Extensive and Intensive Margins of Labour Supply: Working Hours in the US, UK and France

This paper documents the key stylised facts underlying the evolution of labour supply at the extensive and intensive margins in the last forty years in three countries: United-States, United-Kingdom and France. We develop a statistical decomposition that provides bounds on changes at the extensive and intensive margins. This decomposition is also shown to be coherent with the analysis of labour supply elasticities at these margins. We use detailed representative micro-datasets to examine the relative importance of the extensive and intensive margins in explaining the overall changes in total hours worked.labor supply, employment, hours of work

Thread-Modular Static Analysis for Relaxed Memory Models

We propose a memory-model-aware static program analysis method for accurately analyzing the behavior of concurrent software running on processors with weak consistency models such as x86-TSO, SPARC-PSO, and SPARC-RMO. At the center of our method is a unified framework for deciding the feasibility of inter-thread interferences to avoid propagating spurious data flows during static analysis and thus boost the performance of the static analyzer. We formulate the checking of interference feasibility as a set of Datalog rules which are both efficiently solvable and general enough to capture a range of hardware-level memory models. Compared to existing techniques, our method can significantly reduce the number of bogus alarms as well as unsound proofs. We implemented the method and evaluated it on a large set of multithreaded C programs. Our experiments showthe method significantly outperforms state-of-the-art techniques in terms of accuracy with only moderate run-time overhead.Comment: revised version of the ESEC/FSE 2017 pape

On the Modeling of Dynamic-Systems using Sequence-based Deep Neural-Networks

The objective of this thesis is the adaptation and development of sequence-based Neural-Networks (NNs) applied to the modeling of dynamic systems. More specifically, we will focus our study on 2 sub-problems: the modeling of time-series, the modeling and control of multiple-input multiple-output (MIMO) systems. These 2 sub-problems will be explored through the modeling of crops, and the modeling and control of robots. To solve these problems, we build on NNs and training schemes allowing our models to out-perform the state-of-the-art results in their respective fields. In the irrigation field, we show that NNs are powerful tools capable of modeling the water consumption of crops while observing only a portion of what is currently required by reference methods. We further demonstrate the potential of NNs by inferring irrigation recommendations in real-time. In robotics, we show that prioritization techniques can be used to learn better robot dynamic models. We apply the models learned using these methods inside an Model Predictive Control (MPC) controller, further demonstrating their benefits. Additionally, we leverage Dreamer, an Model Based Reinforcement Learning (MBRL) agent, to solve visuomotor tasks. We demonstrate that MBRL controllers can be used for sensor-based control on real robots without being trained on real systems. Adding to this result, we developed a physics-guided variant of DREAMER. This variation of the original algorithm is more flexible and designed for mobile robots. This novel framework enables reusing previously learned dynamics and transferring environment knowledge to other robots. Furthermore, using this new model, we train agents to reach various goals without interacting with the system. This increases the reusability of the learned models and makes for a highly data-efficient learning scheme. Moreover, this allows for efficient dynamics randomization, creating robust agents that transfer well to unseen dynamics.Ph.D

Integer Approximation of Real Valued Preference Curves

A primary challenge of the AFIT Mission Resource Value Assessment Tool is to approximate a given preference curve with integer valued mission ready resources. This thesis evaluated four candidate methods of accomplishing this approximation. The thesis evaluated the implementation of the integer estimation approximation from a purely mathematical perspective. The models were measured against six quality and error measurement standards: convergence on an endpoint, convergence on any interior integer points, characterization of the overall error between the sequence of integer coordinates and the real valued linear function and characterization of the error in each individual dimension of the problem space. Finally, computer processing time was measured and a comparison of the lengths of the real valued linear function and the sequence of integer coordinates used to approximate the function were compared. Based on these measures the Relative Slope Algorithm (RSA) was selected. RSA demonstrated the minimal error and consistently quick processing time. This algorithm will improve the Mission Resource Value Assessment Tool and further its impact on the Advanced Logistic project

Dynamic regulation of quaternary organization of the M1 muscarinic receptor by subtype-selective antagonist drugs

