The method of the factorial moments Fq of continuous order suggested by Hwahas been tested.It is found that this method can not be used to analyse the real experimental data. Some improvement has been made in the method, which. can be used for the real experimental data. The analytic results for the experimental pseudorapidity distributions of charged particles produced in 400GeV/ c pp collisions indecated that the method of the factorial moments of continuous order is available. And there is seemingly multifractal behavior in the process of multiplicity production in pp collisions at 400GeV/c.
Wang Shaoshun, Zhang Jie, Ye Yunxiu and Xiao Chenguo. Factorial Moments Continuous Order and Multifractal Analysisin 400GeV/c pp Collisions[J]. Chinese Physics C, 1997, 21(9): 769-776.
The fragment angular distributios for 16O+159Tb, 16O+197Au and 16O+209Bi reactions have been measured by the solid state track detctors. After subtraction of the contribution of intermediate mass fragment (IMF), the fission angular distributions have been fitted by the transitional state statistical model (TSM) and the single spin standard theory to extract the spins of the fission nuclei. The relation between spinsand bombarding energies is discussed.
Liu Guoxin, Chen Keliang, Yu Xian and DaiGuangxi. Fission Angular Distributions for the Incomplete Fusion Reactions[J]. Chinese Physics C, 1997, 21(9): 777-782.
The 229Ra has been produced in the 232Th(n,a)229Ra reaction by 14MeV neutron irradiation of natural Thorium. The 229Ra activities were separated from irradiated target material by radiochemical separation technique. 18 new γ rays with energies 14.5,15.6,18.8,21.8,22.5,44.0,47.5,55.0,63.0,69.6,93.6,94.1,98.5,102.2,104.5,106.1,161.171.5keV attributed to the 229Ac were observed for the first time by usin γ(X) spectroscopic methods. A partial decay scheme of 229Ra was proposed.
Shen Shuifa, Yuan Shuanggui, Yang Weifan, Fang Keming, Mou Wantong, Zhang Zueqian, Li Zongwei, Zhong Jiquan, Guo Tianrui and Chen Zhantu. Decay Scheme of 229Ra[J]. Chinese Physics C, 1997, 21(9): 783-786.
The light yield, time characteristics of light propagation and attenuation length of some kinds of scintillating fibres are presented in this paper. Comparison of these characteristics of several fibres is also performed.
Wang Xiaolian, Xu Zizong, Hou Yunzhen, Wang Zhaomin, Chen Hongfang, Li Cheng and Zeng Hui. Study of Light Propagation Characteristics of Scintillating Fibres[J]. Chinese Physics C, 1997, 21(9): 787-792.
Wang Jisuo, Sun Changyong and Teng Aiping. Quantum Statistic Properties of Eigenstates of the Annihilation Operatoraqsk of Two Parameter Deformed Harmonic Oscillator[J]. Chinese Physics C, 1997, 21(9): 793-800.
We simulate thermodynamics of SU(2) gauge field on the asymmetric lattices(ξ= 2,3). It shows that Monte-Carlo simulation on the asymmetric lattices is an effective approach of realizing continuous physics limit.
Hu Jingguo. Monte-Carlo Simulation on Asymmetric Lattices for SU(2) Gauge Field Thermodynamics[J]. Chinese Physics C, 1997, 21(9): 801-805.
A recombination factor is employed which can describe the nuclear shadowing and antishadowing effects in the small x region. Based on the consideration of the nuclear momentum conservation, a unified description of the EMC effect, nuclear Drell-Yan process and inelastic J/ψ production is given in terms of the x-rescaling model with recombination factor.
Zhu Yabo and Li Guanglie. A Simple Model Describing EMC Effect,Nuclear Drell-Yan Process and Inelastic J/ψ Production[J]. Chinese Physics C, 1997, 21(9): 806-811.
The non-unique first forbidden beta decay rates for nuclei 208Tl、208Pb and 206—208Hg are calculated using different effective interactions and model space in term of shell model. The calculated logfot are very sensitively dependent on the effective interaction.The beta decay pattems for 206Hg and 208Hg are also compared.
Zhang Changhua and Gu Jinnan. Nuclear Structure in Lead Region (Ⅱ) Non-unique First Forbidden Beta Decay for Nuclei 208Tl,208Pb and 206—208Hg[J]. Chinese Physics C, 1997, 21(9): 812-817.
The fact that. there are. more prolate-deformed nuclei than oblate-deformed ones in the nature is found to be related with the chaotic motion of nucleons. The primary. investigation with classical theory shows that the particle motion in the potential with oblate-plus -octupole deformation could become chaotic in the case of smaller deformation strength as compared with the case of prolate-plus-octupole deformation. The reason is that the negative curvature in the potential surface could appear in the case of smaller deformation strength for the former one rather than for the latter.
Li Junqing. Chaotic Motion of Classical Particles in Axially Symmetric Potentials with Octupole Deformation[J]. Chinese Physics C, 1997, 21(9): 818-824.
In the framework of transport theory, the variation of coherence energy width Γ with neutron-charge ratio N/Z for the 19F+51V dissipative reaction is compre-hensively discussed. It can be concluded that the charge equilibrium is reached in the present colliding system. The charge diffusion coefficent is also extracted from the analysis The proportional relation between Γ distribution width and average interaction time is ascribed to the charge diffusion process. The effects of cross correlation between different isotopes on fluctuation amplitude are also discussed.
Lu Jun, Wang Qi, Zhu Yongtai, Xu Hushan, Li Songlin and Zhang Yuhu. Energy Coherence and Charge Diffusion in Dissipative Reactions[J]. Chinese Physics C, 1997, 21(9): 825-830.
The ground state properties of hypernuclei are studied theoretically using the relativistic mean-field theory with TM1 and NL-SH force parameters. It is found that these new parameters can very satisfactorily reproduce the ground state properties of the above hypernuclei. The parameter sets TM1 and NL-SH give a very similar description on the hypernuclei. The properties of hypernuclei in baryon hyperon systems are insensitive to the force parameters of effective nucleon-nucleon interaction. The influence of the pairing force is taken into account in the study of hypemuclei and it is found that it leads to the even-odd effect.
Shen Yaosong and Ren Zhongzhou. Relativistic Mean-Field Calculation for Hypernuclei[J]. Chinese Physics C, 1997, 21(9): 831-837.
The deformations of the ground and low-lying excited states of the odd-A 157—173Tm isotopic chain are calculated using the Macro-Micro Cranking Model with quasiparticle configuration. Then the decoupling parameters (DPs) of 157—173Tm of [411 1/2] odd proton band. calculated and compared with the experimental results. It is emphasized that the effect of DPs of the odd-A Tm isotopic chain is caused by the γ deformation. It is pointed out that the anomalous DP of 157Tm might be caused by the band-interacting The sensitivity of the DPs to the Nilsson parameter and deformation parameters is also discussed.
Dong Baoguo, Shen Caiwan and Chen Yoagshou. Study of Odd-A Tm Isotopic Chain Properties of Decoupling parameter of [411 1/2] Band[J]. Chinese Physics C, 1997, 21(9): 838-843.
Lithium fluoride(LiF) cluster-based films deposited on monocrystalline silicon with vaporzation/ condensation method have been studied by low energy positron beam and the S parameters and the effective lengths of diffusion (Leff) in position annihilation were measured. The influences on the microstructure of LiF cluster-based films by the conditions of preparation, such as the temperature of tho substrate, the rate of tho vaporization and the partial pressure of Ar are discussed.
Ju Xin, Peng Liangqiang, Deng Bin, Weng Huimin and Han Rongdian. Study on Lithium Fluoride Cluster-Based Nanophase Films by Low Energy Positron Beam[J]. Chinese Physics C, 1997, 21(9): 844-850.
In this paper, the dual-ring double scatterng method of proton beam spreading, such as the relation of beam distribution function with beam uniformity and beam efficiency, the effection of beam misalignment the variation of beam along longitudinal direction are studied. The depths of dual -ring double scatterers corresponding to different beam field are calculated. Liquor scatterer instead of solid one is proposed.
Yu Jianguo and Yu Qingchang. Research for Beam Spreading of Proton Therapy Facility[J]. Chinese Physics C, 1997, 21(9): 851-859.
Spin-dependent relativistic effect on the binding and dissociation of the heavy quarkonium in a thermal environment is investigated . The result shows that the interactions could influence the heavy quarkonium properdes in medium.
Dong Yubing. Spin-Dependent Relativistic Effect on Heavy Quarkonium Properties in Medium[J]. Chinese Physics C, 1997, 21(9): 860-864.


