1,721,073 research outputs found
Dynamical model selection near the quantum-classical boundary
No full textWe discuss a general method of model selection from experimentally recorded time-trace data. This method can be used to distinguish between quantum and classical dynamical models. It can be used in postselection as well as for real-time analysis, and offers an alternative to statistical tests based on state-reconstruction methods. We examine the conditions that optimize quantum hypothesis testing, maximizing one's ability to discriminate between classical and quantum models. We set upper limits on the temperature and lower limits on the measurement efficiencies required to explore these differences, using an experiment in levitated optomechanical systems as an example
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
The Mechanical Psychologist: How Computational Techniques Can Aid Social Researchers in the Analysis of High-Stakes Conversation
Full text linkQualitative coding is an essential observational tool for describing behaviour in the social sciences. However, it traditionally relies on manual, time-consuming, and error-prone methods performed by humans. To overcome these issues, cross-disciplinary researchers are increasingly exploring computational methods such as Natural Language Processing (NLP) and Machine Learning (ML) to annotate behaviour automatically. Automated methods offer scalability, error reduction, and the discovery of increasingly subtle patterns in data compared to human effort alone (N. C. Chen et al., 2018). Despite promising advancements, concerns regarding generalisability, mistrust of automation, and value alignment between humans and machines persist (Friedberg et al., 2012; Grimmer et al., 2021; Jiang et al., 2021; R. Levitan & Hirschberg, 2011; Mills, 2019; Nenkova et al., 2008; Rahimi et al., 2017; Yarkoni et al., 2021).
This thesis investigates the potential of computational techniques, such as social signal processing, text mining, and machine learning, to streamline qualitative coding in the social sciences, focusing on two high-stakes conversational case studies. The first case study analyses political interviewing using a corpus of 691 interview transcripts from US news networks. Psychological behaviours associated with effective interviewing are measured and used to predict conversational quality through supervised machine learning. Feature engineering employs a Social Signal Processing (SSP) approach to extract latent behaviours from low-level social signals (Vinciarelli, Salamin, et al., 2009). Conversational quality, calculated from desired characteristics of interviewee speech, is validated by a human-rater study. The findings support the potential of computational approaches in qualitative coding while acknowledging challenges in interpreting low-level social signals.
The second case study investigates the ability of machines to learn expert-defined behaviours from human annotation, specifically in detecting predatory behaviour in known cases of online child grooming. In this section, the author utilises 623 chat logs obtained from a US-based online watchdog, with expert annotators labelling a subset of these chat logs to train a large language model. The goal was to investigate the machine’s ability to detect eleven predatory behaviours based on expert annotations. The results show that the machine could detect several behaviours with as few as fifty labelled instances, but rare behaviours were frequently over-predicted. The author next implemented a collaborative human-AI approach to investigate the trade-off between human accuracy and machine efficiency. The results suggested that a human-in-the-loop approach could improve human efficiency and machine accuracy, achieving near-human performance on several behaviours approximately fifteen times faster than human effort alone.
The conclusion emphasises the value of increased automation in social sciences while recognising the importance of social scientific expertise in cross-disciplinary re- search, especially when addressing real-world problems. It advocates for technology that augments and enhances human effort and expertise without replacing it entirely. This thesis acknowledges the challenges in interpreting computational signals and the importance of preserving human insight in qualitative coding. The thesis also highlights potential avenues for future research, such as refining computational methods for qualitative coding and exploring collaborative human-AI approaches to address the limitations of automated methods
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
Disease Surveillance using Bayesian Methods
Full text linkDeveloping Markov Chain Monte Carlo (MCMC) algorithms has been an active area of research. Extensions of the original Metropolis-Hastings random walk (MHRW) algorithm, such as Metropolis-adjusted Langevin algorithm (MALA), Hamiltonian Monte Carlo (HMC) and the No-U-Turn Sampler (NUTS), include gradient information about the posterior when proposing parameters in areas of higher probability within the target. Particle- Markov Chain Monte Carlo (p-MCMC) is a similar parameter estimation algorithm that utilises a particle filter to calculate an unbiased estimate of the log-likelihood which can be used in the MHRW algorithm. However, as noted in the literature, obtaining gradients of the log-likelihood w.r.t the parameters is difficult due to operations inherent to the particle filter being non-differentiable. This obstacle has hindered the use of gradient based proposals within p-MCMC. Therefore, in this thesis, a novel method for obtaining the gradient of the log-likelihood w.r.t the parameters by fixing the random number seed within the particle filter is considered. This allows the particle filter to be posed as a deterministic function, i.e. running the particle filter multiple times will result in the same resampling realisations, log-likelihood and associated gradient estimates. When a different resampling realisation occurs between two parameter values, a piecewise continuous estimate of the log-likelihood and gradient occurs.
It is shown that these estimates are still compatible with gradient based proposals such as MALA, HMC and NUTS. A comparison of these samplers is made when estimating the parameters of two state-space models. Results indicate that although NUTS can make multiple gradient evaluations per MCMC iteration, it can produce more accurate estimates in shorter computation time. Frameworks for describing the differentiable particle filter and NUTS in PyTorch and PyMC3, respectively are also provided. This allows the derivatives and partial derivatives to be calculated via automatic differentiation. Particle filters have been used extensively to model and track infectious disease epidemics, with p-MCMC used to estimate the parameters of these models. Although gradient based proposals are used in non-particle methods when modelling epidemiology, the standard proposal when using p-MCMC is the MHRW. Applying the novel differentiable particle filter to two epidemiological models, NUTS can recover the correct parameters in shorter run time when compared to the MHRW proposal.
In the context of epidemiological modelling it is essential for public health officials to understand how a disease spreads through a population. This has recently come to the forefront with the emergence of COVID-19. At the beginning of the pandemic it was vital to gather accurate open-source datasets from which to infer how quickly the virus was spreading. As well as parameter estimation, MCMC algorithms have the ability to make forecasts of quantities of interest. Evaluating these predictions with simple scoring rules gives an indication of how well the model represents reality. The scoring rule normalised estimation error squared (NEES) can detect shortcomings within a model such as incorrect parameters, resulting in forecasts that are over-confident or over-cautious. A detailed description of why being cautious rather than confident is more desirable is provided.
NEES can also be used when evaluating the effectiveness of different open-source datasets when making future predictions. A novel machine learning framework for detecting COVID-19 symptomatic tweets in real-time in multiple languages is outlined. By collating the tweets from the previous 24 hours a time series of symptomatic tweets can be set up per geographic region. It is shown that, when compared with other traditional data sources, such as positive test results, ingesting tweet data can result in more consistent and accurate COVID-19 death predictions in the United States, United Kingdom and European and South American countries
Towards space sustainability using angle-only data from non traditional space surveillance networks
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