Miracle-dz/UniFAR-FTUs huggingface dataset:
Prise en compte des analyses de sûreté de fonctionnement dans l'ingénierie de système dirigée par les modèles SysML

La methode MeDISIS a ete developpee, afin de faciliter les etudes de surete de fonctionnement, au sein de l'ingenierie de systeme dirigee par les modeles. Cet article indique comment, a travers une modelisation des processus d'ingenierie de systeme et de surete de fonctionnement, sont deployes MeDISIS et ses processus. Il souligne egalement l'apport du meta-modele definissant les informations afferentes a la surete de fonctionnement, permettant a travers une base d'informations l'agregation, la perennisation et la tracabilite des connaissances des differents intervenants du projet. MeDISIS appliquee au developpement d'un systeme embarque critique illustre ainsi les gains obtenus lors des phases de specification et conception de ce dernier. A travers ce projet industriel, sont mis en evidence les concepts propres a SysML permettant de preparer, piloter et conduire les etudes de surete de fonctionnement.
MADCAM – THE MULTISPECTRAL ACTIVE DECOMPOSITION CAMERA

A real-time spectral decomposition of streaming three-band image data is obtained by applying linear transformations. The Principal Components (PC), the Maximum Autocorrelation Factors (MAF), and the Maximum Noise Fraction (MNF) transforms are applied. In the presented case study the PC transform appears to provide the best result for separating signal from noise. The main difficulty for the more advanced methods is to obtain good estimates for the correlation structure of the noise since problems arise due to spatial and temporal coding of the video signal. A new MNF transform is proposed that utilised information drawn from the temporal dimension instead of the traditional spatial approach. Using the CIF format (352 ×288) frame rates up to 30 Hz are obtained and in VGA mode (640 ×480) up to 15 Hz.
Personality Measures Link Slower Binocular Rivalry Switch Rates to Higher Levels of Self-Discipline

In this paper we investigated the relation between personality and the rate of perceptual alternations during binocular rivalry. Studies have demonstrated that slower rivalry alternations are associated with a range of clinical conditions. It is less clear whether rivalry dynamics similarly co-vary with individual differences in psychological traits seen across non-clinical population. We assessed rivalry rates in a non-clinical population (n = 149) and found slower rivalry alternations were positively related r(149) = 0.20, p = 0.01 to industriousness, a trait characterized by a high level of self-discipline using the Big Five Aspect Scales (BFAS). Switch rates were also negatively related r(149) = −0.20, p = 0.01 to cognitive disorganization, a schizotypy trait capturing schizophrenia-like symptoms of disorganization using the Oxford-Liverpool Inventory of feelings and Experiences (O-LIFE). Furthermore, we showed that that these relations with personality were unaffected by the inclusion or exclusion of mixed percept in the response analysis. Together these results are relevant to theoretical models of rivalry investigating individual differences in rivalry temporal dynamics and they may reduce concerns about the impact of task compliance in clinical research using rivalry as a potential diagnostic tool.
A MAC layer congestion control mechanism in IEEE 802.11 WLANs

In this paper we propose a novel point-to-point MAC layer congestion control mechanism (MACC) in IEEE 802.11 WLANs. We first analyze the reasons of wired congestion and wireless congestion in TCP and mainly study the latter which TCP could not solve well. Then aiming to alleviate this type of congestion of TCP in WLANs, we bring in the concept of congestion control into MAC layer and propose MACC. The main idea of MACC is using the congestion window to control the sending rate in MAC layer. Being Different from congestion control in TCP which is through reducing the sending rate, MACC controls the congestion through increasing the channel utilization and improving the fairness. Simulation results in various scenarios demonstrate that the performances of network throughput and fairness are improved considerably and also TCP congestion is alleviated much by using MACC.
Partitive mixing of images: a tool for investigating pictorial perception

