Scalable Data Analysis on MapReduce-based Systems

Ph.DDOCTOR OF PHILOSOPH

Post-training corticosterone inhibits the return of fear evoked by platform stress and a subthreshold conditioning procedure in Sprague-Dawley rats

The return of fear is an important issue in anxiety disorder research. Each time a fear memory is reactivated, it may further strengthen overactivation of the fear circuit, which may contribute to long-term maintenance of the fear memory. Recent evidence indicates that glucocorticoids may help attenuate pathological fear, but its role in the return of fear is unclear. In the present study, systemic corticosterone (CORT; 25 mg/kg) administration 1 h after fear conditioning did not impair the consolidation process but significantly suppressed the return of fear evoked by a subthreshold conditioning (SC) procedure and elevated platform (EP) stress. Compared with the SC-induced return of fear, acute stress-induced return was state-dependent. In addition, post-training CORT treatment increased the adrenocorticotropic response after EP stress, which indicates that the drug-induced suppression of the return of fear may possibly derive from its regulation effect of the hypothalamic-pituitary-adrenal axis reactivity to stress. These results suggest that post-training CORT administration may help inhibit the return of fear evoked by EP or SC stress. The possible mechanisms involved in the high-dose CORT-induced suppression of the SC- and EP-induced return of fear are discussed. (C) 2015 Elsevier Inc. All rights reserved.</p

Robust Self-Supervised Multi-Instance Learning with Structure Awareness

Multi-instance learning (MIL) is a supervised learning where each example is a labeled bag with many instances. The typical MIL strategies are to train an instance-level feature extractor followed by aggregating instances features as bag-level representation with labeled information. However, learning such a bag-level representation highly depends on a large number of labeled datasets, which are difficult to get in real-world scenarios. In this paper, we make the first attempt to propose a robust Self-supervised Multi-Instance LEarning architecture with Structure awareness (SMILEs) that learns unsupervised bag representation. Our proposed approach is: 1) permutation invariant to the order of instances in bag; 2) structure-aware to encode the topological structures among the instances; and 3) robust against instances noise or permutation. Specifically, to yield robust MIL model without label information, we augment the multi-instance bag and train the representation encoder to maximize the correspondence between the representations of the same bag in its different augmented forms. Moreover, to capture topological structures from nearby instances in bags, our framework learns optimal graph structures for the bags and these graphs are optimized together with message passing layers and the ordered weighted averaging operator towards contrastive loss. Our main theorem characterizes the permutation invariance of the bag representation. Compared with state-of-the-art supervised MIL baselines, SMILEs achieves average improvement of 4.9%, 4.4% in classification accuracy on 5 benchmark datasets and 20 newsgroups datasets, respectively. In addition, we show that the model is robust to the input corruption

Hiding the Source Based on Limited Flooding for Sensor Networks

Wireless sensor networks are widely used to monitor valuable objects such as rare animals or armies. Once an object is detected, the source, i.e., the sensor nearest to the object, generates and periodically sends a packet about the object to the base station. Since attackers can capture the object by localizing the source, many protocols have been proposed to protect source location. Instead of transmitting the packet to the base station directly, typical source location protection protocols first transmit packets randomly for a few hops to a phantom location, and then forward the packets to the base station. The problem with these protocols is that the generated phantom locations are usually not only near the true source but also close to each other. As a result, attackers can easily trace a route back to the source from the phantom locations. To address the above problem, we propose a new protocol for source location protection based on limited flooding, named SLP. Compared with existing protocols, SLP can generate phantom locations that are not only far away from the source, but also widely distributed. It improves source location security significantly with low communication cost. We further propose a protocol, namely SLP-E, to protect source location against more powerful attackers with wider fields of vision. The performance of our SLP and SLP-E are validated by both theoretical analysis and simulation results

A Large Sample Survey of Tibetan People on the Qinghai-Tibet Plateau: Current Situation of Depression and Risk Factors

Background: A depressive state is a negative emotional state characterized by abnormal dejection and unpleasant mood. Long-term depressive symptoms can result in psychological disorders such as depression. However, little is known about the depression status and risk factors of the Tibetan people on the Qinghai-Tibet Plateau. Objective: This study explores the depression status of the Tibetan people to better promote ethnic minorities&#39; physical and mental health. Participants and Setting: The Center for Epidemiologic Studies Depression Scale (CES-D) was administered to 24,141 Tibetan people from Yushu Prefecture; the average age was 34.33 years (SD = 9.18, range = 18-94 years). Materials and Methods: Participants completed questionnaires collecting demographic information and evaluating symptoms of depression. Results: The depression prevalence was higher at high altitudes, and there may be a significant positive correlation between depression rates and altitude. Significant differences were found for each demographic variable. Participants with depressive symptoms (scores &gt;= 8) accounted for 52.3% of the total sample, and participants with depression (scores &gt;= 14) accounted for 28.6%. The binary logistic regression results showed that alcohol drinkers, unmarried participants, participants with high self-assessed socioeconomic status, participants with a high income level, and those with a middle-school education were more likely to be depressed. Conclusions: The results provide the first evidence that the prevalence of depression in Tibetans of the Qinghai-Tibet Plateau is higher than that in the general Chinese population and that reported in Western studies, a finding that may be related to cultural differences and chronic hypoxia caused by the high altitude. This paper offers insight into the mental health status of people living in plateau areas and provides a basis for formulating pertinent mental health policy.</p