Miracle-dz/UniFAR-FTUs huggingface dataset:
Underuse of an invasive strategy for patients with diabetes with acute coronary syndrome: a nationwide study

Background Guidelines recommend an early invasive strategy for patients with diabetes with acute coronary syndromes (ACS). We investigated if patients with diabetes with ACS are offered coronary angiography (CAG) and revascularisation to the same extent as patients without diabetes. Methods and results The study is a nationwide cohort study linking Danish national registries containing information on healthcare. The study population comprises all patients hospitalised with first-time ACS in Denmark during 2005–2007 (N=24 952). Diabetes was defined as claiming of a prescription for insulin and/or oral hypoglycaemic agents within 6 months prior to the ACS event. Diabetes was present in 2813 (11%) patients. Compared with patients without diabetes, patients with diabetes were older (mean 69 vs 67 years, p<0.0001), less often males (60% vs 64%, p=0.0001) and had more comorbidity. Fewer patients with diabetes underwent CAG: cumulative incidence 64% vs 74% for patients without diabetes, HR=0.72 (95% CI 0.69 to 0.76, p<0.0001); adjusted for age, sex, previous revascularisation and comorbidity HR=0.78 (95% CI 0.74 to 0.82, p<0.0001). More patients with diabetes had CAG showing two-vessel or three-vessel disease (53% vs 38%, p<0.0001). However, revascularisation after CAG revealing multivessel disease was less likely in patients with diabetes (multivariable adjusted HR=0.76, 95% CI 0.68 to 0.85, p<0.0001). Conclusions In this nationwide cohort of patients with incident ACS, patients with diabetes were found to be less aggressively managed by an invasive treatment strategy. The factors underlying the decision to defer an invasive strategy in patients with diabetes are unclear and merit further investigation.
Fast Tracing of Microtubule Centerlines in Electron Tomograms

The organization and role of the cytoskeleton, particularly the network formed by microtubules, is an active topic of research in cell biology. To study the role of these tubular shaped macromolecules, 25nm in diameter, information about their length, number, spatial distribution and dynamics must be gathered. Currently, the only technique that provides the needed resolution is electron microscopy. To gather 3d spatial information, samples are cut in sections of 300nm thickness to allow a projection with an electron beam. The volume is then reconstructed from projections with different tilt angles, a technique called electron tomography. Unfortunately, samples cannot be tilted up to 90◦ in the microscope, which results in an artifact in electron tomograms known as ‘missing wedge’. This artifact as well as angular sampling, imperfections in the sample preparation and destructive effects caused by the electron beam, result in a low signal-to-noise ratio in electron tomograms making their automatic processing particularly challenging. Fig. 1 shows examples of microtubules in electron tomograms. To draw reliable conclusions about the structure of the microtubule network, e.g. during cell division, microtubules need to be identified and their end types classified precisely in a large number of specimens. Manual analysis is often unfeasible: The midsection of a centrosome in the mitotic spindle of C. Elegans, for example, contains between 600 and 1000 microtubules. With current methods, manual segmentation of centerlines in one of these sections takes 10 to 30 hours depending on the image quality. Therefore, biological research on microtubules using electron tomography is currently often based on single observations as opposed to deriving statistics from many samples. On the other hand, automatic extraction and classification on the centerlines remains an open problem and has not yet been solved with the needed quality (see [2]). The major goal for any attempt to solve this problem is to reduce the time spent manually without compromising quality. For large scale analyses at least one order of magnitude of speedup is needed. Segmentation and classification can be divided into two tasks: The tracing of the centerlines and the classification of microtubule ends as being open or closed not a trivial assignment as can be seen in Fig. 1. Since the major bottleneck is the tracing of the centerlines, we describe how the time spent manually on this task can already
The Timing and Method of Payment in Mergers when Acquirers Are Financially Constrained

Although acquisitions are a popular form of investment, the link between firms’ financial constraints and acquisition policies is not well understood. We develop a model in which financially constrained bidders approach targets, decide how much to bid and whether to bid in cash or in stock. In equilibrium, financial constraints do not affect the identity of the winning bidder, but they lower bidders’ incentives to approach the target. Auctions are initiated by bidders with low constraints or high synergies. The use of cash is positively related to synergies and the acquirer’s gains from the deal and negatively to financial constraints. Received March 3, 2016; editorial decision August 18, 2017 by Editor Itay Goldstein. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
A Knowledge Plane for Wireless Mesh Networks?

