Miracle-dz/UniFAR-FTUs huggingface dataset:
ARPHA: an FDIR architecture for Autonomous Spacecrafts based on Dynamic Probabilistic Graphical Models

This paper introduces a formal architecture for on-board diagnosis, prognosis and recovery called ARPHA. ARPHA is designed as part of the ESA/ESTEC study called VERIFIM (Verification of Failure Impact by Model checking). The goal is to allow the design of an innovative on-board FDIR process for autonomous systems, able to deal with uncertain system/environment interactions, uncertain dynamic system evolution, partial observability and detection of recovery actions taking into account imminent failures. We show how the model needed by ARPHA can be built through a standard fault analysis phase, finally producing an extended version of a fault tree called EDFT; we discuss how EDFT can be adopted as a formal language to represent the needed FDIR knowledge, that can be compiled into a corresponding Dynamic Decision Network to be used for the analysis. We also discuss the software architecture we are implementing following this approach, where on-board FDIR can be implemented by exploiting on-line inference based on the junction tree approach typical of probabilistic graphical models.
Automatic texture atlas generation from trimmed NURBS models

A Texture Atlas is a two dimensional representation of a 3D model usable for paint systems or as a sewing pattern.The field of texture atlas generation from polygonal models has been well exploited in the recent years. The developedalgorithms work on piecewise linear surface representations, but not on parametric surfaces like NURBS,that are still the main surface representation in CAD systems. If a texture atlas is generated from a triangulatedNURBS model, the result cannot be edited further in a CAD system, since the separation into charts is not basedon the separate NURBS patches of the original model. We present a method for automatic generation of a textureatlas directly from trimmed NURBS models, while preserving the original NURBS representation. The resultingtexture atlas is build of several charts, each consisting of the original NURBS patches sewn together.
A study of a nonlinear filtering problem for tracking an extended target

The paper presents an analysis of a nonlinear filtering problem corresponding to tracking of an extended target whose shape is modelled by an ellipse. The measurements of target extent are assumed to be available in addition to the usual positional measurements. Using Cramer-Rao bounds we establish the best achievable error performance for this highly nonlinear problem. The theoretical bounds are used to examine the performance as a function of measurement accuracy, observer-target geometry and prior knowledge of shape parameters. Finally an extended Kalman filter (KF) and an unscented KF are developed for this application and their performance (consistency and RMS error) are examined.
Energy efficient continuous location determination for pedestrian information systems

Nowadays, many modern mobile computing devices such as smartphones are equipped with GPS hardware but using it is rather energy demanding. The systems' uptime strongly depends on the frequency of performed positioning requests. Especially if applications require a continuous location determination (navigation system, tourist guides, etc.), energy becomes an important and limiting factor. Most of these applications do not need exact position data, but less precise (semantic) location information. In this paper, we discuss the idea of postponing position measures until the location could have changed. We focus on walking pedestrians. We present the theoretical framework and implementation aspects. The evaluation results show that our approach requires less energy than GPS polling, but offers a suitable precision.
A Novel Mouse c-fos Intronic Promoter That Responds to CREB and AP-1 Is Developmentally Regulated In Vivo

Background The c-fos proto-oncogene is an archetype for rapid and integrative transcriptional activation. Innumerable studies have focused on the canonical promoter, located upstream from the transcriptional start site. However, several regulatory sequences have been found in the first intron. Methodology/Principal Findings Here we describe an extremely conserved region in c-fos first intron that contains a putative TATA box, and functional TRE and CRE sites. This fragment drives reporter gene activation in fibroblasts, which is enhanced by increasing intracellular calcium and cAMP and by cotransfection of CREB or c-Fos/c-Jun expression vectors. We produced transgenic mice expressing a lacZ reporter controlled by the intronic promoter. Lac Z expression of this promoter is restricted to the developing central nervous system (CNS) and the mesenchyme of developing mammary buds in embryos 12.5 days post-conception, and to brain tissue in adults. RT-QPCR analysis of tissue mRNA, including the anlage of the mammary gland and the CNS, confirms the existence of a novel, nested mRNA initiated in the first intron. Conclusions/Significance Our results provide evidence for a novel, developmentally regulated promoter in the first intron of the c-fos gene.
Wind-driven pyroelectric energy harvesting device

