The goal of this chapter is to provide a Role and Reference Grammar (RRG henceforth) analysis of cleft sentences in Persian with respect to the role of interaction over the domains of syntax, semantics and information structure. Clefting is functionally motivated by thematically marked expression of a single proposition via bi-partition syntax in the sense that a sequence of two clauses including a matrix and a cleft clause is employed in order to bring into focus an element that can be otherwise expressed as a non-focal element in the unmarked predicate-focus structure. This deviation from the unmarked predicate-focus structure feeds cleft sentences with a non-isomorphic/non-compositional feature that can be accounted for in terms of a constructional framework that encompasses the morphosyntactic, semantic and informational properties that a cleft construction has. RRG is equipped with a bi-lateral syntax-semantics representation that is enriched with discourse-pragmatic considerations which enables it to facilitate processing of linguistic properties of cleft constructions by means of constructional schemas. Non-compositionality in a cleft construction implies that the semantic composition of the whole cleft construction is not the "computable sum of the meaning of its parts" (Lambrecht 1994: 230). That is why a construction-based scrutiny will be necessary to explain how the mapping between semantics and information structure in clefting can be elucidated with respect to the syntactic juxtaposition of the constituents. It will be also discussed that there should be a necessity to distinguish between emphatic in in clefting and anaphoric in in extraposition in the Persian language with regard to the role of information structure that is distinctively formalized in the syntactic templates of the two constructions on the basis of the RRG constructional framework.


Background: An Ebola outbreak of unparalleled size is currently affecting several countries in West Africa, and international efforts to control the outbreak are underway. However, the efficacy of these interventions, and their likely impact on an Ebola epidemic of this size, is unknown. Forecasting and simulation of these interventions may inform public health efforts. Methods: We use existing data from Liberia and Sierra Leone to parameterize a mathematical model of Ebola and use this model to forecast the progression of the epidemic, as well as the efficacy of several interventions, including increased contact tracing, improved infection control practices, the use of a hypothetical pharmaceutical intervention to improve survival in hospitalized patients. Findings: Model forecasts until Dec. 31, 2014 show an increasingly severe epidemic with no sign of having reached a peak. Modeling results suggest that increased contact tracing, improved infection control, or a combination of the two can have a substantial impact on the number of Ebola cases, but these interventions are not sufficient to halt the progress of the epidemic. The hypothetical pharmaceutical intervention, while impacting mortality, had a smaller effect on the forecasted trajectory of the epidemic. Interpretation: Near-term, practical interventions to address the ongoing Ebola epidemic may have a beneficial impact on public health, but they will not result in the immediate halting, or even obvious slowing of the epidemic. A long-term commitment of resources and support will be necessary to address the outbreak.


This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically discover a mapping from the beliefs generated by the DPNs to the actions that enable active sensors to gather the most useful observations. The resulting method is evaluated on a simulation of a chemical leak localization task and the results demonstrate 1) that the integrated approach can learn policies that perform effective sensor management, 2) that inference based on a correct observation model, which the DPNs make feasible, is critical to performance, and 3) that the system scales to larger versions of the task.


This article focuses on three instances of the use of armed force in international relations. The three instances that are explored are the intervention by NATO in Kosovo, the armed attack by mainly the USA and the UK against Afghanistan and the war against Iraq in 2003. The purpose of this investigation is to examine the legality of the coercive measures in order to ascertain the effects that these actions had in relation to article 2(4) of the UN Charter . The proposed justifications for the attacks differ and these are carefully scrutinized against the jus ad bellum as to determine the legality of the attacks. The notion of humanitarian intervention was used as a ground for justification by various international scholars to explain the use of force in Kosovo, but this concept is not recognized in terms of international law. The attack on Afghanistan was based on article 51 of the UN Charter . The attacks were directed at Afghanistan as this state harboured the terrorists responsible for the attacks on the USA. The mere harbouring of terrorists does not give rise to the use of armed force on the basis of article 51 and as such the use of coercive measures against Afghanistan was illegal. The use of force in Iraq was mainly based on the doctrine of pre-emptive force which is alien to international law. The USA and its coalition partners also acted in contravention with the jus ad bellum in this regard. The author poses certain proposals in relation to the jus ad bellum and stresses the importance of article 2(4) which must ensure that international relations are not once more regulated by the use of armed force.


