Query: AMP-Inspired Deep Networks for Sparse Linear Inverse Problems

Relevant passages:
Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures <=SEP=> Model-based methods and deep neural networks have both been tremendously successful paradigms in machine learning. In model-based methods, we can easily express our problem domain knowledge in the constraints of the model at the expense of difficulties during inference. Deterministic deep neural networks are constructed in such a way that inference is straightforward, but we sacrifice the ability to easily incorporate problem domain knowledge. The goal of this paper is to provide a general strategy to obtain the advantages of both approaches while avoiding many of their disadvantages. The general idea can be summarized as follows: given a model-based approach that requires an iterative inference method, we unfold the iterations into a layer-wise structure analogous to a neural network. We then de-couple the model parameters across layers to obtain novel neural-network-like architectures that can easily be trained discriminatively using gradient-based methods. The resulting formula combines the expressive power of a conventional deep network with the internal structure of the model-based approach, while allowing inference to be performed in a fixed number of layers that can be optimized for best performance. We show how this framework can be applied to the non-negative matrix factorization to obtain a novel non-negative deep neural network architecture, that can be trained with a multiplicative back-propagation-style update algorithm. We present experiments in the domain of speech enhancement, where we show that the resulting model is able to outperform conventional neural network while only requiring a fraction of the number of parameters. We believe this is due to the ability afforded by our framework to incorporate problem level assumptions into the architecture of the deep network. arXiv.org This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2014 201 Broadway, Cambridge, Massachusetts 02139

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems <=SEP=> Martı́n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng Google Research∗ Abstract

Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning <=SEP=> Pilot contamination posts a fundamental limit on the performance of massive multiple-input-multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To address this problem, we propose estimation of only the channel parameters of the desired links in a target cell, but those of the interference links from adjacent cells. The required estimation is, nonetheless, an underdetermined system. In this paper, we show that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. Our strategy is inspired by the observation that for a cellular network, the channel from user equipment to a base station is composed of only a few clustered paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. As a result, if the signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators, the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination.

Bayesian Compressive Sensing <=SEP=> The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directly, but rather a set of related measurements, with the new measurements a linear combination of the original underlying N-dimensional signal. The number of required compressive-sensing measurements is typically much smaller than N, offering the potential to simplify the sensing system. Let f denote the unknown underlying N-dimensional signal, and g a vector of compressive-sensing measurements, then one may approximate f accurately by utilizing knowledge of the (under-determined) linear relationship between f and g, in addition to knowledge of the fact that f is compressible in B. In this paper we employ a Bayesian formalism for estimating the underlying signal f based on compressive-sensing measurements g. The proposed framework has the following properties: i) in addition to estimating the underlying signal f, "error bars" are also estimated, these giving a measure of confidence in the inverted signal; ii) using knowledge of the error bars, a principled means is provided for determining when a sufficient number of compressive-sensing measurements have been performed; iii) this setting lends itself naturally to a framework whereby the compressive sensing measurements are optimized adaptively and hence not determined randomly; and iv) the framework accounts for additive noise in the compressive-sensing measurements and provides an estimate of the noise variance. In this paper we present the underlying theory, an associated algorithm, example results, and provide comparisons to other compressive-sensing inversion algorithms in the literature.

Large Scale Distributed Deep Networks <=SEP=> Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. We show that these same techniques dramatically accelerate the training of a more modestlysized deep network for a commercial speech recognition service. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm.

A 3-D Camera With Adaptable Background Light Suppression Using Pixel-Binning and Super-Resolution <=SEP=> This paper presents a CMOS time-of-flight (TOF) 3-D camera employing a column-level background light (BGL) suppression scheme for high-resolution outdoor imaging. The use of the column-level approach minimizes a pixel size for high-density pixel arrays. Pixel-binning and super-resolution can be adaptably applied for an optimal BGL suppression at given spatiotemporal resolutions. A prototype sensor has been fabricated by using 0.11 μm CMOS processes. The sensor achieved a fill factor of 24% in a pixel pitch of 5.9 μm which is the smallest among all the reported TOF cameras up to date. Measurement results showed the temporal noise of 1.47 cm-rms with a 100 ms integration time at a demodulation frequency of 12.5 MHz using a white target at 1 m distance. The non-linearity was measured as 1% over the range of 0.75 m ~ 4 m. The BGL suppression over 100 klx was achieved from indoor and outdoor experiments, while the BGL-induced offset was maintained less than 2.6 cm under 0 ~ 100 klx.

