It is a requirement that a visa applicant must fill in or complete his or her application form in a manner that all questions are answered, and no incorrect answers are given or provided. There is also a requirement that visa applicants must not provide incorrect information during interviews with the Minister for Immigration (‘Minister’), […]

The post Visa cancelled due to incorrect information given or provided to the Department of Home Affairs appeared first on Visa Australia - Immigration Lawyers & Registered Migration Agents.

The World Health Organization has announced that Coronavirus (COVID-19) is a pandemic. The migration situation is changing rapidly throughout Australia. As an Australian citizen or permanent resident, can I still enter Australia? There is no restriction on Australian citizens or permanent residents entering Australia at this stage. However, those arriving in Australia will be required […]

The post Coronavirus (COVID-19) and Visas for Australia appeared first on Visa Australia - Immigration Lawyers & Registered Migration Agents.

Seasonal Worker Programme and Pacific Labour Scheme workers can extend their stay for up to 12 months to work for approved employers as long as pastoral care and accommodation needs of workers are met to minimise health risks to visa holders and the community. Approved employers under the Seasonal Worker Programme and Pacific Labour Scheme […]

The post What can I do if I am on a working holiday or seasonal worker visa in the Coronavirus (COVID-19) crisis? appeared first on Visa Australia - Immigration Lawyers & Registered Migration Agents.

If you’re a Temporary Skill Shortage visa holder – what should you do if you have been stood down or your work hours are reduced by your employer? The Australian Government has announced that Temporary Skill Shortage visa holders who have been stood down, but not laid off, will maintain their visa validity and businesses […]

The post Employer sponsored temporary work visas (482 and 457) and Coronavirus (COVID-19) appeared first on Visa Australia - Immigration Lawyers & Registered Migration Agents.

International students who have been in Australia for longer than 12 months who find themselves in financial hardship will be able to access their Australian superannuation. The Government will undertake further engagement with the international education sector who already provide some financial support for international students facing hardship. International students working in supermarkets will have […]

The post Student visa holders and New Zealand citizens in Australia and the Coronavirus (COVID-19) crisis? appeared first on Visa Australia - Immigration Lawyers & Registered Migration Agents.

Hello! I have updated this very popular post to include a free downloadable PDF of this checklist.  Preparation is key to successful management of any project, and design projects are no different. The more preparation that both client and designer do right at the start, the more smoothly the work will go. I find checklists […]

https://www.anychart.com/blog/2020/05/06/javascript-choropleth-map-tutorial/

By The University of Sheffield Giant icebergs leave trail of carbon sequestration in their wake – a month after they have passed Geographers analysed 175 satellite images of ocean colour which is an indicator of phytoplankton productivity at the ocean’s … Continue reading →

Photos and other images are used in different fields, so those who know how to work with high-resolution mockups are in demand as professionals. It is useful to be able to take photos, draw, edit...

Tags: coronavirus, FiveThirtyEight, modeling

Made withVisme Infographic Maker

Get To Know COVID-19 is an interactive infographic build on the Visme platform that allows for the inclusion of animation, URL links, interactive content and playable videos.

Of course it’s timely information, but this is also an experiment with embedding the fully interactive infographic here in the blog post. A designer can go overboard, but good use of animation can attract the reader’s attention to important information or better demonstrate data. Below is the static JPG image of the infographic.

I am disappointed that the data included in the infographic isn’t visualized. It’s just shown as text numbers. That doesn’t give the reader any context or visual cues on how to perceive those data values.

What is Coronavirus/COVID-19?

According to the World Health Organization (WHO), COVID-19 is a respiratory disease caused by a newly discovered type of coronavirus.

This new strain of virus, also known as SARS-CoV-2, has not been previously identified in humans. It can be transmitted from person to person via respiratory droplets, such as when an infected person coughs or sneezes within 6 feet of contact.

The WHO has declared the virus as a global pandemic and a crisis unlike any other in the 75-year history of the UN.

Not an infographic today.

Free online classes, discounts on software, extended trial periods, free online data sources, etc. as a result of the COVID-19 pandemic.

As the Organizer for the DFW Data Visualization Meetup Group, I've started this publicly viewable Google Sheet for the local DataViz community listing various resources that companies are making available during the pandemic. Turns out, these are valuable to DataViz designers everywhere, not just DFW, so I'm sharing the link with all of you.

