Miracle-dz/UniFAR-FTUs huggingface dataset:
Degradation and regeneration in mc‐Si after different gettering steps

Light and elevated temperature induced degradation (LeTID) affects significantly the performance of multicrystalline (mc) Si passivated emitter and rear cell (PERC) solar cells, and underlying mechanisms of LeTID are still unknown. In this work LeTID and following regeneration of an industrial mc‐Si PERC solar cell is compared to differently processed minority charge carrier lifetime samples under illumination (1 sun) and elevated temperature (75 °C). LeTID on cell level reveals the same kinetics compared to lifetime samples. Varying the processing sequence has a significant effect on LeTID of lifetime samples. Ungettered samples with fired SiNx:H surface passivation show a very strong LeTID and regeneration effect, with degradation kinetics being similar for all wafer areas irrespective of initial material quality. In contrast, regeneration sets in earlier in good quality areas. Differently gettered samples with lower contamination level than ungettered samples are less sensitive to LeTID, while overall degradation and regeneration behavior is strongly influenced by applied gettering sequences. Al‐gettered samples show a more pronounced degradation effect than P‐gettered samples, leading to the assumption that P‐gettering is more effective in the reduction of LeTID sensitive defects. If the gettering step is less effective, in lifetime samples after degradation a beginning regeneration effect could be observed. A model is presented, describing LeTID in boron as well as gallium doped mc‐Si being based on impurities that can be gettered and redistributed during high temperature steps. Using this experimental approach helps to clarify the underlying mechanisms of LeTID and leads to a better understanding of degradation and regeneration mechanisms in mc Si. Copyright © 2016 John Wiley & Sons, Ltd
An algebraic parameter estimation approach to the adaptive observer-controller based regulation of the boost converter

In this article, a new approach is proposed for the problem of output voltage reference trajectory tracking in a boost converter with unknown, piecewise constant, load resistance values, under the restriction of noisy output voltage measurements alone. We use a nonlinear algebraic parameter estimation approach for the fast computation of the actual value of the stepwise changing load. With this information, a reduced order nonlinear observer for the inductor current is updated as well as a static passive output feedback control law derived on the basis of the exact tracking error dynamics and Lyapunov stability considerations. The average nonlinear adaptive observer-controller scheme is implemented on the switch regulated converter using the averaging features of a Sigma - Delta modulator.
The relevance of personality assessment in estimating the risk of onset and the outcome of major depressive disorder

In the past two decades, numerous studies have focused on the relationship between the psychobiological model of temperament and character and the development and evolution of major depressive disorder. This interest has been generated primarily because this particular model was developed as a tool for a comprehensive diagnosis of mental disorders. Such a diagnosis model, based on fewer diagnostic categories and a more phenomenological and person oriented approach seems to be supported by more recent research. The aim of this paper was to review the latest developments in this area, but in the context of the initial development of the psychobiological model of temperament and character, i.e. as a tool for the comprehensive diagnosis of depressed individuals. Data published so far supports the following observations: (1) high harm avoidance and low self-directedness are risk factors for the development of major depressive disorder, but further research is needed to clearly establish the role of the other dimensions or their facets as predictors for the development of a depressive episode; (2) although some evidence has been obtained so far regarding the use of harm avoidance, novelty seeking, reward dependence and cooperativeness in predicting treatment response in major depressive disorder, further research is needed to clarify and/or to replicate these findings; and (3) data on temperament and character dimensions related to relapse in major depressive disorder are insufficient, although some evidence has been brought to support the hypothesis that high harm avoidance scores, and low self-directedness and novelty seeking scores might serve as predictors; further prospective studies need to be carried out to establish their utility in this respect.
GreenSlot: Scheduling energy consumption in green datacenters

In this paper, we propose GreenSlot, a parallel batch job scheduler for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenSlot predicts the amount of solar energy that will be available in the near future, and schedules the workload to maximize the green energy consumption while meet- ing the jobs' deadlines. If grid energy must be used to avoid dead- line violations, the scheduler selects times when it is cheap. Our results for production scientific workloads demonstrate that Green-Slot can increase green energy consumption by up to 117% and decrease energy cost by up to 39%, compared to a conventional scheduler. Based on these positive results, we conclude that green datacenters and green-energy-aware scheduling can have a significant role in building a more sustainable IT ecosystem.
E-cadherin regulates metastasis of pancreatic cancer in vivo and is suppressed by a SNAIL/HDAC1/HDAC2 repressor complex.

