Aurelia Butler

This is the first time AI has been used to automatically discover vulnerabilities in this type of system, examples of which are used by Google Maps and Facebook.
The experts, from Imperial’s Computational Privacy Group, looked at attacks on query-based systems (QBS) — controlled interfaces through which analysts can query data to extract useful aggregate information about the world. They then developed a new AI-enabled method called QuerySnout to detect attacks on QBS.
QBS give analysts access to collections of statistics gathered from individual-level data like location and demographics. They are currently used in Google Maps to show live information on how busy an area is, or in Facebook’s Audience Measurement feature to estimate audience size in a particular location or demographic to help with advertising promotions.
In their new study, published as part of the 29th ACM Conference on Computer and Communications Security, the team including the Data Science Institute’s Ana Maria Cretu, Dr Florimond Houssiau, Dr Antoine Cully and Dr Yves-Alexandre de Montjoye found that powerful and accurate attacks against QBS can easily be automatically detected at the pressing of a button.
According to Senior Author Dr Yves-Alexandre de Montjoye: “Attacks have so far been manually developed using highly skilled expertise. This means it was taking a long time for vulnerabilities to be discovered, which leaves systems at risk.
“OuerySnout is already outperforming humans at discovering vulnerabilities in real-world systems.”
The need for query-based systems More

Using a miniature camera and a customized deep neural network, Cornell researchers have developed a first-of-its-kind wristband that tracks the entire body posture in 3D.
BodyTrak is the first wearable to track the full body pose with a single camera. If integrated into future smartwatches, BodyTrak could be a game-changer in monitoring user body mechanics in physical activities where precision is critical, said Cheng Zhang, assistant professor of information science and the paper’s senior author.
“Since smartwatches already have a camera, technology like BodyTrak could understand the user’s pose and give real-time feedback,” Zhang said. “That’s handy, affordable and does not limit the user’s moving area.”
A corresponding paper, “BodyTrak: Inferring Full-body Poses from Body Silhouettes Using a Miniature Camera on a Wristband,” was published in the Proceedings of the Association for Computing Machinery (ACM) on Interactive, Mobile, Wearable and Ubiquitous Technology, and presented in September at UbiComp 2022, the ACM international conference on pervasive and ubiquitous computing.
BodyTrak is the latest body-sensing system from the SciFiLab — based in the Cornell Ann S. Bowers College of Computing and Information Science — a group that has previously developed and leveraged similar deep learning models to track hand and finger movements, facial expressions and even silent-speech recognition.
The secret to BodyTrak is not only in the dime-sized camera on the wrist, but also the customized deep neural network behind it. This deep neural network — a method of AI that trains computers to learn from mistakes — reads the camera’s rudimentary images or “silhouettes” of the user’s body in motion and virtually re-creates 14 body poses in 3D and in real time.
In other words, the model accurately fills out and completes the partial images captured by the camera, said Hyunchul Lim, a doctoral student in the field of information science and the paper’s lead author.
“Our research shows that we don’t need our body frames to be fully within camera view for body sensing,” Lim said. “If we are able to capture just a part of our bodies, that is a lot of information to infer to reconstruct the full body.”
Maintaining privacy for bystanders near someone wearing such a sensing device is a legitimate concern when developing these technologies, Zhang and Lim said. They said BodyTrak mitigates privacy concerns for bystanders since the camera is pointed toward the user’s body and collects only partial body images of the user.
They also recognize that today’s smartwatches don’t yet have small or powerful enough cameras and adequate battery life to integrate full body sensing, but could in the future.
Along with Lim and Zhang, paper co-authors are Matthew Dressa ’22, Jae Hoon Kim ’23 and Ruidong Zhang, a doctoral student in the field of information science; Yaxuan Li of McGill University; and Fang Hu of Shanghai Jian Tong University.
Story Source:
Materials provided by Cornell University. Original written by Louis DiPietro. Note: Content may be edited for style and length. More

