Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Labor Market Regulations Affect Labor Earnings in Ecuador?

Although Ecuador may have the most cumbersome labor market regulations in Latin America, these are not a major source of segmentation of the labor market. The reason: the benefits mandated are fully fungible with wages. Ecuadorian labor costs are said to be high because of a large array of mandated benefits. But there are several reasons to doubt that labor market regulations, cumbersome as they are, are responsible for segmentation of the labor market, let alone slow growth and increased inequality. And available evidence on the regulations' impact on the labor market is not compelling, as MacIsaac and Rama show. Using the 1994 Living Standards Measurement Survey, they show that the impact of mandated benefits is mitigated by a reduction of the base earnings on which they are calculated. Therefore, Ecuador's labor regulations do raise take-home pay, but less than the vast number of benefits would suggest. The increase in labor costs induced by compliance with labor regulations is even smaller than the corresponding increase in take-home pay, because mandated benefits are not subject to social security contributions or payroll taxes. Despite mandated benefits, wage differentials between industries are comparable to those in Bolivia, a country otherwise similar to Ecuador, yet known to have flexible labor markets. Cumbersome as they are, Ecuador`s labor market regulations cannot be held responsible for most labor market segmentation. Compliance with these regulations is associated with significantly higher take-home pay only in the public sector and where trade unions are active - and it is unclear that merely changing the labor code would bring wages down in those two areas. And the most dramatic earnings gap, the one between jobs in agriculture and the rest of the economy, appears to be largely independent of either unions or labor laws. Drastically streamlining the labor laws would be welcome, but only moderate change should be expected from such a reform. This paper is a product of the Poverty and Human Resources Division, Policy Research Department. The research was initiated in the context of a poverty assessment for Ecuador undertaken by Country Department III, Latin America and the Caribbean. The study was funded by the Bank's Research Support Budget under research project The Impact of Labor Market Policies and Institutions on Economic Performance (RPO 680-96).
Analysis on Attention Mechanism Application Schemes for Automatic Question Answering Systems

An automatic question answering system is generally composed of the question analysis module, the information retrieval module, the answer selection module, etc. The core component of automatic question answering system is the answer selection module, which focuses on extracting adequate information from the questions and answers and representing them effectively. The performance of answer selection directly determines the quality of the answers submitted to users. In this paper, we studied the answer selection schemes, especially the Attentive LSTM scheme, focusing on the application of the attention mechanism commonly utilized in deep learning models. Meanwhile, a self-attentive LSTM is proposed by combining the Attentive LSTM with self-attention which is capable of extracting the local features and the global features of texts at the same time. In addition, a multi-attentive LSTM is proposed so that multiple parts of information in the question are available for the answer. We performed a series of experiments based on the datasets InsuranceQA and TrecQA, compared and analyzed on the performances of the above schemes.
Application of a Macromolecular Contrast Agent for Detection of Alterations of Tumor Vessel Permeability Induced by Radiation

Permeability of tumor vasculature can be a major barrier to successful drug delivery, particularly for high molecular weight agents such as monoclonal antibodies and their diagnostic or therapeutic conjugates. In this study, changes in permeability of SCCVII tumor vessels after radiation treatment were evaluated by dynamic magnetic resonance imaging as a function of time after irradiation using a generation-8 polyamidoamine dendrimer (G8-Gd-D)-based magnetic resonance imaging contrast agent shown previously to be confined to tumor blood vessels. Tumor irradiation consisted of either single doses (2–15 Gy) or various daily fractionated doses (5 days). A single radiation dose of 15 Gy resulted in significant transient image enhancement of the tumor tissue with a maximum occurring between 7 and 24 hours after radiation treatment. No observable enhancement was recorded for fractionated radiation doses. Use of dynamic magnetic resonance imaging coupled with G8-Gd-D provides an exquisite methodology capable of defining the timing of enhanced permeability of macromolecules in tumors after irradiation. Such information might be applied to optimize the efficacy of subsequent or concurrent therapies including radiolabeled antibodies or other anticancer agents in combination with external beam therapies.
Autophagy is a critical regulator of memory CD8+ T cell formation

During infection, CD8+ T cells initially expand then contract, leaving a small memory pool providing long lasting immunity. While it has been described that CD8+ T cell memory formation becomes defective in old age, the cellular mechanism is largely unknown. Autophagy is a major cellular lysosomal degradation pathway of bulk material, and levels are known to fall with age. In this study, we describe a novel role for autophagy in CD8+ T cell memory formation. Mice lacking the autophagy gene Atg7 in T cells failed to establish CD8+ T cell memory to influenza and MCMV infection. Interestingly, autophagy levels were diminished in CD8+ T cells from aged mice. We could rejuvenate CD8+ T cell responses in elderly mice in an autophagy dependent manner using the compound spermidine. This study reveals a cell intrinsic explanation for poor CD8+ T cell memory in the elderly and potentially offers novel immune modulators to improve aged immunity. DOI: http://dx.doi. org/10.7554/eLife.03706. 001
Computation of Word Associations Based on Co-occurrences of Words in Large Corpora

