Informatique, BioInformatique, Systèmes Complexes

RepeatMasker is a program that screens DNA sequences for interspersed repeats and low-complexity DNA sequences. In this chapter, we present the procedure to routinely use this program on a personal computer.

This paper presents the design and first test on a simulator of a vehicle trajectory-planning algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The proposed algorithm is designed to run on a fail-safe embedded environment with low computational power, such as an engine control unit, to be implementable in commercial vehicles of the near future. The target platform has a clock frequency of less than 150 MHz, 150 kB RAM of memory, and a 3-MB program memory. The trajectory planning is performed by a two-step algorithm. The first step defines the feasible maneuvers with respect to the environment, aiming at minimizing the risk of a collision. The output of this step is a target group of maneuvers in the longitudinal direction (accelerating or decelerating), in the lateral direction (changing lanes), and in the combination of both directions. The second step is a more detailed evaluation of several possible trajectories within these maneuvers. The trajectories are optimized to additional performance indicators such as travel time, traffic rules, consumption, and comfort. The output of this module is a trajectory in the vehicle frame that represents the recommended vehicle state (position, heading, speed, and acceleration) for the following seconds.

Recently, Generative Adversarial Networks (GANs) have received enormous progress, which makes them able to learn complex data distributions in particular faces. More and more efficient GAN architectures have been designed and proposed to learn the different variations of faces, such as cross pose, age, expression and style. These GAN based approaches need to be reviewed, discussed, and categorized in terms of architectures, applications, and metrics. Several reviews that focus on the use and advances of GAN in general have been proposed. However, the GAN models applied to the face, that we call facial GANs, have never been addressed. In this article, we review facial GANs and their different applications. We mainly focus on architectures, problems and performance evaluation with respect to each application and used datasets. More precisely, we reviewed the progress of architectures and we discussed the contributions and limits of each. Then, we exposed the encountered problems of facial GANs and proposed solutions to handle them. Additionally, as GANs evaluation has become a notable current defiance, we investigate the state of the art quantitative and qualitative evaluation metrics and their applications. We concluded the article with a discussion on the face generation challenges and proposed open research issues.

Faced with the challenges associated with sustainably feeding the world’s growing population, the food industry is increasingly relying on operations research (OR) techniques to achieve economic, environmental and social sustainability. It is therefore important to understand the context-specific model-oriented applications of OR techniques in the sustainable food supply chain (SFSC) domain. While existing food supply chain reviews provide an excellent basis for this process, the explicit consideration of sustainability from a model-oriented perspective along with a structured outline of relevant SFSC research techniques are missing in extant literature. We attempt to fill this gap by reviewing 83 related scientific journal publications that utilise mathematical modelling techniques to address issues in SFSC. To this end, we first identify the salient dimensions that include economic, environmental and social issues in SFSC. We then review the models and methods that use these dimensions to solve issues that arise in SFSC. We identify some of the main challenges in analytical modelling of SFSC as well as future research directions.

There are wafer fabrication processes in cluster tools that require wafer revisiting. If a swap strategy is applied to dual-arm cluster tools handling wafer revisiting, a three-wafer periodical process is formed with three wafers completed in each period. Such a period contains three cycles in a revisiting process and three cycles in a nonrevisiting one. Hence, analysis and scheduling of such tools become very complicated. In this paper, a Petri net (PN) model is developed to describe their operations. Based on it, it is found that, if a swap strategy is applied, such tools are always in a transient state. A systematic method is then presented to analyze their performance. With the help of the proposed PN model, this work, for the first time, derives the optimality conditions of three-wafer period scheduling. Industrial application examples are given to show the results.

