It's been a little while since we had an Unseen Restaurant event. I don't really know what happens over the summer – I always mean to hold one but somehow time gets away from me. Anyway – we are now back in business.
If you don't know, the Unseen Restaurant is an occasional restaurant that I hold at home. It is advertised through a Facebook group and people attend on a first come first served basis – one menu, one price, all profits to Shelter for Humanity.
This month I decided to do a vegetarian menu. I have lots of veggie friends and I really wanted them to experience the restaurant.
I'm not a vegetarian but I love to eat good vegetarian food from time to time but I get really tired of boring vegetarian menus in pubs and restaurants. So I decided to stretch myself and piece together a 5 course menu pitched towards the fine dining end of the spectrum. Now, I'm not talking Michelin stars here just elegant, tasty home cooking. At least, I hope that's what I achieved; there was nothing elegant about watching me muddle through the multiple processes involved!
The weather here in Cornwall is all over the place at the moment, mild and sunny one day then wild and windy the next so I devised a menu that traversed the journey from summer through to autumn. That's a pretentious way of saying I did 2 cold courses because it was easier!
The meal started with a dish I have blogged before – chilled pea soup with parmesan crisps and parmesan ice cream – the ice cream caused quite a stir and was generally thought to be nicer than it sounded. I love this recipe because the pea soup just tastes so much of pea – I know that's a daft thing to say but it also happens to be true – try it and see. This time, because I was cooking for vegetarians, I made the soup with vegetable bouillon not chicken stock and, because I was serving 5 courses, I presented it in small tea cups rather than bowls.
The second course was courgette rolls, filled with lemony ricotta cheese and served on a red pepper coulis, drizzled with basil oil. Simple, tasty and most of the work done in advance. Just what you need with a challenging menu.
This was followed by a main course of mushroom strudel served on a roasted parsnip puree with a truffle sauce. I confess that I bought the sauce but it tasted so good that it really didn't matter.
A short pause and we moved on to dessert. I had spent a lot of time picking blackberries and wanted them to be the main feature. Blackberry and apple crumble is a bit of a staple at this time of year but I wanted to give it a bit of a twist. I'm not a great fan of "deconstructing" pudding for no real purpose but I guess that's what I did.
I served a roasted Royal Gala apple on a spiced Bramley apple puree with blackberry sorbet and blackberry jelly. I created a crumble from crushed oat cookies and toasted hazelnuts and sprinkled it on top. I can honestly say I have never baked biscuits before just to crush them up as soon as they were cool – I'm glad I kept a few though they were delicious.
Actually, I lied a little then. I didn't serve the apple puree but I did find it in the fridge the following morning… I used some when I re-staged the dish for my photo.
By this point most of my guests were stuffed (I guess the dishes weren't quite elegant enough) but there was one last course to go. I served up a chunk of mature cheddar with some homemade crackers and a slice of the Damson Cheese that I made last month.
This entry was posted in Blog, Recipes, Vegetarian and tagged vegetarian fine dining, vegetarian recipes by Fiona. Bookmark the permalink.
The food was amazing Fi. Not only was it delicious it was very pretty to look at too.
The menu sounds fabulous, & those pics… wow!