Recent theoretical studies of the nonlinear wave-particle interactions for relativistic particles have shown that Landau resonant orbits could be efficiently accelerated along the mean background magnetic field for propagation angles θ in close proximity to a critical propagation θ c associated with a Hopf–Hopf bifurcation condition. In this report, we extend previous studies to reach greater modeling capacities for the study of electrons in radiation belts by including longitudinal wave effects and inhomogeneous magnetic fields. We find that even though both effects can limit the surfatron acceleration of electrons in radiation belts, gains in energy of the order of 100 keV, taking place on one tenth of a millisecond, are sufficiently strong for the mechanism to be relevant to radiation belt dynamics.


Sheep was one of the first domesticated livestock species in the Anatolia region of contemporary Iran and eventually spread world-wide. Previous studies have shown that French sheep populations likely harbour a large part of European domesticated sheep diversity in a relatively small geographical region, offering a powerful model for the study of adaptation. We studied the diversity of 27 French sheep populations by genotyping 542 individuals for more than 500 000 SNPs. We found that French sheep breeds were divided into two main groups, corresponding to northern and southern origins and additionally we identified admixture events between northern and southern populations. The genetic diversity of domesticated animals results from adaptation of populations to constraints imposed by farmers and environmental conditions. We identified 126 genomic regions likely affected by selection. In many cases, we found evidence for parallel selection events in different genetic backgrounds, most likely for different mutations. Some of these regions harbour genes potentially involved in morphological traits (SOCS2, NCAPG/LCORL, MSRB3), coat colour (MC1R) and adaptation to environmental conditions (ADAMTS9). Closer inspection of two of these regions clarified their evolutionary history: at the LCORL/NCAPG locus we found evidence for introgression of an adaptive allele from a southern population into northern populations and by resequencing MC1R in some breeds we confirmed different mutations in this gene are responsible for the same phenotypic trait. Our study illustrates how dense genetic data in multiple populations allows the deciphering of evolutionary history of populations and of their adaptive mutations.


The intrinsic atomistic variability of nano-scale integrated circuit (IC) technology must be taken into account when analysing circuit designs to predict likely yield. These ?atomistic? variabilities are random in nature and are so great that new circuit analysis techniques are needed which adopt a statistical treatment of the variability of device performances. Monte Carlo (MC) based statistical techniques aim to do this by analysing many randomized copies of the circuit. The randomization can take into account correlation between parameters due to both intra-die and inter-die effects. A major problem is the computational cost of carrying out sufficient analyses to produce statistically reliable results. The use of principal components analysis (PCA) and ?Statistical Behavioural Circuit Blocks (SBCB)? is investigated as a means of reducing the dimensionality of the analysis, and this is combined with an implementation of ?Statistical Blockade (SB)? to achieve significant reduction in the computational costs. The purpose of SBCBs is to model the most important aspects of the device?s or circuit building block?s behaviour, to an acceptable accuracy, with a relatively small number of parameters. The SB algorithm applies Extreme Value Theory (EVT) to circuit analysis by eliminating randomised parameter vectors that are considered unlikely to produce ?rare event? circuits. These circuits are needed for circuit yield failure predictions and occur on the ?tails? of Gaussian-like probability distributions for circuit performances. Versions of the circuit analysis program ?SPICE? with a Python harness called RandomSPICE are used to produce SBCBs by generating and statistically analysing randomized transistor-level versions of the sub-blocks for which behavioural models are required. The statistical analysis of circuits employing these sub-blocks is achieved by a new MATLAB harness called RandomLA. The computational time savings that may be achieved are illustrated by the statistical analysis of representative circuits. A computation time reduction of 98.7% is achieved for a commonly used asynchronous circuit element. Quasi-Monte Carlo (QMC) analysis with ?low discrepancy sequences (LDS)? is introduced for further computation reduction. QMC analysis using SBCB behavioural models with SB is evaluated by applying it to more complex examples and comparing the results with those of transistor level simulations. The analysis of SRAM arrays is taken as a case study for VLSI circuits containing up to 1536 transistors, modeled with parameters appropriate to 35nm technology. Significantly faster statistical analysis is shown to be possible when the aim is to obtain predictions of the yield for fabrication. Saving of up to 99.85% in computation time was obtained with larger circuits.