Plant genomes encode hundreds of receptor kinases and peptides, but the number of known plant receptor-ligand pairs is limited. We report that the Arabidopsis leucine-rich repeat receptor kinase LRR-RK MALE DISCOVERER 1-INTERACTING RECEPTOR LIKE KINASE 2 (MIK2) is the receptor for the SERINE RICH ENDOGENOUS PEPTIDE (SCOOP) phytocytokines. MIK2 is necessary and sufficient for immune responses triggered by multiple SCOOP peptides, suggesting that MIK2 is the receptor for this divergent family of peptides. Accordingly, the SCOOP12 peptide directly binds MIK2 and triggers complex formation between MIK2 and the BRASSINOSTEROID INSENSITIVE 1-ASSOCIATED KINASE 1 (BAK1) co-receptor. MIK2 is required for resistance to the important root pathogen Fusarium oxysporum. Notably, we reveal that Fusarium proteomes encode SCOOP-like sequences, and corresponding synthetic peptides induce MIK2-dependent immune responses. These results suggest that MIK2 may recognise Fusarium-derived SCOOP-like sequences to induce immunity against Fusarium. The definition of SCOOPs as MIK2 ligands will help to unravel the multiple roles played by MIK2 during plant growth, development and stress responses.

The finite-time event-triggered stabilization is studied for a class of discrete-time nonlinear Markov jump singularly perturbed models with partially unknown transition probabilities (TPs). T-S fuzzy strategy is adopted to characterize the related nonlinear Markov jump singularly perturbed models. The control objective is to make sure that the system states remain within a bounded domain during a fixed-time interval. First, a mode-dependent event-triggered scheme is constructed to reduce the communication burden and save the network bandwidth. On that basis, by using a new Lyapunov function, a developed finite-time stability criterion is derived for the corresponding system to avoid an ill-conditioned issue due to a small singular perturbation parameter. Moreover, the mode-dependent fuzzy controller gain and the event-triggered parameter are co-designed under the framework of partially unknown TPs. Finally, the feasibility of the main results is provided to verify the finite-time event-triggered control strategy.

European funding under framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for nearly 2 years. The VPH network of excellence (NoE) is helping in the development of common standards, open-source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also helping to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by framework 6 strategy for a European physiome (STEP) project in 2006. It is now time to assess the accomplishments of the last 2 years and update the STEP vision for the VPH. We consider the biomedical science, healthcare and information and communications technology challenges facing the project and we propose the VPH Institute as a means of sustaining the vision of VPH beyond the time frame of the NoE.

Energy-efficient scheduling is highly necessary for energy-intensive industries, such as glass, mould or chemical production. Inspired by a real-world glass-ceramics production process, this paper investigates a bi-criteria energy-efficient two-stage hybrid flow shop scheduling problem, in which parallel machines with eligibility are at stage 1 and a batch machine is at stage 2. The performance measures considered are makespan and total energy consumption. Time-of-use (TOU) electricity prices and different states of machines (working, idle and turnoff) are integrated. To tackle this problem, a mixed integer programming (MIP) is formulated, based on which an augmented ε-constraint (AUGMECON) method is adopted to obtain the exact Pareto front. A problem-tailored constructive heuristic method with local search strategy, a bi-objective tabu search algorithm and a bi-objective ant colony optimisation algorithm are developed to deal with medium- and large-scale problems. Extensive computational experiments are conducted, and a real-world case is solved. The results show effectiveness of the proposed methods, in particular the bi-objective tabu search.

Human pose estimation and action recognition are related tasks since both problems are strongly dependent on the human body representation and analysis. Nonetheless, most recent methods in the literature handle the two problems separately. In this article, we propose a multi-task framework for jointly estimating 2D or 3D human poses from monocular color images and classifying human actions from video sequences. We show that a single architecture can be used to solve both problems in an efficient way and still achieves state-of-the-art or comparable results at each task while running with a throughput of more than 100 frames per second. The proposed method benefits from high parameters sharing between the two tasks by unifying still images and video clips processing in a single pipeline, allowing the model to be trained with data from different categories simultaneously and in a seamlessly way. Additionally, we provide important insights for end-to-end training the proposed multi-task model by decoupling key prediction parts, which consistently leads to better accuracy on both tasks. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU RGB+D) demonstrate the effectiveness of our method on the targeted tasks. Our source code and trained weights are publicly available at https://github.com/dluvizon/deephar.

