Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor fault diagnosis of singular delayed LPV systems with inexact parameters: an uncertain system approach

ABSTRACT In this paper, sensor fault diagnosis of a singular delayed linear parameter varying (LPV) system is considered. In the considered system, the model matrices are dependent on some parameters which are real-time measurable. The case of inexact parameter measurements is considered which is close to real situations. Fault diagnosis in this system is achieved via fault estimation. For this purpose, an augmented system is created by including sensor faults as additional system states. Then, an unknown input observer (UIO) is designed which estimates both the system states and the faults in the presence of measurement noise, disturbances and uncertainty induced by inexact measured parameters. Error dynamics and the original system constitute an uncertain system due to inconsistencies between real and measured values of the parameters. Then, the robust estimation of the system states and the faults are achieved with H∞ performance and formulated with a set of linear matrix inequalities (LMIs). The designed UIO is also applicable for fault diagnosis of singular delayed LPV systems with unmeasurable scheduling variables. The efficiency of the proposed approach is illustrated with an example.
Interpreting data mining results with linked data for learning analytics: motivation, case study and directions

Learning Analytics by nature relies on computational information processing activities intended to extract from raw data some interesting aspects that can be used to obtain insights into the behaviours of learners, the design of learning experiences, etc. There is a large variety of computational techniques that can be employed, all with interesting properties, but it is the interpretation of their results that really forms the core of the analytics process. In this paper, we look at a specific data mining method, namely sequential pattern extraction, and we demonstrate an approach that exploits available linked open data for this interpretation task. Indeed, we show through a case study relying on data about students' enrolment in course modules how linked data can be used to provide a variety of additional dimensions through which the results of the data mining method can be explored, providing, at interpretation time, new input into the analytics process.
Phenotyping of epidermal dendritic cells: clinical applications of a flow cytometric micromethod.

BACKGROUND The differential diagnosis of inflammatory skin diseases is largely based on the patient's history and the morphological analysis of the skin lesion. Laboratory data, such as serum IgE-level and prick and patch tests, may be helpful but do not assess individual lesions. The assumption of our approach is that each individual lesion is associated with a specific microenvironment and that the immunophenotype of the two epidermal dendritic cell populations, Langerhans cells (LC) and inflammatory dendritic epidermal cells (IDEC), reflects this environment in a disease-specific manner. METHODS A flow cytometric micromethod was developed to directly analyze individual inflammatory human skin lesions. Crude epidermal single cell suspensions were prepared by trypsinization, stained for three-color analysis with different monoclonal antibodies and the vital stain 7-amino-actinomycin-D, and finally analyzed on a single laser equipped FACScan flow cytometer. RESULTS With a limited set of cell surface markers, such as FcepsilonRI, FcgammaRII/CD32, CD1b and CD36, highly specific diagnostic criteria for atopic dermatitis and inflamed human skin could be established. CONCLUSIONS Phenotyping of epidermal dendritic cells is a useful procedure helpful in differential diagnosis of inflammatory skin diseases.
Aggregation Patterns in Stressed Bacteria.

We study the formation of spot patterns seen in bacterial colonies when the bacteria are subjected to oxidative stress due to hazardous by-products of respiration. The cell density is coupled to a chemoattractant concentration as well as to nutrient and waste fields. The model combines the propagation of a front of motile bacterial radially outward from an initial site, a Turing instability of the uniformly dense state, and a reduction of motility for cells sufficiently far behind the front. The wide variety of patterns seen in the experiments is reproduced by the model by varying the details of the initiation of the chemoattractant emission as well as the transition to a nonmotile phase.
2011 IRCAM AUDIO MUSIC SIMILARITY SYSTEM #2

The Universal Background Model (UBM) aims at modeling the overall data distribution. It consists of a classical Gaussian Mixture Model. The UBM is usually composed of Gaussian models with diagonal covariance matrix. The loss of modeling ability due the diagonal covariance matrix can be compensated by increasing the number of Gaussian in the mixture The UBM is trained using a large and representative set of data by using the Expectation Maximization (EM) algorithm. The system is provided with a pre trained UBM.
A grouped decoding algorithm for non-binary LDPC code with Quasi-orthogonal STBC in MIMO system

We extend the group decoding algorithm [1] for space time block code (STBC) to the non-binary low density parity check code (LDPC) with Quasi-Orthogonal STBC in multiple-input and multiple-output (MIMO) system. A Gaussian based detection is proposed to reduce the decoding complexity in this paper. The complexity involved grows linearly with the number of transmit antennas and size of Galois Field. The simulation result shows that the Gaussian based detection is reasonable to employ in this system.
Deformation behavior of electrospun poly(L‐lactide‐co‐ε‐caprolactone) nonwoven membranes under uniaxial tensile loading

