Abstract

We find a single parameter family of genuinely entangled three qubit pure states, called the maximally Bell inequality violating states (MBV), which exhibit maximum Bell inequality violation by the reduced bipartite system for a fixed amount of genuine tripartite entanglement quantified by the so called tangle measure. This in turn implies that there holds a complementary relation between the Bell inequality violation by the reduced bipartite systems and the tangle present in the three qubit states, not necessarily pure. The MBV states also exhibit maximum Bell inequality violation by the reduced bipartite systems of the three qubit pure states with a fixed amount of genuine tripartite correlation quantified by the generalized geometric measure, a genuine entanglement measure of multiparty pure states, and the discord monogamy score, a multipartite quantum correlation measure from information theoretic paradigm. The aforementioned complementary relation has also been established for three qubit pure states for the generalized geometric measure and the discord monogamy score respectively. The complementarity between the Bell inequality violation by the reduced bipartite systems and the genuine tripartite correlation suggests that the Bell inequality violation in the reduced two qubit system comes at the cost of the total tripartite correlation present in the entire system.

Abstract

Parallel graph algorithms continue to attract a lot of research attention given their applications to several fields of sciences and engineering. Efficient design and implementation of graph algorithms on modern manycore accelerators has to however contend with a host of challenges including not being able to reach full memory system throughput and irregularity. Of late, focusing on real-world graphs, researchers are addressing these challenges by using decomposition and preprocessing techniques guided by the structural properties of such graphs. In this direction, we present a new GPU algorithm for obtaining an ear decomposition of a graph. Our implementation of the proposed algorithm on an NVidia Tesla K40c improves the state-of-the-art by a factor of 2.3x on average on a collection of real-world and synthetic graphs. The improved performance of our algorithm is due to our proposed characterization that identifies edges of the graph as redundant for the purposes of an ear decomposition. We then study an application of the ear decomposition of a graph in computing the betweenness-centrality values of nodes in the graph. We use an ear decomposition of the input graph to systematically remove nodes of degree two. The actual computation of betweenness-centrality is done on the remaining nodes and the results are extended to nodes removed in the previous step. We show that this approach improves the state-of-the-art for computing betweenness-centrality on an NVidia K40c GPU by a factor of 1.9x on average over a collection of real-world graphs.

Abstract

Energy efficiency is an area of growing importance in datacenters. While the energy proportionality wall poses a challenge to further improve the dynamic power range of servers, heterogeneous systems offer a new opportunity to achieve higher energy efficiency by improving the match between application workload demands on the large heterogeneous system configuration space. This paper proposes a model-driven energy proportionality analysis of heterogeneous clusters consisting of server nodes with different performance-to-power ratio (PPR). Our analysis shows that inter-node heterogeneity has a positive effect of scaling the energy proportionality wall by exposing configurations with sub-linear energy proportionality. Secondly, analysis of these sub-linear configurations on the 95th percentile response time shows that heterogeneity is beneficial in workloads where the PPR of wimpy nodes is higher than brawny nodes.

Abstract

Many Integrated Core (MIC) architecture systems are becoming increasingly popular for HPC applications as they have the dual-advantage of accelerating vector processing and a general-purpose programming model. One of the key challenges for energy-efficient execution on MIC architecture systems is to determine time and energy-efficient configurations among a large system configuration space. Given a parallel program with an execution time deadline and an energy budget, we propose a measurement-based analytical modeling approach to determine these system configurations. In contrast to current approaches, we model both inter-and intra-core resource overlaps, memory contention among threads within a core and memory contention across multiple cores. The model is validated against direct measurement using six representative HPC applications on Intel Xeon Phi system. We show that a Pareto frontier consisting of optimal configurations exist for a parallel program executing on MIC architecture systems. To further understand the Pareto frontier, we use the performance-to-power ratio metric (PPR), that quantifies the amount of useful computations performed per unit energy during the execution. The proposed approach can be used to determine what thread affinity is suitable for offloading execution to accelerators such as Xeon Phi and save energy.

Abstract

We present the detailed results of the application of mathematical optimization algorithms to transistor sizing in a full-adder cell design, to obtain the maximum expected fabrication yield. The approach takes into account all the fabrication process parameter variations specified in an industrial PDK, in addition to operating condition range and NBTI aging. The final design solutions present transistor sizing, which depart from intuitive transistor sizing criteria and show dramatic yield improvements, which have been verified by Monte Carlo SPICE analysis.

