Conditional Dynamic Mutual Information-Based Feature Selection

With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in the post-analysis process. Thus, it is necessary to eliminate irrelevant features from data before being fed into learning algorithms. Currently, many endeavors have been attempted in this field and many outstanding feature selection methods have been developed. Among different evaluation criteria, mutual information has also been widely used in feature selection because of its good capability of quantifying uncertainty of features in classification tasks. However, the mutual information estimated on the whole dataset cannot exactly represent the correlation between features. To cope with this issue, in this paper we firstly re-estimate mutual information on identified instances dynamically, and then introduce a new feature selection method based on conditional mutual information. Performance evaluations on sixteen UCI datasets show that our proposed method achieves comparable performance to other well-established feature selection algorithms in most cases

MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates

Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates need to be collected at a server. However, when being deployed at mobile edge networks, clients may have unpredictable availability and drop out of the training process, which hinders the convergence of FL. This paper tackles such a critical challenge. Specifically, we first investigate the convergence of the classical FedAvg algorithm with arbitrary client dropouts. We find that with the common choice of a decaying learning rate, FedAvg oscillates around a stationary point of the global loss function, which is caused by the divergence between the aggregated and desired central update. Motivated by this new observation, we then design a novel training algorithm named MimiC, where the server modifies each received model update based on the previous ones. The proposed modification of the received model updates mimics the imaginary central update irrespective of dropout clients. The theoretical analysis of MimiC shows that divergence between the aggregated and central update diminishes with proper learning rates, leading to its convergence. Simulation results further demonstrate that MimiC maintains stable convergence performance and learns better models than the baseline methods

Multi-Criteria Inventory Classification and Root Cause Analysis Based on Logical Analysis of Data