text:
Izbiska bezeichnet Orte in Polen:
 Izbiska, ein Dorf in der Gmina Wadowice Górne in der Woiwodschaft Karpatenvorland
 Izbiska, ein Dorf in der Gmina Teresin in der Woiwodschaft Masowien
 Izbiska, ein Dorf in der Gmina Stegna in der Woiwodschaft Pommern
 Izbiska, ein Dorf in der Gmina Miedźno in der Woiwodschaft Schlesien
 Izbiska; ein Weiler in der Gmina Rydzyna in der Woiwodschaft Großpolen

redpajama_set_name: RedPajamaWikipedia


text:
Opposition councillors demand to know total cost of Montreal's Formula E race
Just how much did last month's Formula E race cost? The opposition parties at City Hall say they aren't getting the answers they're looking for.
While July's event was supposed to cost $24M, city says total cost won't be known until next year
CBC News · Posted: Aug 21, 2017 7:43 PM ET | Last Updated: August 21, 2017
Felix Rosenqvist of Sweden, heads toward the finish line on his way to winning the pole position for the Montreal Formula ePrix electric car race Sunday, July 30. The races have gone smoothly but area residents say the race has caused significant disruptions. (Ryan Remiorz/Canadian Press )
Just how much did last month's Formula E race cost?
The opposition parties at City Hall say they aren't getting the answers they're looking for.
"It's like being at the dentist and trying to pull teeth on this one. And I suspect it's because the news is not good," said Marvin Rotrand, a city councillor and leader of the Coalition Montreal party.
"Just because Mayor (Denis) Coderre says everything was great, doesn't mean it was great. We need to have all the data."
The City had announced that the event would cost Montreal taxpayers about $24 million to host the international high-speed electric car race for the first time.
Montreal to foot $24M bill for Formula E race, but other cities pay nothing to host same event
Coderre defends $24 M price tag of Montreal's Formula E race
But on Monday, Montreal city council approved additional costs for the race worth $5.3 million.
Original price tag at $24M
The original $24-million price tag was criticized by many, especially after learning that other cities — such as Berlin, Hong Kong, Paris or New York — didn't spend any public money to host the same event.
The price included the costs of:
Building and dismantling the track.
Protection of the track.
Race fees.
Road work.
Engineering and inspection services.
Parking compensation for residents within the perimeter of the track
Rotrand said even though it's known that the cost of holding the event was more than $24 million, there are still many unanswered questions.
Marvin Rotrand, leader of Coalition Montreal, says there are no clear answers about the final price tag on last month's Formula E race. (CBC)
He and other councillors want to know if any of the $10-million line of credit for Montreal, c'est électrique, the organization responsible for the event, was spent.
They also want answers about the number of tickets sold versus those given away for free.
Ontario Street bar owners condemn city order to remove terrasses for Formula E race
Red flag for east-end residents as city builds Formula E track
"[Coderre] said the race was great for the economy in Montreal. If it didn't sell a lot of tickets and bring a lot of tourists in, or sell a lot of hotel rooms, I don't see how it's particularly good for the economy of Montreal," Rotrand said.
Coderre said the focus for this year was not on ticket sales, but on ensuring this first edition would be a success.
'Mission accomplished': Denis Coderre says race was success, despite complaints
Coderre also added that Montreal, c'est électrique will tally up the costs and release the numbers on March 1, 2018. That will be after the municipal election in November 2017.
Added to the tally will have to be the Regional Transit Authority's calculations. All STM bus and Metro service was free for race weekend.
With files from CBC's Sean Henry
Coderre defends $24M price tag of Montreal's Formula E race this summer
Formula E racing is in infancy but will drive broader innovation

redpajama_set_name: RedPajamaCommonCrawl


text:
Jazz Gallery Mentorship Concert: Kris Davis Group feat. David Leon
Jazz Gallery Mentorship Series
We've partnered with the Jazz Gallery to continue the tradition of jazz mentorship. Here accomplished pianist Kris Davis takes budding saxophonist David Leon under her wing for a performance inspired by Davis's approach to composition and bandleading.
Learn more about Kris and David
https://krisdavis.net/bio/
http://www.davidleonjazz.com/
The Jazz Gallery serves as an international cultural center where the youngest generation of emerging professional jazz musicians are nurtured with opportunities to collaborate with their peers, discover and refine their creativity, and perform in front of eager audiences. Through residencies, workshops and exhibitions, we provide a platform for artists to discover their unique voice and a home for established musicians to continue to experiment and grow. Together with The National Jazz Museum in Harlem and The Jazz Gallery, artists and audiences come together from around the world to explore new creative ideas, collaborate and celebrate jazz as a dynamic art form that reflects our ever-changing world.
Thursday November 15, 20187:00 pm
National Jazz Museum in Harlem 58 West 129th Street
Harlem, New York, NY 10027
$0-$10reserve online >
Main banner photo by Richard Conde The Jazz Museum in Harlem is a 501(c)3 charitable organization. All donations are fully tax deductible. Copyright © 2013 The National Jazz Museum in Harlem. All Rights Reserved.


Google Pushes New Update Codename "Pigeon"
Google is at it again. A few days ago (July 24th) Google pushed out a major update for local search codename "Pigeon". As I am writing this, there aren't many details out yet, but we are going to be watching how it affects business closely.
What we do know already is that the new algorithm update is attempting to provide more relevant and accurate local searched. This is not a penalty-based update though, like Penguin and Panda. Those updates were aimed at cleaning up low quality content.
The most noticed change so far is the lower numbers of listings in listing packs.
This number of listings in the local pack, according to MOZ Cast is a decline from 12.1% on July 23rd to only 3.3% on July 27th.
If you have encountered a significant traffic decrease over the past few days, this may be due to the disappearance of local listings. What we would recommend, in the short term, run a PPC campaign to regain those losses. In the long term, focus on getting web search listings for specific identified keywords.
Prior to this update, the local search algorithm was somewhat independent. Now it appears to be more tied to traditional web search ranking signals. In other words, local results will now take into effect your domain authority, backlinks, META, etc.
Run a competition report to see how you compare with your competitors on content, backlinks, social and SEO. There are many places to do this, but that is just one. Another is SEO Power Suite.
What we are seeing with the first post-update research, Yelp and other local directory sites have seen a considerable boost in ranking. It is crucial now to ensure you are listed on the most influential sites to your industry.
Research local directories like Yelp, YP, Trip Advisor, Urban Spoon, Manta, Merchant Circle and ensure you are on them if they are relevant to your industry. You should already be there, but if not, get there!
We've been preaching this for years now. Claim, correct, update your business Google + listing. Ensure all of your information is correct and don't forget to verify your address. While we can't attribute directly yet that this is having an impact, it only makes sense.
Make sure you have a LOCAL phone number on your Google+ listing as well to help identify the locale of your business.
Link and verify your website too while you are setting up your profile. Remember, as with all social profiles, complete is best.
Include a service/product keyword in your business title for maximum efficiency.
There are great tools like SEO Power Suite to help you track these changes and their impact on your business and competitors.
Let us know below if you see changes in your local rankings.


text:
Ebba Larsdotter Sparre (* 1626; † 19. März 1662) war eine schwedische Hofdame und bekannt als die beste Freundin von Königin Kristina.

Leben und Wirken
Ebba war die Tochter des Marschalls und Staatsmanns Lars Eriksson Sparre (1590–1644) und der Märta Banér, außerdem Enkelin des Kanzlers Erik Larsson Sparre (1550–1600). Ihr Spitzname war la belle comtesse (: die schöne Gräfin), da sie eine berühmte Schönheit am Königshof war. Sie spielte häufig die Rolle der Venus im Amateur-Ballett, das vom Adel am Hofe aufgeführt wurde. Ihre innige Beziehung zu Königin Kristina gab Anlass zu Spekulationen, dass sie ein Liebespaar waren. Allerdings wurden diese niemals bestätigt.

Kristina verbrachte die meiste Zeit mit Ebba. Außerdem führte sie Ebba zu dem englischen Botschafter Withelocke, als ihren "Bett-Kollegen" und versicherte ihm, dass ihr Intellekt genauso auffällig wie ihr Körper war. Als Königin Kristina Schweden verließ, schrieb sie leidenschaftliche Liebesbriefe an Sparre, in denen sie sagte, dass sie Ebba immer lieben werde. Allerdings waren solche emotionalen Briefe relativ häufig zu dieser Zeit, und Kristina hätte auch, im gleichen Stil, an eine Frau, die sie nie getroffen hätte, aber deren Schriften sie bewunderte, so geschrieben.

Ebba war verlobt mit Bengt Gabrielsson Oxenstierna, löste die Verlobung aber, um auf Initiative der Königin den Grafen Jakob Kasimir De la Gardie zu heiraten, den Bruder von Magnus Gabriel De la Gardie, den Favoriten der Königin. Die beiden heirateten 1652. Allerdings war die Ehe unglücklich und Ebba blieb Kristinas einziger weiblicher Liebling. Nachdem Kristina Schweden verließ, blieben die beiden in Kontakt.

Im Jahre 1661 lud Kristina Ebba ein, sie in Hamburg zu besuchen und sie wünschte sich, auch Ebba zu sehen, während sie einen Besuch in Schweden machte, doch dies wurde von Ebbas Familie verhindert.

Literatur

Weblinks

 genealogische Übersicht

Einzelnachweise

Hofdame
Christina (Schweden)
Schwede
Geboren 1626
Gestorben 1662
Frau


text:
\section{Introduction \\[-0.2cm]}\label{sec:intro}
Human action recognition from videos has been researched for decades, since this topic plays a key role in various areas such as intelligent surveillance, human-robot interaction, robot vision and so on. Although significant progress has been achieved in the past few years, building an accurate, fast and efficient system for the recognition of actions in unseen videos is still a challenging task due to a number of obstacles, \textit{e.g.} changes in camera viewpoint, occlusions, background, speed of motion, etc. Traditional approaches on video-based action recognition \cite{weinland2011survey} have focused on extracting hand-crafted local features and building motion descriptors from RGB sequences. Many spatio-temporal representations of human motion have been proposed and widely exploited with success such as SIFT \cite{lowe2004distinctive}, HOF \cite{laptev2008learning} or Cuboids \cite{dollar2005behavior}. However, one of the major limitations of these approaches is the lack of 3D structure from the scene and recognizing human actions based only on RGB information is not enough to overcome the current challenges of the field.

