Google's next iPhone killer, the Pixel XL has been revealed in a new leaked image. According to Android Police, the picture shows a second generation Pixel XL, the larger of the two Pixel devices, with a 6inch screen. It is believed the handset will be made by LG, and will feature a radical'squeezable' frame. According to Android Police, the picture shows a second generation Pixel XL, the larger of the two Pixel devices, with a 6inch screen. The site says it is'exceptionally confident' the image is real.

Join us at the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) for a full day workshop that will bring together international stakeholders in robotics to examine best practices for accelerating robotics innovation through strategic policy frameworks. This is a unique opportunity to learn from people who have played a significant role in designing and implementing major strategic robotics initiatives around the globe. Objectives In the past decade, a number of governing bodies and industry consortia have developed strategic roadmaps to guide investment and development of robotic technology. With the roadmaps from the US, South Korea, Japan and EU etc. well underway, the time is right to take stock of these strategic robotics initiatives to see what is working, what is not, and what best practices in roadmap development might be broadly applied to other regions. The objective of this two-part workshop is to examine the process of how these policy frameworks came to be created in the first place, how they have been tailored to local capabilities and strengths, and what performance indicators are being used to measure their success -- so that participants may draw from international collective experience as they design and evaluate strategic robotics initiatives for their own regions.

The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results. Many scientific fields however realized the shortcomings of frequentist reasoning and in the most radical cases even banned its use in publications. We should do the same: just as we have embraced the Bayesian paradigm in the development of new machine learning methods, so we should also use it in the analysis of our own results. We argue for abandonment of NHST by exposing its fallacies and, more importantly, offer better - more sound and useful - alternatives for it.

Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using forecasted weather data. In this study, the performance of several commonly used forecasting methods in the presence of weather predictors with uncertainty is assessed and compared. Accordingly, both observed and forecasted weather data are collected, then the influential predictors for solar PV generation forecasting model are selected using several measures. Using observed and forecasted weather data, an analysis on the uncertainty of weather variables is represented by MAE and bootstrapping. The energy forecasting model is trained using observed weather data, and finally, the performance of several commonly used forecasting methods in solar energy forecasting is simulated and compared for a real case study.

The topology of a power grid affects its dynamic operation and settlement in the electricity market. Real-time topology identification can enable faster control action following an emergency scenario like failure of a line. This article discusses a graphical model framework for topology estimation in bulk power grids (both loopy transmission and radial distribution) using measurements of voltage collected from the grid nodes. The graphical model for the probability distribution of nodal voltages in linear power flow models is shown to include additional edges along with the operational edges in the true grid. Our proposed estimation algorithms first learn the graphical model and subsequently extract the operational edges using either thresholding or a neighborhood counting scheme. For grid topologies containing no three-node cycles (two buses do not share a common neighbor), we prove that an exact extraction of the operational topology is theoretically guaranteed. This includes a majority of distribution grids that have radial topologies. For grids that include cycles of length three, we provide sufficient conditions that ensure existence of algorithms for exact reconstruction. In particular, for grids with constant impedance per unit length and uniform injection covariances, this observation leads to conditions on geographical placement of the buses. The performance of algorithms is demonstrated in test case simulations.

Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family of structured prediction models (Belanger and McCallum, 2016). An energy function over candidate structured outputs is given by a deep network, and predictions are formed by gradient-based optimization. This paper presents end-to-end learning for SPENs, where the energy function is discriminatively trained by back-propagating through gradient-based prediction. In our experience, the approach is substantially more accurate than the structured SVM method of Belanger and McCallum (2016), as it allows us to use more sophisticated non-convex energies. We provide a collection of techniques for improving the speed, accuracy, and memory requirements of end-to-end SPENs, and demonstrate the power of our method on 7-Scenes image denoising and CoNLL-2005 semantic role labeling tasks. In both, inexact minimization of non-convex SPEN energies is superior to baseline methods that use simplistic energy functions that can be minimized exactly.

Organizations have spent considerable amounts of time, effort, and money to implement the proper security systems and protocols – but most IT professionals still worry about data security. Part of the challenge is being able to accurately and quickly monitor how secure your data is. In my experiences, most organizations generally keep their sensitive data secure but don't regularly monitor or audit the data, due to the costs and time commitment they need for analyzing access patterns and ensuring there have been no intrusions. In many organizations, IT professionals would be unable to provide a clear location of all sensitive data throughout their organization. In a Ponemon report titled "The State of Data Centric Security", 57% of survey respondents said their biggest security concern is not knowing where their sensitive data lives.

When it comes to digital transformation, businesses that are ahead of the curve are bullish about a particular set of technologies, according to a report from SAP and Oxford Economics based on a survey of more than 3,000 senior executives. First, these digital transformation leaders are a rarified breed, according to the SAP Digital Transformation Executive Study. Only 3 percent of respondents have completed digital transformation projects spanning their organizations. More than half (55 percent) are piloting programs, while 22 percent are in the planning stages. In total, only 100 out of the 3,000 enterprises polled for the study qualified as leaders.

July 14, 2017 --According to Norse mythology, the god Odin has two ravens that fly all over Midgard to gather information. Their names are Huginn and Muninn, the Old Norse words for "thought" and "memory." The ancient storytellers who bestowed these names on the birds were onto something: A new study finds that ravens can flexibly plan for events outside their present sensory awareness, a cognitive skill once considered exclusive to humans and other great apes. This research does more than just reveal that the raven is smarter than we thought, that apes' intellectual abilities are less unique than we thought, and that a mammalian lineage is a not a prerequisite for complex thinking. It also adds to the growing body of evidence that intelligence has evolved more than once.

With just two months to go before Apple is expected to reveal its next smartphone, claims the firm is experiencing problems that could result in a delay are mounting. In the latest warning, Bank of America Merrill Lynch lowered its estimates for Apple's iPhone shipments by 11 million for the year, and suggest it could be delayed by a month. 'Our conversations with the Supply Chain suggest that the iPhone 8 will ship 3-4 weeks delayed given technological issues which Apple and its suppliers are working through,' analysts Wamsi Mohan and Stefano Pascale said in a report Wednesday, citing a recent trip to Asia, according to CNBC. New claims suggest that Apple is working'feverishly' to fix software problems with its wireless charging and 3D face recognition systems. The analysts said problems with finger print and 3-D sensors were to blame for the delay.