Emerging Technologies and Access to Spectrum Resources: the Case of Short-Range Systems

Traditional regulatory arrangements have constrained access to radio frequency spectrum. This has resulted in artificial scarcity of spectrum. The paper addresses the issue of whether technological developments in short-range systems (e.g. cognitive radios and ultra wideband) might promote access to spectrum - possibly using market mechanisms such as trading - and reduce spectrum shortages.spectrum policy, spectrum access, emerging spectrum-using technology, Telecommunications, regulation, infrastructure

The Performance of European Full Service Airlines after Liberalisation: An Econometric Analysis

Deregulation in the airline industry has forced full service airlines to change their strategies in order to respond to increasing challenges. In this paper, an econometric analysis of the possible determinants of economic performance of full service airlines after liberalisation has been carried out. A fixed effects model was used and the performance of ten European full service airlines has been analysed over a period of 11 years. Variables considered in this analysis were the number and type of aircraft in the fleet, the number and type of destinations, investments, number of employees and alliances. The analysis suggests that full service airlines should adjust fleet composition and re-organise operations on their routes in order to react to the increasingly competitive environment.

Adversarial Sets for Regularising Neural Link Predictors

In adversarial training, a set of models learn together by pursuing competing goals, usually defined on single data instances. However, in relational learning and other non-i.i.d domains, goals can also be defined over sets of instances. For example, a link predictor for the is-a relation needs to be consistent with the transitivity property: if is-a(x_1, x_2) and is-a(x_2, x_3) hold, is-a(x_1, x_3) needs to hold as well. Here we use such assumptions for deriving an inconsistency loss, measuring the degree to which the model violates the assumptions on an adversarially-generated set of examples. The training objective is defined as a minimax problem, where an adversary finds the most offending adversarial examples by maximising the inconsistency loss, and the model is trained by jointly minimising a supervised loss and the inconsistency loss on the adversarial examples. This yields the first method that can use function-free Horn clauses (as in Datalog) to regularise any neural link predictor, with complexity independent of the domain size. We show that for several link prediction models, the optimisation problem faced by the adversary has efficient closed-form solutions. Experiments on link prediction benchmarks indicate that given suitable prior knowledge, our method can significantly improve neural link predictors on all relevant metrics.Comment: Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 201

Convolutional 2D Knowledge Graph Embeddings

Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs. However, these models learn less expressive features than deep, multi-layer models -- which potentially limits performance. In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets. We also show that the model is highly parameter efficient, yielding the same performance as DistMult and R-GCN with 8x and 17x fewer parameters. Analysis of our model suggests that it is particularly effective at modelling nodes with high indegree -- which are common in highly-connected, complex knowledge graphs such as Freebase and YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer from test set leakage, due to inverse relations from the training set being present in the test set -- however, the extent of this issue has so far not been quantified. We find this problem to be severe: a simple rule-based model can achieve state-of-the-art results on both WN18 and FB15k. To ensure that models are evaluated on datasets where simply exploiting inverse relations cannot yield competitive results, we investigate and validate several commonly used datasets -- deriving robust variants where necessary. We then perform experiments on these robust datasets for our own and several previously proposed models and find that ConvE achieves state-of-the-art Mean Reciprocal Rank across most datasets.Comment: Extended AAAI2018 pape

Los Estudios del español sefardí (judeoespañol, ladino). Aportaciones, métodos y problemas actuales

Rassegna critica dei principali contributi allo studio del giudeospagnolo apparsi negli ultimi dieci ann