The rapid development of depth-sensing time-of-flight camera technology has helped in dealing with this problem, which is considered complex for traditional cameras. Low-cost and easy-to-use depth cameras are able to provide detailed 3D structural information of human motion. In particular, most of the current depth cameras have integrated real-time skeleton estimation and tracking frameworks \cite{ye2014real}, facilitating the collection of skeletal data. This is a high-level representation of the human body, which is suitable for the problem of motion analysis.  Hence, exploiting skeletal data for 3D action recognition opens up opportunities for addressing the limitations of RGB-based solutions and many skeleton-based action recognition approaches have been proposed \cite{Wang2012MiningAE,xia2012view,chaudhry2013bio,Vemulapalli2014HumanAR,ding2016profile}. However, depth sensors have some significant drawbacks with respect to 3D pose estimation. For instance, they are only able to operate up to a limited distance and within a limited field of view. Moreover, a major drawback of depth cameras is the inability to work in bright light, especially sunlight \cite{zhang2012microsoft}.
\begin{figure}[h]
\begin{center}
 \includegraphics[width=9.5cm,height=3.5cm]{pull-figure.png}
 \vspace*{-0.4cm}
\end{center}
\caption{Overview of the proposed method. In the estimation stage, we first run OpenPose \cite{cao2017realtime} -- a real-time, state-of-the-art multi-person 2D pose detector to generate 2D human body keypoints. A deep neural network is then trained to produce 3D poses from the 2D detections. In the recognition stage, the 3D estimated poses are encoded into a compact image-based representation and finally fed into a deep convolutional network for supervised classification task, which is automatically searched by the ENAS algorithm \cite{pmlr-v80-pham18a}.}
\label{fig:1}
\end{figure}
Our focus in this paper is therefore to propose a 3D skeleton-based action recognition approach without depth sensors. Specifically, we are interested in building a unified deep framework for both 3D pose estimation and action recognition from RGB video sequences. As shown in Figure \ref{fig:1}, our approach consists of two stages. In the first, \textit{estimation stage}, the system recovers the 3D human poses from the input RGB video. In the second, \textit{recognition stage}, an action recognition approach is developed and stacked on top of the 3D pose estimator in a unified framework, where the estimated 3D poses are used as inputs to learn the spatio-temporal motion features and predict action labels.

There are four hypotheses that motivate us to build a deep learning framework for human action recognition from 3D poses. First, actions can be correctly represented through the 3D pose movements \cite{johansson1975visual,gu2010action}. Second, the 3D human pose has a high-level of abstraction with much less complexity compared to RGB and depth streams. This makes the training and inference processes much simpler and faster. Third, depth cameras are able to provide highly accurate skeletal data for 3D action recognition. However, they are expensive and not always available (\textit{e.g.} for outdoor scenes). A fast and accurate approach of 3D pose estimation from only RGB input is highly desirable. Fourth, state-of-the-art 2D pose detectors \cite{cao2017realtime,10.1007/978-3-319-46484-8_29} are able to provide 2D poses with a high degree of accuracy in real-time. Meanwhile, deep networks have proved their capacity to learn complex functions from high-dimensional data. Hence, a simple network model can also learn a \textit{mapping} to convert 2D poses into 3D.

The effectiveness of the proposed method is evaluated on public benchmark datasets (Human3.6M \cite{6682899}, MSR Action3D \cite{li2010action}, and SBU \cite{yun2012two}). Far beyond our expectations, the experimental results demonstrate state-of-the-art performances on the targeted tasks (Section \ref{sect:4.3}) and support our hypotheses above. Furthermore, we show that this approach has a low computational cost (Section \ref{sect:4.4}). Overall, our main contributions are as follows: \\[0.15cm]
\hspace*{0.5cm} $\bullet$ First, we present a two-stream, lightweight neural network to recover 3D human poses from RGB images provided by a monocular camera. Our proposed method achieves state-of-the-art result on 3D human pose estimation task and benefits action recognition.\\[0.15cm]
\hspace*{0.5cm} $\bullet$ Second, we propose to put an action recognition approach on top of the 3D pose estimator to form a unified framework for 3D pose-based action recognition. It takes the 3D estimated poses as inputs, encodes them into a compact image-based representation and finally feeds to a deep convolutional network, which is designed automatically by using a neural architecture search algorithm. Surprisingly, our experiments show that we reached state-of-the-art results on this task, even when compared with methods using depth cameras.\\[0.15cm]
The rest of this paper is organized as follows. We present a review of the related work in Section \ref{sect:2}. The proposed method is explained in Section \ref{sect:3}. Experiments are provided in Section \ref{sect:4} and Section \ref{sect:5} concludes the paper.\\[-0.75cm]
\section{Related work \\[-0.2cm]} \label{sect:2}
This section reviews two main topics that are directly related to ours, \textit{i.e.} 3D pose estimation from RGB images and 3D pose-based action recognition. Due to the limited size of a conference paper, an extensive literature review is beyond the scope of this section. Instead, the interested reader is referred to the surveys of Sarafianos \emph{et al}\bmvaOneDot \cite{Sarafianos20163DHP} for recent advances in 3D human pose estimation and Presti \emph{et al}\bmvaOneDot \cite{presti20163d} for 3D skeleton-based action recognition.\\[-0.8cm]
\subsection{3D human pose estimation \\[-0.1cm]}
The problem of 3D human pose estimation has been intensively studied in the recent years. Almost all early approaches for this task were based on feature engineering \cite{4020735,10.1007/978-3-642-33765-9_41,6682899}, while the current state-of-the-art methods are based on deep neural networks \cite{li20143d,tekin2016direct,pavlakos2017coarse,pavllo20183d,VNect_SIGGRAPH2017,Katircioglu2018}. Many of them are regression-based approaches that directly predict 3D poses from RGB images via 2D/3D heatmaps. For instance, Li \emph{et al}\bmvaOneDot \cite{li20143d} designed a deep convolutional network for human detection and pose regression. The regression network learns to predict 3D poses from single images using the output of a body
part detection network. Tekin \emph{et al}\bmvaOneDot \cite{tekin2016direct} proposed to use a deep network to learn a regression mapping that directly estimates the 3D pose in a given frame of a sequence from a spatio-temporal volume centered on it. Pavlakos \emph{et al}\bmvaOneDot \cite{pavlakos2017coarse} used multiple fully convolutional networks to construct a volumetric stacked hourglass architecture, which is able to recover 3D poses from RGB images. Pavllo \emph{et al}\bmvaOneDot \cite{pavllo20183d} exploited a temporal dilated convolutional network \cite{Yu2016MultiScaleCA} for estimating 3D poses. However, this approach led to a significant increase in the number of parameters as well as the required memory. Mehta \emph{et al}\bmvaOneDot \cite{VNect_SIGGRAPH2017} introduced a real-time approach to predict 3D poses from a single RGB camera. They used ResNets \cite{7780459} to jointly predict 2D and 3D heatmaps as regression tasks. Recently, Katircioglu \emph{et al}\bmvaOneDot \cite{Katircioglu2018} introduced a deep regression network for predicting 3D human poses from monocular images via 2D joint location heatmaps. This architecture is in fact an overcomplete autoencoder that learns a high-dimensional latent pose representation and accounts for joint dependencies, in which a Long Short-Term Memory (LSTM) network \cite{Hochreiter1997LongSM} is used to enforce temporal consistency on 3D pose predictions.

To the best of our knowledge, several studies \cite{pavlakos2017coarse,VNect_SIGGRAPH2017,Katircioglu2018} stated that regressing the 3D pose from 2D joint locations is difficult and not too accurate. However, motivated by Martinez \emph{et al}\bmvaOneDot \cite{martinez_2017_3dbaseline}, we believe that a simple neural network can learn effectively a \textit{direct 2D-to-3D mapping}. Therefore, this paper aims at proposing a simple, effective and real-time approach for 3D human pose estimation that benefits action recognition. To this end, we design and optimize a two-stream deep neural network that performs 3D pose predictions from the 2D human poses. These 2D poses are generated by a state-of-the-art 2D detector that is able to run in real-time for multiple people. We empirically show that although the proposed approach is computationally inexpensive, it is still able to improve the state-of-the-art.\\[-0.8cm]
\subsection{3D pose-based action recognition \\[-0.1cm]}
Human action recognition from skeletal data or 3D poses is a challenging task. Previous works on this topic can be divided into two main groups of method. The first group \cite{Lv2006RecognitionAS,Wang2012MiningAE,Vemulapalli2014HumanAR} extracts hand-crafted features and uses probabilistic graphical models, \textit{e.g.} Hidden Markov Model (HMM) \cite{Lv2006RecognitionAS} or Conditional Random Field (CRF) \cite{Han2010DiscriminativeHA} to recognize actions. However, almost all of these approaches require a lot of feature engineering. The second group \cite{Liu2016SpatioTemporalLW,Du2015HierarchicalRN,Shahroudy2016NTURA} considers the 3D pose-based action recognition as a time-series problem and proposes to use Recurrent Neural Networks with Long-Short Term Memory units (RNN-LSTMs) \cite{Hochreiter1997LongSM} for modeling the dynamics of the skeletons. Although RNN-LSTMs are able to model the long-term temporal characteristics of motion and have advanced the state-of-the-art, this approach feeds raw 3D poses directly into the network and just considers them as a kind of low-level feature. The large number of input features makes RNNs very complex and may easily lead to overfitting. Moreover, many RNN-LSTMs act merely as classifiers and cannot extract high-level features for recognition tasks \cite{Sainath2015ConvolutionalLS}.

