1,720,987 research outputs found
Distributed optical fibre sensing methods for localisation of disturbances and the position of optical fibre cables in the ground
No full textThis paper firstly presents a novel optical sensor technology developed to locate, from above ground, the lateral position of a buried dielectrically sheathed optical cable. Then, optical sensor methods for the distributed sensing and longitudinal positioning of time-varying acoustic disturbances are presented. It is hoped to present results of a novel field-trial capable OTDR based system in the verbal presentation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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Hierarchical Methods for Optimal Long-Term Planning
Full text linkThis thesis addresses the problem of generating goal-directed plans involving very many elementary actions. For example, to achieve a real-world goal such as earning a Ph.D., an intelligent agent may carry out millions of actions at the level of reading a word or striking a key. Given computational constraints, it seems that such long-term planning must incorporate reasoning with high-level actions (such as delivering a conference talk or typing a paragraph of a research paper) that abstract over the precise details of their implementations, despite the fact that these details must eventually be determined for the actions to be executed. This multi-level decision-making process is the subject of hierarchical planning.To most effectively plan with high-level actions, one would like to be able to correctly identify whether a high-level plan works, without first considering its low-level implementations. The first contribution of this thesis is an "angelic" semantics for high-level actions that enables such inferences. This semantics also provides bounds on the costs of high-level plans, enabling the identification of provably high-quality (or even optimal) high-level solutions.Effective hierarchical planning also requires algorithms to efficiently search through the space of high-level plans for high-quality solutions. We demonstrate how angelic bounds can be used to speed up search, and introduce a novel decomposed planning framework that leverages task-specific state abstraction to eliminate many redundant computations. These techniques are instantiated in the Decomposed, Angelic, State-abstracted, Hierarchical A* (DASH-A*) algorithm, which can find hierarchically optimal solutions exponentially faster than previous algorithms
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Towards Trustworthy Machine Learning
Full text linkReal-world applications of machine learning often have complex objectives and safety-critical constraints. Contemporary machine learning systems excel at achieving high average-case performance at tasks with simple procedurally specified objectives, but they struggle at many more demanding real-world tasks. In this thesis, we work towards developing trustworthy machine learning systems that understand human values and reliably optimize them.Machine learning’s key insight was that it is often easier to learn an algorithm than to write it down directly—yet many machine learning systems still have a hard-coded, procedurally specified objective. The field of reward learning applies this insight to instead learn the objective itself. As there is a many-to-one mapping between reward functions and objectives, we start by introducing the notion of equivalence classes consisting of reward functions that specify the same objective.In the first part of the dissertation, we apply this notion of equivalence classes to three distinct settings. First, we study reward function identifiability: what set of reward functions is compatible with the data? We start by categorizing the equivalence classes of reward functions that induce the same data. By comparing these to the aforementioned optimalpolicy equivalence class, we can determine whether a given data source provides sufficient information to recover the optimal policy.Second, we address the fundamental question of how similar or dissimilar two reward function equivalence classes are. We introduce a distance metric over these equivalence classes, the Equivalent-Policy Invariant Comparison (EPIC), and show rewards with low EPIC distance induce policies with similar returns even under different transition dynamics. Finally, weintroduce an interpretability method for reward function equivalence classes. The method selects the easiest to understand representative from the equivalence class, and then visualizes the representative function.In the second part of the dissertation, we study the adversarial robustness of models. We start by introducing a physically realistic threat model consisting of an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial to the defender. We train the adversary using deep RL against a frozen state-of-the-artdefender that was trained via self-play to be robust to opponents. We find this attack reliably wins against state-of-the-art simulated robotics RL agents, and superhuman Go programs.Finally, we investigate ways to improve agent robustness. We find adversarial training is ineffective, however population-based training offers hope as a partial defense: it does not prevent the attack, but it does increase the computational burden of the attacker. Using explicit planning also helps, as we find that defenders with large amounts of search are harder to exploit
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Efficient inference algorithms for near-deterministic systems
Full text linkThis thesis addresses the problem of performing probabilistic inference in stochastic systems where the probability mass is far from uniformly distributed among all possible outcomes. Such near-deterministic systems arise in several real-world applications. For example, in human physiology, the widely varying evolution rates of physiological variables make certain trajectories much more likely than others; in natural language, a very small fraction of all possible word sequences accounts for a disproportionately high amount of probability under a language model. In such settings, it is often possible to obtain significant computational savings by focusing on the outcomes where the probability mass is concentrated. This contrasts with existing algorithms in probabilistic inference---such as junction tree, sum product, and belief propagation algorithms---which are well-tuned to exploit conditional independence relations. The first topic addressed in this thesis is the structure of discrete-time temporal graphical models of near-deterministic stochastic processes. We show how the structure depends on the ratios between the size of the time step and the effective rates of change of the variables. We also prove that accurate approximations can often be obtained by sparse structures even for very large time steps. Besides providing an intuitive reason for causal sparsity in discrete temporal models, the sparsity also speeds up inference. The next contribution is an eigenvalue algorithm for a linear factored system (e.g., dynamic Bayesian network), where existing algorithms do not scale since the size of the system is exponential in the number of variables. Using a combination of graphical model inference algorithms and numerical methods for spectral analysis, we propose an approximate spectral algorithm which operates in the factored representation and is exponentially faster than previous algorithms. The third contribution is a temporally abstracted Viterbi (TAV) algorithm. Starting with a spatio-temporally abstracted coarse representation of the original problem, the TAV algorithm iteratively refines the search space for the Viterbi path via spatial and temporal refinements. The algorithm is guaranteed to converge to the optimal solution with the use of admissible heuristic costs in the abstract levels and is much faster than the Viterbi algorithm for near-deterministic systems. The fourth contribution is a hierarchical image/video segmentation algorithm, that shares some of the ideas used in the TAV algorithm. A supervoxel tree provides the abstraction hierarchy for this application. The algorithm starts working with the coarsest level supervoxels, and refines portions of the tree which are likely to have multiple labels. Several existing segmentation algorithms can be used to solve the energy minimization problem in each iteration, and admissible heuristic costs once again guarantee optimality. Since large contiguous patches exist in images and videos, this approach is more computationally efficient than solving the problem at the finest level of supervoxels. The final contribution is a family of Markov Chain Monte Carlo (MCMC) algorithms for near-deterministic systems when there exists an efficient algorithm to sample solutions for the corresponding deterministic problem. In such a case, a generic MCMC algorithm's performance worsens as the problem becomes more deterministic despite the existence of the efficient algorithm in the deterministic limit. MCMC algorithms designed using our methodology can bridge this gap. The computational speedups we obtain through the various new algorithms presented in this thesis show that it is indeed possible to exploit near-determinism in probabilistic systems. Near-determinism, much like conditional independence, is a potential (and promising) source of computational savings for both exact and approximate inference. It is a direction that warrants more understanding and better generalized algorithms
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