Diamonds aren't just a luxury item--as one of the hardest naturally occurring materials in existence, they are vital components in many industrial drills, medical devices, and even space-grade materials. But recent scientific advancements show it's not just their durability that's impressive, but their data storage capabilities. According to a study published on November 27th in the journal Nature Photonics, researchers at China's University of Science and Technology in Hefei have achieved a record-breaking diamond storage density of 1.85 terabytes per cubic centimeter. CDs, solid state drives, and Blu-ray discs are well suited to handle most general data storage needs, but that increasingly isn't the case for projects requiring massive amounts of digitized information. The artificial intelligence industry as well as quantum and supercomputers often need petabytes, not gigabytes or even terabytes, of information storage.

Hefei National Laboratory, Hefei 230088, China We study quantum-classical separations between classical and quantum supervised learning models based on constant depth (i.e., shallow) circuits, in scenarios with and without noises. This unconditional near-optimal quantum-classical separation originates from the quantum nonlocality property that distinguishes quantum circuits from their classical counterparts. We further derive the noise thresholds for demonstrating such a separation on near-term quantum devices under the depolarization noise model. We prove that this separation will persist if the noise strength is upper bounded by an inverse polynomial with respect to the system size, and vanish if the noise strength is greater than an inverse polylogarithmic function. In addition, for quantum devices with constant noise strength, we prove that no super-polynomial classical-quantum separation exists for any classification task defined by shallow Clifford circuits, independent of the structures of the circuits that specify the learning models. Quantum machine learning studies the interplay between relation problem, obtaining a separation originating from the machine learning and quantum physics [1-6]. In recent years, classical hardness of simulating the intrinsic nonlocality property a number of quantum learning algorithms have been proposed of quantum mechanics. In particular, it is proved that a [7-19], which may offer potential quantum advantages shallow quantum circuit can solve a relation problem such that over their classical counterparts.

A robotic chemist working autonomously in a lab has developed an oxygen-producing catalyst from minerals found in Martian meteorites. The same procedure could one day be used to provide oxygen for astronauts on Mars. Sending supplies to a future Martian colony by spacecraft would be extremely expensive, which makes producing materials with Mars's natural resources an appealing option. But this can be difficult because there are fewer available elements on Mars than on Earth. Yi Luo at the University of Science and Technology of China in Hefei and his colleagues have developed a fully automated robot chemist.

Abstract--Early screening for anxiety and appropriate interventions are essential to reduce the incidence of self-harm and suicide in patients. Due to limited medical resources, traditional methods that overly rely on physician expertise and specialized equipment cannot simultaneously meet the needs for high accuracy and model interpretability . Multimodal data can provide more objective evidence for anxiety screening to improve the accuracy of models. The large amount of noise in multimodal data and the unbalanced nature of the data make the model prone to overfitting. However, it is a non-differentiable problem when high-dimensional and multimodal feature combinations are used as model inputs and incorporated into model training. This causes existing anxiety screening methods based on machine learning and deep learning to be inapplicable. Therefore, we propose a multimodal data-driven anxiety screening framework, namely MMD-AS, and conduct experiments on the collected health data of over 200 seafarers by smartphones. The proposed framework's feature extraction, dimension reduction, feature selection, and anxiety inference are jointly trained to improve the model's performance. In the feature selection step, a feature selection method based on the Improved Fireworks Algorithm is used to solve the non-differentiable problem of feature combination to remove redundant features and search for the ideal feature subset. The experimental results show that our framework outperforms the comparison methods. Furthermore, anxiety disorders are accompanied by immune disorders [2], and interfere with cognitive functions through memory and attention [3], thereby affecting normal life and work. Early anxiety assessment and appropriate interventions can greatly reduce the rate of self-harm and suicide in patients [4]. Psychological scales and routine health checks with professional medical equipment are traditional anxiety screening methods. The Self-rating Anxiety Scale (SAS) [5] and the Generalized Anxiety Disorder-7 (GAD-7) [6] are two psychological scales that are currently used for anxiety screening. Anxiety frequently results in a variety of symptoms or behavioral modifications, such as breathlessness [7], variations in blood pressure [8] and heart rate [9], perspiration, tense muscles, and dizziness [10]. These objective signs can also be used as an important basis for anxiety screening. However, due to the limitation of lacking of medical resources in remote areas and high cost, routine health examinations such as Magnetic Resonance Imaging (MRI) [11], Computed T omography (CT), electrocardiogram (ECG) [12], [13] and electroencephalogram (EEG) [9], [14], may not be available. Haimiao Mo and Shuai Ding are with the School of Management, Hefei University of T echnology, Anhui Hefei 23009, China, also with the Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, China.