Developing multiple hypotheses in behavioral ecology <=SEP=> Researchers in behavioral ecology are increasingly turning to research methods that allow the simultaneous evaluation of hypotheses. This approach has great potential to increase our scientific understanding, but researchers interested in the approach should be aware of its long and somewhat contentious history. Also, prior to implementing multiple hypothesis evaluation, researchers should be aware of the importance of clearly specifying a priori hypotheses. This is one of the more difficult aspects of research based on multiple hypothesis evaluation, and we outline and provide examples of three approaches for doing so. Finally, multiple hypothesis evaluation has some limitations important to behavioral ecologists; we discuss two practical issues behavioral ecologists are likely to face.

Exploiting Software: How to Break Code <=SEP=> To be useful, software must respond to events in a predictable manner. The results can then be used as a window to the interior workings of the code, revealing some of the mechanisms of operations, which may be used to find ways to make it fail in a dangerous way. To some, the window is as clear as a six inch thick pane of lead, but to those with a high level of understanding it can be clear, or at the very least serve as a keyhole. This is an allusion to the old detective stories where someone looks through the keyhole to see what is behind the door. For these reasons, no software that interacts with humans can ever be considered completely secure, and human error in the development of the software can leave the equivalent of keyholes throughout the code.

A tutorial on cross-layer optimization in wireless networks <=SEP=> This tutorial paper overviews recent developments in optimization-based approaches for resource allocation problems in wireless systems. We begin by overviewing important results in the area of opportunistic (channel-aware) scheduling for cellular (single-hop) networks, where easily implementable myopic policies are shown to optimize system performance. We then describe key lessons learned and the main obstacles in extending the work to general resource allocation problems for multihop wireless networks. Towards this end, we show that a clean-slate optimization-based approach to the multihop resource allocation problem naturally results in a "loosely coupled" cross-layer solution. That is, the algorithms obtained map to different layers [transport, network, and medium access control/physical (MAC/PHY)] of the protocol stack, and are coupled through a limited amount of information being passed back and forth. It turns out that the optimal scheduling component at the MAC layer is very complex, and thus needs simpler (potentially imperfect) distributed solutions. We demonstrate how to use imperfect scheduling in the cross-layer framework and describe recently developed distributed algorithms along these lines. We conclude by describing a set of open research problems

Learning to guide task and motion planning using score-space representation <=SEP=> In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning problem instance, and how to transfer knowledge from one problem instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning problem instance, called score space, where we represent a problem instance in terms of performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning problems. Results indicate that our approach perform orders of magnitudes faster than an unguided planner.

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques <=SEP=> OBJECTIVES
The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)'s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model.


METHODS
The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information.


RESULTS
Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871).


CONCLUSIONS
With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction.

Multispectral pedestrian detection: Benchmark dataset and baseline <=SEP=> With the increasing interest in pedestrian detection, pedestrian datasets have also been the subject of research in the past decades. However, most existing datasets focus on a color channel, while a thermal channel is helpful for detection even in a dark environment. With this in mind, we propose a multispectral pedestrian dataset which provides well aligned color-thermal image pairs, captured by beam splitter-based special hardware. The color-thermal dataset is as large as previous color-based datasets and provides dense annotations including temporal correspondences. With this dataset, we introduce multispectral ACF, which is an extension of aggregated channel features (ACF) to simultaneously handle color-thermal image pairs. Multi-spectral ACF reduces the average miss rate of ACF by 15%, and achieves another breakthrough in the pedestrian detection task.

Indoor scene segmentation using a structured light sensor <=SEP=> In this paper we explore how a structured light depth sensor, in the form of the Microsoft Kinect, can assist with indoor scene segmentation. We use a CRF-based model to evaluate a range of different representations for depth information and propose a novel prior on 3D location. We introduce a new and challenging indoor scene dataset, complete with accurate depth maps and dense label coverage. Evaluating our model on this dataset reveals that the combination of depth and intensity images gives dramatic performance gains over intensity images alone. Our results clearly demonstrate the utility of structured light sensors for scene understanding.

