Miracle-dz/UniFAR-FTUs huggingface dataset:
The development of core standards for editing in South Africa

Abstract South African editors2 currently work within a highly unregulated industry. One factor contributing to this lack of regulation is the absence of clear standards that define the role of the editor and demarcate the tasks and skills required to work in the various sectors of the industry. As a solution to this (and other problems faced by South African editors), this article postulates that it is possible to develop a set of core standards that are specific to the South African editing industry. The development of such a set of standards could contribute towards the raised professional status of editors in South Africa, the development of clear and appropriate forms of regulation (and accreditation), and the standardisation of editor training. This article reports on the findings of an empirical investigation that surveyed South African editors from all sectors of the industry on the tasks and skills associated with their everyday work. Various statistical analyses are utilised to extrapolate a lis.
Reinforcement learning in swarms that learn

This paper introduces an approach to reinforcement learning by cooperating agents using a variation of the actor critic method. This is made possible by considering behavior patterns of swarms in the context of approximation spaces. Rough set theory introduced by Zdzislaw Pawlak in 1982 provides a ground for deriving pattern-based rewards within approximation spaces. The framework provided by an approximation space makes it possible to derive pattern-based reference rewards used to estimate action preferences. Approximation spaces are used to derive action-based reference rewards at the swarm intelligence level. Two different forms of the actor critic reinforcement learning method are considered as a part of a study of learning in real-time by a swarm. The contribution of this article is the presentation of a new actor critic method defined in the context of approximation spaces. An ecosystem designed to facilitate study of reinforcement learning by swarms is briefly described. In addition, the results of ecosystem experiments for two forums of the actor critic method are given.
IFT20 is required for opsin trafficking and photoreceptor outer segment development

Intraflagellar transport (IFT) 20 is required for the formation of photoreceptor outer segments, and deletion of this gene in mature cells leads to opsin accumulation in the cell body. IFT20 interacts with opsin photopigments both in the context of the IFT particle and independent of the particle, suggesting that opsin receptors are cargo for the IFT system.
Erasing the Past: A New Identity for the Damoclean Pathogen Causing South American Leaf Blight of Rubber

Background South American leaf blight (SALB) of rubber has been the main constraint to production in its neotropical centre of origin since commercial plantations were first established. The fungal causal agent was identified and described more than a century ago but its precise placement within the Ascomycota still remains uncertain. Indeed, such is the ambiguity surrounding the pathogen that each of the spore morphs would, according to their present classification, be placed in different ascomycete families: the Microcyclus sexual morph in the Planistromellaceae and the two purported asexual morphs - Fusicladium and Aposphaeria – in the Venturiaceae and Lophiostomataceae, respectively. Given the historical importance of the fungus and the ever-menacing threat that it poses to rubber production in the Palaeotropics – and, thus to the rubber industry and to the global economy – its phylogeny, as well as its biology, should be resolved as a matter of urgency. Methods and Results Here, six genomic regions (LSU rRNA, mtSSU, MCM7, EF-1α, Act and ITS) were used for reconstructing the molecular phylogeny of the SALB fungus based on material collected throughout Brazil. The analyses support the classification of the fungus in the family Mycosphaerellaceae s. str. (Capnodiales, Dothideomycetes) and place it firmly within the clade Pseudocercospora s. str. , now accepted as one of the distinct genera within Mycosphaerellaceae. The new combination Pseudocercospora ulei is proposed and the life cycle of the fungus is confirmed, based on both experimental and phylogenetic evidence, with the Aposphaeria morph shown to have a spermatial rather than an infective-dispersal function. Conclusions Because the phylogeny of the SALB fungus has now been clarified, new insights of its epidemiology and genomics can be gained following comparison with closely-related, better-researched crop pathogens.
Thermal Performance Enhancement of Adaptive Optics by Use of a Deformable Secondary Mirror

