University of Lorraine

About: University of Lorraine is a education organization based out in Nancy, France. It is known for research contribution in the topics: Population & Nonlinear system. The organization has 11942 authors who have published 25010 publications receiving 425227 citations. The organization is also known as: Lorraine University.

Improved Density Dependent Correction for the Description of London Dispersion Forces.

Abstract: The Tkatchenko–Scheffler method for calculating dispersion correction to standard density-functional theory, which uses fixed neutral atoms as a reference to estimate the effective volumes of atoms-in-molecule and to calibrate their polarizabilities and dispersion coefficients, fails to describe the structure and the energetics of ionic solids. Here, we propose a more appropriate partitioning, based on the iterative Hirshfeld scheme, where the fractionally charged atomic reference state is determined self-consistently. We show that our new method extends the applicability of the original method in particular to study ionic systems and adsorption phenomena on surfaces of ionic solids.

Cognitive impairments in alcohol-dependent subjects.

Abstract: Chronic excessive alcohol consumption induces cognitive impairments mainly affecting executive functions, episodic memory, and visuospatial capacities related to multiple brain lesions. These cognitive impairments not only determine everyday management of these patients, but also impact on the efficacy of management and may compromise the abstinence prognosis. Maintenance of lasting abstinence is associated with cognitive recovery in these patients, but some impairments may persist and interfere with the good conduct and the efficacy of management. It therefore appears essential to clearly define neuropsychological management designed to identify and evaluate the type and severity of alcohol-related cognitive impairments. It is also essential to develop cognitive remediation therapy so that the patient can fully benefit from the management proposed in addiction medicine units.

Fault and Error Tolerance in Neural Networks: A Review

Abstract: Beyond energy, the growing number of defects in physical substrates is becoming another major constraint that affects the design of computing devices and systems. As the underlying semiconductor technologies are getting less and less reliable, the probability that some components of computing devices fail also increases, preventing designers from realizing the full potential benefits of on-chip exascale integration derived from near atomic scale feature dimensions. As the quest for performance confronts permanent and transient faults, device variation, and thermal issues, major breakthroughs in computing efficiency are expected to benefit from unconventional and new models of computation, such as brain-inspired computing. The challenge is then to find not only high-performance and energy-efficient, but also fault-tolerant computing solutions. Neural computing principles remain elusive, yet as source of a promising fault-tolerant computing paradigm. In the quest to fault tolerance can be translated into scalable and reliable computing systems, hardware design itself and/or to use circuits even with faults has further motivated research on neural networks, which are potentially capable of absorbing some degrees of vulnerability based on their natural properties. This paper presents a survey on fault tolerance in neural networks manly focusing on well-established passive techniques to exploit and improve, by design, such potential but limited intrinsic property in neural models, particularly for feedforward neural networks. First, fundamental concepts and background on fault tolerance are introduced. Then, we review fault types, models, and measures used to evaluate performance and provide a taxonomy of the main techniques to enhance the intrinsic properties of some neural models, based on the principles and mechanisms that they exploit to achieve fault tolerance passively. For completeness, we briefly review some representative works on active fault tolerance in neural networks. We present some key challenges that remain to be overcome and conclude with an outlook for this field.

Flatness-Based Trajectory Planning/Replanning for a Quadrotor Unmanned Aerial Vehicle

Abstract: A flatness-based flight trajectory planning/replanning strategy is proposed for a quadrotor unmanned aerial vehicle (UAV). In the nominal situation (fault-free case), the objective is to drive the system from an initial position to a final one without hitting the actuator constraints while minimizing the total time of the mission or minimizing the total energy spent. When actuator faults occur, fault-tolerant control (FTC) is combined with trajectory replanning to change the reference trajectory in function of the remaining resources in the system. The approach employs differential flatness to express the control inputs to be applied in the function of the desired trajectories and formulates the trajectory planning/replanning problem as a constrained optimization problem.