Biodegradable poly(L-lactide-co-e-caprolactone) copolymers with different L-lactide (LLA)/e-caprolactone (CL) ratios of 75/25 and 50/50 were electrospun into fine fibers. The deformation behavior of the electrospun membranes with randomly oriented structures was evaluated under uniaxial tensile loading. The electrospun membrane with a higher LLA content showed a significantly higher tensile modulus but a similar maximum stress and a lower ultimate strain in comparison with the membrane with a lower LLA content. The beaded fibers that formed in the membranes caused lower tensile properties. X-ray diffraction and differential scanning calorimetry results suggested that the electrospun fine fibers developed highly oriented structures in CL-unit sequences during the electrospinning process even though the concentration was only 25 wt %. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 3205–3212, 2005
A question of trust: user-centered design requirements for an informatics intervention to promote the sexual health of African-American youth.

OBJECTIVE We investigated the user requirements of African-American youth (aged 14-24 years) to inform the design of a culturally appropriate, network-based informatics intervention for the prevention of HIV and other sexually transmitted infections (STI). MATERIALS AND METHODS We conducted 10 focus groups with 75 African-American youth from a city with high HIV/STI prevalence. Data analyses involved coding using qualitative content analysis procedures and memo writing. RESULTS Unexpectedly, the majority of participants' design recommendations concerned trust. Youth expressed distrust towards people and groups, which was amplified within the context of information technology-mediated interactions about HIV/STI. Participants expressed distrust in the reliability of condoms and the accuracy of HIV tests. They questioned the benevolence of many institutions, and some rejected authoritative HIV/STI information. Therefore, reputational information, including rumor, influenced HIV/STI-related decision making. Participants' design requirements also focused on trust-related concerns. Accordingly, we developed a novel trust-centered design framework to guide intervention design. DISCUSSION Current approaches to online trust for health informatics do not consider group-level trusting patterns. Yet, trust was the central intervention-relevant issue among African-American youth, suggesting an important focus for culturally informed design. Our design framework incorporates: intervention objectives (eg, network embeddedness, participation); functional specifications (eg, decision support, collective action, credible question and answer services); and interaction design (eg, member control, offline network linkages, optional anonymity). CONCLUSIONS Trust is a critical focus for HIV/STI informatics interventions for young African Americans. Our design framework offers practical, culturally relevant, and systematic guidance to designers to reach this underserved group better.
Stock Price Forecasting Using Exogenous Time Series and Combined Neural Networks

Time series forecasting is useful in many researches areas. The use of models that provide a reliable prediction in financial time series may to bring valuable profits for the investors. An intelligent agent can be built from a suitable prediction model, to make operations in stock market daily. Furthermore, even that the investor had caution about the use of an automatic agent to make operations he can to use the prediction model as a valuable decision support. A methodology based on information obtained from exogenous series was used in combination with a neural network to predict stock series. Exogenous series were selected by analyzing the correlation between the series with the stocks series used. In this way, the prediction was obtained by not just using the previous values of the series but also by using information external to the main series. Additionally, the best trained neural networks were used in a combination to improve the prediction capacity of single networks. To evaluate the proposed models for prediction, some known metrics were used plus a proposed one Prediction in Direction and Accuracy (PDA), which uses some features to determine if a model has a great accuracy and trend in prediction. Through this novel metric, we have used an evolutionary algorithm to choose the best trained models in order to obtain better results. Experiments with two of the most important Brazilian companies’ stock quotes have shown the usefulness of the proposed prediction system to generate profits in investments.
Societal Computing

Social Computing research focuses on online social behavior and using artifacts derived from it for providing recommendations and other useful community knowledge. Unfortunately, some of that behavior and knowledge incur societal costs, particularly with regards to Privacy, which is viewed quite differently by different populations as well as regulated differently in different locales. But clever technical solutions to those challenges may impose additional societal costs, e.g., by consuming substantial resources at odds with Green Computing, another major area of societal concern. We propose a new crosscutting research area, Societal Computing, that focuses on the technical tradeoffs among computational models and application domains that raise significant societal issues. This dissertation, advised by Prof. Gail Kaiser, will focus on privacy concerns in the context of Societal Computing and will aim to address research topics such as design patterns and architectures for privacy tradeoffs, better understanding of users' privacy requirements so that tradeoffs with other areas such as green computing can be dealt with in a more effective manner, and better visualization techniques for making privacy and its tradeoffs more understandable.
Object-Based 3-D Building Change Detection on Multitemporal Stereo Images

