SHANGHAI--A Chinese man whose son was killed while driving a Tesla Motors Inc. TSLA -0.82 % vehicle applied to a local Beijing court to investigate whether the car's Autopilot driving system was engaged. In January, 23-year-old Gao Yaning died in a crash in the northeastern province of Hebei while driving a Tesla Model S. Six months later his father, Gao Jubin, filed a lawsuit accusing Tesla of exaggerating Autopilot's capabilities. At a court hearing Tuesday, he asked for an independent investigation of the cause of the crash. "The family insists the investigation should be done by a third party, rather than Tesla," said Cui Qiuna, a lawyer for the Gao family. The court will study the family's request.

Who says machine learning can't be fun? A crew of us from SAS went to San Francisco for the recent KDD conference, which bills itself as "a premier interdisciplinary conference, [which] brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data." We brought these buttons with us, and they were a huge hit! But we weren't at KDD just to have fun, of course. We came to learn and share, in our booth and in many other ways.

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.

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.

Hybrid clustering combines partitional and hierarchical clustering for computational effectiveness and versatility in cluster shape. In such clustering, a dissimilarity measure plays a crucial role in the hierarchical merging. The dissimilarity measure has great impact on the final clustering, and data-independent properties are needed to choose the right dissimilarity measure for the problem at hand. Properties for distance-based dissimilarity measures have been studied for decades, but properties for density-based dissimilarity measures have so far received little attention. Here, we propose six data-independent properties to evaluate density-based dissimilarity measures associated with hybrid clustering, regarding equality, orthogonality, symmetry, outlier and noise observations, and light-tailed models for heavy-tailed clusters. The significance of the properties is investigated, and we study some well-known dissimilarity measures based on Shannon entropy, misclassification rate, Bhattacharyya distance and Kullback-Leibler divergence with respect to the proposed properties. As none of them satisfy all the proposed properties, we introduce a new dissimilarity measure based on the Kullback-Leibler information and show that it satisfies all proposed properties. The effect of the proposed properties is also illustrated on several real and simulated data sets.

Molecular profiling data (e.g., gene expression) has been used for clinical risk prediction and biomarker discovery. However, it is necessary to integrate other prior knowledge like biological pathways or gene interaction networks to improve the predictive ability and biological interpretability of biomarkers. Here, we first introduce a general regularized Logistic Regression (LR) framework with regularized term $\lambda \|\bm{w}\|_1 + \eta\bm{w}^T\bm{M}\bm{w}$, which can reduce to different penalties, including Lasso, elastic net, and network-regularized terms with different $\bm{M}$. This framework can be easily solved in a unified manner by a cyclic coordinate descent algorithm which can avoid inverse matrix operation and accelerate the computing speed. However, if those estimated $\bm{w}_i$ and $\bm{w}_j$ have opposite signs, then the traditional network-regularized penalty may not perform well. To address it, we introduce a novel network-regularized sparse LR model with a new penalty $\lambda \|\bm{w}\|_1 + \eta|\bm{w}|^T\bm{M}|\bm{w}|$ to consider the difference between the absolute values of the coefficients. And we develop two efficient algorithms to solve it. Finally, we test our methods and compare them with the related ones using simulated and real data to show their efficiency.

U.S. stocks inched higher Tuesday in another cautious day of trading as investors kept an eye on central banks in the U.S. and Japan. Healthcare and household goods companies led the way, while energy companies slipped. Major market indexes were higher all day but returned most of those gains at the close of trading. They rose just enough to cancel out Monday's small losses. Drug companies helped healthcare stocks make modest gains, while Exxon Mobil fell on reports that it's being investigated by securities regulators.

At a recent Further With Ford event their futurist, Sheryl Connelly, talked about the growth of our world's mega-cities. She used Beijing China as an example. Beijing has 20 million people living in its metropolitan area. A typical driving commute is 5 hours! They have one highway with 50 lanes.

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."