Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

Proportional navigation with delayed line-of-sight rate

This brief discusses the convergence analysis of proportional navigation (PN) guidance law in the presence of delayed line-of-sight (LOS) rate information. The delay in the LOS rate is introduced by the missile guidance system that uses a low cost sensor to obtain LOS rate information by image processing techniques. A Lyapunov-like function is used to analyze the convergence of the delay differential equation (DDE) governing the evolution of the LOS rate. The time-to-go until which decreasing behaviour of the Lyapunov-like function can be guaranteed is obtained. Conditions on the delay for finite time convergence of the LOS rate are presented for the linearized engagement equation. It is observed that in the presence of line-of-sight rate delay, increasing the effective navigation constant of the PN guidance law deteriorates its performance. Numerical simulations are presented to validate the results.

Robustness of early warning signals of regime shifts in time-delayed ecological models

Various ecological and other complex dynamical systems may exhibit abrupt regime shifts or critical transitions, wherein they reorganize from one stable state to another over relatively short time scales. Because of potential losses to ecosystem services, forecasting such unexpected shifts would be valuable. Using mathematical models of regime shifts, ecologists have proposed various early warning signals of imminent shifts. However, their generality and applicability to real ecosystems remain unclear because these mathematical models are considered too simplistic. Here, we investigate the robustness of recently proposed early warning signals of regime shifts in two well-studied ecological models, but with the inclusion of time-delayed processes. We find that the average variance may either increase or decrease prior to a regime shift and, thus, may not be a robust leading indicator in time-delayed ecological systems. In contrast, changing average skewness, increasing autocorrelation at short time lags, and reddening power spectra of time series of the ecological state variable all show trends consistent with those of models with no time delays. Our results provide insights into the robustness of early warning signals of regime shifts in a broader class of ecological systems.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Antithyroid drugs and their analogues: synthesis, structure, and mechanism of action

Thyroid hormones are essential for the development and differentiation of all cells of the human body. They regulate protein, fat, and carbohydrate metabolism. In this Account, we discuss the synthesis, structure, and mechanism of action of thyroid hormones and their analogues. The prohormone thyroxine (14) is synthesized on thyroglobulin by thyroid peroxidase (TPO), a heme enzyme that uses iodide and hydrogen peroxide to perform iodination and phenolic coupling reactions. The monodeiodination of T4 to 3,3',5-triiodothyronine (13) by selenium-containing deiodinases (ID-1, ID-2) is a key step in the activation of thyroid hormones. The type 3 deiodinase (ID-3) catalyzes the deactivation of thyroid hormone in a process that removes iodine selectively from the tyrosyl ring of T4 to produce 3,3',5'-triiodothyronine (rT3). Several physiological and pathological stimuli influence thyroid hormone synthesis. The overproduction of thyroid hormones leads to hyperthyroidism, which is treated by antithyroid drugs that either inhibit the thyroid hormone biosynthesis and/or decrease the conversion of T4 to T3. Antithyroid drugs are thiourea-based compounds, which indude propylthiouracil (PTU), methimazole (MM I), and carbimazole (CBZ). The thyroid gland actively concentrates these heterocyclic compounds against a concentration gradient Recently, the selenium analogues of PTU, MMI, and CBZ attracted significant attention because the selenium moiety in these compounds has a higher nucleophilicity than that of the sulfur moiety. Researchers have developed new methods for the synthesis of the selenium compounds. Several experimental and theoretical investigations revealed that the selone (C=Se) in the selenium analogues is more polarized than the thione (C=S) in the sulfur compounds, and the selones exist predominantly in their zwitterionic forms. Although the thionamide-based antithyroid drugs have been used for almost 70 years, the mechanism of their action is not completely understood. Most investigations have revealed that MMI and PTU irreversibly inhibit TPO. PTU, MTU, and their selenium analogues also inhibit ID-1, most likely by reacting with the selenenyl iodide intermediate. The good ID-1 inhibitory activity of Pill and its analogues can be ascribed to the presence of the -N(H)-C(=O)- functionality that can form hydrogen bonds with nearby amino add residues in the selenenyl sulfide state. In addition to the TPO and ID-1 inhibition, the selenium analogues are very good antioxidants. In the presence of cellular reducing agents such as GSH, these compounds catalytically reduce hydrogen peroxide. They can also efficiently scavenge peroxynitrite, a potent biological oxidant and nitrating agent.

Differential Action of Glycoprotein Hormones: Significance in Cancer Progression

Growth of multicellular organisms depends on maintenance of proper balance between proliferation and differentiation. Any disturbance in this balance in animal cells can lead to cancer. Experimental evidence is provided to conclude with special reference to the action of follicle-stimulating hormone (FSH) on Sertoli cells, and luteinizing hormone (LH) on Leydig cells that these hormones exert a differential action on their target cells, i.e., stimulate proliferation when the cells are in an undifferentiated state which is the situation with cancer cells and promote only functional parameters when the cell are fully differentiated. Hormones and growth factors play a key role in cell proliferation, differentiation, and apoptosis. There is a growing body of evidence that various tumors express some hormones at high levels as well as their cognate receptors indicating the possibility of a role in progression of cancer. Hormones such as LH, FSH, and thyroid-stimulating hormone have been reported to stimulate cell proliferation and act as tumor promoter in a variety of hormone-dependent cancers including gonads, lung, thyroid, uterus, breast, prostate, etc. This review summarizes evidence to conclude that these hormones are produced by some cancer tissues to promote their own growth. Also an attempt is made to explain the significance of the differential action of hormones in progression of cancer with special reference to prostate cancer.

