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A 1-year-old Rialto boy drowned in his family's backyard pool Saturday.
Rialto firefighters received calls regarding the drowning about noon in the 700 block of South Ironwood Avenue, said Rialto fire Battalion Chief Frank Becker. San Bernardino County fire dispatchers gave family members instructions on how to perform CPR on the child, who had no pulse and was not breathing.
Rialto police arrived and took over CPR. Rialto firefighters came to the scene and took the child to a hospital where he was pronounced dead, Becker said.
Family members told firefighters that the boy was left unsupervised momentarily. He walked through a gate and climbed into an above-ground pool.

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Contra Costa and Solano Counties Provide Over 218,000 Meals to Fight Hunger in Local Community
18th annual Counties Care Holiday Food Fight raises $109,000 for local residents
CONCORD, Calif. (April 26, 2022) – Officials from Contra Costa and Solano Counties recently hosted their annual "Counties Care Holiday Food Fight" dedicated to raising funds to provide nutritious food in local communities.
The annual Counties Care Holiday Food Fight is a friendly competition to raise money for the Food Bank of Contra Costa and Solano. Spearheaded by individual county departments, the competition has raised a combined total of $2.6 million since 2003.
The results from this year's Food Fight:
- Solano County raised more than $56,700, which will provide over 113,400 meals
- Contra Costa County raised $52,700, which will provide over 105,400 meals
- In total, both counties will help provide more than 218,000 meals to feed neighbors in need
Melinda Sandy, Holiday Food Fight Coordinator, comments: "The Food Bank works every day to support the people of Solano County, and this event is a wonderful opportunity to help them deliver food to those who need it most. The county employees are the real heart of this program. Our results are just another reminder that when we all work together we can make a huge impact in our community."
Solano County earned its second consecutive and 11th overall championship – taking home the coveted 'Big Apple Trophy' once again. However, both counties have made a positive impact in serving the 1 in 6 community members who turn to the Food Bank for support.
Tammi Ackerman, Holiday Food Fight Assistance Coordinator comments: "Basics like food remain a high-priority need for many members of our community, and we are so thankful for the County employees who helped us raise funds to meet that need during this year's event. We all share the same goal–building a brighter, stronger future for our local residents."
Joel Sjostrom, Food Bank president and CEO, comments: "This year's contest came at a crucial time. With the cost of living on the rise, especially the price of groceries, even more community members will turn to us for support. We are so grateful for this ongoing partnership with our two home counties. Their efforts truly make a difference."
The Food Bank is serving an increased number of people–an average of 250,000 people each month–due to the lingering impacts of the pandemic and anticipates even more need as the cost of groceries continue to increase. When paired with rising gas and utility prices, putting food on the table is becoming nearly impossible for many, especially the most vulnerable members of our community.
For the months of April and May, The Behring Global Educational Foundation is matching each gift made to the Food Bank of Contra Costa and Solano to reach a goal of providing 2 million meals. To learn more or make a donation to the Food Bank, visit foodbankccs.org/2millionmealchallenge.


text:
FOF #998 – Pretty in Pink
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As our 1,000th show draws near and the chickens are coming home to roost! One of our earliest guests, and one of the world's first video bloggers, Victoria Lamarr returns to rekindle her podcasting flame, […]
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Nice!! I like the new commenting system. (I have this same system on my blog… yeah that, the thing I never use anymore…)
I like the new look of your blog. We are still working on some of the stuff.
On Sat, Jun 6, 2009 at 1:59 AM,
Call me- we're trying to figure out weather to keep disqus or not. Right now it doesn't let you post code (like flickr pictures, and youtube videos) which is a HUGE bummer. WHY GOD WHY???
I always like your podcast until this episode "aired".
Let me just say a few things:
1. "Pin" might mean penis in Vietnamese, not in THE Asian language. Please note that unlike USA, Asia is full of cultural diversities and languages. One of the languages is Vietnamese.
2. Please get your fact right about why Chinese ancestors consume 'penis' before calling Chinese as crazy. They are only as sane or insane as you are.
3. I have live in China for years but never heard of people eating chicken bitten by snake to cure sickness, let alone banning it. Don't always believe American news media, most of them are just bullshit, especially Fox News.
4. Be a bitch, it's funny. Don't be a racist.


text:
I Introduction and Motivation
Recently there has been a huge increase in utilizing Convolutional Neural Networks (CNNs) [chakradhar2010dynamically, chen2014big, vgg2014] to solve several real-life challenges such as traffic categorization [luo2015traffic], [kalliatakis2017detection], weather forecasting [zhang1998forecasting, grecu1999detection], etc due to its high prediction accuracy/categorization in the aforementioned target applications. One particular case for harnessing the efficacy of visual CNN based prediction model is Intelligent Transportation Systems (ITS), which is becoming an important pillar in the modern "smart city" framework.
Traffic load categorization is challenging given the increase in vehicles on road. Some of the popular ways of monitoring and categorizing traffic load from videos include vehicle based assess method [asmaa2013road, hu2012real, riaz2013traffic, luo2015traffic] and holistic approach [derpanis2011classification, porikli2004traffic, ess2009segmentation]. In vehicle based assess methodologies, either vehicles are first localized on the road with a background subtraction method [jung2001content, andrews2013highway, asmaa2013road] or the vehicles are localized with moving feature keypoints [kamijo2000traffic, hu2012real, riaz2013traffic]. Whereas, in holistic approach, a macroscopic analysis of traffic flow is understood through global representation of a scene, which is obtained by accounting for spatio-temporal features except tracking using background subtraction and moving feature keypoints [chan2005classification, porikli2004traffic, derpanis2011classification].
In recent times, there has also been emergence of several methods capable of monitoring and analyzing traffic using motionless analysis of videos [luo2015traffic, luo2016traffic, dey2018mat], where videos of traffic are broken into frames instead, and the frames are analyzed for further computation or prediction. The main motivation to utilize methodologies consisting of motionless analysis of video is that it is difficult to stream high-frame rate videos gathered by a large network of interconnected cameras due to bandwidth limitation. Hence, streaming low-frame rate videos on these camera networks is very common. In many cases, it is challenging to stream more than 2 frames per second due to the limited bandwidth of the network when these cameras stream over a WIFI network [luo2015traffic, dey2018mat]. Moreover, to analyze video in real-time without motion features over a WIFI network is difficult due to communication bandwidth constraint and hence, it is better to analyze the image fames on the camera enabled embedded device itself [dey2018mat] instead of relying on a server system over th WIFI network. Another motivation to devise such approaches in embedded devices is the afford-ability of such devices instead of employing powerful server systems used for analysis purposes[dey2018mat]
. Therefore, the approach of analyzing videos without motion features on the embedded device is not just beneficial for categorizing traffic load but could be extended to several computer vision based real-world application that requires analysis of low-frame rate videos. Although motionless analysis of videos have their own benefits, they also come with limitations described with the following observations.