An adaptive optics system is being built for the 6.5 m Multiple Mirror Telescope (MMT) conversion on Mount Hopkins for diffraction‐limited observations in the near‐infrared. At the heart of the system is a deformable secondary mirror which introduces corrections to the optical wavefront. By compensating these errors at the telescope’s secondary, the system has been optimized for low thermal emissivity and high photon throughput. The scientific productivity of the facility will thereby be enhanced in comparison with a telescope equipped with more conventional adaptive optics. The Large Binocular Telescope under construction on Mount Graham will also use adaptive secondary mirrors. This paper explores the benefit to both facilities in terms of the integration time required to achieve a given signal‐to‐noise ratio. The gain is found to be substantial in the photometric bands K, L, M, and N.
Expressway Management Risk Evaluation Based on Fuzzy Neural Networks

On basis of management risks evaluation system for expressway projects, a risk evaluation model, which was based on the improved BP neural network and fuzzy synthetic evaluation, was established, trained and simulated in this study. The numerical value of various risky factors was confirmed using fuzzy synthetic evaluation method. Furthermore, the neural network model was designed and its BP algorithm was improved to overcome the limitation of multiple-dimensions input. This evaluation based on the fuzzy neural network model can objectively evaluate management risks and effectively avoid the subjectivity of confirming the risky factors’ weight. The fuzzy neural network model can help the managers to make sound decision on managing the risks in advance and to obtain an optimum solution.
CIRCUIT TECHNIQUES FOR POWER-AWARE MICROPROCESSORS BY OĞUZ ERGİN

Energy dissipation of the superscalar microprocessors increases drastically with increasing clock frequencies and increasing number of transistors. Reducing energy dissipation of a processor is important for further pushing performance and to avoid increasing cooling costs. In this thesis we introduce dissipate-on-match comparators and present zero-byte encoding to reduce energy dissipation in the superscalar datapath. Modern superscalar datapaths use aggressive execution reordering to exploit instruction level parallelism (ILP). Comparators, either explicit or embedded into contentaddressable logic, are used extensively throughout such designs to implement several key out-of-order execution mechanisms and support the memory hierarchy. The traditional comparator designs dissipate energy on a mismatch in any bit position. As mismatches occur with a much higher frequency than matches in many situations, considerable improvements in energy dissipation can be gained by using comparators that dissipate energy predominantly on a full match and dissipate little or no energy on partial or complete mismatches. We introduce a series of dissipate-on-match comparator designs, including designs for comparing long arguments. Further we show how comparators, used in modern datapaths, can be chosen and organized judiciously based on the microarchitectural-level statistics to minimize the energy dissipation. We use the actual layout data and realistic bit patterns of the comparands (obtained from the simulated execution of SPEC 2000
Minimum word error training of long short-term memory recurrent neural network language models for speech recognition

This paper describes minimum word error (MWE) training of recurrent neural network language models (RNNLMs) for speech recognition. RNNLMs are usually trained to minimize a cross entropy of estimated word probabilities against the correct word sequence, which corresponds to maximum likelihood criterion. However, this training does not necessarily maximize a performance measure in a target task, i.e. it does not minimize word error rate (WER) explicitly in speech recognition. To solve such a problem, several discriminative training methods have already been proposed for n-gram language models, but those for RNNLMs have not sufficiently investigated. In this paper, we propose a MWE training method for RNNLMs, and report significant WER reductions when we applied the MWE method to a standard Elman-type RNNLM and a more advanced model, a Long Short-Term Memory (LSTM) RNNLM. We also present efficient MWE training with N-best lists on Graphics Processing Units (GPUs).
Generalized lifting for sparse image representation and coding

