meSAT: multiple encodings of CSP to SAT

One approach for solving Constraint Satisfaction Problems (CSP) (and related Constraint Optimization Problems (COP)) involving integer and Boolean variables is reduction to propositional satisfiability problem (SAT). A number of encodings (e.g., direct, log, support, order) for this purpose exist as well as specific encodings for some constraints that are often encountered (e.g., cardinality constraints, global constraints). However, there is no single encoding that performs well on all classes of problems and there is a need for a system that supports multiple encodings. We present a system that translates specifications of finite linear CSP problems into SAT instances using several well-known encodings, and their combinations. We also present a methodology for selecting a suitable encoding based on simple syntactic features of the input CSP instance. Thorough evaluation has been performed on large publicly available corpora and our encoding selection method improves upon the efficiency of existing encodings and state-of-the-art tools used in comparison.     

A comparative performance study of common and popular task‐centric programming frameworks

Programmers today face a bewildering array of parallel programming models and tools, making it difficult to choose an appropriate one for each application. An increasingly popular programming model supporting structured parallel programming patterns in a portable and composable manner is the task‐centric programming model. In this study, we compare several popular task‐centric programming frameworks, including Cilk Plus, Threading Building Blocks, and various implementations of OpenMP 3.0. We have analyzed their performance on the Barcelona OpenMP Tasking Suite benchmark suite both on a 48‐core AMD Opteron 6172 server and a 64‐core TILEPro64 embedded many‐core processor. Our results show that the OpenMP offers the highest flexibility for programmers, and this flexibility comes to a cost. Frameworks supporting only a specific and more restrictive model, such as Cilk Plus and Threading Building Blocks, are generally more efficient both in terms of performance and energy consumption. However, Intel's implementation of OpenMP tasks performs the best and closest to the specialized run‐time systems. Copyright © 2013 John Wiley & Sons, Ltd.     

A Multicast-Enabled Delivery Framework for QoE Assurance of Over-The-Top Services in Multimedia Access Networks

Over-The-Top (OTT) video services are becoming more and more important in today’s broadband access networks. While original OTT services only offered short duration medium quality videos, more recently, premium content such as high definition full feature movies and live video are offered as well. For operators, who see the potential in providing Quality of Experience (QoE) assurance for an increased revenue, this introduces important new network management challenges. Traditional network management paradigms are often not suited for ensuring QoE guarantees as the provider does not have any control on the content’s origin. In this article, we focus on the management of an OTT-based video service. We present a loosely coupled architecture that can be seamlessly integrated into an existing OTT-based video delivery architecture. The framework has the goal of resolving the network bottleneck that might occur from high peaks in the requests for OTT video services. The proposed approach groups the existing Hypertext Transfer Protocol (HTTP) based video connections to be multicasted over an access network’s bottleneck and then splits them again to reconstruct the original HTTP connections. A prototype of this architecture is presented, which includes the caching of videos and incorporates retransmission schemes to ensure robust transmission. Furthermore, an autonomic algorithm is presented that allows to intelligently select which OTT videos need to be multicasted by making a remote assessment of the cache state to predict the future availability of content. The approach was evaluated through both simulation and large scale emulation and shows a significant gain in scalability of the prototype compared to a traditional video delivery architecture.     

A note on the radial wall jet with swirl

Summary The aim of this paper is a determinations of the space flow geometry of turbulent radial wall jet with swirl by means of a similarity analysis. It is assumed that except for a very thin layer near the wall, the flow field of the turbulent radial wall jet is similar. The wall conditions for a Newtonian fluid flow used in a paper by the present authors [11] — and satisfactory only for a laminar regime — are substituted by a suitable condition often employed in 3-D turbulent boundary layer problems. It is assumed that the direction of the shear-stress resultant is the same as the direction of the mean velocity-gradient resultant.With 3 Figures     

Formulating the template ILP consistency problem as a constraint satisfaction problem

Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In this paper we employ constraint satisfaction techniques to model and solve a problem known as template ILP consistency, which assumes that the structure of a hypothesis is known and the task is to find unification of the contained variables. In particular, we present a constraint model with index variables accompanied by a Boolean model to strengthen inference and hence improve efficiency. The efficiency of models is demonstrated experimentally.     

Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent neural networks (RNNs) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine learning approaches, such as support vector machines (SVMs), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESNs), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforwardway.The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studiedyet.Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the time series. The feature selection method consists of a greedy hybrid filter-wrapper method using a NB classifier, which selects in each iteration the feature that improves prediction the best and shows little multicollinearity with the already selected set. Least squares regression with noise was used to train the linear readout function of the ESN to mitigate sensitivity to noise and overfitting. Fisher labeling was used to deal with the unbalanced data set. Parameter sweeps were performed to determine the optimal parameter values for the different classifiers. The area under the curve (AUC) and maximum balanced accuracy are used as performance measures. The required execution time was also measured.Results: The classification performance of the ESN shows significant difference at the 5% level compared to the performance of the SVM or the NB classifier combined with FE and FS. The NB+FE+FS, with an average AUC of 0.874, has the best classification performance. This classifier is followed by the ESN, which has an average AUC of 0.849. The SVM+FE+FS has the worst performance with an average AUC of 0.838. The computation time needed to pre-process the data and to train and test the classifier is significantly less for the ESN compared to the SVM andNB.Conclusion: It can be concluded that the use of ESN has an added value in predicting the need for dialysis through the analysis of time series data. The ESN requires significantly less processing time, needs no domain knowledge, is easy to implement, and can be configured using rules ofthumb.     

Randomized trial of internet-delivered self-help with telephone support for pathological gamblers.

Although effective therapies for pathological gambling exist, their uptake is limited to 10% of the target population. To lower the barriers for help seeking, the authors tested an online alternative in a randomized trial (N = 66). The participants were pathological gamblers not presenting with severe comorbid depression. A wait-list control was compared with an 8-week Internet-based cognitive behavior therapy program with minimal therapist contact via e-mail and weekly telephone calls of less than 15 min. Average time spent on each participant, including phone conversations, e-mail, and administration, was 4 hr. The Internet-based intervention resulted in favorable changes in pathological gambling, anxiety, depression, and quality of life. Composite between-group effect size (Cohen's d) at posttreatment was 0.83. Follow-ups carried out in the treatment group at 6, 18, and 36 months indicated that treatment effects were sustained (ds = 2.58, 1.96, and 1.98). This evidence is in support of Internet-delivered treatment for pathological gamblers. However, it is not clear how effective the treatment is for more severely depressed individuals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Encoding Information on the Excited State of a Molecular Spin Chain

The quantum states of nano-objects can drive electrical transport properties across lateral and local-probe junctions. This raises the prospect, in a solid-state device, of electrically encoding information at the quantum level using spin-flip excitations between electron spins. However, this electronic state has no defined magnetic orientation and is short-lived. Using a novel vertical nanojunction process, these limitations are overcome and this steady-state capability is experimentally demonstrated in solid-state spintronic devices. The excited quantum state of a spin chain formed by Co phthalocyanine molecules coupled to a ferromagnetic electrode constitutes a distinct magnetic unit endowed with a coercive field. This generates a specific steady-state magnetoresistance trace that is tied to the spin-flip conductance channel, and is opposite in sign to the ground state magnetoresistance term, as expected from spin excitation transition rules. The experimental 5.9 meV thermal energy barrier between the ground and excited spin states is confirmed by density functional theory, in line with macrospin phenomenological modeling of magnetotransport results. This low-voltage control over a spin chain's quantum state and spintronic contribution lay a path for transmitting spin wave-encoded information across molecular layers in devices. It should also stimulate quantum prospects for the antiferromagnetic spintronics and oxides electronics communities.     

Underapproximation of procedure summaries for integer programs

International Journal on Software Tools for Technology Transfer - We show how to underapproximate the procedure summaries of recursive programs over the integers using off-the-shelf analyzers for...     

Antifungal activity of lauric acid derivatives against Aspergillus niger

The antifungal effects of lauric acid and four lauric acid derivatives (monolauroylglycerol, D-laurate A, T-laurate A, 6-O-lauroysucrose) were tested on the spore germination and the growth rate of Aspergillus niger DMF 0801. The results showed that the tested substances varied in their antifungal activity and they also confirmed the relation of the structure of tested substances and their antifungal effects. Monolauroylglycerol at concentration level between 0.2 and 1.8 mmol l^-1 caused the highest inhibition of spore outgrowths whereas 6-O-lauroysucrose at concentration between 0.05 and 1.8 mmol l^-1 did not exhibit inhibition of spore germination. The inhibition of colony growth rate was detected mainly at presence of 6-O-lauroysucrose.