Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks

Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.

Chaperone expression profiles correlate with distinct physiological states of Plasmodium falciparum in malaria patients

Background: Molecular chaperones have been shown to be important in the growth of the malaria parasite Plasmodium falciparum and inhibition of chaperone function by pharmacological agents has been shown to abrogate parasite growth. A recent study has demonstrated that clinical isolates of the parasite have distinct physiological states, one of which resembles environmental stress response showing up-regulation of specific molecular chaperones. Methods: Chaperone networks operational in the distinct physiological clusters in clinical malaria parasites were constructed using cytoscape by utilizing their clinical expression profiles. Results: Molecular chaperones show distinct profiles in the previously defined physiologically distinct states. Further, expression profiles of the chaperones from different cellular compartments correlate with specific patient clusters. While cluster 1 parasites, representing a starvation response, show up-regulation of organellar chaperones, cluster 2 parasites, which resemble active growth based on glycolysis, show up-regulation of cytoplasmic chaperones. Interestingly, cytoplasmic Hsp90 and its co-chaperones, previously implicated as drug targets in malaria, cluster in the same group. Detailed analysis of chaperone expression in the patient cluster 2 reveals up-regulation of the entire Hsp90-dependent pro-survival circuitries. In addition, cluster 2 also shows up-regulation of Plasmodium export element (PEXEL)-containing Hsp40s thought to have regulatory and host remodeling roles in the infected erythrocyte. Conclusion: In all, this study demonstrates an intimate involvement of parasite-encoded chaperones, PfHsp90 in particular, in defining pathogenesis of malaria.

Design of nonequivalent self-routing networks based on a matrix model

In earlier work, nonisomorphic graphs have been converted into networks to realize Multistage Interconnection networks, which are topologically nonequivalent to the Baseline network. The drawback of this technique is that these nonequivalent networks are not guaranteed to be self-routing, because each node in the graph model can be replaced by a (2 × 2) switch in any one of the four different configurations. Hence, the problem of routing in these networks remains unsolved. Moreover, nonisomorphic graphs were obtained by interconnecting bipartite loops in a heuristic manner; the heuristic nature of this procedure makes it difficult to guarantee full connectivity in large networks. We solve these problems through a direct approach, in which a matrix model for self-routing networks is developed. An example is given to show that this model encompases nonequivalent self-routing networks. This approach has the additional advantage in that the matrix model itself ensures full connectivity.

Social networks for volunteered geographic information - A case study of OpenStreetMap

The world of mapping has changed. Earlier, only professional experts were responsible for map production, but today ordinary people without any training or experience can become map-makers. The number of online mapping sites, and the number of volunteer mappers has increased significantly. The development of the technology, such as satellite navigation systems, Web 2.0, broadband Internet connections, and smartphones, have had one of the key roles in enabling the rise of volunteered geographic information (VGI). As opening governmental data to public is a current topic in many countries, the opening of high quality geographical data has a central role in this study. The aim of this study is to investigate how is the quality of spatial data produced by volunteers by comparing it with the map data produced by public authorities, to follow what occurs when spatial data are opened for users, and to get acquainted with the user profile of these volunteer mappers. A central part of this study is OpenStreetMap project (OSM), which aim is to create a map of the entire world by volunteers. Anyone can become an OpenStreetMap contributor, and the data created by the volunteers are free to use for anyone without restricting copyrights or license charges. In this study OpenStreetMap is investigated from two viewpoints. In the first part of the study, the aim was to investigate the quality of volunteered geographic information. A pilot project was implemented by following what occurs when a high-resolution aerial imagery is released freely to the OpenStreetMap contributors. The quality of VGI was investigated by comparing the OSM datasets with the map data of The National Land Survey of Finland (NLS). The quality of OpenStreetMap data was investigated by inspecting the positional accuracy and the completeness of the road datasets, as well as the differences in the attribute datasets between the studied datasets. Also the OSM community was under analysis and the development of the map data of OpenStreetMap was investigated by visual analysis. The aim of the second part of the study was to analyse the user profile of OpenStreetMap contributors, and to investigate how the contributors act when collecting data and editing OpenStreetMap. The aim was also to investigate what motivates users to map and how is the quality of volunteered geographic information envisaged. The second part of the study was implemented by conducting a web inquiry to the OpenStreetMap contributors. The results of the study show that the quality of OpenStreetMap data compared with the data of National Land Survey of Finland can be defined as good. OpenStreetMap differs from the map of National Land Survey especially because of the amount of uncertainty, for example because of the completeness and uniformity of the map are not known. The results of the study reveal that opening spatial data increased notably the amount of the data in the study area, and both the positional accuracy and completeness improved significantly. The study confirms the earlier arguments that only few contributors have created the majority of the data in OpenStreetMap. The inquiry made for the OpenStreetMap users revealed that the data are most often collected by foot or by bicycle using GPS device, or by editing the map with the help of aerial imageries. According to the responses, the users take part to the OpenStreetMap project because they want to make maps better, and want to produce maps, which have information that is up-to-date and cannot be found from any other maps. Almost all of the users exploit the maps by themselves, most popular methods being downloading the map into a navigator or into a mobile device. The users regard the quality of OpenStreetMap as good, especially because of the up-to-dateness and the accuracy of the map.