Observation 1: Although several implementations of such methods were able to achieve high prediction accuracy on known dataset [luo2015traffic, luo2016traffic, dey2018mat], but in some test cases the analysis were not accurate at all. The reason for failed prediction/analysis is that in some cases it is difficult to predict the label of an image frame from a video if the ground truth of the image is overlapping with several other categories (labels) instead of falling under one category. For example, in the dataset of traffic released by UCSD [luo2015traffic, datasetChan2005classification], which consists of light, heavy and traffic jam categories, two frames (images) belonging to the same category of video are predicted differently by the CNN model [dey2018mat]
. The reason for such behavior is that the CNN predicts the label and probability of it occurring on the instantaneous image frame. In Fig.1, we notice that a trained CNN model [dey2018mat] with an overall prediction accuracy of 81.25% predicted the wrong label for a frame, which falls under Light category but was instead predicted as Heavy. However, both the fames belong to the same video under the Light category. If the ground truth111Ground truth of the image frame in this case is the information gained through empirical evidence as opposed to the inference made by the CNN model. of the two images () are compared then it is justifiable that the prediction by the CNN is in fact accurate due to the fact that the traffic projected in Fig. 1. is more congested than the traffic projected in 1.. In reality the analysis of each image frame of the video should also portray the overall analysis222Here, overall analysis of video means the analysis of the video as a whole as opposed to the analysis of each image frame of the video. for the video instead of the frame itself in order to convey the temporal prediction. Since, each individual image frame of a video could lead to different analysis result (prediction/label), the temporal prediction is the prediction analysis over time. This limitation is due to the fact that the trained CNN model only predicts the label or analyzes the current image frame without taking past image frames into consideration. Therefore, the challenge is to analyze and predict videos just from the image frames without motion features of the video and yet give accurate temporal prediction results for the video as well. Although there have been some recent studies, which focused on future predictions of motion in ego-centric videos [yagi2017future, lai2018people], such as predicting the future position of a person based on the current image frame, but no study to our best knowledge have tried to predict the scene333Scene is a place where a human being could navigate or can act within. [zhou2014learning, xiao2010sun] of a video from image frames taking predictions from immediate previous frames into account to provide a more holistic analysis over a time period. Hence, we call such an analysis as Temporal Motionless Analysis of Video (TMAV) and several target applications of CNN such as traffic categorization require such kind of analysis in comparison with traditional one [luo2015traffic, luo2018traffic, dey2018mat].
Observation 2: In the study [dey2018mat], Dey et al. proposed a methodology to implement a CNN which is trained on a configurable embedded device and it was utilized to categorize traffic on the same device. Although this study gave a novel approach on analyzing traffic using video cameras in low bandwidth network without the need to communicate the image frames over the WIFI network, the study also had energy consumption and device lifespan reliability issues (shown later in the section). When the CNN model is implemented and trained on a multi-processor system-on-a-chip (MPSoC) such as Odroid XU4 [odroidxu4], the maximum temperature of the CPU reached 93.72°C on an average and the power consumption peaked at 10.63 Watt on an average during the training period. Reaching a high operating temperature for a long period of time is an important factor in the reduction of lifespan of the device. In some studies [chantem2008temperature, coskun2007temperature, zhou2016thermal] it has been found that an increase in the operating temperature by 10-15° centigrades could reduce the lifespan of the device by 2. An increase in operating temperature of the device could be both temporal and spatial [coskun2007temperature], where increase in temperature over a time period is known as an increase in temporal thermal gradient and an increase in operating temperature with respect to space is called an increase in spatial thermal gradient. Additionally, an increase in energy consumption on embedded devices is harmful for battery operation itself [panigrahi2001battery, lahiri2002battery], especially in low-power embedded devices. Thus it is important to design not just an energy-efficient CNN model, which is able to achieve desired analysis of video without motion features, but at the same time does not affect the lifespan reliability of the system adversely.
In order to overcome the limitations of the existing approaches we propose IRON-MAN: Integrated RatiONal prediction and Motionless ANalysis of videos using CNN, which is capable of performing , in MPSoCs. To this end, this paper makes the following contributions:
An energy efficient prediction methodology (IRON-MAN) which integrates predictions of previous frames of a video to predict the current frame and hence analyze the video without using motion features.
A new metric named Energy Consumption per Training Image (), which will enable the choice of suitable CNN model for real-world applications on embedded devices keeping energy-efficiency in mind.
Validation of the proposed approach on a real hardware platform, the Odroid-XU4 [odroidxu4].
Comparative study of energy consumption using metric and temporal thermal gradient on the device while training the model on the device and off the device444Training a CNN model could be performed both on the embedded device or off the device. During 'off the device training' it could be performed on a more resourceful device with higher computing resources (CPU/GPU/Memory) and then the weights and parameters of the trained CNN model are saved and transfered to the embedded system for prediction/analysis..
Effect on lifespan of the device utilizing CNN based approaches on such platforms
Ii Related Work
Majority of traffic analysis and categorization approaches before 2015 were mostly performed using the following methodologies:
Vehicle based methodologies where either vehicles are first localized on the road with a background subtraction method [jung2001content, andrews2013highway, asmaa2013road] or the vehicles are localized with moving feature keypoints [hu2012real, riaz2013traffic]. In these methodologies the resulting tracks are concatenated together to identify key features of traffic such as traffic lanes, average traffic speed, average traffic density, etc.
A holistic approach, where a macroscopic analysis of traffic flow is understood through global representation of a scene, which is obtained by accounting for spatio-temporal features except tracking using background subtraction and moving feature keypoints [chan2005classification, porikli2004traffic, derpanis2011classification].
Although the aforementioned methodologies are highly effective to analyze traffic, the biggest limiting factor is the cost of sophisticated camera-network involved and the requirement for high-frame-rate videos to compute reliable motion features. To break away from this trend of traffic analysis, in 2015 Luo et al. [luo2015traffic]
proposed a methodology to use various image processing and CNN based approaches to analyze traffic without moving features. In this paper the authors used four different visual descriptors such as bag of visual words (BOVW), Vector of Locally Aggregated Descriptors (VLAD), improved Fisher Vector (IFV) and Locality-constrained Linear Coding (LLC), and have also used pre-trained deep CNN models such as Caffe and VGG to analyze traffic and predict categorization of the same. The approach taken by Luo et al. to use popular image processing and CNN methods to classify traffic is novel and solves the low-frame-rate video streaming issue. In another extended paper published by Luo et al.[luo2016traffic], the researchers have used SegCNN and RegCNN to analyze and classify traffic. In both the aforementioned papers the authors are training and classifying traffic images after the video frames are transferred to the server from the interconnected camera network. But installing and implementing such hardware infrastructure to analyze traffic in developing countries is a challenging issue[mokhaareview] due to the associated implementation costs [dey2018mat]. In another study [luo2018traffic]
, Luo et al. utilizes image segmentation, Deep Learning[lecun2015deep] and analysis techniques with CNN to learn features from the traffic scene and categorize without motion features. The traffic analysis methodologies proposed in [luo2015traffic, luo2016traffic, luo2018traffic] are not capable of being implemented on low powered embedded devices during the training phase because such methodologies utilize a lot of computing resources due to the computational cost involved. In [luo2015traffic, luo2016traffic, luo2018traffic], training of the CNN model has to be performed on a powerful computer with a lot of computing resources and then use the trained CNN on the device for prediction/analysis, which makes the methodologies inflexible to new training dataset. Every time the models require to be trained on a powerful computing device and only be used on the embedded device for prediction/analysis.
In the study [dey2018mat], Dey et al. proposed a traffic categorization methodology using CNN where the training could be performed on the device mimicking a human being's ability to learn from its surroundings and makes such an approach more flexible as opposed to [luo2015traffic, luo2016traffic, luo2018traffic]. However, neither of the studies in [luo2015traffic, luo2016traffic, luo2018traffic, dey2018mat] are capable of performing temporal analysis of video, where a holistic analysis of video could be provided instead of analyzing each image frame.
Other state-of-the-art methodologies include detecting & counting the numbers of cars and computing traffic density based on that using CNN-based vehicle detectors with high accuracy at near real time [zhang2015integrated, cai2016unified, zhou2016image]. Although this way of detecting traffic density could still be classified as a vehicle based approach and has become popular in recent times but there are associated challenges with these methods as follows:
Training and test data should belong to the same dataset taken from the same camera with same configuration and hence require consistency in training.
Cars detected need to be within a particular range or scope of the image and these methodologies fail to detect cars, which are far away in the images captured.
These methodologies performed poorly if the captured images were occluded, especially in case of heavy traffic & jam.
Compared to all the aforementioned works, we propose an easy to train CNN model, which do not require a lot of images in the training dataset, with combination of transfer learning555Learning achieved by taking the convolutional base of a pre-trained network, running the new data of 4 traffic categories through it and training a new randomly initialized classifier, continuous learning666Learning achieved by re-training the classifier with wrong predictions till operating period of the system and Bayesian update777Bayesian update is the ability to update the probability for a hypothesis as more new information or evidence becomes available. capabilities on the MPSoC.
