Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

Mobile landmark search (MLS) recently receives increasing attention for its great practical values. However, it still remains unsolved due to two important challenges. One is high bandwidth consumption of query transmission, and the other is the huge visual variations of query images sent from mobile devices. In this paper, we propose a novel hashing scheme, named as canonical view based discrete multi-modal hashing (CV-DMH), to handle these problems via a novel three-stage learning procedure. First, a submodular function is designed to measure visual representativeness and redundancy of a view set. With it, canonical views, which capture key visual appearances of landmark with limited redundancy, are efficiently discovered with an iterative mining strategy. Second, multi-modal sparse coding is applied to transform visual features from multiple modalities into an intermediate representation. It can robustly and adaptively characterize visual contents of varied landmark images with certain canonical views. Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises. In this part, we develop a new augmented Lagrangian multiplier (ALM) based optimization method to directly solve the discrete binary codes. We can not only explicitly deal with the discrete constraint, but also consider the bit-uncorrelated constraint and balance constraint together. Experiments on real world landmark datasets demonstrate the superior performance of CV-DMH over several state-of-the-art methods

Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes---specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.Comment: Accepted to AAAI 202

A Differential Evolution-Based Routing Algorithm for Environmental Monitoring Wireless Sensor Networks

The traditional Low Energy Adaptive Cluster Hierarchy (LEACH) routing protocol is a clustering-based protocol. The uneven selection of cluster heads results in premature death of cluster heads and premature blind nodes inside the clusters, thus reducing the overall lifetime of the network. With a full consideration of information on energy and distance distribution of neighboring nodes inside the clusters, this paper proposes a new routing algorithm based on differential evolution (DE) to improve the LEACH routing protocol. To meet the requirements of monitoring applications in outdoor environments such as the meteorological, hydrological and wetland ecological environments, the proposed algorithm uses the simple and fast search features of DE to optimize the multi-objective selection of cluster heads and prevent blind nodes for improved energy efficiency and system stability. Simulation results show that the proposed new LEACH routing algorithm has better performance, effectively extends the working lifetime of the system, and improves the quality of the wireless sensor networks

miR-21 Protects Against Ischemia/Reperfusion-Induced Acute Kidney Injury by Preventing Epithelial Cell Apoptosis and Inhibiting Dendritic Cell Maturation

Renal tubular injury and innate immune responses induced by hypoxia contribute to acute kidney injury. Accumulating evidence suggests that miR-21 overexpression protects against kidney ischemia injury. Additionally, miR-21 emerges as a key inhibitor in dendritic cell maturation. Thus, we hypothesized that miR-21 protects the kidney from IR injury by suppressing epithelial cell damage and inflammatory reaction. In this study, we investigated effects of miR-21 and its signaling pathways (PTEN/AKT/mTOR/HIF, PDCD4/NFκ-B) on kidney ischemia/reperfusion (IR) injury in vitro and in vivo. The results revealed that IR increased miR-21, HIF1α, and 2α expression in vivo and in vitro. MiR-21 interacted with HIF1α and 2α through the PTEN/AKT/mTOR pathway. Moreover, inhibition of miR-21 activated PDCD4/NFκ-B pathways, which are critical for dendritic cell maturation. Renal IR triggers local inflammation by inducing the dendritic cell maturation and promoting the secretion of IL-12, IL-6, and TNF-α cytokines. Knockdown of miR-21 intensified the effect of IR on tubular epithelial cell apoptosis and dendritic cell maturation. Our results suggested that IR-inducible miR-21 protects epithelial cells from IR injury via a feedback interaction with HIF (PTEN/AKT/mTOR/HIF/miR-21) and by inhibiting maturation of DCs through the PDCD4/NF-κB pathway. These findings highlight new therapeutic opportunities in AKI