In the literature, 3D human pose estimation and action recognition are closely related. However, both problems are generally considered as two distinct tasks \cite{cheronICCV15}. Although some approaches have been proposed for tackling the problem of jointly predicting 3D poses and recognizing actions in RGB images or video sequences \cite{5540235,7298734,luvizon20182d}, they are data-dependent and require a lot of feature engineering, except the work of Luvizon \emph{et al}\bmvaOneDot \cite{luvizon20182d}. Unlike in previous studies, we propose a multitask learning framework for 3D pose-based action recognition by reconstructing 3D skeletons from RGB images and exploiting them for action recognition in a joint way. Experimental results on public and challenging datasets show that our framework is able to solve the two tasks in an effective way. \\[-0.8cm]
\section{Proposed Method\\[-0.2cm]} \label{sect:3}
We explain the proposed method is this section. First, our approach for 3D human pose estimation is presented. We then introduce our solution for 3D pose-based action recognition. \\[-0.8cm]
\subsection{Problem definition \\[-0.1cm]}
Given an RGB video clip of a person who starts to perform an action at time $t = 0$ and ends at $t = T$, the problem studied in this work is to generate a sequence of 3D poses $\mathcal{P} = (\textbf{p}_0, ..., \textbf{p}_T)$, where $\textbf{p}_i \in \mathbb{R}^{3 \times M}$, $i \in \{0, ..., T\}$ at the estimation stage. The generated $\mathcal{P}$ is then used as input for the recognition stage to predict the corresponding action label $\mathcal{A}$ by a supervised learning model. See Figure \ref{fig:1} for an illustration of the problem. \\[-0.8cm]
\subsection{3D human pose estimation \\[-0.1cm]}
Given an input RGB image $\textbf{I} \in \mathbb{R}^{W \times H \times 3}$, we aim to estimate the body joint locations in the 3-dimensional space, noted as $\hat{\textbf{p}}_{3D}$ $\in \mathbb{R}^{3 \times M}$. To this end, we first run the state-of-the-art human 2D pose detector, namely OpenPose \cite{cao2017realtime}, to produce a series of 2D keypoints $\textbf{p}_{2D} \in \mathbb{R}^{2 \times N}$. To recover the 3D joint locations, we try to learn a \textit{direct 2D-to-3D mapping} $f_r$: $\textbf{p}_{2D} \xmapsto{f_r} \hat{\textbf{p}}_{3D}$. This transformation can be implemented by a deep neural network in a supervised manner
\begin{equation}
    \hat{\textbf{p}}_{3D} = f_r(\textbf{p}_{2D}, \theta),
\end{equation}
where $\theta$ is a set of trainable parameters of the function $f_r$. To optimize $f_r$, we minimize the prediction error over a labelled dataset of $\mathcal{C}$ poses by solving the optimization problem
\begin{equation}
   \underset{\theta}{\operatorname{arg\,min}} \hspace{0.2cm}  \frac{1}{\mathcal{C}} \sum_{n=1}^{\mathcal{C}} \mathcal{L} (f_r(\textbf{x}_i), \textbf{y}_i).
\end{equation}
Here $\textbf{x}_i$ and $\textbf{y}_i$ are the input 2D poses and the ground truth 3D poses, respectively; $\mathcal{L}$ denotes a loss function. In our implementation the robust Huber loss \cite{huber1992robust} is used to deal with outliers. \\[-0.7cm]
\subsubsection{Network design}
State-of-the-art deep learning architectures such as ResNet \cite{7780459}, Inception-ResNet-v2 \cite{Szegedy2016Inceptionv4IA}, DenseNet \cite{Huang2017DenselyCC}, or NASNet \cite{Zoph2017NeuralAS} have achieved an impressive performance in supervised learning tasks with high dimensional data, \textit{e.g.} 2D or 3D images. However, the use of these architectures \cite{7780459,Szegedy2016Inceptionv4IA,Huang2017DenselyCC,Zoph2017NeuralAS} on low dimensional data like the coordinates of the 2D human joints could lead to overfitting. Therefore, our design is based on a simple and lightweight multilayer network architecture without the convolution operations. In the design process, we exploit some recent improvements in the optimization of the modern deep learning models \cite{7780459,Huang2017DenselyCC}. Concretely, we propose a two-stream network. Each stream comprises linear layers, Batch Normalization (BN) \cite{Ioffe2015BatchNA}, Dropout \cite{Hinton2012ImprovingNN}, SELU \cite{Klambauer2017SelfNormalizingNN} and Identity connections \cite{7780459}. During the training phase, the first stream takes the  ground truth 2D locations as input. The 2D human joints predicted by OpenPose \cite{cao2017realtime} are inputted to the second stream. The outputs of the two streams are then averaged. Figure \ref{fig:2} illustrates our network design. Note that learning with the ground truth 2D locations for both of these streams could lead to a higher level of performance. However, training with the 2D OpenPose detections could improve the generalization ability of the network and makes it more robust during the inference, when only the OpenPose's 2D output is used to deal with action recognition in the wild.
\begin{figure}[h]
\begin{center}
 \includegraphics[width=10cm,height=4.2cm]{2d-to-3d.png}
 \vspace*{-0.4cm}
\end{center}
   \caption{Diagram of the proposed two-stream network for training our 3D pose estimator.}
\label{fig:2}
\end{figure}\\[-1cm]
\subsection{3D pose-based action recognition \\[-0.1cm]}
In this section, we explain how to integrate the estimation stage with the recognition stage in a unified framework. Specifically, the proposed recognition approach is stacked on top of the 3D pose estimator. To explore the high-level information of the estimated 3D poses, we encode them into a compact image-based representation. These intermediate representations are then fed to a Deep Convolutional Neural Network (D-CNNs) for learning and classifying actions. This idea has been proven effective in \cite{Pham2018ExploitingDR,Pham2018SkeletalMT,s19081932}. Thus, the spatio-temporal patterns of a 3D pose sequence are transformed into a single color image as a global representation called Enhanced-SPMF \cite{s19081932} via two important elements of a human movement: 3D poses and their motions. Due to the limited space available, detailed description of the Enhanced-SPMF is not included. We refer the interested reader to the work described in \cite{s19081932} for further technical details. Figure \ref{fig:3} visualizes some Enhanced-SPMF representations from samples of the MSR Action3D dataset \cite{li2010action}.
\begin{figure}[ht]
\begin{center}
 \includegraphics[width=10cm,height=1.8cm]{SPMF-and-SPMF-Enhanced.png}
 \vspace*{-0.4cm}
\end{center}
   \caption{Immediate image-based representations for the recognition stage.}
\label{fig:3}
\end{figure}\\
\hspace*{0.5cm} For learning and classifying the obtained images, we propose to use the Efficient Neural Architecture Search (ENAS) \cite{pmlr-v80-pham18a} -- a recent state-of-the-art technique for automatic design of deep neural networks. The ENAS is in fact an extension of an important advance in deep learning called NAS \cite{Zoph2017NeuralAS}, which is able to automatize the designing process of convolutional architectures on a dataset of interest. This method proposes to search for optimal building blocks (called \textit{cells}, including \textit{normal cells} and \textit{reduction cells}) and the final architecture is then constructed from the best cells achieve. In NAS, an RNN is used. It first samples a candidate architecture called \textit{child model}. This child model is then trained to convergence on the desired task and reports its performance. Next, the RNN uses the performance as a guiding signal to find a better architecture. This process is repeated for many times, making NAS computationally expensive and time-consuming (\textit{e.g.} on CIFAR-10, NAS needs 4 days with 450 GPUs to discover the best architecture). ENAS has been proposed to improve the efficiency of NAS. Its key idea of ENAS \cite{pmlr-v80-pham18a} is the use of sharing parameters among child models, which helps reducing the time of training each child model from scratch to convergence. State-of-the-art performance has been achieved by ENAS on  well known public datasets. We encourage the readers to refer to the original paper \cite{pmlr-v80-pham18a} for more details. Figure \ref{fig:4} illustrates the entire pipeline of our approach for the recognition stage. \\[-0.6cm]
\begin{figure}[ht]
\begin{center}
 \includegraphics[width=13cm,height=2.2cm]{proposed-model.png}
 \vspace*{-0.8cm}
\end{center}
   \caption{Illustration of the proposed approach for 3D pose-based action recognition.}
\label{fig:4}
\end{figure}  \\[-1.2cm]
\section{Experiments \\[-0.2cm]} \label{sect:4}
\subsection{Datasets and settings \\[-0.1cm]}
We evaluate the proposed method on three challenging datasets: Human3.6M, MSR Action3D and SBU Kinect Interaction. The Human3.6M is used for evaluating 3D pose estimation. Meanwhile, the other two datasets are used for validating action recognition. The characteristics of each dataset are as follows.\\[0.15cm]
\textbf{Human3.6M} \cite{6682899}: This is a very large-scale dataset containing 3.6 million different 3D articulated poses captured from 11 actors for 17 actions, under 4 different viewpoints. For each subject, the dataset provides 32 body joints, from which only 17 joints are used for training and computing scores. In particular, 2D joint locations and 3D poses ground truth are available for evaluating supervised learning models.\\[0.15cm]
\textbf{MSR Action3D} \cite{li2010action}: This dataset contains 20 actions, performed by 10 subjects. Our experiment was conducted on 557 video sequences of the MSR Action3D, in which the whole dataset is  divided into three subsets: AS1, AS2, and AS3. There are 8 actions classes for each subset. Half of the data is selected for training and the rest is used for testing.\\[0.15cm]
\textbf{SBU Kinect Interaction} \cite{yun2012two}: This dataset contains a total of 300 interactions, performed by 7 participants for 8 actions. This is a challenging dataset due to the fact that it contains pairs of actions that are difficult to distinguish such as \textit{exchanging objects} -- \textit{shaking hands} or \textit{pushing} -- \textit{punching}. We randomly split the whole dataset into 5 folds, in which 4 folds are used for training and the remaining 1 fold is used for testing.\\[-0.7cm]
\subsection{Implementation details \\[-0.1cm]}
The proposed networks were implemented in Python with Keras/TensorFlow backend. The two streams of the 3D pose estimator are trained separately with the same hyperparameters setting, in which we use mini-batches of 128 poses with 0.25 dropout rate. The weights are initialized by the He initialization \cite{He2015DelvingDI}. Adam optimizer \cite{Kingma2014AdamAM} is used with default parameters. The initial learning rate is set to 0.001 and is decreased by a factor of 0.5 after every 50 epochs. The network is trained for 300 epochs from scratch on the Human3.6M dataset \cite{6682899}. For action recognition task, we run OpenPose \cite{cao2017realtime} to generate 2D detections on MSR Action3D \cite{li2010action} and SBU Kinect Interaction \cite{yun2012two}. The pre-trained 3D pose estimator on Human3.6M \cite{6682899} is then used to provide 3D poses. We use standard data pre-processing and augmentation techniques such as randomly cropping and flipping on these two datasets due to their small sizes. To discover optimal recognition networks, we use ENAS \cite{pmlr-v80-pham18a} with the same parameter setting as the original work. Concretely, the shared parameters $\omega$  are trained with Nesterov's accelerated gradient descent \cite{Nesterov} using Cosine learning rate \cite{Loshchilov2016SGDRSG}. The candidate architectures are initialized by He initialization \cite{He2015DelvingDI} and trained by Adam optimizer \cite{Kingma2014AdamAM} with a learning rate of 0.00035. Additionally, each search is run for 200 epochs.\\[-0.75cm]
\subsection{Experimental results and comparison \\[-0.1cm]} \label{sect:4.3}
\subsubsection{Evaluation on 3D pose estimation}
We evaluate the effectiveness of the proposed 3D pose estimation network using the standard protocol of the Human3.6M dataset \cite{6682899,pavlakos2017coarse,martinez_2017_3dbaseline,VNect_SIGGRAPH2017}. Five subjects S1, S5, S6, S7, S8 are used for training and the rest two subjects S9, S11 are used for evaluation. Experimental results are reported by the average error in millimeters between the ground truth and the corresponding predictions over all joints. Much to our surprise, our method outperforms the previous best result from the literature \cite{martinez_2017_3dbaseline} by 3.1mm, corresponding to an error reduction of 6.8\% even when combining the ground truth 2D locations with the 2D OpenPose detections. This result proves that our network design can learn to recover the 3D pose from the 2D joint locations with a remarkably low error rate, which to the best of our knowledge, has established a new state-of-the-art on 3D human pose estimation (see Table \ref{tab:1} and Figure \ref{fig:5}). \\[-0.55cm]
\begin{table}[ht]
\caption{\label{tab:1} Experimental results and comparison with previous state-of-the-art 3D pose estimation approaches on the Human3.6M dataset \cite{6682899}.}
\vspace*{-0.2cm}
\begin{center}
\begin{tabular}{p{1.5cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}p{0.25cm}}
\hline
\hline
{\tiny Method} &  {\tiny Direct.} & {\tiny Disc.} & {\tiny Eat} & {\tiny Greet} & {\tiny Phone} & {\tiny Photo} & {\tiny Pose} & {\tiny Purch.} & {\tiny Sit} & {\tiny SitD} & {\tiny Smoke} & {\tiny Wait} & {\tiny WalkD} & {\tiny Walk} & {\tiny WalkT} & {\tiny Avg} \\
\hline
 \cellcolor{gray!30} {\tiny Ionescu \emph{et al}\bmvaOneDot \cite{6682899}$^{\dagger}$} & \cellcolor{gray!30} {\tiny 132.7} & \cellcolor{gray!30} {\tiny 183.6} & \cellcolor{gray!30} {\tiny 132.3} & \cellcolor{gray!30} {\tiny 164.4} & \cellcolor{gray!30} {\tiny 162.1} & \cellcolor{gray!30} {\tiny 205.9} & \cellcolor{gray!30} {\tiny 150.6} & \cellcolor{gray!30} {\tiny 171.3} & \cellcolor{gray!30} {\tiny 151.6} & \cellcolor{gray!30} {\tiny 243.0} & \cellcolor{gray!30} {\tiny 162.1} & \cellcolor{gray!30} {\tiny 170.7} & \cellcolor{gray!30} {\tiny 177.1} & \cellcolor{gray!30} {\tiny 96.6} & \cellcolor{gray!30} {\tiny 127.9} & \cellcolor{gray!30} {\tiny 162.1} \\
 {\tiny Du \emph{et al}\bmvaOneDot \cite{10.1007/978-3-319-46493-0_2}$^{\star}$} & {\tiny 85.1} & {\tiny 112.7} & {\tiny 104.9} & {\tiny 122.1} & {\tiny 139.1} & {\tiny 135.9} & {\tiny 105.9} & {\tiny 166.2} & {\tiny 117.5} & {\tiny 226.9} & {\tiny 120.0} & {\tiny 117.7} & {\tiny 137.4} & {\tiny 99.3} & {\tiny 106.5} & {\tiny 126.5} \\
 \cellcolor{gray!30} {\tiny Tekin \emph{et al}\bmvaOneDot \cite{tekin2016direct} } & \cellcolor{gray!30} {\tiny 102.4} & \cellcolor{gray!30} {\tiny 147.2} & \cellcolor{gray!30} {\tiny88.8} & \cellcolor{gray!30} {\tiny 125.3} & \cellcolor{gray!30} {\tiny 118.0} & \cellcolor{gray!30} {\tiny 182.7} & \cellcolor{gray!30} {\tiny112.4} & \cellcolor{gray!30} {\tiny129.2} & \cellcolor{gray!30} {\tiny138.9} & \cellcolor{gray!30} {\tiny 224.9} & \cellcolor{gray!30} {\tiny 118.4} & \cellcolor{gray!30} {\tiny138.8} & \cellcolor{gray!30} {\tiny 126.3} & \cellcolor{gray!30} {\tiny 55.1} & \cellcolor{gray!30} {\tiny 65.8} & \cellcolor{gray!30} {\tiny 125.0} \\
 {\tiny Park \emph{et al}\bmvaOneDot \cite{10.1007/978-3-319-49409-8_15}$^{\star}$} & {\tiny 100.3} & {\tiny 116.2} & {\tiny 90.0} & {\tiny 116.5} & {\tiny 115.3} & {\tiny 149.5} & {\tiny 117.6} & {\tiny 106.9} & {\tiny 137.2} & {\tiny 190.8} & {\tiny 105.8} &{\tiny 125.1} & {\tiny 131.9} & {\tiny 62.6} & {\tiny 96.2} & {\tiny 117.3}\\
 \cellcolor{gray!30} {\tiny Zhou \emph{et al}\bmvaOneDot \cite{780906}$^{\star}$} &  \cellcolor{gray!30} {\tiny 87.4} & \cellcolor{gray!30} {\tiny 109.3} & \cellcolor{gray!30} {\tiny 87.1} & \cellcolor{gray!30} {\tiny 103.2} & \cellcolor{gray!30} {\tiny 116.2} & \cellcolor{gray!30} {\tiny 143.3} & \cellcolor{gray!30} {\tiny 106.9} & \cellcolor{gray!30} {\tiny 99.8} & \cellcolor{gray!30} {\tiny 124.5} & \cellcolor{gray!30} {\tiny 199.2} & \cellcolor{gray!30} {\tiny 107.4} & \cellcolor{gray!30} {\tiny 118.1} & \cellcolor{gray!30} {\tiny 114.2} & \cellcolor{gray!30} {\tiny 79.4} & \cellcolor{gray!30} {\tiny 97.7} & \cellcolor{gray!30} {\tiny 113.0} \\
{\tiny Zhou \emph{et al}\bmvaOneDot \cite{Zhou2016DeepKP}$^{\star}$} & {\tiny 91.8} & {\tiny 102.4} & {\tiny 96.7} & {\tiny 98.8} & {\tiny 113.4} & {\tiny 125.2} & {\tiny 90.0} & {\tiny 93.8} & {\tiny 132.2} & {\tiny 159.0} & {\tiny 107.0} & {\tiny 94.4} & {\tiny 126.0} & {\tiny 79.0} & {\tiny 99.0} & {\tiny 107.3}\\
\cellcolor{gray!30} {\tiny Pavlakos \emph{et al}\bmvaOneDot \cite{pavlakos2017coarse}} & \cellcolor{gray!30} {\tiny 67.4} & \cellcolor{gray!30} {\tiny 71.9} & \cellcolor{gray!30} {\tiny 66.7} & \cellcolor{gray!30} {\tiny 69.1} &  \cellcolor{gray!30} {\tiny 72.0} &  \cellcolor{gray!30} {\tiny 77.0} &  \cellcolor{gray!30} {\tiny 65.0} &  \cellcolor{gray!30} {\tiny 68.3} &  \cellcolor{gray!30} {\tiny 83.7} &  \cellcolor{gray!30} {\tiny 96.5} & \cellcolor{gray!30} {\tiny 71.7} & \cellcolor{gray!30} {\tiny 65.8} &  \cellcolor{gray!30} {\tiny 74.9} & \cellcolor{gray!30} {\tiny 59.1} & \cellcolor{gray!30} {\tiny 63.2} & \cellcolor{gray!30} {\tiny 71.9} \\
{\tiny Mehta \emph{et al}\bmvaOneDot \cite{8374605}$^{\star}$} & {\tiny 67.4} & {\tiny 71.9} & {\tiny 66.7} & {\tiny 69.1} & {\tiny 71.9} & {\tiny 65.0} & {\tiny 68.3} & {\tiny 83.7} & {\tiny 120.0} & {\tiny 66.0} & {\tiny 79.8} & {\tiny 63.9} & {\tiny 48.9} & {\tiny 76.8} & {\tiny53.7} & {\tiny 68.6} \\
\cellcolor{gray!30} {\tiny Martinez \emph{et al}\bmvaOneDot \cite{martinez_2017_3dbaseline}$^{\star}$} & \cellcolor{gray!30} {\tiny 51.8} & \cellcolor{gray!30} {\tiny 56.2} & \cellcolor{gray!30} {\tiny 58.1} & \cellcolor{gray!30} {\tiny 59.0} & \cellcolor{gray!30} {\tiny 69.5} & \cellcolor{gray!30} {\tiny 55.2} & \cellcolor{gray!30} {\tiny 58.1} & \cellcolor{gray!30} {\tiny 74.0} & \cellcolor{gray!30} {\tiny 94.6} & \cellcolor{gray!30} {\tiny 62.3} & \cellcolor{gray!30} {\tiny 78.4} & \cellcolor{gray!30} {\tiny 59.1} & \cellcolor{gray!30} {\tiny 49.5} & \cellcolor{gray!30} {\tiny 65.1} & \cellcolor{gray!30} {\tiny 52.4} & \cellcolor{gray!30} {\tiny 62.9} \\
{\tiny Liang \emph{et al}\bmvaOneDot \cite{LIANG20181}} & {\tiny 52.8} & {\tiny 54.2} & {\tiny 54.3} & {\tiny 61.8} & {\tiny 53.1} & {\tiny 53.6} & {\tiny 71.7} & {\tiny 86.7} & {\tiny 61.5} & {\tiny 53.4} & {\tiny 67.2} & {\tiny 54.8} & {\tiny 53.4} & {\tiny 47.1} & {\tiny 61.6} & {\tiny 59.1} \\
\cellcolor{gray!30} {\tiny Luvizon \emph{et al}\bmvaOneDot \cite{luvizon20182d}} & \cellcolor{gray!30} {\tiny 49.2} & \cellcolor{gray!30} {\tiny 51.6} & \cellcolor{gray!30} {\tiny 47.6} & \cellcolor{gray!30} {\tiny 50.5} & \cellcolor{gray!30} {\tiny 51.8} & \cellcolor{gray!30} {\tiny 48.5} & \cellcolor{gray!30} {\tiny 51.7} & \cellcolor{gray!30} {\tiny 61.5} & \cellcolor{gray!30} {\tiny 70.9} & \cellcolor{gray!30} {\tiny 53.7} & \cellcolor{gray!30} {\tiny 60.3} & \cellcolor{gray!30} {\tiny 48.9} & \cellcolor{gray!30} {\tiny 44.4} & \cellcolor{gray!30} {\tiny 57.9} & \cellcolor{gray!30} {\tiny 48.9} & \cellcolor{gray!30} {\tiny 53.2} \\
 {\tiny Martinez \emph{et al}\bmvaOneDot \cite{martinez_2017_3dbaseline}$^{\dagger}$} &  {\tiny 37.7} &  {\tiny 44.4} &  {\tiny 40.3} &  {\tiny 42.1} &  {\tiny 48.2} &  {\tiny 54.9} &  {\tiny 44.4} &  {\tiny 42.1} &  {\tiny 54.6} &  {\tiny 58.0} &  {\tiny 45.1} &  {\tiny 46.4} &  {\tiny 47.6} &  {\tiny 36.4} &  {\tiny 40.4} &  {\tiny 45.5} \\
\hline
\cellcolor{gray!30} {\tiny \textbf{Ours}$^{\dagger,\star}$} & \cellcolor{gray!30} {\tiny \textbf{36.6}} & \cellcolor{gray!30} {\tiny \textbf{43.2}} & \cellcolor{gray!30} {\tiny \textbf{38.1}} & \cellcolor{gray!30} {\tiny \textbf{40.8}} & \cellcolor{gray!30} {\tiny \textbf{44.4}} & \cellcolor{gray!30} {\tiny \textbf{51.8}} & \cellcolor{gray!30} {\tiny \textbf{43.7}} & \cellcolor{gray!30} {\tiny \textbf{38.4}} & \cellcolor{gray!30} {\tiny \textbf{50.8}} & \cellcolor{gray!30} {\tiny \textbf{52.0}} & \cellcolor{gray!30} {\tiny \textbf{42.1}} & \cellcolor{gray!30} {\tiny \textbf{42.2}} & \cellcolor{gray!30} {\tiny \textbf{44.0}} & \cellcolor{gray!30} {\tiny \textbf{32.3}} & \cellcolor{gray!30} {\tiny \textbf{35.9}} & \cellcolor{gray!30} {\tiny \textbf{42.4}}\\
\hline
\hline \\[-0.75cm]
\end{tabular}
\end{center}
\scriptsize{\hspace*{0.25cm} The symbol $^{\star}$ denotes that a 2D detector was used and the symbol $^{\dagger}$ denotes the ground truth 2D joint locations were used.} \\[-0.3cm]
\end{table}
\begin{figure}[ht]
\begin{center}
 \includegraphics[width=12.9cm,height=1.8cm]{pose_visualization.png}
 \vspace*{-0.5cm}
\end{center}
   \caption{Visualization of 3D output of the estimation stage with some samples on the test set of Human3.6M \cite{6682899}. For each example, from left to right are 2D poses, 3D ground truths and our 3D predictions, respectively.}
\label{fig:5}
\end{figure}
\subsubsection{Evaluation on action recognition \\[-0.1cm]}
Table~\ref{table2} reports the experimental results and comparisons with state-of-the-art methods on the MSR Action3D dataset \cite{li2010action}. The ENAS algorithm \cite{pmlr-v80-pham18a} is able to explore a diversity of network architectures and the best design is identified based on its validation score. Thus, the final architecture achieved a total average accuracy of 97.98\% over three subset AS1, AS2 and AS3. This result outperforms many previous studies \cite{li2010action,Chen2013RealtimeHA,Vemulapalli2014HumanAR,Du2015HierarchicalRN,Liu2016SpatioTemporalLW,Wang2016GraphBS,WengSpatioTemporalNN,Xu2015SpatioTemporalPM,lee2017ensemble}, and among them, many are depth sensor-based approaches. Figure \ref{fig:6} provides a schematic diagram of the best cells and optimal architecture found by ENAS on the AS1 subset \cite{li2010action}. For the SBU Kinect Interaction dataset \cite{yun2012two}, the best model achieved an accuracy of 96.30\%, as shown in Table~\ref{table3}. Our reported results indicated an important observation that by using only the 3D predicted poses, we are able to outperform previous works reported in \cite{Song2017AnES,Liu2016SpatioTemporalLW,8322204,ke2017new,DBLP:conf/bmvc/TasK18,Wang2017ModelingTD,liu2018skeleton} and reach state-of-the-art results provided in \cite{zhang2019view,s19081932}, which deploy accurate skeletal data provided by Kinect v2 sensor.
\begin{table}
\parbox{.45\linewidth}{
\centering
\caption{Test accuracies (\%) on the MSR Action3D dataset \cite{li2010action}.}
\vspace*{-0.3cm}
\begin{tabular}{ccccc}
\label{table2}\\
\hline
{\scriptsize \hspace*{0.01cm} \textbf{Method}} & {\scriptsize \hspace*{0.01cm}  \textbf{AS1}} & {\scriptsize \hspace*{0.01cm} \textbf{AS2}} & {\scriptsize \hspace*{0.01cm} \textbf{AS3}} &  {\scriptsize \hspace*{0.01cm} \textbf{Aver.}}  \\
 \hline
 {\scriptsize \hspace*{0.01cm} Li \emph{et al}\bmvaOneDot \cite{li2010action}} & {\scriptsize \hspace*{0.01cm} 72.90} & {\scriptsize \hspace*{0.01cm} 71.90}  & {\scriptsize \hspace*{0.01cm} 71.90} &  {\scriptsize \hspace*{0.01cm} 74.70} \\
{\scriptsize \cellcolor{gray!30}  Chen \emph{et al}\bmvaOneDot \cite{Chen2013RealtimeHA}} & {\scriptsize \cellcolor{gray!30} 96.20} & {\scriptsize \cellcolor{gray!30} 83.20}  & {\scriptsize \cellcolor{gray!30} 92.00} &  {\scriptsize \cellcolor{gray!30} 90.47} \\
{\scriptsize \hspace*{0.01cm}  Vemulapalli \emph{et al}\bmvaOneDot \cite{Vemulapalli2014HumanAR}} & {\scriptsize \hspace*{0.01cm}  95.29} & {\scriptsize \hspace*{0.01cm}  83.87}  & {\scriptsize  \hspace*{0.01cm} 98.22} &  {\scriptsize \hspace*{0.01cm} 92.46} \\
{\scriptsize \cellcolor{gray!30} Du \emph{et al}\bmvaOneDot \cite{Du2015HierarchicalRN}} & {\scriptsize \cellcolor{gray!30}    99.33} & {\scriptsize \cellcolor{gray!30}  94.64}  & {\scriptsize \cellcolor{gray!30}   95.50} &  {\scriptsize \cellcolor{gray!30}  94.49} \\
{\scriptsize  \hspace*{0.01cm} Liu \emph{et al}\bmvaOneDot \cite{Liu2016SpatioTemporalLW}} & {\scriptsize \hspace*{0.01cm} N/A} & {\scriptsize  \hspace*{0.01cm} N/A}  & {\scriptsize  \hspace*{0.01cm} N/A} &  {\scriptsize  \hspace*{0.01cm} 94.80}
 \\
{\scriptsize \cellcolor{gray!30} Wang \emph{et al}\bmvaOneDot \cite{Wang2016GraphBS}} & {\scriptsize \cellcolor{gray!30}   93.60} & {\scriptsize  \cellcolor{gray!30}  95.50}  & {\scriptsize  \cellcolor{gray!30}   95.10} &  {\scriptsize \cellcolor{gray!30}   94.80}
 \\
{\scriptsize  \hspace*{0.01cm} Wang \emph{et al}\bmvaOneDot \cite{WengSpatioTemporalNN}} & {\scriptsize  \hspace*{0.01cm} 91.50} & {\scriptsize   \hspace*{0.01cm} 95.60}  & {\scriptsize   \hspace*{0.01cm} 97.30} &  {\scriptsize  \hspace*{0.01cm} 94.80} \\
{\scriptsize \cellcolor{gray!30} Xu \emph{et al}\bmvaOneDot \cite{Xu2015SpatioTemporalPM}} & {\scriptsize\cellcolor{gray!30} 99.10} & {\scriptsize \cellcolor{gray!30} 92.90}  & {\scriptsize  \cellcolor{gray!30} 96.40} &  {\scriptsize \cellcolor{gray!30} 96.10} \\
{\scriptsize \hspace*{0.01cm} Lee \emph{et al}\bmvaOneDot \cite{lee2017ensemble}} & {\scriptsize  \hspace*{0.01cm} 95.24} & {\scriptsize  \hspace*{0.01cm} 96.43}  & {\scriptsize   \hspace*{0.01cm}  100.0} &  {\scriptsize  \hspace*{0.01cm} 97.22} \\
{\scriptsize \cellcolor{gray!30} \hspace*{0.01cm} Pham \emph{et al}\bmvaOneDot \cite{s19081932}} & {\scriptsize \cellcolor{gray!30}  \hspace*{0.01cm} 98.83} & {\scriptsize  \cellcolor{gray!30} \hspace*{0.01cm} 99.06}  & {\scriptsize  \cellcolor{gray!30} \hspace*{0.01cm}  99.40} &  {\scriptsize  \cellcolor{gray!30} \hspace*{0.01cm} 99.10} \\
\hline
{\scriptsize  \hspace*{0.01cm} \textbf{Ours}} & {\scriptsize   \hspace*{0.01cm} \textbf{97.87} } & {\scriptsize   \hspace*{0.01cm} \textbf{96.81}}  & {\scriptsize   \hspace*{0.01cm} \textbf{99.27}} &  {\scriptsize  \hspace*{0.01cm} \textbf{97.98}} \\
\hline
\hline
\end{tabular}}
\hfill
\parbox{.45\linewidth}{
\centering
\caption{Test accuracies (\%) on the SBU Kinect Interaction dataset \cite{yun2012two}.}
\vspace*{-0.3cm}
\begin{tabular}{cc}
\label{table3}\\
\hline
\hspace*{0.01cm} {\scriptsize \textbf{Method}} & {\scriptsize \hspace*{0.3cm} \textbf{Acc.}} \\
\hline
{\scriptsize \hspace*{0.01cm} Song \emph{et al}\bmvaOneDot \cite{Song2017AnES}} & {\scriptsize \hspace*{0.32cm} 91.51} \\
{\scriptsize \cellcolor{gray!30} Liu \emph{et al}\bmvaOneDot \cite{Liu2016SpatioTemporalLW}} & {\scriptsize \cellcolor{gray!30}  \hspace*{0.25cm} 93.30}\\
{\scriptsize  \hspace*{0.01cm}  Weng \emph{et al}\bmvaOneDot \cite{8322204}} & {\scriptsize   \hspace*{0.32cm} 93.30} \\
{\scriptsize   \cellcolor{gray!30} Ke \emph{et al}\bmvaOneDot \cite{ke2017new}} & {\scriptsize \cellcolor{gray!30}  \hspace*{0.25cm} 93.57} \\
{\scriptsize   \hspace*{0.01cm} Tas \emph{et al}\bmvaOneDot \cite{DBLP:conf/bmvc/TasK18}} & {\scriptsize  \hspace*{0.32cm} 94.36} \\
{\scriptsize \cellcolor{gray!30}   \hspace*{0.01cm} Wang \emph{et al}\bmvaOneDot  \cite{Wang2017ModelingTD}} & {\scriptsize  \cellcolor{gray!30} \hspace*{0.32cm} 94.80} \\
{\scriptsize   \hspace*{0.01cm}  Liu \emph{et al}\bmvaOneDot \cite{liu2018skeleton}} & {\scriptsize  \hspace*{0.32cm} 94.90}  \\
{\scriptsize \cellcolor{gray!30}   \hspace*{0.01cm} Zang \emph{et al}\bmvaOneDot  \cite{zhang2019view} (using VA-RNN)} & {\scriptsize  \cellcolor{gray!30} \hspace*{0.32cm} 95.70} \\
{\scriptsize \hspace*{0.01cm} Zhang \emph{et al}\bmvaOneDot  \cite{zhang2019view} (using VA-CNN)} & {\scriptsize   \hspace*{0.32cm} 97.50} \\
{\scriptsize \cellcolor{gray!30} \hspace*{0.01cm} Pham \emph{et al}\bmvaOneDot  \cite{s19081932}} & {\scriptsize \cellcolor{gray!30}  \hspace*{0.32cm} 97.86} \\
\hline
{\scriptsize    \textbf{Ours} } & {\scriptsize   \hspace*{0.4cm} \textbf{96.30}}  \\
\hline
\hline
\end{tabular}}
\end{table}
\begin{figure}[ht]
\begin{center}
\includegraphics[width=5.5cm,height=4.5cm]{normall_cell.png}\includegraphics[width=5.5cm,height=4.5cm]{reduction_cell.png} \hspace{0.1cm} \includegraphics[width=1.5cm,height=4.5cm]{model.png}\\
\scriptsize{ \hspace*{2cm} (a) \hspace{5.2cm} (b) \hspace{3cm} (c)}
\vspace*{-0.5cm}
\end{center}
   \caption{Diagram of the top performing \textit{normal cell} (a) and \textit{reduction cell} (b) discovered by ENAS \cite{pmlr-v80-pham18a} on AS1 subset \cite{li2010action}. They were then used to construct the final network architecture (c). We recommend the interested readers to \cite{pmlr-v80-pham18a} to better understand this procedure.}
\label{fig:6}
\end{figure}
\subsection{Computational efficiency evaluation  \\[-0.1cm]} \label{sect:4.4}
On a single GeForce GTX 1080Ti GPU  with 11GB memory, the runtime of OpenPose \cite{cao2017realtime} is less than 0.1\textit{s} per frame on a image size of 800 $\times$ 450 pixels. On the Human3.6M dataset \cite{6682899}, the 3D pose estimation stage takes around 15\textit{ms} to complete a pass (forward + backward) through each stream with a mini-batches of size 128. Each epoch was done within 3 minutes. For the action recognition stage, our implementation of ENAS algorithm takes about 2 hours to find the final architecture ($\sim$2.3M parameters) on each subset of MSR Action3D dataset \cite{li2010action}, whilst it takes around 3 hours on the SBU Kinect Interaction dataset \cite{yun2012two} to discover the best architecture ($\sim$3M parameters). With small architecture sizes, the discovered networks require low computing time for the inference stage, making our approach more practical for large-scale problems and real-time applications.
\section{Conclusions \\[-0.2cm]} \label{sect:5}
In this paper, we presented a unified deep learning framework for joint 3D human pose estimation and action recognition from RGB video sequences. The proposed method first runs a state-of-the-art 2D pose detector to estimate 2D locations of body joints. A deep neural network is then designed and trained to learn a direct 2D-to-3D mapping and predict human poses in 3D space. Experimental results demonstrated that the 3D human poses can be effectively estimated by a simple network design and training methodology over 2D keypoints. We also introduced a novel action recognition approach based on a compact image-based representation and automated machine learning, in which an advanced neural architecture search algorithm is exploited to discover the best performing architecture for each recognition task. Our experiments on public and challenging action recognition datasets indicated that the proposed framework is able to reach state-of-the-art performance, whilst requiring less computation budget for training and inference. Despite that, our method naturally depends on the quality of the output of the 2D detectors. Hence, a limitation is that it cannot recover 3D poses from 2D failed output. To tackle this problem, we are currently expanding this study by adding more visual evidence to the network in order to further gains in performance. The preliminary results are encouraging.

