Faster subsequence recognition in compressed strings

Computation on compressed strings is one of the key approaches to processing massive data sets. We consider local subsequence recognition problems on strings compressed by straight-line programs (SLP), which is closely related to Lempel--Ziv compression. For an SLP-compressed text of length $\bar m$, and an uncompressed pattern of length $n$, C{\'e}gielski et al. gave an algorithm for local subsequence recognition running in time $O(\bar mn^2 \log n)$. We improve the running time to $O(\bar mn^{1.5})$. Our algorithm can also be used to compute the longest common subsequence between a compressed text and an uncompressed pattern in time $O(\bar mn^{1.5})$; the same problem with a compressed pattern is known to be NP-hard

Improved Approximate String Matching and Regular Expression Matching on Ziv-Lempel Compressed Texts

We study the approximate string matching and regular expression matching problem for the case when the text to be searched is compressed with the Ziv-Lempel adaptive dictionary compression schemes. We present a time-space trade-off that leads to algorithms improving the previously known complexities for both problems. In particular, we significantly improve the space bounds, which in practical applications are likely to be a bottleneck

A response to “Trends in tropical tree growth: re-analysis confirms earlier findings”

We recently demonstrated that growth trends from tree rings from Van der Sleen et al. (2015) and Groenendijk et al. (2015) are affected by demographic biases. In particular, clustered age distributions led to a negative bias in their growth trends. In a response, they challenge our analysis and present an alternative correction approach. We here show that their arguments are incorrect and based on misunderstanding of our analysis, and that their alternative approach does not work

A Faster Implementation of Online Run-Length Burrows-Wheeler Transform

Run-length encoding Burrows-Wheeler Transformed strings, resulting in Run-Length BWT (RLBWT), is a powerful tool for processing highly repetitive strings. We propose a new algorithm for online RLBWT working in run-compressed space, which runs in $O(n\lg r)$ time and $O(r\lg n)$ bits of space, where $n$ is the length of input string $S$ received so far and $r$ is the number of runs in the BWT of the reversed $S$. We improve the state-of-the-art algorithm for online RLBWT in terms of empirical construction time. Adopting the dynamic list for maintaining a total order, we can replace rank queries in a dynamic wavelet tree on a run-length compressed string by the direct comparison of labels in a dynamic list. The empirical result for various benchmarks show the efficiency of our algorithm, especially for highly repetitive strings.Comment: In Proc. IWOCA201

The role of community acceptance in planning outcomes for onshore wind and solar farms: An energy justice analysis

The deployment of renewable technologies as part of climate mitigation strategies have provoked a range of responses from various actors, bringing public acceptance to the forefront of energy debates. A key example is the reaction of communities when renewable projects are proposed in their local areas. This paper analyses the effect that community acceptance has had on planning applications for onshore wind and solar farms in Great Britain between 1990 and 2017. It does this by compiling a set of indicators for community acceptance and testing their association with planning outcomes using binomial logistic regression. It identifies 12 variables with statistically significant effects: 4 for onshore wind, 4 for solar farms, and 4 spanning both. For both technologies, the visibility of a project, its installed capacity, the social deprivation of the area, and the year of the application are significant. The paper draws conclusions from these results for community acceptance and energy justice, and discusses the implications for energy decision-making

Exploring ecosystem markets for the delivery of public goods in the UK

Environmental restoration and conservation challenges go beyond what can be financed publicly. There are significant opportunities for private investment in the delivery of public goods, benefitting both commercial organisations whose business relies on ecosystem services, as well as landowners, land managers and the general public. Thus, public-private financing of natural capital improvement presents an opportunity to increase the availability of funding for payments for ecosystem services that provide environmental and societal benefits. Though public-private partnerships for the financing of ecosystem services is in its infancy in the UK. This new report explores the voluntary ecosystem services market in the UK. This is achieved by developing an understanding of how key actors (schemes, stakeholder engagement initiatives, trading platforms and supporting modelling tools) operate, and by identifying possible synergies, examples of good practice and challenges to implementation. Topics covered include, understanding how the identified actors account for the social distribution of ecosystem services, how values are attributed to ecosystem services, and the legal obligations linked to ventures’ operation

Using social media, machine learning and natural language processing to map multiple recreational beneficiaries

Information and numbers on the use and appreciation of nature are valuable information for protected area (PA) managers. A promising direction is the utilisation of social media, such as the photo-sharing website Flickr. Here we demonstrate a novel approach, borrowing techniques from machine learning (image analysis), natural language processing (Latent Semantic Analysis (LSA)) and self-organising maps (SOM), to collect and interpret >20,000 photos from the Camargue region in Southern France. From the perspective of Cultural Ecosystem Services (CES), we assessed the relationship between the use of the Camargue delta and the presence of natural elements by consulting local managers. Clustering algorithms applied to results of the LSA data revealed six distinct user groups, which included those interested in nature, ornithology, religious pilgrimage, general tourists and aviation enthusiasts. For each group, we produced high-resolution spatial and seasonal maps, which matched known recreational attractions and annual festivals in the Camargue. The accuracy of the group identification, and the spatial and temporal patterns of photo activity, in the Camargue delta were evaluated by local managers of the Camargue regional park. This study demonstrates how PA managers can harness social-media to monitor recreation and improve their management decision making

Practical Evaluation of Lempel-Ziv-78 and Lempel-Ziv-Welch Tries

We present the first thorough practical study of the Lempel-Ziv-78 and the Lempel-Ziv-Welch computation based on trie data structures. With a careful selection of trie representations we can beat well-tuned popular trie data structures like Judy, m-Bonsai or Cedar

Dynamic Fluctuation Phenomena in Double Membrane Films

Dynamics of double membrane films is investigated in the long-wavelength limit including the overdamped squeezing mode. We demonstrate that thermal fluctuations essentially modify the character of the mode due to its nonlinear coupling to the transversal shear hydrodynamic mode. The corresponding Green function acquires as a function of the frequency a cut along the imaginary semi-axis. Fluctuations lead to increasing the attenuation of the squeezing mode it becomes larger than the `bare' value.Comment: 7 pages, Revte

Landscape aesthetics: Spatial modelling and mapping using social media images and machine learning

Cultural ecosystem services such as aesthetic value are highly context-specific and often present difficulties in their assessment. Here we present a case study in the northern English Protected Area of the Yorkshire Dales National Park. Utilising publicly available images, paired-comparison surveys, probability modelling, machine-learning based text annotations, natural language processing and regression analysis, we developed a spatial model to predict and map landscape aesthetics across the whole site. The predictive model found eighteen significant variables, including the positive role of rural areas, mountainous landforms and vegetation for aesthetic value. Finally, we demonstrate the potential of our approach to varying size datasets and partial paired-comparison matrices, finding a very good agreement with only 20% of paired comparisons. This study demonstrates the use of freely available data and mostly open source tools to ascertain landscape aesthetic value in a large Protected Area