Miracle-dz/UniFAR-FTUs huggingface dataset:
Training for the Urban Unemployed: A Reevaluation of Mexico's Training Program, Probecat

For many years the government of Mexico has implemented a large training program for the unemployed. The program has been evaluated twice before with similar methodologies. These two evaluations yielded encouraging results in that the program apparently reduces unemployment and increases earnings. This article suggests that both evaluations may suffer from inappropriate controls for the endogeneity of program participation. Using the availability of the program at the state level as a determinant of individual participation, the article uses the data of the second evaluation to indicate that Probecat does not decrease unemployment, nor does it increase wages.
Free Generalized Gamma Convolutions

The so-called Bercovici-Pata bijection maps the set of classical infinitely divisible laws to the set of free infinitely divisible laws. The purpose of this work is to study the free infinitely divisible laws corresponding to the classical Generalized Gamma Convolutions (GGC). Characterizations of their free cumulant transforms are derived as well as free integral representations with respect to the free Gamma process. A random matrix model for free GGC is built consisting of matrix random integrals with respect to a classical matrix Gamma process. Nested subclasses of free GGC are shown to converge to the free stable class of distributions.
Application-Oriented Scheduling in Multicluster Grids

Grid computing appeared in the mid 1990s with the vision of sharing geographically dispersed large computational resources for executing computation-intensive scientific applications. Today, we can name numerous grid projects that run successfully to solve challenging scientific problems such as the grid project of European Organization for Nuclear Research (CERN), which combines thousands of computers worldwide (over 200 sites in about 30 countries) to store and analyze huge amounts of data, which are produced by the Large Hadron Collider (LHC) at CERN. The resources in a grid system are typically heterogeneous since they belong to different administrative domains, and they are managed by proprietary policies. To cope with this heterogeneity, a grid relies on a layer of middleware, which offers transparent access to the distributed resources and simplifies the collaboration between organizations. Grids also need high-level scheduling systems that use grid middleware in order to map application tasks to resources and then manage their execution on behalf of users. However, scheduling in grids is challenging due to the dynamic nature of the grid resources as well as to the lack of control of those resources. The wide variety in the structural and the communication characteristics of the applications submitted to grids further complicate grid scheduling, and may lead to poor or unpredictable performance unless these characteristics are taken into account. In this thesis we address the challenge of designing and analyzing realistic and practical application-oriented scheduling mechanisms in multicluster grid systems. Application-oriented scheduling focuses on the optimization of user-centric performance criteria, such as application execution time, with methods that are specialized for different types of applications. In this thesis we cover a wide-range of grid application types, including parallel applications that may need co-allocation or malleability, bags-of-tasks that can benefit from cycle scavenging, and workflow applications that may push the system to its limits with their computation and data requirements. We investigate the performance of our scheduling mechanisms and policies in a real multicluster grid system, the DAS, using our KOALA multicluster grid scheduler, as well as with simulations using realistic scenarios.
Vulnerability and public service delivery in China from 1985 to 1999

This research focused on the determinants of vulnerability, as measured by the variable of expenditure on food consumption, by the quality of public service delivery as measured by three education variables (amount of students per teacher in primary education, amount of students per teacher in secondary education and amount of students per teacher in higher education) and two health variables (amount of doctors per bed and the amount of beds per hospital). To measure the impact of public service delivery on vulnerability we will use OLS regression, Fixed Effect, Two Stage Least Square (TSLS) and TSLS with Fixed Effect. The instrumental of the TSLS regressions are a group of political decentralization and inequality variables. Lags of respective one and two years are introduced as an additional robustness test. The conclusion is threefold: (i) the quality of primary education has a negative impact on vulnerability, probably due to the selection bias of children from poor families being taken out of school; (ii) the quality of the education service in higher education has a positive impact on vulnerability; and (iii) quality of health care has, at most, only a partial positive effect on reducing vulnerability.
On Arbitrage, Information Costs, Compound Options and the Valuation of the Firm and its Assets

This paper presents a simple framework for the valuation of compound options within a context of incomplete information. Information costs are linked to the theory of signaling, agency models and generic stocks in the spirit of Merton's (1987) model of capital market equilibrium with incomplete information. We propose some ideas to explain arbitrage in financial markets in the presence of information costs. The use of these costs is important in the valuation of equity of some firms in the "new economy" like Internet stocks. Equity in these firms cannot be valued in an appropriate way by a model ignoring information uncertainty. When deriving the compound call option formula, we consider a call option on a stock, which is itself an option on the assets of the firm. Our methodology incorporates shadow costs of incomplete information on the firm's assets as well as the effects of leverage in the capital structure. The compound option formula is derived using two approaches: the standard Black and Scholes approach and the martingale method. The formula can be useful in the valuation of several corporate liabilities in the presence of information uncertainty about the firm and its cash flows. Our analysis can be used for the valuation of several real options.
Orthogonal H-decompositions

