Contribution to supervised representation learning: algorithms and applications.

278 p.In this thesis, we focus on supervised learning methods for pattern categorization. In this context, itremains a major challenge to establish efficient relationships between the discriminant properties of theextracted features and the inter-class sparsity structure.Our first attempt to address this problem was to develop a method called "Robust Discriminant Analysiswith Feature Selection and Inter-class Sparsity" (RDA_FSIS). This method performs feature selectionand extraction simultaneously. The targeted projection transformation focuses on the most discriminativeoriginal features while guaranteeing that the extracted (or transformed) features belonging to the sameclass share a common sparse structure, which contributes to small intra-class distances.In a further study on this approach, some improvements have been introduced in terms of theoptimization criterion and the applied optimization process. In fact, we proposed an improved version ofthe original RDA_FSIS called "Enhanced Discriminant Analysis with Class Sparsity using GradientMethod" (EDA_CS). The basic improvement is twofold: on the first hand, in the alternatingoptimization, we update the linear transformation and tune it with the gradient descent method, resultingin a more efficient and less complex solution than the closed form adopted in RDA_FSIS.On the other hand, the method could be used as a fine-tuning technique for many feature extractionmethods. The main feature of this approach lies in the fact that it is a gradient descent based refinementapplied to a closed form solution. This makes it suitable for combining several extraction methods andcan thus improve the performance of the classification process.In accordance with the above methods, we proposed a hybrid linear feature extraction scheme called"feature extraction using gradient descent with hybrid initialization" (FE_GD_HI). This method, basedon a unified criterion, was able to take advantage of several powerful linear discriminant methods. Thelinear transformation is computed using a descent gradient method. The strength of this approach is thatit is generic in the sense that it allows fine tuning of the hybrid solution provided by different methods.Finally, we proposed a new efficient ensemble learning approach that aims to estimate an improved datarepresentation. The proposed method is called "ICS Based Ensemble Learning for Image Classification"(EM_ICS). Instead of using multiple classifiers on the transformed features, we aim to estimate multipleextracted feature subsets. These were obtained by multiple learned linear embeddings. Multiple featuresubsets were used to estimate the transformations, which were ranked using multiple feature selectiontechniques. The derived extracted feature subsets were concatenated into a single data representationvector with strong discriminative properties.Experiments conducted on various benchmark datasets ranging from face images, handwritten digitimages, object images to text datasets showed promising results that outperformed the existing state-ofthe-art and competing methods

Ensemble learning via feature selection and multiple transformed subsets: Application to image classification

[EN]In the machine learning field, especially in classification tasks, the model's design and construction are very important. Constructing the model via a limited set of features may sometimes bound the classification performance and lead to non-optimal performances that some algorithms can provide. To this end, Ensemble learning methods were proposed in the literature. These methods' main goal is to learn a set of models that provide features or predictions whose joint use could lead to a performance better than that obtained by the single model. In this paper, we propose three variants of a new efficient ensemble learning approach that was able to enhance the classification performance of a linear discriminant embedding method. As a case study we consider the efficient "Inter-class sparsity discriminative least square regression" method. We seek the estimation of an enhanced data representation. Instead of deploying multiple classifiers on top of the transformed features, we target the estimation of multiple extracted feature subsets obtained by multiple learned linear embeddings. These are associated with subsets of ranked original features. Multiple feature subsets were used for estimating the transformations. The derived extracted feature subsets were concatenated to form a single data representation vector that is used in the classification process. Many factors were studied and investigated in this paper including (Parameter combinations, number of models, different training percentages, feature selection methods combinations, etc.). Our proposed approach has been benchmarked on different image datasets of various sizes and types (faces, objects and scenes). The proposed scheme achieved competitive performance on four face image datasets (Extended Yale B, LFW-a, Gorgia and FEI) as well as on the COIL20 object dataset and the Outdoor Scene dataset. We measured the performance of our proposed schemes in comparison to (the single model ICS_DLSR, RDA_GD, RSLDA, PCE, LDE, LDA, SVM as well as the KNN algorithm) The conducted experiments showed that the proposed approach can enhance the classification performance in an efficient manner compared to the single-model based learning and was able to outperform its competing methods