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Home / Business / Cook's 'secret' $275 billion deal behind Apple's China success
Cook's 'secret' $275 billion deal behind Apple's China success
Apple CEO Tim Cook reportedly signed a massive 275-billion-dollar deal with China in order to help the company get into the market. The "secret" meeting took place back in 2016.
An extensively researched article by The Information states that Cook personally lobbied the officials to crack a closer alliance with the Chinese leadership in order to resolve the apparent bad reputation the company had in the country.
In 2016, Apple was facing successive regulatory blockades in the most-populated country and by 2021, the same country has become the second-largest consumer of Apple products followed by the US.
According to the reports, the repeated threats against Apple Pay, iCloud and App Store, made Cook make repeated trips to China to come up with the deal.
Cook finally landed a "memorandum of understanding" between Apple and the National Development and Reform Commission to come to a settlement for which the regulators will allow Apple in the market.
This was followed by Apple investing 1 billion dollars in Didi Chuxing, which announced its exit from the NYSE last month.
Starting from this, all the regulatory lawsuits against Apple were slowly lifted and the company was allowed to close important business deals as well as train workers in China.
The report also claimed that the deal included clauses that meant that Apple will help China develop new technologies as well as train the Chinese youth.
Apple was also supposed to trade with more Chinese part-makers, software firms, and universities contributing to the upheaval of the economy. Apple also promised monetary investments in retail stores, research and development facilities, and other projects which roughly amount to 275 billion dollars through 2021 and if the deal helps both the parties, it will be automatically extended till 2022.
The report also focused on the fact that the company is severely reliant on Cook's communicative and negotiating mastery for cracking new horizons for the company.
As the present CEO has already hinted at his resignation during the 2020s, the report has surely piled on more pressure on the company hierarchy to find a suitable successor to the illustrious leader.
Africa overcomes recession provoked by COVID, returns to growth in 2021


