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China, U.S. voice AI firms battle in world's largest car market

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DALIAN, China -- The race for supremacy in AI-powered automotive voice recognition is heating up in China, as the world's biggest auto market increasingly becomes a standard-bearer for technology.


Researchers create new learning model for prosthetic limbs - The Robot Report

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Below, a prosthetic hand robot used by the team. Researchers at Shenyang University of Technology and the University of Electro-Communications in Tokyo are trying to figure out how to make prosthetic hands respond to arm movements. For the last decade, scientists have been trying to figure out how to use surface electromyography (EMG) signals to control prosthetic limbs. EMG signals are electrical signals that cause our muscles to contract. They can be recorded by inserting electrode needles into the muscle.


Chinese province targets journalists and foreign students with planned new surveillance system

The Japan Times

Security officials in one of China's largest provinces have commissioned a surveillance system they say they want to use to track journalists and international students among other "suspicious people," documents reviewed by Reuters showed. A July 29 tender document published on the Henan provincial government's procurement website -- reported in the media for the first time -- details plans for a system that can compile individual files on such persons of interest coming to Henan using 3,000 facial recognition cameras that connect to various national and regional databases. A 5 million yuan ($782,000) contract was awarded on Sept. 17 to Chinese tech company Neusoft, which was required to finish building the system within two months of signing the contract, separate documents published on the Henan government procurement website showed. It is unclear if the system is currently operating. Shenyang-based Neusoft did not respond to requests for comment. China is trying to build what some security experts describe as one of the world's most sophisticated surveillance technology networks, with millions of cameras in public places and increasing use of techniques such as smartphone monitoring and facial recognition.


Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems

arXiv.org Machine Learning

The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specially, we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) assisted variational sequence model on an augmented recurrent input space, which could induce rich stochastic sequence dependency. Besides, in order to alleviate the issue of inconsistency between training and predicting as well as improving the mining of dynamic patterns, we (i) propose using a hybrid output as input at next time step, which brings training and predicting into alignment; and (ii) further devise a generalized auto-regressive strategy that encodes all the historical dependencies at current time step. Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems. Finally, we apply our sequence model to a real-world centrifugal compressor sensor data forecasting problem, and again verify its outstanding performance by quantifying the time series predictive distribution.


Intel sells Nand memory, storage business to SK hynix for USD 9 billion

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The acquisition includes the Nand SSD business, the Nand component and wafer business, and the Dalian Nand memory manufacturing facility in China. Intel will however keep its distinct Intel Optane business. The companies hope to close the deal after receiving all of the necessary governmental approvals in late 2021. Under the agreement, SK hynix will first acquire the Nand SSD business (including NAND SSD-associated IP and employees), and the Dalian facility, with a first payment of USD 7 billion. SK hynix will acquire the remaining assets, including IP related to the manufacture and design of Nand flash wafers, R&D employees, and the Dalian fab workforce, upon final closing sometime in March 2025 with the remaining payment of USD 2 billion.


AI technology can predict vanadium flow battery performance and cost

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Vanadium flow batteries (VFBs) are promising for stationary large-scale energy storage due to their high safety, long cycle life, and high efficiency. The cost of a VFB system mainly depends on the VFB stack, electrolyte, and control system. Developing a VFB stack from lab to industrial scale can take years of experiments due to complex factors, from key materials to battery architecture. Novel methods to accurately predict the performance and cost of a VFB stack and further system are needed in order to accelerate the commercialization of VFBs. Recently, a research team led by Prof. Li Xianfeng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences proposed a machine learning-based strategy to predict and optimize the performance and cost of VFBs.


Review of Machine-Learning Methods for RNA Secondary Structure Prediction

arXiv.org Machine Learning

Secondary structure plays an important role in determining the function of non-coding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine-learning technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on machine-learning technologies and a tabularized summary of the most important methods in this field. The current pending issues in the field of RNA secondary structure prediction and future trends are also discussed.


A more parameter-efficient SOTA bottleneck! (2020/07)

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CNN are great blablabla… Let's get to the point. SOTA for image classification on Imagenet is EfficientNet with 88.5% top 1 accuracy in 2020. In this article, I introduce a combination of EfficientNet and Efficient Channel Attention (ECA) to highlight the results of the ECA paper from Tianjin/Dalian/Harbin universities. MobileNetV2 is composed of multiple blocks which are called linear bottlenecks or inverted residuals (they're almost the same). Linear Bottleneck is a residual layer composed of one 1x1 convolution, followed by a 3x3 depthwise convolution, then finally a 1x1 convolution.


Robotic waiters and nurses drive China's unmanned economy

#artificialintelligence

At a hotel in Shanghai, a staffer placed a packaged meal into a robotic waiter and entered the room number of a suspected coronavirus patient on the waiter's touch screen. The robot then automatically made its way to the room. "Please take your meal," the robot said, notifying the guest to take the bento box -- eliminating the need for human-to-human contact. The coronavirus pandemic is boosting demand for robots in China's service sector, where automation is helping restaurant and hotel operators navigate staffing shortages and infection risks. The robot used by the Shanghai hotel was developed by Keenon Robotics, founded in that city in 2010.


Coronavirus robots are patrolling hospitals to help curb the spread of the virus

#artificialintelligence

In the fight against coronavirus, doctors have been given a helping hand, thanks to coronavirus patrol robots . The robots are being used in hospitals in Shenyang in China's northeastern Liaoning province, in the hopes of preventing the virus from spreading. The bots can quickly check people's temperatures and identities, and even disinfect them, according to AFP. AFP explained: "The hospital uses the robot to reduce the pressure on front-line medical staff and to avoid cross infections from the COVID-19 coronavirus." These aren't the only robots being used to curb the spread of the coronavirus.