Due to the rapid process of urbanization, there is an increasing demand for detecting building changes over time using very high-resolution (VHR) images. Traditional two-dimensional (2-D) change detection methods are limited due to the image perspective variation and illumination discrepancies. One current trend for building detection combines the use of orthophotos and digital surface models (DSMs), because of its robustness against false changes, as well as its capability of providing volumetric information. In this paper, we propose an object-based three-dimensional (3-D) building change detection framework based on supervised classification, which makes use of the height, spectral, and shape information in a combined fashion with object-based analysis. The proposed method follows the following steps: First, a synergic mean-shift segmentation method is applied on the orthophoto with the constraints of the DSM, which derives segments with homogenous spectrum and height. In a second step, the segments are classified with a hybrid decision tree and SVM approach, and then the segments of the building class are merged as building objects for change detection. An initial change indicator (CI) is then computed for each building object concerning height and spectral information. Finally, an adaptive CI updating strategy based on segment overlapping is proposed and the traffic light system based on a dual threshold is used to identify the change status of each building as “change,” “no-change,” and “uncertain change”. The experimental results on scanned aerial stereo images have demonstrated that our proposed framework is able to achieve high-detection accuracy on images with limited spectral quality.
Preventive intervention for maltreated preschool children: impact on children's behavior, neuroendocrine activity, and foster parent functioning.

OBJECTIVE This article describes the results of a pilot study that evaluated the effectiveness of the Early Intervention Foster Care (EIFC) program in the period immediately following a child's placement in a new foster home. METHOD Data were collected from an EIFC group, a regular foster care group, and a community comparison group-each with 10 participants-via questionnaires for children and their caretakers and salivary cortisol sampling. RESULTS EIFC foster parents adopted and maintained positive parenting strategies, EIFC children's behavioral adjustment improved, and changes occurred in several salivary cortisol measures. Moreover, regular foster care children exhibited decrements in functioning in several areas over the same time period. CONCLUSIONS Results are discussed with regard to how such research fits into a larger program of prevention research for high-risk preschool children.
Apparent Age Estimation Using Ensemble of Deep Learning Models

In this paper, we address the problem of apparent age estimation. Different from estimating the real age of individuals, in which each face image has a single age label, in this problem, face images have multiple age labels, corresponding to the ages perceived by the annotators, when they look at these images. This provides an intriguing computer vision problem, since in generic image or object classification tasks, it is typical to have a single ground truth label per class. To account for multiple labels per image, instead of using average age of the annotated face image as the class label, we have grouped the face images that are within a specified age range. Using these age groups and their age-shifted groupings, we have trained an ensemble of deep learning models. Before feeding an input face image to a deep learning model, five facial landmark points are detected and used for 2-D alignment. We have employed and fine tuned convolutional neural networks (CNNs) that are based on VGG-16 [24] architecture and pretrained on the IMDB-WIKI dataset [22]. The outputs of these deep learning models are then combined to produce the final estimation. Proposed method achieves 0.3668 error in the final ChaLearn LAP 2016 challenge test set [5].
Anteroposterior axis patterning by early canonical Wnt signaling during hemichordate development

The Wnt family of secreted proteins has been proposed to play a conserved role in early specification of the bilaterian anteroposterior (A/P) axis. This hypothesis is based predominantly on data from vertebrate embryogenesis as well as planarian regeneration and homeostasis, indicating that canonical Wnt (cWnt) signaling endows cells with positional information along the A/P axis. Outside of these phyla, there is strong support for a conserved role of cWnt signaling in the repression of anterior fates, but little comparative support for a conserved role in promotion of posterior fates. We further test the hypothesis by investigating the role of cWnt signaling during early patterning along the A/P axis of the hemichordate Saccoglossus kowalevskii. We have cloned and investigated the expression of the complete Wnt ligand and Frizzled receptor complement of S. kowalevskii during early development along with many secreted Wnt modifiers. Eleven of the 13 Wnt ligands are ectodermally expressed in overlapping domains, predominantly in the posterior, and Wnt antagonists are localized predominantly to the anterior ectoderm in a pattern reminiscent of their distribution in vertebrate embryos. Overexpression and knockdown experiments, in combination with embryological manipulations, establish the importance of cWnt signaling for repression of anterior fates and activation of mid-axial ectodermal fates during the early development of S. kowalevskii. However, surprisingly, terminal posterior fates, defined by posterior Hox genes, are unresponsive to manipulation of cWnt levels during the early establishment of the A/P axis at late blastula and early gastrula. We establish experimental support for a conserved role of Wnt signaling in the early specification of the A/P axis during deuterostome body plan diversification, and further build support for an ancestral role of this pathway in early evolution of the bilaterian A/P axis. We find strong support for a role of cWnt in suppression of anterior fates and promotion of mid-axial fates, but we find no evidence that cWnt signaling plays a role in the early specification of the most posterior axial fates in S. kowalevskii. This posterior autonomy may be a conserved feature of early deuterostome axis specification.
Three-dimensional Radar Imaging Based on Sparse Measurements