Removal of wisdom teeth could be done while you are sedated and being carefully monitored.
1) An incision is made in the gums.
expose the crown of the tooth.
3) The tooth is then extracted as a whole or under certain circumstances, it may be necessary to be removed in pieces. This is generally done for severely impacted wisdom teeth.
4) The area is stitched closed and allowed to heal.
BLEEDING. A certain amount of bleeding is to be expected following surgery. Slight bleeding, oozing, or slight blood traces in the saliva is not uncommon. To help minimize bleeding site, avoid any sort of exercise or excitement. Excessive bleeding may be controlled by first rinsing or wiping any old clots from your mouth, then placing a gauze pad over the area and biting firmly for thirty minutes. This action should create a blood clot and bleeding will stop; however, if bleeding does not stop repeat as necessary. As an alternative to the gauze gently bite on a moistened tea bag for thirty minutes. The tannic acid in the tea bag helps to form a clot by contracting bleeding vessels. If bleeding does not subside, call the office at 916-265-2980 for additional instructions.
ANTIBIOTICS. If American River Dental has prescribed antibiotics, take the tablets or liquid as directed. Antibiotics will be given to help prevent infection. Call the office in the event of a rash or other unfavorable reaction at 916-265-2980.
NAUSEA. Occasionally a prescribed pain medicine may induce nausea and/or vomiting following surgery. Do not take anything by mouth for at least an hour including the prescribed medicine. You should then sip on coke, tea or ginger ale. You should sip slowly over a fifteen-minute period. When the nausea subsides you can begin taking solid foods and the prescribed medicine.
- NUMBNESS. As discussed in your pre-surgery consultation numbness of the lip, chin, or tongue can occur, but there is no cause for alarm. This is usually a temporary condition. If you do experience numbness be careful not to bite your lip of tongue as you will not feel the action. Call American River Dental if you have any questions.
- SUTURES. Sutures may be placed the area of surgery to minimize post-operative bleeding and to help healing. Sometimes they become dislodged, this is no cause for alarm. Just remove the suture from your mouth and discard it. In about a week post surgery the sutures will be removed by American River Dental . It is a simply procedure that does not require any anesthesia or needles and only takes a few minutes.