For some wafer fabrication processes in cluster tools, e.g., atomic layer deposition (ALD), wafer revisiting is required. Typically, in such processes, wafers need to visit two consecutive processing steps several times. Such a revisiting process can be denoted as (mi, mi + 1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sup> , where i means the ith-step and m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> and m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i + 1</sub> mean the corresponding quantity of the processing modules in i and (i+1)th steps, and h the number of visiting times. This paper conducts a study for scheduling single-arm cluster tools with such a wafer revisiting process. The system is modeled by Petri nets (PNs) to guarantee the feasibility of robot activities. Based on the model, a deadlock avoidance policy is presented. With the control policy, cycle time analysis for the revisiting process is made. With the fact that wafer processing times are much longer than robot movement times in cluster tools, it is shown that, when m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> = m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i + 1</sub> = 1, i.e., each step has only one processing module, the optimal one-wafer cyclic schedule is deterministic and unique, and the minimal cycle time can be calculated by an analytical expression. It is also shown that, when m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> = 1 and m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> + 1 = 2 or m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> = 2 and m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> + 1 = 1, the optimal one-wafer cyclic schedule can be obtained by finding h deterministic schedules and the one with the least cycle time. A novel analytical method is finally presented to schedule the overall system containing such reentrant wafer flow. This represents a significant advance in single-arm cluster equipment automation.

MOTIVATION: Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally partially observed processes may be difficult to estimate even if the structure of interactions is given. RESULTS: Using the same approach as Sitz et al. proposed in another context, we derive non-linear state-space models from ODEs describing biological networks. In this framework, we apply Unscented Kalman Filtering (UKF) to the estimation of both parameters and hidden variables of non-linear state-space models. We instantiate the method on a transcriptional regulatory model based on Hill kinetics and a signaling pathway model based on mass action kinetics. We successfully use synthetic data and experimental data to test our approach. CONCLUSION: This approach covers a large set of biological networks models and gives rise to simple and fast estimation algorithms. Moreover, the Bayesian tool used here directly provides uncertainty estimates on parameters and hidden states. Let us also emphasize that it can be coupled with structure inference methods used in Graphical Probabilistic Models. AVAILABILITY: Matlab code available on demand.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents the design and the practical implementation of vehicle steering assistance that helps the driver avoid unintended lane departure. A switching strategy is built to govern the driver–assistance interaction, and the resulting hybrid system is formalized as an input/output (I/O) hybrid automaton. Composite Lyapunov functions, polyhedral-like invariant sets, and linear matrix inequality (LMI) methods constitute the heart of the approach used to design the lane-departure avoidance (LDA) system. The practical implementation of this steering assistance in a prototype vehicle confirms the effectiveness of this approach. </para>

This paper presents automatic parallel parking for a passenger vehicle, with highlights on a path-planning method and on experimental results. The path-planning method consists of two parts. First, the kinematic model of the vehicle, with corresponding geometry, is used to create a path to park the vehicle in one or more maneuvers if the spot is very narrow. This path is constituted of circle arcs. Second, this path is transformed into a continuous-curvature path using clothoid curves. To execute the generated path, control inputs for steering angle and longitudinal velocity depending on the traveled distance are generated. Therefore, the traveled distance and the vehicle pose during a parking maneuver are estimated. Finally, the parking performance is tested on a prototype vehicle.

Main task in driving safety is the understanding and prevention of risky situations. While looking closer at the accidents data analysis, it appears that vehicle loss of control represents a huge part of car accidents. Preventing such kind of accidents, using assistance systems needs several type of information about vehicle state and vehicle-road interaction phenomenon. Longitudinal velocity, acceleration and yaw rate are easily measured using low cost sensors that are actually mounted in standard on a large part of vehicles. However, other parameters, which have a major impact on vehicle dynamics, are more difficult to measure using vehicle industry technology sensors. These are for example the used friction coefficient and the sideslip angle. Using an appropriate vehicle model and available measurements, the vehicle state as well as the road/tire interaction forces are reconstructed by implementing an extended Kalman filter. Thereafter, we evaluate the used friction coefficient and the sideslip angle estimates. Simulation and estimation results are then compared to real measurements collected by an equipped test vehicle on Satory test track.