In certain cases, images of different scenes can be mixed to produce an image of a novel scene. For example, an image of a pink sphere can be additively mixed from suitable images of a red and a white sphere. Three ways in which scenes can differ are considered: in the spectral composition of the illuminant and in the spectral and the geometric reflectance of scene objects. Sufficient conditions are given for mixing to produce images that correspond to possible scenes. Examples illustrate ways that mixtures can be used as stimuli in psychophysical experiments concerned with pictorial perception.
Pathway Analysis for Genome-Wide Association Study of Basal Cell Carcinoma of the Skin

Background Recently, a pathway-based approach has been developed to evaluate the cumulative contribution of the functionally related genes for genome-wide association studies (GWASs), which may help utilize GWAS data to a greater extent. Methods In this study, we applied this approach for the GWAS of basal cell carcinoma (BCC) of the skin. We first conducted the BCC GWAS among 1,797 BCC cases and 5,197 controls in Caucasians with 740,760 genotyped SNPs. 115,688 SNPs were grouped into gene transcripts within 20 kb in distance and then into 174 Kyoto Encyclopedia of Genes and Genomes pathways, 205 BioCarta pathways, as well as two positive control gene sets (pigmentation gene set and BCC risk gene set). The association of each pathway with BCC risk was evaluated using the weighted Kolmogorov-Smirnov test. One thousand permutations were conducted to assess the significance. Results Both of the positive control gene sets reached pathway p-values<0.05. Four other pathways were also significantly associated with BCC risk: the heparan sulfate biosynthesis pathway (p = 0.007, false discovery rate, FDR = 0.35), the mCalpain pathway (p = 0.002, FDR = 0.12), the Rho cell motility signaling pathway (p = 0.011, FDR = 0.30), and the nitric oxide pathway (p = 0.022, FDR = 0.42). Conclusion We identified four pathways associated with BCC risk, which may offer new insights into the etiology of BCC upon further validation, and this approach may help identify potential biological pathways that might be missed by the standard GWAS approach.
Comparative optical measurements of airspeed and aerosols on a DC-8 aircraft

NASA Dryden supported a cooperative flight test program on the NASA DC-8 aircraft in November 1993. This program evaluated optical airspeed and aerosol measurement techniques. Three brassboard optical systems were tested. Two were laser Doppler systems designed to measure freestream-referenced airspeed. The third system was designed to characterize the natural aerosol statistics and airspeed. These systems relied on optical backscatter from natural aerosols for operation. The DC-8 aircraft carried instrumentation that provided real-time flight situation information and reference data on the aerosol environment. This test is believed to be the first to include multiple optical airspeed systems on the same carrier aircraft, so performance could be directly compared. During 23 hr of flight, a broad range of atmospheric conditions was encountered, including aerosol-rich layers, visible clouds, and unusually clean (aerosol-poor) regions. Substantial amounts of data were obtained. Important insights regarding the use of laser-based systems of this type in an aircraft environment were gained. This paper describes the sensors used and flight operations conducted to support the experiments. The paper also briefly describes the general results of the experiments.
Correlating features and code using a compact two-sided trace analysis approach

Software developers are constantly required to modify and adapt application features in response to changing requirements. The problem is that just by reading the source code, it is difficult to determine how classes and methods contribute to the runtime behavior of features. Moreover, dependencies between system features are not obvious, consequently software maintenance operations often result in unintended side effects. To tackle these problems, we propose a compact feature-driven approach (i.e., summarized trace information) based on dynamic analysis to characterize features and computational units of an application. We extract execution traces to achieve an explicit mapping between features and classes using two complementary perspectives. We apply our approach to two case studies and we report our findings.
Anti-CD70 Immunocytokines for Exploitation of Interferon-γ-Induced RIP1-Dependent Necrosis in Renal Cell Carcinoma

