Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing

We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.

On the Complexity of Universal Leader Election

Electing a leader is a fundamental task in distributed computing. In its implicit version, only the leader must know who is the elected leader. This article focuses on studying the message and time complexity of randomized implicit leader election in synchronous distributed networks. Surprisingly, the most "obvious" complexity bounds have not been proven for randomized algorithms. In particular, the seemingly obvious lower bounds of Ω(m) messages, where m is the number of edges in the network, and Ω(D) time, where D is the network diameter, are nontrivial to show for randomized (Monte Carlo) algorithms. (Recent results, showing that even Ω(n), where n is the number of nodes in the network, is not a lower bound on the messages in complete networks, make the above bounds somewhat less obvious). To the best of our knowledge, these basic lower bounds have not been established even for deterministic algorithms, except for the restricted case of comparison algorithms, where it was also required that nodes may not wake up spontaneously and that D and n were not known. We establish these fundamental lower bounds in this article for the general case, even for randomized Monte Carlo algorithms. Our lower bounds are universal in the sense that they hold for all universal algorithms (namely, algorithms that work for all graphs), apply to every D, m, and n, and hold even if D, m, and n are known, all the nodes wake up simultaneously, and the algorithms can make any use of node's identities. To show that these bounds are tight, we present an O(m) messages algorithm. An O(D) time leader election algorithm is known. A slight adaptation of our lower bound technique gives rise to an Ω(m) message lower bound for randomized broadcast algorithms. 

An interesting fundamental problem is whether both upper bounds (messages and time) can be reached simultaneously in the randomized setting for all graphs. The answer is known to be negative in the deterministic setting. We answer this problem partially by presenting a randomized algorithm that matches both complexities in some cases. This already separates (for some cases) randomized algorithms from deterministic ones. As first steps towards the general case, we present several universal leader election algorithms with bounds that tradeoff messages versus time. We view our results as a step towards understanding the complexity of universal leader election in distributed networks.

New FMO Type to Flag ROI in H264/AVC

This paper presents a new type of Flexible Macroblock Ordering (FMO) type for the H.264 Advanced Video Coding (AVC) standard, which can more efficiently flag the position and shape of regions of interest (ROIs) in each frame. In H.264/AVC, 7 types of FMO have been defined, all of which are designed for error resilience. Most previous work related to ROI processing has adopted Type-2 (foreground & background), or Type-6 (explicit), to flag the position and shape of the ROI. However, only rectangular shapes are allowed in Type-2 and for non-rectangular shapes, the non-ROI macroblocks may be wrongly flagged as being within the ROI, which could seriously affect subsequent processing of the ROI. In Type-6, each macroblock in a frame uses fixed-length bits to indicate to its slice group. In general, each ROI is assigned to one slice group identity. Although this FMO type can more accurately flag the position and shape of the ROI, it incurs a significant bitrate overhead. The proposed new FMO type uses the smallest rectangle that covers the ROI to indicate its position and a spiral binary mask is employed within the rectangle to indicate the shape of the ROI. This technique can accurately flag the ROI and provide significantly savings in the bitrate overhead. Compared with Type-6, an 80% to 90% reduction in the bitrate overhead can be obtained while achieving the same accuracy.

Artificial neural network model to predict compositional viscosity over a broad range of temperatures

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as ILs) [C _n-mim] [NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. © 2010 IEEE.

An Investigation of Compressor Map Width Enhancement and the Inducer Flow Field Using Various Configurations of Shroud Bleed Slot

This paper describes an investigation of various shroud bleed slot configurations of a centrifugal compressor using CFD with a manual multi-block structured grid generation method. The compressor under investigation is used in a turbocharger application for a heavy duty diesel engine of approximately 400hp. The baseline numerical model has been developed and validated against experimental performance measurements. The influence of the bleed slot flow field on a range of operating conditions between surge and choke has been analysed in detail. The impact of the returning bleed flow on the incidence at the impeller blade leading edge due to its mixing with the main through-flow has also been studied. From the baseline geometry, a number of modifications to the bleed slot width have been proposed, and a detailed comparison of the flow characteristics performed. The impact of slot variations on the inlet incidence angle has been investigated, highlighting the improvement in surge and choked flow capability. Along with this, the influence of the bleed slot on stabilizing the blade passage flow by the suction of the tip and over-tip vortex flow by the slot has been considered near surge.

"Je ne sais plus du tout qui je suis": The Phenomenology of Cancer in Anne Cuneo’s 'Une cuillerée de bleu'

Taking as its case study Anne Cuneo’s autopathography, Une cuillerée de bleu (1979), and supported by a phenomenological reading of the subjective realities of illness, including feelings of alienation, fragmentation, and passivity, this article considers how the experience of cancer affects the patient’s perception of her body, self, and world.

Extraordinary mechanical properties of monatomic C3N2 chain

One-dimensional monatomic chains are promising candidates for technical applications in the field of nanoelectronics due to their unique mechanical, electrical and optical properties. In particular, we investigate the mechanical properties including Young's modulus, ultimate strength and ultimate strain, which are necessities for the stability of the materials by the Car-Parrinello molecular dynamics in this work. The comparative studies for the alternating carbon-nitrogen (C₃N₂) chain and carbon chains (carbyne) of different lengths show that the carbon-nitrogen (C-N) chain is obviously stronger and stiffer than carbynes. Thus the C-N chain, which has been found in decomposition products of the nitromethane explosive simulations, could be a superior nano-mechanical material than the carbyne chain. Furthermore, it is found that the bond order of weakest bond in monatomic chains is positively correlated with Young's modulus and ultimate strength of materials.

Operators for Similarity Search: Semantics, Techniques and Usage Scenarios

This book provides a comprehensive tutorial on similarity operators. The authors systematically survey the set of similarity operators, primarily focusing on their semantics, while also touching upon mechanisms for processing them effectively.

The book starts off by providing introductory material on similarity search systems, highlighting the central role of similarity operators in such systems. This is followed by a systematic categorized overview of the variety of similarity operators that have been proposed in literature over the last two decades, including advanced operators such as RkNN, Reverse k-Ranks, Skyline k-Groups and K-N-Match. Since indexing is a core technology in the practical implementation of similarity operators, various indexing mechanisms are summarized. Finally, current research challenges are outlined, so as to enable interested readers to identify potential directions for future investigations.

In summary, this book offers a comprehensive overview of the field of similarity search operators, allowing readers to understand the area of similarity operators as it stands today, and in addition providing them with the background needed to understand recent novel approaches.

Introduction: Embodiment, Identity, and the Patient’s Story

This introduction looks at the links between medicine and narrative, arguing that patients’ stories have a valuable role to play in patient-centered healthcare. Two particular areas are addressed: first, the ways in which first-person expressions of embodied experience may contribute to medicine’s empathetic endeavor; and second, the lack of research to date into autopathography in the French-speaking world.

French Autopathography

Reacting against the assumption that ill people ‘surrender’ their bodies to medicine, first-person illness narratives attempt to restore the patient’s voice to an often dehumanizing and bewildering medical experience. This special issue complements recent medical humanities scholarship on English-language illness narratives by investigating a distinctly rich tradition of French autopathography. Diverse approaches and methodologies will be used to consider first-person perspectives on a range of illnesses, disabilities and disorders, including AIDS, cancer, physical pain, mental health issues, anorexia, and locked-in syndrome. The issue aims to promote interdisciplinary dialogue across genres (literature, film, philosophy) and examine the creative potential that lies at the interface of medicine and the arts.