A Study of Early Afterdepolarizations in a Model for Human Ventricular Tissue

Sudden cardiac death is often caused by cardiac arrhythmias. Recently, special attention has been given to a certain arrhythmogenic condition, the long-QT syndrome, which occurs as a result of genetic mutations or drug toxicity. The underlying mechanisms of arrhythmias, caused by the long-QT syndrome, are not fully understood. However, arrhythmias are often connected to special excitations of cardiac cells, called early afterdepolarizations (EADs), which are depolarizations during the repolarizing phase of the action potential. So far, EADs have been studied mainly in isolated cardiac cells. However, the question on how EADs at the single-cell level can result in fibrillation at the tissue level, especially in human cell models, has not been widely studied yet. In this paper, we study wave patterns that result from single-cell EAD dynamics in a mathematical model for human ventricular cardiac tissue. We induce EADs by modeling experimental conditions which have been shown to evoke EADs at a single-cell level: by an increase of L-type Ca currents and a decrease of the delayed rectifier potassium currents. We show that, at the tissue level and depending on these parameters, three types of abnormal wave patterns emerge. We classify them into two types of spiral fibrillation and one type of oscillatory dynamics. Moreover, we find that the emergent wave patterns can be driven by calcium or sodium currents and we find phase waves in the oscillatory excitation regime. From our simulations we predict that arrhythmias caused by EADs can occur during normal wave propagation and do not require tissue heterogeneities. Experimental verification of our results is possible for experiments at the cell-culture level, where EADs can be induced by an increase of the L-type calcium conductance and by the application of I-Kr blockers, and the properties of the emergent patterns can be studied by optical mapping of the voltage and calcium.

RAPID HUMAN ACTION RECOGNITION IN H.264/AVC COMPRESSED DOMAIN FOR VIDEO SURVEILLANCE

This paper discusses a novel high-speed approach for human action recognition in H. 264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of our work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can effect in reduced hardware utilization and fast recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust in outdoor as well as indoor testing scenarios. We have tested our method on two benchmark action datasets and achieved more than 85% accuracy. The proposed algorithm classifies actions with speed (>2000 fps) approximately 100 times more than existing state-of-the-art pixel-domain algorithms.

Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a `footprint' in the generator potential that obscures incoming signals. These three processes reduce information rates by similar to 50% in generator potentials, to similar to 3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation.

Design and Synthesis of New Benzimidazole-Carbazole Conjugates for the Stabilization of Human Telomeric DNA, Telomerase Inhibition, and Their Selective Action on Cancer Cells

Cell-permeable small molecules that enhance the stability of the G-quadruplex (G4) DNA structures are currently among the most intensively pursued ligands for inhibition of the telomerase activity. Herein we report the design and syntheses of four novel benzimidazole carbazole conjugates and demonstrate their high binding affinity to G4 DNA. Si nuclease assay confirmed the ligand mediated G-quadruplex DNA protection. Additional evidence from Telomeric Repeat Amplification Protocol (TRAP-LIG) assay demonstrated efficient telomerase inhibition activity by the ligands. Two of the ligands showed IC50 values in the sub-micromolar range in the TRAP-LIG assay, which are the best among the benzimidazole derivatives reported so far. The ligands also exhibited cancer cell selective nuclear internalization, nuclear condensation, fragmentation, and eventually antiproliferative activity in long-term cell viability assays. Annexin V-FITC/PI staining assays confirm that the cell death induced by the ligands follows an apoptotic pathway. An insight into the mode of ligand binding was obtained from the molecular dynamics simulations.

2-(4-Chlorobenzyl)-6-arylimidazo2,1-b]1,3,4]thiadiazoles: Synthesis, cytotoxic activity and mechanism of action

The cytotoxic activity of a new series of 2-(4'-chlorobenzyl)-5,6-disubstituted imidazo2,1-b]1,3,4]wthiadiazoles against different human and murine cancer cell lines is reported. Among the tested compounds, two derivatives namely 2-(4-chlorobenzyl)-6-(2-oxo-2H-chromen-3-yl)imidazo2,1-1)]1,3,4]th iadiazole-5-carbaldehyde 4i and 2-(4-chlorobenzyl)-6-(2-oxo-2H-chromen-3-ypimidazo2,1-1)]1,3,4]thi adiazol-5-yl thiocyanate 5i emerged as the most potent against all the cell lines. To investigate the mechanism of action, we selected compounds 4i for cell cycle study, analysis of mitochondrial membrane potential and Annexin V-FITC flow cytometric analysis and DNA fragmentation assay. Results showed that 4i induced cytotoxicity by inducing apoptosis without arresting the cell cycle. (C) 2014 Elsevier Masson SAS. All rights reserved.

NUMERICAL STUDIES OF DYNAMO ACTION IN A TURBULENT SHEAR FLOW. I.

We perform numerical experiments to study the shear dynamo problem where we look for the growth of a large-scale magnetic field due to non-helical stirring at small scales in a background linear shear flow in previously unexplored parameter regimes. We demonstrate the large-scale dynamo action in the limit where the fluid Reynolds number (Re) is below unity while the magnetic Reynolds number (Rm) is above unity; the exponential growth rate scales linearly with shear, which is consistent with earlier numerical works. The limit of low Re is particularly interesting, as seeing the dynamo action in this limit would provide enough motivation for further theoretical investigations, which may focus attention on this analytically more tractable limit of Re < 1 compared to the more formidable limit of Re > 1. We also perform simulations in the regimes where (i) both (Re, Rm) < 1, and (ii) Re > 1 and Rm < 1, and compute all of the components of the turbulent transport coefficients (alpha(ij) and alpha(ij)) using the test-field method. A reasonably good agreement is observed between our results and the results of earlier analytical works in similar parameter regimes.

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.