Innovation, specialization and growth in a model of structural change

The aim of this paper is to investigate the nexus between demand patterns and innovation as it stems from research efforts and the extent of specialization. In the proposed model an innovation race conducted by entrants investing in research and development against established incumbents raises productivity at the industry level and leads to a shift in the aggregate demand pattern and consequently to a redistribution of the profit fund among industries and a restructuring of the production process in each industry. The paper argues that the degree of development as reflected in a demand share distribution is characterized by a corresponding distribution of specialized sectors that becomes more even across industries as the development process proceeds and investigates the consequences in terms of economic growth.

The environment of the SN-less GRB 111005A at z = 0.0133

The collapsar model has proved highly successful in explaining the properties of long gamma-ray bursts (GRBs), with the most direct confirmation being the detection of a supernova (SN) coincident with the majority of nearby long GRBs. Within this model, a long GRB is produced by the core-collapse of a metal-poor, rapidly rotating, massive star. The detection of some long GRBs in metal-rich environments, and more fundamentally the three examples of long GRBs (GRB 060505, GRB 060614 and GRB 111005A) with no coincident SN detection down to very deep limits is in strong contention with theoretical expectations. In this paper we present MUSE observations of the host galaxy of GRB 111005A, which is the most recent and compelling example yet of a SN-less, long GRB. At z=0.01326, GRB 111005A is the third closest GRB ever detected, and second closest long duration GRB, enabling the nearby environment to be studied at a resolution of 270 pc. From the analysis of the MUSE data cube, we find GRB 111005A to have occurred within a metal-rich environment with little signs of ongoing star formation. Spectral analysis at the position of the GRB indicates the presence of an old stellar population (tau > 10 Myr), which limits the mass of the GRB progenitor to M_ZAMS<15 Msolar, in direct conflict with the collapsar model. Our deep limits on the presence of any SN emission combined with the environmental conditions at the position of GRB 111005A necessitate the exploration of a novel long GRB formation mechanism that is unrelated to massive stars.Comment: Now accepted by A&A. Manuscript replaced to match accepted version. Some additional discussion added, and velocity map of the host galaxy now include

Predicting and Explaining Privacy Risk Exposure in Mobility Data

Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people’s whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they often (i) have high computational complexity; and (ii) are not able to provide users with a justification of the reported risks. In this paper, we propose expert, a new framework for the prediction and explanation of privacy risk on mobility data. We empirically evaluate privacy risk on real data, simulating a privacy attack with a state-of-the-art privacy risk assessment framework. We then extract individual mobility profiles from the data for predicting their risk. We compare the performance of several machine learning algorithms in order to identify the best approach for our task. Finally, we show how it is possible to explain privacy risk prediction on real data, using two algorithms: Shap, a feature importance-based method and Lore, a rule-based method. Overall, expert is able to provide a user with the privacy risk and an explanation of the risk itself. The experiments show excellent performance for the prediction task

Learning Early Exit Strategies for Additive Ranking Ensembles

Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees. We propose LEAR, a novel - learned - technique aimed to reduce the average number of trees traversed by documents to accumulate the scores, thus reducing the overall query response time. LEAR exploits a classifier that predicts whether a document can early exit the ensemble because it is unlikely to be ranked among the final top-k results. The early exit decision occurs at a sentinel point, i.e., after having evaluated a limited number of trees, and the partial scores are exploited to filter out non-promising documents. We evaluate LEAR by deploying it in a production-like setting, adopting a state-of-the-art algorithm for ensembles traversal. We provide a comprehensive experimental evaluation on two public datasets. The experiments show that LEAR has a significant impact on the efficiency of the query processing without hindering its ranking quality. In detail, on a first dataset, LEAR is able to achieve a speedup of 3x without any loss in NDCG@10, while on a second dataset the speedup is larger than 5x with a negligible NDCG@10 loss (&lt; 0.05%)