Feelings of dual-insecurity among European workers: A multi-level analysis

This article analyses European Social Survey data for 22 countries. We assess the relationship between feelings of employment and income insecurity (dual-insecurity) among workers and national flexicurity policies in the areas of lifelong learning, active labour market policy, modern social security systems and flexible and reliable contractual arrangements. We find that dual-insecurity feelings are lower in countries that score better on most flexicurity polices, but these effects are in all cases outweighed by levels of GDP per capita. Thus feelings of insecurity are reduced more by the affluence of a country than by its social policies. However, affluence is strongly correlated with the policy efforts designed to reduce insecurity, especially active labour market policies and life-long learning, two policy areas that are threatened with cuts as a result of austerity

Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks

Feature learning, or the ability of deep neural networks to automatically learn relevant features from raw data, underlies their exceptional capability to solve complex tasks. However, feature learning seems to be realized in different ways in fully-connected (FC) or convolutional architectures (CNNs). Empirical evidence shows that FC neural networks in the infinite-width limit eventually outperform their finite-width counterparts. Since the kernel that describes infinite-width networks does not evolve during training, whatever form of feature learning occurs in deep FC architectures is not very helpful in improving generalization. On the other hand, state-of-the-art architectures with convolutional layers achieve optimal performances in the finite-width regime, suggesting that an effective form of feature learning emerges in this case. In this work, we present a simple theoretical framework that provides a rationale for these differences, in one hidden layer networks. First, we show that the generalization performance of a finite-width FC network can be obtained by an infinite-width network, with a suitable choice of the Gaussian priors. Second, we derive a finite-width effective action for an architecture with one convolutional hidden layer and compare it with the result available for FC networks. Remarkably, we identify a completely different form of kernel renormalization: whereas the kernel of the FC architecture is just globally renormalized by a single scalar parameter, the CNN kernel undergoes a local renormalization, meaning that the network can select the local components that will contribute to the final prediction in a data-dependent way. This finding highlights a simple mechanism for feature learning that can take place in overparametrized shallow CNNs, but not in shallow FC architectures or in locally connected neural networks without weight sharing.Comment: 22 pages, 5 figures, 2 tables. Comments are welcom

Universal mean-field upper bound for the generalization gap of deep neural networks

Modern deep neural networks (DNNs) represent a formidable challenge for theorists: according to the commonly accepted probabilistic framework that describes their performance, these architectures should overfit due to the huge number of parameters to train, but in practice they do not. Here we employ results from replica mean field theory to compute the generalization gap of machine learning models with quenched features, in the teacher-student scenario and for regression problems with quadratic loss function. Notably, this framework includes the case of DNNs where the last layer is optimized given a specific realization of the remaining weights. We show how these results-combined with ideas from statistical learning theory-provide a stringent asymptotic upper bound on the generalization gap of fully trained DNN as a function of the size of the dataset P. In particular, in the limit of large P and N-out (where N-out is the size of the last layer) and N-out << P, the generalization gap approaches zero faster than 2N(out)/P, for any choice of both architecture and teacher function. Notably, this result greatly improves existing bounds from statistical learning theory. We test our predictions on a broad range of architectures, from toy fully connected neural networks with few hidden layers to state-of-the-art deep convolutional neural networks

Serum cholesterol levels in neutropenic patients with fever

Hypocholesterolemia, which often accompanies infectious diseases has been suggested to serve as a prognostic marker in hospitalized patients. Even though patients with chemotherapyinduced leukopenia are at high risk of infection and mortality, only limited information is available on serum cholesterol levels in these patients. We therefore measured serum cholesterol levels in 17 patients with hematological malignancies during chemotherapyinduced neutropenia and correlated it with clinical outcome. Patients with fever (>38.5 degreesC) showed a significant decrease in serum cholesterol levels within 24 hours. Eight days after onset of the fever nonsurvivors had significantly lower serum cholesterol levels (median 2.09 mmol/l, range 0.492.79, n=6) compared to survivors (median 3.23 mmol/l, range 1.684.86, n=11). Cholesterol levels in survivors returned to baseline levels at the time of discharge from the hospital. At the onset of fever, serum levels of inflammatory cytokines interleukin-6, tumor necrosis factor (TNF) and soluble TNF receptors p55 and p75 were elevated in all patients, but only TNF and TNF receptor p75 levels were significantly different in survivors and nonsurvivors. Our data suggest that a decrease in serum cholesterol levels is a prognostic marker in neutropenic patients with fever. Release of inflammatory cytokines may in part be responsible for hypocholesterolemia in these patients

