The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Fault-Tolerant Real-Time Streaming with FEC thanks to Capillary Multi-Path Routing

Erasure resilient FEC codes in off-line packetized streaming rely on time diversity. This requires unrestricted buffering time at the receiver. In real-time streaming the playback buffering time must be very short. Path diversity is an orthogonal strategy. However, the large number of long paths increases the number of underlying links and consecutively the overall link failure rate. This may increase the overall requirement in redundant FEC packets for combating the link failures. We introduce the Redundancy Overall Requirement (ROR) metric, a routing coefficient specifying the total number of FEC packets required for compensation of all underlying link failures. We present a capillary routing algorithm for constructing layer by layer steadily diversifying multi-path routing patterns. By measuring the ROR coefficients of a dozen of routing layers on hundreds of network samples, we show that the number of required FEC packets decreases substantially when the path diversity is increased by the capillary routing construction algorithm

Deregulation and R&D in Network Industries: The Case of the Electricity Industry

Electricity reform has coincided with a significant decline in energy R&D activities. Technical progress is crucial for tackling many energy and environmental issues as well as for long-term efficiency improvement. This paper reviews the industrial organisation literature on innovation to explore the causes of this decline, and shows that it was predicted by the pre-reform literature. More recent evidence endorses this conclusion. At the same time, R&D productivity and innovative output appear to have improved in both electric utilities and equipment suppliers, in line with general improvements in the operating efficiency of the sector. Despite this, a lasting decline in basic R&D and innovation input into basic research may negatively affect development of radical technological innovation in the long run. There is a need for reorientation of energy technology policies and spending toward more basic research, engaging more firms in R&D, encouraging collaborative research, and exploring public private partnerships

The structure of R&D collaboration networks in the European Framework Programmes

Using a large and novel data source, we study the structure of R&D collaboration net-works in the first five EU Framework Programmes (FPs). The networks display proper-ties typical for complex networks, including scale-free degree distributions and the small-world property. Structural features are common across FPs, indicating similar network formation mechanisms despite changes in governance rules. Several findings point towards the existence of a stable core of interlinked actors since the early FPs with integration increasing over time. This core consists mainly of universities and research organisations. We observe assortative mixing by degree of projects, but not by degree of organisations. Unexpectedly, we find only weak association between central projects and project size, suggesting that different types of projects attract different groups of actors. In particular, large projects appear to have included few of the pivotal actors in the networks studied. Central projects only partially mirror funding priorities, indicating field-specific differences in network structures. The paper concludes with an agenda for future research.R&D collaboration, EU Framework Programmes, Complex Networks, Small World Effect, Centrality Measures, European Research Area

Irregular Convolutional Neural Networks

Convolutional kernels are basic and vital components of deep Convolutional Neural Networks (CNN). In this paper, we equip convolutional kernels with shape attributes to generate the deep Irregular Convolutional Neural Networks (ICNN). Compared to traditional CNN applying regular convolutional kernels like ${3\times3}$, our approach trains irregular kernel shapes to better fit the geometric variations of input features. In other words, shapes are learnable parameters in addition to weights. The kernel shapes and weights are learned simultaneously during end-to-end training with the standard back-propagation algorithm. Experiments for semantic segmentation are implemented to validate the effectiveness of our proposed ICNN.Comment: 7 pages, 5 figures, 3 table