Pyroelectric materials have recently received attention for harvesting waste heat owing to their potential to convert temperature fluctuations into useful electrical energy. One of the main challenges in designing pyroelectric energy harvesters is to provide a means to induce a temporal heat variation in a pyroelectric material autonomously from a steady heat source. To address this issue, we propose a new form of wind-driven pyroelectric energy harvester, in which a propeller is set in rotational motion by an incoming wind stream. The speed of the propeller’s shaft is reduced by a gearbox to drive a slider-crank mechanism, in which a pyroelectric material is placed on the slider. Thermal cycling is obtained as the reciprocating slider moves the pyroelectric material across alternative hot and cold zones created by a stationary heat lamp and ambient temperature, respectively. The open-circuit voltage and closed-circuit current are investigated in the time domain at various wind speeds. The device was experimentally tested under wind speeds ranging from 1.1 to 1.6 m s−1 and charged an external 100 nF capacitor through a signal conditioning circuit to demonstrate its effectiveness for energy harvesting. Unlike conventional wind turbines, the energy harvested by the pyroelectric material is decoupled from the wind flow and no mechanical power is drawn from the transmission; hence the system can operate at low wind speeds (<2 m s−1).
Optimization of the OpenFlow controller in wireless environments for enhancing mobility

OpenRoads or OpenFlow Wireless is an open-source platform for deploying an innovative and realistic strategy for different services in wireless networks. It provides a wireless extension for OpenFlow. It is developed to support existing Wireless Local Area Networks (WLANs) and Worldwide Interoperability for Microwave Access (WiMAX) networks. It can provide several mobility managers and run them concurrently in the network including hard handover, informed handover, n-casting and Hoolock. However, the provided mobility support for flow-based routing, where flows of one source taking different paths through multiple wireless access points or base stations, is not simple and hard to be deployed in the traditional routing algorithms. This paper proposes an intelligent mobility enhancement control and then develops an algorithm to decide which neighbor switches need to be selected for the installation of new flow entries and to allocate the appropriate idle-timeout for the selected switches. The proposed approach provides a simple solution to solve the user mobility problem in wireless OpenFlow environments which can handle the fast migration of user addresses (e.g. IP addresses) between several wireless access points and base stations. This approach leads to improvement in the end users' experience.
UB.dmirg: Learning Textual Entailment Relationships Using Lexical Semantic Features

This paper describes our Recognizing Textual Entailment (RTE) system developed at University of Ballarat, Australia for participation in the Text Analysis Conference RTE 2010 competition. This year, we participated in the Main task and used a machine learning approach for learning textual entailment relationships using parse-free lexical semantic features. For this, we employed FrameNet and WordNet resources to extract event-based and semantic features from both hypotheses and texts. Our system also used the longest common substring of lemmas when learning the entailment relationships.
Demethyleneberberine Protects against Hepatic Fibrosis in Mice by Modulating NF-κB Signaling

Demethyleneberberine (DMB) is an essential metabolite of Berberine (BBR) in vivo. Recent reports have revealed multiple novel therapeutic applications of BBR. However, the pharmacological activities of DMB remain to be elucidated. This study aimed to demonstrate the hepatoprotective and anti-fibrotic effects of DMB both in vitro and in vivo. Here we showed that DMB protects against thioacetamide (TAA)-induced hepatic fibrosis in mice and exhibits a higher safety profile as compared to BBR. Flow cytometry and Western blotting analysis showed that DMB is able to suppress the activation of hepatic stellate cells (HSCs) and induce cell apoptosis through the nuclear factor-κB (NF-κB) cascade. Immunohistochemical (IHC) and quantitative polymerase chain reaction (qPCR) analysis indicated that DMB also has inhibitory effects on collagen synthesis and is able to increase collagen degradation by blocking the transforming growth factor β 1 (TGF-β1)-Smad signaling and reducing the expression of matrix metalloproteinases (MMPs) and tissue inhibitors of MMP (TIMPs). These findings indicate that DMB has the potential to attenuate hepatic fibrosis via suppressing HSC activation.
Approche bayésienne des modèles à équations structurelles