Iii Proposed Methodology: IRON-MAN
In our proposed approach, we utilize the concept of Hybrid Training Method [dey2018mat], where we train our model both during offline (training period) and online (runtime/post-training period) modes. IRON-MAN has two modules in it: Training and Prediction (as shown in Fig. 2). The strength of our approach is that it provides temporal analysis of videos without motion features and we call this type of prediction as Temporal Motionless Analysis of Videos ().
In the learning module, we use transfer learning888Learning achieved by taking the convolutional base of a pre-trained network, running the new data of 4 traffic categories through it and training a new randomly initialized classifier [transferLearning2010, zeiler2014transferLearning] by utilizing an existing pre-trained network and train the classifier with our data categories. First, we train the pre-trained CNN with our dataset, which could be either performed on the MPSoC or on a powerful computing system of which the computing resources could be leveraged to improve training time. After the initial phase of training is complete, we evaluate the overall prediction accuracy of the trained CNN. If the overall prediction accuracy () of the CNN is equal or more than the desired quality of experience () [dey2018mat], then we utilize the CNN for prediction in the prediction module, but if the desired prediction accuracy is not achieved then we retrain the CNN with the failed predicted images. This retrain methodology is bio-inspired and mimics one of the key intelligence feature of a human being, which is learning from the surrounding environment to adapt. When a human being meets a new environment and is not aware of the rules and regulations associated with it, the human tries to adjust and adapt by learning the new set of rules and regulations by correcting its mistakes arising from the newly learnt inaccurate rules. We have utilized the same concept in our approach as well.
Once, after the retraining of the CNN, when the desired prediction accuracy is achieved we use the trained CNN in the prediction module. Now, instead of providing prediction result for each individual image frame, we integrate the final prediction by taking previous image frames into account. Our CNN model's prediction is inspired by Bayes' theorem and sequential Bayesian updating [doucet2000sequential], where the model updates the possibility of the prediction label occurring by taking the probability of the label occurring in the previous frames. This approach is again bio-inspired and is adopted from the ideology of humans updating their knowledge using Bayesian logic. Detailed algorithm and inner working of the IRON-MAN is provided in the following two subsections.
Iii-a Training Module
For the proposed approach, any pre-trained CNN model such as VGG [vgg2014], ResNet [resnet2016], etc could be selected. The training module itself consist of two part training: Offline and Online mode. During the offline mode, the CNN is trained with stock images from a dataset stored on a memory. After the initial training period (offline) the CNN is then fed with live images from the camera and the prediction for each image frame is evaluated. Upon failure in prediction for each image, the image is stored in a stack implementation called "reFeed Image Stack". After utilizing cross-validation technique using live frames (images) from the camera, the overall prediction accuracy () of the CNN model is evaluated. If the overall prediction accuracy is equal or more than the desired quality of experience () then the training process concludes and the CNN is ready to start predicting categories in the prediction module. The governing equation to check for the suitability of the CNN for further prediction is provided in Eq. 1[dey2018mat], where is the dataset consisting of images, is any image in the dataset and is the prediction accuracy of the CNN for . But if the desired quality of experience is not met in terms of prediction accuracy then the CNN is trained with the failed prediction images, which are stored in the reFeed image stack. We call this as reTrain approach so that the CNN can achieve a higher localized prediction accuracy. Here, the term localized prediction accuracy means the prediction accuracy of the model for a set of images that is restricted to a specific task or place. Algorithm for the Learning module is provided in Algo. 1.
Note: Using the proposed approach, training could be performed both on the MPSoC or on a more powerful computing device. If the CNN is trained on a device other than the MPSoC, then after the aforementioned training phases (offline and online) are complete, the CNN model's parameters and weights could be saved and migrated to the MPSoC to act as the Prediction module. Utilizing this method of training the CNN can also improve reliability (lifespan) and energy efficiency of the device due to the required operating resources during the training period being high. Here, the operating resources represent the computing resources such as CPU, GPU, memory, etc. required during the operation of an executing application.
Iii-B Prediction Module
From Bayes' Theorem [doucet2000sequential] we can represent the expression representing the Bayesian Update Scheme is as follows:
In Eq. 2, posterior is the revised probability of an event occurring after taking new information into consideration, prior is the probability of the event assessed before revising posterior and likelihood is the probability that an event which has already occurred would yield a specific outcome. Now, using sequential update scheme where we take the past into account and the modified expression for Bayesian Update Scheme is as follows:
In Eq. 3, current is the probability of some entity occurring whereas the new likelihood is the Bayesian Update taking posterior from the past into account. This approach sometimes is also called a Recursive Bayesian Update. For our scenario, we are trying to predict the current probability for the label (category), which becomes the new posterior in the equation, current is the probability prediction of the category of the image frame provided by the CNN and new likelihood is the probability of the category occurring in some previous time steps. Here, the reason to mention some previous time steps
is because the number of previous time steps to take into consideration will be a heuristic choice of the user. In our case, we call the number of previous frames (images), which is considered to provide an integrated prediction for the chosen category, asFrame Window. Frame Window consists of N number of frames, which are taken into consideration.
If we consider that the prediction for the category in the current frame as , prediction for the same category in the previous frame as and the total prediction accuracy of the model as then the updated equation for Bayes' Theorem is as follows:
Eq. 4 could be utilized to predict the frame using the prediction of previous frame as follows:
In Eq. 5, is the updated prediction using Bayes' Theorem for the same category by the CNN model. We should also note that both and are conjugate priors for our scenario since they belong to the same category as the posterior (
) and hence in the same probability distribution family. Now, depending on theFrame Window, the evaluation of will vary, which leads us to an updated equation as follows:
Eq. 6 is the governing equation, which is utilized to predict the probability of the category during the Frame Window.
In the Prediction module, IRON-MAN has a queue implementation of the image stack (called as Image Queue), where the number of frames are stored and is defined by the user to denote the size of the Frame Window. When an image frame comes from the camera for prediction, the images stored at position of the Image Queue is popped out and the image frame is pushed in the position of the queue while everything getting shifted a place in the middle just like in a first-in-first-out (FIFO) queue implementation. When prediction for a particular frame is required, the prediction of the frame by the CNN model is provided as well as the prediction of the Frame Window is provided by using the Eq. 6. After utilizing Eq. 6 the updated prediction () for a specific category is compared with the the updated prediction of other categories and the label for the maximum value of the prediction is provided as output. The detailed algorithm for the Prediction module is provided in Algo. 2.
Iii-C ECTI: A new metric to choose suitability of a CNN model in embedded systems
If we consider as the total execution time period required to train the CNN with a dataset consisting of number of images to achieve a validation prediction accuracy of , as the quality of experience, and the average power consumption per second during the training period as then the equation for Energy Consumption per Training Image () could be defined as follows:
The unit of is (), where is represented in hours and in kilo-Watt (). To choose the most suitable CNN for an embedded application we have to select the CNN with the least value of .
|Image frames
|Normal predictions without IRON-MAN
|Predictions by IRON-MAN
|(Label) Empty Fluid Heavy Jam
|(Fluid) | 0.0100 | 0.7930 | 0.1683 | 0.0286
(Label) Empty Fluid Heavy Jam
(Fluid) 0.01 0.793 0.1683 0.0286
|(Heavy) 0.0131 0.3091 0.5098 0.1681
|(Fluid) 0.0001 0.1986 0.0798 0.0048
|(Jam) 0.0131 0.2754 0.3399 0.3717
|(Fluid) 0.0 0.0524 0.0267 0.0018
|Image frames
|Normal predictions without IRON-MAN
|Predictions by IRON-MAN
|(Label) No-Obstruction Obstruction
|(Obstruction) 0.3270 0.6730
(Label) No-Obstruction Obstruction
(Obstruction) 0.3270 0.6730
|(No-Obstruction) 0.5010 0.4990
|(Obstruction) 0.1843 0.3166
|(Obstruction) 0.4600 0.5400
|(Obstruction) 0.1047 0.1908
Iv Experimental Results
Iv-a Dataset Used
We have performed our validation on two different test cases: traffic categorization and pedestrian obstruction.