Abstract. In conventional three-dimensional Inverse Synthetic Aperture Radar (ISAR) imaging, high range-resolution is attained by transmitting a wideband signal and high cross-range resolution is attained by increasing the observation azimuth and elevation. Consequently, large collection time and data storage is needed for this three-dimensional imaging method. This paper presents three-dimensional imaging approaches by using the sparse measurements and convex optimization. This approach could decrease data collection time and data storage amount greatly; the proposed approaches have been verified using numerical simulations of point target, which show that it could recover the image exactly and maintain the imaging resolution.
Leukemia diagnosis: today and tomorrow

The European LeukemiaNet (ELN) is composed of several work packages, four of them being directly involved in the various aspects of diagnosis. On the occasion of the annual ELN meeting of 2015 in Mannheim, these four work packages collectively examined the current situation and future prospects of cytomorphology, flow cytometry, cytogenetics, next‐generation sequencing, and minimal residual disease detection in the context of leukemia diagnosis and follow‐up. This document summarizes the outcome of this compendium.
A new method to characterize conformality of BARC coatings

In the semiconductor manufacturing industry, a bottom anti-reflective coating (BARC) is used to minimize thin film interference effects by reducing reflected light. As substrate topography becomes more complex with efforts to design more complex circuits, the effect of reflected radiation becomes more critical. The degree of conformality of the BARC coating plays an important role in lithography performance, which in turn affects the design of plasma etching processes. In this study, we propose a new method to measure the BARC coating conformality. The relationship between film thickness and horizontal distance from the step can be described by an exponential function. We found this profile is related to the properties of the coating material, such as molecular weight, the composition of formulation, the polymer structure, Tg of the polymer, thermal flow capability, and the crosslinking reaction, but is independent of step height, step width, and BARC thickness. The pitch affects the shape of the coating profile only when the spacing of features is smaler than a threshold that is related to coating material properties. The curvature of the profile indicates the uniformity of BARC thickness across the topography, which is a very good parameter to quantitatively describe the conformality of BARC coatings. Studies on Brewer Science BARC products confirm that the proposed conformality measuring method is in excellent agreement with observations. This method offers the option to separately consider the effects of coating processing, topography type, film thickness, and inherent material properties. It affords the predictability of BARC behavior for coatings that cover different topographies.
Trust Policies for Semantic Web Repositories

The increasing reliance on information gathered from the Web and other Internet technologies (P2P networks, e-mails, blogs, wikis, etc.) raises the issue of trust. Trust policies are needed to filter out untrustworthy information. This filtering task can be leveraged by the increasing availability of Semantic Web metadata that describes the information retrieved. It is necessary, however, to adequately model the concept of trustworthiness; otherwise one may end up with operational trust measures that lack a clear meaning. It is also important to have a path from one’s trust requirements to concrete trust policies through Semantic Web technologies. This paper proposes a horn logic model for trust policies, grounded on a real-world model of trust that offers justification for trust decisions and controlled trust measurement. We also propose the use of this model to enhance existing Semantic Web repositories with a trust layer.
MDG-Based State Enumeration By Retiming And Circuit Transformation

Multiway Decision Graphs (MDGs) have recently been proposed as an efficient representation for RTL designs. In this paper, we illustrate the MDG-based formal verification technique on the example of the Island Tunnel Controller. We investigate several techniques on how to deal with the nontermination problem of abstract state exploration, including a novel method based on retiming and circuit transformation. We provide comparative experimental results for the verification of a number of properties for the example using two well-known ROBDD-based verification tools, namely, SMV (Symbolic Model Verifier) and VIS (Verification Interacting with Synthesis), and we show the strength of the MDG approach to handling arbitrary data widths.