A statistical model is presented which predicts the strengths of word-associations from the relative frequencies of the common occurrences of words in large bodies of text. These predictions are compared with the Minnesota association norms for 100 stimulus words. The average agreement between the predicted and the observed responses is only slightly weaker than the agreement between the responses of an arbitrary subject and the responses of the other subjects. It is shown that the approach leads to equally good results for both English
The dynamical evolution of Taurus-Auriga-type aggregates

Star formation in the Taurus–Auriga (TA) molecular clouds is producing binary-rich aggregates containing at most a few dozen systems within a region spanning one pc without massive stars. This environment is very different to another well-studied starforming event which produced the Orion Nebula cluster (ONC). The ONC contains a few thousand systems within a region of one pc including massive stars. Differences between these two environments have been found. Notably, the ONC has a significantly smaller binary proportion but a significantly larger number of isolated brown dwarfs (BDs) per star than TA. The aim of the present project is to investigate if these differences can be explained through stellar-dynamical evolution alone. The stellar-dynamical issue is very relevant because dense environments destroy binaries liberating BD companions, possibly leading to the observed difference between the TA and ONC populations. Here a series of high-precision N body models of TA-like embedded aggregates are presented, assuming the standard reference star-formation model for the input populations according to which stars and BDs form with the same kinematical, spatial and binary properties. After a discussion of the general evolution of the aggregates, it is shown that the binary population indeed remains mostly unevolved. Therefore, TA-type star formation cannot have added significantly to the Galactic-field population. The standard model leads to BDs tracing the stellar distribution, apart from a high-velocity tail (v >1 km/s) which leads to a more widely distributed spatial distribution of single BDs. The slow-moving BDs, however, retain a high binary proportion, this being an important observational diagnostic for testing against the embryo-ejection hypothesis. Inferences about the IMF and the binarystar orbital distribution functions are made in two accompanying papers with useful implications for star formation and the possible origin of BDs.
Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment

Reconstruction of neural circuitry at single-synapse resolution is a key target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain Drosophila ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections as well as dynamically adjust and synthesize image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM Drosophila brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.
Trends in medical management of acute myocardial infarction, Auckland 1983-90.

AIMS To examine changes in the medical management of acute myocardial infarction in the Auckland region between 1983 and 1990. METHODS 6190 patients aged 25-64 years with myocardial infarction were identified utilising a population based register, the ARCOS study. Data were collected on resuscitation attempts, transportation to hospital, coronary care unit (CCU) stay and medication use before and during the CCU stay. RESULTS The median time delay between onset of symptoms and arrival at the CCU decreased by approximately 7 minutes per year. The average length of CCU stay decreased from 2.9 to 2.7 days and there were between hospital variations in length of CCU stay. The most striking change in drug therapy before the event was the increase in the use of antiplatelet agents from 5% in 1983 to 18.3% in 1990. There was also a big increase in the proportion of patients using antiplatelets (3.5 fold) and thrombolytic drugs (5.5 fold) during the CCU stay, in 1990, 50% of patients received thrombolytic agents and 56% received antiplatelet agents. CONCLUSION The use of drugs of proven benefit could be further increased although it appears that major improvements in mortality rates will come primarily from primary prevention.
Cortical oscillatory dynamics of tactile selective sensation - for a novel type of somatosensory Brain-computer Interface

In this study, cortical oscillatory dynamics with respect to tactile selective sensation tasks were investigated. Subjects were required to perform three tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation of the left hand (SS-L), 2) selective sensation of the right hand (SS-R), 3) bilateral selective sensation (SS-B). Even-related (de)synchronization (ERD/ERS) analysis revealed a clear contralateral activation of somatosensory cortex during the SS-L and SS-R tasks, and a bilateral activation during SS-B tasks. Additionally, we found a clear ERS in occipital region of the brain during all the SS tasks. Diverse activation pattern among SS-L, SS-R and SS-B offers novel brain signals for somatosensory Brain-computer Interfaces.
Sub-images based image hashing with non-negative factorization

