Abstract

MapReduce is a widely used framework for distributed computing. Data shuffling between the Map phase and Reduce phase of a job involves a large amount of data transfer across servers, which in turn accounts for increase in job completion time. Recently, Coded MapReduce has been proposed to offer savings with respect to the communication cost incurred in data shuffling. This is achieved by creating coded multicast opportunities for shuffling through repeating Map tasks at multiple servers. We consider a server-rack architecture for MapReduce and in this architecture, propose to divide the total communication cost into two: intrarack communication cost and cross-rack communication cost. Having noted that cross-rack data transfer operates at lower speed as compared to intrarack data transfer, we present a scheme termed as Hybrid Coded MapReduce which results in lower cross-rack communication than Coded MapReduce at the cost of increase in intra-rack communication. In addition, we pose the problem of assigning Map tasks to servers to maximize data locality in the framework of Hybrid Coded MapReduce as a constrained integer optimization problem. We show through simulations that data locality can be improved considerably by using the solution of optimization to assign Map tasks to servers.

Abstract

Bilingualism has been found to delay onset of dementia and this has been attributed to an advantage in executive control in bilinguals. However, the relationship between bilingualism and cognition is complex, with costs as well as benefits to language functions. To further explore the cognitive consequences of bilingualism, the study used Frontotemporal dementia (FTD) syndromes, to examine whether bilingualism modifies the age at onset of behavioral and language variants of Frontotemporal dementia (FTD) differently. Case records of 193 patients presenting with FTD (121 of them bilingual) were examined and the age at onset of the first symptoms were compared between monolinguals and bilinguals. A significant effect of bilingualism delaying the age at onset of dementia was found in behavioral variant FTD (5.7 years) but not in progressive nonfluent aphasia (0.7 years), semantic dementia (0.5 years …

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 large-scale 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 …

Abstract

The neurocomputational model described here proposes that two dimensions involved in computation of reward prediction errors i.e magnitude and time could be computed separately and later combined unlike traditional reinforcement learning models. The model is built on biological evidences and is able to reproduce various aspects of classical conditioning, namely, the progressive cancellation of the predicted reward, the predictive firing from conditioned stimuli, and delineation of early rewards by showing firing for sooner early rewards and not for early rewards that occur with a longer latency in accordance with biological data.

Abstract

utomatic photo aesthetic assessment is a challenging artificial intelligence task. Existing computational approaches have focused on modeling a single aesthetic score or a class (good or bad), however these do not provide any details on why the photograph is good or bad, or which attributes contribute to the quality of the photograph. To obtain both accuracy and human interpretation of the score, we advocate learning the aesthetic attributes along with the prediction of the overall score. For this purpose, we propose a novel multitask deep convolution neural network, which jointly learns eight aesthetic attributes along with the overall aesthetic score. We report near human performance in the prediction of the overall aesthetic score. To understand the internal representation of these attributes in the learned model, we also develop the visualization technique using back propagation of gradients. These visualizations highlight the important image regions for the corresponding attributes, thus providing insights about model's representation of these attributes. We showcase the diversity and complexity associated with different attributes through a qualitative analysis of the activation maps.

Abstract

Papillary Renal Cell Carcinoma (PRCC) is a heterogeneous disease accounting for 10%-15% of renal cell carcinomas. A comprehensive analysis is required to find the genes that are responsible for the stage progression in PRCC. The advent of next generation sequencing techniques (NGS) has produced a lot of high throughput data from patients that can be analyzed to address this problem. The low sample size, noise and high dimensionality of the data though enhances the complexity, requiring the use of sophisticated methods. In our study we propose a machine learning pipeline fulfilling a two-fold objective: 1) To find suitable genes that could serve as potential biomarkers for stage progression in PRCC. 2) To build a classifier using the above biomarkers that can predict the stage of a given patient. The RNA-Seq data of PRCC was taken and divided into training set (80%) and test set (20%). Different …

Abstract

Feminine facial features enhance the expressive cues associated with happiness but not sadness. This makes a woman look happier than a man even when their smiles have the same intensity. So, to correctly infer the actual happiness of a woman, one would have to subtract the effect of these facial features. We hypothesised that our perceptual system would apply this subtraction for women, but not for men. This implies that this female-specific subtraction would cause one to infer a man to be happier than a woman if both are matched for facial appearance and expression intensity. We tested this using androgynous virtual faces with equal expression intensity. As predicted, happy men were inferred to be happier than happy women, but sad men were not inferred to be sadder than sad women, supporting our hypothesis of a gender- and emotion-specific perceptual correction.

Abstract

This paper discusses the development of an optimal wheel-torque controller for a compliant modular robot. The wheel actuators are the only actively controllable elements in this robot. For this type of robots, wheel-slip could offer a lot of hindrance while traversing on uneven terrains. Therefore, an effective wheel-torque controller is desired that will also improve the wheel-odometry and minimize power consumption. In this work, an optimal wheel-torque controller is proposed that minimizes the traction-to-normal force ratios of all the wheels at every instant of its motion. This ensures that, at every wheel, the least traction force per unit normal force is applied to maintain static stability and desired wheel speed. The lower this is, in comparison to the actual friction coefficient of the wheel-ground interface, the more margin of slip-free motion the robot can have. This formalism best exploits the redundancy offered by a modularly designed robot. This is the key novelty of this work. Extensive numerical and experimental studies were carried out to validate this controller. The robot was tested on four different surfaces and we report an overall average slip reduction of 44% and mean wheel-torque reduction by 16%.

Abstract

Dynamic scene understanding is a challenging problem and motion segmentation plays a crucial role in solving it. Incorporating semantics and motion enhances the overall perception of the dynamic scene. For applications of outdoor robotic navigation, joint learning methods have not been extensively used for extracting spatio-temporal features or adding different priors into the formulation. The task becomes even more challenging without stereo information being incorporated. This paper proposes an approach to fuse semantic features and motion clues using CNNs, to address the problem of monocular semantic motion segmentation. We deduce semantic and motion labels by integrating optical flow as a constraint with semantic features into dilated convolution network. The pipeline consists of three main stages ie Feature extraction, Feature amplification and Multi Scale Context Aggregation to fuse the semantics and flow features. Our joint formulation shows significant improvements in monocular motion segmentation over the state of the art methods on challenging KITTI tracking dataset.

Abstract

This paper presents a novel and generic reactionless visual servo controller for a satellite-based multi-arm space robot. The controller is designed to complete the task of visually servoing the robot’s end-effectors to a desired pose, while maintaining minimum attitude disturbance on the base-satellite. Task function approach is utilized to coordinate the servoing process and attitude of the base satellite. A redundancy formulation is used to define the tasks. The visual serving task is defined as a primary task, while regulating attitude of the base satellite to zero is defined as a secondary task. The secondary task is defined through a quadratic optimization problem, in such a way that it does not affect the primary task, and simultaneously minimizes its cost function. Stability analysis of the proposed control methodology is also discussed. A set of numerical experiments are carried out on different multi-arm space robotic systems. These systems are a planar dual-arm robot, a spatial dual-arm robot, and a three-arm planar robot. The results of the simulation experiments show efficacy, generality and applicability of the proposed control methodology.