Miracle-dz/UniFAR-FTUs huggingface dataset:
The Microeconomic Implications of Input Market Regulations: Cross-Country Evidence from Within the Firm

We investigate the microeconomic effects of labor regulations that protect employment and are expected to increase rigidity in labor markets. We exploit a unique outlet-level dataset obtained from a multinational food chain operating about 2,840 retail outlets in 43 countries outside the United States. The dataset provides information on output, input costs and labor costs at a weekly frequency over a 4-year period, allowing us to examine the consequences of increased rigidity at a much more detailed level than has been possible with commonly available annual frequency or aggregate data. We find that higher levels of the index of labor market rigidity are associated with significantly lower output elasticity of labor demand, as well as significantly higher levels of hysteresis (measured as the elasticity of current labor costs with respect to the previous week’s). Specifically, an increase of one standard deviation in the labor regulation rigidity index (1) reduces the response of labor cost to a one standard deviation increase in output (revenue) by about 4.5 percentage points (from 26.8 percent to 22.3 percent); and (2) increases the response of labor cost to a one standard deviation increase in lagged labor cost by about 9.6 percentage points (from 17.0 percent to 26.6 percent). Our estimates imply an increase in gross misallocation of labor ranging from 2 to 10 percent of total labor costs, for an increase in the index of labor regulation from the 25th to the 75th percentile. Finally, we find evidence that the Company delayed entry and operates fewer outlets in countries with more rigid labor laws. Overall, the data imply a significant impact of labor laws on labor adjustment and related decisions at the micro level. ∗Stephen M. Ross School of Business, University of Michigan, email: laf@umich.edu, jagadees@umich.edu. We thank Kathryn Shaw, Nick Bloom, Oriana Bandiera, Lee Branstetter, Richard Freeman, Jan Svejnar and participants at seminars at the University of Colorado, the University of California, Berkeley, Ohio State University, the University of Michigan as well as the NBER productivity group and the 2007 AEA session attendees for their comments. All remaining errors are our own.
Comparative Evaluation of Reinforcement Learning with Scalar Rewards and Linear Regression with Multidimensional Feedback

This paper presents a comparative evaluation of two learning approaches. The first approach is a conventional reinforcement learning algorithm for direct policy search which uses scalar rewards by definition. The second approach is a custom linear regression based algorithm that uses multidimensional feedback instead of a scalar reward. The two approaches are evaluated in simulation on a common benchmark problem: an aiming task where the goal is to learn the optimal parameters for aiming that result in hitting as close as possible to a given target. The comparative evaluation shows that the multidimensional feedback provides a significant advantage over the scalar reward, resulting in an order-ofmagnitude speed-up of the convergence. A real-world experiment with a humanoid robot confirms the results from the simulation and highlights the importance of multidimensional feedback for fast learning.
Open Access book publisher

For a long time, the main concern of the pattern recognition research community has been to achieve high accuracy. For example, in the area of Multi-Classifier Systems (MCS), researchers have developed very powerful techniques that combine large number of classifiers through complex combination schemes to achieve satisfactory accuracies. However, this was at the expense of complexity. Even powerful single classifiers (e.g. SVM) have very high complexity. This might give the impression that high accuracies could not be achieved without sacrificing recognition time
Change In Cycle Length During Narrow Complex Tachycardia: What Is The Mechanism?