Relationship of body condition score and blood urea and ammonia to pregnancy in Italian Mediterranean buffaloes

The relationship of body condition score ( BCS) and blood urea and ammonia to pregnancy outcome was examined in Italian Mediterranean Buffalo cows mated by AI. The study was conducted on 150 buffaloes at 145 +/- 83 days in milk that were fed a diet comprising 14.8% crude protein, 0.9 milk forage units . kg(-1) dry matter and a non- structural carbohydrate/ crude protein ratio of 2.14. The stage of the oestrous cycle was synchronised by the Ovsynch- TAI programme and blood urea and ammonia levels were assessed on the day of AI. Energy corrected milk ( ECM) production and BCS were recorded bi- weekly. The pregnancy risk was 46.7% and was slightly lower in buffaloes with BCS 7.5. There were no significant differences in ECM, urea and ammonia between pregnant and non- pregnant buffaloes. However, pregnancy outcome was higher ( P = 0.02) in buffaloes with blood urea < 6.83 mmol . L-1. The likelihood of pregnancy for buffaloes with low urea blood level was 2.6 greater than for high urea level and exposure to a high urea level lowered the probability of pregnancy by about 0.25. The findings indicate that buffaloes are similar to cattle and increased blood levels of urea are associated with reduced fertility when animals are mated by AI

Controlling Adult Stem Cell Behavior Using Nanodiamond-Reinforced Hydrogel: Implication in Bone Regeneration Therapy

Nanodiamonds (NDs) have attracted considerable attention as drug delivery nanocarriers due to their low cytotoxicity and facile surface functionalization. Given these features, NDs have been recently investigated for the fabrication of nanocomposite hydrogels for tissue engineering. Here we report the synthesis of a hydrogel using photocrosslinkable gelatin methacrylamide (GelMA) and NDs as a three-dimensional scaffold for drug delivery and stem cell-guided bone regeneration. We investigated the effect of different concentration of NDs on the physical and mechanical properties of the GelMA hydrogel network. The inclusion of NDs increased the network stiffness, which in turn augmented the traction forces generated by human adipose stem cells (hASCs). We also tested the ability of NDs to adsorb and modulate the release of a model drug dexamethasone (Dex) to promote the osteogenic differentiation of hASCs. The ND-Dex complexes modulated gene expression, cell area, and focal adhesion number in hASCs. Moreover, the integration of the ND-Dex complex within GelMA hydrogels allowed a higher retention of Dex over time, resulting in significantly increased alkaline phosphatase activity and calcium deposition of encapsulated hASCs. These results suggest that conventional GelMA hydrogels can be coupled with conjugated NDs to develop a novel platform for bone tissue engineering

Female labour force projections using microsimulation for six EU countries

We project medium to long term trends in labour force participation and employment for selected low-participation European Union countries (Italy, Spain, Ireland, Hungary and Greece), with Sweden as a benchmark, by means of a dynamic microsimulation model. By 2020, only Sweden will be above the Europe 2020 target of 75% employment rate, though Ireland will be close; the target will be approached by all other countries only at the end of the simulation period at 2050, with the exception of Hungary. Our forecasts, that fully take into account the uncertainty coming from the estimation of all the processes in the microsimulation, significantly depart from the official projections of the European Commission for two of the six countries under analysis

Fabrication of a Double-Cross-Linked Interpenetrating Polymeric Network (IPN) Hydrogel Surface Modified with Polydopamine to Modulate the Osteogenic Differentiation of Adipose-Derived Stem Cells

This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Applied Materials and Interfaces, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://doi.org/10.1021/acsami.8b05200.Hydrogel surface properties can be modified to form bioactive interfaces to modulate the osteogenic differentiation of stem cells. In this work, a hydrogel made of gelatin methacrylamide (GelMA) and alginate was designed and tested as a scaffold to control stem-cell osteogenic differentiation. The hydrogel’s surface was treated with polydopamine (pDA) to create an adhesive layer for the adsorption of the osteoinductive drug dexamethasone (Dex). The presence of the pDA coating enhanced Dex adsorption and retention over 21 days. This effect resulted in a delay in the osteogenic differentiation of hASCs cultured on the hydrogel treated with a pDA layer