock for stem cell therapy for type 1 diabetes

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by the specific destruction of pancreatic islet β cells and is characterized as the absolute insufficiency of insulin secretion. Current insulin replacement therapy supplies insulin in a non-physiological way and is associated with devastating complications. Experimental islet transplantation therapy has been proven to restore glucose homeostasis in people with severe T1DM. However, it is restricted by many factors such as severe shortage of donor sources, progressive loss of donor cells, high cost, etc. As pluripotent stem cells have the potential to give rise to all cells including islet β cells in the body, stem cell therapy for diabetes has attracted great attention in the academic community and the general public. Transplantation of islet β-like cells differentiated from human pluripotent stem cells (hPSCs) has the potential to be an excellent alternative to islet transplantation. In stem cell therapy, obtaining β cells with complete insulin secretion in vitro is crucial. However, after much research, it has been found that the β-like cells obtained by in vitro differentiation still have many defects, including lack of adult-type glucose stimulated insulin secretion, and multi-hormonal secretion, suggesting that in vitro culture does not allows for obtaining fully mature β-like cells for transplantation. A large number of studies have found that many transcription factors play important roles in the process of transforming immature to mature human islet β cells. Furthermore, PDX1, NKX6.1, SOX9, NGN3, PAX4, etc., are important in inducing hPSC differentiation in vitro. The absent or deficient expression of any of these key factors may lead to the islet development defect in vivo and the failure of stem cells to differentiate into genuine functional β-like cells in vitro. This article reviews β cell maturation in vivo and in vitro and the vital roles of key molecules in this process, in order to explore the current problems in stem cell therapy for diabetes

Serum neurofilament light chain: a predictive marker for outcomes following mild-to-moderate ischemic stroke

BackgroundBiomarkers that reflect brain damage or predict functional outcomes may aid in guiding personalized stroke treatments. Serum neurofilament light chain (sNfL) emerges as a promising candidate for fulfilling this role.MethodsThis prospective, observational cohort investigation included 319 acute ischemic stroke (IS) patients. The endpoints were the incidence of early neurological deterioration (END, an elevation of two or more points in the National Institute of Health stroke scale score within a week of hospitalization compared with the baseline) and functional outcome at 3 months (an mRS score of >2 at 3 months was categorized as an unfavorable/poor functional outcome). The association of sNfL, which was assessed within 24 h of admission, with END and unfavorable functional outcomes at follow-up was assessed via multivariate logistic regression, whereas the predictive value of sNfL for unfavorable functional outcomes and END was elucidated by the receiver operating characteristic curve (ROC).ResultsOf 319 IS individuals, 89 (27.90%) suffered from END. sNfL not only reflects the severity of stroke measured by NIHSS score (p < 0.05) but also closely related to the severity of age-related white matter changes. Higher initial NIHSS score, severe white matter lesions, diabetes mellitus, and upregulated sNfL were significant predictors of END. Similarly, the multivariate logistic regression analysis results showed that elevated sNfL, a higher baseline NIHSS score, and severe white matter lesions were substantially linked with unfavorable outcomes for 3 months. Similarly, sNfL was valuable for the prediction of the 3 months of poor outcome (95%CI, 0.504–0.642, p = 0.044). Kaplan–Meier analysis shows that patients with elevated sNfL levels are more likely to reach combined cerebrovascular endpoints (log-rank test p < 0.05).ConclusionThis investigation suggests that sNfL can serve as a valuable biomarker for predicting END and 3-month poor functional outcomes after an IS and has the potential to forecast long-term cardiovascular outcomes

Safety Early Warning Research for Highway Construction Based on Case-Based Reasoning and Variable Fuzzy Sets

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods’ effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established

Design of Synchronous “Plug & Play” QKD-WDM-PON for Efficient Quantum Communications

We propose a new design of quantum key distribution (QKD) - WDM-PON with "plug & play" scheme and synchronization. Simulations show that the design can improve the quantum key generation rate 3-4 times over conventional scheme

A power storage station placement algorithm for power distribution based on electric vehicle

Inspired by the concept of energy internet and energy problems including greenhouse gas emission and regional shortage, this article will propose an idea of electric power distribution using city bus lines running electric vehicles. The designed power distribution system includes renewable energy sources as the energy input, power storage stations placed at some bus stops as the fixed energy output, and bus lines running electric vehicles as the connections between them. As the basic and priority problem, choosing the suitable location to place power storage stations can not only reduce the total number of construction but also increase the utilization rate of each one. Main work involves the two branch algorithms for solving the placement problem; simulation while using real-world transportation data of two city bus maps and analysis about the advantages and disadvantages of algorithms