Structural equation models (SEMs) are multivariate latent variable models used to model causality structures in data. A Bayesian estimation and validation of SEMs is proposed and identifiability of parameters is studied. The latter study shows that latent variables should be standardized in the analysis to ensure identifiability. This heuristics is in fact introduced to deal with complex identifiability constraints. To illustrate the point, identifiability constraints are calculated in a marketing application, in which posterior draws of the constraints are derived from the posterior conditional distributions of parameters. Mots clés : modèles à équations structurelles, variables latentes, algorithme de Gibbs, identifiabilité, méthodes bayésiennes, marketing 1 Modèles à équations structurelles
Multilayer perceptron and neural networks

The attempts for solving linear inseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning algorithm. Also called the generalized delta algorithm because it expands the training way of the adaline network, it is based on minimizing the difference between the desired output and the actual output, through the downward gradient method (the gradient tells us how a function varies in different directions). Training a multilayer perceptron is often quite slow, requiring thousands or tens of thousands of epochs for complex problems. The best known methods to accelerate learning are: the momentum method and applying a variable learning rate. The paper presents the possibility to control the induction driving using neural systems.
Pros, Cons, and Alternatives to Weight Based Cost Estimating

Summary/Conclusions During architecture studies and conceptual design studies, details of any new items to be traded arerarely known: Part counts, features of individual parts, the manufacturing processes to be used, and eventhe weight are not easily obtainable. WBS details are not available without large expenditures of effort.What is available are the major choices of parameters and the choices of how to produce and how toprocure. Consequently, development and production cost models that do not need weight nor details aboutthe hardware are especially useful for studies at these levels. An ideal model would include factors thatallow examination not only ofthe change in cost due to design choices, but also the change in cost due tochanges in approaches to manufacturing, testing, and oversight.RECM and LLEGO are examples of such models. RECM has been incorporated by NASA intoNAFCOM. It has also been successfully used by PWR for many contractual and internal efforts. LLEGOhas been used by KSC.
Which aspects of novice programmers' usage of an IDE predict learning outcomes

We present the preliminary analysis of a study whose long term aim is to track IDE usage to identify novice-programmers in need of support. Our analysis focused on the activity of 24 dyads on a 3 week assignment. We correlated frequencies of events such as use of code generation and of the debugger with assignment grades, final exam grades, and the difference in rankings within dyad on the final exam. Our results show several significant correlations. In particular, code generation and debugging are correlated with the final grade, and running in non-debug mode is correlated with differences in ranking. These results are encouraging as they show that it is possible to predict learning outcomes with simple frequency data and suggest more complex indicators could achieve robust prediction.
Semi-supervised training of a voice conversion mapping function using a joint-autoencoder

Recently, researchers have begun to investigate Deep Neural Network (DNN) architectures as mapping functions in voice conversion systems. In this study, we propose a novel StackedJoint-Autoencoder (SJAE) architecture, which aims to find a common encoding of parallel source and target features. The SJAE is initialized from a Stacked-Autoencoder (SAE) that has been trained on a large general-purpose speech database. We also propose to train the SJAE using unrelated speakers that are similar to the source and target speaker, instead of using only the source and target speakers. The final DNN is constructed from the source-encoding part and the target-decoding part of the SJAE, and then fine-tuned using back-propagation. The use of this semi-supervised training approach allows us to use multiple frames during mapping, since we have previously learned the general structure of the acoustic space and also the general structure of similar source-target speaker mappings. We train two speaker conversions and compare several system configurations objectively and subjectively while varying the number of available training sentences. The results show that each of the individual contributions of SAE, SJAE, and using unrelated speakers to initialize the mapping function increases conversion performance.
Multiple Hypothesis Tracking (MHT) for Space Surveillance: Results and Simulation Studies