"We're thrilled to have Kris as the newest member of Renew Crew," says Rob White, Vice President of Renew Crew. "His military background provides him with the ability to follow franchise systems and build and lead a team in his local market."
Renew Crew of Denton, Texas will officially open for business in July, 2016 after Kris finishes his last military assignment and relocates to Texas. The location will provide eco-friendly outdoor cleaning and sealing for decks, patios, siding, gutters, driveways and other outdoor surfaces in the Greater Denton area including McKinney and Lake Dallas.
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Past Panelists
Get Tickets!
Community Circles (CirCos)
CirCos: Informal and guided discussions with the aim of exchanging ideas and forming allies on topics of common interest in panel or round table format. You can talk about whatever you want.
Organizers also get free tickets to RIIAA.
August 14: deadline to submit proposals.
August 18: notification of accepted proposals
August 28: from 17 to 19 h CDT, sessions in two blocks of parallel sessions of 1 hr each.
Questions, have an idea?
Send an email to circos@riiaa.org
The RIIAA Community Circles (CirCos), inspired in Birds of Feather (BoF) seeks to build and strengthen the community in LATAM interested in Artificial Intelligence (AI) topics through parallel, interactive, informal and self-organized sessions. CirCos provide a space for interested attendees to meet in small groups to exchange ideas and to form allies on issues of common interest. These topics may be directly or indirectly related to the topics covered in RIIAA. That is to say, you decide the session topic!
We invite the community to submit proposals for the topics to be discussed at the CirCos. The process is very simple! The proposal includes a brief description (250 characters maximum), a list of possible panelists (maximum 5), and the name of a couple of organizers. This year, we accept the proposals of CirCos in the following tracks:
AI in society
Examples: How can we create an AI laboratory in a rural community? How can industry, academia and government better collaborate to advance AI in LATAM? What practices can institutions and organizations adopt to increase the participation of minority groups (women, indigenous groups, etc.) within the field?
Tecnological
Praxis and theory of AI
Examples: What tricks, best practices and / or processes do you use with the X software (X = PyTorch, Jax, TensorFlow, etc.)? What do you think is the future of Y (Y = deep learning, transformers, reinforcement learning)? How do you make your work reproducible in ML? Do you want to build a new collaborative dataset?
AI and other fields
Examples: How has field X changed (X = Bioinformatics, Chemistry, Geophysics, Public Health, etc.) due to progress in AI? What innovative AI applications have emerged in your field of knowledge? What are AI-based solutions that have been developed to combat COVID-19?
Each CirCo (session) lasts 1 hour and takes the form of a round table or panel. We recommend a panel for formal discussions and a round table for informal ones. In either case, the moderator determines the scope and general objectives of the discussion. Then, the speakers establishing the points to be discussed, and finally there is a Q&A with attendees. Overall, the session should aim to promote the exchange of ideas with the rest of the attendees, leaving most of the session time for this purpose.
Interested in organizing a CirCo? Hopefully yes!
Deadline for submission: August 14, 2020
The organizers of a CirCo and the exhibitors (maximum 5) will receive a free ticket to RIIAA.
Submit a proposal!
Questions? Do you want some encouragement? Send an email to circos@riiaa.org
Chairs CirCos 2020
Luz Angélica Caudillo Mata, R&D Scientist, MDA Corporation
Benjamín Sánchez-Lengeling, Research Scientist, Google
Submit your application now and help build our community!
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Subclasses of MenuComponent in java.awt