Our current chip technology is largely based on silicon. Only in very special components a small amount of germanium is added. But there are good reasons to use higher germanium contents in the future: The compound semiconductor silicon-germanium has decisive advantages over today’s silicon technology in terms of energy efficiency and achievable clock frequencies.
The main problem here is to establish contacts between metal and semiconductor on a nanoscale in a reliable way. This is much more difficult with a high proportion of germanium than with silicon. The team at TU Wien, however, together with research teams from Linz and Thun (Switzerland), has now shown that this problem can be solved — with contacts made of crystalline aluminium of extremely high quality and a sophisticated silicon germanium layer system. This enables different interesting contact properties — especially for optoelectronic and quantum components.
The problem with oxygen
“Every semiconductor layer is automatically contaminated in conventional processes; this simply cannot be prevented at the atomic level,” says Masiar Sistani from the Institute for Solid State Electronics at TU Wien. First and foremost, it is oxygen atoms that accumulate very quickly on the surface of the materials — an oxide layer is formed.
With silicon, however, this is not a problem: silicon always forms exactly the same kind of oxide. “With germanium, however, things are much more complicated,” explains Masiar Sistani. “In this case, there is a whole range of different oxides that can form. But that means that different nanoelectronic devices can have very different surface compositions and therefore different electronic properties.”
If you now want to connect a metallic contact to these components, you have a problem: Even if you try very hard to produce all these components in exactly the same way, there are still inevitably massive differences — and that makes the material complex to handle for use in the semiconductor industry. More

Universitat Autònoma de Barcelona researchers have developed a magnetic material capable of imitating the way the brain stores information. The material makes it possible to emulate the synapses of neurons and mimic, for the first time, the learning that occurs during deep sleep.
Neuromorphic computing is a new computing paradigm in which the behavior of the brain is emulated by mimicking the main synaptic functions of neurons. Among these functions is neuronal plasticity: the ability to store information or forget it depending on the duration and repetition of the electrical impulses that stimulate neurons, a plasticity that would be linked to learning and memory.
Among the materials that mimic neuron synapses, memresistive materials, ferroelectrics, phase change memory materials, topological insulators and, more recently, magneto-ionic materials stand out. In the latter, changes in the magnetic properties are induced by the displacement of ions within the material caused by the application of an electric field. In these materials it is well known how the magnetism is modulated when applying the electric field, but the evolution of magnetic properties when voltage is stopped (that is, the evolution after the stimulus) is difficult to control. This makes it complicated to emulate some brain-inspired functions, such as maintaining the efficiency of learning that takes place even while the brain is in a state of deep sleep (i.e., without external stimulation).
This study, led by researchers from the UAB Department of Physics Jordi Sort and Enric Menéndez, in collaboration with the ALBA Synchrotron, the Catalan Institute of Nanoscience and Nanotechnology (ICN2) and the ICMAB, proposes a new way of controlling the evolution of magnetization both in the stimulated and in the post-stimulus states.
The researchers have developed a material based on a thin layer of cobalt mononitride (CoN) where, by applying an electric field, the accumulation of N ions at the interface between the layer and a liquid electrolyte in which the layer has been placed can be controlled. “The new material works with the movement of ions controlled by electrical voltage, in a manner analogous to our brain, and at speeds similar to those produced in neurons, of the order of milliseconds,” explain ICREA research professor Jordi Sort and Serra Húnter Tenure-track Professor Enric Menéndez. “We have developed an artificial synapse that in the future may be the basis of a new computing paradigm, alternative to the one used by current computers,” Sort and Menéndez point out.
By applying voltage pulses, it has been possible to emulate, in a controlled way, processes such as memory, information processing, information retrieval and, for the first time, the controlled updating of information without applied voltage. This control has been achieved by modifying the thickness of the cobalt mononitride layers (which determines the speed of the ions motion), and the frequency of the pulses. The arrangement of the material allows the magnetoionic properties to be controlled not only when the voltage is applied but also, for the first time, when the voltage is removed. Once the external voltage stimulus disappears, the magnetization of the system can be reduced or increased, depending on the thickness of the material and the protocol how the voltage has been previously applied.
This new effect opens a whole range of opportunities for new neuromorphic computing functions. It offers a new logic function that allows, for example, the possibility of mimicking the neural learning that occurs after brain stimulation, when we sleep profoundly. This functionality cannot be emulated by any other type of existing neuromorphic materials.
“When the thickness of the cobalt mononitride layer is below 50 nanometers and with a voltage applied at a frequency greater than 100 cycles per second, we have managed to emulate an additional logic function: once the voltage is applied, the device can be programmed to learn or to forget, without the need for any additional input of energy, mimicking the synaptic functions that take place in the brain during deep sleep, when information processing can continue without applying any external signal,” highlight Jordi Sort and Enric Menendez.
The research, published in Materials Horizons, has been led by researchers from the UAB Department of Physics Jordi Sort, also a researcher at the Catalan Institute for Research and Advanced Studies (ICREA), and Enric Menéndez (Serra Húnter Tenure-track Professor). and with the participation of Zhengwei Tan, Julius de Rojas and Sofia Martins, researchers from the UAB Department of Physics; Aitor Lopeandia, from the Physics Department of the UAB and the Catalan Institute of Nanoscience and Nanotechnology (ICN2); Alberto Quintana, from the Barcelona Institute of Materials Science (ICMAB-CSIC); Javier Herrero-Martín, from the ALBA Synchrotron; José L. Costa-Krämer, from the Institute of Micro and Nanotechnology (IMN-CNM-CSIC); and researchers from CNR-SPIN in Italy, and from IMEC and Quantum Solid State Physics (KU Leuven) in Belgium. More