Metastatic renal cell carcinoma (RCC) is an incurable disease in clear need of new therapeutic interventions. In early-phase clinical trials, the cytokine IFN-γ showed promise as a biotherapeutic for advanced RCC, but subsequent trials were less promising. These trials, however, focused on the indirect immunomodulatory properties of IFN-γ, and its direct anti-tumor effects, including its ability to kill tumor cells, remains mostly unexploited. We have previously shown that IFN-γ induces RIP1 kinase-dependent necrosis in cells lacking NF-κB survival signaling. RCC cells display basally-elevated NF-κB activity, and inhibiting NF-κB in these cells, for example by using the small-molecule proteasome blocker bortezomib, sensitizes them to RIP1-dependent necrotic death following exposure to IFN-γ. While these observations suggest that IFN-γ-mediated direct tumoricidal activity will have therapeutic benefit in RCC, they cannot be effectively exploited unless IFN-γ is targeted to tumor cells in vivo. Here, we describe the generation and characterization of two novel ‘immunocytokine’ chimeric proteins, in which either human or murine IFN-γ is fused to an antibody targeting the putative metastatic RCC biomarker CD70. These immunocytokines display high levels of species-specific IFN-γ activity and selective binding to CD70 on human RCC cells. Importantly, the IFN-γ immunocytokines function as well as native IFN-γ in inducing RIP1-dependent necrosis in RCC cells, when deployed in the presence of bortezomib. These results provide a foundation for the in vivo exploitation of IFN-γ-driven tumoricidal activity in RCC.
A generalization of blind source separation algorithms for convolutive mixtures based on second-order statistics

We present a general broadband approach to blind source separation (BSS) for convolutive mixtures based on second-order statistics. This avoids several known limitations of the conventional narrowband approximation, such as the internal permutation problem. In contrast to traditional narrowband approaches, the new framework simultaneously exploits the nonwhiteness property and nonstationarity property of the source signals. Using a novel matrix formulation, we rigorously derive the corresponding time-domain and frequency-domain broadband algorithms by generalizing a known cost-function which inherently allows joint optimization for several time-lags of the correlations. Based on the broadband approach time-domain, constraints are obtained which provide a deeper understanding of the internal permutation problem in traditional narrowband frequency-domain BSS. For both the time-domain and the frequency-domain versions, we discuss links to well-known, and also, to novel algorithms that constitute special cases. Moreover, using the so-called generalized coherence, links between the time-domain and the frequency-domain algorithms can be established, showing that our cost function leads to an update equation with an inherent normalization ensuring a robust adaptation behavior. The concept is applicable to offline, online, and block-online algorithms by introducing a general weighting function allowing for tracking of time-varying real acoustic environments.
Infrastructuring participatory development in information technology

In this paper we present our experience in building a socio-technical infrastructure for supporting social innovation in Information Technology. We start by describing a case study on the design and use of a smartphone application for the canteen services of a local university; based on this, we propose what we call the hourglass approach to support participatory design and development in Information Technology. The hourglass is defined by the intersection of two co-evolving dimensions of infrastructuring: the social and the technical ones. Different subsets of the community, characterized by the increasing involvement of self-selected volunteers, position themselves along the two axes and have different roles in the design and use of the generated artefact. We conclude by discussing how this approach can help addressing some of the current challenges (i.e. scale, milieu and responsibilities) of social innovation in Information Technology.
Detection of Textured Areas in Images Using a Disorganization Indicator Based on Component Counts

texture detection, texture analysis, texture synthesis, image analysis An algorithm is presented for the detection of textured areas in digital images. Texture detection has potential application to image enhancement, tone correction, defect detection, content classification and image segmentation. For example, texture detection may be combined with a color model and other descriptors to detect objects in an image, such as sky, which is generally smooth, and foliage, which is textured.
Hemolysis Is Associated with Low Reticulocyte Production Index and Predicts Blood Transfusion in Severe Malarial Anemia