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class
CheckboxMenuItem

This class represents a check box that can be included in a menu.

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The MenuBar class encapsulates the platform's
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When you remove the constriction of the self, God is.
Follow 👉 @complete.self.knowledge for complete self-knowledge.
True, thank you for sharing!
We are dream characters within the eternal dream of "God", and also cells within the body of "God". But there's an "observer" that experiences all things from "God" to human to ant, only it goes through each perspective one at a time, and that "observer" is Consciousness.
From my own experience, I find this to be true.


To help kick off the growing season, the JK Community Farm is seeking teams of volunteers to participate in its first annual Plantathon. Volunteers will sow 9,000 vegetable on seven acres of land to help support the work of the farm, a nonprofit started by JK Moving Services to alleviate hunger in the community. The farm began last year to provide chemical free, healthy produce and protein to those struggling with food insecurity in the region. The farm donates the food to nonprofit partners, including Loudoun Hunger Relief, Food for Others and Arlington Food Assistance Center. The Plantathon will raise awareness about food insecurity in the region and help support the farm's expansion to serve the greater region.


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As our world advances into the digital age, our physical structures are becoming increasingly outdated. A prime example of this is in hospitals: one of the organizations we depend on most as a society. A recent report cites that today's hospitals are, on average, two decades old. Aging hospitals and healthcare facilities, alongside progressions in technology quickly creates a gap in what facilities are able to support with existing infrastructure and without expensive electrical retrofits.
Essential to healthcare are the digital devices that doctors and nurses use to care for and monitor patients — and without the proper infrastructure to support the electrical demands, power is distributed dangerously and inefficiently in hospitals. According to data collected by the U.S. Fire Administration, 22 percent of unconfined, larger and more serious fires in hospitals over a span of three years were caused by electrical malfunctions. Consider the following components of healthcare facilities as areas of improvement in charging technology.
Patient care rooms are central to the patient experience. It's to hospitals' advantage to make these rooms as comfortable as possible, especially for patients that may be staying for extended periods of time, those with anxiety about medical procedures and everyone in between. The ability to provide plenty of available power sources to keep personal electronic devices powered, like phones and tablets, is a small, yet significant benefit.
We know that medical equipment is a non-negotiable when it comes to needing power directly from outlets. Those plugs monopolize available outlets. But, to keep patients happy, facilities want power sources not directly designated for medical equipment. That way, patients' devices remain charged so they can remain connected with loved ones and the outside world though they're spending time in a medical facility.
The way this power delivery will be accomplished in the future is likely via a wireless power delivery system since plugs are at a premium in hospitals. Think: charging pads or furniture with charging technology. This enables hospitals to forego retrofitting the entire facility with more electrical outlets, rather, using add-on technology to transmit power. This could even become a competitive differentiator for elective medical procedures.
As the move to EMRs becomes more prevalent, the need for non-stationary, electronic devices that record patient data increases too. Perhaps the most private and sensitive of the areas for electronic innovation, these devices must remain charged in a safe and secure way. Healthcare personnel never want to be caught in a situation where they need to access a patient's file right away, but cannot due to a dead device.
Even if a device starts a shift fully charged, it can easily burn through the battery, especially if it's an older device that has a shorter and less dependable battery life. One way more innovative hospitals are combatted this issue is with large charging carts that charge multiple devices at once. This way, there is designated power efficiently distributed for these devices in one convenient area.
It goes without saying the level of importance that surgical tools have. They must always be charged sufficiently and have a power source nearby to ensure they are functioning to the fullest when needed. Similar to patient care rooms, operating rooms already have a plethora of wires working to power the equipment. What many may not know is that tools have the opportunity to be charged via wireless charging, similar to cell phones. The use of wireless charging stations eliminates the need to attach devices to charging cords, and tools can simply be sterilized and placed on the charging mat for the next use. This saves outlet space for mission-critical pieces of equipment and alleviates some of the cords that are cluttering ORs.
Between 2011 and 2015, fires caused more than $50 billion in damages to healthcare facilities. Power delivery technology is evolving – it's no longer acceptable or compliant to overload outlets – at risk of your facility and your patients.
While our hospitals and long-term care facilities are an average of two decades old, that doesn't mean that they can't be modified to fit modern conveniences like power accessibility. In fact, because power delivery is an essential function of all hospitals and many medical devices, considering progressive, safe methods for power transmission is a must for facility professionals. Powering devices in a compliant, safe and price effective way is the way of the future.
Cliff Weinstein is CEO and president of FLI Charge.