I’ll continue to update this list as I learn about new resources during the pandemic. Please use the submission link in the spreadsheet if you know of any DataViz-related offers or deals I should add!

-Randy

COVID-19 (aka Coronavirus) has obviously been a hot topic recently, especially within the media. But how dangerous is this new virus?

The Covid-19 #Coronavirus Infographic Data Pack on Information is Beautiful gathers the current data around the world (version above is from March 31, 2020) and makes the virus more tangible to understand. The infographic makes comparisons to other diseases when it comes to incubation times and number of deaths, as well as reporting who is dying from it.

Created by David McCandless, Omid Kashan, Fabio Bergamaschi, Dr Stephanie Starling, Univers Labs

From Information Is Beautiful:

We made an infographic of the best COVID-19 / Coronavirus charts floating around, plus some of our own – all with the latest data

We’ll plan to keep it updated every few days.

They have also made all of their data accessible with a Google Sheet link: bit.ly/COVID19-DATA

With so many good and bad COVID-19 charts being published at a frantic pace, I can appreciate the design and effort here to gather some of the best data and the best visualizations together in one place.

A couple of mysterious sharks have caught the fancy of the town. Maybe it's the cabin fever finally setting in, or perhaps this is what happens when you go too long without washing your mask, but Bethelites are going wild for two people in inflatable shark suits who pop up randomly around town.

Roy Horn, famed tiger handler and co-star of the magic duo known as Siegfried and Roy, died of complications from the coronavirus in a hospital in Las Vegas on Friday. He was 75 years old. "Today, the world has lost one of the greats of magic, but I have lost my best friend," Siegfried Fischbacher said in a statement. "From the moment we met, I knew Roy and I, together, would change the world." "There could be no Siegfried without Roy, and no Roy without Siegfried." This is a developing story. Please check back for updates.

With the coronavirus pandemic, many folks switched to working online. Things like teaching, business meetings and other face-to-face activities have been replaced with video calls. Home has become both home and workplace, and admit it: your wardrobe totally reflects this. Creative duo The Workmans shows this “fashion crossover” in their latest photo series #COVIDwear. The […]

The post #COVIDwear: a hilarious photo series showing quarantine fashion of remote workers appeared first on DIY Photography.

Consider that $u_0$ nodes are aware of some piece of data $d_0$. This note derives the expected time required for the data $d_0$ to be disseminated through-out a network of $n$ nodes, when communication between nodes evolves according to a graphical Markov model $overline{ mathcal{G}}_{n,hat{p}}$ with probability parameter $hat{p}$. In this model, an edge between two nodes exists at discrete time $k in mathbb{N}^+$ with probability $hat{p}$ if this edge existed at $k-1$, and with probability $(1-hat{p})$ if this edge did not exist at $k-1$. Each edge is interpreted as a bidirectional communication link over which data between neighbors is shared. The initial communication graph is assumed to be an Erdos-Renyi random graph with parameters $(n,hat{p})$, hence we consider a emph{stationary} Markov model $overline{mathcal{G}}_{n,hat{p}}$. The asymptotic "$u_0$-expected flooding time" of $overline{mathcal{G}}_{n,hat{p}}$ is defined as the expected number of iterations required to transmit the data $d_0$ from $u_0$ nodes to $n$ nodes, in the limit as $n$ approaches infinity. Although most previous results on the asymptotic flooding time in graphical Markov models are either emph{almost sure} or emph{with high probability}, the bounds obtained here are emph{in expectation}. However, our bounds are tighter and can be more complete than previous results.

We study an equivariant extension of the Batalin-Vilkovisky formalism for quantizing gauge theories. Namely, we introduce a general framework to encompass failures of the quantum master equation, and we apply it to the natural equivariant extension of AKSZ solutions of the classical master equation (CME). As examples of the construction, we recover the equivariant extension of supersymmetric Yang-Mills in 2d and of Donaldson-Witten theory.

The article is devoted to the expansion of iterated Stratonovich stochastic integrals of arbitrary multiplicity $k$ $(kinmathbb{N})$ based on the generalized iterated Fourier series. The case of Fourier-Legendre series as well as the case of trigonotemric Fourier series are considered in details. The obtained expansion provides a possibility to represent the iterated Stratonovich stochastic integral in the form of iterated series of products of standard Gaussian random variables. Convergence in the mean of degree $2n$ $(nin mathbb{N})$ of the expansion is proved. Some modifications of the mentioned expansion were derived for the case $k=2$. One of them is based of multiple trigonomentric Fourier series converging almost everywhere in the square $[t, T]^2$. The results of the article can be applied to the numerical solution of Ito stochastic differential equations.

