Lifeboat Foundation

Meta has been fined €265 million ($275.5 million) by the Irish data protection commission (DPC) for a massive 2021 Facebook data leak exposing the information of hundreds of million users worldwide.

This concludes the DPC’s investigation of potential GDPR violations by Meta, launched on April 14, 2021, following the publishing of data belonging to 533 million Facebook users on a hacker forum.

The exposed data included personal information, such as mobile numbers, Facebook IDs, names, genders, locations, relationship statuses, occupations, dates of birth, and email addresses.

An annual APS video prize went to supercomputer simulations, control of chaotic Faraday waves, and studies of a large bubble in a bottle.

An algorithm that already predicts how proteins fold might also shed light on the physical principles that dictate this folding.

Proteins control every cell-level aspect of life, from immunity to brain activity. They are encoded by long sequences of compounds called amino acids that fold into large, complex 3D structures. Computational algorithms can model the physical amino-acid interactions that drive this folding [1]. But determining the resulting protein structures has remained challenging. In a recent breakthrough, a machine-learning model called AlphaFold [2] predicted the 3D structure of proteins from their amino-acid sequences. Now James Roney and Sergey Ovchinnikov of Harvard University have shown that AlphaFold has learned how to predict protein folding in a way that reflects the underlying physical amino-acid interactions [3]. This finding suggests that machine learning could guide the understanding of physical processes too complex to be accurately modeled from first principles.

Predicting the 3D structure of a specific protein is difficult because of the sheer number of ways in which the amino-acid sequence could fold. AlphaFold can start its computational search for the likely structure from a template (a known structure for similar proteins). Alternatively, and more commonly, AlphaFold can use information about the biological evolution of amino-acid sequences in the same protein family (proteins with similar functions that likely have comparable folds). This information is helpful because consistent correlated evolutionary changes in pairs of amino acids can indicate that these amino acids directly interact, even though they may be far in sequence from each other [4, 5]. Such information can be extracted from the multiple sequence alignments (MSAs) of protein families, determined from, for example, evolutionary variations of sequences across different biological species.

Kathryn Tunyasuvunakool grew up surrounded by scientific activities carried out at home by her mother—who went to university a few years after Tunyasuvunakool was born. One day a pendulum hung from a ceiling in her family’s home, Tunyasuvunakool’s mother standing next to it, timing the swings for a science assignment. Another day, fossil samples littered the dining table, her mother scrutinizing their patterns for a report. This early exposure to science imbued Tunyasuvunakool with the idea that science was fun and that having a career in science was an attainable goal. “From early on I was desperate to go to university and be a scientist,” she says.

Tunyasuvunakool fulfilled that ambition, studying math as an undergraduate, and computational biology as a graduate student. During her PhD work she helped create a model that captured various elements of the development of a soil-inhabiting roundworm called Caenorhabditis elegans, a popular organism for both biologists and physicists to study. She also developed a love for programming, which, she says, lent itself naturally to a jump into software engineering. Today Tunyasuvunakool is part of the team behind DeepMind’s AlphaFold—a protein-structure-prediction tool. Physics Magazine spoke to her to find out more about this software, which recently won two of its makers a Breakthrough Prize, and about why she’s excited for the potential discoveries it could enable.

All interviews are edited for brevity and clarity.

In a scene from “Star Wars: Episode IV—A New Hope,” R2D2 projects a three-dimensional hologram of Princess Leia making a desperate plea for help. That scene, filmed more than 45 years ago, involved a bit of movie magic—even today, we don’t have the technology to create such realistic and dynamic holograms.

Generating a freestanding 3D hologram would require extremely precise and fast control of light beyond the capabilities of existing technologies, which are based on liquid crystals or micromirrors.

An international group of researchers, led by a team at MIT, has spent more than four years tackling this problem of high-speed optical beam forming. They have now demonstrated a programmable, wireless device that can control light, such as by focusing a beam in a specific direction or manipulating the light’s intensity, and do it orders of magnitude more quickly than commercial devices.

While many studies have investigated the underpinnings of the mammalian motor system (i.e., the collection of neural networks that allow mammals to move in specific ways), some questions remain unanswered. One of these questions relates to the ways in which recurring or stable behaviors are maintained in the brain.

Some theories and research findings suggest that the neural activity underlying stable behaviors is itself very stable. Others, however, hinted at the possibility that the activity of individual motor neurons might change considerably over time, despite the production of similar behavioral patterns.

Researchers at Harvard University have recently tried to move toward the resolution of this long-standing debate, by observing the behavior and neural activity of rodents. Their findings, published in Nature Neuroscience, suggest that the activity of single neurons in brain regions associated with movement and physical behavioral patterns is highly stable over time.

Quantum memory devices can store data as quantum states instead of binary states, as classical computer memories do. While some existing quantum memory technologies have achieved highly promising results, several challenges will need to be overcome before they can be implemented on a large scale.

Researchers at the AWS Center for Quantum Networking and Harvard University have recently developed a promising quantum memory capable of error detection and with a lifetime or coherence time (i.e., the time for which a quantum memory can hold a superposition without collapsing) exceeding 2 seconds. This memory, presented in a paper in Science, could pave the way towards the creation of scalable quantum networks.

Quantum networks are systems that can distribute entangled quantum bits, or qubits, to users who are in different geographic locations. While passing through the networks, qubits are typically encoded as photons (i.e., single particles of light).

UCLA researchers and their colleagues have discovered a new physics principle governing how heat transfers through materials, and the finding contradicts the conventional wisdom that heat always moves faster as pressure increases.

Up until now, the common belief has held true in recorded observations and scientific experiments involving different materials such as gases, liquids and solids.

The researchers detailed their discovery in a study published last week by Nature. They have found that boron arsenide, which has already been viewed as a highly promising material for heat management and advanced electronics, also has a unique property. After reaching an extremely high pressure that is hundreds of times greater than the pressure found at the bottom of the ocean, boron arsenide’s thermal conductivity actually begins to decrease.

Cornell University researchers have created an interface that allows users to handwrite and sketch within computer code—a challenge to conventional coding, which typically relies on typing.

The pen-based interface, called Notate, lets users of computational, digital notebooks open drawing canvases and handwrite diagrams within lines of traditional, digitized computer code.

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