Independent film

Summary

An independent film, independent movie, indie film, or indie movie is a feature film or short film that is produced outside the major film studio system, in addition to being produced and distributed by independent entertainment companies (or, in some cases, distributed by major companies). Independent films are sometimes distinguishable by their content and style and the way in which the filmmakers' personal artistic vision is realized. Usually, but not always, independent films are made with considerably lower budgets than major studio films. It is not unusual for well-known actors who are cast in independent features to take substantial pay cuts for a variety of reasons: if they truly believe in the message of the film; they feel indebted to filmmaker for a career break; their career is otherwise stalled or they feel unable to manage a larger commitment to a studio film; the film offers an opportunity to showcase a talent that has not gained traction in the studio system; or simp

Official source

https://en.wikipedia.org/wiki/Independent_film

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EMG Pattern Recognition Using Decomposition Techniques for Constructing Multiclass Classifiers

Claudio Bruschini, Christian Enz, Huaiqi Huang, Jörn Justiz, Tao Li

To improve the dexterity of multi-functional myoelectric prosthetic hand, more accurate hand gesture recognition based on surface electromyographic (sEMG) signal is needed. This paper evaluates two types of time-domain EMG features, one independent feature and one combined feature including four features. The selected features from eight subjects with 13 finger movements were tested with four decomposed multi-class support vector machines (SVM), four decomposed linear discriminant analyses (LDA) and a multi-class LDA. The classification accuracy, training, and classification time are compared. The results have shown that the combined features decrease error rate, and binary tree based decomposition multi-class classifiers yield the highest classification success rate (88.2%) with relatively low training and classification time.

IEEE2016

Network Coding

Mahdi Jafari Siavoshani

The demand for higher throughput and better efficiency are two important challenges for future communication networks. During the past decades, a lot of research studies have been devoted to investigating and proposing near optimal and efficient schemes and algorithms for point-to-point communication. However, in communication networks, especially in wireless systems, we require more intricate algorithms and coding schemes that are optimized for networks rather than for point-to-point communications. In recent years, the network coding paradigm has opened new opportunities for network information flow algorithms. In the first part of the thesis, we consider a non-coherent transmission scenario in a network performing randomized linear network coding. Our main goal is to find the optimal performance in terms of communication rate in such a transmission scenario and to discover the optimal coding scheme to achieve it. It is observed by Koetter et al. [1] that because the network performs an unknown linear transformation, coding over subspaces spanned by the source packets could be a reasonable coding scheme. In order to make this claim information-theoretically justified, we study a multiplicative matrix channel over a finite field with a uniform and independent distribution over the transfer matrix. The capacity for this unicast communication scenario is characterized and it is shown that coding over subspaces is indeed optimal. A similar result is also derived for the two users multiple access problem in such a non-coherent network coding scenario. We then generalize this model by proposing a more universal scenario which is based on an arbitrarily varying channel approach. This result shows the optimality of subspace coding for a wider class of matrix channels, i.e., the channels where only the rank distribution of the transfer matrix is known. Moreover, the above results show that the overhead of schemes based on coding vectors is negligible for many practical situations, i.e., the situations where the rank of the transfer matrix is concentrated around some integer number. Next, we observe that in a network performing randomized linear network coding, the coding vectors carry topological and state-dependent information about the network. Considering the subspaces spanned by the coding vectors at the relay nodes of the network, we investigate the properties of these subspaces and leverage them for some practical problems including network tomography, network management, and Byzantine attack detection. In the last part of the thesis, we consider the problem of secret key sharing among multiple nodes in a network, in the presence of a passive eavesdropper. We assume that there exists a broadcast channel from one of the trusted nodes to the rest of them (including the eavesdropper). Moreover, we assume that the trusted entities can discuss over a public channel overheard by everyone. The secrecy key generation capacity for this problem is still unknown (in general, there exist only some upper and lower bounds). For the erasure broadcast channel as well as the linear deterministic wireless broadcast channel, we propose optimal and efficient schemes that enable arbitrary number of legitimate entities to share a secret key among themselves. By extending these results, we propose achievability schemes for the non-coherent network coding broadcast channel and the state-dependent Gaussian wireless broadcast channel.

EPFL2012

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Microsimulation of urban transportation and land use evolution require base year, individual characteristics and disaggregate locations of the households and persons living in the study area. On the other hand, mainly due to privacy reasons, at best the census and travel survey, which are the primary sources of the data, provide only cross tabulations at various level of spatial aggregations (sector, commune, region, and country) and a small sample of the individual level information (microdata) that usually doesnt have the spatial information attached to it. This necessitates generation of the baseline population using some synthetic means. Currently, various variants of the Iterative Proportional Fitting (IPF) are predominantly used to generate the base year synthetic population. IPF essentially creates clones of the individual records of households and persons from microdata in a way that the marginal at one or more levels of spatial aggregations are satisfied. In the process of doing so, the IPF ensures that the correlation structure of the sample is preserved in the synthesized populations. The key shortcomings of IPF include: a) losing the heterogeneity that may not have been captured in the microdata, due to cloning rather than true synthesis of the population b) over reliance on the accuracy of the data to determine the cloning weights c) very poor scalability with respect to the increased demand in the number of characteristics of the population that need to be synthesized. In order to overcome these shortcomings and move the research in population synthesis for microsimulations significantly forward, we propose a Markov Chain Monte Carlo Simulation based approach that its core uses Gibbs and Metropolis-Hasting sampling methods. This approach, instead of cloning the microdata, generates the joint distribution of the characteristics of the households, persons, and the associations between them, by using any available data on these three dimensions. The associations are defined in terms of the position (head, adult, kid etc.) a person can take in a household. The problem is divided into generating two types of independent and un-normalized distributions i.e. agent distributions and association distributions for each realization of agents. The resulting joint distribution out of these independent distributions is thus the best possible representation of the real population, given all the available information. The required synthetic population for an urban microsimulation can then be generated by simply taking a realization out of this un-normalized joint distribution. This way the population synthesis can become seamless part of these urban microsimulations and thus it can also be included in the sensitivity analysis of them. In terms of the implementation, we have developed a C++ based code and are testing the methodology by generating the synthetic population for Brussels, Belgium. We are also in the process of synthesizing the population for Switzerland, where we have access to the entire census. This way, we will be able to compare the performance of our proposed methodology with IPF and combinatorial optimization based methods in terms of reproducing the actual population.

2012

Network Coding

Mahdi Jafari Siavoshani

EPFL2012

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2012