BACKGROUND & AIMS Early metastasis is a hallmark of pancreatic ductal adenocarcinoma and responsible for >90% of pancreatic cancer death. Because little is known about the biology and genetics of the metastatic process, we desired to elucidate molecular pathways mediating pancreatic cancer metastasis in vivo by an unbiased forward genetic approach. METHODS Highly metastatic pancreatic cancer cell populations were selected by serial in vivo passaging of parental cells with low metastatic potential and characterized by global gene expression profiling, chromatin immunoprecipitation, and in vivo metastatic assay. RESULTS In vivo selection of highly metastatic pancreatic cancer cells induced epithelial-mesenchymal transition (EMT), loss of E-cadherin expression, and up-regulation of mesenchymal genes such as Snail. Genetic inactivation of E-cadherin in parental cells induced EMT and increased metastasis in vivo. Silencing of E-cadherin in highly metastatic cells is mediated by a transcriptional repressor complex containing Snail and histone deacetylase 1 (HDAC1) and HDAC2. In line, mesenchymal pancreatic cancer specimens and primary cell lines from genetically engineered Kras(G12D) mice showed HDAC-dependent down-regulation of E-cadherin and high metastatic potential. Finally, transforming growth factor beta-driven E-cadherin silencing and EMT of human pancreatic cancer cells depends on HDAC activity. CONCLUSIONS We provide the first in vivo evidence that HDACs and Snail play an essential role in silencing E-cadherin during the metastatic process of pancreatic cancer cells. These data link the epigenetic HDAC machinery to EMT and metastasis and provide preclinical evidence that HDACs are promising targets for antimetastatic therapy.
Convex Combination and Covariance Intersection Algorithms in Distributed Fusion

In a distributed estimation or tracking system, local estimates are first generated from individual sensors. The state estimates of associated objects are then fused to generate the global estimates. The fusion algorithm has to deal with correlated estimation errors due to common past information or common process noise. Most approaches to estimation fusion use a convex combination of the local estimates to minimize the mean square error. Recently an alternative approach to fusion, covariance intersection, has been proposed and is claimed to be more robust. This paper provides a set-theoretic interpretation of the covariance intersection approach and develops a tighter bound for the estimation error. Numerical results are used to compare the performance of the different fusion approaches, and the probability for the bound to be true is computed for some examples. Critical parameters that affect the performance of a fusion algorithm are identified.
A multiresolution enhancement to generic classifiers of subcellular protein location images

We propose an algorithm for the classification of fluorescence microscopy images depicting the spatial distribution of proteins within the cell. The problem is at the forefront of the current trend in biology towards understanding the role and function of all proteins. The importance of protein subcellular location was pointed out by Murphy, whose group produced the first automated system for classification of images depicting these locations, based on diverse feature sets and combinations of classifiers. With the addition of the simplest multiresolution features, the same group obtained the highest reported accuracy of 91.5% for the denoised 2D HeLa data set. Here, we aim to improve upon that system by adding the true power of multiresolution adaptivity. In the process, we build a system able to work with any feature sets and any classifiers, which we denote as a generic classification system (GCS). Our system consists of multiresolution (MR) decomposition in the front, followed by feature computation and classification in each subband, yielding local decisions. This is followed by the crucial step of combining all those local decisions into a global one, while at the same time ensuring that the resulting system does no worse than a no-decomposition one. On a nondenoised data set and a much smaller number of features (a combination of texture and Zernicke moment features) and a neural network classifier, we obtain a high accuracy of 89.8%, effectively proving that the space-frequency localized information in the subbands adds to the discriminative power of the system
The preparation and properties study of methoxy functionalized silicone-modified epoxy resins

In this work, a series of siloxane epoxy resins (E3074) were synthesized through reaction of poly(methylphenylsiloxane) (DC-3074) with epoxy resin. The chemical structure of the resultant epoxy resin was determined by FTIR, GPC, and epoxy equivalent weight (EEW) tests. The mechanical measurements indicated that the tensile strength of E3074 was lower than that of neat epoxy resin after modification, while their elongations at break improved markedly when compared with that of epoxy resin. SEM revealed that the particle size of silicone had a significant effect on the mechanical properties of the modified epoxy resin. TGA results showed that the thermal stability of E3074 serials was better than that of epoxy resin. The char residue of E3074 at 600°C increased with the incremental content of silicone. DMA tests displayed that the addition of silicone effectively enhanced the damping properties of epoxy resin. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40212.
Path protection with pre-identification for MPLS networks