Wireless Mesh Software Defined Networks (wmSDN) <=SEP=> In this paper we propose to integrate Software Defined Networking (SDN) principles in Wireless Mesh Networks (WMN) formed by OpenFlow switches. The use of a centralized network controller and the ability to setup arbitrary paths for data flows make SDN a handy tool to deploy fine-grained traffic engineering algorithms in WMNs. However, centralized control may be harmful in multi-hop radio networks formed by commodity devices (e.g. Wireless Community Networks), in which node isolation and network fragmentation are not rare events. To exploit the pros and mitigate the cons, our framework uses the traditional OpenFlow centralized controller to engineer the routing of data traffic, while it uses a distributed controller based on OLSR to route: i) OpenFlow control traffic, ii) data traffic, in case of central controller failure. We implemented and tested our Wireless Mesh Software Defined Network (wmSDN) showing its applicability to a traffic engineering use-case, in which the controller logic balances outgoing traffic among the Internet gateways of the mesh. Albeit simple, this use case allows showing a possible usage of SDN that improves user performance with respect to the case of a traditional mesh with IP forwarding and OLSR routing. The wmSDN software toolkit is formed by Open vSwitch, POX controller, OLSR daemon and our own Bash and Python scripts. The tests have been carried out in an emulation environment based on Linux Containers, NS3 and CORE tools.

Closed loop speed control of chopper fed DC motor for industrial drive application <=SEP=> In this paper a comparative transient and robustness analysis of closed loop speed control employing different linear controllers for the same dc motor using 4 quadrant chopper is investigated. The controller configurations can be broadly classified under (i) Integer order PID controller (ii) linear state space observer and (iii) fractional order PID controller. All of them exhibit superior performances, the first one is the conventional controller used in industries but the later two are modern controllers that have rich potential for industry use and have large advantage over the conventional controllers. The closed loop control of chopper fed DC motor is shown with the 1st quadrant operation of chopper circuit.

Input Sentence Splitting and Translating <=SEP=> We propose a method to split and translate input sentences for speech translation in order to overcome the long sentence problem. This approach is based on three criteria used to judge the goodness of translation results. The criteria utilize the output of an MT system only and assumes neither a particular language nor a particular MT approach. In an experiment with an EBMT system, in which prior methods cannot work or work badly, the proposed split-and-translate method achieves much better results in translation quality.

The Alternating Decision Tree Learning Algorithm <=SEP=> The application of boosting procedures to decision tree algorithms has been shown to produce very accurate classi ers. These classiers are in the form of a majority vote over a number of decision trees. Unfortunately, these classi ers are often large, complex and diÆcult to interpret. This paper describes a new type of classi cation rule, the alternating decision tree, which is a generalization of decision trees, voted decision trees and voted decision stumps. At the same time classi ers of this type are relatively easy to interpret. We present a learning algorithm for alternating decision trees that is based on boosting. Experimental results show it is competitive with boosted decision tree algorithms such as C5.0, and generates rules that are usually smaller in size and thus easier to interpret. In addition these rules yield a natural measure of classi cation con dence which can be used to improve the accuracy at the cost of abstaining from predicting examples that are hard to classify.

High-quality speech coding with SampleRNN <=SEP=> We provide a speech coding scheme employing a generative model based on SampleRNN that, while operating at significantly lower bitrates, matches or surpasses the perceptual quality of state-of-theart classic wide-band codecs. Moreover, it is demonstrated that the proposed scheme can provide a meaningful rate-distortion trade-off without retraining. We evaluate the proposed scheme in a series of listening tests and discuss limitations of the approach.

A natural language planner interface for mobile manipulators <=SEP=> Natural language interfaces for robot control aspire to find the best sequence of actions that reflect the behavior intended by the instruction. This is difficult because of the diversity of language, variety of environments, and heterogeneity of tasks. Previous work has demonstrated that probabilistic graphical models constructed from the parse structure of natural language can be used to identify motions that most closely resemble verb phrases. Such approaches however quickly succumb to computational bottlenecks imposed by construction and search the space of possible actions. Planning constraints, which define goal regions and separate the admissible and inadmissible states in an environment model, provide an interesting alternative to represent the meaning of verb phrases. In this paper we present a new model called the Distributed Correspondence Graph (DCG) to infer the most likely set of planning constraints from natural language instructions. A trajectory planner then uses these planning constraints to find a sequence of actions that resemble the instruction. Separating the problem of identifying the action encoded by the language into individual steps of planning constraint inference and motion planning enables us to avoid computational costs associated with generation and evaluation of many trajectories. We present experimental results from comparative experiments that demonstrate improvements in efficiency in natural language understanding without loss of accuracy.