Scientific use cases show promise, but challenges remain for complex data analytics.
Check out Fundamentals of Deep Learning by Nikhil Buduma to learn about key concepts in this complex and exciting field.
Deep learning is enjoying unprecedented success in a wide variety of commercial applications. Around 10 years ago, very few practitioners could have predicted that deep learning-powered systems would surpass human-level performance in computer vision and speech recognition tasks. At Lawrence Berkeley National Laboratory, we are confronted with some of the most challenging data analytics problems in science. While there are similarities between commercial and scientific applications in terms of the overall analytics tasks (classification, clustering, anomaly detection, etc.), a priori, there is no reason to believe that the underlying complexity of scientific data sets would be comparable to ImageNet. Are deep learning methods powerful enough to produce state-of-the-art performance for scientific analytics tasks? In this article, we present a number of case studies, chosen from diverse scientific disciplines, that illustrate the power of deep learning methods for enabling scientific discovery.
Galaxy models have numerous applications in astronomy. For example, a slight distortion in a galaxy's appearance may indicate the gravitational pull of nearby dark matter. Dark matter is hypothesized to be five times as common as ordinary matter, but there is no consensus on whether it exists. Without a model of what a galaxy looks like unaltered, there is no basis for concluding that a distortion is present.
Because galaxies have much in common, a model fit to a sample of galaxies can accurately represent the whole population of galaxies. Shared characteristics include spiral "arms'' (see Figure 1 below), rings (Figure 2), and brightness that decreases with distance from the center—even among irregular galaxies (Figure 3). These commonalities are high-level features, not easily described at the level of individual pixels.
To date, most neural-network successes have been on supervised learning problems: given an input, predict an output. If the predicted output does not match the correct answer, adjust the network's weights. For galaxy models, there is no correct output. Rather, we seek a probabilistic model of images that assigns high probability to images of galaxies, while assigning a total probability of one to all possible images. A neural network specifies a conditional probability within this model.
In the probabilistic model, an unobserved random vector $z$ is drawn from a multivariate standard normal distribution. A neural network maps $z$ to a mean and a covariance matrix that parameterize a high-dimensional multivariate normal distribution, with one dimension per pixel of a galaxy image. The neural network may have as many layers and nodes as are helpful for representing the map. Figure 4 shows the mean of a multivariate normal distribution for a particular galaxy image, and Figure 5 shows the diagonal of the covariance matrix. Finally, a galaxy image is sampled from this multivariate normal distribution.
One may take either of two perspectives on our procedure for learning the weights of the neural network from galaxy images: algorithmic and statistical. Algorithmically, our procedure trains an autoencoder. The input is an image, the low-dimensional $z$ vector is a narrow layer in the middle of the network, noise is added, and the output is a reconstruction of the input. The loss measures the difference between the input and the output. Our choice of loss function and the type of noise we add to the autoencoder, however, follow from the statistical model. With these choices, training the autoencoder is equivalent to learning an approximate posterior distribution on the unobserved vector $z$, through a technique called "variational inference." The posterior distribution for a galaxy tells us what we want to know: how the galaxy most likely appears (i.e., the posterior's mode) and the amount of uncertainty about its appearance. It combines our prior knowledge of what galaxies generally look like with what we learn about a particular galaxy from its image.
We implemented the proposed variational autoencoder (VAE) using Mocha.jl, a neural network framework in Julia based on Caffe. We trained our model with 43,444 galaxy images, each cropped around one prominent galaxy and downscaled to 69 x 69 pixels. On 97.2% of galaxy images in a hold-out set, the proposed VAE assigns higher probability to the image than the current common practice of modeling galaxies with bivariate Gaussian densities.
Figure 1. A prototypical spiral galaxy. Source: European Space Agency & NASA on Wikimedia Commons.
Figure 2. A ring galaxy. Source: NASA, ESA, and The Hubble Heritage Team (AURA/STScI) on Wikimedia Commons.
Figure 3. An irregular galaxy. Source: ESA/Hubble, NASA, D. Calzetti (UMass) and the LEGUS Team on Wikimedia Commons.
Figure 4. The mean intensity for each pixel in a 69 pixel x 69 pixel image of a particular galaxy. Source: Generated by Jeffrey Regier and Jon McAullife, used with permission.
Figure 5. The variance for each pixel's intensity in a 69 pixel x 69 pixel image of a particular galaxy. Source: Generated by Jeffrey Regier and Jon McAullife, used with permission.
Extreme weather events pose great potential risk on ecosystem, infrastructure, and human health. Analyzing extreme weather in the observed record (satellite and weather station products) and characterizing changes in extremes in simulations of future climate regimes is an important task. Typically, the climate community specifies patterns through hand-coded, multivariate threshold conditions. Such criteria are usually subjective, and there is often little agreement in the community on the specific algorithm that should be used. We have explored a completely different paradigm, namely that of training a deep learning-based system on human ground-truth labeled data, to learn pattern classifiers.
Our first step was to consider the problem of supervised classification for centered, cropped patches of tropical cyclones and atmospheric rivers. We identified 5,000-10,000 patches and trained a vanilla convolutional network (in Caffe) with hyperparameter tuning (in Spearmint). We observed 90-99% classification accuracy for the supervised classification task. The next step was to consider a unified network that could simultaneously perform pattern classification for multiple types of patterns (tropical cyclones, atmospheric rivers, extra-tropical cyclones, etc.) and localize these patterns with a bounding box. This is the more advanced, semi-supervised formulation of the problem. Our current network is depicted in Figure 6.
Figure 6. Semi-supervised architecture for climate pattern localization and classification. Figure courtesy of Evan Racah, LBNL.
Figure 7. Sample results (ground truth: Green, DL prediction: red) for weather patterns and their location. Figure courtesy of Evan Racah, LBNL.
Figure 7 highlights some of the sample results obtained by the semi-supervised architecture. While further tweaks on the architecture are ongoing, t-SNE plots of the underlying clusters reveal that the method has the capability to discover new coherent fluid flow structures in the data set. The current architecture operates on instantaneous snapshots; we are extending the model to incorporate time explicitly to make more accurate predictions.
Upcoming astronomical sky surveys will obtain measurements of tens of billions of galaxies, enabling precision measurements of the parameters that describe the nature of dark energy, the force that is accelerating the expansion of the universe. For example, maps can be constructed of the matter in the universe—regular matter and dark matter—using the technique of gravitational lensing. Characterizing these mass maps allows us to distinguish between different theoretical models of dark energy.
We explore emerging deep learning techniques to identify new ways of rapidly analyzing cosmological map data. These models offer the potential to identify unexpected features in mass maps that give new insight into the structure of the universe. We developed an unsupervised Denoising Convolutional Autoencoder model in order to learn an abstract representation directly from our data. This model uses a convolution-deconvolution architecture, which is fed with input data from simulations of a theoretical universe (corrupted with binomial noise to prevent over-fitting). We use four convolutional layers, two bottleneck layers, and four deconvolution layers, implemented using the Lasagne package. It is trained using 10,000 images of mass maps, each 128x128 pixels. Our model effectively trains itself to minimize the mean-squared error between the input and the output using gradient descent, resulting in a model that, theoretically, is broad enough to tackle other similarly structured problems. Using this model, we were able to successfully reconstruct simulated mass maps and identify the structures in them (see Figure 8). We also determined which structures had the highest "importance" (i.e., which structures were most typical of the data, see Figure 9). We note that the structures of highest importance in our reconstruction were around high mass concentrations, which correspond to massive clusters of galaxies.
Figure 8. Left: mass map from cosmological simulation. Right: reconstruction of this map using our convolutional autoencoder. We smooth the map, so small detail is lost, but the features of the maps are accurately reproduced. Figure courtesy of Shiwangi Singh and Debbie Bard. LBNL.
Figure 9. Left: mass map from cosmological simulation. Right: saliency map of the features most important for reconstructing the map using our convolutional autoencoder. We see that the areas of high mass concentration are more important than areas of low mass. Figure courtesy of Shiwangi Singh and Debbie Bard.
We also developed a supervised Convolutional Neural Network (CNN) with four hidden layers for classification of cosmological mass maps from two different simulated theoretical models. The CNN uses a softmax classifier, which minimizes a binary cross-entropy loss between the estimated distribution and true distribution. In other words, given an unseen convergence map, the trained CNN determines probabilistically which theoretical model fits the data best. Trained using 5,000 maps (128x128 pixels) from two theoretical models, this preliminary work demonstrates that we can classify the cosmological model that produced the convergence maps with 80% accuracy (see Figure 10).
Figure 10. A t-SNE plot with 2D representation of feature vector extracted from mass maps. Red and blue represent mass maps from two different theoretical models of cosmology. Figure courtesy of Shiwangi Singh and Debbie Bard, LBNL.
The seemingly effortless ability to produce the complex grammatical structures and acoustic patterns that comprise speech is unique to the human species. The seminal work of Penfield and Boldrey in the 1930s showed that different parts of the body (including the vocal tract) are represented in spatially localized areas in the brain. How the brain generates temporal patterns of neural activity, like those seen in Figure 11, across a set of interconnected brain areas to orchestrate the articulators of the vocal tract remains an open question.
Figure 11. Neural recordings from the human cortical surface during the production of speech. The top row shows a schematic configuration of the vocal tract required to produce the consonants 'b', 'd', and 'g', while the middle row shows spectrograms (acoustic power at different frequencies as a function of time) of the produced speech sound ('ba','da', and 'ga'). The colored traces below show the spatiotemporal patterns of neural activity recorded during the produced speech. These speech sounds are produced by overlapping, yet distinct, spatiotemporal patterns of cortical activity. Figure courtesy of Jesse Livezey and Kris Bouchard.
Brains are composed of nonlinear processing elements (neurons), which exhibit a general hierarchical organization as signals are successively processed. Therefore, we posited that the hierarchical and nonlinear processing of deep neural networks (DNNs) would be well matched to the complex neural dynamics of speech production. On large (millions of examples), complex data sets, DNNs have demonstrated their ability to outperform traditional methods across many tasks. However, this improvement has not been demonstrated on neuroscience analytics tasks, which typically have much smaller (thousands of examples) data sets.
In recent work, we found that, even on the relatively small data sets typically attained in neuroscience experiments, DNNs outperform traditional methods for decoding (i.e., translating) brain signals into produced speech, attaining state-of-the-art speech classification performance (up to 39% accuracy, 25x over chance). Additionally, the performance of DNNs scales better than traditional classifiers as the training data set size grows, maximizing the return on relatively limited, but highly valuable data. The input data set comprised of 85 channels and 250 time-sample signals, which is classified into 1-of-57 categories. Data sets for an individual subject typically only have about 2,000 training examples, necessitating extensive hyperparameter search for best performance. The best networks had one or two hidden layers with hyperbolic-tangent nonlinearities, and were trained on GPUs and CPUs using the Theano library. Each model trained relatively quickly (30 minutes), but hundreds of models were trained during hyperparameter search. These results suggest that DNNs could become the state-of-the-art for brain-machine interfaces in the future, and that more work is needed to develop best practices for training deep networks on small data sets.
Beyond their capabilities to decode brain signals, which is critical for prosthetic purposes, we also investigated the utility of DNNs as an analytical tool to reveal structure in neuroscience data. We found that DNNs were able to extract a rich hierarchy of cortical organization during speech from noisy single-trial recordings. The extracted hierarchy (see Figure 12) provides insight into the cortical basis of speech control. We expect deep learning's application to data analysis problems in neuroscience to grow as larger and more complex neural data sets are collected.
Figure 12. Dendrogram constructed from the confusions (i.e., errors) made between syllables by the DNN trained to classify speech from human brain activity. We observed a hierarchical structure of linguistic features, providing insight into the cortical organization of speech motor control. Figure courtesy of Jesse Livezey and Kris Bouchard.
The Daya Bay Reactor Neutrino Experiment searches for physics beyond the Standard Model of Particle Physics by measuring properties of antineutrinos, elementary subatomic particles produced by beta-decay in a nuclear reactor. Physicists monitor the light produced in large tanks of a detector medium, called liquid scintillator, looking for the characteristic double flash of light coming from an antineutrino interaction. Other background processes also produce flashes of light. Some backgrounds, such as cosmic-ray muons, are easy to identify, but others (like decays of Lithium-9 isotopes produced by the muons) closely mimic the neutrino signal. Separating the antineutrino signal from backgrounds is a difficult task; it can lead to systematic uncertainty and a reduction in signal efficiency as real antineutrino events are inadvertently rejected.
Currently, the Daya Bay analysis uses the time and total energy to discriminate signal from background. But there is information in the spatial distribution of light, which leaves open the possibility of better discrimination. By using unsupervised techniques from deep learning, we may learn the features that identify antineutrino events as distinct from Lithium-9 decays. With knowledge of the identifying features, we can update our current analysis cuts to enhance their discriminating power and improve the precision of our neutrino measurement.
The power of unsupervised deep learning is apparent in a case study performed to separate the antineutrino signal from a well-understood background caused by two uncorrelated flashes of light. In our case study, real data, rather than simulated data, was used to train the neural network. This is uncommon in supervised learning, but it works well in the unsupervised regime because it eliminates uncertainties and bias due to discrepancies between simulated and real data. A previous study used unsupervised learning to address a related signal-to-background problem at the Daya Bay experiment.
and the decoding phase, in which an encoding is decompressed in an attempt to recover the original physics event image.
To successfully recover the original uncorrupted image, the autoencoder must learn how to infer missing information from the corrupted image it's provided. When trained correctly, the autoencoder will create encodings that contain information on the important distinguishing features of the input images.
Figure 13. Architecture of the denoising autoencoder used for this study. Figure courtesy of Samuel Kohn, LBNL.
By using the t-SNE dimensional reduction algorithm, we can visualize the 16-dimensional encodings on the 2D cartesian plane. In Figure 14, it is clear that the neural network distinguishes our signal events from the accidental background—without being trained on individual event labels. This is a promising advance, which helps validate the use of an unsupervised neural network trained on real data. We will continue using our trial data set of accidental background events to improve the network architecture and determine which features are important to the neural network. After the technique is developed further, we will apply it to the physics problem of separating Lithium-9 background.
Figure 14. A t-SNE plot showing the encodings of signal (blue) and background (red) events. The separation of blue from red indicates the neural network recognized features of the signal that are not present in the background, and vice-versa. Figure courtesy of Samuel Kohn, LBNL.
The Large Hadron Collider collides protons 40 million times per second at the highest energies ever achieved. Each collision produces sprays of resulting particles that are detected in instruments, such as the ATLAS detector (Figure 15), with hundreds of millions of channels of electronics attempting to discover new, previously unknown particles. The high luminosity upgrade of the LHC (HL-LHC) is expected to increase the rate of collisions by an order of magnitude. Data from the current detector already totals hundreds of petabytes. The only way to handle this volume and complexity of data is to rapidly filter away the bulk of it using both "triggers" on the detector and filters in offline data analysis. After triggers have down-sampled the data (to around 200 events per second), it is then reconstructed into objects such as particle tracks and energy deposits, reducing to hundreds of dimensions per event. This is then further down-sampled to analysis data, which has tens of dimensions depending on the particular physics of interest. Figure 16 shows a collision event illustrating some of these detector signals and the higher-level reconstructed objects.
The 2013 Nobel Prize in Physics for the theory of the Higgs Boson was a direct result of the detection of this particle at the LHC. The Higgs Boson completed the Standard Model of Particle Physics, and the exact nature of the new physics beyond the Standard Model is not well known. Therefore, the accuracy and speed of the triggering, reconstruction, and physics analysis algorithms directly impacts the ability of the experiment to discover new phenomena—more than ever before. It is already clear that the current approach and algorithms for filtering data will struggle to scale computationally to the next phase of the LHC, and they run the risk of overlooking more exotic new physics signals. It is therefore vital to explore innovative, efficient methods to tackle data filtering. Methods that apply machine learning to run a physics analysis at the scale and rates of the initial detector signals or raw data have the potential to make new discoveries that will change our understanding of fundamental physics.
Figure 15. The ATLAS Detector at the LHC. Figure courtesy of CERN, used with permission.
Figure 16. A particle collision in the ATLAS detector, showing deposits in the calorimeter and reconstructed jets. Source: ATLAS experiment, copyright CERN, used with permission.
Figure 17. A simulated "image" from signals in the "calorimeter" part of the simulated LHC detector as input into convolutional network. Simulation is produced using the simulation packages Pythia and Delphes. Figure courtesy of Wahid Bhimji.
Deep learning offers the potential to learn novel selection filters to extract rare new physics signals that are more accurate than existing approaches, more flexible for alternative new physics signatures, and also computationally tractable to run on large dimensionality input data (corresponding to detector channels). We are exploring classifiers trained on simulations of new physics and well as anomaly detection algorithms that only use background (known Standard Model) samples for training. The signals that are output from particle physics detectors can be thought of as images (such as Figure 17), and therefore we exploit convolutional architectures. An example architecture, showing the layers of the neural network we use for classification is shown below.
Figure 18. Example architecture for classification of data from the LHC. Figure courtesy of Thorsten Kurth.
Our approach is novel in that it uses data from the detector before it has been reconstructed into high-level physics objects using high-resolution images (currently 227x227 pixels). This allows us to learn more sensitive patterns that may not be exploited by existing physics algorithms. Using large models and data sets requires scaling across multiple compute nodes, which is also novel for deep learning in this science domain.
We currently achieve classification performance in excess of the simple selections on high-level reconstructed features typically used for selecting this kind of physics, demonstrating the suitability of these kind of architectures. We are also scaling these architectures to large numbers of compute resources and beginning to explore anomaly detection that doesn't require simulating the new physics of interest.
Performance and scaling: Deep learning methods are computationally expensive. Our current experiments process 1-100GB-sized data sets, and take anywhere from one day to one week to converge on many-core architectures. This is prohibitive for hyperparameter tuning. There is a critical need to improve single-node performance for many-core architectures, and to scale networks using data and model parallelism on O(1000) nodes.
Complex data: Scientific data comes in many shapes and sizes. 2D images can have anywhere from 3-1000 channels, 3D structured and unstructured grids are common, both sparse and dense data sets are prevalent in the field, and one can often find graph structures that encode key relationships. It is important that deep learning methods/software be able to operate on such data sets.
Lack of labeled data: Scientists do not have ready access to vast amounts of high-quality, labeled images. Even if some communities were to self-organize and conduct labeling campaigns, it is unlikely we would have high-quality, ImageNet-style databases will millions of images. Much of domain science will always operate in the unsupervised (i.e., unlabeled data) or semi-supervised (i.e., limited labeled data for some classes) regimes. It is therefore very important that deep learning researchers continue to demonstrate compelling results in light of limited training data.
Hyperparameter tuning: Domain science experts have limited intuition into the effect of tweaking network configurations (number and depth of convolutional layers); types of non-linear/pooling functions; learning rates; optimization schemes; training curricula, etc., for their specific problem. In order for deep learning to be broadly employed in science, it is important that we package capabilities for automatically tuning these hyperparameters.
Interpretability: In contrast to commercial applications, where it is perhaps acceptable to have a black-box, near-perfect predictor, domain scientists need to understand and explain the function that the neural network is learning to the rest of their communities. They need to understand what features are being learned, whether the features are physically meaningful/insightful, and whether non-linear functions on the learned features are analogous to physical processes. In an ideal case, choices of functions and features would be constrained based on our understanding of domain sciences. Currently, this important link is missing, and we are hopeful that the next generation of deep learning researchers will try to bridge the interpretability gap.
We have presented a number of successful applications of deep learning from diverse scientific disciplines at Lawrence Berkeley National Laboratory as well as the open challenges. It would be fair to summarize our empirical experience with deep learning as being extremely encouraging. We believe it is simply a matter of time before deep learning will be explored (and adopted) by several domain science disciplines. We will caution that some domain sciences will make more rigorous demands on the theoretical foundations and properties of deep learning networks. We would like to encourage deep learning researchers to engage the domain science community as a rich source of interesting problems.
This post is a collaboration between contributors from Lawrence Berkeley National Laboratory, UC Berkeley, UCSF, and the University of Montreal.
Article image: Sample results (ground truth: Green, DL prediction: red) for weather patterns and their location. (source: Courtesy of Evan Racah, LBNL, used with permission.).
This webcast looks at how Elasticsearch is taking search engine technology and branching it out to provide insightful analysis of large datasets.
Smart cities and smart nations run on data.
Making search smarter through better human-computer interaction.