Abstract : The need to accurately track breakups and satellite clusters, as well as to resolve uncorrelated tracks (UCTs), especially uncorrelated optical observations (UCOs), requires new, robust, and autonomous methods for space surveillance to enable the development and maintenance of the space catalog and to support the over- all SSA mission. This paper presents results on the performance of a newly-developed, statistically-robust, system-level, multiple hypothesis tracking (MHT) capability for joint catalog maintenance, UCT/UCO resolution, and initial orbit determination, in multiple regimes of space. A recent companion paper described some of the unique components contained within the authors' implementation of MHT, including the multi-frame data association problem and the statistical framework used for scoring the likelihood that a sequence of measurements, UCTs/UCOs, and other reports emanate from a common object (either newly-discovered or already catalogued). As demonstrated by the results in this paper, the MHT system provides superior tracking performance (compared to existing methods for data association) in realistic multi-sensor multi- object tracking scenarios over multiple regimes of space. Specifically, we demonstrate that the prototype MHT system can accurately and efficiently process tens of thousands of UCTs/UCOs emanating from thousands of space objects in real-time on a single processor.
Assessment of quality of life in patients with visual impairments using a new visual function questionnaire: the VFQ-J11

Background The purpose of the present study was to assess the vision-related quality of life (QOL) of visually impaired patients using the Japanese 11-item National Eye Institute Visual Function Questionnaire (VFQ-J11). Comparisons with the 25-item version (VFQ-25) and the EuroQoL Index using a large group of patients with various degrees of impairments and various causative diseases were performed. Methods A total of 232 visually impaired Japanese patients were recruited from six ophthalmology departments in Japan. Information on ophthalmic findings and patient backgrounds was collected, and information on QOL and utility assessments was collected from the patients by means of survey questionnaires. Results The average age of patients was 69.6±14.3 years. Both the vision-related QOL scores (VFQ-25 composite and VFQ-J11) were significantly associated with better and worse visual acuity (VA) in visually impaired subjects (all P<0.01). VFQ-J11 was comparable to VFQ-25 regardless of causative diseases. VFQ-25 composite and the VFQ-J11 scores were concurrently associated with a range of systemic medical disorders. EuroQoL Index had a significant association with better eye VA (P<0.01), but not with worse eye VA, or any systemic disorders. Conclusion VFQ-J11 provides valid data on vision-related QOL and is less of a burden for patients with vision problems.
The Group Latent Variable Approach to Probit Binary Classifications

This paper considers the binary classification with the probit model under the expectation-maximization (EM) algorithm. Usually, in the Bayesian approach of the probit model, the latent variables are introduced to handle with the intractable problem. For each training sample, there is a corresponding latent variable. However, the EM algorithm requires matrix inversions which demand for the expansive computational cost when the number of training samples is large. To overcome this problem, we employ the group latent-variable approach where for each of the training samples there are corresponding multiple latent variables instead of just one. The major advantage of this approach, which is originated from Bayesian backfitting, is that there are no requirements for matrix inversions in the EM algorithm for the probit model. In this paper, to obtain sparse classifiers the Laplacian prior is employed and the method to control the degree of sparseness is presented. Although the sparsity of the classifier is not determined in a full automatic way, it can be controlled by specifying just one parameter. In other words, we are free to choose the degree of sparseness. The proposed method is compared with support vector machine, relevance vector machine, and generalized LASSO.
A Semantic Data Value Vocabulary Supporting Data Value Assessment and Measurement Integration

