Multigranular scale speech recognition: tehnological and cognitive view

We propose a Multigranular Automatic Speech Recognizer. The hypothesis is that speech signal contains information distributed on more different time scales. Many works from various scientific fields ranging from neurobiology to speech technologies, seem to concord on this assumption. In a broad sense, it seems that speech recognition in human is optimal because of a partial parallelization process according to which the left-to-right stream of speech is captured in a multilevel grid in which several linguistic analyses take place contemporarily. Our investigation aims, in this view, to apply these new ideas to the project of more robust and efficient recognizers

Beni collettivi e infrastrutture per la competitività

L'articolo sviluppa un'analisi sul ruolo e le caratteristiche delle infrastrutture economiche nei processi di sviluppo a scala regionale. Dopo una rassegna delle principale infrastrutture economiche e delle specifiche condizioni regolative che ne assicurano la produzione, il saggio prende in esame tre servizi economici che hanno attraversisato una fase di profonda riorganizzazione: il sistema fieristico, quello bancario e i servizi di pubblica utilità

Balancing spreads of influence in a social network

The personalization of our news consumption on social media has a tendency to reinforce our pre-existing beliefs instead of balancing our opinions. To tackle this issue, Garimella et al. (NIPS’17) modeled the spread of these viewpoints, also called campaigns, using the independent cascade model introduced by Kempe, Kleinberg and Tardos (KDD’03) and studied an optimization problem that aims to balance information exposure when two opposing campaigns propagate in a network. This paper investigates a natural generalization of this optimization problem in which μ different campaigns propagate in the network and we aim to maximize the expected number of nodes that are reached by at least ν or none of the campaigns, where μ ≥ ν ≥ 2. Following Garimella et al., despite this general setting, we also investigate a simplified one, in which campaigns propagate in a correlated manner. While for the simplified setting, we show that the problem can be approximated within a constant factor for any constant μ and ν, for the general setting, we give reductions leading to several approximation hardness results when ν ≥ 3. For instance, assuming the gap exponential time hypothesis to hold, we obtain that the problem cannot be approximated within a factor of n−g(n) for any g(n) = o(1) where n is the number of nodes in the network. We complement our hardness results with an Ω(n−1/2)-approximation algorithm for the general setting when ν = 3 and μ is arbitrary

Industrial districts, urban areas or both? The location behaviour of foreign and domestic firms in an Italian manufacturing region

The present paper aims at exploring the location behaviour of manufacturing firms, according to their ownership: domestic firms (henceforth DOMs) and inward foreign direct investments (henceforth IFDIs). This issue is empirically addressed by using data on manufacturing IFDIs and on DOMs in Veneto (north-east Italy) from the Reprint, AIDA and ISTAT databases. Veneto is an industrial district region, specialized in the Made-in-Italy sectors, hosting a central metropolitan area (Padua) and attracting a high share of IFDIs. Geo-referenced mapping and econometric analysis (counterfactual) are developed to explore the location behaviour of the two groups of firms. In line with previous work, findings show that IFDIs are more likely to be located in areas close to the main urban centres, such as the metropolitan area of Padua, to exploit the advantages of complex environments and higher connectivity. However, they also tend to locate in district areas more often than their DOMs counterfactual, suggesting the objective of acquiring a system of specialized productive knowledge and skills developed within a district ecosystem, and hardly reproducible in other contexts

Estimating hidden fishing activity hotspots from vessel transmitted data

Monitoring fishery activity is essential for resource planning and guaranteeing fisheries sustainability. Large fishing vessels constantly and continuously communicate their positions via Automatic Identification System (AIS) or Vessel Monitoring Systems (VMSs). These systems can use radio or Global Positioning System (GPS) devices to transmit data. Processing and integrating these big data with other fisheries data allows for exploring the relations between socio-economic and ecosystem assets in marine areas, which is fundamental in fishery monitoring. In this context, estimating actual fishing activity from time series of AIS and VMS data would enhance the correct identification of fishing activity patterns and help assess regulations' effectiveness. However, these data might contain gaps because of technical issues such as limited coverage of the terrestrial receivers or saturated transmission bands. Other sources of data gaps are adverse meteorological conditions and voluntary switch-offs. Gaps may also include hidden (unreported) fishing activity whose quantification would improve actual fishing activity estimation. This paper presents a workflow for AIS/VMS big-data analysis that estimates potential unreported fishing activity hotspots in a marine area. The workflow uses a statistical spatial analysis over vessel speeds and coordinates and a multi-source data integration approach that can work on multiple areas and multiple analysis scales. Specifically, it (i) estimates fishing activity locations and rebuilds data gaps, (ii) estimates the potential unreported fishing hour distribution and the unreported-over-total ratio of fishing hours at a 0.01 degrees spatial resolution, (iii) identifies potential unreported fishing activity hotspots, (iv) extracts the stocks involved in these hotspots (using global-scale repositories of stock and species observation data) and raises an alert about their possible endangered, threatened, and protected (ETP) status. The workflow is also a free-to-use Web Service running on an open science-compliant cloud computing platform with a Web Processing Service (WPS) standard interface, allowing efficient big data processing. As a study case, we focussed on the Adriatic Sea. We reconstructed the monthly reported and potential unreported trawling activity in 2019, using terrestrial AIS data with a 5-min sampling period, containing similar to 50 million records transmitted by similar to 1,600 vessels. The results highlight that the unreported fishing activity hotspots especially impacted Italian coasts and some forbidden and protected areas. The potential unreported activity involved 33 stocks, four of which were ETP species in the basin. The extracted information agreed with expert studies, and the estimated trawling patterns agreed with those produced by the Global Fishing Watch