In-Network Computation in Random Wireless Networks: A PAC Approach to Constant Refresh Rates with Lower Energy Costs

We propose a method to compute a probably approximately correct (PAC) normalized histogram of observations with a refresh rate of Theta(1) time units per histogram sample on a random geometric graph with noise-free links. The delay in computation is Theta(root n) time units. We further extend our approach to a network with noisy links. While the refresh rate remains Theta(1) time units per sample, the delay increases to Theta(root n log n). The number of transmissions in both cases is Theta(n) per histogram sample. The achieved Theta(1) refresh rate for PAC histogram computation is a significant improvement over the refresh rate of Theta(1/log n) for histogram computation in noiseless networks. We achieve this by operating in the supercritical thermodynamic regime where large pathways for communication build up, but the network may have more than one component. The largest component however will have an arbitrarily large fraction of nodes in order to enable approximate computation of the histogram to the desired level of accuracy. Operation in the supercritical thermodynamic regime also reduces energy consumption. A key step in the proof of our achievability result is the construction of a connected component having bounded degree and any desired fraction of nodes. This construction may also prove useful in other communication settings on the random geometric graph.

Fast distributed approximation algorithms for vertex cover and set cover in anonymous networks

We present a distributed algorithm that finds a maximal edge packing in O(Δ + log* W) synchronous communication rounds in a weighted graph, independent of the number of nodes in the network; here Δ is the maximum degree of the graph and W is the maximum weight. As a direct application, we have a distributed 2-approximation algorithm for minimum-weight vertex cover, with the same running time. We also show how to find an f-approximation of minimum-weight set cover in O(f2k2 + fk log* W) rounds; here k is the maximum size of a subset in the set cover instance, f is the maximum frequency of an element, and W is the maximum weight of a subset. The algorithms are deterministic, and they can be applied in anonymous networks.

Optimisation problems in wireless sensor networks

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating–dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating–dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs – these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating–dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

Voluntary Energy Harvesting Relays and Selection in Cooperative Wireless Networks

The use of energy harvesting (EH) nodes as cooperative relays is a promising and emerging solution in wireless systems such as wireless sensor networks. It harnesses the spatial diversity of a multi-relay network and addresses the vexing problem of a relay's batteries getting drained in forwarding information to the destination. We consider a cooperative system in which EH nodes volunteer to serve as amplify-and-forward relays whenever they have sufficient energy for transmission. For a general class of stationary and ergodic EH processes, we introduce the notion of energy constrained and energy unconstrained relays and analytically characterize the symbol error rate of the system. Further insight is gained by an asymptotic analysis that considers the cases where the signal-to-noise-ratio or the number of relays is large. Our analysis quantifies how the energy usage at an EH relay and, consequently, its availability for relaying, depends not only on the relay's energy harvesting process, but also on its transmit power setting and the other relays in the system. The optimal static transmit power setting at the EH relays is also determined. Altogether, our results demonstrate how a system that uses EH relays differs in significant ways from one that uses conventional cooperative relays.

