Lifeboat Foundation

Demand is growing for effective new technologies to cool buildings, as climate change intensifies summer heat. Now, scientists have just designed a transparent window coating that could lower the temperature inside buildings, without expending a single watt of energy. They did this with the help of advanced computing technology and artificial intelligence. The researchers report the details today (November 2) in the journal ACS Energy Letters.

Cooling accounts for about 15% of global energy consumption, according to estimates from previous research studies. That demand could be lowered with a window coating that could block the sun’s ultraviolet and near-infrared light. These are parts of the solar spectrum that are not visible to humans, but they typically pass through glass to heat an enclosed room.

Energy use could be even further reduced if the coating radiates heat from the window’s surface at a wavelength that passes through the atmosphere into outer space. However, it’s difficult to design materials that can meet these criteria simultaneously and at the same time can also transmit visible light, This is required so they don’t interfere with the view. Eungkyu Lee, Tengfei Luo, and colleagues set out to design a “transparent radiative cooler” (TRC) that could do just that.

Generate seamless video from still images with just one click.

Available now: https://bit.ly/3E373m1

Watch this video on Streamable.

The last decade has marked a profound change in how we perceive and talk about Artificial Intelligence. The concept of learning, once confined in the corner of AI, has now become so important some people came up with the new term “Machine Intelligence”[1][2][3] as to make clear the fundamental role of Machine Learning in it and further depart form older symbolic approaches.

Recent Deep Learning (DL) techniques have literally swept away previous AI approaches and have shown how beautiful, end-to-end differentiable functions can be learned to solve incredibly complex tasks involving high-level perception abilities.

Yet, since DL techniques have been proven shining only with a large number of labeled examples, the research community has now shifted his attention towards Unsupervised and Reinforcement Learning, both aiming to solve equivalently complex tasks but without (or less as possible) explicit supervision.

Researchers built an AI that gives out money based on who started with less resources—and humans preferred it.

Summary: Bergmann glial cell synaptic engulfing in the cerebellum was enhanced during motor learning in mice.

Source: Tohoku University.

Tohoku University researchers have shown that Bergmann glial cells, astrocyte-like cells in the cerebellum, ‘eat’ their neighboring neuronal elements within healthy living brain tissue.

Using non-invasive techniques to manipulate our emotions, it might be possible to curtail the screaming horrors that plague our sleep.

A study conducted on 36 patients diagnosed with a nightmare disorder showed that a combination of two simple therapies reduced the frequency of their bad dreams.

Scientists invited the volunteers to rewrite their most frequent nightmares in a positive light and then playing sound associated with positive experiences as they slept.

Spaceship Neptune is the latest space tourism venture for a company is planning balloon flights to the edge of space.

Although these studies collectively suggest that the DLPFC plays a major role in making risky choices, a question remains as to whether its activity mediates risky choice via probability weighting, via marginal utility (value) of monetary outcomes, or both.

In the present study we causally address the hypothesis that the DLPFC is involved both in the subjective valuation of a monetary reward and in probability weighting. The hypothesis was not preregistered but was formulated prior to the collection of the data. The hypothesis was well grounded in the existing literature on the role of DLPFC in risk taking. Several previous studies mentioned the possible role of the lateral PFC in separate components of choice under risk, such as reward magnitude, reward probability, and expected value^14,25,26. However, previous studies including those exploring causal role of the DLPFC in risky choice with non-invasive brain stimulation were not focusing on the estimation of the risk preference parameters but rather on observing changes purely on a behavioural level. Therefore, in the present study we used an experimental design that is typically employed in economic studies estimating risk preference parameters²⁷. We combined offline repetitive TMS over the left and right DLPFC and sham over the right DLPFC, performed in a randomized and counterbalanced order, with a random lottery pair (RLP) task, which is widely used in economics to estimate the degree of risk aversion as well as the curvature of the probability weighting function on an individual level.

Following offline TMS, subjects completed a computerized task consisting of 96 binary lottery choice questions presented in random order. Using the hierarchical Bayesian modeling approach, we then estimated the structural parameters of risk preferences (degree of risk aversion and the curvature of the probability weighting function) and analyzed the obtained posterior distributions to determine the effect of stimulation on model parameters.