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Man, 22, dies after taking 'unknown substance' at Lost Paradise music festival
By Megan Gorrey and Sally Rawsthorne & Josh Dye
Updated December 30, 2018 — 9.27pm first published at 9.19am
A Brisbane man is dead and two people fell ill after they took an "unknown substance" at a music festival on the NSW Central Coast, reigniting the debate over whether pill testing should be trialled at the events.
Josh Tam, 22, was taken to Gosford Hospital from the Lost Paradise music festival at Glenworth Valley, near Gosford, about 8pm on Saturday. He died a short time later.
Josh Tam, 22, died at the weekend after falling ill at the Lost Paradise music festival near Gosford, NSW. Credit:Facebook
Mr Tam was a QUT student, Marist College Ashgrove old boy and former GPS rugby player.
Tributes flowed on social media on Sunday night, as friends of Mr Tam left heart emojis and wrote supportive messages on Facebook.
"Stick together and stay strong," one person said.
"Lost a good one. Love you brother," another person said.
About 11,000 people have attended the Lost Paradise music festival, which runs until Tuesday.
Investigations into Mr Tam's cause of death are continuing. Police said another man and a woman were treated in hospital after they took the substance.
Brisbane Water Police District Commander Acting Superintendent Rod Peet said one of Mr Tam's friends had the "unfortunate experience" of having to identify his body at the hospital.
"The best safety message is don't take drugs," he said.
Acting Superintendent Peet said revellers' "methods of secretion are becoming sophisticated" amid a heavy police presence at the event and people were not "carrying drugs in their pockets".
"We've found drugs in Vegemite jars, aerosol containers, and on one instance, someone had stuffed drugs into the stuffing of a barbecue chicken.
"If [police] need to search more chickens to find drugs, they'll be doing that."
The latest incident follows the deaths of three young people from suspected drug overdoses at music festivals since September, reigniting debate over drug policing and pill testing schemes.
Greens MP David Shoebridge said the death again showed why the government should reconsider its position on pill testing, which he said was needed to "reduce the unknowns".
"Both the coalition and Labor are refusing to call off the dogs and support pill testing, it is so dreadfully sad to see politics failing young people like this.''
"Young people want practical advice, real-time accurate information and a government that helps them keep safe, not one who's only plan is to lecture and punish them."
The Berejiklian government remains stridently opposed to introducing a pill testing trial in NSW.
"I am devastated to hear another life has been lost at a music festival, Premier Gladys Berejiklian said on Sunday.
"I extend my deepest sympathies to the young man's family and friends.
"There is no such thing as a safe illegal drug and I urge everyone who is attending a festival to stay safe and think carefully about the decisions they make around drugs."
A Lost Paradise spokeswoman labelled the man's death at the "drug-free" event "very distressing" on Sunday morning.
"Our sincerest thoughts and condolences are with the family and friends of the deceased. The matter is the subject of a police investigation and we will continue to cooperate and provide whatever assistance we can."
She said in a statement that a "great deal of planning and effort goes into ensuring the safety and welfare of our festival-goers and event staff".
"We work closely with local police to try to ensure festival-goers respect our drug free policy and NSW Ambulance to provide extensive medical support across the festival site."
Baulkham Hills teenager Callum Brosnan, 19, died at the Knockout Games of Destiny music festival on December 9.
In September, 23-year-old Joseph Pham and 21-year-old Diana Nguyen died at the Defqon. 1 music festival at Penrith.
About 11,000 people attended Lost Paradise, which started on Friday and finishes on Tuesday.
Police handed 50 revellers court attendance notices for drug possession and charged three people with drug offences.
They included a Drummoyne man, 21, allegedly caught with 105 MDMA pills. An Elanora Heights man, 23, was found with 80 MDMA pills and 65 bags of cocaine, police said.
Police issued seven cautions for cannabis. Police drug dogs have searched 184 people and 97 vehicles at the festival.
In October, Ms Berejiklian promised tougher penalties for drug dealers who caused a death at music festivals, in response to an expert panel into drug-related deaths at the events.
Megan Gorrey is the Urban Affairs reporter at The Sydney Morning Herald.Connect via Twitter or email.
Sally Rawsthorne is a Crime Reporter for The Sydney Morning Herald.Connect via Twitter or email.
Josh Dye is a news reporter with The Sydney Morning Herald.Connect via Twitter or email.

