Ordonnancement sous contraintes (m,k)-firm et combinatoire des mots

Dans ce papier, nous montrons une nouvelle méthode d'analyse de l'ordonnançabilité des ensembles de tâches sous contraintes (m,k)-firm en utilisant des propriétés des mots mécaniques (théorie des mots). Nous nous intéressons à l'ordonnancement sous contrainte de (m,k) pattern fixe. Dans un premier temps, nous montrons que les patterns introduits dans la littérature se caractérisent bien sous la forme de mots mécaniques. Les preuves d'ordonnançabilité en sont ainsi simplifiées. En identifiant les défauts de ces patterns, nous proposons ensuite une nouvelle technique basée sur la ligne cellulaire pour déterminer les (m,k) patterns des tâches. Les résultats expérimentaux montrent que cette nouvelle technique permet une amélioration de la région ordonnançable

Graceful Degradation of the Quality of Control through Data Drop Policy

International audienceSharing a network link in a networked control system (NCS) may result in the network overload leading to the non respect of the specified periodicity of control system, this may cause an unpredictable performance degradation of the system. In this paper, we consider an overload management technique which selectively drops the data packets of the NCS to avoid network overload. The investigated problems are: how to design the controller under packet drops and how to distribute the packet drops in a packet delivery sequence so that the quality of control (QoC) is optimized. The paper first gives the optimal LQ-controller using the conventional technique to reduce the deterioration in QoC due to packet drops. Then, the paper proposes a methodology for deriving a distribution of the packet drops in the packet delivery sequence so that the QoC is optimal. Finally, an efficient algorithm is given to reduce the computation complexity of the proposed methodology. This proposal contributes to the co-design of the controller and the resource performance management process at the implementation level

Analysis of networked control system with packet drops governed by (m,k)-firm constraint

In this paper we study the effect of the packet drop process governed by (m,k)-firm constraint in the feedback loop of a control system. Our approach is to first analyse the stability and the optimality of such a system, then by considering the amplitude variance of the system state as the system performance, we propose a method to optimally distribute the packets to offer a better system performance

Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations

Existing research predominantly focuses on developing powerful language learning models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in preserving efficacy in a multilingual context. To bridge this gap, this paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. Firstly, by utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages, thus addressing the issue of training data scarcity in xMR tasks. Based on the collected dataset, we propose different training strategies to build powerful xMR LLMs, named MathOctopus, notably outperform conventional open-source LLMs and exhibit superiority over ChatGPT in few-shot scenarios. Notably, MathOctopus-13B reaches 47.6% accuracy which exceeds ChatGPT 46.3% on MGSM testset. Beyond remarkable results, we unearth several pivotal observations and insights from extensive experiments: (1) When extending the rejection sampling strategy to the multilingual context, it proves effective for model performances, albeit limited. (2) Employing parallel corpora for math Supervised Fine-Tuning (SFT) across multiple languages not only significantly enhances model performance multilingually but also elevates their monolingual performance. This indicates that crafting multilingual corpora can be regarded as a vital strategy for enhancing model performance in a specific language, especially in mathematical reasoning tasks. For instance, MathOctopus-7B improves its counterparts that trained on English from 42.2% to 50.8% on GSM8K testset.Comment: Work in Progres

Task Handler Based on (m,k)-firm Constraint Model for Managing a Set of Real-Time Controllers

URL : http://rtns07.irisa.frInternational audienceIn this paper, we study how to schedule a set of real-time tasks where each task implements a control law. These tasks share a limited computing resource. The set of tasks can switch on line from one given configuration to another one depending on the working modes of the global application. This means that some tasks may appear while other ones may be no longer activated and that the WCET of some tasks may be modified. We propose a scheduling architecture for the handling of such task instances. At each mode switching, the task handler consider the new task parameters; then it determines on line a (m,k)-constraint based scheduling strategy to apply to each task; this strategy aims to selectively discard task instances so that the schedulability of tasks is guaranteed and the overall control performance is maintained at a high level

Robust beam splitter with fast quantum state transfer through a topological interface

The Su-Schrieffer-Heeger (SSH) model, commonly used for robust state transfers through topologically protected edge pumping, has been generalized and exploited to engineer diverse functional quantum devices. Here, we propose to realize a fast topological beam splitter based on a generalized SSH model by accelerating the quantum state transfer (QST) process essentially limited by adiabatic requirements. The scheme involves delicate orchestration of the instantaneous energy spectrum through exponential modulation of nearest neighbor coupling strengths and onsite energies, yielding a significantly accelerated beam splitting process. Due to properties of topological pumping and accelerated QST, the beam splitter exhibits strong robustness against parameter disorders and losses of system. In addition, the model demonstrates good scalability and can be extended to two-dimensional crossed-chain structures to realize a topological router with variable numbers of output ports. Our work provides practical prospects for fast and robust topological QST in feasible quantum devices in large-scale quantum information processing.Comment: To be published in Frontiers of Physic

Optimal LQ-controller Design and Data Drop Distribution under (m,k)-firm constraint

The share of the network link in a networked control system (NCS) may result in the network overload where the periodicity of control system could be infected, causing an unpredictable performance degradation in control system. In this paper, we consider an overload management technique which selectively drops the data packets of the NCS to avoid the network overload. The problems investigated are: how to design the controller under packet drops and how to distribute the packet drops in a packet delivery sequence so that the quality of control (QoC) is optimized. The paper first gives the optimal LQ-controller by using the conventional technique to reduce the deterioration in QoC due to packet drops. Then, the paper proposes a methodology for deriving the distribution of the packet drops in the packet delivery sequence so that the QoC is optimal. To reduce the computation complexity of the proposed methodology, a computationally cheaper algorithm is also given. This proposal contributes to the co-design of the controller and the resource performance management process at the implementation level

Image restoration with point-spread function regularization and active learning

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid, and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research

Overload Management Through Selective Data Dropping

During system and network overload periods, excessive delay or even data loss may occur. To maintain the quality of control of an NCS, the implementation system (including both computer and network) overload must be correctly handled. In this chapter, as an alternative to the explicit sampling period adjustment, we present an indirect sampling period adjustment approach which is based on selective sampling data dropping according to the (m, k)-firm model. The interest of this alternative is its easy implementation despite having less adjustment quality, since only the multiples of the basic sampling period can be exploited. Upon overload detection, the basic idea is to selectively drop some samples according to the (m, k)-firm model to avoid long consecutive data drops. The consequence is that the shared network and processor will be less loaded. However, the control stability and performance must still be maintained to an acceptable level. This can be achieved by keeping either the total control tasks on a same processor or the messages sharing a same network bandwidth schedulable under the (m, k)-firm constraint

Impact of a (m,k)-firm Data Dropouts Policy on the Quality of Control

http://ieeexplore.ieee.orgIn this paper, we propose a method for the design of an operational architecture of a Networked Control System (NCS). We consider a control system whose main goal is to control the position of a cart moving along a rail. The implementation of the controller is done through a distributed architecture in which a shared network supports the transmission of the samples between the sensor and the controller. For efficiently handling network congestion, we propose to apply a selective sample drop policy according to a (m,k)-pattern in order to decrease the network bandwidth required by this application during network overload periods. The paper shows how to determine the values of the parameter k that preserve the stability of the system and then how to identify the value of m and the (m,k)-pattern in order to optimize the system performance