Major spontaneous variation in cycle length during supraventricular tachycardia is quite an uncommon phenomenon, which sometimes can mislead a correct diagnosis. We describe a patient who developed spontaneous variation in cycle length during electrophysiologic study in whom the coronary sinus cannulation was extremely difficult. In this situation, careful inspection of the mechanisms associated with this variation and classic pacing maneuvers are important to make a correct diagnosis of the supraventricular tachycardia.
Whose Job Will be Taken Over by a Computer? The Role of Personality in Predicting Job Computerizability over the Lifespan

Economists estimate that 47% of US jobs will be computerized in the future. This paper tests the prospective role of a comprehensive range personality factors on selection into more (or less) computerizable jobs. We used a US representative high school sample (N = 346 660) and a longitudinal design. At baseline, we measured social background, intelligence, personality traits and vocational interests. In two follow–ups (11 and 50 years later), we recorded occupations and coded their probability of being computerized based on the skills required and technological developments. Multiple regressions showed that, regardless of social background, people who were more intelligent, mature, interested in arts, and sciences at baseline, and selected into jobs that had a lower probability of computerization. On average, a one standard deviation increase in each of these traits predicted an average of 4 percentage points drop in the probability of one's job of being computerized. At the US population level, this is equivalent with saving 5.8 million people from losing their future careers to computerization. Most effects replicated across time. Path analyses showed that educational attainment mediated these effects and some direct effects remained. This highlights the importance of personality on occupational selection and in shaping the labour market. Copyright © 2017 European Association of Personality Psychology
Autophagy-mediated HMGB1 release promotes gastric cancer cell survival via RAGE activation of extracellular signal-regulated kinases 1/2

High mobility group box-B1 (HMGB1), an autophagy activator, is crucial in tumorigenesis. However, its extracellular role and signaling in gastric cancer remain unclear. Samples were collected from gastric cancer patients and healthy controls. Immunohistochemistry and immunocytochemistry were used to determine the localization of HMGB1 in gastric cancer tissues, four gastric carcinoma cell lines (BGC-823, SGC-7901, MKN-28 and MKN-45) and a gastric epithelial cell line GES-1. Western blot analysis and ELISA were used to assess the effects of gefitinib, an epidermal growth factor receptor inhibitor, on autophagy and HMGB1 release in BGC-823 cells. MTT assay and western blot analysis assessed the effects of extracellular HMGB1 on cell proliferation and signaling transduction. Released HMGB1 promoted proliferation through activation of ERK1/2 MAPK. HMGB1 expression in gastric cancer tissues and serum was significantly increased compared to the controls and healthy serum. Gastric carcinoma cells showed an increased HMGB1 in the nuclei and cytoplasm, whereas GES-1 cells exhibited a lower HMGB1 with nuclear localization. Gefitinib increased autophagy and cytoplasmic HMGB1 release from the BGC-823 cells. Extracellular HMGB1 in autophagic cell supernatant promoted proliferation that was abolished by glycyrrhizic acid, an HMGB1 inhibitor. BGC-823 cells incubated with HMGB1 had increased ERK1/2 phosphorylation, while levels of JNK, p38 or AKT were not affected. Blocking RAGE-HMGB1 interaction with antibody or siRNA suppressed the ERK1/2 activation and gastric cancer cell growth, indicating that RAGE-mediated ERK1/2 signaling was necessary for tumor progression.
Predicting political systems using economic, environmental, and relational variables

This article continues the elaboration of a multiple factor model for predicting the political systems that emerge in specific economic, environmental and relational contexts. While anthropologists have long sought to describe the emergence of ‘the state’ or of modern bureaucracies, only recently have predictive models of political systems begun to emerge. Political scientists have long upheld the ideology that ‘democratic’ political systems can simply be exported or created, independent of the economic and environmental context as well as the realities of military power of one's neighbors, or that systems where the contexts have changed can still be called ‘democratic’ even though none of the conditions or attributes of such systems continue to exist. The causal arrow for predicting political systems appears to be in reverse of the ideological dogma of contemporary social sciences that generally seeks to deny the principles of evolutionary adaptation. Political systems appear to arise historically as a function of contextual variables and not independent of them, though technologies that today can refashion ecosystems and economies can also establish some types of (mostly authoritarian) political systems. The science of politics in this piece presents an alternative to the ideological (and theological, anti-evolutionary) approaches of contemporary ‘political science’.
Answer Retrieval From Extracted Tables

