Emilia Díaz-Struck is research editor and Latin American coordinator for the International Consortium of Investigative Journalists (ICIJ). She oversees data projects and has been involved in some major cross-border investigations including the Panama Papers, the Paradise Papers and the Offshore Leaks. The ICIJ receives vast amounts of files from whistleblowers and uses AI-powered technologies to sift through that data more efficiently. For our interview series with women working on the intersection of AI and journalism, Emilia spoke to us about how exactly AI is deployed and what impact it will have on investigative journalism. JournalismAI: You have a very diverse background in journalism, having worked with major organisations such as The Washington Post, the Press and Society Institute of Venezuela and co-founding your own news site Armando.info. How did you initially move into a data-driven role?

We study an urban bike lane planning problem based on the fine-grained bike trajectory data, which is made available by smart city infrastructure such as bike-sharing systems. The key decision is where to build bike lanes in the existing road network. As bike-sharing systems become widespread in the metropolitan areas over the world, bike lanes are being planned and constructed by many municipal governments to promote cycling and protect cyclists. Traditional bike lane planning approaches often rely on surveys and heuristics. We develop a general and novel optimization framework to guide the bike lane planning from bike trajectories. We formalize the bike lane planning problem in view of the cyclists' utility functions and derive an integer optimization model to maximize the utility. To capture cyclists' route choices, we develop a bilevel program based on the Multinomial Logit model. We derive structural properties about the base model and prove that the Lagrangian dual of the bike lane planning model is polynomial-time solvable. Furthermore, we reformulate the route choice based planning model as a mixed integer linear program using a linear approximation scheme. We develop tractable formulations and efficient algorithms to solve the large-scale optimization problem. Via a real-world case study with a city government, we demonstrate the efficiency of the proposed algorithms and quantify the trade-off between the coverage of bike trips and continuity of bike lanes. We show how the network topology evolves according to the utility functions and highlight the importance of understanding cyclists' route choices. The proposed framework drives the data-driven urban planning scheme in smart city operations management.

Clustering analysis has been widely used in trust evaluation on various complex networks such as wireless sensors networks and online social networks. Spectral clustering is one of the most commonly used algorithms for graph-structured data (networks). However, the conventional spectral clustering is inherently difficult to work with large-scale networks due to the fact that it needs computationally expensive matrix manipulations. To deal with large networks, in this paper, we propose an efficient graph clustering algorithm, KCoreMotif, specifically for large networks by exploiting k-core decomposition and motifs. The essential idea behind the proposed clustering algorithm is to perform the efficient motif-based spectral clustering algorithm on k-core subgraphs, rather than on the entire graph. More specifically, (1) we first conduct the k-core decomposition of the large input network; (2) we then perform the motif-based spectral clustering for the top k-core subgraphs; (3) we group the remaining vertices in the rest (k-1)-core subgraphs into previously found clusters; and finally obtain the desired clusters of the large input network. To evaluate the performance of the proposed graph clustering algorithm KCoreMotif, we use both the conventional and the motif-based spectral clustering algorithms as the baselines and compare our algorithm with them for 18 groups of real-world datasets. Comparative results demonstrate that the proposed graph clustering algorithm is accurate yet efficient for large networks, which also means that it can be further used to evaluate the intra-cluster and inter-cluster trusts on large networks.

COVID-19 impact will also be included and considered for forecast. Global Artificial Intelligence in Market research report provides detail information about Market Introduction, Market Summary, Global market Revenue (Revenue USD), Market Drivers, Market Restraints, Market Opportunities, Competitive Analysis, Regional and Country Level. Global Artificial Intelligence in Marketing market is valued at USD 6.99 Billion in 2018 and expected to reach USD 37.08 Billion by 2025 with the CAGR of 26.98% over the forecast period. Growing adoption of customer-centric marketing strategies and increased use of social media for advertising are some of the factors which are expected to drive the growth of Global Artificial Intelligence in Marketing Market. Artificial intelligence (AI) in marketing is the process of utilizing data models, mathematics and algorithms to generate insights that can be used by marketers. Marketers will use AI-derived insights to guide future decisions about campaign spending, strategy and content topics.

