A Bayesian Framework for Active Learning

Copyright © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We describe a Bayesian framework for active learning for non-separable data, which incorporates a query density to explicitly model how new data is to be sampled. The model makes no assumption of independence between queried data-points; rather it updates model parameters on the basis of both observations and how those observations were sampled. A `hypothetical' look-ahead is employed to evaluate expected cost in the next time-step. We show the efficacy of this algorithm on the probabilistic high-low game which is a non-separable generalisation of the separable high-low game introduced by Seung et al. Our results indicate that the active Bayes algorithm performs significantly better than passive learning even when the overlap region is wide, covering over 30% of the feature space

Finite driving rate and anisotropy effects in landslide modeling

In order to characterize landslide frequency-size distributions and individuate hazard scenarios and their possible precursors, we investigate a cellular automaton where the effects of a finite driving rate and the anisotropy are taken into account. The model is able to reproduce observed features of landslide events, such as power-law distributions, as experimentally reported. We analyze the key role of the driving rate and show that, as it is increased, a crossover from power-law to non power-law behaviors occurs. Finally, a systematic investigation of the model on varying its anisotropy factors is performed and the full diagram of its dynamical behaviors is presented.Comment: 8 pages, 9 figure

Weaving Concurrency in eXecutable Domain-Specific Modeling Languages

International audienceThe emergence of modern concurrent systems (e.g., Cyber-Physical Systems or the Internet of Things) and highly-parallel platforms (e.g., many-core, GPGPU pipelines, and distributed platforms) calls for Domain-Specific Modeling Languages (DSMLs) where concurrency is of paramount importance. Such DSMLs are intended to propose constructs with rich concurrency semantics, which allow system designers to precisely define and analyze system behaviors. However , specifying and implementing the execution semantics of such DSMLs can be a difficult, costly and error-prone task. Most of the time the concurrency model remains implicit and ad-hoc, embedded in the underlying execution environment. The lack of an explicit concurrency model prevents: the precise definition, the variation and the complete understanding of the semantics of the DSML, the effective usage of concurrency-aware analysis techniques, and the exploitation of the concurrency model during the system refinement (e.g., during its allocation on a specific platform). In this paper, we introduce a concurrent executable metamodeling approach, which supports a modular definition of the execution semantics , including the concurrency model, the semantic rules, and a well-defined and expressive communication protocol between them. Our approach comes with a dedicated metalanguage to specify the communication protocol, and with an execution environment to simulate executable models. We illustrate and validate our approach with an implementation of fUML, and discuss the modularity and applicability of our approach

Distributed Consensus, Revisited

We provide a novel model to formalize a well-known algorithm, by Chandra and Toueg, that solves Consensus among asynchronous distributed processes in the presence of a particular class of failure detectors (Diamond S or, equivalently, Omega), under the hypothesis that only a minority of processes may crash. The model is defined as a global transition system that is unambigously generated by local transition rules. The model is syntax-free in that it does not refer to any form of programming language or pseudo code. We use our model to formally prove that the algorithm is correct

Task shifting and integration of HIV care into primary care in South Africa: The development and content of the streamlining tasks and roles to expand treatment and care for HIV (STRETCH) intervention

Background: Task shifting and the integration of human immunodeficiency virus (HIV) care into primary care services have been identified as possible strategies for improving access to antiretroviral treatment (ART). This paper describes the development and content of an intervention involving these two strategies, as part of the Streamlining Tasks and Roles to Expand Treatment and Care for HIV (STRETCH) pragmatic randomised controlled trial. Methods: Developing the intervention: The intervention was developed following discussions with senior management, clinicians, and clinic staff. These discussions revealed that the establishment of separate antiretroviral treatment services for HIV had resulted in problems in accessing care due to the large number of patients at ART clinics. The intervention developed therefore combined the shifting from doctors to nurses of prescriptions of antiretrovirals (ARVs) for uncomplicated patients and the stepwise integration of HIV care into primary care services. Results: Components of the intervention: The intervention consisted of regulatory changes, training, and guidelines to support nurse ART prescription, local management teams, an implementation toolkit, and a flexible, phased introduction. Nurse supervisors were equipped to train intervention clinic nurses in ART prescription using outreach education and an integrated primary care guideline. Management teams were set up and a STRETCH coordinator was appointed to oversee the implementation process. Discussion: Three important processes were used in developing and implementing this intervention: active participation of clinic staff and local and provincial management, educational outreach to train nurses in intervention sites, and an external facilitator to support all stages of the intervention rollout

Numerical analysis of seepage–deformation in unsaturated soils

A coupled elastic–plastic finite element analysis based on simplified consolidation theory for unsaturated soils is used to investigate the coupling processes of water infiltration and deformation. By introducing a reduced suction and an elastic–plastic constitutive equation for the soil skeleton, the simplified consolidation theory for unsaturated soils is incorporated into an in-house finite element code. Using the proposed numerical method, the generation of pore water pressure and development of deformation can be simulated under evaporation or rainfall infiltration conditions. Through a parametric study and comparison with the test results, the proposed method is found to describe well the characteristics during water evaporation/infiltration into unsaturated soils. Finally, an unsaturated soil slope with water infiltration is analyzed in detail to investigate the development of the displacement and generation of pore water pressure

Selection of yeast strains for bioethanol production from UK seaweeds

Macroalgae (seaweeds) are a promising feedstock for the production of third generation bioethanol, since they have high carbohydrate contents, contain little or no lignin and are available in abundance. However, seaweeds typically contain a more diverse array of monomeric sugars than are commonly present in feedstocks derived from lignocellulosic material which are currently used for bioethanol production. Hence, identification of a suitable fermentative microorganism that can utilise the principal sugars released from the hydrolysis of macroalgae remains a major objective. The present study used a phenotypic microarray technique to screen 24 different yeast strains for their ability to metabolise individual monosaccharides commonly found in seaweeds, as well as hydrolysates following an acid pre-treatment of five native UK seaweed species (Laminaria digitata, Fucus serratus, Chondrus crispus, Palmaria palmata and Ulva lactuca). Five strains of yeast (three Saccharomyces spp, one Pichia sp and one Candida sp) were selected and subsequently evaluated for bioethanol production during fermentation of the hydrolysates. Four out of the five selected strains converted these monomeric sugars into bioethanol, with the highest ethanol yield (13 g L−1) resulting from a fermentation using C. crispus hydrolysate with Saccharomyces cerevisiae YPS128. This study demonstrated the novel application of a phenotypic microarray technique to screen for yeast capable of metabolising sugars present in seaweed hydrolysates; however, metabolic activity did not always imply fermentative production of ethanol

Hypoxia inducible factor‐2α importance for migration, proliferation, and self‐renewal of trunk neural crest cells

Background: The neural crest is a transient embryonic stem cell population. Hypoxia inducible factor (HIF)‐2α is associated with neural crest stem cell appearance and aggressiveness in tumors. However, little is known about its role in normal neural crest development. Results: Here, we show that HIF‐2α is expressed in trunk neural crest cells of human, murine, and avian embryos. Knockdown as well as overexpression of HIF‐2α in vivo causes developmental delays, induces proliferation, and self‐renewal capacity of neural crest cells while decreasing the proportion of neural crest cells that migrate ventrally to sympathoadrenal sites. Reflecting the in vivo phenotype, transcriptome changes after loss of HIF‐2α reveal enrichment of genes associated with cancer, invasion, epithelial‐to‐mesenchymal transition, and growth arrest. Conclusions: Taken together, these results suggest that expression levels of HIF‐2α must be strictly controlled during normal trunk neural crest development and that dysregulated levels affects several important features connected to stemness, migration, and development