Miracle-dz/UniFAR-FTUs huggingface dataset:
Développement d'un système agroenvironnemental d'aide à la décision pour la réduction de la pollution de l'eau par des sources agricoles diffuses

Phosphorus (P) is an essential element for crop and animal production. The high P concentration accelerates the eutrophication of receiving fresh waters. This process may be accelerated by the P losses from agricultural lands. In Quebec, the nonpoint sources of P are recognized as the main source of P in surface water bodies (MENVQ, 1999). The P concentration in several agricultural watersheds in Quebec was found ranging from 0.1 to 0.2 mg/l (Gangbazo, 1997; Painchaud, 1996), which is higher than the guideline limit for Quebec’s Rivers (0.03 mg/l) (MENVQ, 2001). Therefore, recently attention has been drawn towards the control of agricultural P nonpoint sources to minimize the surface water degradation. Despite its importance, controlling P loads from agricultural nonpoint sources by using limiting parameters such as soil test phosphorus (STP) or threshold of P saturation % is not sufficient to assess the risk of P losses from cropland (Sharpley et al., 2002). Neither is a simple reduction in P applications through fertilization an acceptable solution. Moreover, the problem with this method is that conventional soil test analysis is designed to estimate the availability of agronomic P present in the soil than the potential of P loss (Sharpley et al., 1996). This project seeks to work with stakeholders to build a comprehensive and robust modeling framework to reduce phosphorus loads in rural watersheds. Stakeholders will be involved in evaluating and recommending P levels that run off their lands, aiming to attain acceptable water quality standards. This will permit the development of a policy and decision-making framework for use by watershed managers, policy makers, government regulatory agencies and agroenvironmental consultants. The quality of local water resources will be improved and more reliable and cost-effective P management plans will be achieved. The overall result will be an improvement in the quality of life and health of rural communities and an improvement in the socio-economic and environmental conditions of rural watersheds.
Opto-mechanical design and assembly of a Sagnac configuration of Entangled Photon Source for space applications: preliminary results

Entangled photon pairs are a key resource for a number of applications of quantum physics, such as quantum teleportation [1] and quantum cryptography [2]. A vital step towards the global-scale implementation of such applications via ground-to-satellite and inter-satellite links is the development of robust, space-proof entangled photon sources (EPS). In a simple description, entangled photons can be considered a pair of particles which are intimately correlated. In fact they are part of the same quantum state: any measurement on one of them affects the state of the other. Photon pairs can be entangled in a number of degrees of freedom (e.g. time, energy, momentum, polarization), whereby polarizationentanglement is the most established for applications in free-space optics. In this case, both photons are anti-correlated in their polarizations, i.e. measuring one particular polarization for one photon yields a perpendicular polarization state for the partner photon – the two photons are entangled irrespectively of their spatial separation (Einstein referred to this as spooky action at a distance). To date the best-developed method for the generation of photon pairs is spontaneous parametric down-conversion (SPDC) in second-order nonlinear crystals. In the SPDC process, a high energy pump beam (=405nm) spontaneously can produce a pair of photons at lower energy (=810nm). The efficient generation of photon pairs requires very strict characteristics on the illuminating beam, such e.g. its size, shape, and polarization state. For these photon pairs to be entangled in their polarizations there must be a superposition of two distinct pair-generation possibilities each with orthogonal polarized photon pairs. To achieve this with a single nonlinear crystal we have chosen a set-up based on Sagnac interferometer [3]. Thus, beams must travel equal optical paths along both sides and focus on the crystal at the same point.
Ultrastructural examination of left internal mammary artery under electron microscopy in patients with chronic kidney disease who underwent coronary bypass surgery

Aim: To investigate the vascular damage of internal mammary artery graft with electron microscope secondary to chronic renal failure transmission in patients who underwent coronary artery bypass grafting surgery. Method: A total of 30 patients (10 patients with chronic renal failure and 20 patients without chronic renal failure) who underwent coronary artery bypass graft surgery were included in this prospective study. Left internal mammary artery graft was harvested as conventional fashion with no touch technique. Samples were prepared and then examined with the transmission electron microscope. Every arterial sample was individually examined ultrastructurally, and the changes were recorded. Then the samples of the control group and chronic renal failure group were compared. Results: There were no significant differences between chronic renal failure group and the control group in terms of demographics, comorbidities, intraoperative data and postoperative outcomes, and the groups were statistically similar (p<0.05). Moreover, no statistically significance was detected in terms of structure and ultrastructure between the groups. Conclusion: The results of our study revealed that no ultrastructural changes were observed in the structure of IMA, suggesting that this graft would provide a good graft patency.
Acceleration and fragmentation of CORINE land cover changes in the United Kingdom from 2006–2012 detected by Copernicus IMAGE2012 satellite data

