Lego's next release is a digital brick loaded with sensors that add new layers of interactivity to its play sets. WIRED got exclusive access to the Lego labs where the Smart Brick was born. The secretive division of 237 staff based here and in London, Boston, and Singapore is dedicated to thinking up what comes next for the world's largest toy brand. In front of me, on a plain white table, is a batch of prototypes of Lego's new Smart Brick, the final version of which is a small, sensor-laden 2-by-4 black brick with a big brain. No outsider has seen these prototypes, all of which represent stages of a journey Lego has been charting over the past eight years. Lego hopes this innovation, which lands in stores March 1, will safeguard the future of its plastic empire. The diminutive proportions of the finished Smart Brick belie the fact that the thing is exceedingly clever. Inside is a tiny custom chip running bespoke software that can communicate with onboard sensors to monitor and react to motion, orientation, and magnetic fields. It's also likely no exaggeration that the Smart Brick could represent the most radical product Lego has produced since Jens Nygaard Knudsen, the company's former longtime chief designer, created the minifigure nearly 50 years ago.

However, on-body displacement of sensors is inevitable since the device cannot be firmly worn at a fixed position across different sessions.

Appendix A provides derivations supporting Section 3 in the main paper. In this section we provide detailed derivations of the ST -DGMRF joint distribution, for both first-order transition models (Section A.1) and higher-order transition models (Section A.2). A.1 Joint distribution The LDS (see Section 2.2 and 3.1 in the main paper) defines a joint distribution over system states First, note that Eq. (1) can be written as a set of linear equations x We make use of this property in the DGMRF formulation and in the conjugate gradient method. Eq. 11 is converted into a discrete-time dynamical system by approximating ρ We consider two ST -DGMRF variants that capture different amounts of prior knowledge. DGMRF transition matrices can be parameterized accordingly. The air quality dataset is based on hourly PM2.5 measurements obtained from [ The raw PM2.5 measurements are log-transformed and standardized to zero mean and unit Ca. 50% of the nodes are masked out (purple nodes within We use a simple MLP with one hidden layer of width 16 with ReLU activations and no output non-linearity. The DGMRF parameters are not shared across time, allowing for dynamically changing spatial covariance patterns.

Lack of samples per minute or per hour (metrics 1 and 2) are helping to understand 4G networking issues.

The ability to sense and reconstruct 3D appearance and geometry is critical to applications in vision, graphics, and beyond.

The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fields. Despite the increasing popularity of deep learning and remote sensing, the majority of researchers still use decision trees and random forests for Landsat image analysis due to the prevalence of small labeled datasets and lack of foundation models. In this paper, we introduce SSL4EO-L, the first ever dataset designed for Self-Supervised Learning for Earth O bservation for the Landsat family of satellites (including 3 sensors and 2 product levels) and the largest Landsat dataset in history (5M image patches). Additionally, we modernize and re-release the L7 Irish and L8 Biome cloud detection datasets, and introduce the first ML benchmark datasets for Landsats 4-5 TM and Landsat 7 ETM+ SR. Finally, we pre-train the first foundation models for Landsat imagery using SSL4EO-L and evaluate their performance on multiple semantic segmentation tasks.