Current approaches to providing robust network connections which are tolerant to failures involve restoration schemes which mainly focus on reserving backup paths. In this paper we propose a technique for which avoids the extra cost for reserving, by pre-identifying (but not reserving) the backup paths. We present and analyze an algorithm to solve this problem and study a practical special case in detail. Through simulations we show that our model is significantly more cost-efficient than backup path reservation. We also show how this model can fit into the MPLS architecture
Edinburgh’s Submission to all Tracks of the WMT 2009 Shared Task with Reordering and Speed Improvements to Moses

PCT No. PCT/JP90/01270 Sec. 371 Date Apr. 1, 1992 Sec. 102(e) Date Apr. 1, 1992 PCT Filed Oct. 2, 1990 PCT Pub. No. WO91/04751 PCT Pub. Date Apr. 18, 1991.A drug for preventing the absorption of food materials dissolved during digestion, a drug for preventing obesity, a drug for treating hyperlipemia, a drug for treating diabetes mellitus, and a drug for preventing constipation, wherein the flocculant and other auxiliary additives at request, are coated with the aquatic enteric material.
Composite Discriminant Factor analysis

We propose a linear dimensionality reduction method, Composite Discriminant Factor (CDF) analysis, which searches for a discriminative but compact feature subspace that can be used as input to classifiers that suffer from problems such as multi-collinearity or the curse of dimensionality. The subspace selected by CDF maximizes the performance of the entire classification pipeline, and is chosen from a set of candidate subspaces that are each discriminative. Our method is based on Partial Least Squares (PLS) analysis, and can be viewed as a generalization of the PLS1 algorithm, designed to increase discrimination in classification tasks. We demonstrate our approach on the UCF50 action recognition dataset, two object detection datasets (INRIA pedestrians and vehicles from aerial imagery), and machine learning datasets from the UCI Machine Learning repository. Experimental results show that the proposed approach improves significantly in terms of accuracy over linear SVM, and also over PLS in terms of compactness and efficiency, while maintaining or improving accuracy.
Retaining volunteers in volunteer computing projects

Volunteer computing projects (VCPs) have been set up by groups of scientists to recruit members of the public who are asked to donate spare capacity on their personal computers to the processing of scientific data or computationally intensive models. VCPs serve two purposes: to acquire significant computing capacity and to educate the public about science. A particular challenge for these scientists is the retention of volunteers as there is a very high drop-out rate. This paper develops recommendations for scientists and software engineers setting up or running VCPs regarding which strategies to pursue in order to improve volunteer retention rates. These recommendations are based on a qualitative study of volunteers in a VCP (climateprediction.net). A typology of volunteers has been developed, and three particularly important classes of volunteers are presented in this paper: for each type of volunteer, the particular benefits they offer to a project are described, and their motivations for continued participation in a VCP are identified and linked to particular strategies. In this way, those setting up a VCP can identify which types of volunteers they should be particularly keen to retain, and can then find recommendations to increase the retention rates of their target volunteers.
Student diversity in engineering education: Insights to build a glocal program

This article investigates the impact of cultural and professional diversity of students on engineering education. A study of international post-graduate programs is used to obtain insights with reference to student's relationships with colleagues, school staff and faculty, student's cognition about the program and learning process. The article shows how cultural and professional diversity can be addressed by leveraging a strategy based on interdisciplinarity, action learning and open learning environment. Findings are used to envision a model favoring the adoption of a “glocal” approach in the design of engineering programs. The model can be of value for practitioners and educators involved in the creation of multicultural learning initiatives to address the culture-specific needs of students while adhering, at the same time, to the global trends in engineering education.
Multiscale cluster lens mass mapping - I. Strong lensing modelling

We propose a novel technique to refine the modelling of galaxy cluster mass distribution using gravitational lensing. The idea is to combine the strengths of both ‘parametric’ and ‘non-parametric’ methods to improve the quality of the fit. We develop a multiscale model that allows sharper contrast in regions of higher density where the number of constraints is generally higher. Our model consists of (i) a multiscale grid of radial basis functions with physically motivated profiles and (ii) a list of galaxy-scale potentials at the location of the cluster member galaxies. This arrangement of potentials of different sizes allows us to reach a high resolution for the model with a minimum number of parameters. We apply our model to the well-studied cluster Abell 1689. We estimate the quality of our mass reconstruction with a Bayesian Monte Carlo Markov Chain sampler. For a selected subset of multiple images, we manage to halve the errors between the positions of predicted and observed images compared to previous studies. This is due to the flexibility of multiscale models at intermediate scale between cluster and galaxy scale. The software developed for this paper is part of the public lenstool package which can be found at http://www.oamp. fr/cosmology/lenstool.
A Middleware for Managing Non-Functional Requirements in Cloud PaaS

