Technical change and employment growth in services: Analytical and policy challenges

It is generally acknowledged that employment in our economies is increasingly dependent on services. European countries are continuing their gradual move towards a service-based economy with today nearly 70% of the total labour force being employed in service activities. It is also generally acknowledged that services provide the key to future employment growth. However, relatively little attention has been paid to the effects of technical change on services. It is essential to understand whether it will help to develop new markets and welfare or whether it will further the trends of automation. The future of work is at stake in these processes, and the answers are not straightforward. This paper does not attempt to answer all these broad issues. It analyses the dynamics of technical change in services, how it relates with the dynamics of employment and it explores the policies to be pursued at all levels

Innovationsnetzwerke in Ostdeutschland: ein noch zu wenig genutztes Potential zur regionalen Humankapitalbildung

In Ostdeutschland ist die Kooperation regionaler Akteure noch nicht hinreichend. Zur Stimulierung des wirtschaftlichen Wachstums hat die Bundesregierung verschiedene Initiativen gestartet, um die Vernetzung und Kooperation von Unternehmen und anderen Akteuren in den neuen Bundesländern zu fördern. Eine Initiative ist das Förderprogramm InnoRegio des Bundesministeriums für Bildung und Forschung (BMBF). Im Rahmen des Förderprogramms werden nicht nur der Aufbau innovationsorientierter Netzwerke sowie Produkt- und Prozessinnovationen, sondern auch Projekte zur Aus- und Weiterbildung gefördert, da trotz hoher Arbeitslosigkeit innovative Unternehmen häufig keine qualifizierten Arbeitskräfte finden. Dabei könnten die Innovationsnetzwerke ein Ansatz sein, über eine verstärkte Kooperation der Unternehmen mit anderen regionalen Akteuren das Problem des Fachkräftemangels zu verringern

Intrinsic radiosensitivity of human pancreatic tumour cells and the radiosensitising potency of the nitric oxide donor sodium nitroprusside.

A panel of eight human pancreatic tumour cell lines displayed high intrinsic radioresistance, with mean inactivation doses between 2.4 and 6.5 Gy, similar to those reported for melanoma and glioblastoma. The radiosensitising potency of sodium nitroprusside, a bioreductive nitric oxide donor, was assessed in a model of metabolism-induced hypoxia in a cell micropellet. Sodium nitroprusside at 0.1 mM revealed a radiosensitising effect with an overall enhancement ratio of 1.9 compared with 2.5 for oxygen. Radiosensitising activity correlated with the enhancement of single-strand DNA breakage caused by radiation. In suspensions with cell densities of between 3% and 30% (v/v), the half-life of sodium nitroprusside decreased from 31 to 3.2 min, suggesting a value of around 1 min for micropellets. Despite this variation, the radiosensitising activity was similar in micropellets and in diluted cell suspensions. S-nitroso-L-glutathione was found to possess radiosensitising activity, consistent with a possible role of natural thiols in the storing of radiobiologically active nitric oxide adducts derived from sodium nitroprusside. As measured by a nitric oxide-specific microsensor, activation of sodium nitroprusside occurred by bioreduction, whereas S-nitroso-L-glutathione showed substantial spontaneous decomposition. Both agents appear to exert radiosensitising action through nitric oxide as its scavenging by carboxy phenyltetramethylimidazolineoxyl N-oxide (carboxy-PTI0) and oxyhaemoglobin resulted in attenuated radiosensitisation. Sodium nitroprusside was at least 10-fold more potent than etanidazole, a 2-nitroimidazole used as a reference. Our data suggest that sodium nitroprusside, a drug currently used for the treatment of hypertension, is a potential tumour radioresponse modifier

Combinatorial Bounds and Characterizations of Splitting Authentication Codes

We present several generalizations of results for splitting authentication codes by studying the aspect of multi-fold security. As the two primary results, we prove a combinatorial lower bound on the number of encoding rules and a combinatorial characterization of optimal splitting authentication codes that are multi-fold secure against spoofing attacks. The characterization is based on a new type of combinatorial designs, which we introduce and for which basic necessary conditions are given regarding their existence.Comment: 13 pages; to appear in "Cryptography and Communications