Composite MR image reconstruction and unaliasing for general trajectories using neural networks

In rapid parallel magnetic resonance imaging, the problem of image reconstruction is challenging. Here, a novel image reconstruction technique for data acquired along any general trajectory in neural network framework, called ``Composite Reconstruction And Unaliasing using Neural Networks'' (CRAUNN), is proposed. CRAUNN is based on the observation that the nature of aliasing remains unchanged whether the undersampled acquisition contains only low frequencies or includes high frequencies too. Here, the transformation needed to reconstruct the alias-free image from the aliased coil images is learnt, using acquisitions consisting of densely sampled low frequencies. Neural networks are made use of as machine learning tools to learn the transformation, in order to obtain the desired alias-free image for actual acquisitions containing sparsely sampled low as well as high frequencies. CRAUNN operates in the image domain and does not require explicit coil sensitivity estimation. It is also independent of the sampling trajectory used, and could be applied to arbitrary trajectories as well. As a pilot trial, the technique is first applied to Cartesian trajectory-sampled data. Experiments performed using radial and spiral trajectories on real and synthetic data, illustrate the performance of the method. The reconstruction errors depend on the acceleration factor as well as the sampling trajectory. It is found that higher acceleration factors can be obtained when radial trajectories are used. Comparisons against existing techniques are presented. CRAUNN has been found to perform on par with the state-of-the-art techniques. Acceleration factors of up to 4, 6 and 4 are achieved in Cartesian, radial and spiral cases, respectively. (C) 2010 Elsevier Inc. All rights reserved.

Interaction Energy Based Protein Structure Networks

The three dimensional structure of a protein is formed and maintained by the noncovalent interactions among the amino acid residues of the polypeptide chain These interactions can be represented collectively in the form of a network So far such networks have been investigated by considering the connections based on distances between the amino acid residues Here we present a method of constructing the structure network based on interaction energies among the amino acid residues in the protein We have investigated the properties of such protein energy based networks (PENs) and have shown correlations to protein structural features such as the clusters of residues involved in stability formation of secondary and super secondary structural units Further we demonstrate that the analysis of PENs in terms of parameters such as hubs and shortest paths can provide a variety of biologically important information such as the residues crucial for stabilizing the folded units and the paths of communication between distal residues in the protein Finally the energy regimes for different levels of stabilization in the protein structure have clearly emerged from the PEN analysis

H2O2 has a role in cellular regulation

H2O2, in addition to producing highly reactive molecules through hydroxyl radicals or peroxidase action, can exert a number of direct effects on cells, organelles and enzymes. The stimulations include glucose transport, glucose incorporation into glycogen, HMP shunt pathway, lipid synthesis, release of calcium from mitochondria and of arachidonate from phospholipids, poly ADP ribosylation, and insulin receptor tyrosine kinase and pyruvate dehydrogenase activities. The inactivations include glycolysis, lipolysis, reacylation of lysophospholipids, ATP synthesis, superoxide dismutase and protein kinase C. Damages to DNA and proteoglycan and general cytotoxicity possibly through oxygen radicals were also observed. A whole new range of effects will be opened by the finding that H2O2 can act as a signal transducer in oxidative stress by oxidizing a dithiol protein to disulphide form which then activates transcription of the stress inducible genes. Many of these direct effects seem to be obtained by dithiol-disulphide modification of proteins and their active sites, as part of adaptive responses in oxidative stress.

Discriminative learning of Bayesian networks via factorized conditional log-likelihood

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.

Circuit layout through an analogy with neural networks

Standard-cell design methodology is an important technique in semicustom-VLSI design. It lends itself to the easy automation of the crucial layout part, and many algorithms have been proposed in recent literature for the efficient placement of standard cells. While many studies have identified the Kerninghan-Lin bipartitioning method as being superior to most others, it must be admitted that the behaviour of the method is erratic, and that it is strongly dependent on the initial partition. This paper proposes a novel algorithm for overcoming some of the deficiencies of the Kernighan-Lin method. The approach is based on an analogy of the placement problem with neural networks, and, by the use of some of the organizing principles of these nets, an attempt is made to improve the behavior of the bipartitioning scheme. The results have been encouraging, and the approach seems to be promising for other NP-complete problems in circuit layout.

Quality-of-Transmission-Aware Manycasting Over Optical Burst-Switched Networks

Many next-generation distributed applications, such as grid computing, require a single source to communicate with a group of destinations. Traditionally, such applications are implemented using multicast communication. A typical multicast session requires creating the shortest-path tree to a fixed number of destinations. The fundamental issue in multicasting data to a fixed set of destinations is receiver blocking. If one of the destinations is not reachable, the entire multicast request (say, grid task request) may fail. Manycasting is a generalized variation of multicasting that provides the freedom to choose the best subset of destinations from a larger set of candidate destinations. We propose an impairment-aware algorithm to provide manycasting service in the optical layer, specifically OBS. We compare the performance of our proposed manycasting algorithm with traditional multicasting and multicast with over provisioning. Our results show a significant improvement in the blocking probability by implementing optical-layer manycasting.