Abstract

An index coding (IC) problem consisting of a server and multiple receivers with different side-information and demand sets can be equivalently represented using a fitting matrix.A scalar linear index code to a given IC problem is a matrix representing the transmitted linear combinations of the message symbols. The length of an index code is then the number of transmissions (or equivalently, the number of rows in the index code). An IC problem I ext is called an extension of another IC problem I if the fitting matrix of Iis a sub matrix of the fitting matrix of I ext. We first present a straight forward mordern extension Iextof an IC problem I for which an index code is obtained by concatenating m copies of an index code of I. The length of the codes is the same for both I and Iext,and if the index code for I has optimal length then so does the extended code for I ext. More generally, an extended IC problem of I having the same optimal length as I is said to be a rank-invariant extension of I. We then focus on2-order rank-invariant extensions of I, and present constructions of such extensions based on in volutory permutation matrices.

Abstract

The index coding problem is a problem of efficient broadcasting with side-information. In uniprior index coding,the sets of side-information symbols possessed by different receivers are disjoint. For single uniprior index coding, in which each receiver has a single unique side-information symbol, a polynomial complexity construction of an optimal index code is known from prior work. In this work, we model the uniprior index coding problem as a super graph, and focus on a class of uniprior problems defined on special supergraphs known as generalized cycles in which the sizes of the demand set andthe side-information set are equal at each receiver. For such problems, we prove upper and lower bounds on the optimal broadcast rate. Using a connection with Eulerian directed graphs,we also show that the upper and lower bounds are equal fora subclass of uniprior problems. We show the NP-hardness offinding the lower bound for uniprior problems on generalized cycles, hence contrasting such uniprior problems with single uniprior problems. Finally, we look at a simple extension of the generalized cycle uniprior class for which we give bounds on the optimal rate and show an explicit scheme which achieves the upper bound

Abstract

Coded caching schemes on broadcast networks with user caches help to offload traffic from peak times to off-peak times by prefetching information from the server to the usersduring off-peak times and thus serving the users more efficiently during peak times using coded transmissions. We consider the problem of secretive coded caching which was proposed recently,in which a user should not be able to decode any information about any file that the user has not demanded. We propose a new secretive coded caching scheme which has a lower average rate compared to the existing state-of-the-art scheme, for the same memory available at the users. The proposed scheme is based one exploiting the presence of common demands between multiple users

Abstract

Linear index coding can be formulated as an inter-ference alignment problem, in which precoding vectors of theminimum possible length are to be assigned to the messagesin such a way that the precoding vector of a demand (at somereceiver) is independent of the space of the interference (non side-information) precoding vectors. An index code has rate1lif theassigned vectors are of lengthl. In this paper, we introduce thenotion of strictly rate1Lmessage subsets which must necessarilybe allocated precoding vectors from a strictlyL-dimensionalspace (L=1,2,3) in any rate13code. We develop a generalnecessary condition for rate13feasibility using intersections ofstrictly rate1Lmessage subsets. We apply the necessary conditionto show that the presence of certain interference configurationsmakes the index coding problem rate13infeasible. We alsoobtain a class of index coding problems, containing certaininterference configurations, which are rate13feasible based on theidea ofcontractionsof an index coding problem. Our necessaryconditions for rate13feasibility and the class of rate13feasibleproblems obtained subsume all such known results for rate13index coding

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 …