Question answering (QA) on table data, which contains densely packed information in two-dimensional form, is a challenging information retrieval task. Data can be placed at a distance from the metadata describing it. The metadata itself can be difficult to identify given the layout of a particular table. This paper describes a QA system for tables created with both machine learning and heuristic table extraction methods. Our approach creates a cell document for each table cell. A probabilistic language model selects the most likely cell documents for the information need. The performance of the system is tested with government statistical data, and errors are analyzed in order to improve the system. We also apply these improvements on another type of table data set and show the experimental results.
Pharmacological and Non Pharmacological Strategies in the Management of Coronary Artery Disease and Chronic Kidney Disease

Patients with advanced chronic kidney disease (CKD), including those treated with dialysis, are at high risk for the development of cardiovascular disease (CVD). CVD accounts for 45-50% of deaths among dialysis patients. Therapy of acute and chronic coronary heart disease (CHD) that is effective in the general population is frequently less effective in patients with advanced CKD. Drug therapy in such patients may require dose modification in some cases. Oral anti-platelet drugs are less effective in those with advanced CKD than in persons with normal or near normal renal function. The intravenous antiplatelet drugs eptifibatide and tirofiban both require dose reductions in patients with advanced CKD. Enoxaparin requires dose reduction in early stage CKD and is contraindicated in hemodialysis patients. Unfractionated heparin and warfarin maybe used without dose adjustment in CKD patients. Atenolol, acetbutolol and nadolol may require dose adjustments in CKD. Metoprolol and carvedilol do not. Calcium channel blockers and nitrates do not require dose adjustment, whereas ranolazine does. Angiotensin converting enzyme inhibitors and angiotensin receptor blockers may safely be used in CKD patients with close observation for hyperkalemia. The safety of spironolactone in such patients is questionable. Statins are less effective in reducing cardiovascular complication in CKD patients and their initiation is not recommended in dialysis patients. Coronary artery bypass grafting is associated with higher short-term mortality, but better long-term morbidity and mortality than percutaneous coronary interventions in patients with advanced CKD with non-ST segment ACS and chronic CHD.
Correlation analysis based on MIMO channel measurements in an indoor environment

Multiple-input-multiple-output (MIMO) systems have the potential to achieve very high capacities, depending on the propagation environment. Capacity increases as signal correlation decreases. We present the measurements of a MIMO system under strong and weak line-of-sight conditions. The system capacity decreases as the distance from the transmitter increases. Indeed the transmitter correlation increases as the distance increases. The receiver correlation is lower than the transmitter correlation under both propagation conditions.
Neuroendocrine cells in eutopic endometrium of women with endometriosis.

BACKGROUND Endometriosis is a common gynaecological disease, but the pathogenesis of endometriosis and pathophysiological basis for endometriosis-associated painful symptoms are still uncertain. Little is known about neuroendocrine (NE) cells in the uterus. METHODS For this study, 38 premenopausal women with histologically diagnosed ovarian endometrioma or peritoneal endometriosis and 24 women without endometriosis were selected. Biopsy samples from eutopic endometrium were used for immunohistochemical staining to detect synaptophysin (SYN) and neuron-specific enolase (NSE) expression in women with and without endometriosis. RESULTS There were substantially more NE cells of eutopic endometrium stained with SYN and NSE in women with endometriosis than in those without endometriosis (3.8 +/- 1.8 versus 0.5 +/- 0.7/mm2, P < 0.001, and 2.8 +/- 2.1 versus 0.4 +/- 0.6/mm2, respectively, P < 0.001). These cells were scattered in the epithelium of endometrial glands. At all stages of the menstrual cycle, the densities of NE cells stained with SYN and NSE were greater in women with endometriosis than in those without endometriosis (P < 0.05). CONCLUSIONS These results suggest that NE cells in eutopic endometrium probably play some role in the pathogenesis or symptoms of endometriosis.
Massively Parallel Real-time Reasoning with Very Large Knowledge Bases: an Interim Report

