Density estimation in high-dimensional settings is an important and challenging statistical problem.Traditional methods based on kernel smoothing are inefficient in high dimensions due to the difficulties in specifying appropriate location-adaptive kernels. In this work, we introduce pre-training, a key idea behind many cutting-edge AI technologies, to the context of non-parametric density estimation. By establishing a pre-trained neural network that can recommend an appropriate location-adaptive kernel for each sample point, efficient density estimation with adaptive kernels is achieved in high dimensions. A wide range of numerical experiments show that this strategy is highly effective for improving density-estimation accuracy, when the target distribution is close to the distribution family for pre-training. When the target distribution is substantially different from the pre-training distribution family, the benefit from the proposed pre-training strategy may be diluted, but can be reactivated by an additional fine-tuning procedure.

The CMAP (Cultural Mapping and Pattern Analysis) visualization toolkit is an open-source suite for analyzing and visualizing text data--from qualitative fieldnotes and in-depth interview transcripts to historical documents and web-scraped data such as message board posts or blogs. The toolkit is designed for scholars integrating pattern analysis, data visualization, and explanation in qualitative and/or computational social science (CSS). Despite the existence of off-the-shelf commercial qualitative data analysis software, there remains a shortage of highly scalable open-source options capable of handling large datasets and supporting advanced statistical and language modeling. The foundation of the toolkit is a pragmatic approach that aligns research tools with social science project goals--empirical explanation, theory-guided measurement, comparative design, or evidence-based recommendations--guided by the principle that research paradigms and questions should determine methods. Consequently, the CMAP visualization toolkit offers a wide range of possibilities through the adjustment of a relatively small number of parameters and allows seamless integration with other Python tools.

Ethnography (social scientific methods that illuminate how people understand, navigate and shape the real world contexts in which they live their lives) and machine learning (computational techniques that use big data and statistical learning models to perform quantifiable tasks) are each core to contemporary social science. Yet these tools have remained largely separate in practice. This chapter draws on a growing body of scholarship that argues that ethnography and machine learning can be usefully combined, particularly for large comparative studies. Specifically, this paper (a) explains the value (and challenges) of using machine learning alongside qualitative field research for certain types of projects, (b) discusses recent methodological trends to this effect, (c) provides examples that illustrate workflow drawn from several large projects, and (d) concludes with a roadmap for enabling productive coevolution of field methods and machine learning.

recognizing Web browsing signatures can complement other behavioral biometrics such as keystroke authentication to verify a claim of identity and/or identify persons of interest. The deluge of available digital traces enables the cognitive analysis of behavioral traits that differentiate between users and predict their online behavior. Recommendation systems have long capitalized on this capability to personalize search queries but have not exploited the temporal structure of preferences. This paper claims that spatio-temporal patterns of category of website visited by time of access can uniquely characterize and identify users. We present some exploratory approaches in user identification based on recurrent neural networks and empirical results based on clickstream data obtained through a user study and through an internet data provider.

Intelligent systems rely on pattern recognition and signature-based approaches for a wide range of sensors enhancing situational awareness. For example, autonomous systems depend on environmental sensors to perform their tasks and secure systems depend on anomaly detection methods. The availability of large amount of data requires the processing of data in a "streaming" fashion with online algorithms. Yet, just as online learning can enhance adaptability to a non-stationary environment, it introduces vulnerabilities that can be manipulated by adversaries to achieve their goals while evading detection. Although human intelligence might have evolved from social interactions, machine intelligence has evolved as a human intelligence artifact and been kept isolated to avoid ethical dilemmas. As our adversaries become sophisticated, it might be time to revisit this question and examine how we can combine online learning and reasoning leading to the science of deceptive and counter-deceptive machines.

As a designer, developer and digital marketer for Mazepress, David Alexander uses personal assistants to handle basic tasks to improve his productivity. Some are quick and simple uses such as checking time zones that his clients are in and converting currencies. Other use cases are more advanced. For example, he asks for daily sales reports and other details from his Shopify stores. Alexander admits these uses are fairly basic, but he is looking forward to the day when personal assistants start to mature into something smarter and more proactive.

A new report shows the finding of researchers attempting to teach killer whales mimic human sounds, and yes, even speak. You have to hear it to believe it. Scientists have demonstrated for the first time that orcas, also known as killer whales, can mimic human words, including "Amy," "Bye-Bye" and "One-Two-Three." Wikie, a 14-year-old female killer whale housed at Marineland Aquarium in Antibes, France, was tested by researchers including José Z. Abramson to get her to speak. Wikie had previously participated in an action imitation study, so she already knew the "copy" command, giving her a leg (or a fin) up when it came to "speaking."

Graphical models based on conditional independence support concise encodings of the subjective belief of a single agent. A natural question is whether the consensus belief of a group of agents can be represented with equal parsimony. We prove, under relatively mild assumptions, that even if everyone agrees on a common graph topology, no method of combining beliefs can maintain that structure. Even weaker conditions rule out local aggregation within conditional probability tables. On a more positive note, we show that if probabilities are combined with the logarithmic opinion pool (LogOP), then commonly held Markov independencies are maintained. This suggests a straightforward procedure for constructing a consensus Markov network. We describe an algorithm for computing the LogOP with time complexity comparable to that of exact Bayesian inference.