This paper proposes a robust and secure perceptual image hashing technique based on random sub-image selection, non-negative matrix factorization and DWT transform where the hashes are extracted in a binary form. The binary hash values are obtained with a perfect control over the probability distribution of hash bits. A key is used randomize the feature vector. Experimentally, the proposed technique has been shown to yield a good performance with respect to robustness against image processing operations including JPEG lossy compression, additive noise and geometric attacks such as rotation, translation and more secure of image hashing scheme.
Attraction-Repulsion Expectation-Maximization Algorithm for Image Reconstruction and Sensor Field Estimation

In this paper, we propose an attraction-repulsion expectation-maximization (AREM) algorithm for image reconstruction and sensor field estimation. We rely on a new method for density estimation to address the problems of image reconstruction from limited samples and sensor field estimation from randomly scattered sensors. Density estimation methods often suffer from undesirable phenomena such as over-fitting and over-smoothing. Specifically, various density estimation techniques based on a Gaussian mixture model (GMM) tend to cluster the Gaussian functions together, thus resulting in over-fitting. On the other hand, other approaches repel the Gaussian functions and yield over-smooth density estimates. We propose a method that seeks an equilibrium between over-fitting and over-smoothing in density estimation by incorporating attraction and repulsion forces among the Gaussian functions and determining the optimal balance between the competing forces experimentally. We model the attractive and repulsive forces by introducing the Gibbs and inverse Gibbs distributions, respectively. The maximization of the likelihood function augmented by the Gibbs density mixture is solved under the expectation-maximization (EM) method. Computer simulation results are provided to demonstrate the effectiveness of the proposed AREM algorithm in image reconstruction and sensor field estimation.
Managerial Optimism and Debt Contract Design: The Case of Syndicated Loans

We examine the impact of managerial optimism on the inclusion of performancepricing provisions in syndicated loan contracts (PSD). Given their upwardly biased expectations about the firm’s future cash flow, optimistic managers may view PSD as a relatively cheap form of financing. Indeed, we find that optimistic managers are more likely to issue PSD, and choose contracts with greater risk-compensation than rational managers. Consistent with their biased expectations, firms with optimistic managers perform worse than firms with rational managers after issuing PSD. Our results indicate that behavioral aspects can affect contract design in the market for syndicated loans.
Techniques of DNA Microarray Data Pre-processing Based on the Complex Use of Bioconductor Tools and Shannon Entropy

The paper presents the comparison analysis of various techniques of DNA microarrays data pre-processing in order to choose the optimal combination of the methods in terms of the minimum value of Shannon entropy criterium. The Bioconductor package tools of R software were used during the simulation process. The DNA microarray data of patients, which were investigated on lung cancer from database Array Express, were used as the experimental data. The algorithm of step-by-step procedure of the data processing for purpose of determination of the optimal combination of the methods has been proposed as the results of the research. The results of the simulation have shown that the optimal combination of the methods for the investigated data is the following one: rma method background correction, invariant set method normalization and mas method PM correction and summarization. This combination of the methods corresponds to the minimum value of the Shannon entropy criterion.
The Microarchitecture of a Pipelined WaveScalar Processor : An RTL-based Study

WaveScalar is a recently introduced architecture designed to capitalize on the vast number of transistors available on modern processes. Prior work introduced the architecture and used simulation-based results to demonstrate its performance-efficiency compared to conventional designs. But can it really be built in commercially viable area budgets and will it achieve a clock speed comparable to more conventional superscalars? This paper answers these questions. We have designed a synthesizable RTL model of the WaveScalar microarchitecture, called the WaveCache. This includes its execution substrate, memory system, and interconnect. Using the TSMC 90nm process and latest design tools, this model synthesizes to a chip of 252mm and achieves a clock rate of 25 FO4. This paper describes its RTL implementation and couples it with results from cycle-level simulation to illustrate the key performance-area-delay trade-offs in WaveCache design.
Gene Expression Profiling for Diagnosis of Triple-Negative Breast Cancer: A Multicenter, Retrospective Cohort Study