Background Falciparum Malaria, an infectious disease caused by the apicomplexan parasite Plasmodium falciparum, is among the leading causes of death and morbidity attributable to infectious diseases worldwide. In Gabon, Central Africa, one out of four inpatients have severe malarial anemia (SMA), a life-threatening complication if left untreated. Emerging drug resistant parasites might aggravate the situation. This case control study investigates biomarkers of enhanced hemolysis in hospitalized children with either SMA or mild malaria (MM). Methods and Findings Ninety-one children were included, thereof 39 SMA patients. Strict inclusion criteria were chosen to exclude other causes of anemia. At diagnosis, erythrophagocytosis (a direct marker for extravascular hemolysis, EVH) was enhanced in SMA compared to MM patients (5.0 arbitrary units (AU) (interquartile range (IR): 2.2–9.6) vs. 2.1 AU (IR: 1.3–3.9), p<0.01). Furthermore, indirect markers for EVH, (i.e. serum neopterin levels, spleen size enlargement and monocyte pigment) were significantly increased in SMA patients. Markers for erythrocyte ageing, such as CD35 (complement receptor 1), CD55 (decay acceleration factor) and phosphatidylserine exposure (annexin-V-binding) were investigated by flow cytometry. In SMA patients, levels of CD35 and CD55 on the red blood cell surface were decreased and erythrocyte removal markers were increased when compared to MM or reconvalescent patients. Additionally, intravascular hemolysis (IVH) was quantified using several indirect markers (LDH, α-HBDH, haptoglobin and hemopexin), which all showed elevated IVH in SMA. The presence of both IVH and EVH predicted the need for blood transfusion during antimalarial treatment (odds ratio 61.5, 95% confidence interval (CI): 8.9–427). Interestingly, this subpopulation is characterized by a significantly lowered reticulocyte production index (RPI, p<0.05). Conclusions Our results show the multifactorial pathophysiology of SMA, whereby EVH and IVH play a particularly important role. We propose a model where removal of infected and non-infected erythrocytes of all ages (including reticulocytes) by EVH and IVH is a main mechanism of SMA. Further studies are underway to investigate the mechanism and extent of reticulocyte removal to identify possible interventions to reduce the risk of SMA development.
Study on an improved phosphate cement binder for the development of fiber-reinforced inorganic polymer composites

Magnesium phosphate cement (MPC) has been proven to be a very good repair material for deteriorated concrete structures. It has excellent adhesion performance, leading to high bonding strength with old concrete substrates. This paper presents an experimental study into the properties of MPC binder as the matrix of carbon fiber sheets to form fiber-reinforced inorganic polymer (FRIP) composites. The physical and mechanical performance of the fresh mixed and the hardened MPC paste, the bond strength of carbon fiber sheets in the MPC matrix, the tensile strength of the carbon FRIP composites and the microstructure of the MPC matrix and fiber-reinforced MPC composites were investigated. The test results showed that the improved MPC binder is well suited for developing FRIP composites, which can be a promising alternative to externally-bonded fiber-reinforced polymer (FRP) composites for the strengthening of concrete structures. Through the present study, an in-depth understanding of the behavior of fiber-reinforced inorganic MPC composites has been achieved.
Properties of the Topographic Product of Experts

In this paper, we show how a topographic mapping can be created from a product of experts. We learn the parameters of the mapping using gradient descent on the negative logarithm of the probability density function of the data under the model. We show that the mapping, though retaining its product of experts form, becomes more like a mixture of experts during training.
Rectifying Self Organizing Maps for Automatic Concept Learning from Web Images

We attack the problem of learning concepts automatically from noisy web image search results. Going beyond low level attributes, such as colour and texture, we explore weakly-labelled datasets for the learning of higher level concepts, such as scene categories. The idea is based on discovering common characteristics shared among subsets of images by posing a method that is able to organise the data while eliminating irrelevant instances. We propose a novel clustering and outlier detection method, namely Rectifying Self Organizing Maps (RSOM). Given an image collection returned for a concept query, RSOM provides clusters pruned from outliers. Each cluster is used to train a model representing a different characteristics of the concept. The proposed method outperforms the state-of-the-art studies on the task of learning low-level concepts, and it is competitive in learning higher level concepts as well. It is capable to work at large scale with no supervision through exploiting the available sources.
Fault Isolation By Manifold Learning

