Lifeboat Foundation

We discuss a perturbative and non-instantaneous reheating model, adopting a generic post-inflationary scenario with an equation of state w. In particular, we explore the Higgs boson-induced reheating, assuming that it is achieved through a cubic inflaton-Higgs coupling ϕ|H|2. In the presence of such coupling, the Higgs doublet acquires a ϕ-dependent mass and a non-trivial vacuum–expectation–value that oscillates in time and breaks the Standard Model gauge symmetry. Furthermore, we demonstrate that the non-standard cosmologies and the inflaton-induced mass of the Higgs field modify the radiation production during the reheating period. This, in turn, affects the evolution of a thermal bath temperature, which has remarkable consequences for the ultraviolet freeze-in dark matter production.

Proteins are the molecular machines that sustain every cell and organism, and knowing what they look like will be critical to untangling how they function normally and malfunction in disease. Now researchers have taken a huge stride toward that goal with the development of new machine learning algorithms that can predict the folded shapes of not only proteins but other biomolecules with unprecedented accuracy.

In a paper published today in Nature, Google DeepMind and its spinoff company Isomorphic Labs announced the latest iteration of their AlphaFold program, AlphaFold3, which can predict the structures of proteins, DNA, RNA, ligands and other biomolecules, either alone or bound together in different embraces. The findings follow the tail of a similar update to another deep learning structure-prediction algorithm, called RoseTTAFold All-Atom, which was published in March in Science.

In this study, graduate student Keito Kobayashi and Professor Shunsuke Fukami from Tohoku University, along with Dr. Kerem Camsari from the University of California, Santa Barbara, and their colleagues, developed a near-future heterogeneous version of a probabilistic computer tailored for executing probabilistic algorithms and facile manufacturing.

“Our constructed prototype demonstrated that excellent computational performance can be achieved by driving pseudo random number generators in a deterministic CMOS circuit with physical random numbers generated by a limited number of stochastic nanomagnets,” says Fukami. “Specifically speaking, a limited number of probabilistic bits (p-bits) with a stochastic magnetic tunnel junction (s-MTJ), should be manufacturable with a near-future integration technology.”

The researchers also clarified that the final form of the spintronics probabilistic computer, primarily composed of s-MTJs, will yield a four-order-of-magnitude reduction in area and a three-order-of-magnitude reduction in energy consumption compared to the current CMOS circuits when running probabilistic algorithms.

Argonne National Laboratory scientists have used anomaly detection in the ATLAS collaboration to search for new particles, identifying a promising anomaly that could indicate new physics beyond the Standard Model.

Scientists used a neural network, a type of brain-inspired machine learning algorithm, to sift through large volumes of particle collision data in a study that marks the first use of a neural network to analyze data from a collider experiment.

Particle physicists are tasked with mining this massive and growing store of collision data for evidence of undiscovered particles. In particular, they’re searching for particles not included in the Standard Model of particle physics, our current understanding of the universe’s makeup that scientists suspect is incomplete.

A recent book diagnoses a blind spot in our knowledge and calls for a revolution that includes human subject experience in the equation.

Philosopher Nick Bostrom popularized the idea superintelligent AI could erase humanity. His new book imagines a world in which algorithms have solved every problem.

Northrop Grumman and Umbra have been awarded small contracts by the Defense Advanced Research Projects Agency (DARPA) to continue to the second phase of a program designed to collect data from radar-equipped satellites flying in formation and develop innovative algorithms to process the data for military applications.

Umbra’s contract under the Distributed Radar Image Foundation Technology (DRIFT) program is for $6 million and will last for six months and Northrop Grumman’s is for $2 million and covers one year, a DARPA spokesperson said.

Scientists have introduced a groundbreaking form of quantum entanglement known as frequency-domain photon number-path entanglement. This leap in quantum physics involves an innovative tool called a frequency beam splitter, which has the unique ability to alter the frequency of individual photons with a 50% success rate.

For years, the scientific community has delved into spatial-domain photon number-path entanglement, a key player in the realms of quantum metrology and information science. This concept involves photons arranged in a special pattern, known as NOON states, where they’re either all in one pathway or another, enabling groundbreaking applications like super-resolution imaging that surpasses traditional limits, the enhancement of quantum sensors, and the development of quantum computing algorithms designed for tasks requiring exceptional phase sensitivity.

In a new paper published in Light Science & Application, a team of scientists, led by Professor Heedeuk Shin from Department of Physics, Pohang University of Science and Technology, Korea, have developed an entangled states in the frequency domain, a concept akin to spatial-domain NOON states but with a significant twist: instead of photons being divided between two paths, they’re distributed between two frequencies. This advancement has led to the successful creation of a two-photon NOON state within a single-mode fiber, showcasing an ability to perform two-photon interference with double the resolution of its single-photon counterpart, indicating remarkable stability and potential for future applications.

Sepsis is a condition in which the body responds improperly. More specifically, the infection causes the organs in the body to shut down. This is a serious illness which could lead to extremely low blood pressure or septic shock. In this case, permanent damage to the lungs, kidneys, liver, and other organs can occur. Unfortunately, if the damage is extensive enough, it could be fatal. Common symptoms associated with sepsis includes alteration of mental status, shallow breathing, sweating out of context, lightheadedness, chills, and other symptoms associated with infection or fever. Sepsis can lead to septic shock and raises the risk of death. Symptoms of septic shock include inability to stand, sleepiness, and extreme confusion. Interestingly, symptoms can vary between people, and it is important to monitor bodily changes to detect sepsis as early as possible. Bacterial, viral, or fungal infections can lead to sepsis including common infections such as pneumonia, urinary tract infections, and burns, among others. It is critical to see a doctor if you suspect you are not getting better or if your symptoms worsen. Early detection of sepsis can help improve survival rate and prevent permanent organ damage. Treatments include antibiotics, increased fluids, vasopressors to increase blood pressure, and steroids. Although scientists and physicians have worked to understand sepsis and how to treat it, other discoveries are yet to be made.

A recent study in Nature Immunology by Dr. Antoine Roquilly from Nantes University in France, demonstrated that patients that experienced sepsis build strong immune cells that aid in the prevention of tumor development. It was previously unknown how the immune landscape was shaped after a patient recovered from sepsis. Roquilly and his team wanted to understand the relationship between these exposed immune cells and the risk of developing cancer in the future.

Roquilly’s research team first analyzed big datasets that consisted of information from patients who survived sepsis. Researchers were able to determine the risk of cancer prevalence up to 10 years following the discharge from the hospital for sepsis patients. Interestingly, sepsis survivors had lower risk of developing cancer compared to those that did not have sepsis.