زواج القاصر في المحاكم الشرعية السنيّة في لبنان

This research deals with the consolidation of the role of the Sunni Sharia courts in Lebanon in matters of marriage as a contract and proof, originally extensive on the issue of the marriage of minors, with a statement of its ruling, mentioning the controls and guarantees of this practically, in whole and in detail. Hence, the required conditions are lawfully and legally met and their existence is established after a series of required procedures in accordance with the principles . The same applies to making sure that they are prepared psychologically, physically, and physiologically. In addition, all of this was related to the permission of the competent Sharia judge alone, pointing to some of the unprecedented legislative challenges that afflict the Muslim family in order to target the personal status systems in Lebanon through intellectual and behavioral confrontation: Intellectual Invasion, explaining the dangers of raised suspicious, honeyed- poisoned laws , which tickles feelings, and marked in regulating minor marriages and family violence, as well as its religious danger, noting that they completely contradict the Lebanese constitution in letter and spirit. Indicating the blatant infringement on the functional jurisdiction of the Sharia judiciary in the personal status laws, suggesting after all the determination of the age of marriage, revealing the ideal of customary marriage contracts and how to treat them, concluding with a set of recommendations and suggestions that can represent the aspects of ideal solutions. In order to extract jurisprudential and legal opinions, this research is divided into an introduction, four chapters, and a conclusion

A Hybrid Approach combining ANN-based and Conventional Demapping in Communication for Efficient FPGA-Implementation

In communication systems, Autoencoder (AE) refers to the concept of replacing parts of the transmitter and receiver by artificial neural networks (ANNs) to train the system end-to-end over a channel model. This approach aims to improve communication performance, especially for varying channel conditions, with the cost of high computational complexity for training and inference. Field-programmable gate arrays (FPGAs) have been shown to be a suitable platform for energy-efficient ANN implementation. However, the high number of operations and the large model size of ANNs limit the performance on resource-constrained devices, which is critical for low latency and high-throughput communication systems. To tackle his challenge, we propose a novel approach for efficient ANN-based remapping on FPGAs, which combines the adaptability of the AE with the efficiency of conventional demapping algorithms. After adaption to channel conditions, the channel characteristics, implicitly learned by the ANN, are extracted to enable the use of optimized conventional demapping algorithms for inference. We validate the hardware efficiency of our approach by providing FPGA implementation results and by comparing the communication performance to that of conventional systems. Our work opens a door for the practical application of ANN-based communication algorithms on FPGAs.Comment: Available at: https://ieeexplore.ieee.org/document/983569

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Oil spills are regrettably common and have socioeconomic implications on communities and disastrous consequences on the marine ecosystem and maritime life. The European Space Agency (ESA) has stated that worldwide spillage exceeds 4.5 million tons of oil annually, where 45% of the amount is due to operative discharges from ships. To alleviate the severity of oil spills and promptly react to such incidents, it is crucial to have oil-spill monitoring systems, which enable an effective contingency plan to dictate the best actions for dealing with oil spills. A quick and efficient intervention requires the (1) detection of oil slicks, (2) thickness estimation, and (3) oil classification. The European Maritime Safety Agency (EMSA) highlighted in 2016 the need to use drones as complementary systems supporting satellite maritime surveillance. While multiple sensors could be used, active radars appear to be prominent for oil spill monitoring. In this chapter, we present recent advances in drone-based radar remote sensing as an effective oil spill monitoring system. It shows from the system-level perspective the capability of radar systems on drones, using high spectral resolution and parallel scanning, to perform the above-required functionalities (1, 2, and 3) and provide valuable information to contain the damage

Stagewise Newton Method for Dynamic Game Control with Imperfect State Observation

International audienceIn this letter, we study dynamic game optimal control with imperfect state observations and introduce an iterative method to find a local Nash equilibrium. The algorithm consists of an iterative procedure combining a backward recursion similar to minimax differential dynamic programming and a forward recursion resembling a risksensitive Kalman smoother. A coupling equation renders the resulting control dependent on the estimation. In the end, the algorithm is equivalent to a Newton step but has linear complexity in the time horizon length. Furthermore, a merit function and a line search procedure are introduced to guarantee convergence of the iterative scheme. The resulting controller reasons about uncertainty by planning for the worst case disturbances. Lastly, the low computational cost of the proposed algorithm makes it a promising method to do output-feedback model predictive control on complex systems at high frequency. Numerical simulations on realistic robotic problems illustrate the risk-sensitive behavior of the resulting controller

Do Fiduciary Duties Contained in Federal Tax Laws Effectively Promote National Health Care Policies and Practices ?

International audienc

Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control

We introduce Crocoddyl (Contact RObot COntrol by Differential DYnamic Library), an open-source framework tailored for efficient multi-contact optimal control. Crocoddyl efficiently computes the state trajectory and the control policy for a given predefined sequence of contacts. Its efficiency is due to the use of sparse analytical derivatives, exploitation of the problem structure, and data sharing. It employs differential geometry to properly describe the state of any geometrical system, e.g. floating-base systems. We have unified dynamics, costs, and constraints into a single concept-action-for greater efficiency and easy prototyping. Additionally, we propose a novel multiple-shooting method called Feasibility-prone Differential Dynamic Programming (FDDP). Our novel method shows a greater globalization strategy compared to classical Differential Dynamic Programming (DDP) algorithms, and it has similar numerical behavior to state-of-the-art multiple-shooting methods. However, our method does not increase the computational complexity typically encountered by adding extra variables to describe the gaps in the dynamics. Concretely, we propose two modifications to the classical DDP algorithm. First, the backward pass accepts infeasible state-control trajectories. Second, the rollout keeps the gaps open during the early "exploratory" iterations (as expected in multiple-shooting methods). We showcase the performance of our framework using different tasks. With our method, we can compute highly-dynamic maneuvers for legged robots (e.g. jumping, front-flip) in the order of milliseconds

Efficient FPGA implementation of an ANN-based demapper using cross-layer analysis

In the field of communication, autoencoder (AE) refers to a system that replaces parts of the traditional transmitter and receiver with artificial neural networks (ANNs). To meet the system performance requirements, it is necessary for the AE to adapt to the changing wireless-channel conditions at runtime. Thus, online fine-tuning in the form of ANN-retraining is of great importance. Many algorithms on the ANN layer are developed to improve the AE’s performance at the communication layer. Yet, the link of the system performance and the ANN topology to the hardware layer is not fully explored. In this paper, we analyze the relations between the design layers and present a hardware implementation of an AE-based demapper that enables fine-tuning to adapt to varying channel conditions. As a platform, we selected field-programmable gate arrays (FPGAs) which provide high flexibility and allow to satisfy the low-power and low-latency requirements of embedded communication systems. Furthermore, our cross-layer approach leverages the flexibility of FPGAs to dynamically adapt the degree of parallelism (DOP) to satisfy the system-level requirements and to ensure environmental adaptation. Our solution achieves 2000× higher throughput than a high-performance graphics processor unit (GPU), draws 5× less power than an embedded central processing unit (CPU) and is 5800× more energy efficient compared to an embedded GPU for small batch size. To the best of our knowledge, such a cross-layer design approach combined with FPGA implementation is unprecedented