The Myth of Millions of Annual Self-Defense Gun Uses: A Case Study of Survey Overestimates of Rare Events <=SEP=> A criminologist has been using national self-report surveys to estimate the incidence of self-defense gun use in the United States (Kleck 199 I). His most recent estimate is that civilians use guns in self-defense against offenders more than 2.5 million times each year (Kleck and Gertz 1995). This figure has been widely cited, not only by the National Rifle Association but in the media and in Congress. A criminologist in Canada is touting high self-defense estimates for that country, derived from similar self-report surveys (Mauser 1995). All attempts at external validation of the 2.5 million figure show that it is an enormous overestimate (Hemenway, in press). For example, in 34% of the times a gun was used for self-defense, the offender was allegedlycommitting a burglary. In other words, guns were reportedly used by defenders for self-defense in approximately 845,000 burglaries. From sophisticated victimization surveys, however,we know that there were fewer than 6 million burglaries in the year of the survey and in only 22% of those cases was someone certainly at home (1.3 million burglaries). Since only 42% of U.S. households own firearms,

One Century of Brain Mapping Using Brodmann Areas* <=SEP=> 100 years after their publication, Brodmann’s maps of the cerebral cortex are universally used to locate neuropsychological functions. On the occasion of this jubilee the life and work of Korbinian Brodmann are reported. The core functions of each single Brodmann area are described and Brodmann’s views on neuropsychological processes are depicted. 100 Jahre nach ihrer Veröffentlichung wird Brodmanns Kartierung des zerebralen Kortex universell zur Lokalisation neuropsychologischer Funktionen eingesetzt. Anlässlich dieses Jubiläums werden Leben und Werk von Korbinian Brodmann dargestellt. Die wesentlichen Funktionen der einzelnen Brodmann-Areale werden beschrieben und Brodmanns Ansichten über neuropsychologische Prozesse wiedergegeben.

CONTEXTUAL CLASSIFICATION OF POINT CLOUD DATA BY EXPLOITING INDIVIDUAL 3D NEIGBOURHOODS <=SEP=> The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.

Examining the factors that influence early adopters' smartphone adoption: The case of college students <=SEP=> The influence of early adopters on potential adopters’ decisions of whether or not to adopt a product is known to be critical. In this paper, we examine the factors that influence the adoption behavior of smartphone early adopters by looking at smartphone adoption behavior of college students, because a large portion of the early adopters of smartphones are college students. Our focus is on the effect of normative peer influence on a college student’s smartphone adoption. We also examine the influence of other factors such as selfinnovativeness, self-efficacy, the decision maker’s attitudes towards a product, financial burden of using the product, familial influence, and other demographic factors (e.g., age and gender). College students’ adoption behavior is studied using logit and probit choice models developed based on random utility theory. The discrete choice models are empirically estimated using survey data. We find important influence of friends, financial burden, and other family members on the smartphone adoption of college students who adopted smartphones earlier than other students. 2013 Elsevier Ltd. All rights reserved.

The calvaria of Sangiran 38, Sendangbusik, Sangiran Dome, Java. <=SEP=> We describe in detail Sangiran 38 (S38), an adult partial calvaria recovered in 1980 from the Bapang (Kabuh) Formation of the Sangiran Dome near the hamlet of Sendangbusik, Java. Several other hominins (Bukuran, Hanoman 1, and Bs 9706) recovered in the vicinity come from either the upper-most Sangiran (Pucangan) or lower-most Bapang formations. S38 is from the lower Bapang Formation, which (40)Ar/(39)Ar age estimates suggest spans between 1.47 and 1.58Ma. Anatomical and metric comparisons with a worldwide set of 'early non-erectus'Homo, and Homo erectus (sensu lato) fossils indicate S38 is best considered a member of H. erectus. Although smaller in size, S38 is similar in overall morphology to the Bukuran specimen of similar age and provenance. The S38 calvaria exhibits several depressed lesions of the vault consistent with a scalp or systemic infection or soft tissue cyst.

Using Discriminant Eigenfeatures for Image Retrieval <=SEP=> |This paper describes the automatic selection of features from an image training set using the theories of multi-dimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the eeectiveness of these Most Discriminating Features for view-based class retrieval from a large database of widely varying real-world objects presented as \well-framed" views, and compare it with that of the principal component analysis.

