Bibliographic analysis considers the author's research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. This gives rise to the Citation Network Topic Model (CNTM). We propose a novel and efficient inference algorithm for the CNTM to explore subsets of research publications from CiteSeerX. The publication datasets are organised into three corpora, totalling to about 168k publications with about 62k authors. The queried datasets are made available online. In three publicly available corpora in addition to the queried datasets, our proposed model demonstrates an improved performance in both model fitting and document clustering, compared to several baselines. Moreover, our model allows extraction of additional useful knowledge from the corpora, such as the visualisation of the author-topics network. Additionally, we propose a simple method to incorporate supervision into topic modelling to achieve further improvement on the clustering task.

N 2015, the mining industry in Poland reported 2158 dangerous incidents with 19 casualties and 12 severe injuries [1]. Underground mining work poses a number of threats including fires, methane outbreaks or seismic tremors and bumps. Monitoring and decision support systems might play an essential role in limiting the number of incidents and their prevention. Such systems, often based on machine learning or data mining techniques, can be effectively applied to lessen the danger to employees and prevent potential losses arising from lost and damaged equipment, see, e.g., [2], [3], [4]. In this paper, we present a model for predicting dangerous seismic events in coal mines.

We present Probabilistic Doxastic Temporal (PDT) Logic, a formalism to represent and reason about probabilistic beliefs and their temporal evolution in multi-agent systems. This formalism enables the quantification of agents beliefs through probability intervals and incorporates an explicit notion of time. We discuss how over time agents dynamically change their beliefs in facts, temporal rules, and other agents beliefs with respect to any new information they receive. We introduce an appropriate formal semantics for PDT Logic and show that it is decidable. Alternative options of specifying problems in PDT Logic are possible. For these problem specifications, we develop different satisfiability checking algorithms and provide complexity results for the respective decision problems. The use of probability intervals enables a formal representation of probabilistic knowledge without enforcing (possibly incorrect) exact probability values. By incorporating an explicit notion of time, PDT Logic provides enriched possibilities to represent and reason about temporal relations.

The Dirichlet process and its extension, the Pitman-Yor process, are stochastic processes that take probability distributions as a parameter. These processes can be stacked up to form a hierarchical nonparametric Bayesian model. In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics. In particular, we propose a general framework for designing these Bayesian models, which are called topic models in the computer science community. We then propose a specific nonparametric Bayesian topic model for modelling text from social media. We focus on tweets (posts on Twitter) in this article due to their ease of access. We find that our nonparametric model performs better than existing parametric models in both goodness of fit and real world applications.

Several machine learning problems arising in natural language processing can be modeled as a sequence labeling problem. Gaussian processes (GPs) provide a Bayesian approach to learning such problems in a kernel based framework. We develop Gaussian process models based on pseudo-likelihood to solve sequence labeling problems. The pseudo-likelihood model enables one to capture multiple dependencies among the output components of the sequence without becoming computationally intractable. We use an efficient variational Gaussian approximation method to perform inference in the proposed model. We also provide an iterative algorithm which can effectively make use of the information from the neighboring labels to perform prediction. The ability to capture multiple dependencies makes the proposed approach useful for a wide range of sequence labeling problems. Numerical experiments on some sequence labeling problems in natural language processing demonstrate the usefulness of the proposed approach.

Abstract--One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for specific graph signal denoising and interpolation problems. The philosophy, to design the ARMA coefficients independently from the underlying graph, renders the ARMA graph filters suitable in static and, particularly, time-varying settings. The latter occur when the graph signal and/or graph topology are changing over time. We show that in case of a time-varying graph signal our approach extends naturally to a two-dimensional filter, operating concurrently in the graph and regular time domain. We also derive the graph filter behavior, as well as sufficient conditions for filter stability when the graph and signal are time-varying. The analytical and numerical results presented in this paper illustrate that ARMA graph filters are practically appealing for static and time-varying settings, as predicted by theoretical derivations. Keywords-- distributed graph filtering, signal processing on graphs, infinite impulse response graph filters, autoregressive moving average graph filters, time-varying graph signals, time-varying graphs. Due to their ability to capture the complex relationships present in many high-dimensional datasets, graphs have emerged as a favorite tool for data analysis.

Do you know that some of the content you read wasn't written by human beings? Automated Insights states, its software created one billion stories last year, many with no human intervention. This content you are reading was written by a real person, but just think, what are the chances you haven't consumed that type of content without knowing it? So what does it mean for content marketing? Forbes contributor Jayson DeMers has a futuristic approach to that question: "Right now, human writers are available at a variety of expertise levels and costs, making it possible for any company to find a good fit for their own content needs. In the not-too-distant future, algorithms will outperform all of them, and at a far lower cost. Essentially, human writers will be completely out of the equation."

If you believe everything you read, you are probably quite worried about the prospect of a superintelligent, killer AI. The Guardian, a British newspaper, warned recently that "we're like children playing with a bomb," and a recent Newsweek headline reads, "Artificial Intelligence Is Coming, and It Could Wipe Us Out." Numerous such headlines, fueled by comments from as the likes of Elon Musk and Stephen Hawking, are strongly influenced by the work of one man: professor Nick Bostrom, author of the philosophical treatise Superintelligence: Paths, Dangers, and Strategies. Bostrom is an Oxford philosopher, but quantitative assessment of risks is the province of actuarial science. He may be dubbed the world's first prominent "actuarial philosopher," though the term seems an oxymoron given that philosophy is an arena for conceptual arguments, and risk assessment is a data-driven statistical exercise. So what do the data say?

Salesforce (CRM), the Customer Success Platform and world's #1 CRM company, today unveiled Salesforce Einstein, bringing the power of artificial intelligence to every Salesforce user. With Salesforce Einstein, any company will be able to deliver more predictive and personalized customer experiences across sales, service, marketing, commerce and more. Salesforce Einstein is a breakthrough innovation that embeds advanced AI capabilities in the Salesforce Platform--in fields, objects, workflows, components and more--so everyone will be able to build AI-powered apps that get smarter with every interaction, using clicks or code. "With Salesforce Einstein, we are delivering the world's smartest CRM," said Marc Benioff, chairman and CEO, Salesforce. "Einstein is now every customer's data scientist, making it easy for everyone to take advantage of best-in-class AI capabilities in the context of their business."

Drone designs are usually a choice between flexibility and endurance. You can either go with a multirotor that'll let you hover and make pinpoint landings, or you can go with a flying wing, which can handle bigger payloads and longer ranges. Finding a compromise is difficult, and usually, it's also very messy. Amazon and Google, for example, are both working on delivery drones that have a whole bunch of frequently superfluous motors and propellers that help the drone to transition between hovering and efficient forward flight. Delft University of Technology in the Netherlands has a history of managing to make successful drones that combine the best features of VTOL and fixed-wing flight.