Increasing Anteroposterior Genital Hiatus Widening Does Not Limit Apical Descent for Prolapse Staging during Valsalva’s Maneuver: Effect on Symptom Severity and Surgical Decision Making

Objective: Determine if anteroposterior genital hiatus (GH) widening obscures rather than facilitates signs and symptoms, inadvertently altering management decisions for women with pelvic organ prolapse (POP) during Valsalva's Maneuver, at a given total vaginal length (TVL). Methods: We performed a retrospective cohort with nested cross-sectional study of patients who underwent POP surgery. Data from obstetric and gynecologic history, preoperative and postoperative physical examinations, and 20-item Pelvic Floor Distress Inventory (PFDI-20) and 7-item Pelvic Floor Impact Questionnaire (PFIQ-7) scores were extracted. Study participants were compared in 2 groups: anteroposterior widened (>3 cm) and not widened (<=3 cm) GH, for baseline leading edge and POP stage, while controlling for TVL. Baseline PFDI-20 and PFIQ-7 scores were evaluated within GH groups. Delta GH, PFDI-20, and PFIQ-7 scores after apical suspension with and without posterior colporrhaphy were compared to assess the clinical value of the procedure. Results: Study participants with anteroposterior GH widening during Valsalva maneuver had greater baseline leading edge descent and higher POP stage compared with those without anteroposterior GH widening after controlling for TVL. Baseline PFDI-20 and PFIQ-7 scores were similar within both GH categories controlling for prolapse severity. Adding posterior colporrhaphy to apical suspension resulted in a greater anteroposterior GH reduction without improving delta PFDI-20 or PFIQ-7 scores. Conclusions: Facilitation through herniation rather than obscuration from anteroposterior GH widening explains why patients will not be undertreated based on signs and symptoms of disease. Adding posterior colporrhaphy to apical suspension more effectively reduces anteroposterior GH widening without differential improvement in symptoms rendering the operation to no more than a cosmetic procedure

Nouvelle topologie de déphaseurs analogiques agiles large bande

National audienceUn nouveau type de déphaseur analogique à variation continue de phase est présenté. Il est basé sur l'association du déphaseur de Schiffman et de varicaps assurant l'agilité en phase. Cette topologie assure un nouveau compromis particulièrement intéressant en terme de bande passante, de plage de variation de phase, de pertes d'insertion, de compacité et de coût. L'application principale visée est le réseau d'antennes à balayage. Un prototype à une seule cellule a été conçue sur la bande 4-7 GHz. La validation expérimentale montre un très bon accord avec la simulation avec une agilité en phase de plus de 180° et permet d'envisager les 360° avec seulement deux cellules, tout en assurant un niveau de pertes d'insertion très raisonnable pour un déphaseur analogique

An All-Pass Topology to Design a 0-360° Continuous Phase Shifter with Low Insertion Loss and Constant Differential Phase Shift

International audienceIn this paper, an analog phase shifter is designed by using a novel all-pass topology. The phase shift can be continuously adjusted from 0 up to 380° by biasing varactor-diodes while maintaining the differential phase shift constant across the 6.7 GHz - 7.7 GHz band. This two-stage circuit is simple and compact with respectively insertion losses of 2.9 dB +- 1.3 dB, return losses better than 9.4 dB and a differential phase shift flatness of +- 11° in the worst case. With a 90.5°/dB Figure-of-Merit, this topology presents an interesting trade-off between low-cost, low loss, large phase-shift range, phase flatness and bandwidth. Measurements are discussed and carefully compared to current competing topologies

Nouveau déphaseur variable analogique large-bande

National audienceDans cet article, une nouvelle topologie de déphaseur analogique variable est proposée. Elle est obtenue en inter-combinant un déphaseur de Schiffman et un déphaseur passe-tout mixte distribué/localisé. Ce déphaseur compact présente l'avantage d'une forte agilité en phase pour une variation limitée de la capacité de la varicap. Les mesures du dispositif réalisé en technologie hybride microruban confirme une variation continue du déphasage jusqu'à 180° pour un circuit à une seule cellule. Sur l'ensemble de la bande considérée, i.e. 4-7 GHz, les pertes d'insertion sont à 1,8 dB+- 1 dB. Cette topologie présente un compromis entre plage de variation, compacité, pertes, platitude de phase particulièrement intéressant