COVID-19 lockdowns reveal the resilience of Adriatic Sea fisheries to forced fishing effort reduction

The COVID-19 pandemic provides a major opportunity to study fishing effort dynamics and to assess the response of the industry to standard and remedial actions. Knowing a fishing fleet’s capacity to compensate for effort reduction (i.e., its resilience) allows differentiating governmental regulations by fleet, i.e., imposing stronger restrictions on the more resilient and weaker restrictions on the less resilient. In the present research, the response of the main fishing fleets of the Adriatic Sea to fishing hour reduction from 2015 to 2020 was measured. Fleet activity per gear type was inferred from monthly Automatic Identification System data. Pattern recognition techniques were applied to study the fishing effort trends and barycentres by gear. The beneficial effects of the lockdowns on Adriatic endangered, threatened and protected (ETP) species were also estimated. Finally, fleet effort series were examined through a stock assessment model to demonstrate that every Adriatic fishing fleet generally behaves like a stock subject to significant stress, which was particularly highlighted by the pandemic. Our findings lend support to the notion that the Adriatic fleets can be compared to predators with medium-high resilience and a generally strong impact on ETP species

Un approccio innovativo al testing psicopatologico: Taleia. parte II: attendibilità e validità del test

Studies reliability and validity of TALEIA (Test for AxiaL Evaluation and Interview for Clinical, Personnel, and Guidance Applications). Retest (N = 123, one-weak interval) shows average r = 0.83, and correlations between parallel forms (N = 139) average to r = 0.74. An ACP (N = 280) on TALEIA, 16 PF, and PNP shows a four factors structure explaining 53% of the total variance, the first factor having psychopathological content and the others showing continuity between normal personality traits and clinical or personality disorders. A sample of «certified pathological» (N = 436), «certified normals» (N = 773 males), and two samples of «non-certified normals» (N = 386 draftees and N = 156 male students asking for school guidance) were compared. The MANOVA results were significant (p < 0.001), and post-hoc analyses (HSD test corrected for different size) showed significant differences related both to clinical status (pathologicals vs. normals) and to different situations (guidance and military draft). These results seem to allow a professional use of TALEIA, if different parameters for different applications are provided

Exploitation and status of European stocks

Report about the outcome of four workshops in 2016, about the assessment of all European stock

IFNAR1-Signalling Obstructs ICOS-mediated Humoral Immunity during Non-lethal Blood-Stage Plasmodium Infection

Funding: This work was funded by a Career Development Fellowship (1028634) and a project grant (GRNT1028641) awarded to AHa by the Australian National Health & Medical Research Council (NHMRC). IS was supported by The University of Queensland Centennial and IPRS Scholarships. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Subjective hunger, gastric upset, and sleepiness in response to altered meal timing during simulated shiftwork

Shiftworkers report eating during the night when the body is primed to sleep. This study investigated the impact of altering food timing on subjective responses. Healthy participants (n = 44, 26 male, age Mean ± SD = 25.0 ± 2.9 years, BMI = 23.82 ± 2.59kg/m2) participated in a 7-day simulated shiftwork protocol. Participants were randomly allocated to one of three eating conditions. At 00:30, participants consumed a meal comprising 30% of 24 h energy intake (Meal condition; n = 14, 8 males), a snack comprising 10% of 24 h energy intake (Snack condition; n = 14; 8 males) or did not eat during the night (No Eating condition; n = 16, 10 males). Total 24 h individual energy intake and macronutrient content was constant across conditions. During the night, participants reported hunger, gut reaction, and sleepiness levels at 21:00, 23:30, 2:30, and 5:00. Mixed model analyses revealed that the snack condition reported significantly more hunger than the meal group (p < 0.001) with the no eating at night group reporting the greatest hunger (p < 0.001). There was no difference in desire to eat between meal and snack groups. Participants reported less sleepiness after the snack compared to after the meal (p < 0.001) or when not eating during the night (p < 0.001). Gastric upset did not differ between conditions. A snack during the nightshift could alleviate hunger during the nightshift without causing fullness or increased sleepiness.Charlotte C Gupta, Stephanie Centofanti, Jillian Dorrian , Alison M Coates, Jacqueline M Stepien, David Kennaway, Gary Wittert, Leonie Heilbronn, Peter Catcheside, Manny Noakes, Daniel Coro, Dilushi Chandrakumar and Siobhan Bank