A Latent Class Binomial Logit Methodology for the Analysis of Paired Comparison Choice Data

A latent class model for identifying classes of subjects in paired comparison choice experiments is developed. The model simultaneously estimates a probabilistic classification of subjects and the logit models' coefficients relating characteristics of objects to choices for each respective group among two alternatives in paired comparison experiments. A modest Monte Carlo analysis of algorithm performance is presented. The proposed model is illustrated with empirical data from a consumer psychology experiment that examines the determinants of perceived consumer risk. The predictive validity of the method is assessed and compared to that of several other procedures. The sensitivity of the method to (randomly) eliminate comparisons, which is important in view of reducing respondent fatigue in the task, is investigated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74931/1/j.1540-5915.1993.tb00508.x.pd

Construction of an Immigrant Integration Composite Indicator through the Partial Least Squares Structural Equation Model K-Means

Integration is a multidimensional process, which can take place in different ways and at different times in relation to each of the single economic, social, cultural, and political dimensions. Hence, examining every single dimension is important as well as building composite indexes simultaneously inclusive of all dimensions in order to obtain a complete description of a complex phenomenon and to convey a coherent set of information. In this paper, we aim at building an immigrant integration composite indicator (IICI), able to measure the different aspects related to integration such as employment, education, social inclusion, active citizenship, and on the basis of which to simultaneously classify territorial areas such as European regions. For this application, the data collected in 274 European regions from the European Social Survey (ESS), Round 8, on immigration have been used

Lassoing and corraling rooted phylogenetic trees

The construction of a dendogram on a set of individuals is a key component of a genomewide association study. However even with modern sequencing technologies the distances on the individuals required for the construction of such a structure may not always be reliable making it tempting to exclude them from an analysis. This, in turn, results in an input set for dendogram construction that consists of only partial distance information which raises the following fundamental question. For what subset of its leaf set can we reconstruct uniquely the dendogram from the distances that it induces on that subset. By formalizing a dendogram in terms of an edge-weighted, rooted phylogenetic tree on a pre-given finite set X with |X|>2 whose edge-weighting is equidistant and a set of partial distances on X in terms of a set L of 2-subsets of X, we investigate this problem in terms of when such a tree is lassoed, that is, uniquely determined by the elements in L. For this we consider four different formalizations of the idea of "uniquely determining" giving rise to four distinct types of lassos. We present characterizations for all of them in terms of the child-edge graphs of the interior vertices of such a tree. Our characterizations imply in particular that in case the tree in question is binary then all four types of lasso must coincide

Recognizing Treelike k-Dissimilarities

A k-dissimilarity D on a finite set X, |X| >= k, is a map from the set of size k subsets of X to the real numbers. Such maps naturally arise from edge-weighted trees T with leaf-set X: Given a subset Y of X of size k, D(Y) is defined to be the total length of the smallest subtree of T with leaf-set Y . In case k = 2, it is well-known that 2-dissimilarities arising in this way can be characterized by the so-called "4-point condition". However, in case k > 2 Pachter and Speyer recently posed the following question: Given an arbitrary k-dissimilarity, how do we test whether this map comes from a tree? In this paper, we provide an answer to this question, showing that for k >= 3 a k-dissimilarity on a set X arises from a tree if and only if its restriction to every 2k-element subset of X arises from some tree, and that 2k is the least possible subset size to ensure that this is the case. As a corollary, we show that there exists a polynomial-time algorithm to determine when a k-dissimilarity arises from a tree. We also give a 6-point condition for determining when a 3-dissimilarity arises from a tree, that is similar to the aforementioned 4-point condition.Comment: 18 pages, 4 figure

A survey on feature weighting based K-Means algorithms

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Classification [de Amorim, R. C., 'A survey on feature weighting based K-Means algorithms', Journal of Classification, Vol. 33(2): 210-242, August 25, 2016]. Subject to embargo. Embargo end date: 25 August 2017. The final publication is available at Springer via http://dx.doi.org/10.1007/s00357-016-9208-4 © Classification Society of North America 2016In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.Peer reviewedFinal Accepted Versio