In this paper we define DaVe: a data value vocabulary that allows for the comprehensive representation of data value. This vocabulary enables users to extend it using data value dimensions as required in the context at hand. DaVe caters for the lack of consensus on what characterises data value, and also how to model it. This vocabulary will allow users to monitor and asses data value throughout any value creating or data exploitation efforts, therefore laying the basis for effective management of value and efficient value exploitation. It also allows for the integration of diverse metrics that span many data value dimensions and which most likely pertain to a range of different tools in different formats. This data value vocabulary is based on requirements extracted from a number of value assessment use cases extracted from literature, and is evaluated using Gruber’s ontology design criteria, and by instantiating it in a deployment case study.
Tree and filter optimization for audio compression in a wavelet-based perceptual audio coder

This paper outlines a new perceptual low bit rate audio coding scheme based on adapted wavelet representations. It claims wavelet tree and filter adaptation attending to a perceptual entropy-based method. To achieve such adaptive structure, a periodized wavelet packet transform is performed for each audio frame. After the transform, the encoder employs scalar adaptive quantization, controlled by an enhanced psycho-acoustic model, followed by entropy coding and bit packing. An adapted Huffman algorithm that assumes Laplacian distributions for wavelet coefficients is used for entropy coding. A relevant feature of our coder is the method to translate the psycho-acoustic information to the wavelet domain. It allows using orthonormal wavelets with any compact support. Experimental results indicate that our coder ensures ‘transparent’ coding of monophonic CD quality audio signals at bit rates lower than 64 kb/s for all the test signals. Key-Words: audio coding, wavelets, best basis selection, perceptual entropy, Laplacian distribution.
Shrinkage mappings and their induced penalty functions

Many optimization problems that are designed to have sparse solutions employ the ℓ1 or ℓ0 penalty functions. Consequently, several algorithms for compressive sensing or sparse representations make use of soft or hard thresholding, both of which are examples of shrinkage mappings. Their usefulness comes from the fact that they are the proximal mappings of the ℓ1 and ℓ0 penalty functions, meaning that they provide the solution to the corresponding penalized least-squares problem. In this paper, we both generalize and reverse this process: we show that one can begin with any of a wide class of shrinkage mappings, and be guaranteed that it will be the proximal mapping of a penalty function with several desirable properties. Such a shrinkage-mapping/penalty-function pair comes ready-made for use in efficient algorithms. We give an example of such a shrinkage mapping, and use it to advance the state of the art in compressive sensing.
Prognostic scoring systems for myelodysplastic syndromes (MDS) in a population‐based setting: a report from the Swedish MDS register

The myelodysplastic syndromes (MDS) have highly variable outcomes and prognostic scoring systems are important tools for risk assessment and to guide therapeutic decisions. However, few population‐based studies have compared the value of the different scoring systems. With data from the nationwide Swedish population‐based MDS register we validated the International Prognostic Scoring System (IPSS), revised IPSS (IPSS‐R) and the World Health Organization (WHO) Classification‐based Prognostic Scoring System (WPSS). We also present population‐based data on incidence, clinical characteristics including detailed cytogenetics and outcome from the register. The study encompassed 1329 patients reported to the register between 2009 and 2013, 14% of these had therapy‐related MDS (t‐MDS). Based on the MDS register, the yearly crude incidence of MDS in Sweden was 2·9 per 100 000 inhabitants. IPSS‐R had a significantly better prognostic power than IPSS (P < 0·001). There was a trend for better prognostic power of IPSS‐R compared to WPSS (P = 0·05) and for WPSS compared to IPSS (P = 0·07). IPSS‐R was superior to both IPSS and WPSS for patients aged ≤70 years. Patients with t‐MDS had a worse outcome compared to de novo MDS (d‐MDS), however, the validity of the prognostic scoring systems was comparable for d‐MDS and t‐MDS. In conclusion, population‐based studies are important to validate prognostic scores in a ‘real‐world’ setting. In our nationwide cohort, the IPSS‐R showed the best predictive power.