Figure 2: A schematic illustration of the transition from non precipitating closed Benard cells to precipitating open cells and onward to nearly complete rainout and elimination of the clouds. In the closed Benard cells (A) the convection is propelled by radiative cooling from the tops of the extensive deck of clouds with small drops. The onset of drizzle breaks the cloudcover (B, C). The propulsion of the convection undergoes transition from radiative cooling at the top of the fully cloudy MBL (orange arrows in B) to surface heating at the bottom of the partly cloudy MBL (orange arrows at D) causes a reversal of the convection from closed to open Benard cells (D). The process can continue to a runaway effect of cleansing by the CCN and direct condensation into drizzle that directly precipitates and prevents the cloud formation altogether (E). The satellite strip is a 300 km long excerpt from the box in Figure 3.
Although the proposed mechanism is still a hypothesis that remains to be validated, it is already established that the change of regimes from closed to open cells occurs abruptly at rather low aerosol concentrations. The values in which the transition occur are within AOD<0.1, centered at the pris- tine background of AOD=∼0.06 (Smirnov et al., 2002). The transition from open cells to the super clean cloudless state obviously occurs at extremely low aerosols concentrations. This situation provides a huge sensitivity to very small differ- ences in aerosol amounts that can lead to a bifurcation of the state of the MBL and the respective cloud radiative forcing. The strong decrease of cloudcover at the smallest values of aerosol optical depth (see Fig. 1, especially at the upper left panel for the South Atlantic) provides some supporting evi- dence for the climatological importance of the processes that were shown here on the basis of a few case studies. The vali- dation of our proposed mechanism may lead to a different ap- proach in the model calculations of cloud-mediated aerosol forcing – i.e. so far most of the studies showed linear or log- arithmic dependence between aerosols and cloud properties. Here we suggest mechanism that reacts more like a step func- tion between almost 100% cloud fraction (close cells) to less than 40% in case of open cells, and then near zero cloud frac- tion for the super-clean areas.
downwind (and to a lesser extent, cross-wind) edge of the forest over all wind directions. This edge effect is most pronounced at higher regional average wind speeds (41 m s 1 ). These patterns are consistent with a forest-breeze circulation driven by locally enhanced heating over the forest 1,4 , as previously observed for Landes 8 . In calm conditions, convergence over the forest favours cloud development. Under moderate winds, the thermally induced surface pressure gradient weakens at the upwind edge, due to advection of cool air into the forest. By contrast at the downwind edge, the local forest breeze opposes the synoptic ﬂow, enhancing convergence and providing favourable conditions for convection. No clear relation between patterns of cloud frequency and regional wind conditions was found for the smaller Sologne forest (Supplementary Figs 14 and 16) where edge effects are more difﬁcult to detect. We also did not ﬁnd a strong dependence between synoptic conditions (measured by sea level pressure) and difference in cloud frequency (Supplementary Fig. 17), suggesting that differences in cloudcover between forest and surrounding areas occur under a range of synoptic conditions possibly involving different dominant mechanisms. The thermally induced forest breeze is likely to be strongest in June and July. Analysis of land surface temperature (LST) observations (Fig. 2d and Supplementary Fig. 11) reveals a progressive increase in LST over the surrounding areas during the summer season. The stable LST over forest probably reﬂects sustained evapo- transpiration rates throughout the summer season, whereas the strong increase in LST over surrounding areas (up to 10 K for Sologne) is indicative for declining evaporation rates. The associated increase in thermal convection will counteract the increased thermal convection over forest due to the lower albedo.
N s indicates the cloudcover fraction of the most dominant cloud type C s described in Table 3.
One of the problems of comparing SYNOP surface mea- surements to satellite derived measurements is that, while looking at the same cloud, the determined cloud fraction may be different. Part of this difference may be explained by tak- ing into account the distance of the cloud layer to the ob- server and the path through which it is observed: for the hu- man observer on the ground the cloud is at a few hundred meter or a few kilometer in altitude, seen through the hazy boundary layer which limits the observer to about 20–30 km radius, while the satellite instrument observes the same cloud from above at 800 km altitude, through mostly optically thin air. An other issue is the definition of a cloud: a hu- man observer may define a cloud as the portion of the sky that is “white” on a blue-ish background, while the satellite instrument is using calibrated spectral radiances or broad- band measurements. Cloud fractions reported in the SYNOP reports are classified by the eye of humans and, although trained for this work and following standards set by the WMO, the results may differ per individual. A satellite in- strument stays the same, and should give similar results in similar cases in all parts of the world (when degradation of the instrument is not taken into account). Cloud fractions re- ported by surface observers are supposed to be independent of the cloud optical thickness but geometrical effects with clouds with a larger vertical structure may increase the re- ported cloudcover.
Figure 5. Variations of three upscaling factors (EF, EF r and R g ) at different upscaling moments when transient cloud appeared before upscaling time
Figure 6. Comparisons of daily LE derived from three methods and simulated daily LE from ALEX at all upscaling moments when transient cloud appeared before upscaling time
Conf. Acoustics, Speech and Signal Process. (ICASSP) (2007).
 E LLIS , D., AND P OLINER , G. Identifying cover songs with chroma
features and dynamic programming beat tracking. In Int. Conf. Acoustics, Speech and Signal Process. (ICASSP) (2007), vol. 4.
a b .
