Climate change impacts and adaptations are the subjects to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

Causal inference often relies on the counterfactual framework, which requires that treatment assignment is independent of the outcome, known as strong ignorability. Approaches to enforcing strong ignorability in causal analyses of observational data include weighting and matching methods. Effect estimates, such as the average treatment effect (ATE), are then estimated as expectations under the reweighted or matched distribution, P . The choice of P is important and can impact the interpretation of the effect estimate and the variance of effect estimates. In this work, instead of specifying P, we learn a distribution that simultaneously maximizes coverage and minimizes variance of ATE estimates. In order to learn this distribution, this research proposes a generative adversarial network (GAN)-based model called the Counterfactual $\chi$-GAN (cGAN), which also learns feature-balancing weights and supports unbiased causal estimation in the absence of unobserved confounding. Our model minimizes the Pearson $\chi^2$ divergence, which we show simultaneously maximizes coverage and minimizes the variance of importance sampling estimates. To our knowledge, this is the first such application of the Pearson $\chi^2$ divergence. We demonstrate the effectiveness of cGAN in achieving feature balance relative to established weighting methods in simulation and with real-world medical data.

Innovation ecosystems can be naturally described as a collection of networked entities, such as experts, institutions, projects, technologies and products. Representing in a machine-readable form these entities and their relations is not entirely attainable, due to the existence of abstract concepts such as knowledge and due to the confidential, non-public nature of this information, but even its partial depiction is of strong interest. The representation of innovation ecosystems incarnated as knowledge graphs would enable the generation of reports with new insights, the execution of advanced data analysis tasks. An ontology to capture the essential entities and relations is presented, as well as the description of data sources, which can be used to populate innovation knowledge graphs. Finally, the application case of the Universidad Politecnica de Madrid is presented, as well as an insight of future applications.

Alcatel 3L may feature similar technology found in the leading smartphones, but it can be purchased for a sixth of the price. The handset, developed by TCL Communications, debuted at CES in Las Vegas with a price tag of $155 and includes an AI-powered triple rear cameras setup. The system includes a 48-megapixel sensor, a 12-megapixel and a 5-megapixel for ultra wide shots. The Alcatel 3L will be released in'select markets across Europe, Asia, Africa and the Middle East in the beginning of this year, reports CNET. Alcatel 3L may features similar technology found in the leading smartphones, but it can be purchased for a sixth of the price.

For a country that holds 12 percent of the planet's water supply, Brazil faces significant water management issues. In addition to its commonly known sanitation problems, the country's infrastructure lends itself to distribution issues, including fraudulent use. Fraudulent water use can be particularly hard to track and identify, and often goes unaddressed for significant periods of time – especially in highly populated areas where physically checking people's homes and water meters isn't an option. Instead, companies need to find ways to swiftly identify and eliminate fraudulent water activity which impacts an already scarce supply and costs communities money. To address this challenge, a utilities company from Mato Grosso, Brazil recently worked with a group of data engineers at ScientificCloud. The goal was to develop a solution that could better locate fraudulent water usage by tracking data patterns based on home location and property attributes. As a Sao Paolo-based data science company that develops and deploys machine learning (ML) and artificial intelligence (AI)-powered applications, ScientificCloud understood these problems first hand.

Rising number of contact centers and necessity for compliance and risk management across several verticals have led the companies to invent solutions in speech analytics which will aid companies to comprehend the changing necessities of customers. Several organizations functioning in diverse industrial domains have been evolving interests for the transcription and analyzing of customers and structural media and uptake rational decisions for the management of business and consumers with the help of speech and text intelligence. This is the main factor that is responsible for the growth of the speech analytics market and a protuberant driving factor in the growing demands for speech analytics in several industrial applications. This rising demand can also be accredited to the burdens on businesses for safeguarding their rational assets for improving agility and competence in business operations via the all-embracing insights quarried in the Voice of Customer (VoC). Speech analytics is used in sectors such as customer experience management, agent performance, business processes, compliance and risk management, and market intelligence.

