Knowledge-based Query Expansion in Real-Time Microblog Search

Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we propose a new language modeling approach for microblog retrieval by inferring various types of context information. In particular, we expand the query using knowledge terms derived from Freebase so that the expanded one can better reflect users' search intent. Besides, in order to further satisfy users' real-time information need, we incorporate temporal evidences into the expansion method, which can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on two official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods.Comment: 9 pages, 9 figure

Behavioral Intention Prediction in Driving Scenes: A Survey

In the driving scene, the road agents usually conduct frequent interactions and intention understanding of the surroundings. Ego-agent (each road agent itself) predicts what behavior will be engaged by other road users all the time and expects a shared and consistent understanding for safe movement. Behavioral Intention Prediction (BIP) simulates such a human consideration process and fulfills the early prediction of specific behaviors. Similar to other prediction tasks, such as trajectory prediction, data-driven deep learning methods have taken the primary pipeline in research. The rapid development of BIP inevitably leads to new issues and challenges. To catalyze future research, this work provides a comprehensive review of BIP from the available datasets, key factors and challenges, pedestrian-centric and vehicle-centric BIP approaches, and BIP-aware applications. Based on the investigation, data-driven deep learning approaches have become the primary pipelines. The behavioral intention types are still monotonous in most current datasets and methods (e.g., Crossing (C) and Not Crossing (NC) for pedestrians and Lane Changing (LC) for vehicles) in this field. In addition, for the safe-critical scenarios (e.g., near-crashing situations), current research is limited. Through this investigation, we identify open issues in behavioral intention prediction and suggest possible insights for future research.Comment: 254 reference

Robust Face Recognition System Based on a Multi-Views Face Database

In this chapter, we describe a new robust face recognition system base on a multi-views face database that derives some 3-D information from a set of face images. We attempt to build an approximately 3-D system for improving the performance of face recognition. Our objective is to provide a basic 3-D system for improving the performance of face recognition. The main goal of this vision system is 1) to minimize the hardware resources, 2) to obtain high success rates of identity verification, and 3) to cope with real-time constraints. Using the multi-views database, we address the problem of face recognition by evaluating the two methods PCA and ICA and comparing their relative performance. We explore the issues of subspace selection, algorithm comparison, and multi-views face recognition performance. In order to make full use of the multi-views property, we also propose a strategy of majority voting among the five views, which can improve the recognition rate. Experimental results show that ICA is a promising method among the many possible face recognition methods, and that the ICA algorithm with majority-voting is currently the best choice for our purposes

Extreme rainfall and snowfall alter responses of soil respiration to nitrogen fertilization : a 3-year field experiment

Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Global Change Biology 23 (2017): 3403-3417, doi:10.1111/gcb.13620.Extreme precipitation is predicted to be more frequent and intense accompanying global warming, and may have profound impacts on soil respiration (Rs) and its components, i.e., autotrophic (Ra) and heterotrophic (Rh) respiration. However, how natural extreme rainfall or snowfall events affect these fluxes are still lacking, especially under nitrogen (N) fertilization. In this study, extreme rainfall and snowfall events occurred during a 3-year field experiment, allowing us to examine their effects on the response of Rs, Rh and Ra to N supply. In normal rainfall years of 2011/2012 and 2012/2013, N fertilization significantly stimulated Rs by 23.9% and 10.9%, respectively. This stimulation was mainly due to the increase of Ra because of N-induced increase in plant biomass. In the record wet year of 2013/2014, however, Rs was independent on N supply because of the inhibition effect of the extreme rainfall event. Compared with those in other years, Rh and Ra were reduced by 36.8% and 59.1%, respectively, which were likely related to the anoxic stress on soil microbes and decreased photosynthates supply. Although N supply did not affect annual Rh, the response ratio (RR) of Rh flux to N fertilization decreased firstly during growing season, increased in nongrowing season and peaked during spring thaw in each year. Nongrowing season Rs and Rh contributed 5.5–16.4% to their annual fluxes, and were higher in 2012/2013 than other years due to the extreme snowfall inducing higher soil moisture during spring thaw. The RR of nongrowing season Rs and Rh decreased in years with extreme snowfall or rainfall compared to those in normal years. Overall, our results highlight the significant effects of extreme precipitation on responses of Rs and its components to N fertilization, which should be incorporated into models to improve the prediction of carbon-climate feedbacks.This research was funded by the Chinese Academy of Sciences (XDB15020100) and the National Natural Science Foundation of China (31561143011).2017-12-2

Dietary supplementation of <em>Astragalus</em> fermentation products improves the growth performance, immunological characteristics, and disease resistance of crucian carp (<em>Carassius auratus</em>)