The link between chronic pain and a loss of appetite may finally be understood – in mice at least. Zhi Zhang at the University of Science and Technology of China in Hefei and his colleagues injected mice with bacteria that provoke chronic pain. Ten days later, these mice were eating less frequently and for shorter periods of time compared with control mice that had been injected with saline. When the first group of mice were later given pain medication, they ate normally, the researchers wrote in a paper published in Nature Metabolism. To better understand the neuronal activity responsible for this change in behaviour, the researchers analysed the brains of the first group of mice while the animals were in chronic pain.

Researchers from the Hefei Institutes of Physical Science (HFIPS) of the Chinese Academy of Sciences (CAS) have proposed a new artificial intelligence framework for target detection that provides a new solution for fast and high-precision real-time online target detection. Relevant results were published in Expert Systems with Applications. In recent years, deep learning theory has driven the rapid development of artificial intelligence technology. Object detection technology based on deep learning theory is also successful in many industrial applications. Current research focuses on improving the speed or accuracy of target detection and fails to take efficiency and accuracy into account. How to achieve fast and accurate object detection has become an important challenge in the field of artificial intelligence.

A research team led by Prof. Gao Ge and Jiang Li from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has investigated the fault diagnosis of a pulse width modulation converter and proposed a neural network fault diagnosis algorithm to solve existing problems in this field. Results were published in IEEE Transactions on Power Electronics. Pulse width modulation has the advantages of high efficiency, high power density and high reliability. But due to the complexity of the drive systems and the diversity of fusion joint operation, pulse-width modulating voltage source converter systems are prone to suffer critical failures. Therefore, research on fault diagnostic technology is of deep concern, especially open-circuit fault diagnosis, which was what scientists have been focusing in this study.

In the future, operation routes and recommended pick-up points will continue to be expanded. The vehicles launched by Apollo Go this time have 46 safety guarantee technologies and 59 travel service designs, and they have redundancy of all sensors and computing units, which greatly improves the safety. At the same time, they are equipped with innovative functions such as independent control of four door locks, voice interaction and intelligent doors. The city of Hefei has provided policy support for the commercial pilot business of Apollo Go. In March this year, the city issued the Management Specification for Road Test and Demonstration Application of Intelligent Vehicles in Hefei, providing policy guarantees for road testing of intelligent vehicles.

An artificial intelligence (AI) institute in Hefei, in China's Anhui province, says it has developed software that can gauge the loyalty of Communist Party members – something that, if true, would be considered a breakthrough, but has sparked public outcry. Analysts said China has improved its AI-powered surveillance, using big data, machine learning, facial recognition and AI to "get into the brains and minds of its people," building what many call a draconian digital dictatorship. The institute posted a video called "The Smart Political Education Bar," on July 1 to boast about its "mind-reading" software, which it said would be used on party members to "further solidify their determination to be grateful to the party, listen to the party and follow the party." In the video, a subject was seen scrolling through online material that promotes party policy at a kiosk, where the institute said its AI software was monitoring his reaction to see how attentive he was to the party's thought education. The post, however, was taken down shortly after sparking a public outcry among Chinese netizens.

Researchers in the eastern Chinese province of Anhui say they have developed a device that can determine loyalty to the ruling Chinese Communist Party (CCP) using facial scans. A short video uploaded to the Weibo account of the Hefei Comprehensive National Science Center on June 30 said the project was an example of "artificial intelligence empowering party-building." The Weibo post was later deleted, but a text summary of the video, produced in honor of the CCP's July 1 anniversary, remained available on the Internet Archive on Monday. "Guaranteeing the quality of party-member activities is turning into a problem in need of coordination," the text said. "This equipment is a kind of smart ideology, using AI technology to extract and integrate facial expressions, EEG readings and skin conductivity ... making it possible to ascertain the levels of concentration, recognition and mastery of ideological and political education so as to better understand its effectiveness," the description said.