Abstract

Computational modeling of the spontaneous dynamics over the whole brain provides critical insight into the spatiotemporal organization of brain dynamics at multiple resolutions and their alteration to changes in brain structure (e.g. in diseased states, aging, across individuals). Recent experimental evidence further suggests that the adverse effect of lesions is visible on spontaneous dynamics characterized by changes in resting state functional connectivity and its graph theoretical properties (e.g. modularity). These changes originate from altered neural dynamics in individual brain areas that are otherwise poised towards a homeostatic equilibrium to maintain a stable excitatory and inhibitory activity. In this work, we employ a homeostatic inhibitory mechanism, balancing excitation and inhibition in the local brain areas of the entire cortex under neurological impairments like lesions to understand global functional …

Abstract

Stroke is the leading cause of severe chronic disability and the second cause of death worldwide with 15 million new cases and 50 million stroke survivors. The post stroke chronic disability may be ameliorated with early neuro rehabilitation where non-invasive brain stimulation (NIBS) techniques can be used as an adjuvant treatment to hasten the effects. However, the heterogeneity in the lesioned brain will require individualized NIBS intervention where innovative neuroimaging technologies of portable electroencephalography (EEG) and functional-near-infrared spectroscopy (fNIRS) can be leveraged for Brain State Dependent Electrotherapy (BSDE). In this hypothesis and theory article, we propose a computational approach based on excitation-inhibition (E-I) balance hypothesis to objectively quantify the post stroke individual brain state using online fNIRS-EEG joint imaging. One of the key events that occurs following Stroke is the imbalance in local excitation-inhibition (that is the ratio of Glutamate/GABA) which may be targeted with NIBS using a computational pipeline that includes individual “forward models” to predict current flow patterns through the lesioned brain or brain target region. The current flow will polarize the neurons which can be captured with excitation-inhibition based brain models. Furthermore, E-I balance hypothesis can be used to find the consequences of cellular polarization on neuronal information processing which can then be implicated in changes in function. We first review evidence that shows how this local imbalance between excitation-inhibition leading to functional

Abstract

An ElectroCardiogram (ECG) inter-patient heartbeat time series classification method by a hierarchical system of based on support vector machine and Decision rule, using full heart-beat time series by alignment of R-peaks of all beats, is proposed. PQRST Time series of heart-beats having converted into equal length series by alignment of R-peaks of all heart-beats based on R-peak of largest length PQRST series in the data and by padding zeroes to the smaller length series on either side, was used in this experimentation. The main objective of this paper is to identify the abnormalities in ECG heart beats based on AAMI Categorization. Experiments were conducted on ECG data of 44 patients obtained from MIT-BIH Arrhythmia database. Results were compared with existing methods such as weighted support vector machine (SVM), hierarchical SVM and weighted linear discriminant analysis (LDA). Comparative …

Abstract

Early detection of cardiovascular diseases can prevent the premature deaths caused by abnormal heartbeat problems. Application of unsupervised classification by Extreme learning machine is addressed for ElectroCardiogram (ECG) heart-beat time series clustering by a hybrid of Extreme learning machine and Decision rule using full heart-beat time series by alignment of R-peaks of all beats is proposed in this work. PQRST Time series of heart-beats having converted into equal length series by alignment of R-peaks of all heart-beats based on R-peak of largest length PQRST series in the data and by padding zeroes to the smaller length series on either side, was used in this experimentation. The main objective of this paper is to identify the abnormalities in ECG heart beats based on AAMI Categorization. Because of the large patient specific characteristics in ECG heartbeat morphology across individuals, the …

Abstract

This paper focuses on evaluating the gains obtained through cooperative spectrum sensing in the real world while using cyclostationary-based mobile sensors. In cooperative sensing(CS), different secondary users (SUs) in a geographical neighbor-hood cooperate to detect the presence of a primary user (PU).Compared with single-user sensing, cooperation provides diversity gains in the face of multi path fading and shadowing. The effectiveness of CS is demonstrated by analyzing data acquired in two extensive field measurement campaigns. The first measurement campaign (MC-I) focuses on measurements at fixed locations,whereas the second measurement campaign (MC-II) focuses on a scenario where measurements are taken inside a moving car.These measurements are carried out for DVB-T channels in the Capital Region of Finland, which consists of urban and suburban environments. Hard decision rules such as OR,AND,and MAJOR-ITYand a soft decision rule such as sum of cyclostationary test statistics (SUM) are employed, and their detection performances are compared with a cyclostationary-based single-user detector. A performance parameter of relative increase in probability of detection (RIPD) is used to efficiently demonstrate the cooperation gain obtained relative to local sensing. It is shown that cooperation can significantly improve the performance of a sensor severely affected by fading and shadowing effects. Furthermore, it is shown that increasing the number of collaborating users beyond few users(five to eight) does not, in practice, bring significant improvement in terms of the expected RIPD. The performances of CS schemes evaluated from MC-I are also compared with the corresponding simulated CS results using empirical channel models and terrain data for the same experimental parameters. It is shown that the use of empirical or theoretical models may result in detection errors in practical conditions, and measurements should be used to improve the accuracy in such scenarios.