This thesis investigates the use of the Generalized Lifting method for the sparse representation of images, with application to lossy compression. The Generalized Lifting method is a signal decomposition scheme with perfect reconstruction that enables the use of non-linear operators. In this thesis, the application of the method is conducted in the wavelet domain, with the objective of increasing the sparsity of the wavelet signal through the use of a non-linear, invertible, context-based, Generalized Lifting mapping operation. The idea of using the Generalized Lifting method in the wavelet domain is similar to those of the adaptive directional lifting methods and bandelets. Three approximations to the Generalized Lifting decomposition are studied: In the rst case, in which the image to be coded belongs to a class; the pdf is estimated for the class of images, and this statistical model is used to decompose the image. The second approach is conceived for generic images. The idea is to perform a global estimation of the pdf of the image, and to use this estimation for the Generalized Lifting decomposition. Most of the energy in the wavelet subbands is located along the contours of the image; therefore, a set of context-based, contrast-invariant models of contours has been devised as the realization of the pdf that is used to decompose the image. The third solution is a local adaptive pdf estimation approach, also devised for generic images, in which a simpli ed pdf model is used to grasp the local dynamics of the image subbands. The Generalized Lifting signal is encoded with EBCOT, a context-based, bit-plane entropy coder that is the core of the JPEG2000 standard. The changes in the statistics of the wavelet signal produced by the eect of the Generalized Lifting mapping, and their consequences for the performance of the entropy coder are also investigated.
Energy estimation based on hierarchical bus models for power-aware smart cards

Smart cards are one of the smallest computing platforms in use today. Due to their limited resources applications are often simple and less complex. High performance 32-bit smart cards, which were introduced by several vendors in the last years, allow the implementation of complex applications on smart cards. Additional to the high performance processor cores these smart cards contain coprocessors to reach the performance and power consumption goals. The interface between the processor and the coprocessor influences the performance and power consumption and should be evaluated early in the design process. We propose a hierarchical bus model for system-level smart card design which supports accurate energy dissipation estimation. The bus models have been implemented in systemC 2.0 at transaction level layer one (cycle accurate) and layer two (timing estimation). We evaluate accuracy and simulation performance of the models and show their usage as bus functional models for a smart card application.
ADAPTIVE ORTHOGONAL SIGNAL DECOMPOSITION BASED ON EMPIRICAL MODE DECOMPOSITION AND EMPIRICAL WAVELET TRANSFORM

Empirical mode decomposition (EMD) and Empirical wavelet transform (EWT) are recently developed adaptive signal processing tools. These techniques decompose a signal accordingly to its contained information. The main issue with EMD is its lack of theory and in case of EWT a prior knowledge of the signal is required. IMF's obtained as a result of applying EMD are quasi-orthogonal . This paper suggest how to overcome these difficulties. For this Gram-Schmidt orthogonalization procedure is applied to the IMF's generated. The number of orthogonal components obtained determines the number of modes for applying EWT to the same signal.
Oxidative damage induced by cigarette smoke exposure in mice: impact on lung tissue and diaphragm muscle,

OBJECTIVE: To evaluate oxidative damage (lipid oxidation, protein oxidation, thiobarbituric acid-reactive substances [TBARS], and carbonylation) and inflammation (expression of phosphorylated AMP-activated protein kinase and mammalian target of rapamycin [p-AMPK and p-mTOR, respectively]) in the lung parenchyma and diaphragm muscles of male C57BL-6 mice exposed to cigarette smoke (CS) for 7, 15, 30, 45, or 60 days. METHODS: Thirty-six male C57BL-6 mice were divided into six groups (n = 6/group): a control group; and five groups exposed to CS for 7, 15, 30, 45, and 60 days, respectively. RESULTS: Compared with control mice, CS-exposed mice presented lower body weights at 30 days. In CS-exposed mice (compared with control mice), the greatest differences (increases) in TBARS levels were observed on day 7 in diaphragm-muscle, compared with day 45 in lung tissue; the greatest differences (increases) in carbonyl levels were observed on day 7 in both tissue types; and sulfhydryl levels were lower, in both tissue types, at all time points. In lung tissue and diaphragm muscle, p-AMPK expression exhibited behavior similar to that of TBARS. Expression of p-mTOR was higher than the control value on days 7 and 15 in lung tissue, as it was on day 45 in diaphragm muscle. CONCLUSION: Our data demonstrate that CS exposure produces oxidative damage, not only in lung tissue but also (primarily) in muscle tissue, having an additional effect on respiratory muscle, as is frequently observed in smokers with COPD.
Novelty generating machine