redpajama_set_name: RedPajamaCommonCrawl


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A Memoir of Recovery
by Bill Clegg
Bill Clegg has a simple goal: stay clean and sober for 90 days. Six weeks out of his most recent rehab, he returns to New York and starts attending two or three meetings each day. It is in these refuges that he befriends essential new allies. At first, the support is not enough: Clegg relapses with only three days left. That's when the battle to reclaim his life gets reignited. As any recovering addict knows, hitting rock bottom is just the beginning.
April 8-14: Impeachment, Mourning, Sobriety And Soccer
Crack Addict Aspires To 'Ninety Days' Of Sobriety
A Memoir of the Craft
CD/Spoken Word, 8 sound discs (ca. 8 hr.)
The author shares his insights into the craft of writing and offers a humorous perspective on his own experience as a writer.
Jargon To Jabberwocky: 3 Books On Writing Well
Stephen King: The 'Craft' Of Writing Horror Stories
Macabre Master Stephen King Returns To Form
A Wedding In Haiti
by Julia Alvarez
Growing up in the Dominican Republic, author and poet Julia Alvarez says, she was taught to view neighboring Haiti with suspicion. But because of a promise made one night, Haiti, and a particular Haitian boy named Piti, would become ingrained in her heart — so much so that she would find herself smuggling Piti out of Haiti with his new family, and then finding her way back there after the devastating 2010 earthquake.
Haiti: 'The Sister I Hardly Knew'
March 18-24: True Tales From Haiti, Paris, Early Christendom And Hitler's Germany
'A Wedding In Haiti': Making Good On A Promise
The Dressmaker of Khair Khana
Five Sisters, One Remarkable Family, and the Woman Who Risked Everything to Keep Them Safe
by Gayle Tzemach Lemmon
The incredible true account of Kamila Sidiqi who, when her father and brother were forced to flee Kabul, became the sole breadwinner for her five siblings. Armed only with grit and determination, she picked up a needle and thread and created a thriving business of her own and held her family together.
Foreign Affairs: Love In Rome, Battle In Cuba And Mystery In Sweden
In Kabul, A 'Dressmaker' Sows Entrepreneurial Seeds
The Long Goodbye
by Meghan O'Rourke
The author discusses how caring for her terminally ill mother ultimately strengthened the bond between the two, as well as fortified the author's family during the difficult time. By the author of the poetry collection Halflife.
'The Long Goodbye': A Syllabus For Modern Mourning


text:
Biografia
Nato nel 1929 sull'isola di Burano (Venezia), a 23 anni partecipò ai Giochi olimpici di , nell'otto, insieme ad Attorese, Baldan, Bozzato, Enzo, Ghiatto, Nardin, Nuvoli e Smerghetto, arrivando 3º in batteria con il tempo di 6'17"0, non riuscendo ad accedere alla finale neanche grazie al 2º posto nel recupero in 6'15"8.

Note

Collegamenti esterni

redpajama_set_name: RedPajamaWikipedia


Warren Buffett is live on CNBC's "Squawk Box" to discuss his just-announced decision to give both Greg Abel and Ajit Jain the title of Berkshire Hathaway vice chairman. The move makes it very likely one of the two will be Berkshire's next CEO, although Buffett says he has no plans to leave anytime soon.
3. Why Abel and Jain now?


Remove from the Task List window all Tasks that were provided by this scanner.
Notify the framework that the scanner has finished.
Add/remove Tasks for the given file/folder.
Add/remove Tasks that aren't associated with a particular resource.
Notify the framework that the scanner started looking for available Tasks.
file - Resource (file or folder) the tasks are associated with.
tasks - Tasks associated with the given resource or an empty list to remove previously provided Tasks.
tasks - Tasks associated with this TaskScanner or an empty list to remove previously provided Tasks.


Cuish is part of an association of Mezcal leaders in Oaxaca which known for the wide diversity of agaves. The role of Mezcal in this community is of the utmost importance. Whether celebrating social or religious tradition, Mezcal is a part of the ritual. That's why Cuish's process is 100% handmade and traditional, respecting the customs of the different peoples of Oaxaca. In each bottle, you will appreciate the aromas and flavors of each variety of agave. Whether aged for 7 years or 20, you will lavish in the quality.


text:
Share Fallers
Why the Mesoblast (ASX:MSB) share price crashed 45% lower today
The Mesoblast limited (ASX:MSB) share price has crashed 45% lower on Friday. Here's what you need to know…
James Mickleboro
James Mickleboro has been a Motley Fool contributor since late 2015. After studying economics at university back home in the United Kingdom, James came to live in Australia and managed to land a job at an Australian fund manager. This was the start of a love affair with Australian equities and he hasn't looked back since. James is part of the CFA Institute's Chartered Financial Analyst program and hopes it teaches him how to become an astute investor which allows him to help others with their own investing. Outside of reading and researching he spends many a late night watching the English Premier League and Seinfeld reruns.
You can see James' holdings here.
Latest posts by James Mickleboro (see all)
Broker says Life360 (ASX:360) share price has ~80% upside - January 29, 2022 12:34pm
3 'champion stocks' for ASX investors in 2022 - January 29, 2022 11:00am
3 high quality ETFs for ASX investors in February - January 29, 2022 10:02am
Last Updated December 18, 2020 10:51am AEDT
| More on: MSB
The Mesoblast limited (ASX: MSB) share price has returned from its trading halt and crashed significantly lower.
In early trade the biotechnology company's shares are down a massive 45% to $2.08.
Why is the Mesoblast share price crashing lower?
Investors have been selling the company's shares this morning after it provided an update on the randomised controlled trial of its remestemcel-L product in ventilator-dependent patients with moderate to severe acute respiratory distress syndrome (ARDS) due to COVID-19 infection.
According to the release, the Data Safety Monitoring Board (DSMB) has performed a third interim analysis on the trial's first 180 patients.
The trial was aiming to achieve a primary endpoint of 43% reduction in mortality at 30 days for treatment with remestemcel-L on top of maximal care in a trial of 300 patients.
This target was based on pilot data observed during the initial stages of the pandemic when control mortality rates were exceedingly high. It was also prior to new evolving treatment regimens that have reduced disease mortality in ventilated patients.
While the DSMB has reported that there were no safety concerns, importantly, it noted that the trial is unlikely to meet the 30-day mortality reduction endpoint at the planned 300 patient enrolment.
In light of this, the DSMB has recommended that the trial be ended early at just 223 enrolled patients.
While the primary endpoint has not been achieved, management is pushing ahead to see if it achieves its secondary endpoints.
These include days alive off mechanical ventilation at 60 days post randomisation, overall survival, days in intensive care, duration of hospitalisation, and cardiac, neurological, and pulmonary organ damage.
The company has suggested that changes in the treatment regimens for COVID-19 patients are to blame for the trial's failure.
It explained: "During the course of the trial, as the pandemic has evolved, numerous changes in the treatment regimens for COVID-19 patients occurred, including both prior to and while on mechanical ventilation that may have an effect on the mortality endpoint in the trial."
"These include extended management of patients prior to ventilator support, and use of experimental therapies such as dexamethasone, antivirals, and re-purposed immunomodulatory agents. All of these may have changed the natural course of ventilated patients and reduced overall mortality rates during the trial compared to the early stages of the pandemic," it added.
What about the Novartis deal?
Readers may be aware that Mesoblast recently signed a deal with Novartis for remestemcel-L. That agreement will have an initial focus on the development of a treatment for acute respiratory distress syndrome (ARDS), including that associated with COVID-19.
It also sees Novartis pay US$50 million upfront and then upwards of US$1.25 billion in milestone payments.
This news appears to have led to concerns that this deal may now not go ahead.
Though, the release advises that "Mesoblast and Novartis will both analyse these results to identify meaningful clinical outcomes that may guide decisions on the development program for remestemcel-L in non-COVID ARDS."
Motley Fool contributor James Mickleboro has no position in any of the stocks mentioned. The Motley Fool Australia has no position in any of the stocks mentioned. The Motley Fool has a disclosure policy. This article contains general investment advice only (under AFSL 400691). Authorised by Bruce Jackson.
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Rich Ross was inducted into the Hall of Fame very shortly after competeing in his first TFC tournament. Rich won his first TFC tournament by winning every game in one of his first TFC tournaments. This had only been done once before.


With praise and gratitude to Almighty God, the Diocese of London, and St. Peter's Seminary, also announce the ordination of Mr. Paul Kim to the transitional diaconate. A transitional deacon is a seminarian in the last year of preparation for ordination to the priesthood.
The Sacrament of Holy Orders will be conferred by His Excellency Joseph Dabrowski, Auxiliary Bishop of London, on Monday, June 12 at 7pm at Holy Angels Parish, 502 Talbot Street, St. Thomas. All are welcome to attend.
We assure Paul of our support, and prayers, as he prepares for ordination.