To ensure the security of the general mass, crime prevention is one of the most higher priorities for any government. An accurate crime prediction model can help the government, law enforcement to prevent violence, detect the criminals in advance, allocate the government resources, and recognize problems causing crimes. To construct any future-oriented tools, examine and understand the crime patterns in the earliest possible time is essential. In this paper, I analyzed a real-world crime and accident dataset of Denver county, USA, from January 2014 to May 2019, which containing 478,578 incidents. This project aims to predict and highlights the trends of occurrence that will, in return, support the law enforcement agencies and government to discover the preventive measures from the prediction rates. At first, I apply several statistical analysis supported by several data visualization approaches. Then, I implement various classification algorithms such as Random Forest, Decision Tree, AdaBoost Classifier, Extra Tree Classifier, Linear Discriminant Analysis, K-Neighbors Classifiers, and 4 Ensemble Models to classify 15 different classes of crimes. The outcomes are captured using two popular test methods: train-test split, and k-fold cross-validation. Moreover, to evaluate the performance flawlessly, I also utilize precision, recall, F1-score, Mean Squared Error (MSE), ROC curve, and paired-T-test. Except for the AdaBoost classifier, most of the algorithms exhibit satisfactory accuracy. Random Forest, Decision Tree, Ensemble Model 1, 3, and 4 even produce me more than 90% accuracy. Among all the approaches, Ensemble Model 4 presented superior results for every evaluation basis. This study could be useful to raise the awareness of peoples regarding the occurrence locations and to assist security agencies to predict future outbreaks of violence in a specific area within a particular time.

Learning generative probabilistic models that can estimate the continuous density given a set of samples, and that can sample from that density, is one of the fundamental challenges in unsupervised machine learning. In this paper we introduce a new approach to obtain such models based on what we call denoising density estimators (DDEs). A DDE is a scalar function, parameterized by a neural network, that is efficiently trained to represent a kernel density estimator of the data. Leveraging DDEs, our main contribution is to develop a novel approach to obtain generative models that sample from given densities. We prove that our algorithms to obtain both DDEs and generative models are guaranteed to converge to the correct solutions. Advantages of our approach include that we do not require specific network architectures like in normalizing flows, ordinary differential equation solvers as in continuous normalizing flows, nor do we require adversarial training as in generative adversarial networks (GANs). Finally, we provide experimental results that demonstrate practical applications of our technique.

For many years, achievements and discoveries made by scientists are made aware through research papers published in appropriate journals or conferences. Often, established scientists and especially newbies are caught up in the dilemma of choosing an appropriate conference to get their work through. Every scientific conference and journal is inclined towards a particular field of research and there is a vast multitude of them for any particular field. Choosing an appropriate venue is vital as it helps in reaching out to the right audience and also to further one's chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of acceptance. We present three different approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modeling. In all these approaches, we apply Correspondence Analysis (CA) to derive appropriate relationships between the entities in question, such as conferences and papers. Our models show promising results when compared with existing methods such as content-based filtering, collaborative filtering and hybrid filtering.

At the Last Futurist, we enjoy looking at AI Trends and digital transformation trends. In between those two are more broad technology trends. In fact these topics make up the mission statement of this new news site. However the last decade had a lot of technology and gadgets that didn't fare so well in the real world. The decade was mobile all the way, with mass adoption taking place the way we might expect the brain-computer interface (BCI) to achieve mass adoption in a future decade years from now. In the decade ahead the move to automated stores and electric vehicles are real trends, but it's important to differentiate the hype from the reality. Autonomous vehicles, quantum computing going mainstream, better self-learning AI, hang on a second! Even mass adoption of digital currencies is coming faster. From computers to the internet and smart phones, a few generations shows a lot of progress. But technology never stands still. Advertising has scaled a world of surveillance capitalism normalization and an AI-arms race is now taking place. Most technology trends and AI listicles only touch the surface of how humans are embedding technology increasingly into their lives. However looking at it from the perspectives of many industries and across technology and innovation stacks gives a more complete picture. The real world and customer experience are the real tests for new technological innovations and pivots. It will take decades for 3D printing, quantum computing and an AGI to even become mature, but an age of biotechnology and AI in healthcare, education and finance is inevitable. From Huawei, to ByteDance (TikTok), to Didi, China will wage major battles for global market share in 5G, consumer apps, E-commerce, mobile payments and ride sharing, among others. Chinese led tech companies -- with the support of the Chinese Government and venture funds such as Softbank Vision Fund -- can mean that in the 2020s China's ecosystem fully replaces Silicon Valley as the leader of innovation. In 2019, some believe this has already occurred.