The quest for the autonomic management of communication systems dates back to more than two decades ago. In practice, it be- came a compelling need when such systems started growing in size and complexity. The risk, in fact, was that the support/management of the infrastructure became so expensive to be the major design constraint. In telecommunication networks, in particular, autonomicity is perceived practically as an alias of Autonomic Network Management (ANM) (1): proper handling of network complexity, in fact, is perceived as a corner- stone for achieving robustness and performance. But, despite the expec- tations, while ANM is emerging as one of the hottest research topics, little concrete results have been achieved so far. This can be ascribed, on one hand, to the complexity of the issue itself, whose theoretical foun- dations have not been completed so far, and on the other one to the lack of a research platform on which novel solutions can be tested in a controlled and replicable fashion. In such a scenario, due to their re- configurability and ease of deployment, wireless mesh networks provide us the opportunity to design from scratch an autonomic control plane on top of a network of practical interest. In this paper we propose a novel Knowledge Plane, tailored specifically for the WMNs scenario and capable of enabling consistent sharing of services ontology among the entities participating the WMNs. As a case study, we present JANUS, a novel and freely available monitoring framework exploiting the proposed Knowledge Plane.
The Economy as a Polity: The Political Constitution of Contemporary Capitalism

Part I - The Economy as a Polity: Renewing a Debate: Theorising the Economy as a Polity: The Point of View of Contemporary Economics The Problmatique of Economic Modernity: Critical Theory, Political Philosophy and the Analysis of Capitalism Civilizational Perspectives on Capitalism One World Or Three? : Globalization and the Future of Welfare Capitalism. Part II - Capitalism, co-ordination, Constitution: Capitalism, Coordination, and Economic Change: Lessons from the French Political Economy Between Constitutional Democracy and Society: reflections on the Legal-Historical Evolution of Administrative Governance The Turn to Governance and the Problmatique of Constitutionalism The European Social Model and the Constitution-Giving Process in the European Union. Part III - Markets, Mobility, Monetary Regulation: The Basel System of International Monetary relations, 1960-1968 Monetary Regulation and European Economic Policy in Historical Perspective.
International Journal of Physical Sciences Optimal Lead-lag Controller Designing for Reduction of Load Current Total Harmonic Disturtion and Hanmonic with Voltage Control Using Honey Bee Mating Optimization (hbmo)

Increase in world demand load has resulted in new distributed generation (DG) that has entered into the power system. One of the most renewable energies is fuel cell, which is connected to power system using a power electronics interface in microgrid or standalone condition. The highest problem of this switching interface based DGs is the power quality (PQ) and harmonics of currents. Also, the voltage of the DGs should be controlled in islanding condition. In this paper, we present a Lead-Lag optimal controller for controlling one of the most important types of fuel cell, namely proton exchange membrane fuel cell (PEMFC) in islanding mode operation for reducing PQ problems and voltage control. At first, the introduction and implementation of the PEMFC is present and next, during system load variations the proposed controller is designed. The controller should be designed against the demand load variations of fuel cell. Here, the lead-lag controller is used when its coefficients are optimized based on honey bee mating optimization (HBMO). In order to use this algorithm, at first, the problem is written as an optimization problem which includes the objective function and constraints, and then to achieve the most desirable controller, HBMO algorithm is applied to solve the problem. Simulation results are done for various loads in time domain, and the results show the efficiency of the proposed controller in contrast to the previous controllers.
Sparse Tabular Multiagent Q-learning ⁄

Multiagent learning problems can in principle be solved by treating the joint actions of the agents as single actions and applying singleagent Q-learning. However, the number of joint actions is exponential in the number of agents, rendering this approach infeasible for most problems. In this paper we investigate a sparserepresentationoftheQ-functionbyonly considering the joint actions in those states in which coordination is actually required. In all other states single-agent Q-learning is applied. This ofiers a compact state-action value representation,withoutcompromisingmuchinterms of solution quality. We have performed experiments in the predator-prey domain and comparedourmethodtoothermultiagentreinforcementlearningmethodswithpromisingresults.
Hybrid Recommender System for Joining Virtual Communities

The variety of social networks and virtual communities has created problematic for users of different ages and preferences; in addition, since the true nature of groups is not clearly outlined, users are uncertain about joining various virtual groups and usually face the trouble of joining the undesired ones. As a solution, in this study, we introduced the hybrid community recommender system which offers customized recommendations based on user preferences. Although techniques such as content based filtering and collaborative filtering methods are available, these techniques are not enough efficient and in some cases make problems and bring limitations to users. Our method is based on a combination of content based filtering and collaborative filtering methods. It is created by selecting related features of users based on supervised entropy as well as using association rules and classification method. Supposing users in each community or group share similar characteristics, by hierarchical clustering, heterogeneous members are identified and removed. Unlike other methods, this is also applicable for users who have just joined the social network where they do not have any connections or group memberships. In such situations, this method could still offer recommendations.
A Framework for Healthcare Everywhere: BMI Prediction Using Kinect and Data Mining Techniques on Mobiles