The CORINE land cover maps present the longest series of land cover maps with a consistent class labelling system and date back to 1985. This paper presents the results of the CORINE land cover mapping of the United Kingdom for 2012 and the corresponding land cover change map from 2006 to 2012. It compares the rates of change with those of the preceding land cover change map 2000–2006 and finds that land cover change has become smaller in scale, more diverse in types of change and affects more land cover polygons than in the past reporting period. Land cover change from 2006 to 2012 affected almost 60% more land cover polygons than from 2000 to 2006. A greater variety of 165 types of land cover change was detected from 2006 to 2012 than the 67 types of change from 2000 to 2006. The total land cover change area increased by over 21,000 ha or 11% but remained at around 1% of the total land area of the UK. Rotation forestry mostly of conifer forests was a dominant type of land cover change in both periods (53% of overall change from 2000 to 2006 and 54% from 2006 to 2012), followed by growth and replanting of conifer forest. From 2006 to 2012 the replanting rate decreased by almost 15,000 ha compared to 2000–2006 and a smaller decrease in planting of broadleaf and mixed forests was also observed. Urban land take continued from 2006 to 2012 in the UK, with over 16,000 ha of increase in artificial surfaces. The rate of change from other land cover types to artificial surfaces accelerated from 2006 to 2012. However, we urge caution when interpreting the rate of land take, as it includes wind farms in forested areas which leave the forest largely intact apart from an access road and the wind turbine sites. We also found that the inference from the land cover change matrices is dependent on the level of class aggregation (level 1, 2 or 3).
Spatio-Temporal Non-Rigid Registration of 3D Point Clouds of Plants

Analyzing sensor data of plants and monitoring plant performance is a central element in different agricultural robotics applications. In plant science, phenotyping refers to analyzing plant traits for monitoring growth, for describing plant properties, or characterizing the plant's overall performance. It plays a critical role in the agricultural tasks and in plant breeding. Recently, there is a rising interest in using 3D data obtained from laser scanners and 3D cameras to develop automated non-intrusive techniques for estimating plant traits. In this paper, we address the problem of registering 3D point clouds of the plants over time, which is a backbone of applications interested in tracking spatio-temporal traits of individual plants. Registering plants over time is challenging due to its changing topology, anisotropic growth, and non-rigid motion in between scans. We propose a novel approach that exploits the skeletal structure of the plant and determines correspondences over time and drives the registration process. Our approach explicitly accounts for the non-rigidity and the growth of the plant over time in the registration. We tested our approach on a challenging dataset acquired over the course of two weeks and successfully registered the 3D plant point clouds recorded with a laser scanner forming a basis for developing systems for automated temporal plant-trait analysis.
Improving Cross-Dataset Performance of Face Presentation Attack Detection Systems Using Face Recognition Datasets

Presentation attack detection (PAD) is now considered critically important for any face-recognition (FR) based access-control system. Current deep-learning based PAD systems show excellent performance when they are tested in intra-dataset scenarios. Under cross-dataset evaluation the performance of these PAD systems drops significantly. This lack of generalization is attributed to domain-shift. Here, we propose a novel PAD method that leverages the large variability present in FR datasets to induce invariance to factors that cause domain-shift. Evaluation of the proposed method on several datasets, including datasets collected using mobile devices, shows performance improvements in cross-dataset evaluations.1
Mining Spatiotemporal Features of City Traff ic Panagiotis

Short-term traffic forecasting is a field of research that has always attracted significant attention. The recent introduction of Machine Learning techniques in traffic forecasting has broadened the researchers’ horizons, making fresher approaches possible. However, researchers should not disregard the importance of spatiotemporal relations of a road network and classic statistical modeling, which also provide better interpretation. In this paper, we detect the spatiotemporal relationships of the extended 2 ring road network of Xi’an, China using Pearson’s Correlation, Mutual Information and Dynamic Time Warping on the network’s speed time series. The first two give an indication of the spatial dependency between road sections by comparing their speeds’ contributions, while Dynamic Time Warping takes also into account the temporal evolution of the phenomenon. Results show that, although the first approach leads to an accurate Bayesian Network prediction model, the second one leads to an improved accuracy using the same modeling structure.
Comparison of impacts of friction massage, stretching exercises and analgesics on pain relief in primary fibromyalgia syndrome: a randomized clinical trial