From Modeling to Implementation of Virtual Sensors in Body Sensor Networks <=SEP=> Body Sensor Networks (BSNs) represent an emerging technology which has received much attention recently due to its enormous potential to enable remote, real-time, continuous and non-invasive monitoring of people in health-care, entertainment, fitness, sport, social interaction. Signal processing for BSNs usually comprises of multiple levels of data abstraction, from raw sensor data to data calculated from processing steps such as feature extraction and classification. This paper presents a multi-layer task model based on the concept of Virtual Sensors to improve architecture modularity and design reusability. Virtual Sensors are abstractions of components of BSN systems that include sensor sampling and processing tasks and provide data upon external requests. The Virtual Sensor model implementation relies on SPINE2, an open source domain-specific framework that is designed to support distributed sensing operations and signal processing for wireless sensor networks and enables code reusability, efficiency, and application interoperability. The proposed model is applied in the context of gait analysis through wearable sensors. A gait analysis system is developed according to a SPINE2-based Virtual Sensor architecture and experimentally evaluated. Obtained results confirm that great effectiveness can be achieved in designing and implementing BSN applications through the Virtual Sensor approach while maintaining high efficiency and accuracy.

A Novel Economic Sharing Model in a Federation of Selfish Cloud Providers <=SEP=> This paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices.

Binding mechanism of PicoGreen to DNA characterized by magnetic tweezers and fluorescence spectroscopy <=SEP=> Fluorescent dyes are broadly used in many biotechnological applications to detect and visualize DNA molecules. However, their binding to DNA alters the structural and nanomechanical properties of DNA and, thus, interferes with associated biological processes. In this work we employed magnetic tweezers and fluorescence spectroscopy to investigate the binding of PicoGreen to DNA at room temperature in a concentration-dependent manner. PicoGreen is an ultrasensitive quinolinium nucleic acid stain exhibiting hardly any background signal from unbound dye molecules. By means of stretching and overwinding single, torsionally constrained, nick-free double-stranded DNA molecules, we acquired force-extension and supercoiling curves which allow quantifying DNA contour length, persistence length and other thermodynamical binding parameters, respectively. The results of our magnetic tweezers single-molecule binding study were well supported through analyzing the fluorescent spectra of stained DNA. On the basis of our work, we could identify a concentration-dependent bimodal binding behavior, where, apparently, PicoGreen associates to DNA as an intercalator and minor-groove binder simultaneously.

Applications of Random Sampling in Computational Geometry, II <=SEP=> Random sampling is used for several new geometric algorithms. The algorithms are “Las Vegas,” and their expected bounds are with respect to the random behavior of the algorithms. One algorithm reports all the intersecting pairs of a set of line segments in the plane, and requires <italic>&Ogr;</italic>(<italic>A</italic> + <italic>n</italic> log <italic>n</italic>) expected time, where <italic>A</italic> is the size of the answer, the number of intersecting pairs reported. The algorithm requires <italic>&Ogr;</italic>(<italic>n</italic>) space in the worst case. Another algorithm computes the convex hull of a point set in <italic>E</italic><supscrpt>3</supscrpt> in <italic>&Ogr;</italic>(<italic>n</italic> log <italic>A</italic>) expected time, where <italic>n</italic> is the number of points and <italic>A</italic> is the number of points on the surface of the hull. A simple Las Vegas algorithm triangulates simple polygons in <italic>&Ogr;</italic>(<italic>n</italic> log log <italic>n</italic>) expected time. Algorithms for half-space range reporting are also given. In addition, this paper gives asymptotically tight bounds for a combinatorial quantity of interest in discrete and computational geometry, related to halfspace partitions of point sets.

Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey <=SEP=> This paper represents energy efficient routing protocols in WSN. It is a collection of sensor nodes with a set of limited Processor and limited memory unit embedded in it. Reliable routing of packets from the sensor node to its base station is the most important task for the networks. The routing protocols applied for the other networks cannot be used here due to its battery powered nodes This paper gives an overview of the different routing strategies used in wireless sensor networks and gives a brief working model of energy efficient routing protocols in WSN. It also shows the comparison of these different routing protocols based on metrics such as mobility support, stability, issues and latency.