Abstract

Optimal initiation of quantum dynamics of N-H photodissociation of pyrrole on the S0- 1πσ∗( 1A2) coupled electronic states by UV-laser pulses in an effort to guide the subsequent dynamics to dissociation limits is studied theoretically. Specifically, the task of designing optimal laser pulses that act on initial vibrational states of the system for an effective UV-photodissociation is considered by employing optimal control theory. The associated control mechanism(s) for the initial state dependent photodissociation dynamics of pyrrole in the presence of control pulses is examined and discussed in detail. The initial conditions determine implicitly the variation in the dissociation probabilities for the two channels, upon interaction with the field. The optimal pulse corresponds to the objective fixed as maximization of overall reactive flux subject to constraints of reasonable fluence and quantum dynamics. The simple optimal pulses obtained by the use of genetic algorithm based optimization are worth an experimental implementation given the experimental relevance of πσ∗-photochemistry in recent times.

Abstract

A first principles quantum dynamics study of N–H photodissociation of pyrrole on the coupled electronic states is carried out with the aid of an optimally designed UV-laser pulse. A new photodissociation path, as compared to the conventional barrier crossing on the state, opens up upon electronic transitions under the influence of pump-dump laser pulses, which efficiently populate both the dissociation channels. The interplay of electronic transitions due both to vibronic coupling and the laser pulse is observed in the control mechanism and discussed in detail. The proposed control mechanism seems to be robust, and not discussed in the literature so far, and is expected to trigger future experiments on the photochemistry of molecules of chemical and biological importance. The design of the optimal pulses and their application to enhance the overall dissociation probability is carried out within the framework of optimal control theory. The quantum dynamics of the system in the presence of pulse is treated by solving the time-dependent Schr ̈ odinger equation in the semi-classical dipole approximation

Abstract

The power of ConvNets has been demonstrated in a wide variety of vision tasks including pose estimation. But they often produce absurdly erroneous predictions in videos due to unusual poses, challenging illumination, blur, self-occlusions etc. These erroneous predictions can be re- fined by leveraging previous and future predictions as the temporal smoothness constrain in the videos. In this paper, we present a generic approach for pose correction in videos using sequence learning that makes minimal assumptions on the sequence structure. The proposed model is generic, fast and surpasses the state-of-the-art on benchmark datasets. We use a generic pose estimator for initial pose estimates, which are further refined using our method. The proposed architecture uses Long Short-Term Memory ( LSTM ) encoder- decoder model to encode the temporal context and refine the estimations. We show 3.7% gain over the baseline Yang & Ramanan ( YR ) [1] and 2.07% gain over Spatial Fusion Network ( SFN ) [2] on a new challenging YouTube Pose Subset dataset [3]. Keywords-Pose estimation; sequence to sequence learnin

Abstract

Human pose as a query modality is an alternative and rich experience for image and video retrieval. It has interesting retrieval applications in domains such as sports and dance databases. In this work we propose two novel ways for representing the image of a person striking a pose, one looking for parts and other looking at the whole image. These representations are then used for retrieval. Both the representations are obtained using deep learning methods. In the first method, we make the following contributions: (a) We introduce ‘deep poselets’ for pose- sensitive detection of various body parts, built on convolutional neural network (CNN) features. These deep poselets significantly outperform previous instantiations of Berkeley poselets [6], and (b) Using these detec- tor responses, we construct a pose representation that is suitable for pose search, and show that pose retrieval performance is on par with the previous methods. In the second method, we make the follow- ing contributions: (a) We design an optimized neural network which maps the input image to a very low dimensional space where similar poses are close by and dissimilar poses are farther away, and (b) We show that pose retrieval system using these low dimensional representation is on par with the deep poselet representation and is on par with the previous methods. The previous works with which the above two methods are compared include bag of visual words [44], Berkeley poselets [6] and human pose estimation algorithms [52]. All the methods are quantitatively eval- uated on a large dataset of images built from a number of standard benchmarks together with frames from