An H-decomposition of a graph G is a set L of edge-disjoint Hsubgraphs of G, such that each edge of G appears in some element of L. A k-orthogonal H-decomposition of a graph G is a set of k H-decompositions of G, such that any two copies of H in any two distinct H-decompositions have at most one edge in common. We prove that for every fixed graph H and every fixed integer k ≥ 1, if n is sufficiently large then Kn has a k-orthogonal H decomposition if and only if it has an H-decomposition. This occurs whenever ( n 2 ) is a multiple of e(H) and n− 1 is a multiple of the gcd of the degrees of H.
Illumina Sequencing Reveals Aberrant Expression of MicroRNAs and Their Variants in Whitefish (Coregonus lavaretus) Liver after Exposure to Microcystin-LR

Molecular analyses show that challenging fish with microcystin-LR (MC-LR) causes perturbations of microRNA (miRNA) signaling. However, the significance and scope of these alterations is currently unknown. To address this issue, we studied miRNA gene expression in the liver of juvenile whitefish, C. lavaretus, during 28 days of exposure to a subacute dose of MC-LR (100 μg·kg-1 body mass). Using genomic resources of Atlantic salmon (AGKD03), the mature miRNA library of Atlantic salmon (miRBase-21) and bioinformatics tools (sRNAbench), we discovered and annotated a total of 377 distinct mature miRNAs belonging to 93 families of evolutionary conserved miRNAs, as well as 24 novel mature miRNA candidates that were mapped to 14 distinct S. salar miRNA precursors. miRNA-Seq transcriptome profiling of liver tissues revealed differential miRNA expression in control and treated fish at 14 days (73 miRNAs were modulated) and at 28 days (83 miRNAs) of the treatment, subsequently validated by qPCR for nine selected differentially expressed miRNAs. Additional qPCR study confirmed the miRNA-Seq data and revealed consistent, aberrant miRNAs expression profile in the later phase of MC-LR hepatotoxicity (7–28 d). Functional annotation analysis revealed that the aberrantly expressed miRNAs have target genes involved in cytoskeletal remodeling, cell metabolism, cell cycle regulation and apoptosis; dysregulation of these processes in liver cells leads to cirrhosis and hepatocellular carcinoma in humans. To enable deeper insight into the molecular responses of liver cells in fish exposed to MC-LR, we expanded the miRNAome analysis by inclusion of miRNA variants (isomiRs) profiles, and we showed that the isomiR profiles of liver specific MiR122, and a few other miRNAs, correlated with MC-LR treatment. Given the importance of isomiRs for disease biology in mammals, we believe that further research focused on the miRNA isoforms will bring us closer to better understanding the molecular mechanisms of MC-LR hepatotoxicity.
Neutralizing Monoclonal Antibody to Periostin Inhibits Ovarian Tumor Growth and Metastasis

Periostin, an extracellular matrix protein, is reported to be overexpressed in a variety of human cancers and its functions seem to be linked to tumor metastasis. Our previous results show that engineered periostin overexpression promotes ovarian tumor growth and dissemination in vivo. In this study, we developed a neutra-lizing monoclonal antibody to periostin, named MZ-1, and investigated its effects on human ovarian tumor growth and metastasis. Our in vivo studies showed significant growth inhibition by MZ-1 on both subcutaneous and intraperitoneal (i.p.) tumors derived from the periostin-expressing ovarian cancer cell line A2780. In addition, MZ-1 treatment led to a reduction of the metastatic potential of these A2780 i.p. tumors. The in vivo antitumor effects of MZ-1 were linked to its specific inhibition of anchorage-independent growth and survival of periostin-expressing cells, as well as its neutralizing effects on periostin-induced cancer cell migration and invasion. The data suggest that blocking periostin expression may be a novel approach for treating the subset of invasive ovarian tumors that overexpress periostin protein. Mol Cancer Ther; 10(8); 1500–8. ©2011 AACR.
Why do banks syndicate loans

Loan syndication, where a group of banks makes a loan jointly to a single borrower, offers several benefits. Syndication allows banks to diversify, expanding their lending to broader geographic areas and industries. Second, syndication allows banks that are constrained by their capital-asset ratios to participate in loans to larger borrowers. ; Despite these benefits, loan syndication could pose additional risks for the banking system, if the originating or lead banks withhold information about the borrower from participating banks, misleading them into making loans that are riskier than they thought. This study uses data on loan syndications to test the importance of various factors that motivate the participants. Despite a significant number of problem credits among the syndicated loans studied, it finds little evidence of opportunistic behavior by the lead banks in syndications. At the same time, it finds substantial support for the importance of bank regulation, in the form of capital requirements and lending limits, to the existence of the bank syndication market.
Aggregate Implications of a Credit Crunch

