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

Android-applikation för övervakning av ett industriellt automationssystem

The technology in the industry has grown substantially over the years, particularly in the wireless area. Today's smartphones and other devices that connect to a wireless network, has the opportunity to communicate with other devices via the TCP / IP protocol. This places significant demands from the companies in the industry, which believes that there is opportunity to improve productivity in the company. This report describes how an Android application was developed as a bachelor thesis at Tetra Pak AB in Lund. The main goal for the project is to build up an information structure between the beverage processing line automation system and a portable device like iPhone, iPad or Android. The work began with a literature search to find out if a mobile platform today has the ability to monitor production and also control machines through communication between two different operating systems such as Microsoft and Android. We have chosen to use the Android when, comparisons, proved to be the most appropriate environment to meet most of the requirements. The application is supposed to act as a user interface on a portable device which is connected to Tetra Pak's internal network and the process line. The application's functionality consists of four points: Quality Assurance, Manual addition, Maintenance and Information Overview. The automation system consists of a PLC from Rockwell, HMI Wonderware InTouch and an SQL database. The final product was named Tetroid. It contains two of the four functions which is mentioned above, Manual addition and Information Overview

A Predictive Model for Steady State Ozone Concentration at an Urban-Coastal Site

Ground level ozone (O-3) plays an important role in controlling the oxidation budget in the boundary layer and thus affects the environment and causes severe health disorders. Ozone gas, being one of the well-known greenhouse gases, although present in small quantities, contributes to global warming. In this study, we present a predictive model for the steady-state ozone concentrations during daytime (13:00-17:00) and nighttime (01:00-05:00) at an urban coastal site. The model is based on a modified approach of the null cycle of O-3 and NOx and was evaluated against a one-year data-base of O-3 and nitrogen oxides (NO and NO2) measured at an urban coastal site in Jeddah, on the west coast of Saudi Arabia. The model for daytime concentrations was found to be linearly dependent on the concentration ratio of NO2 to NO whereas that for the nighttime period was suggested to be inversely proportional to NO2 concentrations. Knowing that reactions involved in tropospheric O-3 formation are very complex, this proposed model provides reasonable predictions for the daytime and nighttime concentrations. Since the current description of the model is solely based on the null cycle of O-3 and NOx, other precursors could be considered in future development of this model. This study will serve as basis for future studies that might introduce informing strategies to control ground level O-3 concentrations, as well as its precursors' emissions.Peer reviewe

A Predictive Model for Steady State Ozone Concentration at an Urban-Coastal Site

Ground level ozone (O3) plays an important role in controlling the oxidation budget in the boundary layer and thus affects the environment and causes severe health disorders. Ozone gas, being one of the well-known greenhouse gases, although present in small quantities, contributes to global warming. In this study, we present a predictive model for the steady-state ozone concentrations during daytime (13:00–17:00) and nighttime (01:00–05:00) at an urban coastal site. The model is based on a modified approach of the null cycle of O3 and NOx and was evaluated against a one-year data-base of O3 and nitrogen oxides (NO and NO2) measured at an urban coastal site in Jeddah, on the west coast of Saudi Arabia. The model for daytime concentrations was found to be linearly dependent on the concentration ratio of NO2 to NO whereas that for the nighttime period was suggested to be inversely proportional to NO2 concentrations. Knowing that reactions involved in tropospheric O3 formation are very complex, this proposed model provides reasonable predictions for the daytime and nighttime concentrations. Since the current description of the model is solely based on the null cycle of O3 and NOx, other precursors could be considered in future development of this model. This study will serve as basis for future studies that might introduce informing strategies to control ground level O3 concentrations, as well as its precursors’ emissions

A Predictive Model for Steady State Ozone Concentration at an Urban-Coastal Site

Ground level ozone (O3) plays an important role in controlling the oxidation budget in the boundary layer and thus affects the environment and causes severe health disorders. Ozone gas, being one of the well-known greenhouse gases, although present in small quantities, contributes to global warming. In this study, we present a predictive model for the steady-state ozone concentrations during daytime (13:00–17:00) and nighttime (01:00–05:00) at an urban coastal site. The model is based on a modified approach of the null cycle of O3 and NOx and was evaluated against a one-year data-base of O3 and nitrogen oxides (NO and NO2) measured at an urban coastal site in Jeddah, on the west coast of Saudi Arabia. The model for daytime concentrations was found to be linearly dependent on the concentration ratio of NO2 to NO whereas that for the nighttime period was suggested to be inversely proportional to NO2 concentrations. Knowing that reactions involved in tropospheric O3 formation are very complex, this proposed model provides reasonable predictions for the daytime and nighttime concentrations. Since the current description of the model is solely based on the null cycle of O3 and NOx, other precursors could be considered in future development of this model. This study will serve as basis for future studies that might introduce informing strategies to control ground level O3 concentrations, as well as its precursors’ emissions