Novelty detection is one of primary tasks in data mining and machine learning. The task is to differentiate unseen outliers from normal patterns. Though novelty detection has been well-studied for many years and has found a wide range of applications, identifying outliers is still very challenging because of the absence or scarcity of outliers. We observe several characteristics of outliers and normal patterns. First, normal patterns are usually grouped together and form some clusters in high density regions of the data. Second, outliers are very different from the normal patterns, and in turn these outliers are far away from the normal patterns. Third, the number of outliers is very small compared with the size of the dataset. Based on these observations, we can envisage that the decision boundary between outliers and normal patterns usually lies in some low density regions of the data, which is referred to as cluster assumption. The resultant optimization problem is in form of a mixed integer programming. Then, we present a cutting plane algorithm together with multiple kernel learning techniques to solve its convex relaxation. Moreover, we make use of the scarcity of outliers to find a violating solution in cutting plane algorithm.
Active Learning With Drifting Streaming Data

In learning to classify streaming data, obtaining true labels may require major effort and may incur excessive cost. Active learning focuses on carefully selecting as few labeled instances as possible for learning an accurate predictive model. Streaming data poses additional challenges for active learning, since the data distribution may change over time (concept drift) and models need to adapt. Conventional active learning strategies concentrate on querying the most uncertain instances, which are typically concentrated around the decision boundary. Changes occurring further from the boundary may be missed, and models may fail to adapt. This paper presents a theoretically supported framework for active learning from drifting data streams and develops three active learning strategies for streaming data that explicitly handle concept drift. They are based on uncertainty, dynamic allocation of labeling efforts over time, and randomization of the search space. We empirically demonstrate that these strategies react well to changes that can occur anywhere in the instance space and unexpectedly.
A PRACTICIONER'S GUIDE TO EVALUATING THE IMPACTS OF LABOR MARKET PROGRAMS *

1. Intr oduction This note provides an introduction to the impact evaluation of labor market programs, with particular reference to developing countries. Its focus is on the main issues that need to be considered when planning an impact evaluation, including the importance of rigorous design for an evaluation, and on the statistical techniques used to estimate program impacts. To help the exposition, a prototype of a training program is referred to intermittently throughout the note. This hypothetical training program, which we call Get-toWork, provides training to the unemployed to help them find work. The note describes some general issues that are important for any impact evaluation of employment programs, both in the design and analysis stages, regardless of the specific evaluation techniques used. It then describes the main evaluation techniques, including the data requirements and the main assumptions invoked by each.
Choroidal excavation with polypoidal choroidal vasculopathy: a case report

Purpose This is a report of a case of choroidal excavation accompanied by polypoidal choroidal vasculopathy (PCV) and retinal pigment epithelium detachment (PED). Methods A 57-year-old Japanese woman who had begun complaining of metamorphopsia in her left eye 7 months earlier underwent spectral-domain optical coherence tomography (SD-OCT), fluorescein angiography (FA), and indocyanine green angiography (IA), as well as a routine ophthalmological examination. Results The patient’s intraocular pressure, visual acuity, and visual field were within normal range. Ophthalmoscopy revealed a serous macular detachment, soft drusen, exudates, and a reddish-orange elevated lesion in the macula of the left eye. The right eye was normal. SD-OCT revealed two lesions in the left eye. One was a PED accompanied by a notch sign, and the other was a choroidal excavation. Additionally, FA revealed a window defect in the PED, and IA showed typical PCV. Three monthly injections of antivascular endothelial growth factor preserved visual acuity, but failed to have any visible effect on the lesion during the 6-month follow up period. Conclusions This is the first report of choroidal excavation accompanied by PED and PCV. The data suggest that choroidal excavation may be associated with various changes that have not been previously reported. Careful observation of such cases may therefore be necessary.
Human Urocortin 2, a Corticotropin-Releasing Factor (CRF)2 Agonist, and Ovine CRF, a CRF1 Agonist, Differentially Alter Feeding and Motor Activity