The authors sketch a framework within which the contributions to this theme issue can be understood. In particular, they discuss various frames of reference or ways of thinking that can be brought to bear on the challenges that arise in evaluating attempts to govern environmental risks. The discussion is divided into three sections. First, they discuss the factors that have led to the emergence of new ways of governing those corporate activities that are associated with the generation or management of environmental risks. Second, they problematise these new forms of governance, adopting the United Nation's Global Compact as an example, and drawing particularly on the insights derived from the contrasting perspectives associated with communicative and strategic action. As an alternative to both of these perspectives, they next focus on the Foucauldian concept of governmentality. The need for analyses to consider (a) the nature of the ways in which different risks are problematised, (b) the character of different governance regimes, (c) the significance of the organising ideals that guide the operation, and (d) the evolution of the multidimensional processes through which risks are governed, are highlighted. They conclude by suggesting that new 'governmental technologies' are unlikely to enable either 'governance at a distance', for instance, by creating opportunities for new forms of engagement and new spaces of accountability, or 'governance of the self', for example, by instilling values and developing technologies which allow corporations to govern their own activities more effectively.


Acknowledging the user context, e.g., position and activity, provides a natural way to adapt applications according to the user needs. How to actually capture and exploit context, however, is not self-evident and it is tempting to assign the related responsibilities to individual context-consuming applications. Unfortunately, this confuses the user, complicates application development and hinders context-aware semantic computing as a research discipline. In this article, we outline context-aware semantic computing research topics and the state-of-the-art mobile application development frameworks of special interest to us, acknowledging best practices for accessing and modeling sensor context. From the integrated point of view, context-aware semantic computing is demonstrated in terms of a software component called context engine. In order to better understand how theory is tied with practice, we also introduce a simple context engine prototype. Finally, we use the research background and the empirical setting to discuss the significant problems and relevant research directions in context-aware semantic processing.


Like many of the shows in PRX's Radiotopia catalogue of podcasts, Criminal's sensibility and sound partake of the US public radio formula made famous by This American Life: journalistic rigour and gothic yarns. The show tells "stories of people who've done wrong, been wronged, or got caught somewhere in the middle". But it's moved beyond mere crime journalism to something that aspires to a bit more philosophical heft. Most of the stories unspool through the elegant co-narration between host Phoebe Judge and each episode's central protagonist. The effect is almost always seamless, thanks to the expert mixing of Rob Byers, and the painstaking interviewing and editing process necessary to produce a coherent and tonally appropriate narrative. While we get the who-what-where details upfront, the when is not as clear, a key distinction between the daily crime beat of a journalist and the story-first imperative of non-fiction narrative podcasting. This review focuses on the first eight episodes to air after the milestone 50th landed in 2016, listening for signs of how the show is evolving as it matures and moves into its next fifty.


Spatial modulation of liquid crystals can be controlled and adjusted by light polarization, the degree of pretilt on the substrates, anchoring strength, and the experimental geometry. In particular, strong anchoring can affect the liquid crystal orientation in opposite ways, depending on the polarization of the incident light. Here we present a theoretical model that describes the liquid crystal modulation and how it can be controlled and optimized. The model is valid for electric fields with a uniform component that is large with respect to the spatial modulation, a situation typical of spatial light modulators and photorefractive cells.