Lifeboat Foundation

Cardiac alterations in structure and function, namely, the left ventricle, have been intensely studied for decades, in association with aging. In recent times, there has been keen interest in describing myocardial changes that accompany skeletal muscle changes in older adults. Initially described as a cardio-sarcopenia syndrome where alterations in myocardial structure were observed particularly among older adults with skeletal muscle sarcopenia, investigations into this syndrome have spurred a fresh level of interest in the cardiac-skeletal muscle axis. The purpose of this perspective is to summarize the background for this “syndrome of concern,” review the body of work generated by various human aging cohorts, and to explore future directions and opportunities for understanding this syndrome.

The traditional view of cardiovascular aging is that of age-related adaptations in the heart characterized by increased left ventricular (LV) mass (LVM) and LV hypertrophy (LVH), which are often secondary to increased systolic blood pressure mainly mediated by arterial stiffening (1, 2). These changes accumulate throughout the lifetime of an individual, increasing the risk of developing cardiovascular disease (CVD), such as heart failure (HF) and coronary artery disease. The incidence of CVD increases with age, rising from ∼78% among adults aged 60–79 years to ∼90% in those aged above 80 years. CVD is the leading cause of disease burden in the world, with global prevalence doubling from 271 million to 523 million between 1990 and 2019. Incident CVD mortality increased from 12.1 million to 18.6 million in the same period , and accounted for 32% of all deaths. With rapidly aging national populations, these numbers are expected to increase.

A simple design overcomes a substantial limitation on the potential applications for coherent perfect absorbers.

ChatGPT wants to answer your queries, even if it doesn’t know the answer.

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Ultrafast science has begun to tackle the measurement of electronic and chemical processes taking place on the few-femtosecond-to-attosecond timescale. This field requires high-power, extremely short-duration laser pulses. Here we review progress towards the generation of such pulses by Raman scattering in a medium whose component molecules oscillate in phase, which modulates the optical polarizability of the medium and generates high-order Raman sidebands on a field propagating through it. This process may occur with high efficiency and thus lead to sufficient bandwidth for supporting few-femtosecond to attosecond pulses. Significant progress has recently been made in the use of this technique to deliver useable ultrashort pulses in the visible to ultraviolet regions of the spectrum.

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Billeh et al. systematically integrate multi-modal data about neuron types, connectivity, and sensory innervations to create biologically realistic models of the mouse primary visual cortex at two levels of resolution, shared freely as a community resource.

Quantum experiment conducted on Google’s Sycamore 2 computer transferred data across two simulated black holes, adding weight to the holographic principle of the universe.

The researchers discovered a mutation in the thrombospondin-1 (THBS1) gene in three ethnically and geographically diverse families with a history of childhood glaucoma using advanced genome-sequencing technology. The researchers then confirmed their findings in a mouse model that had the genetic mutation and developed glaucoma symptoms due to a previously unknown disease mechanism.

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This increase in pressure not only damages the optic nerve but can also affect other structures in a child’s eye like the cornea. Children with childhood glaucoma typically require surgeries as early as the first three to six months of life, followed by several more operations throughout their childhood.

Game-playing artificial intelligence (AI) systems have advanced to a new frontier. Stratego, the classic board game that’s more complex than chess and Go, and craftier than poker, has now been mastered. Published in Science, we present DeepNash, an AI agent that learned the game from scratch to a human expert level by playing against itself.

DeepNash uses a novel approach, based on game theory and model-free deep reinforcement learning. Its play style converges to a Nash equilibrium, which means its play is very hard for an opponent to exploit. So hard, in fact, that DeepNash has reached an all-time top-three ranking among human experts on the world’s biggest online Stratego platform, Gravon.

Continue reading “Mastering Stratego, the classic game of imperfect information” »

Machine learning has increased considerably in several areas due to its performance in recent years. Thanks to modern computers’ computing capacity and graphics cards, deep learning has made it possible to achieve results that sometimes exceed those experts give. However, its use in sensitive areas such as medicine or finance causes confidentiality issues. A formal privacy guarantee called differential privacy (DP) prohibits adversaries with access to machine learning models from obtaining data on specific training points. The most common training approach for differential privacy in image recognition is differential private stochastic gradient descent (DPSGD). However, the deployment of differential privacy is limited by the performance deterioration caused by current DPSGD systems.

The existing methods for differentially private deep learning still need to operate better since that, in the stochastic gradient descent process, these techniques allow all model updates regardless of whether the corresponding objective function values get better. In some model updates, adding noise to the gradients might worsen the objective function values, especially when convergence is imminent. The resulting models get worse as a result of these effects. The optimization target degrades, and the privacy budget is wasted. To address this problem, a research team from Shanghai University in China suggests a simulated annealing-based differentially private stochastic gradient descent (SA-DPSGD) approach that accepts a candidate update with a probability that depends on the quality of the update and the number of iterations.

Concretely, the model update is accepted if it gives a better objective function value. Otherwise, the update is rejected with a certain probability. To prevent settling into a local optimum, the authors suggest using probabilistic rejections rather than deterministic ones and limiting the number of continuous rejections. Therefore, the simulated annealing algorithm is used to select model updates with probability during the stochastic gradient descent process.