Derived from rapid advances in computer vision and machine learning, video analysis tasks have been moving from inferring the present state to predicting the future state. Vision-based action ...recognition and prediction from videos are such tasks, where action recognition is to infer human actions (present state) based upon complete action executions, and action prediction to predict human actions (future state) based upon incomplete action executions. These two tasks have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as visual surveillance, autonomous driving vehicle, entertainment, and video retrieval, etc. Many attempts have been devoted in the last a few decades in order to build a robust and effective framework for action recognition and prediction. In this paper, we survey the complete state-of-the-art techniques in action recognition and prediction. Existing models, popular algorithms, technical difficulties, popular action databases, evaluation protocols, and promising future directions are also provided with systematic discussions.
Abstract
The management of bacterial infections is becoming a major clinical challenge due to the rapid evolution of antibiotic resistant bacteria. As an excellent candidate to overcome antibiotic ...resistance, antimicrobial peptides (AMPs) that are produced from the synthetic and natural sources demonstrate a broad-spectrum antimicrobial activity with the high specificity and low toxicity. These peptides possess distinctive structures and functions by employing sophisticated mechanisms of action. This comprehensive review provides a broad overview of AMPs from the origin, structural characteristics, mechanisms of action, biological activities to clinical applications. We finally discuss the strategies to optimize and develop AMP-based treatment as the potential antimicrobial and anticancer therapeutics.
The speed with which intelligent systems can react to an action depends on how soon it can be recognized. The ability to recognize ongoing actions is critical in many applications, for example, ...spotting criminal activity. It is challenging, since decisions have to be made based on partial videos of temporally incomplete action executions. In this paper, we propose a novel discriminative multi-scale kernelized model for predicting the action class from a partially observed video. The proposed model captures temporal dynamics of human actions by explicitly considering all the history of observed features as well as features in smaller temporal segments. A compositional kernel is proposed to hierarchically capture the relationships between partial observations as well as the temporal segments, respectively. We develop a new learning formulation, which elegantly captures the temporal evolution over time, and enforces the label consistency between segments and corresponding partial videos. We prove that the proposed learning formulation minimizes the upper bound of the empirical risk. Experimental results on four public datasets show that the proposed approach outperforms state-of-the-art action prediction methods.
•Demonstration of TECT applied in the electronic cooling has been reviewed.•Thermoelectric materials and extraction of physical parameters have been reviewed.•Two thermoelectric analytical ...methodologies were proposed to evaluate TECT.•Effects of governing parameters on the performance of TECT were investigated.
In recent years, thermoelectric cooling technology (TECT) has emerged as one of high efficiency and low energy consumption methodologies for electronic cooling. This paper presented a comprehensive survey of TECT to show a complete foundation on the thermoelectric applications in electronic cooling. Thermoelectric physical parameters, consisting of Seebeck coefficient S, thermal conductivity K, and electric resistance R, are highly dependent on temperatures of thermoelectric heating and cooling sides and they have been simplified into constants when the thermoelectric cooling model was theoretically established. Furthermore, two systematical solution methodologies were proposed, i.e., the thermal resistance network and the effectiveness-number of transfer units, to describe the coefficient of performance (COP). Effects of cooling load, air temperature and all thermal conductances in heating side on the cooling performance have been attempted, regarding surface temperature of electronic devices and COP as evaluation indexes. Our analysis reveals that thermal control for electronics of high heat flux could be achieved by enhancing heat transfer in the hot side of thermoelectric system and increasing the numbers of thermoelectric coolers. Overall, governing parameters and modeling for practical applications have been presented, and the cooling potential of thermoelectric technology for electronic devices could be enhanced further.
Classifying human actions from varied views is challenging due to huge data variations in different views. The key to this problem is to learn discriminative view-invariant features robust to view ...variations. In this paper, we address this problem by learning view-specific and view-shared features using novel deep models. View-specific features capture unique dynamics of each view while view-shared features encode common patterns across views. A novel sample-affinity matrix is introduced in learning shared features, which accurately balances information transfer within the samples from multiple views and limits the transfer across samples. This allows us to learn more discriminative shared features robust to view variations. In addition, the incoherence between the two types of features is encouraged to reduce information redundancy and exploit discriminative information in them separately. The discriminative power of the learned features is further improved by encouraging features in the same categories to be geometrically closer. Robust view-invariant features are finally learned by stacking several layers of features. Experimental results on three multi-view data sets show that our approaches outperform the state-of-the-art approaches.