A key aspect of distributed applications in platform-as-a-service clouds revolves around the definition of requirements for quality of service and the most efficient use of the available resources to achieve them. Matching both the requirements and resources represents a unique challenge: While requirements are generally defined in relatively abstract form and defined as system-wide parameters, the resources are concrete, low-level entities that, by themselves, are only of marginal influence on the requirements. In order to establish a relationship between these two requires both the agglomeration of properties into more abstract ones as well as refining requirements in more detailed, concrete ones, both of which can often only be done to a certain degree. This paper presents a vision how both aspects can be brought to the same level and introduces an approach enhancing component-based cloud applications with non-functional properties in the context of an integrated programming model. This allows developers of such applications to formally declare properties that may influence the application during runtime and document the expectations regarding non-functional aspects of the overall system. This can be used to ensure the health of the application and, in case requirements are violated, actions like the deployment of additional resources can be performed in order to alleviate any degradation of its non-functional state, either by evaluating, choosing and performing pre-defined actions automatically or by manual administrative intervention.
On exploiting structure induced when modelling an intersection of cones in conic optimization

Conic optimization is the problem of optimizing a linear function over an intersection of an affine linear manifold with the Cartesian product of convex cones. However, many real world conic models involves an intersection rather than the product of two or more cones. It is easy to deal with an intersection of one or more cones but unfortunately it leads to an expansion in the optimization problem size and hence to an increase in the computational complexity of solving the optimization problem. In this note we discuss how to handle the intersection of two or more cones. In particular we show that the important special case of the intersection of a linear and a quadratic cone can be handled in a computational efficient way.
Cardiac autonomic patterns preceding occasional vasovagal reactions in healthy humans.

BACKGROUND The wide range of clinical presentation of orthostatic vasovagal syncope suggests different underlying changes in the cardiac autonomic modulation. METHODS AND RESULTS To evaluate the beat-by-beat modifications in the neural control of heart period preceding a syncopal event, we studied RR interval variability in 22 healthy subjects who experienced fainting for the first time during a 90 degrees head-up tilt and in 22 control subjects by means of time-variant power spectral analysis. Sympathetic and vagal modulations to the sinoatrial node were assessed by the normalized power of the low-frequency (LF, approximately 0.1-Hz) and high-frequency (HF, approximately 0.25-Hz) oscillatory components of RR variability. When the patients were supine, no differences were observed in the hemodynamic and spectral parameters of the 2 groups. During the tilt procedure, RR, LFNU, and HFNU (NU=normalized units) values were relatively stable in control subjects. During early tilt (T1), subjects with syncope had reduced RR intervals compared with control subjects. In 13 subjects with syncope, RR decreased while LFNU and LF/HF increased in the last minute of tilt before syncope (T2). Conversely, in the remaining 9 fainters, LFNU and LF/HF decreased from T1 to T2 and HFNU increased slightly. CONCLUSIONS Two different patterns may be recognized in the cardiac autonomic changes preceding an occasional vasovagal event, namely, one characterized by a progressive increase of the marker of cardiac sympathetic modulation up to the onset of syncope, the other by a sympathetic inhibition with an impending vagal predominance. The recognition of different pathophysiological mechanisms in fainters may have important therapeutic implications.
On the Use of Adaptive Nearest Neighbors for Missing Value Imputation

A popular nonparametric treatment of missing value imputation uses methods based on k-nearest neighbors, where the number k of nearest neighbors is fixed without any consideration of the local features of missing values. This article proposes an alternative imputation method based on adaptive nearest neighbors, which takes into account the local features of the data. The proposed method adapts the number of neighbors in imputing the missing values according to the location of the missing values. Efficiency evaluation is then gauged through simulation studies using both simulated and real data. It is shown that the proposed method has distinct advantages over the imputation method based on k-nearest neighbors.
PATH PLANNING OF COOPERATIVE MANIPULATORS USING COEVOLUTIONARY GENETIC ALGORITHM by

This paper presents a new path planning approach using Coevolutionary genetic algorithm (CGA) for automating the path planning of two Cooperative construction manipulators. A methodology based on the concept of configuration space (C-space) technique in conjunction with the Coevolutionary genetic search is used for generating the path. The paper proposes a method for finding the minimum distance and collision free path using CGA. It uses an interference detection algorithm that runs in parallel with CGA to check collision between obstacle and cooperative cranes during path planning. The effectiveness of CGA is compared with other search approaches like A* and Genetic Algorithms GA.