Lifeboat Foundation

US airlines have begun to scale back the number of flights they are offering to customers, citing the skyrocketing cost of fuel that has been exacerbated by the Russian invasion of Ukraine.

Alaska Air said it will reduce its offerings by as much as 5% in the first half of this year citing “the sharp rise in fuel costs.” Allegiant Airlines will cut flights by somewhere between 5% and 10% in the second quarter, the company’s chief financial officer said.

Allegiant’s financial chief said the company plans to scale back its flight schedule primarily during times of weaker demand. His comments were reported by Bloomberg News.

Researchers at Johns Hopkins University have developed a new shock-absorbing material that is super lightweight, yet offers the protection of metal. The stuff could make for helmets, armor and vehicle parts that are lighter, stronger and, importantly, reusable.

The key to the new material is what are known as liquid crystal elastomers (LCEs). These are networks of elastic polymers in a liquid crystalline phase that give them a useful combination of elasticity and stability. LCEs are normally used to make actuators and artificial muscles for robotics, but for the new study the researchers investigated the material’s ability to absorb energy.

The team created materials that consisted of tilted beams of LCE, sandwiched between stiff supporting structures. This basic unit was repeated over the material in multiple layers, so that they would buckle at different rates on impact, dissipating the energy effectively.

Applying machine learning techniques to its rule-based security code scanning capabilities, GitHub hopes to be able to extend them to less common vulnerability patterns by automatically inferring new rules from the existing ones.

GitHub Code Scanning uses carefully defined CodeQL analysis rules to identify potential security vulnerabilities lurking in source code.

In the ongoing effort to scale AI systems without incurring prohibitively high training and compute costs, sparse mixture-of-expert models (MoE) have shown their potential for achieving impressive neural network pretraining speedups by dynamically selecting only the related parameters for each input. This enables such networks to vastly expand their parameters while keeping their FLOPs per token (compute) roughly constant. Advancing MoE models to state-of-the-art performance has however been hindered by training instabilities and uncertain quality during fine-tuning.

To address these issues, a research team from Google AI and Google Brain has published a set of guidelines for designing more practical and reliable sparse expert models. The team tested their recommendations by pretraining a 269B sparse model, which it says is the first to achieve state-of-the-art results on natural language processing (NLP) benchmarks.

The team summarizes their main contributions as:

ATLAS has already spotted more than 66 comets and 700 near-Earth asteroids (two of which actually hit Earth’s atmosphere!).

Now, a new algorithm developed by Brown University bioengineers could be an important step toward such adaptive DBS. The algorithm removes a key hurdle that makes it difficult for DBS systems to sense brain signals while simultaneously delivering stimulation.

“We know that there are electrical signals in the brain associated with disease states, and we’d like to be able to record those signals and use them to adjust neuromodulation therapy automatically,” said David Borton, an assistant professor of biomedical engineering at Brown and corresponding author of a study describing the algorithm. “The problem is that stimulation creates electrical artifacts that corrupt the signals we’re trying to record. So we’ve developed a means of identifying and removing those artifacts, so all that’s left is the signal of interest from the brain.”

Despite having remarkable utility in treating movement disorders such as Parkinson’s disease, deep brain stimulation (DBS) has confounded researchers, with a general lack of understanding of why it works at some frequencies and does not at others. Now a University of Houston biomedical engineer is presenting evidence in Nature Communications Biology that electrical stimulation of the brain at higher frequencies (100Hz) induces resonating waveforms which can successfully recalibrate dysfunctional circuits causing movement symptoms.

“We investigated the modulations in local field potentials induced by electrical stimulation of the subthalamic nucleus (STN) at therapeutic and non-therapeutic frequencies in Parkinson’s disease patients undergoing DBS surgery. We find that therapeutic high-frequency stimulation (130−180 Hz) induces high-frequency oscillations (~300 Hz, HFO) similar to those observed with pharmacological treatment,” reports Nuri Ince, associate professor of biomedical engineering.

For the past couple of decades, deep brain stimulation (DBS) has been the most important therapeutic advancement in the treatment of Parkinson’s disease, a progressive nervous system disorder that affects movement in 10 million people worldwide. In DBS, electrodes are surgically implanted in the deep brain and electrical pulses are delivered at certain rates to control tremors and other disabling motor signs associated with the disease.

Recordings of neural activity during therapeutic stimulation can be used to predict subsequent changes in brain connectivity, according to a study of epilepsy patients published in JNeurosci. This approach could inform efforts to improve brain stimulation treatments for depression and other psychiatric disorders.

Corey Keller and colleagues delivered electrical stimulation from implanted electrodes in 14 patients while recording participants’ brain activity.

Repeated sets of stimulation resulted in progressive changes to the brain’s response to simulation, with stronger responses in brain regions connected to the stimulation site.