Using Knowledge Distillation to improve interpretable models in a retail banking context

This article sets forth a review of knowledge distillation techniques with a focus on their applicability to retail banking contexts. Predictive machine learning algorithms used in banking environments, especially in risk and control functions, are generally subject to regulatory and technical constraints limiting their complexity. Knowledge distillation gives the opportunity to improve the performances of simple models without burdening their application, using the results of other - generally more complex and better-performing - models. Parsing recent advances in this field, we highlight three main approaches: Soft Targets, Sample Selection and Data Augmentation. We assess the relevance of a subset of such techniques by applying them to open source datasets, before putting them to the test on the use cases of BPCE, a major French institution in the retail banking sector. As such, we demonstrate the potential of knowledge distillation to improve the performance of these models without altering their form and simplicity.Comment: 25 pages, 9 figures, 11 table

Drug Abuse-Induced Cardiac Arrhythmias: Mechanisms and Management

Toxicomania is a worldwide emerging problem threatening young population. Several reports highlighted its hazardous cardiovascular effects. Sudden cardiac death secondary to cardiac arrhythmias is the most occupying issue. Different forms of cardiac rhythm disorders may be induced by illicit drug abuse according to the type of drug and the mechanism involved. In this review, we exposed the main ventricular and supraventricular arrhythmia complicating the common recreational drugs, and we explained their different mechanisms as well as the particularities of management

SiC-reinforced A12O3metal composites by directed metal oxidation

A new process, the DIMOX$rm sp{TM}$ Process, for making ceramic matrix and metal matrix composites was developed and commercialized by Lanxide Corporation. This technology is based on the use of a unique directed-metal oxidation process to grow ceramic matrices around pre-placed composite fillers or reinforcements. This thesis attempts to offer a good understanding of the mechanism of the process, as well as the effects of the processing parameters on the process, especially in the presence of a reinforcing material. Metal-ceramic matrix composites were grown into four different SiC powders by the directed oxidation of aluminum alloys in air at various temperatures. Microstructure, microstructural evolution, and growth kinetic studies were performed on these composites as a function of alloy compositions, processing temperature, and preform size. The results are then compared to those of composites processed without SiC-reinforcing particles.The microstructure of the resulting composites consists of three phases: the SiC preform, a continuous $alpha$-$rm Al sb2O sb3$ matrix, and a network of unoxidized metal. The microstructural evolution for composites without SiC starts with an incubation period of variable length. The incubation time decreases with increase in the processing temperature and with increase in the alloy silicon content. The addition of silicon in the alloy decrease the viscosity of the melt and therefore increases the rate of metal supply to the reaction front. However increasing the magnesium content resulted only in a slight decrease of the length of the incubation period.For composites processed with SiC particles, the growth started immediately after introducing the alloy into the hot zone of the furnace. The incubation time was very short and was not sensitive to changes in either temperatures or alloy composition. The preform does not show any evidence of degradation by the molten alloy, however the growth front tends to climb up the surface of the particles. The composite growth rate increased with decreasing in the preform particle size.The oxidative formation of $rm Al sb2O sb3$ matrix composites using Al-Mg and Al-Mg-Si alloys exhibits a linear type of kinetics in both the presence and absence of SiC preforms with an activation energy of 224 kJ/mol

Impact of sampling on learning asymmetric-entropy decision trees from imbalanced data

Learning from imbalanced data is still a challenging problem in spite of more than two decades of continuous development in this field. To deal with this problem, several data-level and algorithmic-level methods are proposed. Hybrid methods, which combine the advantages of the two previous groups, are also gaining increasing popularity. Therefore, in this paper, we put our focus on new hybrid approaches combining different sampling strategies with adapted decision trees to tackle the binary imbalanced problems. Our experiments consider five preprocessing methods and three asymmetric split criteria, which results in fifteen evaluated combinations. Unlike the majority of the studies, we take into account the intrinsic data characteristics in the analysis of each finding in order to gain a deeper understanding in the field of imbalanced data. The achieved findings, supported by statistical tests, end up to learn the extent to which sampling can be advantageous when combined with algorithmic solutions

TFP IN TUNISIAN MANUFACTURING SECTORS: CONVERGENCE OR CATCH-UP WITH OECD MEMBERS?

Total Factor Productivity (TFP) is analyzed for six Tunisian manufacturing sectors: food processing, electrical and metal products, chemical activities, textiles, clothing and leather, building materials and ceramics, miscellaneous products. First, sector-based TFP are calculated over a long period (1983–2002) as well as some sub-periods reflecting changes of local economic policy. Then, using an accounting framework, we decompose the industrial productivity into a reallocation effect (i.e., variation in the relative distribution of sectoral value added), and a pure productivity effect (i.e., the sectoral value-added shares being constant). Secondly, through panel data unit root tests, TFP long-term convergence with or without catch-up is examined with respect to the productive performance of OECD members. Each of the six Tunisian manufacturing sectors is benchmarked by the productive performance of OECD members. The Dickey–Fuller type test that we use allows us to take into account the potential correlation across OECD countries. The empirical analysis highlights two main findings. TFP convergence and catch-up have generally been a joint process. Moreover, the sectors where catch-up occurred were those with the best productive performance and those that succeeded in reducing the productivity gap with regard to the best OECD performers.Productivity, convergence, catch-up, panel unit root