Recently, health-care has become a popular issue. Having a good physique is also commonly regarded as important for being healthy. For evaluating our body status, the Body Mass Index (BMI) is a widely used indicator. However, calculating BMI is inconvenient and requires the physical measuring of people's weights and heights. In this paper, we are interested in building a mobile-based BMI prediction system using Kinect and data mining techniques so that everybody can easily monitor their BMI everywhere by taking a snapshot of their face. The rationale behind this is the intuition that there is a correlation between the shape of one's face and one's BMI values, which people often act on when noticing a friend has either gained or lost weight. Through the evaluations of 50 volunteers, we show that the rules for training BMI prediction match with people's common intuitions.
Postgraduate Education and Human Capital Productivity in Japan

In advanced countries, including Japan, the number of workers with postgraduate degrees is increasing. These highly educated workers are important contributors to innovation. This paper, using published data from the Employment Status Survey, estimates standard wage functions to investigate the effects of postgraduate degree on productivity and the rate of return on postgraduate education. According to the analysis, wage premium for postgraduates relative to undergraduates is about 20% in Japan, which is comparable to the figures found in the United States and the United Kingdom. The premium is larger for female employees. Wage reduction after age 60 is smaller, and retirement age is higher for workers with postgraduate education. Considering the trend toward advanced technology and the growing demand for human capital, postgraduate education is becoming important to vitalize the Japanese economy. At the same time, expansion of postgraduate education may contribute to narrowing the wage gap between male and female workers and increasing labor force participation of elderly people.
Wage Losses for Displaced Workers: Is It Really the Firm That Matters?

Many studies have shown that displaced workers experience substantial wage reductions that are strongly related to their predisplacement job tenure and experience. This paper is an assessment of whether firm closures or downturns in more broadly defined labor markets drive the extreme wage losses of high tenure, high experience displaced workers. I show that the relatively high wage losses of high experience displaced workers are primarily due to their heightened sensitivity to downturns in their state, industry, and occupation. However, the large wage losses of high tenure displaced workers are not explained by their sensitivity to these local downturns. One interpretation of these facts is that firm specialization is linked primarily to firm-specific experience, but not to general labor market experience.
Low power SC CMFB folded cascode OTA optimization

This paper describes a design of a switched capacitor common mode feedback (SC CMFB) folded cascode operational transconductance amplifier for low power and high-speed sigma-delta modulators. An algorithmic driven methodology is developed ending to the optimal transistor geometries. Using a 0.35 mum CMOS process, the OTA circuit has been designed to achieve 82.94 dB DC gain, 526 MHz unity-gain frequency, 560 V/mus slew rate with 1.8 V power supply voltage and a power consumption of only 1.19 mW. The simulated OTA circuit has been applied in a SC integrator to illustrate the versatility of the circuit.
Reflections of Reality in

109 Abstract. There has been considerable debate about the perspectival/optical bases of the naturalism pioneered by Robert Campin and Jan van Eyck. Their paintings feature brilliantly rendered convex mirrors, which have been the subject of much comment, especially iconographical. David Hockney has recently argued that the Netherlandish painters exploited the image-forming capacities of concave mirrors. However, the secrets of the images within the painted mirrors have yet to be revealed. By means of novel, rigorous techniques to analyze the geometric accuracy of the mirrors, unexpected findings emerge, which radically affect our perception of the way in which the paintings have been generated. The authors focus on Jan van Eyck’s Arnolfini Portrait, and the Heinrich von Werl Triptych, here reattributed to Robert Campin. The accuracy of the images in the convex mirrors depicted in these paintings is assessed by applying mathematical techniques drawn from computer vision. The proposed algorithms also allow the viewer to “rectify” the image in the mirror so that it becomes a central projection, thus providing a second view from the back of the painted room. The plausibility of the painters’ renderings of space in the convex mirrors can then be assessed. The rectified images can be used for purposes of three-dimensional reconstruction as well as for measuring accurate dimensions of objects and people. The surprising results presented in this article cast a new light on the understanding of the artists’ techniques and their optical imitation of seen things and potentially require a rethinking of the foundations of Netherlandish naturalism. The results also suggest that the von Werl panels should be reinstated as autograph works by Campin. Additionally, this research represents a further attempt to build a constructive dialogue between two very different disciplines: computer science and art history. Despite their fundamental differences, the two disciplines can learn from and be enriched by each other.
An engineered mammalian band-pass network