Background: Triple-negative breast cancer (TNBC) accounts for 12–20% of all breast cancers. Diagnosis of TNBC is sometimes quite difficult based on morphological assessment and immunohistochemistry alone, particularly in the metastatic setting with no prior history of breast cancer. Methods: Molecular profiling is a promising diagnostic approach that has the potential to provide an objective classification of metastatic tumors with unknown primary. In this study, performance of a novel 90-gene expression signature for determination of the site of tumor origin was evaluated in 115 TNBC samples. For each specimen, expression profiles of the 90 tumor-specific genes were analyzed, and similarity scores were obtained for each of the 21 tumor types on the test panel. Predicted tumor type was compared to the reference diagnosis to calculate accuracy. Furthermore, rank product analysis was performed to identify genes that were differentially expressed between TNBC and other tumor types. Results: Analysis of the 90-gene expression signature resulted in an overall 97.4% (112/115, 95% CI: 0.92–0.99) agreement with the reference diagnosis. Among all specimens, the signature correctly classified 97.6% of TNBC from the primary site (41/42) and lymph node metastasis (41/42) and 96.8% of distant metastatic tumors (30/31). Furthermore, a list of genes, including AZGP1, KRT19, and PIGR, was identified as differentially expressed between TNBC and other tumor types, suggesting their potential use as discriminatory markers. Conclusion: Our results demonstrate excellent performance of a 90-gene expression signature for identification of tumor origin in a cohort of both primary and metastatic TNBC samples. These findings show promise for use of this novel molecular assay to aid in differential diagnosis of TNBC, particularly in the metastatic setting.
Newly Developed TV-Based Cognitive Training Games Improve Car Driving Skills, Cognitive Functions, and Mood in Healthy Older Adults: Evidence From a Randomized Controlled Trial

Background: Cognitive training in a laboratory improves car driving skills of older car drivers. However, it remains unclear whether other types of cognitive training at home have beneficial effects on driving skills. Using our developed cognitive training games that can be played on a television with a set-top box in a person’s home, we investigated the effects of a 6-week cognitive training program on driving skills, which included on-road evaluation (primary outcome), and cognitive functions and emotional states (secondary outcome) in older people. Methods: In this double-blinded randomized control trial (RCT), 60 older licensed drivers were randomly assigned into one of the two groups: a cognitive training game for car driving (CTCD) group and an active control cognitive training game (ACT) group. Participants in the CTCD group played the CTCD (processing speed, dual attention, and speed prediction) for 20 min in five sessions per week for 6 weeks. Participants in the ACT group played the ACT (selecting the larger number; selecting a number from largest to smallest; play a game of rock, article, scissors) for 20 min in five sessions per week for 6 weeks. We measured driving skills, various cognitive functions, and emotional states before and after the 6-week intervention period. Results: Our main results showed that compared to the ACT group, the CTCD group demonstrated improved driving skills (adjusted p = 0.034). Moreover, the CTCD group demonstrated improved inhibition (stroop, adjusted p = 0.042: reverse Stroop, adjusted p = 0.043) and processing speed performance symbol search (SS), adjusted p = 0.049; digit symbol coding (adjusted p = 0.047), compared to the ACT group. The CTCD group scored higher on vigor–activity mood (adjusted p = 0.041) as measured using the Profile of Mood State. Discussion: This randomized controlled trial provides scientific evidence for the benefits of the 6-week CTCD program on driving skills and cognitive functions, such as processing speed, inhibition, and vigor–activity mood, in healthy older people. Our results suggest that cognitive training is useful to improve the driving skills of older adults. Trial registration: This trial was registered at The University Hospital Medical Information Network Clinical Trials Registry (UMIN 000029769). Registered 31 October 2017, https://upload.umin. ac.jp/cgi-open-bin/ctr_e/ctr_view. cgi? recptno=R000034010
SVM-based Technique for Mobile Malware Detection

A paper presents a new technique for the mobile malware detection based on the malware’s network features analysis is proposed. It uses SVM for malicious programs detection. The novel approach provides the ability to detect malware in the mobile devices. As the inference engine for malware detection the support vector machine was used. The detection process is performed by taking into account the malware’s features, captured in the mobile devices. Experimental research showed that the SVMs are able to produce the accurate classification results. Experiments demonstrated, that technique is able to detect different types of malware up to 98.21%.
Properties of Symmetric Fitness Functions

The properties of symmetric fitness functions are investigated. We show that the search spaces obtained from symmetric functions have the zero-correlation structures between fitness and distance. It is also proven that symmetric functions induce a class of the hardest problems in terms of the epistasis variance and its variants. These analyses suggest that the existing quantitative measures cannot discriminate among symmetric functions, which reveals critical limitations of the measures. To take a closer look at the symmetric functions, additional analyses are performed from other viewpoints including additive separability and boundedness. It is shown that additive separability in a symmetric function is closely related to the symmetry of its subfunctions. This elucidates why most of the well-known symmetric fitness functions are additively inseparable. The properties of two-bounded symmetric functions are investigated and they are utilized in designing an efficient algorithm to check additive separability for the two-bounded functions. Throughout this paper, we heavily use the generalized Walsh transform over multary alphabets. Our results have an independent interest as a nontrivial application of the generalized Walsh analysis.