Although rhodopsin-like G protein-coupled receptors can exist as both monomers and non-covalently associated dimers/oligomers, the steady-state proportion of each form and whether this is regulated by receptor ligands is unknown. Herein we address these topics for the M1 muscarinic acetylcholine receptor, a key molecular target for novel cognition enhancers, by employing Spatial Intensity Distribution Analysis. This method can measure fluorescent particle concentration and assess oligomerization states of proteins within defined regions of living cells. Imaging and analysis of the basolateral surface of cells expressing some 50 molecules.microm-2 of the human muscarinic M1 receptor identified an ~75/25 mixture of receptor monomers and dimers/oligomers. Both sustained and shorter-term treatment with the selective M1 antagonist pirenzepine resulted in a large shift in the distribution of receptor species to favor the dimeric/oligomeric state. Although sustained treatment with pirenzepine also resulted in marked upregulation of the receptor, simple mass-action effects were not the basis for ligand-induced stabilization of receptor dimers/oligomers. The related antagonist telenzepine also produced stabilization and enrichment of the M1 receptor dimer population but the receptor subtype non-selective antagonists atropine and N-methylscopolamine did not. In contrast, neither pirenzepine nor telenzepine altered the quaternary organization of the related M3 muscarinic receptor. These data provide unique insights into the selective capacity of receptor ligands to promote and/or stabilize receptor dimers/oligomers and demonstrate that the dynamics of ligand regulation of the quaternary organization of G protein-coupled receptors is markedly more complex than previously appreciated. This may have major implications for receptor function and behavior

Arrimage avec learning resource metadata initiative (LRMI)

Tiré de l'écran-titre (visionné le 27 juin 2017).Parmi ses travaux sur les métadonnées pour les ressources d’enseignement et d’apprentissage (RÉA), le GTN-Québec a misé sur une transition du standard IEEE 1484.12.1–2002 (LOM) vers la norme ISO 19788 (MLR). Nous observons l’émergence de normes concurrentes pour les métadonnées de RÉA, parmi lesquelles, le standard LRMI, soutenu par l’initiative Dublin Core et plusieurs acteurs importants de l’industrie informatique dont Microsoft et Google. Nous avons fait une étude sur les cas d’adoption de cette norme, dans le but de déterminer s’il serait stratégique de développer des outils appropriés pour l’importation, le traitement et l’exportation de métadonnées suivant cette norme. Notre étude montre une adoption limitée, visant surtout l’optimisation pour les moteurs de recherche. Il demeure possible que cet objectif puisse motiver une adoption future du standard et nous recommandons de continuer à observer l’évolution des données LRMI

Characterizing aging in the human brainstem using quantitative multimodal MRI analysis.

Aging is ubiquitous to the human condition. The MRI correlates of healthy aging have been extensively investigated using a range of modalities, including volumetric MRI, quantitative MRI (qMRI), and diffusion tensor imaging. Despite this, the reported brainstem related changes remain sparse. This is, in part, due to the technical and methodological limitations in quantitatively assessing and statistically analyzing this region. By utilizing a new method of brainstem segmentation, a large cohort of 100 healthy adults were assessed in this study for the effects of aging within the human brainstem in vivo. Using qMRI, tensor-based morphometry (TBM), and voxel-based quantification (VBQ), the volumetric and quantitative changes across healthy adults between 19 and 75 years were characterized. In addition to the increased R2* in substantia nigra corresponding to increasing iron deposition with age, several novel findings were reported in the current study. These include selective volumetric loss of the brachium conjunctivum, with a corresponding decrease in magnetization transfer and increase in proton density (PD), accounting for the previously described “midbrain shrinkage.” Additionally, we found increases in R1 and PD in several pontine and medullary structures. We consider these changes in the context of well-characterized, functional age-related changes, and propose potential biophysical mechanisms. This study provides detailed quantitative analysis of the internal architecture of the brainstem and provides a baseline for further studies of neurodegenerative diseases that are characterized by early, pre-clinical involvement of the brainstem, such as Parkinson’s and Alzheimer’s diseases