Gene expression circuitries, which enable cells to detect precise levels within a morphogen concentration gradient, have a pivotal impact on biological processes such as embryonic pattern formation, paracrine and autocrine signalling, and cellular migration. We present the rational synthesis of a synthetic genetic circuit exhibiting band-pass detection characteristics. The components, involving multiply linked mammalian trans-activator and -repressor control systems, were selected and fine-tuned to enable the detection of ‘low-threshold’ morphogen (tetracycline) concentrations, in which target gene expression was triggered, and a ‘high-threshold’ concentration, in which expression was muted. In silico predictions and supporting experimental findings indicated that the key criterion for functional band-pass detection was the matching of componentry that enabled sufficient separation of the low and high threshold points. Using the circuitry together with a fluorescence-encoded target gene, mammalian cells were genetically engineered to be capable of forming a band-like pattern of differentiation in response to a tetracycline chemical gradient. Synthetic gene networks designed to emulate naturally occurring gene behaviours provide not only insight into biological processes, but may also foster progress in future tissue engineering, gene therapy and biosensing applications.
Named Entity Recognition Using Hybrid Machine Learning Approach

This paper presents a hybrid method using machine learning approach for named entity recognition (NER). A system built based on this method is able to achieve reasonable performance with minimal training data and gazetteers. The hybrid machine learning approach differs from previous machine learning-based systems in that it uses maximum entropy model (MEM) and hidden Markov model (HMM) successively. We report on the performance of our proposed NER system using British National Corpus (BNC). In the recognition process, we first use MEM to identify the named entities in the corpus by imposing some temporary tagging as references. The MEM walkthrough can be regarded as a training process for HMM, as we then use HMM for the final tagging. We show that with enough training data and appropriate error correction mechanism, this approach can achieve higher precision and recall than using a single statistical model We conclude with our experimental results that indicate the flexibility of our system in different domains
Reduced-Complexity Turbo Equalization with Multiple Transmit and Receive Antennas over Multipath Fading Channels

| In this paper, nite{length multi{input multi{ output (MIMO) channel{shortening lters are derived and used as preelters for turbo equalizers. These preelters reduce the number of turbo equalizer states signiicantly while maximizing the SNR at its input. Achievable bit error rates for the original and shortened MIMO channel are compared via simulation in a multipath fading environment. I. Introduction Future wireless communication systems promise to ooer a variety of multimedia services which require reliable transmission at high data rates. In order to achieve such high data rates, transmission over multiple{input multiple{output (MIMO) channels is considered. The capacity of MIMO channels shows enormous gains over their constituent single{input single{output (SISO) channels. The outage capacity of a MIMO channel can be shown to increase linearly with the number of transmit antennas as long as there are at least as many receive antennas as transmit antennas. On frequency selective channels, an increasing channel memory leads to a roughly logarithmic growth of the channel capacity. A considerable fraction of the capacity can be achieved using transmit diversity with space-time coded modulation as proposed in TSC98]. However, on frequency{selective channels , an equalizer has to be applied to resolve time dispersion. In wireless communications, we prefer to use a BCJR-MAP or Viterbi equalizer which does not suuer from noise enhancement or error propagation as linear or DFE equalizers do, respectively. The complexity of a BCJR-MAP or Viterbi equalizer, measured by the number of states, increases exponentially with the number of transmit antennas and the channel memory. Even for moderate time dispersion the complexity becomes prohibitive for practical systems, especially when high{order constellations (e.g. 8{PSK) are used to achieve higher spectral eeciencies. The equalization complexity can be reduced by standard reduced complexity methods as described in EQ88]. However, such methods require that the channels from all transmit antennas to all receive antennas are minimum phase. In general, it is not possible to design a nite{length MIMO preelter to meet the minimum{phase requirement exactly. In this paper, we follow a diierent approach. We propose to design a set of receive lters by which the energy of the channel taps is concentrated in a small number of adjacent taps. The equalizer sees a shortened channel and therefore needs to
Linear feature extraction techniques for object recognition: study of PCA and ICA

In this paper, we have compared linear techniques for object recognition. 3D object recognition is the process of matching an object to a scene description to determine the objects identity and / or its pose in space. Several face recognition techniques uses unsupervised statistical methods. The basic idea is to compute the principal components as sequence of image vectors incrementally, without estimating the covariance matrix and at the same time transforming these principal components to the independent directions that maximize the non–Gaussianity of the source. We illustrate the potential of PCA and ICA on a database of 1440 images of 20 different objects captured by CCD camera. The excellent recognition rates achieved in all the performed experiments indicate that the present method is well suited for appearance-based 3D object recognition and pose estimation.
A Cut-Free ExpTime Tableau Decision Procedure for the Logic Extending Converse-PDL with Regular Inclusion Axioms

We give the first cut-free ExpTime (optimal) tableau decision procedure for the logic CPDLreg, which extends Converse-PDL with regular inclusion axioms characterized by finite automata. The logic CPDLreg is the combination of Converse-PDL and regular grammar logic with converse. Our tableau decision procedure uses global state caching and has been designed to increase efficiency and allow various optimization techniques, including on-the-fly propagation of local and global (in)consistency.