This thesis investigates the possibility of improving black box fault diagnosis by a process called manifold learning, which simply stated is a way of finding patterns in recorded sensor data. The idea is that there is more information in the data than is exploited when using simple classification algorithms such as k-Nearest Neighbor and Support Vector Machines, and that this additional information can be found by using manifold learning methods. To test the idea of using manifold learning, data from two different fault diagnosis scenarios is used: A Scania truck engine and an electrical system called Adapt. Two linear and one non-linear manifold learning methods are used: Principal Component Analysis and Linear Discriminant Analysis (linear) and Laplacian Eigenmaps (non-linear). Some improvements are achieved given certain conditions on the diagnosis scenarios. The improvements for different methods correspond to the systems in which they are achieved in terms of linearity. The positive results for the relatively linear electrical system are achieved mainly by the linear methods Principal Component Analysis and Linear Discriminant Analysis and the positive results for the non-linear Scania system are achieved by the non-linear method Laplacian Eigenmaps. The results for scenarios without these special conditions are not improved however, and it is uncertain wether the improvements in special condition scenarios are due to gained information or to the nature of the cases themselves.
Blue Brain Technology: A Subway to Artificial Intelligence

Blue Brain is the name of the world's first virtual brain. A Virtual machine is one that can function as, a very appropriate application of an Artificial Intelligence human brain. Reverse engineering is a foremost concept of implementing the human brain and recreate it at the cellular level inside a complete simulation. The four major motivations behind the Blue Brain Technology are treatment of brain disfunctioning, scientific curiosity about consciousness and human mind, a bottom up approach towards building thinking machine and databases of all neuroscientific research results and related past stories. There are three main steps 2to build the virtual brain are data acquisition, simulation and visualization of results. The mission is undertaking the Blue Brain technology is to gather all existing knowledge of the brain, raise the global research efficiency of reverse engineering and to build a complete theoretical framework.
ADEC: Assured Deletion and Verifiable Version Control for Cloud Storage

Although cloud storage offers a number of attractive features, it also raises various security concerns. In particular, cloud storage of sensitive information, such as medical or trade records, requires measures to be taken not only for privacy protection, but also for assured deletion and verifiable version control. Even though there are methods for realizing both assured deletion and version control in cloud storage, they cannot guarantee the integrity of files and their versioning order. Therefore, we propose a system referred to as ``Assured Deletion and verifiable version Control (ADEC)'', which is implemented as a virtual file system capable of taking snapshots in cloud storage. The main ideas behind ADEC are a scheme for generating chained keys and a scheme for verifiable version control with hysteresis signatures. This method can be used not only for protection of privacy, but also for assured deletion of specific versions and all older versions in a single operation. In addition, the integrity of each file version can be verified with the hysteresis signature scheme, which makes it impossible to implement rollback and reordering attacks.
Mentoring “Inside” and “Outside” the Action of Teaching: A Professional Framework for Mentoring

ABSTRACT This study seeks to contribute to the research on mentored induction by investigating the practices mentors employ in their work with new teachers in two high-need, high-poverty urban elementary schools. Informed by Schwille’s (2008) temporal framework of “educative” mentoring practices occurring “inside” and “outside” the action of teaching, this study investigated the range of practices mentors employed, new teachers’ perceptions of the practices, and if the practices contributed to new teachers’ professional learning. Participants included six new teachers and two induction mentors. Results indicate that “inside” and “outside” mentoring practices are complementary and should be conceived as assisted performance and judiciously selected to promote productive changes in new teachers’ practice. Recommendations for mentoring programs are provided.