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What just happened? In addition to suing Elon Musk, Twitter is also blaming the world’s richest man for its falling revenue. The platform saw its number of users increase in the second quarter of the year, but that hasn’t translated to a healthier bottom line, something it partly blames on the disruption caused by Musk bidding for the company before walking away.

Twitter’s Q2 2022 results show its second-quarter revenue was $1.18 billion, representing both a quarterly and yearly decline, albeit only slightly. However, its average monetizable daily active users (mDAUs) was up 16.6% compared to Q2 2021, reaching 237.8 million globally.

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Two scientific teams have proposed new models of wormholes, one focusing on microscopic wormholes and one focusing on traversible ones for human travel.

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Intel’s Raptor Lake CPU has been benchmarked in Geekbench, and the results are quite impressive.

Normally, robotic arms are controlled by a GUI running on a host PC, or with some kind of analog system that maps human inputs to various degrees of rotation. However, Maurizio Miscio was able to build a custom robotic arm that is completely self-contained — thanks to a companion mobile app that resides on an old smartphone housed inside a control box.

Miscio started his project by making 3D models of each piece, most of which were 3D-printed. These included the gripper, various joints that each give a single axis of rotation, and a large circular base that acts as a stable platform on which the arm can spin. He then set to work attaching five servo motors onto each rotational axis, along with a single SG90 micro servo motor for the gripper. These motors were connected to an Arduino Uno that also had an HC-05 Bluetooth® serial module for external communication.

In order to operate the arm, Miscio developed a mobile app with the help of MIT App Inventor, which presents the user with a series of buttons that rotate a particular servo motor to the desired degree. The app even lets a series of motion be recorded and “played back” to the Uno over Bluetooth for repeated, accurate movements.

A research partnership between Penn State and the Massachusetts Institute of Technology (MIT) could enable an improved method to make a new type of semiconductor that is a few atoms thin and interacts with light in an unusual way. This new semiconductor could lead to new computing and communications technologies that use lower amounts of energy than current electronics.

The new type of semiconductor, tin selenide (SnSe), would be useful for developing a new type of electronics known as “photonics” that use particles of light, or photons, to store, manipulate and transmit information. Traditional electronics use electrons to do this, while photonics use photons. Tin selenide is a binary compound consisting of tin and selenium in a 1:1 ratio.

The material has a peculiar interaction with light that gives it great potential for use in electronics.

Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world.

Daniele Filippetto and colleagues at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) developed the setup to automatically compensate for real-time changes to accelerator beams and other components, such as magnets. Their machine learning approach is also better than contemporary beam control systems at both understanding why things fail, and then using physics to formulate a response. A paper describing the research was published late last year in Nature Scientific Reports.

“We are trying to teach physics to a chip, while at the same time providing it with the wisdom and experience of a senior scientist operating the machine,” said Filippetto, a staff scientist at the Accelerator Technology & Applied Physics Division (ATAP) at Berkeley Lab and deputy director of the Berkeley Accelerator Controls and Instrumentation Program (BACI) program.

Immature human egg cells skip a fundamental metabolic reaction thought to be essential for generating energy, according to the findings of a study by researchers at the Center for Genomic Regulation (CRG) published today in the journal Nature.

By altering their metabolic activity, the cells avoid creating reactive oxygen species, harmful molecules that can accumulate, damage DNA and cause cell death. The findings explain how human egg cells remain dormant in ovaries for up to 50 years without losing their reproductive capacity.

“Humans are born with all the supply of egg cells they have in life. As humans are also the longest-lived terrestrial mammal, egg cells have to maintain pristine conditions while avoiding decades of wear-and-tear. We show this problem is solved by skipping a fundamental metabolic reaction that is also the main source of damage for the cell. As a long-term maintenance strategy, its like putting batteries on standby mode. This represents a brand new paradigm never before seen in animal cells,” says Dr. Aida Rodriguez, postdoctoral researcher at the CRG and first author of the study.

The biggest benchmarking data set to date for a machine learning technique designed with data privacy in mind has been released open source by researchers at the University of Michigan.

Called federated learning, the approach trains learning models on end-user devices, like smartphones and laptops, rather than requiring the transfer of private data to central servers.

“By training in-situ on data where it is generated, we can train on larger real-world data,” explained Fan Lai, U-M doctoral student in computer science and engineering, who presents the FedScale training environment at the International Conference on Machine Learning this week.