The fermentation products of Astragalus have been acknowledged for their ability to enhance immune functions. This study assessed the impact of incorporating Astragalus, fermented with Lactobacillus plantarum and Bacillus coagulans, on crucian carp's growth, disease resistance, and immunological characteristics. The experimental groups were fed with common feed (C), C + Astragalus (A), A + Lactobacillus plantarum (AL), A + Bacillus coagulans (AB), and AL + Bacillus coagulans (ALB). The fermented products were mixed with common feed at a 1:99 ratio, and crucian carp were fed 2% of their body weight for four weeks, with sampling conducted on days 3, 7, 14, 21, and 28. Disease resistance was evaluated using Aeromonas hydrophila (A. hydrophila) at a concentration of 0.2 mL (1.0×10^7 CFU/mL). The final weights in the AL, AB, and ALB groups significantly increased compared to the C group. The ALB group exhibited elevated serum albumin levels, alkaline phosphatase, intestinal lipase, protease enzyme, C3, and IgM gene expression compared to the C group. At the same time, alanine aminotransferase, aspartate aminotransferase, and glucose contents were significantly reduced. The survival rate significantly increased in all experimental groups after treatment with A. hydrophila. In conclusion, Astragalus products fermented with Lactobacillus plantarum and Bacillus coagulans could effectively improve crucian carp's growth, disease resistance, and immune response

Icariside II suppresses ferroptosis to protect against MPP+-Induced Parkinson's disease through Keap1/Nrf2/GPX4 signaling

Parkinson's disease (PD) is recognized as a degenerative and debilitating neurodegenerative disorder. The novel protective role of icariside II (ICS II) as a plant-derived flavonoid compound in neurodegenerative diseases has aroused much attention. Herein, the definite impacts of ICS II on the process of PD and the relevant action mechanism were studied. Human neuroblastoma SK-N-SH cells were challenged with 1-methyl-4-phenylpyridinium ion (MPP+) to construct the PD cell model. MTT assay and flow cytometry analysis, respectively, appraised cell viability and apoptosis. Caspase 3 Activity Assay examined caspase 3 activity. Corresponding kits examined oxidative stress levels. BODIPY 581/591 C11 assay evaluated lipid reactive oxygen species. Iron Assay Kit assessed iron content. Western blot tested the expression of apoptosis-, ferroptosis- and Kelch-like ECH-associated protein 1 (Keap1)/nuclear factor erythroid 2-related factor 2 (Nrf2)/glutathione peroxidase 4 (GPX4) signaling-associated proteins. Molecular docking verified the binding of ICS II with Keap1. The existing experimental results unveiled that ICS II elevated the viability whereas reduced the apoptosis, oxidative stress, and ferroptosis in MPP+-treated SK-N-SH cells in a concentration-dependent manner. Furthermore, ICS II declined Keap1 expression while raised Nrf2, heme oxygenase 1, and GPX4 expression. In addition, ICS II had a strong binding with Keap1 and Nrf2 inhibitor ML385 partially abolished the suppressive role of ICS II in MPP+-triggered apoptosis, oxidative stress, and ferroptosis in SK-N-SH cells. To summarize, ICS II might inhibit apoptosis, oxidative stress, and ferroptosis in the MPP+-stimulated PD cell model, which might be due to the activation of Keap1/Nrf2/GPX4 signaling

STUDY ON MENTAL HEALTH PROBLEMS AND OUNTERMEASURES OF COLLEGE STUDENTS UNDER THE BACKGROUND OF EPIDEMIC PREVENTION AND CONTROL

Background: To explore the mental health problems and countermeasures of college students under the background of epidemic prevention and control. Subjects and methods: A total of 286 students in school were investigated with self-designed social demographic questionnaire, epidemic cognitive behavior, epidemic panic and anxiety questionnaire, depression symptom group scale, heart-filling scale and emotion regulation self-efficacy scale. Results: College students have a certain understanding of COVID-19 transmission routes, preventive measures, etc., and can actively cooperate and understand the country\u27s epidemic prevention measures. The average score of the College Student Anxiety Self-Rating Scala is (55.37+6.13) points.The total score of students emotional regulation self-efficacy is 64.32+10.61, the total score of expressing positive emotions is 24.17+3.55, and the total score of managing negative emotions is 39.69+7.71. Under the epidemic situation, the degree of bad emotions such as panic, anxiety and depression is lower. Conclusions: The mental health status of college students is on the decline. According to the demographic characteristics and anxiety sources of different students, more social support and targeted and personalized intervention measures should be given to promote their positive mental state

Optimized Handover Algorithm Based on Stackelberg Games in CBTC Systems for Urban Rail Transit

Communications Based Train Control (CBTC) systems have become indispensable for train operation to improve the safety and efficiency of train running to the utmost extent at present. In this paper, the problem of train handover in wireless LAN of CBTC systems for urban rail transit was taken into consideration, which was formulated as a market competition with Stackelberg games and cooperative diversity to obtain the reasonable resource allocation for trains. Under the environment of continuous train-to-wayside communications with high reliability, the successful train handover probability was analyzed in detail. Firstly, the relay triggering during handover was introduced, and the price strategies were determined in terms of the bandwidth requested by trains. Then, the utility function of train, based on price and revenue, was designed, and the existence of Nash equilibrium was proved. To achieve the optimal bandwidth strategies of train and network revenue, a distributed iterative learning scheme was proposed to figure out Nash equilibrium. Finally, the train handover process was further analyzed. The results of our study showed that the proposed strategy can motivate relay nodes to participate in cooperation for reasonable resource allocation of networks, the train handover time was less than 50ms and the successful train handover probability was remarkably higher than that of the traditional handover of RSS (Received Signal Strength)