We map structured connectionist models of knowledge representation and reasoning onto existing general purpose massively parallel architectures with the objective of developing and implementing practical, real-time reasoning systems. Shruti, a connectionist knowledge representation and reasoning system which attempts to model reeexive reasoning, serves as our representative connectionist model. Realizations of shruti are developed on the Connection Machine CM-2|an SIMD architecture|and on the Connection Machine CM-5|an MIMD architecture. Though SIMD implementations on the CM-2 are reasonably fast|requiring a few seconds to tens of seconds for answering queries|experiments indicate that SPMD message passing systems are vastly superior to SIMD systems and ooer hundred-fold speedups. The CM-5 implementation can encode large knowledge bases with several hundred thousand (randomly generated) rules and facts, and respond in under 500 milliseconds to a range of queries requiring inference depths of up to eight. This work provides some new insights into the simulation of structured connectionist networks on massively parallel machines and is a step toward developing large yet eecient knowledge representation and reasoning systems.
Aggression in Children With Reactive Attachment Disorder: A Sign of Deficits in Emotional Regulatory Processes?

ABSTRACT Children diagnosed with reactive attachment disorder (RAD) have been reported to exhibit a multitude of symptoms and behaviors that are not captured by the Diagnostic and Statistical Manual of Mental Disorders criteria. One behavior in particular that has been the source of much argument and controversy is aggression. Although limited research on this topic does support an association between aggression and RAD behaviors, the exact nature of this link remains unclear. This study aims to fill this gap by reporting data from a study that examined the behavioral processes that occur in adopted children with RAD. Guided by the principles of grounded theory, this study employed a multi-stage semi-structured interview design. The sample consisted of 5 adoptive families, and included both adult and child participants, including the adopted children with RAD. The findings revealed 5 prominent themes: The adopted children are reported to engage in extreme and prolonged tantrums, known as rages; children were often inconsolable during these rages; these rages are perceived as uncontrollable by children with RAD; these rages are directed toward a discriminate caregiver; and the impetus for these rages appears to be associated with difficulties in adapting to environmental demands. An in-depth discussion that draws a theoretical connection between these rages and possible deficits in brain-based processes responsible for emotional regulation is presented.
Knowledge Sharing Mechanisms in Industrial Research

Previous research has firmly established the importance of knowledge sharing in Research and Development (R&D) settings. However, current theories provide only fragmented insights into the origination of knowledge sharing, and thus offer limited guidance for knowledge management practices in R&D. To integrate and extend these fragmented insights, we undertook two exploratory field studies of knowledge sharing in industrial research organizations. The contributions of this study are the following. First, we introduce three dimensions that differentiate origination mechanisms for knowledge sharing. Second, we show that some of these mechanisms correspond to mechanisms assumed in particular streams of literature, whereas others have been neglected till now. Third, based on our field studies, we show that each of these knowledge-sharing mechanisms have a different value for industrial research practices. Therefore, knowledge management in R&D should facilitate and stimulate a broad portfolio of knowledge-sharing mechanisms.
Fast Morphing of Triangular Meshes in Shape Space

We present a novel approach to morph between two isometric poses of the same non-rigid object given as triangular meshes. We model morphs as linear interpolations in a suitable shape space S. For triangulated 3D polygons, we prove that interpolating linearly in this shape space corresponds to the most isometric morph in R. We then extend this shape space to arbitrary triangulations in 3D using a heuristic approach that utilizes belief propagation on junction trees and show the practical use of the approach in preliminary experiments.
DRY DEPOSITION OF PARTICULATE MATTER EMITTED FROM COTTON GINS - PARTICLE SIZE DISTRIBUTION OF THE PARTICULATE MATTER IN THE DOWNWIND PLUME

