| 1. |
- Balestra, F., et al.
(author)
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NANOSIL network of excellence-silicon-based nanostructures and nanodevices for long-term nanoelectronics applications
- 2008
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In: Materials Science in Semiconductor Processing. - : Elsevier BV. - 1369-8001 .- 1873-4081. ; 11:5-6, s. 148-159
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Journal article (peer-reviewed)abstract
- NANOSIL Network of Excellence [NANOSIL NoE web site < www.nanosil-noe.eu >], funded by the European Commission in the 7th Framework Programme (ICT-FP7, no 216171), aims at European scale integration of the excellent European research laboratories and their capabilities in order to strengthen scientific and technological excellence in the field of nanoelectronic materials and devices for terascale integrated circuits (ICs), and to disseminating the results in a wide scientific and industrial community. NANOSIL is exploring and assessing the science and technological aspects of nanodevices and operational regimes relevant to the n+4 technology node and beyond. It encompasses projects on nanoscale CMOS and beyond-CMOS. Innovative concepts, technologies and device architectures are proposed-with fabrication down to the finest features, and utilising a wide spectrum of advanced deposition and processing capabilities, extensive characterization and very rigorous device modeling. This work is carried out through a network of joint processing, characterization and modeling platforms. This critical interaction strengthens European integration in nanoelectronics and will speed up technological innovation for the nanoelectronics of the next two to three decades.
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| 2. |
- Botta, N., et al.
(author)
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Sequential decision problems, dependent types and generic solutions
- 2017
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In: Logical Methods in Computer Science. - 1860-5974. ; 13:1
-
Journal article (peer-reviewed)abstract
- We present a computer-checked generic implementation for solving finite horizon sequential decision problems. This is a wide class of problems, including inter temporal optimizations, knapsack, optimal bracketing, scheduling, etc. The implementation can handle time-step dependent control and state spaces, and monadic representations of uncertainty (such as stochastic, non-deterministic, fuzzy, or combinations thereof). This level of genericity is achievable in a programming language with dependent types (we have used both Idris and Agda). Dependent types are also the means that allow us to obtain a formalization and computer-checked proof of the central component of our implementation: Bellman's principle of optimality and the associated backwards induction algorithm. The formalization clarifies certain aspects of backwards induction and, by making explicit notions such as viability and reachability, can serve as a starting point for a theory of controllability of monadic dynamical systems, commonly encountered in, e.g., climate impact research.
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