We take an off-the-shelf model with financial frictions and heterogeneity, and study the mapping from a credit crunch, modeled as a shock to collateral constraints, to simple aggregate wedges. We study three variants of this model that only differ in the form of underlying heterogeneity. We find that in all three model variants a credit crunch shows up as a different wedge: efficiency, investment, and labor wedges. Furthermore, all three model variants have an undistorted Euler equation for the aggregate of firm owners. These results highlight the limitations of using representative agent models to identify sources of business cycle fluctuations.
Effect of intermittent high altitude hypoxia on gene expression in rat heart and lung.

Hypoxia has been identified as an important stimulus for gene expression during embryogenesis and in various pathological situations. Its influence under physiological conditions, however, has only been studied occasionally. We therefore investigated the effect of intermittent high altitude hypoxia on the mRNA expression of different cytokines and protooncogenes, but also of other genes described to be regulated by hypoxia, in the left ventricle (LV), the right ventricle (RV), atria and the lung of adult rats after simulation of hypoxia in a barochamber (5000 m, 4 hours to 10 days). Heme oxygenase-1 as well as transforming growth factor-beta1 showed an increased expression in all regions of the heart and the lung at different periods of hypoxia. For lactate dehydrogenase-A, we found a significant up-regulation in the RV and the lung, for lactate dehydrogenase-B up-regulation in the RV, but down-regulation in the LV and the atria. Vascular endothelial growth factor was up-regulated in the RV, the LV and the lung, but down-regulated in the atria. Its receptor Flk-1 mRNA was significantly increased in the atria and RV only. Expression of c-fos was found in the LV and RV only after 4 hours of hypoxia. The level of c-jun was significantly increased in the LV but decreased in the atria. Our data clearly demonstrate that intermittent hypoxia is a modulator of gene expression under physiological conditions. It differently regulates the expression of distinct genes not only in individual organs but even within one organ, i.e. in the heart.
Multiplicatively Separable Preferences and Output Persistence

In the New-Neoclassical Synthesis literature it is customary to use additively separable preferences, very often not campatible with long-run productivity growth and trend infation. The present paper shows that using multiplicatively separable preferences it is possible to gain further insight on the persistence mechanics of this class of models. In particular it is showed that the more leisure and the money-consumption bundle are Edgeworth complement and the less persistent are output deviations after a monetary shock. The basic intuition for this result is that an increase in money supply not only induces economic agents to increase their labour supply, but also raises the opportunity cost for this choice given that agents with more money in their pockets and greater consumption would like to have more leisure too. In addition, empirical estimates not only support multiplicatively and not additively separable preferences, but highlight new problems for the New-Neoclassical Synthesis given that leisure and money (consumption) appear to be Edgeworth complements and not substitutes.
MESMER: minimal ensemble solutions to multiple experimental restraints

MOTIVATION Macromolecular structures and interactions are intrinsically heterogeneous, temporally adopting a range of configurations that can confound the analysis of data from bulk experiments. To obtain quantitative insights into heterogeneous systems, an ensemble-based approach can be employed, in which predicted data computed from a collection of models is compared to the observed experimental results. By simultaneously fitting orthogonal structural data (e.g. small-angle X-ray scattering, nuclear magnetic resonance residual dipolar couplings, dipolar electron-electron resonance spectra), the range and population of accessible macromolecule structures can be probed. RESULTS We have developed MESMER, software that enables the user to identify ensembles that can recapitulate experimental data by refining thousands of component collections selected from an input pool of potential structures. The MESMER suite includes a powerful graphical user interface (GUI) to streamline usage of the command-line tools, calculate data from structure libraries and perform analyses of conformational and structural heterogeneity. To allow for incorporation of other data types, modular Python plugins enable users to compute and fit data from nearly any type of quantitative experimental data. RESULTS Conformational heterogeneity in three macromolecular systems was analyzed with MESMER, demonstrating the utility of the streamlined, user-friendly software. AVAILABILITY AND IMPLEMENTATION https://code.google. com/p/mesmer/
Particle Swarm Optimization aided unscented kalman filter for ballistic target tracking

Tracking of a ballistic target in its reentry phase by considering the radar measurements is a highly complex problem in nonlinear filtering. Kalman Filter (KF) is used to estimate the positions of the target when the measurements are corrupted with noise. If the measurements (range and bearing) are nonlinear then Unscented Kalman filter (UKF) can be used. For obtaining reliable estimate of the target state, filter has to be tuned before the operation, which is offline. Tuning is the process of estimating the process noise covariance matrix (Q) and measurement noise covariance matrix (R) of the filter. This paper presents tuning of UKF using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for ballistic target tracking. Simulations results show that the superiority of PSO tuned UKF over conventional UKF.
HePNC: Design of physical layer network coding with heterogeneous modulations