This article concerns optimal prescribed-time formation control for a class of nonlinear multiagent systems (MASs). Optimal control depends on the solution of the Hamilton–Jacobi–Bellman equation, which is hard to be calculated directly due to its inherent nonlinearity. To overcome this difficulty, the reinforcement learning strategy with fuzzy logic systems is proposed, in which identifier, actor, and critic are used to estimate unknown nonlinear dynamics, implement control behavior, and evaluate system performance, respectively. Different from the existing optimal control algorithms, a new performance index function considering formation error cost and control input energy cost is constructed to achieve optimal formation control of MASs within a prescribed time. The presented control strategy can ensure that the formation error converges to the desired accuracy within a prescribed time. Finally, the validity of the presented strategy is verified via a simulation example.

Early responses of Tregs and effector T cells (Teffs) to their first encounter with tumor cells have been poorly characterized. Here we have shown, in both implanted and in situ-induced mouse tumor models, that the appearance of tumor cells is immediately sensed by CD44hi memory Tregs that are specific for self antigens. The rapid response of these Tregs preceded and prevented activation of naive antitumor Teffs. The relative speed of the Treg versus the Teff response within the first 2-4 days determined the outcome of the antitumor immune response: tolerance or rejection. If antitumor memory Teffs were present at the time of tumor emergence, both Tregs and Teffs were recruited and activated with memory kinetics; however, the Tregs were unable to control the Teffs, which eradicated the tumor cells. This balance between effector and regulatory responses did not depend on the number of Tregs and Teffs, but rather on their memory status. Thus, in the natural setting, dominant tolerogenic immunosurveillance by self-specific memory Tregs protects tumors, just as it protects normal tissues. More generally, our results reveal that the timing of Treg and Teff engagement, determined by their memory status, is an important mode of regulation of immune responses.

Component-Based Software Engineering focuses on the reuse of existing software components. In practice, most components cannot be integrated directly into an application-to-be, because they are incompatible. Software Adaptation aims at generating, as automatically as possible, adaptors to compensate mismatch between component interfaces, and is therefore a promising solution for the development of a real market of components promoting software reuse. In this article, we present our approach for software adaptation which relies on an abstract notation based on synchronous vectors and transition systems for governing adaptation rules. Our proposal is supported by dedicated algorithms that generate automatically adaptor protocols. These algorithms have been implemented in a tool, called Adaptor, that can be used through a user-friendly graphical interface.

The adaptive fuzzy predefined-time tracking control problem for a class of nonlinear systems with output hysteresis is investigated in this article. An inverse model is utilized to capture the output hysteresis phenomenon, and then, the Nussbaum-type function technique is utilized to overcome the difficulty of unknown time-varying control gain caused by output hysteresis. An adaptive fuzzy control scheme under the backstepping framework is developed using the predefined-time stability criterion. Different from the existing predefined-time design approaches, the adaptive law designed in this article is represented as a nonlinear differential equation. Theoretical analysis demonstrates that all signals of the closed-loop systems are bounded, and the tracking error can converge to the neighborhood near the origin within an expected settling time. The developed scheme's feasibility is verified by an example of an electromechanical system.

This article is devoted to the discrete-time sliding mode control (DSMC) for nonlinear semi-Markovian switching systems (S-MSSs). Motivated by the fact that the complete information of the semi-Markov Kernel is difficult to be obtained in practical applications, it is recognized to be partly unknown as the most common mean. By utilizing the prior information of the sojourn-time upper bound for each switching mode, sufficient conditions under the equivalent DSMC law are proposed for the mean square stability. Moreover, the designed DSMC law realizes the finite-time reachability of the sliding region, and makes the sliding dynamics converge to the predesignated sliding region in a finite time. In the end, a numerical example and an electronic throttle model are given to validate the proposed control strategy.