Acoustic mapping techniques have long been used in spatial audio processing for direction of arrival estimation (DoAE). Traditional beamforming methods for acoustic mapping, while interpretable, often rely on iterative solvers that can be computationally intensive and sensitive to acoustic variability. On the other hand, recent supervised deep learning approaches offer feedforward speed and robustness but require large labeled datasets and lack interpretability. Despite their strengths, both methods struggle to consistently generalize across diverse acoustic setups and array configurations, limiting their broader applicability. We introduce the Latent Acoustic Mapping (LAM) model, a self-supervised framework that bridges the interpretability of traditional methods with the adaptability and efficiency of deep learning methods. LAM generates high-resolution acoustic maps, adapts to varying acoustic conditions, and operates efficiently across different microphone arrays. We assess its robustness on DoAE using the LOCATA and STARSS benchmarks. LAM achieves comparable or superior localization performance to existing supervised methods. Additionally, we show that LAM's acoustic maps can serve as effective features for supervised models, further enhancing DoAE accuracy and underscoring its potential to advance adaptive, high-performance sound localization systems.

It can be challenging to perform an integrative statistical analysis of multi-view high-dimensional data acquired from different experiments on each subject who participated in a joint study. Canonical Correlation Analysis (CCA) is a statistical procedure for identifying relationships between such data sets. In that context, Structured Sparse CCA (ScSCCA) is a rapidly emerging methodological area that aims for robust modeling of the interrelations between the different data modalities by assuming the corresponding CCA directional vectors to be sparse. Although it is a rapidly growing area of statistical methodology development, there is a need for developing related methodologies in the Bayesian paradigm. In this manuscript, we propose a novel ScSCCA approach where we employ a Bayesian infinite factor model and aim to achieve robust estimation by encouraging sparsity in two different levels of the modeling framework. Firstly, we utilize a multiplicative Half-Cauchy process prior to encourage sparsity at the level of the latent variable loading matrices. Additionally, we promote further sparsity in the covariance matrix by using graphical horseshoe prior or diagonal structure. We conduct multiple simulations to compare the performance of the proposed method with that of other frequently used CCA procedures, and we apply the developed procedures to analyze multi-omics data arising from a breast cancer study.

For two decades, Arteric has led pharmaceutical and biotech brand teams through the selection and implementation of complex emerging marcom technologies. Scouting the digital landscape and educating clients are how Arteric helps marketing teams see opportunities that others miss and to transform those events into competitive advantage. Arteric's ongoing education program will continue on May 15 at the 2018 BioPharma eMarketing Summit West, where President and Chief Strategist Hans Kaspersetz will share two case studies during his presentation titled "AI - Super Hearing for Healthcare Marketers. BioPharma eMarketing Summit West is a TED-style venue that features an audience of more than 200 marketing executives from pharma, biotech, and medical device organizations. More than 45 thought leaders from companies such as Johnson and Johnson, Pfizer, Daiichi Sankyo, and Microsoft will share insights and case studies on leading-edge issues such as: · Utilizing big data to tell stories and to streamline pipelines · Establishing metrics to understand the effectiveness of a digital marketing strategy · Capitalizing on disruptive marketing "Detecting and capturing audience signals will be a central theme emphasized throughout several Biopharma eMarketing sessions," states Mr. Kaspersetz. "At the core of every Arteric digital strategy is the integration of the reality that customers are experiencing our brands across hundreds of micro-moments.

As if active portfolio managers didn't have enough challenges from computer-driven passive investing strategies, now machines are directly horning in on their territory. San Francisco-based EquBot LLC is launching the first ever exchange-traded fund to use artificial intelligence, according to a company statement on Tuesday. Employing International Business Machines Corp.'s Watson platform, the AI Powered Equity ETF, ticker AIEQ, will attempt to mimic an army of equity research analysts working around the clock, according to Art Amador, co-founder of EquBot. "There has been an explosion of information," Amador said by phone. "AI provides a more informed way of investing."

Uber and Lyft may have changed lives in the Big American City, but they're hardly ubiquitous. Just 15 percent of Americans use these services, according to the Pew Research Center. One-third have never heard of them. The ridesharing giants do have an excellent way to build a bigger, less urban customer base: teaming up with government. In Florida, in New Jersey, and in Colorado, Uber and Lyft have partnered with municipalities to solve first-mile, last-mile problems, ferrying riders to bus stops, train stations, or even their homes for subsidized fares.