Low-complexity motion estimation for the Scalable Video Coding extension of H.264/AVC

The recently standardized Scalable Video Coding(SVC) extension of H.264/AVC allows bitstream scalability with improved rate-distortion efficiency with respect to the classical Simulcasting approach, at the cost of an increased computational complexity of the encoding process. So one critical issue related to practical deployment of SVC is the complexity reduction, fundamental to use it in consumer applications. In this paper, we present a fully scalable fast motion estimation algorithm that enables an excellent complexity performance

New Fast Search Algorithm for Base Layer of H.264 Scalable Video Coding Extension

In this contribution, a fast search motion estimation algorithm for H.264/AVC SVC (scalable video coding) [2] base layer with hierarchical B-frame structure for temporal decomposition is presented and compared with fast search motion estimation algorithm in JSVM software [1], that is the reference software for H.264/AVC SVC. The proposed technique is a block-matching based motion estimation algorithm working in two steps, called Coarse search and Fine search. The Coarse search is performed for each frame in display order, and for each 16x16 macroblock chooses the best motion vector at half pel accuracy. Fine search is performed for each frame in encoding order and finds the best prediction for each block type, reference frame and direction, choosing the best motion vector at quarter pel accuracy using R-D optimization. Both Coarse and Fine Search test 3 spatial and 3 temporal predictors, and add to the best one a set of updates. The spatial predictors for the fine search are the result of the Fine search already performed for the previous blocks, while the temporal predictors are the results of Coarse Search scaled by an appropriate coefficient. This scaling is performed since in the Coarse search each picture is always estimated with respect to the previous one, while in the Fine Search the temporal distance between the current picture and its references depend on the temporaldecomposition level. Moreover in Fine search the number and the value of the updates tested depend on the distance between the current picture and its references. These sets of updates are the result of a huge number of simulations on test sequences with different motion features. The proposed algorithm has been tested on the set of test sequences proposed by JVT group, using different resolutions and temporal decomposition structures. The proposed method can reduce the average coding complexity in terms of motion vector tested from 70 to 90 percent with respect to the Fast-ME JVT method, while the quality loss depends on the GOP dimension, that is the most critical parameter for the performance of the algorithm. In fact for small GOP dimensions (4 or 8) the algorithm has the same quality at equal bit-rate respect to the Fast-ME JVT method for almost all the sequences and better quality for some sequences. For medium and long GOP dimensions (16-32) the algorithm has a quality loss lower than 0.5 dB for all the tested sequences

The polo-like kinase 1 (PLK1) inhibitor NMS-P937 is effective in a new model of disseminated primary CD56+ acute monoblastic leukaemia

CD56 is expressed in 15–20% of acute myeloid leukaemias (AML) and is associated with extramedullary diffusion, multidrug resistance and poor prognosis. We describe the establishment and characterisation of a novel disseminated model of AML (AML-NS8), generated by injection into mice of leukaemic blasts freshly isolated from a patient with an aggressive CD56+ monoblastic AML (M5a). The model reproduced typical manifestations of this leukaemia, including presence of extramedullary masses and central nervous system involvement, and the original phenotype, karyotype and genotype of leukaemic cells were retained in vivo. Recently Polo-Like Kinase 1 (PLK1) has emerged as a new candidate drug target in AML. We therefore tested our PLK1 inhibitor NMS-P937 in this model either in the engraftment or in the established disease settings. Both schedules showed good efficacy compared to standard therapies, with a significant increase in median survival time (MST) expecially in the established disease setting (MST = 28, 36, 62 days for vehicle, cytarabine and NMS-P937, respectively). Importantly, we could also demonstrate that NMS-P937 induced specific biomarker modulation in extramedullary tissues. This new in vivo model of CD56+ AML that recapitulates the human tumour lends support for the therapeutic use of PLK1 inhibitors in AML

Location determinants of green technological entry: evidence from European regions

In this paper, we explore the spatial distribution and the location determinants of new green technology-based firms across European regions. Integrating insights from evolutionary economic geography and the literature on knowledge spillovers, we study the importance of new knowledge creation and the conditioning role played by regional technological relatedness in fostering combinatorial opportunities underlying the process of green technological entry. The analysis is based on a dataset covering over 900 NUTS3 regions for 15 European countries obtained merging economic data from ESPON-Eurostat and patent information from the PATSTAT-CRIOS database for the period 1996–2006. Our results show that the geographical distribution of green technological entry across European regions is not evenly distributed, offering evidence of spatial path dependence. In line with this, we find evidence of a significant role played by the characteristics of the regional innovation system. New green innovators are more likely to develop in regions defined by higher levels of technological activity underlying knowledge spillovers and more dynamism in technological entry. Moreover, our findings point to an inverted-U relationship between regional technological relatedness and green technological entry. Regions whose innovation activity is defined by cognitive proximity to environmental technologies support interactive learning and knowledge spillovers underlying entrepreneurship in this specific area. However, too much relatedness may cause technological lock-ins and reduce the set of combinatorial opportunities