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Our 10 Favorite Anti-Christmas Movies
By Jason Bailey
With the holiday season in full swing, it's easy to get burned out — cable stations are running crappy movies like Jingle All the Way and The Santa Clause 24/7, saccharine holiday music is blasting out of every speaker, and you've still got like half your list to buy, and what the hell do I get these freaks?
Calm down. Have an eggnog. Put some rum in it. And enjoy some of our favorite anti-Christmas movies — not films that hate the holiday, per se (though a couple of them do), but ones that cut through all that warmth and good feeling and get at the darkness underneath. Our contrarian Christmas viewing list is after the jump.
It would be unthinkable to begin this discussion with any film other than Bad Santa, Terry Zwigoff's gloriously mean-spirited, piss-and-vinegar-filled 2003 holiday comedy. Billy Bob Thornton plays Willie T. Stoke (a decidedly W.C. Fieldsian name for the kind of role Fields could have played, had he been born sixty years later), a horny, drunken, pissed-off safecracker who masquerades as a department store Santa every holiday season in order to pull off a Christmas Eve score that he'll live off for the next year. John Requa and Glenn Ficara's script almost seems designed to offend, with scenes of a hammered Santa beating the shit out of papier-mâché animals, urinating on himself in the Santa chair, and sodomizing a clerk in the Plus-Sized dressing room. Acid-tongued and wickedly funny, Bad Santa has rightfully become an anti-holiday classic.
The Ref
"You know what I'm going to get you next Christmas, Mom? A big wooden cross, so that every time you feel unappreciated for your sacrifices, you can climb on up and nail yourself to it." Not many Christmas-themed movies would trot out dialogue like that (made all the more bitter and nasty by the venomous reading it gets from Kevin Spacey), but that only one of the great lines in Ted Demme's terrific 1994 comedy The Ref. Released by Touchstone in March of that year (nice scheduling!), the film was promoted primarily as a vehicle for Denis Leary and his angry-smoker persona. The few who saw it in that initial run were surprised to discover a smart, well-written (by Fisher King scribe Richard LaGravanese), insightful look at a dysfunctional family and a crumbling marriage — a kind of Christmas with George and Martha. Luckily, it found a second life on home video, where we can enjoy Spacey and Judy Davis' holiday-themed sniping forevermore.
Scrooged
Richard Donner's big-budget 1988 revamp of A Christmas Carol ends with plenty of holiday cheer — too much, perhaps. But they've also got quite a bit to make up for, by that point; Donner's portrait of smug, unhappy network executive Frank Cross (played to perfection by Bill Murray) is so bleak, jaded, and nasty that a heartfelt sing-along is about all that can turn it around into an upbeat holiday picture. Not that we're complaining — from the mushroom cloud-heavy holiday promos ("That looked like the Manson family Christmas special!") to discussions of TV nudity ("Well, I'm sure Charles Dickens would have wanted to see her nipples") to a sugarplum fairy with a mean right hook ("the bitch hit me with a toaster!") to a shotgun-wielding disgruntled employee, Scrooged offers plenty of dark laughs on the way to its happy ending.
Bob Clark's 1974 Canadian shocker is the heartwarming holiday tale of a serial killer picking off the residents of a sorority house. Happy yuletide! This low-budget thriller gets the job done with blunt effectiveness — and, predating Halloween, Friday the 13th, and their countless imitators, points the way towards the "slasher film" craze of the 1980s. Director Clark, however, didn't participate in that trend; he'd move on to comedies, including one of the most beloved of all Christmas movies, A Christmas Story.
Silent Night, Deadly Night Part 2
The original 1984 Silent Night, Deadly Night is perhaps the most controversial of all Christmas-themed films. Piggybacking off the idea of slasher movies for every occasion, director Charles Sellier crafted the low-budget tale of young Billy, whose parents are brutally murdered by a man in a Santa suit. Years later, while working at a toy store, Billy is forced to fill in as the store Santa — and donning the red suit sends him on a killing spree (complete with his own catchphrase: "Naughty!"). It's a really terrible movie, and its tasteful Christmastime release led to widespread protests and condemnation — for both the film and its TV spots, which highlighted the axe-wielding Santa. Though it out-grossed the original Nightmare on Elm Street (released the same day) in its opening weekend, distributor Tri-Star pulled the film's ads less than a week into its run, and yanked the film from theaters after two weeks. When it was re-released the following spring, the controversy was the centerpiece of the ad campaign, and the film ultimately turned a tidy profit — leading to four sequels. Why, then, do we recommend the first of those sequels over the original? Two reasons. First, because Silent Night Deadly Night Part 2 features so much footage from the first film (nearly a third of the running time, according to an accounting at Badass Digest) that it's like you're getting a two-for-one by skipping the original. And second, because Part 2 includes the scene above.
Sint (aka Saint Nick )
Dick Maas's Dutch horror/comedy opens with St. Nicholas and his goons on horseback, terrorizing a village which then raises their pitchforks and burns him alive. So yeah, right away, not your average Christmas movie. Maas carries off his tale of a murderous Santa with much greater success than his American counterparts because his film (unlike the Silent Nights) has got a forceful and wicked sense of humor; the filmmaker has clearly ingested copious amounts of American horror movies, and regurgitates them with a wink and nudge. It's not the most festive movie, but it's a lot of fun.
The Ice Harvest
Billy Bob Thornton, the patron saint of anti-Christmas movies, returns to our list with this 2005 caper film, which includes such tidings of comfort and joy as "Only morons are nice on Christmas" and "Christmas Eve. Ho ho fucking ho!" It's the tale of a lawyer (John Cusack) and a businessman (Thornton) who team to rip off a mob boss on Christmas Eve; double-crosses, dead bodies, and holiday drinking ensue. Though promoted as a comedy (which you'd expect from its director, Groundhog Day's Harold Ramis), The Ice Harvest has a surprisingly dark, almost noir streak to it, and has very little time in its tight 92 minutes for holiday pleasantries.
John Landis' 1983 satire isn't as holiday-centered as some of the other films on our list, but is worth revisiting over the season for the scene that finds disgraced (and now drunken) businessman Louis Winthorpe III (Dan Aykroyd) donning a Santa suit to sneak in to his former employer's Christmas party, where he attempts to plant drugs in the desk of his replacement (Eddie Murphy) before stealing a full side of salmon for consumption on a city bus. And that's not even the low point of his holiday.
As with Trading Places, the holiday season is mostly window dressing in Go, director Doug Liman's 1999 Pulp Fiction-style crime triptych. The story itself concerns drug stings, body disposal, lap dances, car chases, gunplay, a three-way, and a giant holiday rave called "Mary Christmas." The ladies (and some of the fellas) are treated to Timothy Olyphant as a pajama-bottom-and-Santa-hat-clad drug dealer; Katie Holmes gives a cute little monologue about the pleasures of holiday surprises. Go concerns itself with the less-wholesome pleasures of the holiday season — and thankfully, no heartwarming lessons are learned.
OF COURSE we didn't forget Gremlins. Joe Dante's 1984 horror comedy not only features the most adorable Christmas gift ever (little Gizmo, the cuddly "Mogwai") that also, ha ha, transforms into a snarling killer monster, but it features what may very well be the darkest holiday story ever put to film: girlfriend Kate's explanation of why she hates Christmas. As told by Phoebe Cates, it is a genuinely disturbing scene, mixing comedy and horror a bit too uneasily for both studio heads (who wanted it out) and executive producer Steven Spielberg (who didn't like it either). But director Dante stood his ground, and gave us the gift of, as it is titled in the YouTube clip above, "the worst Christmas story EVER." Happy holidays!


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Republican Senator Rand Paul said Sunday he's "worried" about Supreme Court nominee Brett Kavanaugh's record on constitutional privacy rights, signaling that he's far from certain to vote for President Donald Trump's pick in the closely divided Senate.
"I'm concerned about Kavanaugh," the Kentucky Republican said on "Fox and Friends" on Sunday.
Paul said he's willing to meet with Kavanaugh and bring an "open mind" to the confirmation process. His vote could be pivotal in a chamber that Republicans control by a 51-49 margin, with Arizona's John McCain absent as he fights brain cancer. There are centrists in both parties who've said they're undecided about how they'll vote on the nominee to replace retiring Justice Anthony Kennedy.
To weigh in on this information provided by Bloomberg, engage in our LIVE CHAT below. Scroll down.


Dax Martinez-Vargas: epic comedy director; screenwriter; screen performer; musician and all round gent. Having made audiences laugh for years and years, Dax needs little in the way of an introduction.
Stop by and check out his spots for Carling, Sky and Burger King to see what type of scale and humour Dax does best. Feel free to ask us about his rap videos for Hershey, Seagate and Adobe too. Special stuff.
What makes Dax Dirty isn't just his dashing good looks, his hair or succession of genre-hopping musical albums. Dax is especially Dirty because of the way he fits big, genre stories into little screen time. Dax is well known for pushing creative boundaries and playing with audience expectations, rightly earning him the title 'King of Mock Epics'.


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#ifndef EVENT_CHARACTER_PICKS_UP_ITEM_H
#define EVENT_CHARACTER_PICKS_UP_ITEM_H

#include "EventHandler.h"

/**
Tokens to identify possible pickups that the player may interact with.
All possible pickup items are listed here.
*/
enum PickupType {
	Pickup_Health, // Health Pickup of any type
	Pickup_Power,  // Weapon power
	Pickup_Coin    // Coin pickup
};

/**
Message to notify that a character has picked up an item
of the given PickupType.
*/
class EventPicksUpItem : public Event {
public:
	EventPicksUpItem(ActorID _id, PickupType _ptype) {
		id = _id;
		ptype = _ptype;
	}

public:
	ActorID id;
	PickupType ptype;
};