RÉSUMÉ : La gestion des stocks de pièces de rechange donne un avantage concurrentiel vital dans de nombreuses industries, en passant par les entreprises à forte intensité capitalistique aux entreprises de service. En raison de la quantité élevée d'unités de gestion des stocks (UGS) distinctes, il est presque impossible de contrôler les stocks sur une base unitaire ou de porter la même attention à toutes les pièces. La gestion des stocks de pièces de rechange implique plusieurs intervenants soit les fabricants d'équipement d'origine (FEO), les distributeurs et les clients finaux, ce qui rend la gestion encore plus complexe. Des pièces de rechange critiques mal classées et les ruptures de stocks de pièces critiques ont des conséquences graves. Par conséquent il est essentiel de classifier les stocks de pièces de rechange dans des classes appropriées et d'employer des stratégies de contrôle conformes aux classes respectives. Une classification ABC et certaines techniques de contrôle des stocks sont souvent appliquées pour faciliter la gestion UGS. La gestion des stocks de pièces de rechange a pour but de fournir des pièces de rechange au moment opportun. La classification des pièces de rechange dans des classes de priorité ou de criticité est le fondement même de la gestion à grande échelle d’un assortiment très varié de pièces. L'objectif de la classification est de classer systématiquement les pièces de rechange en différentes classes et ce en fonction de la similitude des pièces tout en considérant leurs caractéristiques exposées sous forme d'attributs. L'analyse ABC traditionnelle basée sur le principe de Pareto est l'une des techniques les plus couramment utilisées pour la classification. Elle se concentre exclusivement sur la valeur annuelle en dollar et néglige d'autres facteurs importants tels que la fiabilité, les délais et la criticité. Par conséquent l’approche multicritères de classification de l'inventaire (MCIC) est nécessaire afin de répondre à ces exigences. Nous proposons une technique d'apprentissage machine automatique et l'analyse logique des données (LAD) pour la classification des stocks de pièces de rechange. Le but de cette étude est d'étendre la méthode classique de classification ABC en utilisant une approche MCIC. Profitant de la supériorité du LAD dans les modèles de transparence et de fiabilité, nous utilisons deux exemples numériques pour évaluer l'utilisation potentielle du LAD afin de détecter des contradictions dans la classification de l'inventaire et de la capacité sur MCIC. Les deux expériences numériques ont démontré que LAD est non seulement capable de classer les stocks mais aussi de détecter et de corriger les observations contradictoires en combinant l’analyse des causes (RCA). La précision du test a été potentiellement amélioré, non seulement par l’utilisation du LAD, mais aussi par d'autres techniques de classification d'apprentissage machine automatique tels que : les réseaux de neurones (ANN), les machines à vecteurs de support (SVM), des k-plus proches voisins (KNN) et Naïve Bayes (NB). Enfin, nous procédons à une analyse statistique afin de confirmer l'amélioration significative de la précision du test pour les nouveaux jeux de données (corrections par LAD) en comparaison aux données d'origine. Ce qui s’avère vrai pour les cinq techniques de classification. Les résultats de l’analyse statistique montrent qu'il n'y a pas eu de différence significative dans la précision du test quant aux cinq techniques de classification utilisées, en comparant les données d’origine avec les nouveaux jeux de données des deux inventaires.----------ABSTRACT : Spare parts inventory management plays a vital role in maintaining competitive advantages in many industries, from capital intensive companies to service networks. Due to the massive quantity of distinct Stock Keeping Units (SKUs), it is almost impossible to control inventory by individual item or pay the same attention to all items. Spare parts inventory management involves all parties, from Original Equipment Manufacturer (OEM), to distributors and end customers, which makes this management even more challenging. Wrongly classified critical spare parts and the unavailability of those critical items could have severe consequences. Therefore, it is crucial to classify inventory items into classes and employ appropriate control policies conforming to the respective classes. An ABC classification and certain inventory control techniques are often applied to facilitate SKU management. Spare parts inventory management intends to provide the right spare parts at the right time. The classification of spare parts into priority or critical classes is the foundation for managing a large-scale and highly diverse assortment of parts. The purpose of classification is to consistently classify spare parts into different classes based on the similarity of items with respect to their characteristics, which are exhibited as attributes. The traditional ABC analysis, based on Pareto's Principle, is one of the most widely used techniques for classification, which concentrates exclusively on annual dollar usage and overlooks other important factors such as reliability, lead time, and criticality. Therefore, multi-criteria inventory classification (MCIC) methods are required to meet these demands. We propose a pattern-based machine learning technique, the Logical Analysis of Data (LAD), for spare parts inventory classification. The purpose of this study is to expand the classical ABC classification method by using a MCIC approach. Benefiting from the superiority of LAD in pattern transparency and robustness, we use two numerical examples to investigate LAD’s potential usage for detecting inconsistencies in inventory classification and the capability on MCIC. The two numerical experiments have demonstrated that LAD is not only capable of classifying inventory, but also for detecting and correcting inconsistent observations by combining it with the Root Cause Analysis (RCA) procedure. Test accuracy improves potentially not only with the LAD technique, but also with other major machine learning classification techniques, namely artificial neural network (ANN), support vector machines (SVM), k-nearest neighbours (KNN) and Naïve Bayes (NB). Finally, we conduct a statistical analysis to confirm the significant improvement in test accuracy for new datasets (corrections by LAD) compared to original datasets. This is true for all five classification techniques. The results of statistical tests demonstrate that there is no significant difference in test accuracy in five machine learning techniques, either in the original or the new datasets of both inventories

