A Splitting Theorem for Local Cohomology and its Applications

Let $R$ be a commutative Noetherian ring and $M$ a finitely generated $R$-module. We show in this paper that, for an integer $t$, if the local cohomology module $H^{i}_\mathfrak{a}(M)$ with respect to an ideal $\frak a$ is finitely generated for all $i<t$, then $$H^{i}_\mathfrak{a}(M/xM)\cong H^{i}_\mathfrak{a}(M)\oplus H^{i+1}_\mathfrak{a}(M)$for all$\frak a$-filter regular elements$x$containing in a enough large power of$\frak a$and all$i<t-1$. As consequences we obtain generalizations, by very short proofs, of the main results of M. Brodmann and A.L. Faghani (A finiteness result for associated primes of local cohomology modules, Proc. Amer. Math. Soc., 128(2000), 2851-2853) and of H.L. Truong and the first author (Asymptotic behavior of parameter ideals in generalized Cohen-Macaulay module, J. Algebra, 320(2008),158-168).Comment: to appear in J. Algebr

Endogenous Fiscal Policies, Environmental Quality, and Status-Seeking Behavior.

This paper analyzes endogenous fiscal policy and public decision in an endogenous growth model where agents care about social status and environmental quality. The quest for a higher status is assimilated to a preference for capital wealth. The government uses income tax to finance infrastructure and environmental protection, and maximizes individual welfare. We find that accounting for preferences for social status and environmental quality may lead to an allocation of tax revenue in favor of cleanup effort to the detriment of infrastructure. It does not necessary have a negative impact on growth. Status seeking can however harm economic growth and environmental quality when its motive is important enough. Finally, we show that economic growth is consistent with environmental preservation but is not necessarily welfare-improving as in the case of absence of status-seeking behavior.Endogenous policy; endogenous growth; environmental quality; status-seeking; public expenditure; Wagner's law.

Pervasive technology-enhanced learning system integrating working and learning situations

International audienceIn the p-LearNet project, we are interested in TEL systems integrating context-aware corporate learning and working activities for e-retail (shops and hypermarkets). The main issues of the p-LearNet project are: work-integrated learning and customer learning support whatever the place, the time, the organisational and technological contexts of the individual or collective learning and working processes. We propose an adaptive and context-aware model of scenario based on an interdisciplinary approach (education, computer science, social sciences, and business) for a pervasive learning system supporting working and learning situations. This model enables us to choose how to achieve activities according to the current situation. The scenario model is based on a hierarchical task model having the task/method paradigm - methods define how to achieve a task

An FPGA-based Convolution IP Core for Deep Neural Networks Acceleration

The development of machine learning has made a revolution in various applications such as object detection, image/video recognition, and semantic segmentation. Neural networks, a class of machine learning, play a crucial role in this process because of their remarkable improvement over traditional algorithms. However, neural networks are now going deeper and cost a significant amount of computation operations. Therefore they usually work ineffectively in edge devices that have limited resources and low performance. In this paper, we research a solution to accelerate the neural network inference phase using FPGA-based platforms. We analyze neural network models, their mathematical operations, and the inference phase in various platforms. We also profile the characteristics that affect the performance of neural network inference. Based on the analysis, we propose an architecture to accelerate the convolution operation used in most neural networks and takes up most of the computations in networks in terms of parallelism, data reuse, and memory management. We conduct different experiments to validate the FPGA-based convolution core architecture as well as to compare performance. Experimental results show that the core is platform-independent. The core outperforms a quad-core ARM processor functioning at 1.2 GHz and a 6-core Intel CPU with speed-ups of up to 15.69× and 2.78×, respectivel