An engineering approach to analyz e PM dry deposition due to gravitational settling is summarized in this paper. Particle horizontal settling distance is determined based upon wind speed and particle terminal settling velocity. New PSD in the downwind plume is calculated based upon particle horizontal settling distance and source PSD. Results of this research indicate that change of PSD in the downwind plume is a function of wind speed, downwind distance and source PSD. Both MMD and GSD in the downwind plume decrease with increase of downwind distance and source MMD. When the MMD of the source is high , the re wa s a big change in the MMD and GSD of the dust in the downwind plume. Likewise, there was a big change in the MMD and GSD of dust in the downwind plume as downwind distance is increased. However, the change of wind speed has an inverse impact on the change of downwind PSD. MMD and GSD in the downwind plume decrease with decrease of wind speed. Gravitational settling causes significant change of MMD and GSD in the downwind plume and thus, has significant impact on the PM 10 sampling error problems. Future rese arch is needed to quantify this impact for more accurate PM 10 measurement
Path planning in the AFM nanomanipulation of multiple spherical nanoparticles by using a coevolutionary Genetic Algorithm

Recently, Atomic Force Microscope (AFM) based nanorobots have emerged as the ultimate tool for assembling nanostructures. The main obstacle in precise nano-manipulation is the lack of real-time monitoring; therefore, computer simulations have been employed to surmount this limitation. In this paper, an algorithm has been proposed to maximize the effectiveness of the existing computer simulations. The existing simulations only deal with one-dimensional particle paths and disregard the existence of other particles as well as surface roughness. In this paper, a more realistic path, which considers the presence of other particles and surface roughness, has been proposed. The Artificial Potential Field Method has been applied to find the initial sub-optimal paths. Then, a customized Genetic Algorithm has been used to find the optimal solution for the path planning problem. The main goal is to minimize the manipulation time while keeping the applied force within the desired limits and make the nano assembly more practical.
A semi-autonomous framework for human-aware and user intention driven wheelchair mobility assistance

An important aspect to be taken care of while designing assistive robots for mobility is that they need to operate among humans. Thus understanding human spatial social conventions and incorporating them in the assistive solutions, is important. In this paper, we introduce a semi-autonomous framework for assistive wheelchair navigation in human environments, which is driven by the intention of the wheelchair user. Safe and socially compliant motion provided by a user intention driven local motion planner is fused with user teleoperation in order to create such a system. Taking into account the fact that the user is the primary controller, our proposed system aims to provide progressive assistance whenever the user is in danger of collision or at risk of disturbance to other humans. We also thus propose generalized formulations for estimating user intentions and for sharing control within the context of wheelchair mobility assistance, that is adaptable in order to be deployed in real world systems. We then evaluate the proposed framework in simulation in order to obtain a quantitative analysis. We also provide experimental evidence using an off-the-shelf robotized wheelchair equipped with a single 2D laser scanner.
A Comparison of Centrality Measures for Graph-Based Keyphrase Extraction

In this paper, we present and compare various centrality measures for graphbased keyphrase extraction. Through experiments carried out on three standard datasets of different languages and domains, we show that simple degree centrality achieve results comparable to the widely used TextRank algorithm, and that closeness centrality obtains the best results on short documents.
A Super-Resolution Imaging Method Based on Dense Subpixel-Accurate Motion Fields

A super-resolution imaging method suitable for imaging objects moving in a dynamic scene is described. The primary operations are performed over three threads: the computation of a dense inter-frame 2-D motion field induced by the moving objects at a sub-pixel resolution in the first thread. Concurrently, each video image frame is enlarged by the cascode of an ideal low-pass filter and a higher rate sampler, essentially stretching each image onto a larger grid. Then, the main task is to synthesize a higher resolution image from the stretched image of the first frame and that of the subsequent frames subject to a suitable motion compensation. A simple averaging process and/or a simplified Kalman filter may be used to minimize the spatio-temporal noise, in the aggregation process. The method is simple and can take advantage of common MPEG-4 encoding tools. A few experimental cases are presented with a basic description of the key operations performed in the over all process.