Quantum dots are nanoscale crystals capable of emitting light of different colors. Display devices based on quantum dots promise greater power efficiency, brightness and color purity than previous generations of displays. Of the three colors typically required to display full color images — red, green and blue — the last has proved difficult to produce. A new method based on self-organizing chemical structures offers a solution, and a cutting-edge imaging technique to visualize these novel blue quantum dots proved essential to their creation and analysis.
Peer closely at the screen of your device, and you might be able to see the individual picture elements, pixels, that make up the image. Pixels can appear almost any color, but they are not actually the smallest element on your screen as they are typically made up of subpixels which are red, green and blue. The variable intensity of these subpixels gives the individual pixels the appearance of a single color from a palette of billions. The underlying technology behind subpixels has evolved from the days of early color television, and there is now a number of possible options. But the next big leap is likely to be so-called quantum dot light emitting diodes, or QD-LEDs.
Displays based on QD-LEDs already exist, but the technology is still maturing, and current options have some drawbacks, specifically regarding the blue subpixels within them. Of the three primary colors, blue subpixels are the most important. Through a process called down-conversion, blue light is used to generate green and red light. Because of this, blue quantum dots require more tightly controlled physical parameters. This often means blue quantum dots are highly complex and costly to produce, and their quality a critical factor in any display. But now, a team of researchers led by Professor Eiichi Nakamura from the University of Tokyo’s Department of Chemistry has a solution.
“Previous design strategies for blue quantum dots were very top down, taking relatively large chemical substances and putting them through a series of processes to refine them into something that worked,” said Nakamura. “Our strategy is bottom up. We built on our team’s knowledge of self-organizing chemistry to precisely control molecules until they form the structures we want. Think of it like building a house from bricks rather than carving one from stone. It’s much easier to be precise, design the way you want, and is more efficient and cost effective too.”
But it’s not just the way Nakamura’s team produced their blue quantum dot that’s special; when exposed to ultraviolet light, it produces nearly perfect blue light, according to the international standard for measuring color accuracy, known as BT.2020. This is due to the unique chemical makeup of their dot, a hybrid mix of organic and inorganic compounds including lead perovskite, malic acid and oleylamine. And only through self-organization can these be coaxed into the form required, which is a cube of 64 lead atoms, four to a side.
“Surprisingly, one of our biggest challenges was in finding that malic acid was a key piece of our chemical puzzle. It took over a year methodically trying different things to find it,” said Nakamura. “Perhaps less surprising is that our other main challenge was to determine the structure of our blue quantum dot. At 2.4 nanometers, 190 times smaller than the wavelength of the blue light we sought to create with it, the structure of a quantum dot cannot be imaged by conventional means. So, we turned to an imaging tool pioneered by some of our team known as SMART-EM, or ‘cinematic chemistry’ as we like to call it.”
Cinematic chemistry is an evolution of electron microscope imaging that is more akin to shooting a video than taking a still image. For capturing details of the structure of the blue quantum dot, this is essential, as the nanocrystal is actually quite dynamic, so any single image of it would only tell a small piece of its story. Unfortunately, the blue quantum dot is also quite short-lived, though this was expected, and the team is now aiming to improve its stability with the aid of industrial collaboration.
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Materials provided by University of Tokyo. Note: Content may be edited for style and length. More