#endif

redpajama_set_name: RedPajamaGithub


Wandering the streets of Winnipeg, I couldn't shake the feeling that I'd been before. The truth was I'd never stepped foot anywhere in Manitoba prior to my arrival this summer, however, the provincial capital reminded me of Albany, New York, the state capital in which I was born and raised, and for that I liked it instantly.
My impressions of Winnipeg prior to arriving were more neutral – as a pop culture addict, I knew the episode from The Office poked fun at Winnipeg winters, while a mention on The Simpsons Winnipeg's reputation for self deprecation, among dozens of other throw away lines . Neil Young, Nia Vardalos, Anna Paquin, and Winnie the Pooh — a bunch of famous winter sports people I'm clueless about – all hail from here. As a traveler, I knew it is as the gateway to the polar bear mecca of Churchill, a bucket-list trip for wanderers around the world. Traditionally, the rest of Manitoba rarely found its way into the destination daydreams of travelers – but that's starting to change.
Recently, National Geographic named a journey to Winnipeg one of – how's that for an endorsement? Affectionately nicknamed "The Peg" by the residents who love it, the city holds around 75% of the population of Manitoba – who despite their penchant for self-deprecating jokes, seem genuinely proud of the place they live, and for good reason. Throughout my visit I asked people over and over again, "what do you love about living in Winnipeg?," and I always found myself smiling at their answers. Friendly people. Family first. The cultural benefits of a big city balanced with the simplicity of life in a small town, in a simpler time.
Winnipeg works overtime to live up to the reputation boasted on its provincial license plates: Friendly Manitoba. As a cold-hearted New Yorker, I at first reacted with suspicion to the hospitable immigration official who welcomed me at the Winnipeg Airport, to the chatty driver who wrote down his favorite restaurant recommendations for me, and to the random passersby on the street who all seemed to be smiling!
But I quickly caught on. A few days into my trip, when I peered into a café moments after the closed sign was flipped, I was barely shocked when the owner rushed out to apologize that the kitchen was indeed closed, but he'd be happy to make me a coffee if I needed a caffeine fix. And further afield, in Churchill, I just nodded when another traveler expressed shock at the kindness of a bakery owner who's pie she'd gushed over before vowing to return for another slice. When another customer purchased the full pie off the counter the night before our return, the baker remembered that lady from Georgia who promised to come back for one more slice the next day – and so she woke up before dawn to make sure there was one waiting for her. That's just Manitobans, I said.
And while they have their kindness and a love of their country in common, Manitobans are an impressively diverse bunch. The province is home to the largest French-speaking community in Western Canada, the largest urban aboriginal population in the country, and the largest Icelandic population outside Iceland in Gimli!
In Winnipeg, trendy, centrally-located boutique hotels and beautiful, trusted brand hotels alike can be had for $100-150 a night. Dinner at hip restaurants can set you back $30 – including a cocktail! Free entertainment options abound, while popular attractions are competitively priced. Many a Winnipeger confessed to me that the province has a reputation for frugality (when you search "are winnipegers…" Google autofills "friendly" and "cheap" as the suggested searches), a trait that translates to fabulous deals for travelers passing through. Add to it the strength of the American dollar, and US travelers hoping to visit their neighbor to the north have an attractive, affordable destination on their hands.
Many international travelers come to Winnipeg as a stopover en route to the nature-lover's paradise that is Churchill. Unreachable by road, Churchill can only be accessed via train or plane from Winnipeg. After spending a few days in Winnipeg en route to Churchill, I can only conclude you'd be crazy not to extend your mandatory layover and do the same.
And it's not just those going north who stop here. Located in the true geographical center of North America (look at a map if you don't believe me!), Winnipeg is also a frequent stop for those on coast to coast journeys across Canada.
This unpretentious city in the prairie better watch out – Winnipeg has plenty to brag about these days. Foodies are abuzz about the city's culinary status as a hidden gem, architects and activists alike are flocking to the much lauded Canadian Museum for Human Rights, a built-a-trip-around-it kind of museum opened in 2014, and festival addicts are trying to keep the secret of some of Canada's best and biggest celebrations.
Quite frankly, I arrived in Winnipeg sure that it was a destination I'd be able to tick off my list upon departure. Did I think I'd enjoy myself? Of course! The passion of my many local social media followers who cheered me on as I planned the trip made me sure of it!
But I had no idea that it was somewhere I'd be so eager to return to before I even left. Eventually, I gave in and started a list of all the Winnipeg attractions and restaurants I wanted to hit up on my next visit, which I hope will include side trips to the bison-filled Riding Mountain National Park and the lakeside Gimli.
Winnipeg will probably never have the same name recognition that larger Canadian cities like Toronto, Montreal and Vancouver enjoy, just like Albany will never know the fame that New York City does. But I think this mid sized city with the small town smiles is just fine with that. For those that live there and those that pass through, there's plenty to make the heart swell in the providence nicknamed Canada's heart beat.
Where to stay: The Fairmont Winnipeg is perfectly located for access to both the Downtown and the Exchange District, boasts a gorgeous indoor rooftop pool and gym, amazing customer service, and fabulous food options. The Four Points is located within a five minute walk of the airport and is perfect for catching early morning flights. I give a huge thumbs up to both. Other options that caught my eye include the cute The Inn at The Forks, the modern Mere Hotel, and the uber-stylish ALT Hotel.
Where to eat: Stay tuned for a whole post of favorites!
How to get around: Much of the city is walkable. For the bits that aren't, Uber and Lyft are sadly not available, though Unicity Taxis can be ordered by phone or by app and found at some hotels. Though I didn't use it, there is an efficient Winnipeg Transit public bus system that can be navigated with the help of the tool.
Bonus tip: Follow and on Instagram for plenty of pre-trip inspiration!
This post was produced by me, brought to you by .
What a cute town, I'll make sure I'll avoid it!
Ha, well considering how affordable Winnipeg is, you really might have to avoid it… might be too tempting to resist!
I am so pumped to read about you coming to Canada, and I can find more inspiration to travel my own country!
It's fun exploring your own backyard, isn't it?
that is a great post, loved it!!
Thank you! Lots more Canada headed your way soon!
Yay for Winnipeg 🙂 What a great place to call home.
Alright, we'll go to Winnipeg first before we train it up to the belugas with our tanks, cool?
Winnipeg is my home city. I think it is a great city to live and to visit. I have written about its many attractions (the Human Right Museum, the Forks, the hermetic secrets tour of the Legislative Building, Museum of Man and Nature, the French heritage in St. Boniface, the Forks, the historic Exchange District, its festivals and more). The rest of Manitoba has lots to offer too. Lots of lakes in the province. Sadly, very few Winnipeggers have made it to Churchill because that trip is so expensive. I glad you enjoyed both Winnipeg and Churchill.
You have a wonderful place to call home! I hope you'll check out my latest post, where I detail some of the attractions you mention as well.
Thank you I enjoyed this post, I would love to visit Canada one day there are many places in the world I would like to visit if I ever get to them it would be great.
I have yet to meet a Canadian town that I didn't love! That country's got it goin' on!
However, I have to correct you on the "geographical center of North America" claim. Winnipeg is on the longitudinal center, but Rugby, North Dakota is the actual center!
As a Winnipeger myself, I had a big smile across my face as I read this post! So glad you enjoyed your visit!
I did, and I loved the suggestions from so many Winnipegers on social media throughout my trip! Such a helpful, friendly bunch!
As a travel blogger myself who grew up in Winnipeg, I'm excited to see this fresh look at my home town. Great writing, Meihoukai!
I'm a Winnipegger, and have spent the summer working in the tourism industry- can I also recommend some of the many tours in the West End/downtown/exchange district areas? Some highlights include the Exchange District's Death and Debauchery tour, the West End BIZ mural tours (for which I am the tour guide!), and a whole host of restaurant tours, including a patio crawl. Winnipeg has been getting more attention than usual lately and I love it!! We'd love to have you back!
Hey Cailey! I took one of those tours and wrote about it in this post. I'd love to come back too!
I was pleased to read about your visit to my home town!! I no longer live there however it will always be my home! Thank you for the beautiful write up. Winnipeg is often missed in the travels of Canada and it has a lot to offer all year round!
Have you tried the restaurant called 'Dwarf no Cachette' on Provencher? It's amazing if you love Japanese cuisine!
I'm not from Manitoba, and I've yet to go to Winnipeg (even just in passing), but it made me feel proud of my country to read about your wonderful experience there! Manitoba has always seemed to be overlooked as a "boring, flat province", yet it has a lot to offer and is actually quite lovely. I can't wait to read more about your experience there!
I love to fall in love with places other travelers are unimpressed by 🙂 But I think Canadians are too hard on Manitoba. It's beautiful!


Sixt mydriver is a private transfer service which provides a personal, chauffeured airport shuttle to and from the Saarbrücken Airport (SCN), city centre, hotels or events in the city. The Sixt mydriver airport shuttle service will be waiting for you when you arrive in Saarbrücken and ensures a safe, luxury trip to the airport when it is time to leave. Professional chauffeurs will help you with your luggage when you arrive and, if desired, we can track your flight's progress to ensure that if there are any delays due to weather, we can adjust collection and departure times. This ensures that your driver will be waiting for you at the perfect time. Our premium quality airport transfer service provides top name brands like BMW, Audi, Mercedes Benz and Jaguar and a range of cars, estates and passenger van models so you can select a car that suits your needs and lifestyle. The airport transfer service in Saarbrücken offers great comfort, luxury and reliability so that your transportation needs in Saarbrücken will be very well taken care of, whether you need a private transfer for a business trip or family holiday.
Private transfer services provide a stress-free shuttle service to and from the airport and events, leaving you free to enjoy the sights and sounds of Saarbrücken. Our top class, air-conditioned and well-maintained vehicles ensure that you will arrive at your hotel feeling relaxed and unhurried, allowing you to begin your trip in style and comfort. Our experienced and professional chauffeurs know the Saarbrücken area and Germany well and will ensure that you are swiftly collected and dropped off at the correct location, whether you're heading straight to your business meeting or to your accommodation. A private transfer allows you the freedom of the city of Saarbrücken so you can travel to your destination comfortably and in the vehicle of your choice. Create the impression you want by using a private shuttle, which is a low stress option and a very affordable and convenient way to get around Saarbrücken. Sixt mydriver offers fixed rates and the price we quote is the price you pay; this means that you won't be charged extra if traffic or late flights delay your transfer.


text:
Q: Android grid view item aspect ratio (make height little bit higher than the width) I wanted to make items of my grid view perfect squares. I used the method suggested by this answer and it worked well.
Following is the code (Overridden onMeasure method of grid view item)
@Override
public void onMeasure(int widthMeasureSpec, int heightMeasureSpec) {
    super.onMeasure(widthMeasureSpec, widthMeasureSpec);
}

Now I want to make the height of the items 1.2 times bigger than the item width. So, I modified the code like below.
@Override
public void onMeasure(int widthMeasureSpec, int heightMeasureSpec) {
    super.onMeasure(widthMeasureSpec, (int) (widthMeasureSpec * 1.2));
}

But, the items become very long and can't even fit into the screen anymore.
Surprisingly, if I set the heightMeasureSpec to a lesser value than widthMeasureSpec, still the item height gets larger than the width. Following code makes items taller.
@Override
public void onMeasure(int widthMeasureSpec, int heightMeasureSpec) {
    super.onMeasure(widthMeasureSpec, (int) (widthMeasureSpec * 0.5));
}

Can someone explain this behavior? Is there any way I can achieve what I want?