Decision-making techniques for smart grid energy management

This thesis has contributed to the design of suitable decision-making techniques for energy management in the smart grid with emphasis on energy efficiency and uncertainty analysis in two smart grid applications. First, an energy trading model among distributed microgrids (MG) is investigated, aiming to improve energy efficiency by forming coalitions to allow local power transfer within each coalition. Then, a more specific scenario is considered that is how to optimally schedule Electric Vehicles (EV) charging in a MG-like charging station, aiming to match as many as EV charging requirements with the uncertain solar energy generation. The solutions proposed in this thesis can give optimal coalition formation patterns for reduced power losses and achieve optimal performance for the charging station. First, several algorithms based on game theory are investigated for the coalition formation of distributed MGs to alleviate the power losses dissipated on the cables due to power transfer. The seller and buyer MGs can make distributed decisions whether to form a coalition with others for energy trading. The simulation results show that game theory based methods that enable cooperation yield a better performance in terms of lower power losses than a non-cooperative approach. This is because by forming local coalitions, power is transferred within a shorter distance and at a lower voltage. Thus, the power losses dissipated on the transmission lines and caused by power conversion at the transformer are both reduced. However, the merge-and-split based cooperative games have an inherent high computational complexity for a large number of players. Then, an efficient framework is established for the power loss minimization problem as a college admissions game that has a much lower computational complexity than the merge-and-split based cooperative games. The seller and buyer MGs take the role of colleges and students in turn and apply for a place in the opposite set following their preference lists and the college MGs’ energy quotas. The simulation results show that the proposed framework demonstrates a comparable power losses reduction to the merge-and-split based algorithms, but runs 700 and 18000 times faster for a network of 10 MGs and 20 MGs, respectively. Finally, the problem of EV charging using various energy sources is studied along with their impact on the charging station’s performance. A multiplier k is introduced to measure the effect of solar prediction uncertainty on the decision-making process of the station. A composite performance index (the Figure of Merit, FoM) is also developed to measure the charging station’s utility, EV users charging requirements and the penalties for turning away new arrivals and for missing charging deadlines. A two-stage admission and scheduling mechanism is further proposed to find the optimal trade-off between accepting EVs and missing charging deadlines by determining the best value of the parameter k under various energy supply scenarios. The numerical evaluations give the solution to the optimization problem and show that some of the key factors such as shorter and longer deadline urgencies of EVs charging requirements, stronger uncertainty of the prediction error, storage capacity and its initial state will not affect significantly the optimal value of the parameter k

Effect of a structurally modified human granulocyte colony stimulating factor, G-CSFa, on leukopenia in mice and monkeys

<p>Abstract</p> <p>Background</p> <p>Granulocyte colony stimulating factor (G-CSF) regulates survival, proliferation, and differentiation of neutrophilic granulocyte precursors, Recombinant G-CSF has been used for the treatment of congenital and therapy-induced neutropenia and stem cell mobilization. Due to its intrinsic instability, recombinant G-CSF needs to be excessively and/or frequently administered to patients in order to maintain a plasma concentration high enough to achieve therapeutic effects. Therefore, there is a need for the development of G-CSF derivatives that are more stable and active in vivo.</p> <p>Methods</p> <p>Using site-direct mutagenesis and recombinant DNA technology, a structurally modified derivative of human G-CSF termed G-CSFa was obtained. G-CSFa contains alanine 17 (instead of cysteine 17 as in wild-type G-CSF) as well as four additional amino acids including methionine, arginine, glycine, and serine at the amino-terminus. Purified recombinant G-CSFa was tested for its in vitro activity using cell-based assays and in vivo activity using both murine and primate animal models.</p> <p>Results</p> <p>In vitro studies demonstrated that G-CSFa, expressed in and purified from <it>E. coli</it>, induced a much higher proliferation rate than that of wild-type G-CSF at the same concentrations. In vivo studies showed that G-CSFa significantly increased the number of peripheral blood leukocytes in cesium-137 irradiated mice or monkeys with neutropenia after administration of clyclophosphamide. In addition, G-CSFa increased neutrophil counts to a higher level in monkeys with a concomitant slower declining rate than that of G-CSF, indicating a longer half-life of G-CSFa. Bone marrow smear analysis also confirmed that G-CSFa was more potent than G-CSF in the induction of granulopoiesis in bone marrows of myelo-suppressed monkeys.</p> <p>Conclusion</p> <p>G-CSFa, a structurally modified form of G-CSF, is more potent in stimulating proliferation and differentiation of myeloid cells of the granulocytic lineage than the wild-type counterpart both in vitro and in vivo. G-CSFa can be explored for the development of a new generation of recombinant therapeutic drug for leukopenia.</p

Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and synthetic aperture radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra moderate resolution imaging spectroradiometer (MODIS), and Suomi-NPP visible infrared imaging radiometer suite (VIIRS). The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding, i.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of 91% [respectively, mean per-class Intersection-over-Union (mIoU) scores >60%] and generalizes well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement

DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning

Federated learning (FL) attempts to train a global model by aggregating local models from distributed devices under the coordination of a central server. However, the existence of a large number of heterogeneous devices makes FL vulnerable to various attacks, especially the stealthy backdoor attack. Backdoor attack aims to trick a neural network to misclassify data to a target label by injecting specific triggers while keeping correct predictions on original training data. Existing works focus on client-side attacks which try to poison the global model by modifying the local datasets. In this work, we propose a new attack model for FL, namely Data-Agnostic Backdoor attack at the Server (DABS), where the server directly modifies the global model to backdoor an FL system. Extensive simulation results show that this attack scheme achieves a higher attack success rate compared with baseline methods while maintaining normal accuracy on the clean data.Comment: Accepted by Backdoor Attacks and Defenses in Machine Learning (BANDS) Workshop at ICLR 202

Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and Synthetic Aperture Radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra MODIS, and Suomi-NPP VIIRS. The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding. I.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of < 1.5 days. The network produces spatially explicit lake ice maps with pixel-wise accuracies > 91% (respectively, mIoU scores > 60%) and generalises well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement

Proinflammatory cytokine production and insulin sensitivity regulated by overexpression of resistin in 3T3-L1 adipocytes

Resistin is secreted from adipocytes, and high circulating levels have been associated with obesity and insulin resistance. To investigate whether resistin could exert autocrine effects in adipocytes, we expressed resistin gene in 3T3-L1 fibroblasts using a lentiviral vector, and selected several stably-transduced cell lines under blasticidin selection. We observed that 3T3-L1 adipocytes expressing resistin have a decreased gene expression for related transcriptional factors (CCAAT/enhancer binding protein α(C/EBPα) , peroxisome proliferator-activated receptor gamma (PPARγ), and adipocyte lipid binding protein (ALBP/aP2) which is one of target genes for the PPARγ during adipocyte differentiation,. Overexpression of resistin increased the levels of three proinflammatory cytokines, tumor necrosis factor alpha (TNFα), interleukin 6 (IL-6) and monocyte chemoattractant protein-1 (MCP-1), which play important roles for insulin resistance, glucose and lipid metabolisms during adipogenesis. Furthermore, overexpressing resistin in adipocytes inhibits glucose transport 4 (GLUT4) activity and its gene expression, reducing insulin's ability for glucose uptake by 30 %. In conclusion, resistin overexpression in stably transduced 3T3-L1 cells resulted in: 1) Attenuation of programmed gene expression responsible for adipogenesis; 2) Increase in expression of proinflammatory cytokines; 3) Decrease in insulin responsiveness of the glucose transport system. These data suggest a new role for resistin as an autocrine/paracrine factor affecting inflammation and insulin sensitivity in adipose tissue

An Empirical study of Loan loss provisioning hypotheses for Chinese banking industry

This paper provides an empirical study on the loan loss provisions (LLP) of 188 unconsolidated commercial banks in China from the year of 2007 to 2016. The loan loss provisioning in China is under multiple regulatory forces and the regime causes challengers for practical applications. Critical reforms have been made to the LLP regulatory regimes over the past few years. But contradictions arise from these regulatory standards. For the empirical research, a Cost-efficiency analysis is first carry out to estimate the efficiency scores in Chinese Banking industry over the last ten years. The Chinese banking industry was found to be cost efficient, the efficiency scores are then used as a potential explanatory variable for LLP behaviour model. The main estimations are the three LLP behaviour hypotheses regarding business cyclical provisioning, income smoothing and capital management. A joint model using two steps system Generalized Method of Moments (GMM) offers empirical implication for further analysis. While the income smoothing hypothesis are supported by empirical evidence, the cyclical provisions and capital management do not show significant practices in China. In the end, this paper also provided some practical limitation and improvement suggestions for the Loan loss provisioning analysis in China. KEYWORDS: Loan loss provisioning, Chinese Banking, Capital management hypothesis, Income smoothing, Cost-efficienc