Search results for "Hard"
showing 10 items of 2294 documents
La Media Education al Centro Tau. Dalla teoria alla pratica
2022
Il contributo illustra le attività di media education del Centro Tau di Palermo come approccio per la costruzione di una civic intentionality (Mihailidis and Thevenin 2013) nei giovani che frequentano questo centro di aggregazione giovanile collocato in una delle aree più colpite da disagio sociale della città di Palermo.
Canopy Architecture Appraisal by Fractal Dimension of 'Flordastar' Peach Trees Grafted onto Different Rootstocks
2007
The objective of this research was to evaluate the modification of canopy architecture of ''Flordastar'' peach (Prunus persica L. Batsch) grafted onto rootstocks with different vigour, by the use of fractal dimension (D). The hypothesis was that different vigour rootstocks are able to modify the complexity of the branching pattern and that this effect can be assessed by a geometric parameter such as the fractal dimension (D) of the 2D projection of tree branching structure. The observations were carried out in a four-year-old experimental orchard of cv. ''Flordastar'' peach trees grafted onto Ishtara, Barrier, GF677 and MrS 2/5 rootstocks. On digital pictures of leafless, dormant peach tree…
On the impact of forgetting on learning machines
1995
People tend not to have perfect memories when it comes to learning, or to anything else for that matter. Most formal studies of learning, however, assume a perfect memory. Some approaches have restricted the number of items that could be retained. We introduce a complexity theoretic accounting of memory utilization by learning machines. In our new model, memory is measured in bits as a function of the size of the input. There is a hierarchy of learnability based on increasing memory allotment. The lower bound results are proved using an unusual combination of pumping and mutual recursion theorem arguments. For technical reasons, it was necessary to consider two types of memory : long and sh…
Boosting Textual Compression in Optimal Linear Time
2005
We provide a general boosting technique for Textual Data Compression. Qualitatively, it takes a good compression algorithm and turns it into an algorithm with a better compression performance guarantee. It displays the following remarkable properties: (a) it can turn any memoryless compressor into a compression algorithm that uses the “best possible” contexts; (b) it is very simple and optimal in terms of time; and (c) it admits a decompression algorithm again optimal in time. To the best of our knowledge, this is the first boosting technique displaying these properties.Technically, our boosting technique builds upon three main ingredients: the Burrows--Wheeler Transform, the Suffix Tree d…
Work Partitioning on Parallel and Distributed Agent-Based Simulation
2017
Work partitioning is a key challenge with ap- plications in many scientific and technological fields. The problem is very well studied with a rich literature on both distributed and parallel computing architectures. In this paper we deal with the work partitioning problem for parallel and distributed agent-based simulations which aims at (i) balancing the overall load distribution, (ii) minimizing, at the same time, the communication overhead due to agents' inter-dependencies. We introduce a classification taxonomy of work partitioning strategies and present a space-based work partitioning ap- proach, based on a Quad-tree data structure, which enables to: identify a good space partitioning …
Supervised learning of time-independent Hamiltonians for gate design
2018
We present a general framework to tackle the problem of finding time-independent dynamics generating target unitary evolutions. We show that this problem is equivalently stated as a set of conditions over the spectrum of the time-independent gate generator, thus transforming the task to an inverse eigenvalue problem. We illustrate our methodology by identifying suitable time-independent generators implementing Toffoli and Fredkin gates without the need for ancillae or effective evolutions. We show how the same conditions can be used to solve the problem numerically, via supervised learning techniques. In turn, this allows us to solve problems that are not amenable, in general, to direct ana…
On the Influence of Technology on Learning Processes
2014
Probabilistic computations and frequency computations were invented for the same purpose, namely, to study possible advantages of technology involving random choices. Recently several authors have discovered close relationships of these generalizations of deterministic computations to computations taking advice. Various forms of computation taking advice were studied by Karp and Lipton [1], Damm and Holzer [2], and Freivalds [3]. In the present paper, we apply the nonconstructive, probabilistic, and frequency methods to an inductive inference paradigm originally due to Gold [4] and investigate their impact on the resulting learning models. Several trade-offs with respect to the resulting l…
The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions
2018
Part 10: Learning - Intelligence; International audience; Although the field of Learning Automata (LA) has made significant progress in the last four decades, the LA-based methods to tackle problems involving environments with a large number of actions are, in reality, relatively unresolved. The extension of the traditional LA (fixed structure, variable structure, discretized, and pursuit) to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and consequently, most components of the vector will, after a relatively short time, have values that are smal…
Approximate supervised learning of quantum gates via ancillary qubits
2018
We present strategies for the training of a qubit network aimed at the ancilla-assisted synthesis of multi-qubit gates based on a set of restricted resources. By assuming the availability of only time-independent single and two-qubit interactions, we introduce and describe a supervised learning strategy implemented through momentum-stochastic gradient descent with automatic differentiation methods. We demonstrate the effectiveness of the scheme by discussing the implementation of non-trivial three qubit operations, including a Quantum Fourier Transform (QFT) and a half-adder gate.
Adjusting Software Revenue and Pricing Strategies in the Era of Cloud Computing
2016
Competitive forces shape software revenue and pricing models in cloud computing.Different revenue and pricing models lead to different competitive strategies.Software firms apply mixed revenue models, or a hybrid pricing mechanism.Software renting provides flexibility for software providers against competition.Software architecture may either limit possibilities for different revenue models. Recent research has recognized cloud computing as a new paradigm of servitization in which software products are offered based on service contracts. Thus, instead of selling software licenses, software vendors can rent software as a service to customers. However, it is still unclear how software provide…