How does the human visual system compute the speed of a coherent motion stimulus that contains motion energy in different spatiotemporal frequency bands? Here we propose that perceived speed is the result of optimal integration of speed information from independent spatiotemporal frequency tuned channels. We formalize this hypothesis with a Bayesian observer model that treats the channel activity as independent cues, which are optimally combined with a prior expectation for slow speeds. We test the model against behavioral data from a 2AFC speed discrimination task with which we measured subjects' perceived speed of drifting sinusoidal gratings with different contrasts and spatial frequencies, and of various combinations of these single gratings. We find that perceived speed of the combined stimuli is independent of the relative phase of the underlying grating components, and that the perceptual biases and discrimination thresholds are always smaller for the combined stimuli, supporting the cue combination hypothesis.

Psychophysical experiments have demonstrated that the brain integrates information from multiple sensory cues in a near Bayesian optimal manner. The present study proposes a novel mechanism to achieve this. We consider two reciprocally connected networks, mimicking the integration of heading direction information between the dorsal medial superior temporal (MSTd) and the ventral intraparietal (VIP) areas. Each network serves as a local estimator and receives an independent cue, either the visual or the vestibular, as direct input for the external stimulus. We find that positive reciprocal interactions can improve the decoding accuracy of each individual network as if it implements Bayesian inference from two cues.

In many modern applications from, for example, bioinformatics and computer vision, samples have multiple feature representations coming from different data sources. Multiview learning algorithms try to exploit all these available information to obtain a better learner in such scenarios. In this paper, we propose a novel multiple kernel learning algorithm that extends kernel k-means clustering to the multiview setting, which combines kernels calculated on the views in a localized way to better capture sample-specific characteristics of the data. We demonstrate the better performance of our localized data fusion approach on a human colon and rectal cancer data set by clustering patients. Our method finds more relevant prognostic patient groups than global data fusion methods when we evaluate the results with respect to three commonly used clinical biomarkers.

After the messy reporting of the Iowa caucus results, some who build tech for progressive causes say the approach to software development in this space needs rethinking. After the messy reporting of the Iowa caucus results, some who build tech for progressive causes say the approach to software development in this space needs rethinking. Democrats could avoid another tech meltdown like the one that afflicted the Iowa caucuses with a better strategy for building the tools they need, progressive technology specialists say. The origins of the Iowa debacle are in a boom-and-bust cycle that places technology in competition with other priorities as time-crunched campaigns grapple with how best to spend as they hurtle toward an election. "The easy way to spend money, that's reliable, is on advertising," says Evan Henshaw-Plath, a technologist who has built applications for progressive causes and was an early employee at Twitter.

Does the shift to modern, agile data management practices mean EOL for ETL? Today's data-driven organizations are moving data into flexible centralized storage structures, such as data lakes and cloud blob storage, and using new data preparation technologies to assess and transform data for analytics success.

Data gathered from multiple sensors can be effectively fused for accurate monitoring of many engineering applications. In the last few years, one of the most sought after applications for multi sensor fusion has been fault diagnosis. Dempster-Shafer Theory of Evidence along with Dempsters Combination Rule is a very popular method for multi sensor fusion which can be successfully applied to fault diagnosis. But if the information obtained from the different sensors shows high conflict, the classical Dempsters Combination Rule may produce counter-intuitive result. To overcome this shortcoming, this paper proposes an improved combination rule for multi sensor data fusion. Numerical examples have been put forward to show the effectiveness of the proposed method. Comparative analysis has also been carried out with existing methods to show the superiority of the proposed method in multi sensor fault diagnosis.

As the healthcare industry continues to evolve from fee-for-service to value-based payment models, there is an increasing focus on better quality, outcomes and experience – all with significantly lower costs. Healthcare and technology leaders are working together to achieve these goals through digital transformation. This digital transformation will blanket the HIMSS20 Global Conference next month, where IT giant IBM and its Watson Health will have a massive presence. Healthcare IT News interviewed Paul Roma, general manager of IBM Watson Health, to see what he and the company see as the key trends impacting this digital transformation will be at HIMSS20 and beyond. Data security is fundamental to digital transformation, Roma asserted.

Advances in Data Science are lately permeating every field of Transportation Science and Engineering, making it straightforward to imagine that developments in the transportation sector will be data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a "story" intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed to software running on automatic devices, actuators or control systems producing, in turn, complex information flows between users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. The present work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded on this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the everchanging phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within the Data Science realm that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

Failure of applying good maintenance can surely disrupt the whole chain of industrial operations. To overcome this paradigm of maintenance Spark Cognition's analytical solution SparkPredict was introduced. It helped in overcoming the maintenance downtime and thus boosting the overall operational costs savings. SparkPredict analyzes various data whether structured or unstructured. It then uses machine learning techniques to revert with appropriate actions acceptable at that time.

About the Event As the digital transformation in financial services accelerates, it is a must-do for leading firms to gain a competitive advantage by being data literate, having access to the right data mixed with the right analytics strategies. Financial Services organizations are looking to lead with data to uncover transformational insights and enrich every decision across all aspects of their operations. Learn how Qlik is at the forefront of 3rd Generation of Business Intelligence, leveraging AI to drive data literacy across organizations, from the edge to the point of decision. Spend the day with Qlik on Wednesday, February 26 with the top leaders in Financial Services. Our agenda is packed with perspectives, best practices and use cases that explore how new and traditional data sources can blend together to better inform profitability, sales performance, risk management, and customer experiences.