Iv-A1 Traffic categorization dataset
For our traffic categorization experimentation we are using the same dataset used by Luo et al. [luo2015traffic, luo2016traffic] and Dey et al. [dey2018mat] for traffic categorization to validate the performance of our proposed methodology. Mainly two dataset are used in this experiment. The first dataset is released by UCSD traffic control department [datasetChan2005classification]. This dataset contains 254 highway video sequences, all of which are filmed using the same camera containing light, heavy and traffic jams filmed at different periods of the day under different weather conditions. Each UCSD video has a resolution of 320 240 pixels with a frame rate of 10 fps. The video streams from the UCSD dataset were converted to images by processing 1 frame out of every 8 frames (~1.3 fps). This UCSD dataset was used as the testing dataset. Since UCSD dataset is categorized into 3 labels: Light, Medium and Heavy, we manually annotated the images into our desired 4 categories: Jam, Heavy, Fluid, Empty. Another dataset consisting of the 400 images, which is provided by Luo et al.[luo2015traffic] as well as Dey et al.[dey2018mat], captured from highway cameras deployed all over the UK and also consist of several examples of different weather and lighting conditions in order to provide a better training performance. These 400 images are segregated into 4 categories: Jam, Heavy, Fluid, Empty; and each category having 100 images. This dataset was used for training and validation purposes. When the road was empty the images were categorized as Empty and for light traffic it is labeled as Fluid. When the traffic was heavy the images are labeled as Heavy and for slow moving heavy traffic the images are labeled as Jam.
Iv-A2 Pedestrian obstruction dataset
For our pedestrian obstruction experimentation we have used the pedestrian dataset used by Li et al. [li2016new], which consists of more than 5 million images for a total of 32361 labeled vulnerable road users (VRUs), including cyclists, pedestrians, tri-cyclists and motor-cyclists etc. To validate our proposed IRON-MAN approach we have only used the training dataset of [li2016new] consisting of 9742 images for both training and validation, whereas we used the validation dataset from the same study for testing purposes. The images were manually labeled into 2 categories: Obstruction, No-Obstruction. Whenever there were pedestrians in front or nearby the camera view, then it is categorized as obstruction and in contrary it is labeled as no-obstruction.
Iv-B Hardware Setup
We have implemented the methodology on an Odroid-XU4 [odroidxu4], which employs Exynos 5422 MPSoC[exynos5422] used in popular Samsung Note phones and phablets. Exynos 5422 implements ARM's big.LITTLE architecture utilizing 4 ARM Cortex A-15 big CPUs, 4 ARM Cortex A-7 LITTLE CPUs and 6 MALI T628 MP6 GPUs. The Odroid-XU4 does not have an internal power sensor, and we had to use an external power monitor [odroidsmartpower2] with networking capabilities over WIFI to take power consumption readings. Odroid-XU4 platform has 4 temperature sensors on 4 ARM Cortex A-15 big CPU cores, which we have read to monitor temperature behavior during our experiments. For all our experiments we have only utilized 4 A-15 big CPU cores to monitor the operating temperature and power consumption.
Iv-C Experimental Results
Iv-C1 Prediction accuracy evaluation
We chose two pre-Trained CNN models, which were trained on millions of ImageNet images, for our validation. These two CNN models are VGG16[vgg2014] and ResNet50 [resnet2016]. In our experiments, we have chosen the () to be 0.7 i.e. 70%. For VGG16 it took us 360 images to train the pre-Trained CNN for traffic categorization using Transfer Learning and retrain approaches, (see Sec. III-A) and gained a testing prediction accuracy of 98.93%. The average power consumption was 10.63 W on the Odroid-XU4 MPSoC and the total execution time was 97 minutes 44 seconds. Therefore, using the formula in Eq. 7, the Energy Consumption per Training Image comes out to be (average) ().
For ResNet50 it took us 117 minutes and 27 seconds, and 330 images to train the pre-Trained CNN for traffic categorization using transfer learning and retrain approaches. The testing prediction accuracy attained by the model is 92.79%. The average power consumption was 10.59 W. Therefore, using Eq. 7, the Energy Consumption per Training Image comes out to be (average) (). Since both VGG16 and ResNet50 were able to achieve a prediction accuracy higher than (70%) and the ECTI of VGG16 is lower than ResNet50, we have chosen VGG16 as a more suitable CNN model for training on the embedded device, i.e. Odroid-XU4. During training VGG16 and ResNet50, the average maximum temperature achieved by the big CPU cores are 93.60°C (average) and 93.72°C (average) respectively. The maximum operating temperature of the big CPU cores for different CNN models are shown graphically in Fig. 4. We noticed that the maximum baseline temperature, which is the operating temperature of the CPU core when idle i.e. only executing background tasks while on Linux's ondemand power scheme, of the ARM Cortex A-15 big CPU core was 69.24°C (average). Therefore, the deviation of operating temperature while training and the baseline temperature was 24.36 °C (average) for VGG16 and 24.48 °C (average) for ResNet50. Max T. and Max Baseline T. stands for maximum operating temperature and maximum baseline temperature in Fig. 4. In Fig. 5 we show graphical representation of power consumption for both VGG16 and ResNet50 training period. For both Fig. 4 and Fig. 5 we have only shown a snapshot of the total training execution period due to repetition in behavior of the graph.
Now, to prove efficacy of our integrated rational prediction of image frames from the video, we randomly chose a video from UCSD traffic dataset[datasetChan2005classification] and broke the video into image frames, which represents the same category as the video itself. We chose a video from medium traffic category, which corresponds to fluid category and utilized the VGG16 model as the CNN model in IRON-MAN to predict the label of a sequence of image frames as well as the label for video. For the experiment we chose the Frame Window of 3 images representing a video sequence of 4 seconds (approx.). Table. I shows the prediction of labels if we only use VGG16 CNN without our IRON-MAN approach and if we use IRON-MAN as well. The table shows that VGG16 could predict the label of the image frame, but that prediction did not match the label for the whole video, whereas the prediction label for the video using IRON-MAN was achieved with 100% accuracy.
In another experiment (pedestrian obstruction), we chose simultaneous image frames having pedestrians as obstruction from the pedestrian dataset[li2016new] to validate efficacy of IRON-MAN. Table II shows that IRON-MAN was again able to predict whether the path is obstructed by a pedestrian or not using integrated results from previous frames. ResNet50 CNN was utilized in this experiment, which gained a testing prediction accuracy of 72.50% after transfer learning and retraining approaches for this application. For pedestrian obstruction experiment we trained the classifier of ResNet50 and VGG16 on a general purpose computer instead due to massive training dataset and associated energy consumption, and migrated the saved parameters and weights of the model to Odroid-XU4 for further prediction. ResNet50 was selected over VGG16 in this experiment because the model gained a better testing prediction accuracy for this application, which outperformed VGG16's 66.87% testing prediction accuracy for the same.
Iv-C2 Comparison between traditional method & IRON-MAN
If we consider a video consisting of number of image frames need to be analyzed and predicted using CNN based approaches such as VGG and ResNet, traditional method such as VGG or ResNet would analyze each individual image frames from number of images and output the result. Whereas, from the results of experiments provided in Sec. IV-C1, we could notice that IRON-MAN is capable of analyzing , where , number of image frames together and provides a consolidated prediction of frames. Therefore, IRON-MAN is able to categorize video with 100% prediction accuracy during testing and is able to perform TMAV, which could not be performed by traditional CNN based approaches.