Background : Stretching exercises and massage therapy are both suggested for pain relief in fibromyalgia syndrome. Previous studies have not proved their superiority over each other. This study compared the therapeutic effects of friction massage, stretching exercises, and analgesics on pain relief in patients with fibromyalgia syndrome. Methods : We evaluated 129 female patients with the diagnosis of primary fibromyalgia visited at the physical medicine clinics of Baqiyatallah Hospital in Tehran, Iran during 2010- 2011. Patients were randomly divided into three groups: the first group received 400 mg ibuprofen P.O. (3 times per day) and 25 mg nortriptyline (daily) P.O. as analgesic, the second group was treated by friction massage and the third group performed stretching exercises. Patients were assessed three times (initially, after one and four weeks) by visual analogue scale (VAS). Results : The mean age of participants was 60.46 years. The mean age in each treatment group was 46.66 years in medication group, 46.73 years in stretching group and 46.65 years in friction massage group. Changes in VAS score over 4 weeks were 2.4, 3.1 and 1.9, in the first, second, and third groups, respectively. The changes in VAS were significantly different in the first and second groups rather than the controls (P Conclusion : The effect of stretch exercise on pain relief was similar to analgesics, but it was more effective than friction massage. Moreover, the therapeutic effect of stretching exercise on pain relief upon four weeks was more permanent than friction massage but it was similar to analgesics.
Enabling Workload Allocation for Macro Cells and RRH in H-CRAN

: The heterogeneous cloud radio access network (H-CRAN) has become the most prominent way to handle interference and data traffic challenges. The main idea of the H-CRAN is incorporating cloud computing into a heterogeneous network (HetNet) to enhance coordinated multipoint transmission and reception, cooperative radio resource management, and self-organizing networks, which improve both spectral and energy efficiencies of cellular systems. One of the main challenges to balance the data transmission in back haul by decreasing the bandwidth of the backhaul is implemented previously. In this paper we propose that we can also allocate the workload between macro cells and RRHs.
The Impact of Industrial Characteristics and Organizational Climate on Knowledge Management Process and Business Innovation Performance

The aim of this paper is to examine how bioscience industry exploits KMS to effectively manage intellectual property, especially patent documents. A majority of Taiwan’s bioscience firms are small and medium-sized start-up companies that possess many valuable intellectual property and patents/licenses. Nevertheless, due to lack or distortion of information, they are more likely to be charged of patent infringement by overseas companies who own the invention or technology. Four constructs are developed in this research: KMS, business innovation performance, and Bio-KMS benefits. Seven propositions are developed.
A MODIFIED STRUCTURE OF CARRY SELECT ADDER USING CNTFET TECHNOLOGY

Carry Select Adder (CSLA) is one of the fastest adders used in many data processing processors to perform fast arithmetic functions. This work uses a simple and efficient gate level modification to significantly reduce the area and power of the CSLA. The modified design has reduced area and power as compared with multiternary digit (trit) adder design. The adder is based on an efficient single-trit full-adder design with low-complexity encoder and reduced complexity carry generation unit. Further, we optimize the number of encoder and decoder blocks required while putting together several single-trit full-adder units to realize a multitrit adder. Extensive HSPICE simulation results show roughly 79% reduction in power-delay product for three-trit adders and 88% reduction in power-delay product for nine-trit adders in comparison to a direct realization.
Towards Affect-based User Models: a comparative study with various datasets, features and algorithms, for multi-labeled, probabilistic affect detection

—In this paper, we make use of machine learning based techniques to try to ﬁnd the best possible combination of datasets, affect lexicons, features, and algorithms, for the automatic detection of affect in text. For this purpose, we consider as affective categories, the six basic emotions from Paul Ekman ( anger , disgust , fear , happiness , sadness , and surprise ). For the experiments, we count with three different datasets, which contain, respectively, news headlines, fairy tales and blogs sentences, and with two affect lexicons: WordNet-Affect and Roget’s Thesaurus. From this collection of data, we compare the performance of two different classiﬁcation algorithms: Naive Bayes and Support Vector Machines (SVM). The experiments allow us to demonstrate that from the various possible combinations, there are two, in particular, that stand out and are more appropriate for the purpose of this study. We evaluate and discuss the results with the aim of, in the future, building user affect models.
Character Education Model For Junior High School Students Based On Java Ethnopedagogic

The primary aim of this paper is to find an effective and efficient character education model for Javanese ethno pedagogy junior high school students. The Javanese teachings used are the teachings of one of the educational leaders in Indonesia who come from Javanese society, namely Ki Hajar Dewantara, who reads Ing ngarso sung tulodho, ing madyo mangun karso, tut wuri handayani. Data collection uses qualitative methods that are case studies by conducting observations, in-depth interviews, FGDs, and documents. The results were analyzed using the stages of category collection, direct interpretation, searching for category equivalence and naturalistic generalizations. The research subjects were teachers and students of grade 1 junior high school. The results showed that character education for Javanese Ethno pedagogy-based junior high school students was carried out in several stages, namely, training, mentoring and supervision for teachers and through modeling, habituation, KBM, extracurricular activities, and wall writing for students. The findings of this study provide a basic recommendation for government authorities to issue policies implementing character education for culture-based junior high school students. Also, the stages inside proved to be effective and efficient in shaping the personality of students to have a Javanese identity.
Assessing Individual Differences in EFs