FAMILIAL MEDITERRANEAN FEVER: A GENERAL REVIEW

Familial Mediterranean Fever (FMF) is an autosomal recessive inherited disease, which is accompanied by recurrent attacks of fever and serositis. It can be distinguished into two types. Type 1, is associated with recurrent short episodes of inflammation and polyserositis; type 2, is characterized by the accumulation of serum amyloid A mainly in the kidney leading to amyloidosis. The etiology of this disease is due to mutations in the MEFV gene, which encodes the protein “pyrin”. These mutations cause the uncontrolled production of proinflammatory cytokines including interleukin 1. Genetic analysis is important to confirm the diagnosis in the patients. Colchicine is the drug of choice. However, some people are resistant to this drug. In such cases, newer biologic agents have used in the treatment of the disease. This review aims to discuss the most recent advances about FMF including the major symptoms, the diagnosis, the genetics and the management

Study of wavelength switching time in tunable semiconductor micro-ring lasers: experiment and travelling wave description

We report in this paper the wavelength switching features of semiconductor ring lasers that are wavelength tunable based on filtered optical feedback. The filtered feedback provides a wavelength dependent loss mechanism in these devices with which a particular longitudinal mode, and thus a particular wavelength, can be selected by changing the filter characteristics of the feedback channel. We investigate how the wavelength switching speed depends on the amplitude of the modulation of the switching driving signal and on the different phase factors within the filtering branches of the SRL. We compare qualitatively the experimental results with numerical simulations based on a travelling wave model. We also investigate the dynamical behavior of the lasing and nonlasing longitudinal modes in the two channels of the clockwise and the counter-clockwise directions. We show the crucial importance of various phase relation factors on the wavelength switching behavior. Finally, we discuss what limits the switching speed and how we can accelerate it

Analysis and enhancement of wireless LANs in noisy channels

Without a doubt, Wireless Local Area Networks (WLANs) technology has been encountering an explosive growth lately. IEEE 802.11 is the standard associated with this promising technology, which enures shared access to the wireless medium through the distributed coordination function (DCF). Recently, the IEEE 802.11e task group has made extensions to WLANs medium access control (MAC) in order to support quality of service (QoS) traffic. An inherited problem for WLANs, is the volatility of the propagation medium, which is a challenging issue that affects the system performance significantly. Consequently, enhancing the operation of the DCF in noisy environments is of great interest, and has attracted the attention of many researchers. Our first major contribution in the presented thesis, is an analytical and simulation analysis for the binary exponential backoff (BEB) scheme of the DCF, in the presence of channel noise. We show that following the BEB procedure when a host encounters erroneous transmission is needed only if the channel was highly loaded. However, incrementing the contention window (CW) upon each packet failure, whether caused by instantaneous transmission (i.e. collision) or channel noise, will result in the waste of air time if the channel was lightly loaded. Accordingly, we present a hybrid access method that adapts the CW according to the channel load along with the frame error rate (FER). Other means to overcome the channel noise is the adjustment of the transmission rate. Many rate adaptation (RA) algorithms were introduced in the past few years, including the Automatic Rate Fallback (ARF) which is currently implemented in the wireless cards. Yet, many drawbacks are associated with these RA algorithms; specifically, in regard to the techniques and events that should trigger the rate change. Moreover, the IEEE 802.11e QoS flows requirements were not considered with the latter schemes. Accordingly, our next major contribution in this work is the presentation of a novel rate adaptation scheme. The simplicity of the introduced rate adaptation scheme is that it relies on the MAC layer parameters rather than those of the PHY layer when adjusting the rate. Furthermore, our algorithm supports the IEEE 802.11e MAC extensions where QoS traffic requirements were integrated in the procedure of adjusting the bit rate. Hence, strict real-time flow parameters such as delay and maximum drop rate are respected. Finally, we enhance the dynamic assignment of transmission opportunities (TXOPs) in order to offer fair air-time for nodes facing high packet loss rat