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Meet Grace, the healthcare robot COVID-19 created

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By Joyce Zhou

HONG KONG (Reuters) – The Hong Kong team behind celebrity humanoid robot Sophia is launching a new prototype, Grace, targeted at the healthcare market and designed to interact with the elderly and those isolated by the COVID-19 pandemic.

Dressed in a blue nurse’s uniform, Grace has Asian features, collar-length brown hair and a thermal camera in her chest to take your temperature and measure your responsiveness. She uses artificial intelligence to diagnose a patient and can speak English, Mandarin and Cantonese.

“I can visit with people and brighten their day with social stimulation … but can also do talk therapy, take bio readings and help healthcare providers,” Grace told Reuters as she stood next to her “sister”, Sophia, in creator Hanson Robotics’ Hong Kong workshop.

Grace’s resemblance to a healthcare professional and capacity for social interaction is aimed at relieving the burden of front-line hospital staff overwhelmed during the pandemic, said founder David Hanson.

“A human-like appearance facilitates trust and natural engagement because we are wired for human face-to-face interactions,” Hanson said, explaining how Grace can simulate the action of more than 48 major facial muscles, and has a comforting demeanour designed to look a little like anime characters, often a fusion of Asian and Western styles.

Awakening Health intends to mass-produce a beta version of Grace by August, said David Lake, chief executive of the joint venture between Hanson Robotics and Singularity Studio, and there are plans to fully deploy her next year in locations including Hong Kong, mainland China, Japan and Korea.

The cost of making the robots, now akin to luxury car pricing, will decrease once the company is manufacturing tens or hundreds of thousands of units, Hanson added.

Grace’s launch comes as the global impact of the coronavirus has made the need for humanoid robots urgent, said Kim Min-Sun, a communicology professor at the University of Hawaii.

Stuck at home during COVID-19 lockdowns, many people have had their mental states affected with negative thoughts.

“If they can get help through the deployment of these social robots in intimate settings, certainly it will have a positive impact on society,” she said.

(Writing by Farah Master; Editing by Karishma Singh)

Image Credit: Reuters

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Source: https://datafloq.com/read/meet-grace-healthcare-robot-covid-19-created/15291

Big Data

If you did not already know

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Familia google


In the last decade, a variety of topic models have been proposed for text engineering. However, except Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), most of existing topic models are seldom applied or considered in industrial scenarios. This phenomenon is caused by the fact that there are very few convenient tools to support these topic models so far. Intimidated by the demanding expertise and labor of designing and implementing parameter inference algorithms, software engineers are prone to simply resort to PLSA/LDA, without considering whether it is proper for their problem at hand or not. In this paper, we propose a configurable topic modeling framework named Familia, in order to bridge the huge gap between academic research fruits and current industrial practice. Familia supports an important line of topic models that are widely applicable in text engineering scenarios. In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models. Simply through changing the data organization of Familia, software engineers are able to easily explore a broad spectrum of existing topic models or even design their own topic models, and find the one that best suits the problem at hand. With its superior extendability, Familia has a novel sampling mechanism that strikes balance between effectiveness and efficiency of parameter inference. Furthermore, Familia is essentially a big topic modeling framework that supports parallel parameter inference and distributed parameter storage. The utilities and necessity of Familia are demonstrated in real-life industrial applications. Familia would significantly enlarge software engineers’ arsenal of topic models and pave the way for utilizing highly customized topic models in real-life problems. …

Median Absolute Deviation (MAD) google


In statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample. Consider the data (1, 1, 2, 2, 4, 6, 9). It has a median value of 2. The absolute deviations about 2 are (1, 1, 0, 0, 2, 4, 7) which in turn have a median value of 1 (because the sorted absolute deviations are (0, 0, 1, 1, 2, 4, 7)). So the median absolute deviation for this data is 1. …

Temporal Recurrent Network (TRN) google


Most work on temporal action detection is formulated in an offline manner, in which the start and end times of actions are determined after the entire video is fully observed. However, real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, Temporal Recurrent Networks (TRNs), to model greater temporal context of a video frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS’14. The results show that TRN significantly outperforms the state-of-the-art. …

CDF2PDF google


CDF2PDF is a method of PDF estimation by approximating CDF. The original idea of it was previously proposed in [1] called SIC. However, SIC requires additional hyper-parameter tunning, and no algorithms for computing higher order derivative from a trained NN are provided in [1]. CDF2PDF improves SIC by avoiding the time-consuming hyper-parameter tuning part and enabling higher order derivative computation to be done in polynomial time. Experiments of this method for one-dimensional data shows promising results. …

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Source: https://analytixon.com/2021/06/13/if-you-did-not-already-know-1421/

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Predict Future Sales using XGBRegressor

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XGBRegressor |Predict Future Sales using XGBRegressor





















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Source: https://www.analyticsvidhya.com/blog/2021/06/predict-future-sales-using-xgbregressor/

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Decoding the Chi-Square Test - Use, Implementation and Visualization

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Chi-Square Test - Use, Implementation and Visualization





















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Source: https://www.analyticsvidhya.com/blog/2021/06/decoding-the-chi-square-test%e2%80%8a-%e2%80%8ause-implementation-and-visualization/

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Anonymous or Lambda Functions in Python: A Beginner’s Guide!

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Lambda Functions in Python: A Beginner’s Guide! – Analytics Vidhya





















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Source: https://www.analyticsvidhya.com/blog/2021/06/anonymous-or-lambda-functions-in-python-a-beginners-guide/

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