An anthropologist imagines a world in which Neanderthals—and their relationships with the environment and one another—survived evolution.
Discusses models, training neural nets, embeddings, tokens, transformers, language syntax.
Does someone look angry or sad? You can probably offer an answer to that question based on the information you can see just by looking at their face. That’s because facial expressions—or a combination of different small facial movements—can be read by other humans to help understand what a person might be feeling at that exact moment.
Since Darwin’s seminal work on the evolutionary origins of facial expressions of emotion, scientists have been trying to find out which specific combinations of facial movements best represent our six basic emotions: happy, surprise, fear, disgust, anger and sadness. So far, researchers have offered a range of theories—or models—to define which facial movements best match each emotion, but until now no one has been able to show which one is most accurate.
Now, a new study by a team of European researchers led by the University of Glasgow and University of Amsterdam has begun to answer that question. The new study, which is published in Science Advances, shows that even the best models for predicting emotions from facial expressions fall short of the judgment of real human participants. Moreover, different humans themselves may read different emotions from the same facial expression, making it even harder to pinpoint exactly which facial movements are systematically linked with certain emotions.
A team of neuroscientists at the New York University School of Medicine has found a link between chronic pain and overactive pyramidal neurons during sleep periods. In their study, published in the journal Nature Neuroscience, the group conducted experiments with injured mice experiencing chronic pain.
Prior research has shown that there is often a link between chronic pain and insomnia. After experiencing a neural injury of some sort, many patients are left with some degree of lasting pain. This tends to result in poor sleep and sometimes insomnia. Once that happens, the pain becomes worse, and over time becomes chronic. But why this happens has been a mystery. In this new effort, the team in New York conducted experiments with mice hoping to find the answer.
The work involved inducing chronic pain in mice by damaging two of the three branches that make up a group of sciatic nerves. Doing so led to skin sensitivity in the legs. The researchers scanned the brains of each of the mice before and after the damage. They observed that pyramidal neurons in the part of the cerebral cortex responsible for sensation processing in the skin became more active. And over the course of several weeks, the activity increased, peaking during non-REM sleep.
Approximately 9,000 people are admitted to Norwegian hospitals with stroke each year. About half of these patients feel exhausted afterwards, and many patients sleep more during the day than before the stroke. These after-effects are challenging and significantly affect patients’ everyday life.
However, we still have a limited understanding of which factors lead to increased fatigue and daytime sleep after stroke. Our research group therefore wanted to investigate whether cognitive and emotional complaints are related to increased fatigue and sleep during the day.
Our results were recently published in an article in the journal Frontiers in Neurology.
Here is an important reason to stay in touch with friends and family: social isolation causes memory and learning deficits and other behavioral changes. Many brain studies have focused on the effects social deprivation has on neurons, but little is known about the consequences for the most abundant brain cell, the astrocyte.
Researchers at Baylor College of Medicine working with animal models report in the journal Neuron that during social isolation, astrocytes become hyperactive, which in turn suppresses brain circuit function and memory formation. Importantly, inhibiting astrocyte hyperactivity reversed the cognitive deficits associated with social deprivation.
“One thing we have learned during the COVID pandemic is that social isolation can influence cognitive functions, as previous studies suggested,” said co-first author, Yi-Ting Cheng, graduate student in Dr. Benjamin Deneen’s lab at Baylor. “This motivated co-first author Dr. Junsung Woo and me to further investigate the effects of social isolation in the brain, specifically in astrocytes.”
In 2021, the fusion yield of 1.35 MJ was produced at NIF by using indirect drive inertial confinement fusion (ICF), indicating that indirect drive ICF has reached ignition. However, the driving radiation flux on capsule inside Hohlraums is still a puzzle in indirect drive ICF studies. The energy deficit at NIF is still neither well understood nor solved. In this paper, we proposed a scheme to determine the driving radiation flux on the capsule by using the combination of the shock wave technique and the reemitted radiation flux measurement. In this scheme, a witness sample is placed in the Hohlraum center as the surrogate of the capsule. The shock velocity in the witness sample is measured by a streaked optical pyrometer from one side, and the temporal reemitted radiation flux is measured by a space-resolved flat response x-ray detector. Then, the peak of the radiation flux is determined by the shock velocity, and the time behavior of the radiation flux is determined by the reemitted flux through the numerical simulation of radiation hydrodynamic code. The rules for designing the witness sample and an example of applying this scheme to determine the driving radiation flux on capsule inside the octahedral spherical Hohlraum are presented in detail.
Measurements of the magnetic moment of the electron have achieved unprecedented accuracy, showing great potential for the search for physics beyond the standard model.
Despite its remarkable successes, the standard model of particle physics clearly isn’t complete—dark matter, dark energy, and the matter–antimatter asymmetry of the Universe are some of its most flagrant deficiencies. Experimenters thus eagerly search for anomalies that could provide hints on a theory that could complete or replace the standard model. The electron is a key player in this quest: its magnetic moment is both the most precisely measured elementary-particle property and the most accurately verified standard model prediction to date. New measurements by Gerald Gabrielse’s group at Northwestern University in Illinois [1] have determined the value of the electron’s magnetic moment 2.2 times more accurately than the previous best estimate, which was obtained in 2008 [2].
Adapting a method for making graphene, researchers have created a 2D honeycomb material that is predicted to have desirable mechanical, thermal, and optical properties.