A: I didn't face behaviour that you described but usually I use next onMeasure
@Override
protected void onMeasure(int widthMeasureSpec, int heightMeasureSpec) {
    int originalWidth = MeasureSpec.getSize(widthMeasureSpec);
    super.onMeasure(
            widthMeasureSpec,
            MeasureSpec.makeMeasureSpec((int) (originalWidth * 1.1f), MeasureSpec.EXACTLY)
    );
}

A: You should not increment the MeasureSpecs but first get the size from it and then increment it. You should also call setMeasuredDimension(width, height); instead of super.onMeasure(int widthMeasureSpec, int heightMeasureSpec);. Since you are now deciding what the width and height should be.
@Override
public void onMeasure(int widthMeasureSpec, int heightMeasureSpec) {
    int width = MeasureSpec.getSize(widthMeasureSpec);
    int height = (int) (MeasureSpec.getSize(widthMeasureSpec) * 1.2);
    setMeasuredDimension(width, height);
}

redpajama_set_name: RedPajamaStackExchange


Imagine this: It's the hottest day of the summer, your office in the greater Orlando area is packed with customers and employees, and your air conditioning unit stops working. Customers start becoming uncomfortable and begin leaving sooner than they otherwise would have. To top it all off, it's a holiday weekend! You fear that you won't even be able to get in touch with an HVAC company until at least Tuesday. You picture your profits over the next few days disappearing alongside the cool breeze of your air conditioner.
Suddenly, you remember Greens Energy Services has 24/7 commercial AC repair service—including weekends and holidays! You sigh a breath of relief as you pick up the phone to get in touch with the HVAC experts who have been in business for more than 60 years, knowing that their cutting-edge diagnostic equipment and fully-equipped service vehicles will be there soon.
Does this scenario ring a bell? Are you in this situation right now, or have you always feared that it would occur? We're here for you! The next time you need immediate commercial AC repair service, contact Greens Energy Services!
When Should You Schedule Commercial AC Repair Service in Orlando, Florida?
You're too hot – Don't pay for a service that doesn't work. Our heating and cooling professionals can get to the root of the problem!
Your energy bills are too high – If your energy usage has not changed but your bills have, you should call an HVAC expert to inspect and diagnose your AC system.
You hear strange sounds coming from your unit – Some noise coming from your unit is normal, but loud popping or banging sounds are not. Call an air conditioning technician to investigate on your behalf!
For help determining if it's time to repair or replace your AC unit, contact the experts at Greens Energy Services. We're here to help!
Whatever problems your AC system in Central Florida presents, our consultation-style approach will prepare you to make informed decisions by guiding you through the process and advising you of all your options and their costs. You can count on us for an immediate response, quick diagnosis, and accurate quotes. We take pride in doing excellent work that stands the test of time; you can trust that we won't cut corners in our materials, products, or processes, and all new parts come with a one-year warranty.
If you need a quick and accurate assessment of your air conditioning problems, let us help you understand your options. We will consult with you until you feel prepared to make informed decisions. Contact us to get started!
Our family has made a commitment decade after decade to uphold the values of outstanding service at an exceptional value, and we believe that our standards have directly translated to success and longevity. We are dedicated to providing personalized service at competitive rates and plan to keep that undertaking a cornerstone of our business model for decades to come.
A 100% Satisfaction Guarantee – All new Carrier equipment comes with a 100% money-back guarantee within one year of installation. If you're not satisfied, we'll replace the part or remove it and refund the full purchase price!
Work That's Technically Sound – Carrier endorses our team's technical know-how on all aspects of HVAC installation, maintenance, and repair. Additionally, all of our sales representatives have gone to Carrier University, where they learned to offer solutions with the highest level of comfort and value possible.
Certifiable Excellence – We employ NATE-certified technicians and use genuine Carrier parts whenever possible. Our work and our products are rooted in quality to ensure your system performs as promised.
For commercial AC repair service in Orlando, Florida, delivered with quality products, superior customer service, and affordable prices, contact Greens Energy Services today!
Don't lose out on customers or even your own discomfort due to the summer heat. When your air conditioning unit starts to fail, call the commercial AC repair company that's been trusted in Central Florida since 1953. Whether you need a repair, replacement, or maintenance, you can trust that Greens Energy Service will help restore your business to a comfortable environment, all while maintaining your workflow and accommodating your customers.
For unbeatable customer service, quality, efficient work, and value prices, call Greens Energy Services at 407-917-3759, or contact us now to make an appointment!


Nuno Fonseca and Goncalo Malosso, joined their musical experiences of DJ and production in the construction of a Trance track, "Alien Explorer". After good acceptance by the public and friends, decided to make the track several renowned artists for national production of an RMX EP. After this sequence of events, the most logical step was the creation of the UFO project, which is the result of a merger of these two elements.


"Then the word of the Lord came to Isaiah: 'Go and say to Hezekiah, Thus says the LORD, the God of David your father: I have heard your prayer; I have seen your tears… . I will deliver you and this city out of the hand of the king of Assyria'" (vv. 4–6).
The Lord said yes to Hezekiah's prayer. Life would be hard in the short term, and food would be scarce because of what Assyria would inflict on the land, but Jerusalem would be spared (vv. 30–35). Then God sent the angel of the Lord to Assyria's camp, killing 185,000 men. The angel may have done this with a plague of mice, for the ancient Greek historian Herodotus records such a plague afflicting the Assyrian army on the border near Egypt at about this time. Sennacherib himself was murdered some twenty years later by his sons while worshiping his gods. Ironically, the one who defied the only true God was killed in the service of the false gods in whom he trusted (vv. 36–38).
Hezekiah's intercession shows the power of prayer before our sovereign Creator, which is also confirmed in today's passage. This record of Hezekiah's illness and recovery, Isaiah 38:6 reveals, took place before the events recorded in chapter 37. (Biblical authors sometimes do not order their accounts chronologically.) Therefore, the promise Isaiah gave in 37:5–7 was not the Lord's first pledge to deliver Jerusalem from Assyria. In His grace, God repeats His promises to us, increasing our confidence in His Word.
Unlike his father, Ahaz, who lacked faith to ask for a sign from God, we know that Hezekiah asked for a sign of his recovery and Jerusalem's rescue (2 Kings 20:8). God granted this sign, healing Hezekiah and adding fifteen years to his life (Isa. 38:4–6).
God's granting fifteen more years of life to Hezekiah does not mean He changes His mind like we do. Instead, such accounts show us that the Lord has a real relationship with His people in time and responds to our prayers and actions. Hezekiah did not know how God would answer His prayer for healing, but the Lord did. Similarly, God knows how He will answer our prayers even before we offer them, but that must not keep us from interceding for ourselves and others.


Meeting Metropolis – Pennsylvania Convention Center's 2-day meeting/event was produced for an invitation only group of the country's top meeting planners whose meetings represent billions of dollars of revenue. Perfection Events spearheaded the Pennsylvania Marketplace Expo featuring over 100 national and regional exhibitors. In partnership with Stockton & Partners, PE was responsible to: design of show floor layout; creation of scoring criteria and parameters involving exhibition applications and subsequent notification of exhibitors' acceptance or rejection; managing sponsor fulfillment of exhibit space and associated benefits; serving as liaison between multiple functional departments, clients and service providers to ensure the expo was produced and executed according to design.
Perfection Events Inc. designed and delivered an innovative tradeshow, exceeding client and attendee expectations for a holistic experience representing the PA region. The connections formed at the tradeshow were further developed into tangible relationships, bringing significant business to the City.