Iv-C3 Comparative study of IRON-MAN
To evaluate the efficacy of IRON-MAN, we compared the methodology with the state-of-the-art approach for traffic categorization proposed by Luo et al. [luo2018traffic]. Since, the methodology proposed in [luo2018traffic] is closely related to the target application of traffic categorzation without motion features, it is justifiable to use the proposed methodology for a comparative study with IRON-MAN. Additionally, IRON-MAN being the first methodology to perform TMAV in traffic categorization, it would be unjustified to compare the methodology with any other existing approaches.
In [luo2018traffic], the researchers have used SegCNN and RegCNN to analyze and categorize traffic. The main motivation of utilizing SegCNN and RegCNN in their approach is to improve the accuracy of the prediction model even without training the CNN with a large dataset.
Although after the training, Luo et al.'s approach was able to achieve a prediction accuracy of 94.8% during testing for traffic categorization, which is slightly higher (%) than the prediction accuracy achieved by IRON-MAN, but when the approach was tested for temporal analysis of image frames to provide holistic prediction of the video instead, it achieved a prediction accuracy of 65% for the same target application. In comparison IRON-MAN was able to achieve 100% prediction accuracy for temporal analysis of 9 sequential image frames in the frame window as mentioned in Sec. IV-C.
Moreover, given the computation complexity of [luo2018traffic], executing the approach also has an overhead of 5.2 on the Odroid XU4 MPSoC than compared to IRON-MAN. Fig. 3 shows the execution time (in seconds) of analyzing 9 sequential image frames of the same video from the UCSD dataset [datasetChan2005classification]. Each test was performed 5 times on the Odroid XU4, utlizing all eight CPU cores (big.LITTLE CPU cores) and the average execution time for image analysis for each number of image frame is provided in Fig. 3. Since the concept of frame window or temporal analysis of image frames of video does not exist in [luo2018traffic] approach, we sequentially feed each image frames from the video to the methodology to get the output analysis.
From Fig. 3 we can notice that it takes 1.487275 seconds for Luo et al.'s approach to analyze 9 sequential image frames, whereas it takes 0.285775 seconds to analyze 9 sequential image frames in the frame window for the IRON-MAN. Therefore, the overhead associated with Luo et al.'s approach is 5.2 (approx.) on the Odroid XU4 MPSoC, making the IRON-MAN more suitable for real-time execution for traffic categorization application on the embedded devices utilizing MPSoC.
Iv-C4 Effect on lifespan of the device
In the studies [chantem2008temperature, coskun2007temperature, zhou2016thermal], it has been shown that an increase in the operating temperature of an embedded device by 10-15°C could reduce the lifespan of the device by 2. In Sec. IV-C and from the knowledge on thermal deviation in Fig. 4, we have already noticed that if training is performed on the device then there is an increase in operating temperature of the device by 24.36°C (average) for VGG16 and 24.48°C (average) for ResNet50. Therefore, considering that the device lifespan reduces by 2 for every 10°C increase in operating temperature and if training is performed on the device then the lifespan of the same device reduces by 4.872 () for utilizing VGG16 and 4.896 () for utilizing ResNet50. Now, if we consider that lifespan reduces by 2 for an increase of 15°C in operating temperature instead, then the same evaluation changes to 3.248 for VGG16 and 3.264 for ResNet50. Therefore, until the application requires the CNN to be trained on the embedded MPSoC to continue providing desirable analysis it is highly recommended that the CNN is not trained on the embedded system.
V Limitations and Discussion
Lemma 1.
Updated prediction () of the category using Baye's Theorem tends to zero as prediction () for the category in the current frame tends to zero.
In the Eq. 4, if then (see Eq. 8). Hence, for a lower value of we would be achieving a lower value of if prediction () for the same category in the previous image frame is less than or equal to one999Prediction of an image frame could not be more than one since one represents 100% probability of the category occurring and probability could only range from 0 to 100. ().
Theorem 1.
If in the frame window and then the updated prediction () of the category converging to zero increases as increases.
In Eq. 6, if the number of image frames () is greater than one then the updated prediction for the category is represented by Eq. 9.
In the Eq. 9, if we consider the term, as , since the preceding term is part of the product series represented in Eq. 9 and consider as a function () of and , where is the image in the frame window consisting of N image frames, and both and are whole numbers ( and ), then the aforementioned equation (Eq. 9) could be represented as follows:
Since is an integral part of in Eq. 10 (see Eq. 9), using the knowledge from lemma 1 we can say that as then as well. Now, as and then . Therefore, using this knowledge we could state that: and proving the fact that as gets larger and gets smaller (close to zero), the updated prediction () also converges to zero.
Through our empirical data we noticed that the aforementioned theorem holds true and this could be considered as a potential limitation of using Bayes' Theorem along with CNN's prediction as proposed in IRON-MAN. In our experiments, when the number of image frames in the frame window was selected to be more than 7, the prediction for the category became equal to zero for all the classes and hence, a frame window of more than 7 image frames could not be chosen for accurate predictions. Now, if we consider seconds as the time interval between two image frames in the video then because of Lemma 1 and Theorem 1 using IRON-MAN methodology we are only able to analyze a snippet of the video of seconds duration. If we consider the traffic categorization problem where each image frames were taken every 1.3 seconds (see Sec. IV-A) interval then using IRON-MAN we are able to analyze 9.1 (1.3 7) seconds of the video with high prediction accuracy.
In order to overcome the aforementioned limitations of IRON-MAN as mentioned earlier, we need to develop a more robust algorithm so that we are able to analyze larger snippets of videos (larger than 9.1 seconds for traffic categorization problem) without motion features more accurately.
Vi Conclusion
In this paper, we propose IRON-MAN, which is capable of providing Temporal Motionless Analysis of Videos () i.e. analyzing videos without motion features and providing a holistic temporal analysis while utilizing predictions of the past image frames into consideration. Our approach is able to achieve 100% prediction accuracy in analyzing video from the image frames for certain applications and it was also noticed that for our chosen applications VGG16 had better energy-efficiency on Exynos 5422 platform compared to ResNet50 using Energy Consumption per Training Image () metric comparison. Based on the results we have also shown that training CNN based approaches on MPSoCs could lead up to 4.8 (approx.) reduction in lifespan of the embedded device and therefore, it is recommended to perform the training off the embedded device for improved longevity. It is also shown that for traffic categorization application, our approach outperforms the state-of-the-art.


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Levels of Hierarchy in Business
Business hierarchy varies based on the business's size and model, but every organization has some form of hierarchy framework. The levels within this framework determine how the business operates and how decisions are made and distributed throughout the hierarchy. The major benefit of having well defined business roles is the development of clear objectives and a tight organizational structure. When one level of the hierarchy makes a decision and instructs the next level, objectives are clear and things tend to get done efficiently. The defined structure also helps the decision makers be clear and responsibility get delegated effectively.
How Hierarchy Structures Work
The actual hierarchy varies greatly in the business world, but the roles listed here are commonly used – with shareholders at the top and employees at the bottom. A small business can have a simple hierarchy, with a boss that is essentially the CEO and President and a few employees, with no intermediary between the owner and employee. The larger a business becomes, the more complex the hierarchy. A Fortune 500 company will have a CEO who is beholden to a board and to the shareholders. Below the CEO are a number of different department heads, often with each department acting like an entire business of its own.
A tree structure is one popular method of modeling the business hierarchy. In some cases, departments will overlap, and in those cases, the structure is somewhat more difficult to define in exact terms. Outside of the traditional hierarchy, several other models of organization exist, but they are not as popular and arguably not as effective in defining responsibility and organizing a business.
The Shareholders
The shareholders are the people who own the business. They are not always involved in day-to-day operations, and the group can include anyone owning stock in a publicly traded company or the top level owners who control the majority share of ownership. They don't necessarily fit within the operational hierarchy, but they are worth mentioning because shareholders have some control over the direction of a business. They have invested and expect a return on that investment, putting them in a position to pressure the CEO and top level decision makers. In some cases, the company is employee-owned, making each person on the staff a shareholder. This model encourages performance, because the employee is directly affected on a financial level based on performance.