If you hold one wire mesh on top of another one and look through it, you’ll see a larger pattern called a moiré pattern formed by the overlapping grids of the two meshes, which depends on their relative twisted angle. Scientists developing new materials are actively studying moiré patterns in overlapping atomically thin materials — they produce intriguing electronic phenomena that includes unconventional superconductivity and ferromagnetism.
Supercomputer simulations have helped scientists reveal in a bilayer moiré system a new species of an electronic phenomenon called an exciton, which is an electrically neutral quasiparticle, yet one that can carry energy and consists of an electron and electron ‘hole’ that can be created for example by light impinging certain semiconductors and other materials.
The newly discovered excitons were produced by moiré patterns from two-dimensional sheets of exotic semiconductors called transition metal dichalcogenides, with the electron bound to the hole but separated from each other by a characteristic distance in the sheet. This was named an intralayer charge-transfer exciton and was a surprise to the scientists because such excitons do not exist in the individual sheets. The research can be used in the development of new optical sensors and communication technology such as optical fibers and lasers.
Novel Exciton Discovered
“In this work we discovered a novel exciton of unforeseen intralayer charge-transfer characteristics in a moiré superlattice formed by two atomically thin layers of transition metal dichalcogenide materials,” said Steven G. Louie, a distinguished professor of physics at the University of California, Berkeley (UC Berkeley), and a senior faculty scientist at the Lawrence Berkeley National Laboratory (LBNL).
Louie is the corresponding author of research published August 2022 in the journal Nature. In it, the scientists developed computer models that go beyond the conventional parameterized models that have been used to describe moiré systems and moiré excitons. Instead, they performed ab initio calculations that only start with the identity and initial position of the 3,903 atoms of the moiré superlattice unit cell. More

X-rays can be used like a superfast, atomic-resolution camera, and if researchers shoot a pair of X-ray pulses just moments apart, they get atomic-resolution snapshots of a system at two points in time. Comparing these snapshots shows how a material fluctuates within a tiny fraction of a second, which could help scientists design future generations of super-fast computers, communications, and other technologies.
Resolving the information in these X-ray snapshots, however, is difficult and time intensive, so Joshua Turner, a lead scientist at the Department of Energy’s SLAC National Accelerator Center and Stanford University, and ten other researchers turned to artificial intelligence to automate the process. Their machine learning-aided method, published October 17 in Structural Dynamics, accelerates this X-ray probing technique, and extends it to previously inaccessible materials.
“The most exciting thing to me is that we can now access a different range of measurements, which we couldn’t before,” Turner said.
Handling the blob
When studying materials using this two-pulse technique, the X-rays scatter off a material and are usually detected one photon at a time. A detector measures these scattered photons, which are used to produce a speckle pattern — a blotchy image that represents the precise configuration of the sample at one instant in time. Researchers compare the speckle patterns from each pair of pulses to calculate fluctuations in the sample.
“However, every photon creates an explosion of electrical charge on the detector,” Turner said. “If there are too many photons, these charge clouds merge together to create an unrecognizable blob.” This cloud of noise means the researchers must collect tons of scattering data to yield a clear understanding of the speckle pattern. More