A new study in Lancet Digital Health identifies early warning symptoms like chest pain and dyspnea that are moderately associated with imminent sudden cardiac arrest. The research suggests that future digital technologies could combine these symptoms with biometric data to more precisely predict and potentially prevent these life-threatening events.
Federal agencies will send the alerts on Oct. 4 to ensure that the country is prepared to inform the public in the event of a large-scale emergency.
Some metals display an unusually high electrical resistance. Researchers now have an explanation for why.
A liquid droplet pushed by the wind contracts and stretches its way along a surface until it breaks apart.
A new symmetry-based classification could help researchers describe open, many-body quantum systems that display quantum chaos.
The quest for understanding quantum systems of many particles—and the exotic phenomena they display—fascinates theorists and experimentalists alike, but it’s one with many hurdles. The number of the system’s quantum states increases exponentially with size; these states are hard to prepare, probe, and characterize in experiments, and interactions with the environment “open” the system, further increasing the number of states to consider. As a result, open, many-body quantum systems remain a frontier of exploration in physics, for which researchers haven’t developed a systematic theoretical framework. A new study by Kohei Kawabata of Princeton University and colleagues has taken an important step toward developing such a general framework by offering a complete classification of these systems based on symmetry principles [1] (Fig. 1).
The observation of elusive, exotic particles is the key objective of countless studies, as it could open new avenues for research, while also improving present knowledge of the matter contained in the universe and its underlying physics. The quark model, a theoretical model introduced in 1964, predicted the existence of elementary subatomic particles known as quarks in their different configurations.
Quarks and antiquarks (the anti-matter equivalent of quarks) are predicted to be constituents of various subatomic particles. These include “conventional” particles, such as mesons and baryons, as well as more complex particles made up of four or five quarks (i.e., tetraquarks and pentaquarks, respectively).
The Large Hadron Collider beauty (LHCb) experiment, a research effort involving a large group of researchers at different institutes worldwide, has been trying to observe some of these fascinating particles for over a decade, using data collected at CERN’s LHC particle collider in Switzerland. In a recent paper published in Physical Review Letters, they reported the very first observation of a doubly charged tetraquark and its neutral partner.
Deep Learning (DL) performs classification tasks using a series of layers. To effectively execute these tasks, local decisions are performed progressively along the layers. But can we perform an all-encompassing decision by choosing the most influential path to the output rather than performing these decisions locally?
In an article published today in Scientific Reports, researchers from Bar-Ilan University in Israel answer this question with a resounding “yes.” Pre-existing deep architectures have been improved by updating the most influential paths to the output.
Continue reading “Better paths yield better AI: Enhancing pre-existing architectures” »
There’s a proverb in astronomy that goes something like, “black holes have no hair.” This indicates that black holes are extremely straightforward entities under the framework of general relativity. The only necessary characteristics of a black hole are its mass, electric charge, and spin rate. You now know everything there is to know about black holes just from those three numbers. That is to say, they are bare; they lack any further data.
This feature of black holes has been a major source of frustration for astronomers trying to figure out the inner workings of these cosmic behemoths. However, understanding black holes and their inner workings is impossible due to the absence of any kind of “hair” on their surfaces. Unfortunately, black holes continue to be among the universe’s most elusive and baffling features.
The present knowledge of general relativity, however, is essential to the “no-hair” black hole notion. The emphasis of this relativity illustration is on the curved nature of space-time. Any object with enough mass or energy to bend space-time around it will provide that object directions for movement.
Join Dr. Ben Goertzel, the visionary CEO and Founder of SingularityNET, as he delves into the compelling realm of large language models. In this Dublin Tech Summit keynote presentation, Dr. Goertzel will navigate the uncharted territories of AI, discussing the imminent impact of large language models on innovation across industries. Discover the intricacies, challenges, and prospects of developing and deploying these transformative tools. Gain insights into the future of AI, as Dr. Goertzel unveils his visionary perspective on the role of large language models in shaping the AI landscape. Tune in to explore the boundless potentials of AI and machine learning in this thought-provoking session.
Themes: AI & Machine Learning | Innovation | Future of Technology | Language Models | Industry Transformation.
Keynote: Dr. Ben Goertzel, CEO and Founder, SingularityNET
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Summary: The mushroom body—a key area in the brains of arthropods like insects—plays a crucial role in abstract behavioral decision-making.
Contrary to the long-standing belief that insects react purely on stimulus-response, the study shows they can actually make nuanced decisions based on experiences. The researchers recorded feeding behavior alongside neural signals.
This has implications for understanding not just insect behavior but also basic neurobiological principles that are similar in humans.