Board and Advisors
Outside advisers are not uncommon, and in many cases they do not have a stake in the company. Being independent allows them to provide sound advice without any outside influence or motivation. Advisers tend to have specific experience that makes them valuable, but they are either retired or do not have competing interest. Paid and unpaid adviser roles are both normal. In paid positions, the adviser is also commonly called a consultant. Essentially, they are providing expertise that will ultimately benefit the business.
The board members give direction to the CEO and primary decision makers. They pass along shareholder input and are an oversight system with some controlling power. The board can bring major issues to a vote, making it a powerful entity. The individuals on the board can be just about anyone involved with the business. The board may have a CEO, majority shareholder and any other individuals who were assigned an official seat at the table.
Chief Executive Officer and Chief Operating Officer
The CEO runs the entire show and makes the big decisions based on the business's direction and actions. The COO runs everything in terms of operations and processes. Not every business has a COO, but business models heavily dependent on employee processes stand to benefit from this job role. The COO still reports to the CEO, but the two are running the business at the highest levels together. The CEO is responsible for making decisions that will ultimately increase revenue and make the business profitable and viable. She will consider major acquisitions, approve new department creation and really have an eye on every aspect of production or service delivery while monitoring the accounting and return. The CEO has to keep the business cash flow positive while investing in growth and proving the success of business strategy over time. A CEO is heavily criticized, and in many cases will take the credit or blame for major successes and failures.
President and Vice President
The President and Vice President roles are not often very hands-on with the operational level. In a large business with multiple departments, there may be President and Vice President roles for each individual department. For example, a President and Vice President of Marketing will be separate from the same roles in manufacturing, sales, development and account management. These management-level roles function like CEOs of sorts – for their own specific departments. These positions add a controlling level of oversight to specialty departments that are headed by experts in the specific fields.
A company may also utilize overall President and VP roles to manage the big decisions and operations within the company.
Department Heads
Many organizations opt for a department head rather than a President and Vice President for each department. The department head is hands-on and manages the employees in his division. He has some autonomy to make daily decisions that will ultimately drive the department forward, but any big decisions are passed up the food chain. Department heads do often influence major decision making, however, as they understand the detailed reasons for success and failure within their specific job functions. They will meet with their President and Vice President and sometimes with a CEO or COO. They work as intermediaries for the employees and upper-level management to communicate and ensure their departments are meeting performance standards. Without department heads for multifaceted businesses, a gap in communication would exist at the management level.
Supervisors, Managers and Team Leaders
The supervisor, manager and team leadership roles are hands-on and absolutely critical to operations. These people handle the daily workforce tasks by assigning duties to employees and holding them accountable for their workloads. They train new employees and manage hiring, while also sometimes delivering news about layoffs and terminations. They are in constant contact with the workforce and, in many cases, were working as base-level employees before they were promoted into the supervisor job position.
The roles overlap and in some cases are actually the same, depending on the company naming conventions for the job positions. These positions can also stand alone, with a supervisor tasked with handling multiple teams and team leaders, for example. The team leader position is often created for employee-heavy business models. For instance, a large call center will set daily sales goals and break out into teams with leaders to motivate, assist, train and drive sales.
Employees Roles
Employees in their basic roles make up the backbone of the business. They work at the opposite end from management and are responsible for the company's critical daily operations. Employee roles vary greatly, and essentially make up the majority of the job market. The employees are also far more specialized than most managers. They are not responsible for oversight and decision making against an entire department that has numerous moving parts. The employee has a very specific task to complete, and her responsibilities are clearly defined. The only exception here is in the world of small business, where an owner may also fill employee shoes. Sole proprietors and single owner LLCs are responsible for every aspect of the business, but any multiperson operation will delegate tasks and define various job positions.
Independent Contractors
Contractors are important to businesses, but they function outside of the normal hierarchy. The independent contractor or subcontractor is available to fill roles temporarily, or add value to the business by performing tasks that are needed on occasion – but not enough to justify a full-time employee.
In addition to the contractor role, temp employees are used to fill every role from the employee level to upper management. Interim CEOs are even used sometimes to temporarily fill the position until a permanent replacement is found. The role of the contractor and temp employee serves to keep the business running smoothly when staff is short due to any number of reasons. It's also a good method of testing a potential employee before hiring him into a permanent position. However, if the contractor is working in a permanent-type capacity, the company may have to hire him as a full-time employee, with benefits like unemployment, workers compensation and potential health care.
Other Business Structures
The hierarchy business structure is widely used because it's easy to organize and is effective. That said, not all businesses work well under a hierarchy, and numerous other models of organization are used with some regularity. Flat organizations skip the traditional hierarchy and assign no formal job roles or titles. The flat organization is not very common, but it does work in unique businesses. The lack of structure spurs creativity and inspires new ideas, and the model gives workers the ability to make nimble decisions and drive consistent processes.
The lack of responsibility, without a manager or supervisor, does also make the flat structure problematic in terms of accountability. Startups are often flat in the early stages, because they have yet to define a strict hierarchy or organizational structure. They will run flat, with overlapping roles to ensure everything is being done, until permanent employees with specific roles are hired.
Team-based organizations are also common. In some cases, small teams or pods are created to meet goals as a team unit. They may have a higher-up boss or president, but the pods remove the need for department heads – each pod uses a grouping of specialty roles, and the members hold one another accountable. For example, a digital advertising company may create a pod with a sales rep, account manager and technical ad operations employee. The three people all have different but essential skills to obtain and manage new business while delivering the service. They are all working on the same accounts and can communicate efficiently and in a knowledgeable manner about the specific accounts they manage. Such a unit is quite effective, but can have difficulties when business is too slow or too overwhelming for the team to manage. The inability to effectively allocate and move employees around as needed is one major drawback to a dedicated team or pod.
A Holacracy is somewhat similar, in that it's essentially a business environment devoid of bosses. The idea is for individuals to draw on their strengths and drive performance in their specialty areas without answering to a specific manager or boss. The model is heavy on collaboration but does not rely on oversight from higher-ups. If individuals need help or have a weak spot, they will pool resources with others in the company who can round out their project with another set of skills.
Writer Bio
Zach Lazzari is a freelance writer with extensive experience in startups and digital advertising. He has a diverse background with a strong presence in the digital marketing world. Zach has developed and sold multiple successful web properties and manages marketing for multiple clients in the outdoor industry. He has published business content in Angling Trade Magazine and writes white papers and case studies for multiple corporate partners.


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Our Story
Four Bakery is not just a bread shop, it's a revolution.
Whilst most of us happily fill ourselves with food every day without even thinking about reading the label Four offers customers an antidote to the fast-paced and processed produce of modern life.
It won't be a surprise that we have four key values that are intrinsic to the ethos, and recipes, that we serve.
The main inspiration for our name derives from the French word for 'oven' and every day you will discover a fresh batch of four core offerings – loaves, viennoiseries, savouries and brownies but the significance of the name goes deeper than that. Four reaffirms that you don't need much to create something really beautiful. Most of our products contain just four simple quality ingredients plus lots and lots of love. We definitely subscribe to the old adage that 'less is more'.
With a background in finance, Four founder Simon is not one to overlook the small print and is on a mission to make people aware of the hidden preservatives and additives in all foods, especially flour. Four is a place where people can be educated, inspired and enjoy the health benefits of authentic sourdough. We are proud to be members of the Real Bread Campaign which is on a mission to find and share ways to make bread better for us, better for our communities and better for the planet.
Look out for our in-house workshops and masterclasses with Ellen, our Head Baker and Chocolatière when you will get the chance to learn how to bake your very own sourdough bread or how to make your very own chocolate creations.
From the moment you walk into the bakery you can feel the sense of community. As soon as we could we knocked down the wall between the shop and the kitchen to break down as many barriers between our produce and the people that visit us. Customers can now easily watch our expert Pastry Chef de Partie and bakers in action and ask them any questions they may have.
We aim to be as inclusive as possible and offer a broad selection of bakes that cater for as many dietary requirements as possible. All of our bread is vegan-friendly and we also offer vegan and gluten-friendly sweet treats too!
