A new tool, described as “flying handcuffs,” will soon be used by San Francisco police. https://abc7ne.ws/3x2ZZVJ#restraint #police #handcuffs #crime #lasso #…
The bestselling author of Seven Brief Lessons on Physics introduces the mysteries of time, further explored in his new book, The Order of Time.
Time is a mystery that does not cease to puzzle us. Philosophers, artists and poets have long explored its meaning while scientists have found that its structure is different from the simple idea we have of it. From Einstein to quantum theory and beyond, our understanding of time has been undergoing radical transformations. Time flows at a different speed in different places, the past and the future differ far less than we might think, and the very notion of the present evaporates in the vast universe.
Continue reading “The Order of Time: Carlo Rovelli explains that time doesn’t really exist” »
Related channels worth checking out; https://youtu.be/SBx1_V8n-Dg?si=rY8mz2tFxM5V8BkG https://youtu.be/0CSL702JSdY?si=uHv-hc2OSJmpLjlJ
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Northwestern University researchers have developed a new antioxidant biomaterial that someday could provide much-needed relief to people living with chronic pancreatitis.
Users of Photoshop, Substance 3D, and other Adobe products are now required to provide the company with unlimited access to their creations.
This tutorial aims to provide a survey of the Bayesian perspective of causal inference under the potential outcomes framework. We review the causal estimands, assignment mechanism, the general structure of Bayesian inference of causal effects, and sensitivity analysis. We highlight issues that are unique to Bayesian causal inference, including the role of the propensity score, the definition of identifiability, the choice of priors in both low and high dimensional regimes. We point out the central role of covariate overlap and more generally the design stage in Bayesian causal inference. We extend the discussion to two complex assignment mechanisms: instrumental variable and time-varying treatments. We identify the strengths and weaknesses of the Bayesian approach to causal inference. Throughout, we illustrate the key concepts via examples.
Instructor:
We propose and demonstrate the first chip-based 3D printer, consisting of a silicon-photonics chip that emits non-mechanically-reconfigurable beams into photocurable resin, enabling future compact, portable, and low-cost next-generation 3D printers.
Patreon: https://www.patreon.com/seanmcarrollBlog post with audio player, show notes, and transcript: https://www.preposterousuniverse.com/podcast/2024/06/03…