6. Concluding remarks
The k-path vertex cover of a graph G can be easily translated to the transversal of the hypergraph with V (G) as its vertex set whose edges are all the paths of order k in G. Since the corresponding hypergraph is k- uniform, one can infer from the general upper bound of Alon  that ψk(G) ≤ |V (G)|p(G) ln(k)/k, where p(G) is the number of paths of order k in G. More specifically, for the case of 3-uniform hypergraphs, the bound of Thomass´e and Yeo  implies that ψ 3 (G) ≤ (|V (G)|+p(G))/4. As expected, the latter general result is only in some special cases better as our bound from Theorem 9, in particular such case is when G is K3-free and P v∈V (G) d(v)(d(v)−1) 2 −2m <
The problem of allocating cloud resources in performant, robust and energy- efficient ways is of paramount importance in today’s usage of computing in- frastructures. Cloud resources proposed to clients as Infrastructure as a Ser- vice (IaaS) open a large field of investigation regarding how automatic tools can help users to better provision the resources and schedule their computation or storage tasks with regard to the trade-off between rental cost and performance. Indeed it rapidly becomes very difficult for users to manually handle the pro- visioning and scheduling decisions when the workloads involve numerous tasks, which are potentially dependent on others – and such complex workloads are the focus in this paper. In the last decade a number of research papers have contributed new allocation techniques to address this issue. A pitfall of research on IaaS lies in the validation of the models and algorithms proposed, as vali- dation on actual clouds requires infrastructures that are difficult to set up for individual researchers. As a consequence, many researchers evaluate their work through simulation. A number of simulators have been developed for that pur- pose as reviewed in the related work section hereafter. They are typically based on discrete-event simulation, using models for each elementary component of the infrastructure, which are then composed to simulate the whole system and applications running on it.
A basic issue with standard, public CC services is that these services are located far from the shop floor, under external management, which creates performance as well as security issues. This can be alleviated by considering using private CC ser- vices. This is what Morariu et al. do . The shop floor as well as part of the Man- ufacturing Execution System (MES)  is virtualized in a private cloud whereas a public cloud is used for high-level application services. As the physical resources are seen as agents, virtualized in the cloud, there is no intelligence left in the physical layer of the architecture. Experiments show the effect of virtualization on the per- formance of event propagation. A broader discussion of the advantages and disad- vantages of relying on public, private, community and hybrid Cloud Manufacturing solutions is available in . The discussion results in the design of a Hybrid Man- ufacturing Cloud (HMC) infrastructure. However, performance and virtualization issues are not considered. The focus is rather on access control and interoperability issues. These issues are addressed through the use of an ontology and rule-based reasoning with an implementation hosted by a public cloud.
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Covers are different interpretations of the same original musical work. They usually share a similar melodic line, but typically differ greatly in one or several other dimen- sions, such as their structure, tempo, key, instrumentation, genre, etc. Automatic cover detection – the task of finding in an audio database all the covers of one or several query tracks – has long been seen as a challenging theoretical problem in MIR. It is also now an acute practical problem for copyright owners facing continuous expansion of user- generated online content.
3.1 Score-based Combination
As stated in Section 2.1, previous work shows that rejec- tors based on global features such as the tempo or the du- ration of the songs do not produce satisfying results, when taken individually. It makes therefore sense to investigate their combination so that more information is taken into ac- count when comparing two songs. As the global rejectors estimate probabilities of cover identities, we evaluate sev- eral combination rules to take advantage of each feature. Multiple rules have been proposed as a mean of combining probability estimates for classification [7, 8, 15]. We select in particular the product, the sum and the median rules  and evaluate the combination of our probabilistic rejectors with them.
2.3 Cloud Networking
Le Cloud Networking est le service d’interconnexion entre les ressources de types Cloud Computing. Ce service est lié aux opérations nécessaires au niveau des réseaux locaux Local Area Network (LAN), réseaux étendus Wide Area Network (WAN) et aux fonctions de gestion qui doivent se mettre en place pour l’adoption du Cloud Computing . Le Cloud Networking inclut plusieurs acteurs, mais l’acteur principal reste le fournisseur de réseau ’Cloud Carrier’. Il offre de plus en plus de nouveaux services de type Cloud avec des fonctions d’interconnexion et de communications qui sont plus riches, plus flexibles et surtout en ligne avec les caractéristiques et les exigences du Cloud Computing. Comme définie dans [56, 62], le Cloud Networking introduit une nouvelle façon de déployer, configurer et gérer les réseaux et les services de communications. Cette nouvelle philosophie se base sur l’automatisation des opérations complexes du réseau, pour offrir un service à la demande qui soit simple à utiliser et rapide à déployer. L’émergence du Cloud Networking est le résultat de plusieurs facteurs. Parmi ces facteurs, il y a la croissance continue de la demande des ressources d’interconnexion, surtout avec l’augmentation du trafic entre les Clouds, l’apparition des courtiers Cloud ’Cloud Broker’ et l’explosion de l’utilisation des appareils mobiles. Un deuxième facteur est la limite des réseaux traditionnels qui ne sont plus suffisants avec leurs fonctionnalités basiques et leurs approches ’best effort’. Un troisième facteur est lié à l’apparition de nouvelles technologies d’interconnexion qui ont concrétisé le Cloud Networking. Parmi ces technologies, il y a les réseaux virtuels et le Software-defined Networking (SDN).
it is wide; moreover, we have constrained the cloud width (but not height). Thus, we must handle widths and heights asymmetrically.
In some EDA design styles, the interconnect wiring must run between modules, rather than atop them. Thus, ade- quate white space must be allocated throughout the layout to accommodate wiring. Superficially, tag clouds are simi- lar: we should not abut two tags without leaving some white space between them. However, in EDA the amount of white space at any particular area depends on the number of wires that must pass though that area. This is much more com- plicated than with tags, where a fixed boundary, or perhaps one proportional to the font size, is appropriate.