A Review on Joint Models in Biometrical Research

In some fields of biometrical research joint modelling of longitudinal measures and event time data has become very popular. This article reviews the work in that area of recent fruitful research by classifying approaches on joint models in three categories: approaches with focus on serial trends, approaches with focus on event time data and approaches with equal focus on both outcomes. Typically longitudinal measures and event time data are modelled jointly by introducing shared random effects or by considering conditional distributions together with marginal distributions. We present the approaches in an uniform nomenclature, comment on sub-models applied to longitudinal measures and event time data outcomes individually and exemplify applications in biometrical research

Dependability for declarative mechanisms: neural networks in autonomous vehicles decision making.

Despite being introduced in 1958, neural networks appeared in numerous applications of different fields in the last decade. This change was possible thanks to the reduced costs of computing power required for deep neural networks, and increasing available data that provide examples for training sets. The 2012 ImageNet image classification competition is often used as a example to describe how neural networks became at this time good candidates for applications: during this competition a neural network based solution won for the first time. In the following editions, all winning solutions were based on neural networks. Since then, neural networks have shown great results in several non critical applications (image recognition, sound recognition, text analysis, etc...). There is a growing interest to use them in critical applications as their ability to generalize makes them good candidates for applications such as autonomous vehicles, but standards do not allow that yet. Autonomous driving functions are currently researched by the industry with the final objective of producing in the near future fully autonomous vehicles, as defined by the fifth level of the SAE international (Society of Automotive Engineers) classification. Autonomous driving process is usually decomposed into four different parts: the where sensors get information from the environment, the where the data from the different sensors is merged into one representation of the environment, the that uses the representation of the environment to decide what should be the vehicles behavior and the commands to send to the actuators and finally the part that implements these commands. In this thesis, following the interest of the company Stellantis, we will focus on the decision part of this process, considering neural network based solution. Automotive being a safety critical application, it is required to implement and ensure the dependability of the systems, and this is why neural networks use is not allowed at the moment: their lack of safety forbid their use in such applications. Dependability methods for classical software systems are well known, but neural networks do not have yet similar dependable mechanisms to guarantee their trust. This problem is due to several reasons, among them the difficulty to test applications with a quasi-infinite operational domain and whose functions are hard to define exhaustively in the specifications. Here we can find the motivation of this thesis: how can we ensure the dependability of neural networks in the context of decision for autonomous vehicles? Research is now being conducted on the topic of dependability and safety of neural networks with several approaches being considered and our research is motivated by the great potential in safety critical applications mentioned above. In this thesis, we will focus on one category of method that seems to be a good candidate to ensure the dependability of neural networks by solving some of the problems of testing: the formal verification for neural networks. These methods aim to prove that a neural network respects a safety property on an entire range of its input and output domains. Formal verification is already used in other domains and is seen as a trusted method to give confidence in a system, but it remains for the moment a research topic for neural networks with currently no industrial applications. The main contributions of this thesis are the following: a proposal of a characterization of neural network from a software development perspective, and a corresponding classification of their faults, errors and failures, the identification of a potential threat to the use of formal verification. This threat is the erroneous neural network model problem, that may lead to trust a formally validated safety property that does not hold in real life, the realization of an experiment that implements a formal verification for neural networks in an autonomous driving application that is to the best of our knowledge the closest to industrial use. For this application, we chose to work with an ACC (Adaptive Cruise Control) function, which is an autonomous driving function that performs the longitudinal control of a vehicle. The experiment is conducted with the use of a simulator and a neural network formal verification tool. The other contributions of the thesis are the following: theoretical example of the erroneous neural network model problem and a practical example in our autonomous driving experiment, a proposal of detection and recovery mechanisms as a solution to the erroneous model problem mentioned above, an implementation of these detection and recovery mechanisms in our autonomous driving experiment and a discussion about difficulties and possible processes for the implementation of formal verification for neural networks that we developed during our experiments

Student Loans and their effect on Parental Views of Education Financing

Using the 2012 wave National Longitudinal Survey of Youth 1979, this study examines the effect that parents\u27 student debt have on their decision to use tax advantage education vehicles to save for their children\u27s college. We also examine parental decisions on obtaining student loans on behalf of their children. The results show that parents who report having student loans are 61% less likely than those that report no student loan debt to use tax-advantaged education saving vehicles. However, we find no difference in the effect of having student loans on the decision to obtain debt to fund their children\u27s college education

Revised classification of kinases based on bioactivity data: the importance of data density and choice of visualization

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Indoor spraying with chlorfenapyr (a pyrrole insecticide) provides residual control of pyrethroid-resistant malaria vectors in southern Benin.

BACKGROUND: New classes of insecticides with novel modes of action, which can provide effective and prolonged control of insecticide-resistant malaria vector populations, are urgently needed for indoor residual spraying. Such insecticides can be included in a rotation plan to manage and prevent further development of resistance in mosquito vectors of malaria. Chlorfenapyr, a novel pyrrole insecticide with a unique mode of action, is being developed as a long-lasting IRS formulation. METHODS: The efficacy of several formulations of chlorfenapyr alone and as mixtures with alpha-cypermethrin were evaluated in an experimental hut trial against wild pyrethroid-resistant Anopheles gambiae sensu lato in Cové, Benin, in an attempt to identify the most effective and long-lasting formulations for IRS. The trial lasted 12 months. A comparison was made with alpha-cypermethrin and bendiocarb formulations. CDC bottle bioassays were performed to investigate cross-resistance to chlorfenapyr in the local vector population. RESULTS: Mortality rates in World Health Organization (WHO) cylinder bioassays were  95% with bendiocarb thus confirming susceptibility to carbamates in the vector population. CDC bottle bioassays showed no cross-resistance between pyrethroids and chlorfenapyr. Overall mortality of free-flying mosquitoes entering the experimental huts over the 12-month trial was 4% with alpha-cypermethrin and 12% with bendiocarb. The chlorfenapyr solo-formulations induced significantly higher levels of mortality (38-46%) compared to the bendiocarb (12% P  80% mortality in the first month, but this declined sharply to < 20% by the third month while the mortality rates achieved with the chlorfenapyr formulations (38-46%) were persistent lasting 7-10 months. The mixtures induced significantly lower percentage mortality than chlorfenapyr-solo formulations. Wall cone bioassays only showed mortality rates that were consistent with chlorfenapyr IRS treated huts when the exposure time was increased to 2 h. CONCLUSION: Indoor residual spraying with chlorfenapyr (Sylando® 240SC) provides moderate but prolonged control of pyrethroid-resistant malaria vectors compared to pyrethroid and bendiocarb IRS. Wall cone bioassays on chlorfenapyr-treated walls required longer exposure times of 2 h than the customary 30 min indicating that WHO guidelines on residual cone bioassays need to be more insecticide-specific