At Four we recognise the importance of giving back to the community and we work hard to help people having a tougher time than most of us. Not only do we provide Macmillan Cancer Support with free bread on a daily basis, we also actively support and donate to Stand Up to Cancer. Any bread that is left over at the end of the day is donated to Jersey Hospital and to the Family Nursing and Home Care.
We genuinely love what we do. That's why we get up before dawn five days a week so that you can enjoy truly authentic sweet and savoury sourdough, fresh out of the oven.
We love to share our success and we happily give away our carefully prepared sourdough starter for free to anyone who wishes to create their own sourdough bake from home. Just bring in your own jam jar and we will fill it!
At Four we also love to support the success of others. In the shop you will discover produce from other local companies especially those that align with our key values.
We hope you also will love what we have created at Four Bakery and we hope you will join us in our revolution to make bread real again.


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Thriven and throfiction Gu Xi – Chapter 557 – Multiple Strikes madly corn quote-p3
Novel–Astral Pet Store–Astral Pet Store
Chapter 557 – Multiple Strikes wax pipe
Needless to say, that was a indicating, an exaggeration. Even now, the a.n.a.logy could reveal how fast was the rate reached!
So, this woman was nevertheless able to release this energy when she was under the influence of the bell?!
The Situ family members top of your head could not tell himself which he possessed seen a challenging slaughter.
Tang Linzhan struggled to stand up.
The shocking element was she was shifting considerably faster compared to what they ever could with the expertise! "The Shadow Reacher!"
On the flip side, the Situ family members as well as w.a.n.g family acquired a multitude of t.i.tled challenge furry friend warriors. Not actually Venerable the Blade can get away from a real strike, in addition to Tang Ruyan who has been merely for the intermediate posture of your t.i.tled ranking!
Barefoot In The City Of Broken Dreams
In the end, their former intel explained Tang Ruyan as you in the seventh position nonetheless, she acquired arrived at the t.i.tled rank in this short time. How rapid.
"Wait one minute. How about the bell? Provides the impact expired?"
Bang, bang!
masters of french music performance
In the event it theory ended up a fact, then the gossips about Tang Ruyan were probably untrue. Or probably, the Tang friends and family experienced released these types of information about goal!
"Humph, I don't treatment your identiity because you're intending to expire now!" The w.a.n.g household head bellowed, "Everyone, let's eliminate her!"
All things considered, their previous intel detailed Tang Ruyan as you within the seventh position continue to, she possessed arrived at the t.i.tled get ranking in such a limited time. How quick.
The household heads on the Situ and w.a.n.g family members could view the worry in each other's eyeballs. They found it challenging to think that the impressive challenge family pet warrior wouldn't be there. Why else would Tang Ruyan arrive?
Tang Ruyan was in the t.i.tled rate?!
The gal that they had decided upon to banish acquired came back in that crucial time she was combating for any Tang spouse and children and was within the t.i.tled get ranking.
The 3 loved ones propagated the astonishment. Tang Ruyan, as well, didn't be expecting how the Minimal Skeleton would fully stand up so swiftly. She was all set to deal with that line of flame by themselves. She could explain to that the column of flames would not damage her because she got ascertained its lack of strength.
Tang Ruyu remembered Tang Ruyan got merely been with the 7th get ranked. When have she arrive at the t.i.tled rank?!
Yet again, persons had the ability to see Tang Ruyan. She was standing on the wailing dragon.
It had been strange.
Tang Linzhan imagined that was beyond his being familiar with
Three of the family members propagated the astonishment. Tang Ruyan, very, didn't assume the fact that Minimal Skeleton would stand up so swiftly. She was all set to battle that line of fireplace by herself. She could tell that this column of flame would not injure her because she got ascertained its weakness.
So, the Tang Ruyan dealing with would have to be an imposter. Which was the one reasonable reason. Why else would the young lady hold these types of capabilities?
In the event it principle were definitely correct, then a gossips about Tang Ruyan have been probably incorrect. Or maybe, the Tang spouse and children experienced produced these kinds of details on intention!
"You know very well who I am just." Tang Ruyan addressed, coldly.
He didn't summon his conflict pets. But no one thought that men such as that would need a struggle pet to combat against a little bit female! "Be very careful. She is… at the t.i.tled rank!" an elder with the w.a.n.g family members informed others, frightened.
There seemed to be a darker, arched blur on the surroundings.
Just what is that skeleton?!
Doctor Who_ Lungbarrow
Individuals gasped. How frightening!
Astral Pet Store
The w.a.n.g family members mind was the first to provide his feelings rear internet. "Who do you find yourself seriously?"
She is for the t.i.tled ranking?


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Vermont Business Magazine Technology for Tomorrow (T4T) has announced a new partnership with the Comcast NBCUniversal Foundation to expand its digital literacy education across Vermont. Technology for Tomorrow is a non-profit organization that provides various digital technology training programs to older adults, New Americans, and other local communities. In 2022, it has served over 500+ Vermonters with in-person and remote instruction.
The Comcast NBCUniversal Foundation partnership will allow T4T to continue bridging the digital equity gap and double its impact and programming over the next year.
The recent Covid 19 pandemic has only widened the gap and made the need for digital equity and the reduction of social isolation even more critical.
Vijay Desai, T4T Executive Director, and Ranga Chilakamarri, T4T Program Manager, shared this growing demand with the Comcast NBCUniversal Foundation.
Comcast NBCUniversal Foundation recognized the need for this education and supported the effort with a grant award of $15,000, one of the largest for the Williston-based non-profit.
"Every day, I am out in the community teaching these critical skills, and it's no longer a luxury skill set; it's a literal lifesaver," Shelburne-based T4T Program Manager Emmy Hilliard. "We're helping people learn how to access medical records, make appointments online, and keep them connected to those they love by helping them navigate tools like Zoom. All while sharing with them how to protect themselves online; It's essential and deeply purposeful work."
For more information on Technology for Tomorrow's work, please visit their website at www.tech4tomorrow.org.
11.28.2022. Williston. Technology for Tomorrow


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STATEN ISLAND, N.Y. -- To celebrate the Year of the Dog, eat dumplings -- as they represent prosperity, in an old form of Chinese money. Wear red to scare away bad luck; gold to attract good luck. And sweep out the house to ship off unwanted memories from the year prior.
These pointers come courtesy of Snug Harbor Cultural Center and Botanical Garden's education crew, which coordinated a Chinese New Year celebration Saturday at the Livingston campus.
About 150 revelers turned out to enjoy the arts and crafts, nibble on egg rolls and take in the gongs and flash of the Shaolin Kung Fu Temple Lion Dancers shimmying and weaving through the crowd.
The Lion Dance hails from the Han Dynasty, coached Snug educators. But lions don't come originally from China. Instead, the lion symbol traveled to the ancient country thanks to the Silk Road which brought Buddhism from India to China and lion as a symbol of a holy guard. Today, it signifies good luck.
If you were in China during the Chinese New Year, which starts officially on Friday, Feb. 16, said Snug educator Susannah Abbate, you'd be enjoying lo mein noodles, strands that represent long life. But the noodles just weren't practical for this afternoon of demonstrations and tours through the Chinese Scholar's Garden.
Kevin Chau of Tottenville, a board member at Snug Harbor, brought daughter, Samantha, 7, to the festivities.
Said a smiling Chau, "I'm just thrilled to be part of this cultural event. This is a great opportunity for us to highlight the cultural richness of Snug Harbor and particularly to bring awareness of the New York Chinese Scholar's Garden."
Chau pointed out, "It's only one of two authentic gardens in the U.S...every piece of stone was shipped from China. They actually sent over 40 artisans from China who lived here for [about six months]."
Aileen Fuchs, President & CEO of Snug Harbor Cultural Center & Botanical Garden, said, "The Chinese New Year is one of the biggest cultural celebrations in the world. In honor of this festive occasion, we're eager to welcome families back to this event to reconnect with Snug Harbor and get excited about our gardens, grounds, and all the upcoming events we have in store for you this spring and summer."
