117,107 research outputs found
Scalability using effects
This note is about using computational effects for scalability. With this
method, the specification gets more and more complex while its semantics gets
more and more correct. We show, from two fundamental examples, that it is
possible to design a deduction system for a specification involving an effect
without expliciting this effect
Impact of Strain on Drain Current and Threshold Voltage of Nanoscale Double Gate Tunnel Field Effect Transistor: Theoretical Investigation and Analysis
Tunnel field effect transistor (TFET) devices are attractive as they show
good scalability and have very low leakage current. However they suffer from
low on-current and high threshold voltage. In order to employ the TFET for
circuit applications, these problems need to be tackled. In this paper, a novel
lateral strained double-gate TFET (SDGTFET) is presented. Using device
simulation, we show that the SDGTFET has a higher on-current, low leakage, low
threshold voltage, excellent subthreshold slope, and good short channel effects
and also meets important ITRS guidelines.Comment: http://web.iitd.ac.in/~mamidal
Designing Scalable Business Models
Digital business models are often designed for rapid growth, and some relatively young companies have indeed achieved global scale. However despite the visibility and importance of this phenomenon, analysis of scale and scalability remains underdeveloped in management literature. When it is addressed, analysis of this phenomenon is often over-influenced by arguments about economies of scale in production and distribution. To redress this omission, this paper draws on economic, organization and technology management literature to provide a detailed examination of the sources of scaling in digital businesses. We propose three mechanisms by which digital business models attempt to gain scale: engaging both non- paying users and paying customers; organizing customer engagement to allow self- customization; and orchestrating networked value chains, such as platforms or multi-sided business models. Scaling conditions are discussed, and propositions developed and illustrated with examples of big data entrepreneurial firms
The impact of agent density on scalability in collective systems : noise-induced versus majority-based bistability
In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each agent can change its decision after being recruited by a nearby agent. The second model captures the dynamics of dense swarms controlled by the majority rule (i.e., agents switch their opinion to comply with that of the majority of their neighbors). The third model combines the first two, with the aim of studying the role of non-uniform swarm density in the performance of collective decision-making. Based on the three models, we formulate a set of requirements for convergence and scalability in collective decision-making
Scalability of Genetic Programming and Probabilistic Incremental Program Evolution
This paper discusses scalability of standard genetic programming (GP) and the
probabilistic incremental program evolution (PIPE). To investigate the need for
both effective mixing and linkage learning, two test problems are considered:
ORDER problem, which is rather easy for any recombination-based GP, and TRAP or
the deceptive trap problem, which requires the algorithm to learn interactions
among subsets of terminals. The scalability results show that both GP and PIPE
scale up polynomially with problem size on the simple ORDER problem, but they
both scale up exponentially on the deceptive problem. This indicates that while
standard recombination is sufficient when no interactions need to be
considered, for some problems linkage learning is necessary. These results are
in agreement with the lessons learned in the domain of binary-string genetic
algorithms (GAs). Furthermore, the paper investigates the effects of
introducing utnnecessary and irrelevant primitives on the performance of GP and
PIPE.Comment: Submitted to GECCO-200
Scalability of the channel capacity in graphene-enabled wireless communications to the nanoscale
Graphene is a promising material which has been proposed to build graphene plasmonic miniaturized antennas, or graphennas, which show excellent conditions for the propagation of Surface Plasmon Polariton (SPP) waves in the terahertz band. Due to their small size of just a few micrometers, graphennas allow the implementation of wireless communications among nanosystems, leading to a novel paradigm known as Graphene-enabled Wireless Communications (GWC). In this paper, an analytical framework is developed to evaluate how the channel capacity of a GWC system scales as its dimensions shrink. In particular, we study how the unique propagation of SPP waves in graphennas will impact the channel capacity. Next, we further compare these results with respect to the case when metallic antennas are used, in which these plasmonic effects do not appear. In addition, asymptotic expressions for the channel capacity are derived in the limit when the system dimensions tend to zero. In this scenario, necessary conditions to ensure the feasibility of GWC networks are found. Finally, using these conditions, new guidelines are derived to explore the scalability of various parameters, such as transmission range and transmitted power. These results may be helpful for designers of future GWC systems and networks.Peer ReviewedPostprint (author’s final draft
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