Ilse Hakvoort received her PhD in developmental psychology from the University of Amsterdam in 1996, with a Doctoral dissertation titled "Conceptualizations of peace and war from childhood through adolescence". She came as visiting researcher to Göteborg with a post-doc grant in October 1997. At present, Ilse Hakvoort has a position as a lecturer at the department. Her research interests focusses on the development of children's conceptualization of peace and war, learning and development in different socio-cultural contexts, peace-education, conflict resolution in the school context, and children's rights and realities. She is involved in courses on conflict resolution in preschool and school, and children's rights and realities.
A collaboration project has been established between the department and the Government S.K. College (related to the University of Rajasthan) in Sikar, Jaipur, Rajasthan, India. The collaboration project has got financial support from the Swedish International Programme Office for Education and Training (Internationella programkontoret) in the form of a Linnaeus-Palme International Exchange Programme grant. The grant was first given in March 2007, for exchange activities involving sending teachers and students both ways from 2007-08. More information about the Linnaeus Palme grants 2007.
In February 2008 Dr. Ilse Hakvoort visited Jaipur, India and the first detailed discussions took place for a future collaboration with Dr. Amita Agarwal. Dr. Agarwal visited Göteborg in May 2008. The next step of this collaboration was granted by the Linnaeus-Palme collaboration programme with a teachers exchange grant for 2008/2009.
The contact person on the Indian side is Amita Agarwal, Head of the Department of Political Science. She recently completed her post-doctoral research project on "Indo-US Relations and Peace Prospects in the Indian Ocean Region" at the South Asia Studies Center, University of Rajasthan. Amita Agarwal is an active member of several professional associations (national and international) and is at present the State Chancellor of the Rajasthan Chapter of the International Association of Educators for World Peace. Her latest publications include Human Rights for the Survival of Civilization (Kalinga, New Delhi, 2004), Indian Ocean and World Peace (Kanishka, New Delhi, 2000), and a chapter on the human rights of children in the volume Law and Poverty (Zed Press, London, 2006). Amita Agarwal participated in the conference on "Women and Politics in Asia", organised in Halmstad 6-7 June 2003. There she presented a paper titled "Empowering Women Through Reservation in Representative Institutions in India" in a session on Women's Political Participation and Representation.
The next phase of the collaboration between the Dept. of Education, Gothenburg Univerity, and Government S.K. College in Sikar, the organisation of a joint Indo-Swedish conference entitled "Do girls belong to a lesser God? The rights and realities of girl children in the developing and the developed world" was planned for September 2010. However, due to lack of funds, the conference will not be able to hold.


A sucker for secrets! Joe Jonas may know how Game of Thrones ends, but he won't be spilling the beans any time soon.
Sophie Turner, Jonas' fiancee who stars as Sansa Stark on the hit HBO series, revealed during her visit to Good Morning America on Tuesday, April 2, that the musician, 29, has all the answers about the eighth and final season.
Season 8 of Game of Thrones premieres Sunday, April 14, at 9 p.m. ET.


For students looking to fill in a gap year or seeking something unique after college, or for any young person wanting to expand their horizons, becoming an au pair can be an undeniably beneficial experience. If you are someone that wants to live and breathe another language and culture, then heading out into the wide world as an au pair can be a great way to explore without the stress of paying for accommodation or board. Instead, you can live with a local family and either look after their children or do some light chores around the house, earning 'pocket money' and learning first-hand what it is like to live in a new location.
By living with a new family, you will gain an incredible amount of independence. You won't be with your immediate family and while you'll have the support of your host family, you can truly embrace your time and learn more about who you are as a person. However, you will have the opportunity to make new friends with the locals and other au pairs so you can form connections and a support network to cherish for a long time to come.
When you have free time from your daily tasks, you'll be able to utilise your pocket money to travel the country and discover more about your surroundings and the neighbouring towns and cities. For those who want to travel, this alone can be just one of the great reasons to become an au pair.
Find out more on the history of au pairs.
There are so many reasons to become an au pair – if you like a challenge and you want to make a difference, becoming an au pair would be perfect for you. The role can be tiring and challenging at times, but you will gain plenty of new skills, such as leadership, flexibility, adaptability and the ability to work with new people from different walks of life, which will all look fantastic on your CV and impress any future employers. It is very likely that you will grow a bond with the children that you are looking after and the family you are living with, which is a connection that you will carry with you for the rest of your lives. You will be able to create a positive impact for the children and help them to learn and grow and furthermore, they will be able to learn about your culture and language, so they will be benefitting for the future too.
Although the wages associated with being an au pair are not massive, you will still be able to make some money and due to the accommodation and food being supplied as standard, you won't have to worry about these costs. If you want to make more money, you could always get a part-time job in your spare time, too!
Becoming an au pair will really give you an experience which you will never be able to forget. It will give you a chance to learn something new about yourself and help you to grow and develop as an individual. If you want to become an au pair during a gap year, then it will give you an opportunity to have a think about what you really want out of life and the paths you wish to take for your future.
If you're interested in become an au pair, register with Oaktree Care Agency. We'll match you with the right family to ensure your placement runs smoothly from start to finish, so get in touch or fill in our application form, today.


"You guys rock! My furnace is up and running within 24 hours of my online purchase; thank you!"
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"I received the blower yesterday. Perfect replacement. Thanks for the excellent transaction."
"Thank you for you quick response and service. I can't say I have experienced such great service through email in a very long time!"
"I'm writing to let you know how impressed I am by your company, especially my salesman, Josh Covert. He was very patient with me and very helpful, making every effort to answer or seek answers for every question I had on which exact parts to order. He spent a long time on the phone with me to accomplish this, and I fully realize that my order is likely very small compared to others.
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"Just wanted to say thanks! I received the new blower motor today and quickly installed it after removing the old unit. It works great and I am certain I saved big bucks by purchasing the item through you and installing it myself."
"Just wanted to drop you a note and say how well this motor worked out...it fit like a glove and when it started up, it made no noise...unlike the old motor it replaced...This unit is the fan motor on an old gas furnace which has a great cast iron burner, they don't make em like that anymore, so when the fan motor started going out, the technician said we needed to replace the whole furnace...Thanks to you folks, this old furnace is running great again...we thank you and our renter thanks you...Thanks again."
"I received your shipment yesterday, thanks for your quick and accurate service!"
"Thanks for the help. The Fasco D126 worked great as a replacement motor in the Fasco 50755-D500 blower assembly… I could only buy the whole assembly locally. You saved me over a hundred dollars. Thanks again."
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"I received the motor I ordered from you today. Looks good! Thank you for your kind help and quick service. Have a great day!"
"Thanks Josh for the rapid response, your company's customer service is second to none. Got the new replacement motor today, put it in today, and it works great!!!"
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"The motor arrived here today. Excellent service, thanks very much."
"Thanks for all your work on this little order. There's a nice old lady who will be very happy to have her wood fire going again."


text:
ANSIBLE_METADATA = {'status': ['preview'],
                    'supported_by': 'community',
                    'version': '1.0'}

DOCUMENTATION = '''
---
module: logentries
author: "Ivan Vanderbyl (@ivanvanderbyl)"
short_description: Module for tracking logs via logentries.com
description:
    - Sends logs to LogEntries in realtime
version_added: "1.6"
options:
    path:
        description:
            - path to a log file
        required: true
    state:
        description:
            - following state of the log
        choices: [ 'present', 'absent' ]
        required: false
        default: present
    name:
        description:
            - name of the log
        required: false
    logtype:
        description:
            - type of the log
        required: false

notes:
    - Requires the LogEntries agent which can be installed following the instructions at logentries.com
'''
EXAMPLES = '''
# Track nginx logs
- logentries:
    path: /var/log/nginx/access.log
    state: present
    name: nginx-access-log

# Stop tracking nginx logs
- logentries:
    path: /var/log/nginx/error.log
    state: absent
'''

def query_log_status(module, le_path, path, state="present"):
    """ Returns whether a log is followed or not. """

    if state == "present":
        rc, out, err = module.run_command("%s followed %s" % (le_path, path))
        if rc == 0:
            return True

        return False

def follow_log(module, le_path, logs, name=None, logtype=None):
    """ Follows one or more logs if not already followed. """

    followed_count = 0

    for log in logs:
        if query_log_status(module, le_path, log):
            continue

        if module.check_mode:
            module.exit_json(changed=True)

        cmd = [le_path, 'follow', log]
        if name:
            cmd.extend(['--name',name])
        if logtype:
            cmd.extend(['--type',logtype])
        rc, out, err = module.run_command(' '.join(cmd))

        if not query_log_status(module, le_path, log):
            module.fail_json(msg="failed to follow '%s': %s" % (log, err.strip()))

        followed_count += 1

    if followed_count > 0:
        module.exit_json(changed=True, msg="followed %d log(s)" % (followed_count,))

    module.exit_json(changed=False, msg="logs(s) already followed")

def unfollow_log(module, le_path, logs):
    """ Unfollows one or more logs if followed. """

    removed_count = 0

    # Using a for loop incase of error, we can report the package that failed
    for log in logs:
        # Query the log first, to see if we even need to remove.
        if not query_log_status(module, le_path, log):
            continue

        if module.check_mode:
            module.exit_json(changed=True)
        rc, out, err = module.run_command([le_path, 'rm', log])

        if query_log_status(module, le_path, log):
            module.fail_json(msg="failed to remove '%s': %s" % (log, err.strip()))

        removed_count += 1

    if removed_count > 0:
        module.exit_json(changed=True, msg="removed %d package(s)" % removed_count)

    module.exit_json(changed=False, msg="logs(s) already unfollowed")

def main():
    module = AnsibleModule(
        argument_spec = dict(
            path = dict(required=True),
            state = dict(default="present", choices=["present", "followed", "absent", "unfollowed"]),
            name = dict(required=False, default=None, type='str'),
            logtype = dict(required=False, default=None, type='str', aliases=['type'])
        ),
        supports_check_mode=True
    )

    le_path = module.get_bin_path('le', True, ['/usr/local/bin'])

    p = module.params

    # Handle multiple log files
    logs = p["path"].split(",")
    logs = filter(None, logs)

    if p["state"] in ["present", "followed"]:
        follow_log(module, le_path, logs, name=p['name'], logtype=p['logtype'])

    elif p["state"] in ["absent", "unfollowed"]:
        unfollow_log(module, le_path, logs)

# import module snippets
from ansible.module_utils.basic import *

if __name__ == '__main__':
    main()