Physical layer network coding (PNC) has been proposed for the two-way-relay scenario. The existing PNC solutions typically use the same modulation for the source nodes' signals which may not be desirable for practical situations when the amount of data exchanged between the two source nodes are un-equal and their links to the relay node are heterogeneous. In this paper, physical layer network coding with heterogeneous modulations (HePNC) has been proposed to further improve the spectrum and energy efficiency considering the above mentioned heterogeneity. Similar to the existing PNC, HePNC also includes two stages: the multiple access (MA) stage for two source nodes to transmit data to the relay and the broadcast (BC) stage for the relay to broadcast data to both source nodes. The main difference is that at the MA stage, HePNC can select heterogeneous modulation methods according to the channel conditions and the ratio of data to be exchanged. We present two sample designs of HePNC, including QPSK-BPSK and 8PSK-BPSK, and obtain the mapping rules for the relay's de-noising and forwarding based on the neighbor clustering algorithm. In addition, we discuss the end-to-end bit-error-rate, energy efficiency and optimal relay location selection when HePNC is used. Extensive simulations demonstrated that the proposed HePNC can substantially enhance the end-to-end throughput and the energy efficiency compared to the homogeneous PNC, and thus it is a promising technology for future green communication systems.
Multivariate linear regression based activity recognition and classification

In this paper various activities such as sitting, standing and walking are recognized and classified using multivariate linear regression algorithm. Accuracy of recognition has been found to vary in a free living environment compared to the laboratory conditions. Hence the proposed work has considered free living environment. Zephyr BioHarness which has 3-axis accelerometer sensor is used for data acquisition. The proposed algorithm has been simulated in MATLAB. From the results obtained the accuracy of sitting activity is found to be 99.5 ± 0.3%, for standing activity 99.6 ± 0.4% and for walking activity 96.4 ± 0.2%. This shows more accuracy of 6%,7%,4%for sitting, standing and walking activities with the work taken for comparison.
Using Simple Content Features for the Author Profiling Task Notebook for PAN at CLEF 2013

This paper describes the methods we have employed to solve the au- thor profiling task at PAN-2013. Our goal was to use simple features to identify the age group and the gender of the author of a given text. We introduce the fea- tures, detail how the classifiers were trained, and how the experiments were run.
GlueTag Linear Logic based Semantics Construction for LTAG

In this paper we review existing appoaches to semantics construction in LTAG (Lexicalised Tree Adjoining Grammar) which are all based on the notion of derivation (tree)s . We argue that derivation structures in LTAG are not appropriate to guide semantic composition, due to a non-isomorphism, in LTAG, between the syntactic operation of adjunction on the one hand, and the semantic operations of complementation and modiﬁcation, on the other. Linear Logic based “glue” semantics, by now the classical approach to semantics construction within the LFG framework (cf. Dalrymple (1999)) allows for ﬂexible coupling of syntactic and semantic structure. We investigate application of “glue semantics” to LTAG syntax, using as underlying structure the derived tree , which is more appropriate for principle-based semantics construction. We show how linear logic semantics construction helps to bridge the non-isomorphism between syntactic and semantic operations in LTAG. The glue approach allows to capture non-tree local dependencies in control and modiﬁcation structures, and extends to the treatment of scope ambiguity with quantiﬁed NPs and VP adverbials. Finally, glue semantics applies successfully to the adjunction-based analysis of long-distance dependencies in LTAG, which diﬀers signiﬁcantly from the f-structure based analysis in LFG.
Facial expressions and flat affect in schizophrenia, automatic analysis from depth camera data

One of the prominent clinical manifestations of schizophrenia is flat or altered facial activity, and flattening of emotional expressiveness (Flat Affect). In this study we used a structured-light depth camera and dedicated software to automatically measure the facial activity of schizophrenia patients and healthy individuals during a short structured interview. Based on K-means clustering analysis, facial activity was characterized in terms of Typicality, Richness and Distribution of 7 facial-clusters. Thus we found patients' facial activity to be poorer, more typical, and characterized mainly by neutral (flat) expressions. The facial features defined in our study achieved up to 85% correct diagnosis classification rate in a SVM based two-step algorithm, and were in significant correlation with Flat Affect severity. Our results demonstrate how the use of assistive technology and data-driven computational tools allow for a comprehensive description of patients' facial behavior in clinical settings, and may contribute to the reliability and accuracy of psychiatric diagnosis.