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Animals locomote for various reasons: to search for food, find suitable habitat, pursue prey, escape from predators, or seek a mate. The grand scale of biodiversity contributes to the great locomotory design and mode diversity. Various creatures make use of legs, wings, fins and other means to move through the world. In this report, we refer to the locomotion of general biological species as biolocomotion. We present a computational approach to detect biolocomotion in unprocessed video. Significantly, the motion exhibited by the body parts of a biological entity to navigate through an environment can be modeled by a combination of an overall positional advance with an overlaid asymmetric oscillatory pattern, a distinctive signature that tends to be absent in non-biological objects in locomotion. We exploit this key trait of positional advance with asymmetric oscillation along with differences in an object's common motion (extrinsic motion) and localized motion of its parts (intrinsic motion) to detect biolocomotion. An algorithm is developed to measure the presence of these traits in tracked objects to determine if they correspond to a biological entity in locomotion. An alternative algorithm, based on generic features combined with learning is assembled out of components from allied areas of investigation, also is presented as a basis of comparison. A novel biolocomotion dataset encompassing a wide range of moving biological and non-biological objects in natural settings is provided. Also, biolocomotion annotations to an extant camouflage animals dataset are provided. Quantitative results indicate that the proposed algorithm considerably outperforms the alternative approach, supporting the hypothesis that biolocomotion can be detected reliably based on its distinct signature of positional advance with asymmetric oscillation and extrinsic/intrinsic motion dissimilarity.

Acute Coronary Syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morphologically to a plaque rupture. In this paper, we propose methods to classify TCFA using various machine learning classifiers including Feed-forward Neural Network (FNN), K-Nearest Neighbor (KNN), Random Forest (RF) and Convolutional Neural Network (CNN) to figure out a classifier that shows optimal TCFA classification accuracy. In addition, we suggest pixel range based feature extraction method to extract the ratio of pixels in the different region of interests to reflect the physician's TCFA discrimination criteria. A total of 12,325 IVUS images were labeled with corresponding OCT images to train and evaluate the classifiers. We achieved 0.884, 0.890, 0.878 and 0.933 Area Under the ROC Curve (AUC) in the order of using FNN, KNN, RF and CNN classifier. As a result, the CNN classifier performed best and the top 10 features of the feature-based classifiers (FNN, KNN, RF) were found to be similar to the physician's TCFA diagnostic criteria., Comment: Submitted to journal

In computer vision, action recognition refers to the act of classifying an action that is present in a given video and action detection involves locating actions of interest in space and/or time. Videos, which contain photometric information (e.g. RGB, intensity values) in a lattice structure, contain information that can assist in identifying the action that has been imaged. The process of action recognition and detection often begins with extracting useful features and encoding them to ensure that the features are specific to serve the task of action recognition and detection. Encoded features are then processed through a classifier to identify the action class and their spatial and/or temporal locations. In this report, a thorough review of various action recognition and detection algorithms in computer vision is provided by analyzing the two-step process of a typical action recognition and detection algorithm: (i) extraction and encoding of features, and (ii) classifying features into action classes. In efforts to ensure that computer vision-based algorithms reach the capabilities that humans have of identifying actions irrespective of various nuisance variables that may be present within the field of view, the state-of-the-art methods are reviewed and some remaining problems are addressed in the final chapter.

We develop a method to describe the temporal evolution of an interacting system of bosons, for which the field operator expansion is truncated after a finite number $M$ of modes, in a rigorously controlled manner. Using McLachlan's principle of least error, we find a self-consistent set of equations for the many-body state. As a particular benefit, and in distinction to previously proposed approaches, the presently introduced method facilitates the dynamical increase of the number of orbitals during the temporal evolution, due to the fact that we can rigorously monitor the error made by increasing the truncation dimension $M$. The additional orbitals, determined by the condition of least error of the truncated evolution relative to the exact one, are obtained from an initial trial state by steepest $constrained$ descent., Comment: 12 pages of RevTex4-1

We investigate the dynamical mode population statistics and associated first- and second-order coherence of an interacting bosonic two-mode model when the pair-exchange coupling is quenched from negative to positive values. It is shown that for moderately rapid second-order transitions, a new pair-coherent phase emerges on the positive coupling side in an excited state, which is not fragmented as the ground-state single-particle density matrix would prescribe it to be., Comment: 4 pages of RevTex4-1, 4 figures; Rapid Communication in Physical Review A

Electric control of multiple domain walls (DWs) motion is demonstrated by Pt/Co/Pt nanotracks with perpendicular magnetic anisotropy. Due to the weak microstructural disorders with small DW propagation field, the purely current-driven DW motion is achieved in the creep regime at current densities less than 10^7 A/cm^2 at room temperature. It is confirmed that by use of a scanning magneto-optical Kerr effect microscope, several DWs are simultaneously and identically displaced by the same distance in the same direction. Utilizing such DWs motion, we succeed to realize random bits writing and transferring as a device prototype of four-bit shift registers.

Electric current exerts torques-so-called spin transfer torques (STTs)-on magnetic domain walls (DWs), resulting in DW motion. At low current densities, the STTs should compete against disorders in ferromagnetic nanowires but the nature of the competition remains poorly understood. By achieving two-dimensional contour maps of DW speed with respect to current density and magnetic field, here we visualize unambiguously distinct roles of the two STTs-adiabatic and nonadiabatic-in scaling behaviour of DW dynamics arising from the competition. The contour maps are in excellent agreement with predictions of a generalized scaling theory, and all experimental data collapse onto a single curve. This result indicates that the adiabatic STT becomes dominant for large current densities, whereas the nonadiabatic STT-playing the same role as a magnetic field-subsists at low current densities required to make emerging magnetic nanodevices practical.

Spin-polarized electric current exerts torque on local magnetic spins, resulting in magnetic domain-wall (DW) motion in ferromagnetic nanowires. Such current-driven DW motion opens great opportunities toward next-generation magnetic devices controlled by current instead of magnetic field. However, the nature of the current-driven DW motion--considered qualitatively different from magnetic-field-driven DW motion--remains yet unclear mainly due to the painfully high operation current densities J_OP, which introduce uncontrollable experimental artefacts with serious Joule heating. It is also crucial to reduce J_OP for practical device operation. By use of metallic Pt/Co/Pt nanowires with perpendicular magnetic anisotropy, here we demonstrate DW motion at current densities down to the range of 10^9 A/m^2--two orders smaller than existing reports. Surprisingly the current-driven motion exhibits a scaling behaviour identical to the field-driven motion and thus, belongs to the same universality class despite their qualitative differences. Moreover all DW motions driven by either current or field (or by both) collapse onto a single curve, signalling the unification of the two driving mechanisms. The unified law manifests non-vanishing current efficiency at low current densities down to the practical level, applicable to emerging magnetic nanodevices., Comment: This paper has been withdrawn by the authors. New version is available at http://arxiv.org/abs/1006.1216

There have been theoretical studies for generation of optical coherent superposition states. Once the superposition state is generated it is natural to ask if it is possible to amplify it without losing the nonclassical properties of the field state. We consider amplification of the superposition state in various amplifiers such as a sub-Poissonian amplifier, a phase-sensitive amplifier and a classical amplifier. We show the evolution of phase probability distribution functions in the amplifier.