A sparse and spatially constrained generative regression model for fMRI data analysis

In this study, we present an advanced Bayesian framework for the analysis of functional magnetic resonance imaging (fMRI) data that simultaneously employs both spatial and sparse properties. The basic building block of our method is the general linear regression model that constitutes a well-known probabilistic approach. By treating regression coefficients as random variables, we can apply an enhanced Gibbs distribution function that captures spatial constrains and at the same time allows sparse representation of fMRI time series. The proposed scheme is described as a maximum a posteriori approach, where the known expectation maximization algorithm is applied offering closed-form update equations for the model parameters. We have demonstrated that our method produces improved performance and functional activation detection capabilities in both simulated data and real applications.     

Generic Architecture and Mechanisms for Protocol Reconfiguration

The next generation of wireless mobile communications termed beyond 3G (or 4G), will be based on a heterogeneous infrastructure that comprises different wireless networks in a complementary manner. Beyond 3G will introduce reconfiguration capabilities to flexibly and dynamically (i.e., during operation) adapt the wireless protocol stacks to better meet the ever-changing service requirements. For the dynamic reconfiguration of protocol stacks during runtime operation to become a practical capability of mobile communication systems, it is necessary to establish a software architecture that functionally supports reconfiguration. In the present paper, a generic architecture and respective mechanisms to achieve protocol stack and component based protocol layer reconfiguration are proposed.

Lower Bounds on the Approximation Ratios of Leading Heuristics for the Single-Machine Total Tardiness Problem

The weakly NP-hard single-machine total tardiness scheduling problem has been extensively studied in the last decades. Various heuristics have been proposed to efficiently solve in practice a problem for which a fully polynomial time approximation scheme exists (though with complexity O(n ⁷/)). In this note, we show that all known constructive heuristics for the problem, namely AU, MDD, PSK, WI, COVERT, NBR, present arbitrarily bad approximation ratios. The same behavior is shown by the decomposition heuristics DEC/EDD, DEC/MDD, DEC/PSK, and DEC/WI.     

On kernels, defaults and even graphs

Extensions in prerequisite‐free, disjunction‐free default theories have been shown to be in direct correspondence with kernels of directed graphs; hence default theories without odd cycles always have a “standard” kind of an extension. We show that, although all “standard” extensions can be enumerated explicitly, several other problems remain intractable for such theories: Telling whether a non‐standard extension exists, enumerating all extensions, and finding the minimal standard extension. We also present a new graph‐theoretic algorithm, based on vertex feedback sets, for enumerating all extensions of a general prerequisite‐free, disjunction‐free default theory (possibly with odd cycles). The algorithm empirically performs well for quite large theories. This revised version was published online in June 2006 with corrections to the Cover Date.     

Non-invasive analysis of sleep patterns via multimodal sensor input

The monitoring of sleep patterns is of major importance for various reasons such as the detection and treatment of sleep disorders, the assessment of the effect of different medical conditions or medications on the sleep quality, and the assessment of mortality risks associated with sleeping patterns in adults and children. Sleep monitoring by itself is a difficult problem due to both privacy and technical considerations. The proposed system uses a combination of non-invasive sensors to assess and report sleep patterns: a contact-based pressure mattress and a non-contact 3D image acquisition device, which can complement each other. To evaluate our system, we used real data collected in Heracleia Lab’s assistive living apartment. Our system uses Machine Learning techniques to automatically analyze the collected data and recognize sleep patterns. It is non-invasive, as it does not disrupt the user’s usual sleeping behavior and it can be used both at the clinic and at home with minimal cost.     

Enabling Delivery of Mobile Services Over Heterogeneous Converged Infrastructures

Traditionally, end customers have been offered different categories of communication, data and media services (e.g., fixed/mobile voice, fixed/mobile data, broadcasting) through vertically separated, rigidly integrated infrastructures. Major advances in a variety of technological fields, mainly in the area of mobile computing and networking, have created prospects for a fully converged environment, where ubiquitous access to an abundance of value-added services will be offered over a single, as perceived by the users, highly reconfigurable system. This vision can be enabled by the seamless plugging of diverse access networks to a high-speed IP backbone; however the path to its realization poses a variety of additional challenges. The required support of complicated business models and service delivery over highly diverse contexts introduces significant complexity to service management and provision. The present contribution presents object-oriented mediating service management platforms as a catalyst for making these demanding tasks feasible, identifies their desired functionality and provides an overview of such a distributed framework that we have designed and prototyped. Furthermore, we demonstrate that the latter is able to be a critical enabler of flexible, adaptable service provision over future heterogeneous networks, while supporting advanced business paradigms.     

Performance analysis of a solar vapour thermal compression chiller

The influence of the design conditions on the coefficient of performance (COP) of a solar vapour thermal compression chiller was investigated from an operational point of view. Based on a well known model, a number of COP evaluation runs were conducted and the results are presented in graph form as a function of the main design parameters. Through these graphs the influence of the main operating parameters on the performance is quantitatively shown. Finally, the calculated COP is compared to that of a solar absorption system operating under similar conditions.     

A quantitative approach to evaluate Website Archivability using the CLEAR+ method

Website Archivability (WA) is a notion established to capture the core aspects of a website, crucial in diagnosing whether it has the potential to be archived with completeness and accuracy. In this work, aiming at measuring WA, we introduce and elaborate on all aspects of CLEAR+, an extended version of the Credible Live Evaluation Method for Archive Readiness (CLEAR) method. We use a systematic approach to evaluate WA from multiple different perspectives, which we call Website Archivability Facets. We then analyse archiveready.com, a web application we created as the reference implementation of CLEAR+, and discuss the implementation of the evaluation workflow. Finally, we conduct thorough evaluations of all aspects of WA to support the validity, the reliability and the benefits of our method using real-world web data.     

Binding and Docking Interactions of NO,CO and O2 in Heme Proteins as Probed by Density Functional Theory

Dynamics and reactivity in heme proteins include direct and indirect interactions of the ligands/substrates like CO, NO and O₂ with the environment. Direct electrostatic interactions result from amino acid side chains in the inner cavities and/or metal coordination in the active site, whereas indirect interactions result by ligands in the same coordination sphere. Interactions play a crucial role in stabilizing transition states in catalysis or altering ligation chemistry. We have probed, by Density Functional Theory (DFT), the perturbation degree in the stretching vibrational frequencies of CO, NO and O₂ molecules in the presence of electrostatic interactions or hydrogen bonds, under conditions simulating the inner cavities. Moreover, we have studied the vibrational characteristics of the heme bound form of the CO and NO ligands by altering the chemistry of the proximal to the heme ligand. CO, NO and O₂ molecules are highly polarizable exerting vibrational shifts up to 80, 200 and 120 cm⁻¹, respectively, compared to the non-interacting ligand. The importance of Density Functional Theory (DFT) methodology in the investigation of the heme-ligand-protein interactions is also addressed.