New particle formation, growth and apparent shrinkage at a rural background site in western Saudi Arabia

Atmospheric aerosols have significant effects on human health and the climate. A large fraction of these aerosols originates from secondary new particle formation (NPF), where atmospheric vapors form small particles that subsequently grow into larger sizes. In this study, we characterize NPF events observed at a rural background site of Hada Al Sham (21.802 degrees N, 39.729 degrees E), located in western Saudi Arabia, during the years 2013-2015. Our analysis shows that NPF events occur very frequently at the site, as 73 % of all the 454 classified days were NPF days. The high NPF frequency is likely explained by the typically prevailing conditions of clear skies and high solar radiation, in combination with sufficient amounts of precursor vapors for particle formation and growth. Several factors suggest that in Hada Al Sham these precursor vapors are related to the transport of anthropogenic emissions from the coastal urban and industrial areas. The median particle formation and growth rates for the NPF days were 8.7 cm(-3) s(-1) (J(7)(nm)) and 7.4 nm h(-1) (GR(7-12nm)), respectively, both showing highest values during late summer. Interestingly, the formation and growth rates increase as a function of the condensation sink, likely reflecting the common anthropogenic sources of NPF precursor vapors and primary particles affecting the condensation sink. A total of 76 % of the NPF days showed an unusual progression, where the observed diameter of the newly formed particle mode started to decrease after the growth phase. In comparison to most long-term measurements, the NPF events in Hada Al Sham are exceptionally frequent and strong both in terms of formation and growth rates. In addition, the frequency of the decreasing mode diameter events is higher than anywhere else in the world.Peer reviewe

The role of CD8+ T cell clones in immune thrombocytopenia

Immune thrombocytopenia (ITP) is traditionally considered an antibody-mediated disease. However, a number of features suggest alternative mechanisms of platelet destruction. In this study, we use a multi-dimensional approach to explore the role of cytotoxic CD8+ T cells in ITP. We characterised patients with ITP and compared them to age-matched controls using immunophenotyping, next-generation sequencing of T cell receptor (TCR) genes, single-cell RNA sequencing, and functional T cell and platelet assays. We found that adults with chronic ITP have increased polyfunctional, terminally differentiated effector memory CD8+ T cells (CD45RA+CD62L-) expressing intracellular interferon-g, tumour necrosis factor-a, and Granzyme B defining them as TEMRA cells. These TEMRA cells expand when the platelet count falls and show no evidence of physiological exhaustion. Deep sequencing of the T cell receptor showed expanded T cell clones in patients with ITP. T cell clones persisted over many years, were more prominent in patients with refractory disease, and expanded when the platelet count was low. Combined single-cell RNA and TCR sequencing of CD8+ T cells confirmed that the expanded clones are TEMRA cells. Using in vitro model systems, we show that CD8+ T cells from patients with ITP form aggregates with autologous platelets, release interferon-g and trigger platelet activation and apoptosis through TCR-mediated release of cytotoxic granules. These findings of clonally expanded CD8+ T cells causing platelet activation and apoptosis provide an antibody-independent mechanism of platelet destruction, indicating that targeting specific T-cell clones could be a novel therapeutic approach for patients with refractory ITP

BACH2 immunodeficiency illustrates an association between super-enhancers and haploinsufficiency.

The transcriptional programs that guide lymphocyte differentiation depend on the precise expression and timing of transcription factors (TFs). The TF BACH2 is essential for T and B lymphocytes and is associated with an archetypal super-enhancer (SE). Single-nucleotide variants in the BACH2 locus are associated with several autoimmune diseases, but BACH2 mutations that cause Mendelian monogenic primary immunodeficiency have not previously been identified. Here we describe a syndrome of BACH2-related immunodeficiency and autoimmunity (BRIDA) that results from BACH2 haploinsufficiency. Affected subjects had lymphocyte-maturation defects that caused immunoglobulin deficiency and intestinal inflammation. The mutations disrupted protein stability by interfering with homodimerization or by causing aggregation. We observed analogous lymphocyte defects in Bach2-heterozygous mice. More generally, we observed that genes that cause monogenic haploinsufficient diseases were substantially enriched for TFs and SE architecture. These findings reveal a previously unrecognized feature of SE architecture in Mendelian diseases of immunity: heterozygous mutations in SE-regulated genes identified by whole-exome/genome sequencing may have greater significance than previously recognized