Gagarin Capital led the round of funding, which included investment from Matrix Capital, Impulse VC, IMI.VC, and some business angels. The Finnish startup has developed a vertical agricultural system called iFarm Growtune. By growing food closer to consumers and in spaces where conditions can be carefully controlled, iFarm promises to produce food that is fresher while reducing environmental impact. As companies rethink logistics and the environment in the wake of the pandemic, self-contained urban farms hold growing appeal. "The main advantage of indoor farms is that you can be growing all year round, wherever you are," said iFarm cofounder and CEO Max Chizhov.

Researchers have "digitally unwrapped" mummified animals from ancient Egypt, revealing previously undiscovered details about ritualistic mummification. Analysis of the 2,000-year-old animals – which include a bird, a kitten and a snake – was done using a technique that achieves a resolution 100-times higher than a medical CT scan. "Using micro CT we can effectively carry out a post-mortem on these animals, more than 2,000 years after they died in ancient Egypt," said Professor Richard Johnston from Swansea University, who led the research. "We were able to piece together new evidence of how they lived and died, revealing the conditions they were kept in, and possible causes of death. "These are the very latest scientific imaging techniques.

Three mummified animals from ancient Egypt have been digitally unwrapped and dissected by researchers using high-resolution 3D scans. The snake, bird and cat, from the Egypt Centre's collection at Swansea University, are at least 2,000 years old. Ancient texts suggest they were offerings to the souls of the departed, but little was known of their fate. Researchers said the details revealed by the scans were "extraordinary". Using micro CT scanners, which generate 3D images with 100 times the resolution of medical CT scans, the animals' remains were analysed in previously unseen detail, giving an insight into how they were killed and the ritual behind it.

"Machine Learning in Finance Market 2020" report share informative Covid-19 Outbreak data figures as well as important insights regarding some of the market component which is considered to be future course architects for the market. This includes factors such as market size, market share, market segmentation, significant growth drivers, market competition, different aspects impacting economic cycles in the market, demand, expected business up-downs, changing customer sentiments, key companies operating in the Machine Learning in Finance Market, etc. In order to deliver a complete understanding of the global market, the report also shares some of the useful details regarding regional as well as significant domestic markets. The report presents a 360-degree overview and SWOT analysis of the competitive landscape of the industries. The report also incorporates premium quality data figures associated with financial figures of the industry including market size (in USD), expected market size growth (in percentage), sales data, revenue figures and more.

Global AI and Machine Learning Operationalization Software Market provides in-depth analysis of parent market trends, macro-economic indicators and governing factors along with market attractiveness as per segments. Global AI and Machine Learning Operationalization Software Market research report presentation demonstrates and presents an easily understandable market depiction, lending crucial insights on market size, market share as well as latest market developments and notable trends that collectively harness growth in the Global AI and Machine Learning Operationalization Software Market. Global AI and Machine Learning Operationalization Software Market research report presentation demonstrates and presents an easily understandable market depiction, lending crucial insights on market size, market share as well as latest market developments and notable trends that collectively harness growth in the Global AI and Machine Learning Operationalization Software Market. This detailed and meticulously composed market research report on the Global AI and Machine Learning Operationalization Software Market discussed the various market growth tactics and techniques that are leveraged by industry players to make maximum profits in the Global AI and Machine Learning Operationalization Software Market even amidst pandemic situation such as COVID-19. Regional Analysis of the Global AI and Machine Learning Operationalization Software Market Further in the subsequent sections of the report, readers can get an overview and complete picture of all major company players, covering also upstream and downstream market developments such as raw material supply and equipment profiles as well as downstream demand prospects.

Multi-category support vector machine (MC-SVM) is one of the most popular machine learning algorithms. There are lots of variants of MC-SVM, although different optimization algorithms were developed for different learning machines. In this study, we developed a new optimization algorithm that can be applied to many of MC-SVM variants. The algorithm is based on the Frank-Wolfe framework that requires two subproblems, direction finding and line search, in each iteration. The contribution of this study is the discovery that both subproblems have a closed form solution if the Frank-Wolfe framework is applied to the dual problem. Additionally, the closed form solutions on both for the direction finding and for the line search exist even for the Moreau envelopes of the loss functions. We use several large datasets to demonstrate that the proposed optimization algorithm converges rapidly and thereby improves the pattern recognition performance.