This paper addresses the problem of recognizing human interactions from videos. We propose a novel approach that recognizes human interactions by the learned high-level descriptions, interactive ...phrases. Interactive phrases describe motion relationships between interacting people. These phrases naturally exploit human knowledge and allow us to construct a more descriptive model for recognizing human interactions. We propose a discriminative model to encode interactive phrases based on the latent SVM formulation. Interactive phrases are treated as latent variables and are used as mid-level features. To complement manually specified interactive phrases, we also discover data-driven phrases from data in order to find potentially useful and discriminative phrases for differentiating human interactions. An information-theoretic approach is employed to learn the data-driven phrases. The interdependencies between interactive phrases are explicitly captured in the model to deal with motion ambiguity and partial occlusion in the interactions. We evaluate our method on the BIT-Interaction data set, UT-Interaction data set, and Collective Activity data set. Experimental results show that our approach achieves superior performance over previous approaches.
An online system leveraging self-explanation was developed. The theoretical basis and design principles guiding the development of the system were explicated. Four evaluation studies were conducted ...to assess the student-generated explanations component accompanying student-generated questions (SQG) and the embedded designs. The analyzed data revealed several important findings. First, SGQ complemented by student-generated explanations (as compared to SGQ alone) was regarded as promoting learning better by a sizeable majority of the participants, and its facilitating effects on cognitive and affective aspects were noted. Second, the fact that the exact same set of cognitive and affective gains and similar patterns in the spread of responses were found from the explanation-generation for self- and peer-generated questions provided preliminary evidence supporting the "manageable versatility" design principle. Third, the result indicating that a predominant percent of the participants supported online practice on SGQ complemented by student-generated explanations substantiated the "manageable integration" guiding principle. Finally, the finding that a significantly greater percentage of the participants felt multimedia-equipped explanations to be better for SGQ (as compared to text-based explanations), both cognitively and affectively, provided preliminary evidence supporting the effectiveness of the multimedia design in terms of attaining the multiple feedback functions.
This paper addresses the problem of recognizing human interactions with close physical contact from videos. Due to ambiguities in feature-to-person assignments and frequent occlusions in close ...interactions, it is difficult to accurately extract the interacting people. This degrades the recognition performance. We, therefore, propose a hierarchical model, which recognizes close interactions and infers supporting regions for each interacting individual simultaneously. Our model associates a set of hidden variables with spatiotemporal patches and discriminatively infers their states, which indicate the person that the patches belong to. This patch-aware representation explicitly models and accounts for discriminative supporting regions for individuals, and thus overcomes the problem of ambiguities in feature assignments. Moreover, we incorporate the prior for the patches to deal with frequent occlusions during interactions. Using the discriminative supporting regions, our model builds cleaner features for individual action recognition and interaction recognition. Extensive experiments are performed on the BIT-Interaction data set and the UT-Interaction data set set #1 and set #2, and validate the effectiveness of our approach.
Long noncoding RNAs (lncRNAs) are emerging as important regulators during tumorigenesis by serving as competing endogenous RNAs (ceRNAs). In this study, the qRT-PCR results indicated that the lncRNA ...protein disulfide isomerase family A member 3 pseudogene 1 (PDIA3P) was overexpressed in oral squamous cell carcinoma (OSCC) and decreased the survival rate of OSCC patients. CCK-8 and clonal colony formation assays were used to detect the effects of PDIA3P on proliferation. Results revealed that silencing PDIA3P by small interfering RNA (siRNA) inhibited OSCC cell proliferation and repressed tumor growth and reduced the expression of proliferation antigen Ki-67 in vivo. Furthermore, the interaction between PDIA3P and miRNAs was then analyzed by qRT-PCR and luciferase reporter gene assay. We found that PDIA3P negatively regulated miR-185-5p in OSCC cells. Simultaneously, we found that silencing PDIA3P by siRNA suppressed proliferation via miR-185-5p in OSCC cells. Moreover, silencing PDIA3P by siRNA inhibited CCND2 protein (no influence on mRNA levels) expression via miR-185-5p in OSCC cells, and CCND2 facilitated cell proliferation of SCC4 and SCC15 cells induced by sh-PDIA3P#1. Therefore, our study demonstrated that PDIA3P may be a therapeutic target for the treatment of OSCC.
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