Two corticotropin-releasing factor (CRF) receptor families have been identified (CRF1 and CRF2). Whereas anxiogenic-like roles for the CRF1 receptor have been identified, behavioral functions of the CRF2 receptor remain obscure. Urocortin 2 (Ucn 2), a CRF-related peptide that selectively binds CRF2 receptors, was recently identified and recognized for its central anorectic properties. The present study tested the hypothesis that the anorexigenic mode of action of Ucn 2 differed from that of ovine CRF (oCRF), a preferential CRF1 receptor agonist. The behavioral effects of intracerebroventricular administration of Ucn 2 were compared with those of oCRF in nondeprived male Wistar rats (n = 102). Ucn 2 reduced 6-h food and water intake at doses that did not induce visceral illness (0.1, 1, and 10 μg), as indicated by kaolin intake. Ucn 2 retained its potent anorectic activity in rats receiving a highly palatable cafeteria diet, preferentially reducing intake of carbohydrate (CHO)-rich items while sparing intake of mixed-fat/CHO items. In contrast to Ucn 2, oCRF (10 μg) suppressed 6-h intake of cafeteria diet-fed rats without regard to macronutrient composition. Rather, oCRF most potently suppressed intake of preferred food items. Whereas oCRF had short-onset motor-activating effects, Ucn 2 had nondose-dependent, delayed-onset motor-suppressing effects. Thus, central infusion of a CRF2 receptor agonist suppressed intake of both bland and palatable diets without inducing behavioral arousal or malaise, and the profile of anorexigenic effects qualitatively differed from those of a CRF1 receptor agonist. The results suggest the existence of distinct forms of CRF1- and CRF2-mediated anorexia.
Actom sequence models for efficient action detection

We address the problem of detecting actions, such as drinking or opening a door, in hours of challenging video data. We propose a model based on a sequence of atomic action units, termed “actoms”, that are characteristic for the action. Our model represents the temporal structure of actions as a sequence of histograms of actom-anchored visual features. Our representation, which can be seen as a temporally structured extension of the bag-of-features, is flexible, sparse and discriminative. We refer to our model as Actom Sequence Model (ASM). Training requires the annotation of actoms for action clips. At test time, actoms are detected automatically, based on a non parametric model of the distribution of actoms, which also acts as a prior on an action's temporal structure. We present experimental results on two recent benchmarks for temporal action detection, “Coffee and Cigarettes” [12] and the dataset of [3]. We show that our ASM method outperforms the current state of the art in temporal action detection.
UN-sensored very high resolution Land-Cover mapping

Land-Cover databases (LC-DB) are mandatory for environmental purposes, but need to be regularly updated to provide robust and instructive spatial indicators. Moreover, an increasing number of sensors, such as optical and SAR satellite images or Lidar point cloud, allow to cover large areas regularly, and with a very high precision. Thus, automatic methods have to be developed to take into account the complementarity of available observations. In this paper, several fusion methods are proposed and introduced in an existing Land-Cover mapping framework. Those methods are compared on several scenarii (based on optical, SAR and Lidar datasets), and evaluated thanks to a very high resolution LC-DB.
Improving Performance Analysis of Software System Versions Using Change-Based Test Selection

The detection of performance bugs by measurements is very time-consuming since measurements must be repeated often in order to get reliable results. Especially in performance analysis of software system versions (PeASS), where performance bugs are detected by comparing the performance of unit tests of diﬀerent revisions, the testing process takes much time. Using change-based test selection enables to execute only those performance unit tests which may have changed results. This leads to a signiﬁcant reduction of test execution time.