Abstract

Mobile image retrieval allows users to identify visual information about their environment by transmitting image queries to an online image database that has associated annotations (e.g. location, product information, etc.) with the images. However, this is reliant on a network connection to transmit the query and retrieve the information. This chapter examines mobile image retrieval for offline use when the data net- work connection is limited or not available. In this scenario, the entire visual search index must reside on the mobile device itself. More specifically, we are interested in ‘instance retrieval’, where the annotations associated with the images (e.g. building’s name, object information) are returned by the query and not the images themselves. Figure 1 shows an example use case where mobile camera photos are used to identify buildings and landmarks without the need for a network connection. While our targeted instance retrieval does not need to store the images, the entire visual index structure needs to fit on a mobile device, ideally within a small footprint (e.g. 16–32 MB). This small memory footprint serves two purposes. First, while mobile phones have up to 16–32 GB of storage, this is mainly in the form of slower flash memory that is an order of magnitude slower than RAM. Having the entire index within tens of MBs makes it possible for use in a resident application on the phone’s RAM. Second, this small size is inline with common practices for mobile applications; e.g. the iPhone average application size is currently 23 MB. Addition- ally, iPhone apps less than 50 MB can be downloaded using 3G/4G, anything larger

Abstract

In this research paper, noise suppression ability of associative memories (e.g. Hopfield neural network) is briefly summarized. Motivated by this fact, noise suppression abilities of trained convolutional network is discussed. Introducing the concept of null vectors, noise suppression ability of Single Layer Perceptron, Multi-Layer Perceptron and Extreme Learning Machine are discussed.

Abstract

We assess change in metastability to characterize age-effects on the dynamic repertoire of the functional networks at rest. Resting state fMRI signals from each subject (N=48) have been used and metastability is evaluated as the standard deviation of mean phase synchrony of BOLD signals across whole-brain as well as across known resting state networks. The results suggest that significant whole-brain metastability changes occur between middle to old age. We also demonstrate that static time-averaged FC largely undermines age-effects on the interaction between functional networks. Discriminant Function Analysis reveals existence of two different patterns of change in metastability, which maximally discriminates between two different processes of maturation and ageing.

Abstract

What makes a computational neuronal model ’large scale’ ? Is it the number of neurons modeled? Or the number of brain regions modeled in a network? Most of the higher cognitive processes span across co-ordinated activity in a network of different brain areas. However at the same time, the basic information transfer takes place at a single neuron level, together with multiple other neurons. We explore modeling a neural system involving some areas of cortex, the basal ganglia (BG) and thalamus for the process of decision making, using a largescale neural engineering framework, Nengo. Early results tend to replicate the known neural activity patterns as found in the previous action selection model by Guthrie et al. 2013, besides operating with a larger neuronal populations. The power of converting algorithms to efficiently weighed neural networks in Nengo (Stewart et al. 2009 and Bekolay et al. 2013) is exploited in this work. Crucial aspects in a computational model, like parameter tuning and detailed neural implementations, while moving from a simplistic to large-scale model, are studied.

Abstract

The architectural trend towards heterogeneity has pushed heterogeneous computing to the fore of parallel computing research. Heterogeneous algorithms, often carefully handcrafted, have been designed for several important problems from parallel computing such as sorting, graph algorithms, matrix computations, and the like. A majority of these algorithms follow a work partitioning approach where the input is divided into appropriate sized parts so that individual devices can process the “right-sized” parts of the input. However, arriving at a good work partitioning is usually non-trivial and may crucially depend also on the input instance apart from the heterogeneous algorithm used. An extensive empirical search is not helpful as such an empirical search can potentially offset any gains accrued out of heterogeneous algorithms. Other recently proposed approaches too are in general inadequate. In this paper, we propose a simple and effective technique for work partitioning in the context of heterogeneous algorithms. Our technique is based on randomized sampling and therefore can adapt to both the algorithm used and the input instance. Our technique is generic in its applicability as we will demonstrate in this paper. We validate our technique on three problems: multiplying two unstructured sparse matrices (spmm), multiplying two scale-free sparse matrices, and finding the connected components of a graph (CC). For these problems, we show that using our method, we can find the required threshold that is under 10% away from the best possible thresholds respectively.

Abstract

Query recommendation (QR) support search engine to provide alternative queries as a recommendation using similarity-based approaches. In the literature, orthogonal query recommendation (OQR) has been proposed to compute the diversity of QR when the user does not formulate proper queries. The OQR uses dissimilarity measure in QR to recommend completely different queries. In this paper, we propose an approach in QR by extending association rules, diverse patterns, and unbalanced concept hierarchy of search terms. We conceptualize association rules based QR, and order the rules based on confidence and diversity. Subsequently, the high ranked rules based on confidence and diversity are provided in QRs. The experimental results on real world AOL click-through dataset show that the diverse QRs improve the performance significantly