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Efficiently processing broadband signals using convolutional neural networks (CNNs) could enhance the performance of machine learning tools for a wide range of real-time applications, including image recognition, remote sensing and environmental monitoring. However, past studies suggest that performing broadband convolutional processing computations directly in sensors is challenging, particularly when using conventional complementary metal-oxide-semiconductor (CMOS) technology, which underpins the functioning of most existing transistors.

Researchers at Huazhong University of Science and Technology and Nanjing University have recently investigated the possibility of achieving the convolutional processing of broadband signals using an alternative platform, namely van der Waals heterostructures. Their paper, published in Nature Electronics, could ultimately inform the development of better performing image recognition algorithms.

“Our paper was inspired by some our previous research works,” Tianyou Zhai, Xing Zhou and Feng Miao, three of the researchers who carried out the study, told TechXplore. “In studies published in Advanced Materials and Advanced Functional Materials, we realized type-III and type-II band-alignments in different heterostructures. Furthermore, we published a paper in Science Advances, where we realized a reconfigurable neural network vision sensor based on WSe₂.”

Researchers have developed an algorithm that can identify the basic needs of users from the text and images they share on social networks. The experts hope this tool will help psychologists to diagnose possible mental health problems. The study suggests that Spanish-speaking users are more likely to mention relationship problems when feeling depressed than English speakers.

We spend a substantial amount of our time sharing images, videos or thoughts on social networks such as Instagram, Facebook and Twitter. Now, a group of researchers from the Universitat Oberta de Catalunya (UOC) has developed an algorithm that aims to help psychologists diagnose possible mental health problems through the content people post on these platforms.

According to William Glasser’s Choice Theory, there are five basic needs that are central to all human behavior: Survival, Power, Freedom, Belonging and Fun. These needs even have an influence on the images we choose to upload to our Instagram page. “How we present ourselves on social media can provide useful information about behaviors, personalities, perspectives, motives and needs,” explained Mohammad Mahdi Dehshibi, who led this study within the AI for Human Well-being (AIWELL) group, which belongs to the Faculty of Computer Science, Multimedia and Telecommunications at the UOC.

Modern life can be full of baffling encounters with artificial intelligence—think misunderstandings with customer service chatbots or algorithmically misplaced hair metal in your Spotify playlist. These AI systems can’t effectively work with people because they have no idea that humans can behave in seemingly irrational ways, says Mustafa Mert Çelikok. He’s a Ph.D. student studying human-AI interaction, with the idea of taking the strengths and weaknesses of both sides and blending them into a superior decision-maker.

In the AI world, one example of such a hybrid is a “centaur.” It’s not a mythological horse–human, but a human-AI team. Centaurs appeared in chess in the late 1990s, when artificial intelligence systems became advanced enough to beat human champions. In place of a “human versus machine” matchup, centaur or cyborg chess involves one or more computer chess programs and human players on each side.

“This is the Formula 1 of chess,” says Çelikok. “Grandmasters have been defeated. Super AIs have been defeated. And grandmasters playing with powerful AIs have also lost.” As it turns out, novice players paired with AIs are the most successful. “Novices don’t have strong opinions” and can form effective decision-making partnerships with their AI teammates, while “grandmasters think they know better than AIs and override them when they disagree—that’s their downfall,” observes Çelikok.

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Roger Jones, a physicist working at the Large Hadron Collider (LHC) at Cern, explains how the standard model of particle physics may be broken.

Researchers have developed a new kind of logic gate, the fundamental building block from which computers are made. Depending on the kind of logic gate and its rules, two inputs of any combination of 0 and 1 result in an output of either a 1 or 0. A single chip used in creating electronic components like processors and memory modules can contain billions of logic gates.

The newly developed logic gate, which demonstrates the viability of “lightwave electronics,” works orders of magnitudes faster than traditional logic gates. Ordinary logic gates have an input processing delay on the order of nanoseconds, but the new logic gates process inputs in only femtoseconds, a million times shorter than nanoseconds.

The new gates comprise two gold electrodes connected with a graphene wire, which is then zapped with laser pulses, adjusting the pulse’s phase to produce outputs of either a one or a 0. The shortened processing time for the new logic gates means that computers built on the technology would have their processing speeds measured on Petahertz (PHz) scale compared to the current Gigahertz (GHz).

Supernovas might spell the end for the star they happen to, but they aren’t only destructive phenomena. When a star approaches the end of its life and runs out of fuel, it explodes in an enormous outpouring of energy, leaving behind a small, dense core that becomes a black hole or a neutron star. This explosion, though destructive on an epic scale, can also leave behind a beautiful remnant created by the explosion’s shock wave.

A image recently released by the Hubble Space Telescope team shows one such supernova remnant, called DEM L249. Captured by Hubble’s Wide Field Camera 3 instrument and located in the constellation of Mensa, this delicate structure is formed from dust and gas ejected outward from the star’s location by the force of the blast.

“This object — known as DEM L249 — is thought to have been created by a Type 1a supernova during the death throes of a white dwarf,” the Hubble scientists write. “While white dwarfs are usually stable, they can slowly accrue matter if they are part of a binary star system. This accretion of matter continues until the white dwarf reaches a critical mass and undergoes a catastrophic supernova explosion, ejecting a vast amount of material into space in the process.”