Larry Anderson, director of Performing Arts and Public Programs, thanked board members Dr. Ronald Altman and Kevin Chau for their continued support, "which helps us bring fun, educational, and culturally significant programming to the community year after year."
Anderson added, "If you weren't able join us this year, please save the date for next year's Chinese New Year Celebration, which will be held on Saturday, Feb. 9, 2019: the Year of the Pig."


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Storyteller and author Ivan Coyote's latest work, the nonfiction collection Care Of (McClelland & Stewart), is a moving meditation on care and community. Penned during the early months of the pandemic, the book is a collection of notes and letters from audience members to Ivan and their deeply compassionate responses to these letters.
The Star calls the book "strong medicine, best taken in measured doses, and a vital reminder of the value not only of connection, but of every story, every voice."
Ivan Coyote is a writer and storyteller. Born and raised in Whitehorse, Yukon, they are the author of thirteen books, the creator of four films, six stage shows, and three albums that combine storytelling with music. Coyote's books have won the ReLit Award, been named a Stonewall Honour Book, been longlisted for Canada Reads, been shortlisted for the Hilary Weston Prize for nonfiction, and awarded BC and Yukon Book Prize's inaugural Jim Deva Prize for Writing that Provokes. In 2017 Ivan was given an honorary Doctor of Laws from Simon Fraser University for their writing and activism.
Trevor Corkum: In Care Of, you respond to letters, notes, and emails from a range of folks who've written deeply personal messages to you over the years. When and how did you imagine this project becoming a book?
I didn't imagine it as anything other than finally having time to catch up on my correspondence at first. I checked my calendar, and during the waning days of March 2020, when we were all grappling with the early lockdown times and scrambling to find toilet paper and masks, I did an online panel discussion on queer memoir. It was an American thing, mostly, with writers like Michelle Tea and Alexander Chee, and Samra Habib was on it, and she had recently won Canada Reads, I think, and after the panel was done the host, Cameron Esposito, asked all of us which of our books people should buy and I just said "hey, write me a letter? I've been answering mail this month and it's really helping me feel connected to other people, so write me a letter, and a couple of days later I got an email from a publisher asking me if I would consider putting these letters together, if I thought it might work as a manuscript, and it all sort of happened from there. I was already talking to Jared Bland at M and S about the mystery novel I'm working on, and my agent Rachel Letofsky is a total dream and she parlayed it into the two-book deal I signed with M and S.
TC: From reading these powerful messages, it's so clear that so many folks are deeply moved and comforted by your performances and stories. How does it feel to be such a trusted confidante?
IC: It's a complicated thing, that. I get a lot of mail, sometimes containing hard stories, and trauma. I am grateful that the process of writing these letters taught me so much about the importance of the long conversations. The vital process that is the considered response. I will never be able to write all the letters I want to, but I continue to be honoured and awed by the way my stories shake loose stories and letters from other people. I see it as a very sacred exchange.


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To go ahead with unfilled promises
Not sure of success with many misses
This was the day when you left us crying
We did all to save you and kept on trying
It did not prove any result and dark descended
You left for heavenly abode and ascended
It is painful memory never to be erased
All goals and night mare to be chased
Still not way out but to stand and face bravely
Nice days can not be back but must be made happily
You had gone once for all with no indication
Now we all are in dark and face immense suffocation
We are seized with lots of problems and troubles
Now I have realized the life as nothing but bubbles
Yes I know that sadness may not remain for long
it may be treacherous and prove us very wrong
life is not in any way deter us but facilitate to determine
to achieve the goal gracefully and not to undermine
But I am for sure to move along confidently
It is known from the beginning evidently
There was no point in grieving all the times
Sadness was there but to be felt at sometimes
In real sense what can be the great tribute?
To stand on own feet and positively contribute
Towards fulfillment of dream and aspirations
Proving worth without any character abrasions
It will be worth watching to realize his dreams
He should have failed to realize it even as good team
As life is considered as adventurous mission
It will be considered as feat without his liaison
Since he is more with you
You will have to stand fast and pass through
All ordeals that may fall on the way
It will serve no purpose if you loose hope and stay away
What can be real and great tribute?
If you stay sound on feet and contribute
A token geture and show of being successful woman
That he could have not even dreamed of as man
I wish you to be aware and silently watching
You may still notice my eyes are searching
I stand hopelessly along and look at vast horizon
You had left me alone in this world and flown
I have missed not a single moment
Still feel the pulse of long breath for the moment
You used to enter gently with total surprise
How all of sudden it vanished with sudden demise?
But I aim to move along confidently
The chance has come to me incidentally
I shall definitely overcome it with God's grace
I shall proudly bear the confidence on face
To go ahead with unfilled promises


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Fun With Puzzles
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We stayed at the resort for 8 nights. When we arrived and wanted to take a shower, there was no water running out at all. We've let them know and they told us they were going to fix it. When we returned they told us it was fixed, but it wasn't. Weird. We got another room, the shower worked but there were a lot of bugs. Staff was friendly but overall for this price, we expect a working shower and a clean room. We had a flat tire with the scooter twice, and they fixed it without a problem which was very nice. The tour and scooter you can book at the hotel is more expensive then the ones you can find in the centre.
The resort is kind of far from the city center which makes it way more quiet. The pool is nice and it's open 24/7, the outside area is wonderful with plenty of plants. I particular liked the restaurant and the reception. However there is a downside, since the rooms are like huts some bugs get in, nothing that bad unless you have entomophobia. Another good or bad thing depending on the situation is that they are off the electrical grid, they have their own generators. However when we arrived they weren't working for around 6/7h. Overall it's a very good resort.
Positives: the resort had the most AMAZING staff. Very accomodating, entertaining and helpful. I was spending my birthday here and they made sure that our stay would be as comfortable and pleasant as possible! Their bar serves very tasty drinks!! We had requested for particular flavours and they kindly customized it for us. Their restaurant's menu had a good variety as well. We couldnt decide what to order so they recommended the black chicken dish which turned out to be amazingly yummy. Pool was open all day as well... Rain or shine we still enjoyed it. Downside: the distance from downtown was a big factor. We felt like it was too costly when it came to time and money. It also made me feel less in the mood to go out and explore since it was such an effort going back here. We rented the deluxe room/hut which was well equipped with a very large TV, cable, there was a mini bar for coffee etc.. Shower was extremely spacious but the floors were a bit slippery. Hot water didnt work at first but the staff immediately addressed our concern. The furnitureswere already very squeaky and at some point during the day, my boyfriend casually sat on the right side of the bed when we heard a creaking sound. We werent sure if the wood had cracked or it wa just adjusting to his weight.
Our Vacation in Lio Villas Resort was really perfect. It's little bit up from the town and all the tourism and trouble guaranteed a really quiet and relaxed atmosphere. The staff is really amazing, everybody handle you like a Friend and not as a guest, beginning from the Manager Jeffrey over the receptionist Jim to the kitchen/bar staff, which all give their best to make your vacation perfect. The area and the rooms are very clean. Food is delicious! Nothing more to say than thank you for this great stay lio villas!
Top aspect of this resort was the service; the staff couldn't do too much for us. The rooms were comfortable with good air conditioning and clean, well equipped bathrooms. Food choices for breakfast and dinner were surprisingly wide and we didn't have any bad dishes while we were staying. Like most of El Nido this resort isn't close to anything so you need a tricycle to get anywhere; the resort gets you one on request. They also arrange boat tours which looked great but which we were unable to experience due to Typhoon Nona passing while we were here. They're finishing off some infrastructure work so the back part of the site isn't fully gravelled yet, but that wasn't a problem. Pool is central and very pleasant, even in the rain. This is the jungle so you can't avoid insects in your room occasionally, but the beds have good bug nets. If you want to explore El Nido this is a great place to do it from.
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