Executive functions (EFs)—a set of general-purpose control processes that regulate one’s thoughts and behaviors—have become a popular research topic lately and have been studied in many subdisciplines of psychological science. This article summarizes the EF research that our group has conducted to understand the nature of individual differences in EFs and their cognitive and biological underpinnings. In the context of a new theoretical framework that we have been developing (the unity/diversity framework), we describe four general conclusions that have emerged. Specifically, we argue that individual differences in EFs, as measured with simple laboratory tasks, (a) show both unity and diversity (different EFs are correlated yet separable), (b) reflect substantial genetic contributions, (c) are related to various clinically and societally important phenomena, and (d) show some developmental stability.
for a Role in Diminished T Cell Reactivity CTLA-4 in Filarial Infections: Implications

Cathy Steel and Thomas B. Nutmanhttp://www.jimmunol.org/content/170/4/1930J Immunol€2003; 170:1930-1938; ;Referenceshttp://www.jimmunol.org/content/170/4/1930.full#ref-list-1This article cites 62 articles, 39 of which you can access for free at: Subscriptionshttp://jimmunol.org/subscriptionsInformation about subscribing to The Journal of Immunology is online at: Permissionshttp://www.aai. org/ji/copyright.htmlSubmit copyright permission requests at: Email Alertshttp://jimmunol.org/cgi/alerts/etocReceive free email-alerts when new articles cite this article. Sign up at:
Multi Tumor Classification in MR Brain Images through deep Feature Extraction using CNN and Supervised Classifier

: Classification of brain tumor is challenging task for both radiologists and researchers. Brain tumors have different types in shapes and orientations. After tumor classification by radiologists, the treatment is planned to improving the life span of the patient. MRI is the common medical imaging modality used to acquire the brain images due to its high quality in soft tissues and less radiation. Traditional machine learning algorithms classify the brain tumors based on few handcrafted features with expert’s choice, which may lead to decay in performance of Algorithms. Deep learning models became more popular in recent years in classification of Images. CNNs have proved as a master in extracting the huge number of non-handcrafted features to improve the accuracy of classification models. In this paper the MR brain images are classified by taking advantage of CNN for feature extraction and supervised classifiers for classification on publicly available two datasets. The former one is to classify LGG (Benign) and HGG (Malignant), other one is to classify glioma, meningioma and pituitary tumors. The proposed model compared with other machine learning and pre trained CNNs models, the proposed model is hybrid combination of CNN and KNN classifier attained noteworthy performance in terms of overall accuracy of 96.10% and 96.74%.
White Paper # 10 – In situ Temperature , Salinity , an d Velocity Observations

The observing system requirements for in situ temperature, salinity, and velocity (T,S,V) are set by the space/time scales of phenomena that are to be resolved in applications. The process of observing system design is an iterative one, as improved understanding of the requirements is balanced with new technologies that make more comprehensive observations practical. Observing system design is a compromise between what is required and what is feasible from economical and/or logistical perspectives. Observing system designs relevant to the tropical Pacific Ocean are described for TAO/TRITON (Hayes et al., 1991; Anonymous, 2002), the Argo Program (Argo Steering Team, 1998), the Global Drifter Program (Dohan et al., 2010), and the Expendable Bathy-Thermograph (XBT) Networks (Goni et al., 2010).
Modelling the neuroanatomical progression of Alzheimer's disease and posterior cortical atrophy

In order to find effective treatments for Alzheimer's disease (AD), we need to identify subjects at risk of AD as early as possible. To this end, recently developed disease progression models can be used to perform early diagnosis, as well as predict the subjects' disease stages and future evolution. However, these models have not yet been applied to rare neurodegenerative diseases, are not suitable to understand the complex dynamics of biomarkers, work only on large multimodal datasets, and their predictive performance has not been objectively validated. In this work I developed novel models of disease progression and applied them to estimate the progression of Alzheimer's disease and Posterior Cortical atrophy, a rare neurodegenerative syndrome causing visual deficits. My first contribution is a study on the progression of Posterior Cortical Atrophy, using models already developed: the Event-based Model (EBM) and the Differential Equation Model (DEM). My second contribution is the development of DIVE, a novel spatio-temporal model of disease progression that estimates fine-grained spatial patterns of pathology, potentially enabling us to understand complex disease mechanisms relating to pathology propagation along brain networks. My third contribution is the development of Disease Knowledge Transfer (DKT), a novel disease progression model that estimates the multimodal progression of rare neurodegenerative diseases from limited, unimodal datasets, by transferring information from larger, multimodal datasets of typical neurodegenerative diseases. My fourth contribution is the development of novel extensions for the EBM and the DEM, and the development of novel measures for performance evaluation of such models. My last contribution is the organization of the TADPOLE challenge, a competition which aims to identify algorithms and features that best predict the evolution of AD.