A reaction-diffusion model was developed describing the spread of the COVID-19 virus considering the mean daily movement of susceptible, exposed and asymptomatic individuals. The model was calibrated using data on the confirmed infection and death from France as well as their initial spatial distribution. First, the system of partial differential equations is studied, then the basic reproduction number, R0 is derived. Second, numerical simulations, based on a combination of level-set and finite differences, shown the spatial spread of COVID-19 from March 16 to June 16. Finally, scenarios of unlockdown are compared according to variation of distancing, or partially spatial lockdown.

Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another with respect to the dynamics of the scene and the mechanics of the eyes (dynamics). State-of-the-art models focus on learning saliency maps from human data, a process that only takes into account the spatial component of the phenomenon and ignore its temporal and dynamical counterparts. In this work we focus on the evaluation methodology of models of human visual attention. We underline the limits of the current metrics for saliency prediction and scanpath similarity, and we introduce a statistical measure for the evaluation of the dynamics of the simulated eye movements. While deep learning models achieve astonishing performance in saliency prediction, our analysis shows their limitations in capturing the dynamics of the process. We find that unsupervised gravitational models, despite of their simplicity, outperform all competitors. Finally, exploiting a crowd-sourcing platform, we present a study aimed at evaluating how strongly the scanpaths generated with the unsupervised gravitational models appear plausible to naive and expert human observers.

In response to the coronavirus disease (COVID-19) outbreak, there is an ever-increasing number of national governments that are rolling out contact-tracing Apps to aid the containment of the virus. The first hugely contentious issue facing the Apps is the deployment framework, i.e. centralised or decentralised. Based on this, the debate branches out to the corresponding technologies that underpin these architectures, i.e. GPS, QR codes, and Bluetooth. This work conducts a pioneering review of the above scenarios and contributes a geolocation mapping of the current deployment. The vulnerabilities and the directions of research are identified, with a special focus on the Bluetooth-based decentralised scheme.

We show that combining a prediction model (based on neural networks), with a new method of test pooling (better than the original Dorfman method, and better than double-pooling) called 'Grid', we can reduce the number of Covid-19 tests by 73%.

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might convert to the severe stage, for designing effective treatment plan and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of high-dimensional data and learn the shared information across the classification task and the regression task. To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 422 chest computed tomography (CT) scans, where 52 cases were converted to severe on average 5.64 days and 34 cases were severe at admission. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the numbers of training data.

Convolutional neural networks (CNN) have shown promising results for end-to-end speech recognition, albeit still behind other state-of-the-art methods in performance. In this paper, we study how to bridge this gap and go beyond with a novel CNN-RNN-transducer architecture, which we call ContextNet. ContextNet features a fully convolutional encoder that incorporates global context information into convolution layers by adding squeeze-and-excitation modules. In addition, we propose a simple scaling method that scales the widths of ContextNet that achieves good trade-off between computation and accuracy. We demonstrate that on the widely used LibriSpeech benchmark, ContextNet achieves a word error rate (WER) of 2.1\%/4.6\% without external language model (LM), 1.9\%/4.1\% with LM and 2.9\%/7.0\% with only 10M parameters on the clean/noisy LibriSpeech test sets. This compares to the previous best published system of 2.0\%/4.6\% with LM and 3.9\%/11.3\% with 20M parameters. The superiority of the proposed ContextNet model is also verified on a much larger internal dataset.

This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.

In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a non-convex regularisation problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Despite being non-convex, the proposed method has guaranteed convergence via a proximal splitting algorithm and accurately identifies both horizontal and vertical line artefacts in LUS images. In order to reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method and show a considerable performance gain with 87% correctly detected B-lines in LUS images of nine COVID-19 patients. In addition, owing to its fast convergence, which takes around 12 seconds for a given frame, our proposed method is readily applicable for processing LUS image sequences.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.

The Cannabis Issue So many of us are stuck inside right now, and that lack of socializing means we're all probably going a little bit stir crazy.…

Coronavirus Family Tree The University of Washington Virology lab, which is testing samples for coronavirus, tweeted last night.…

After Governor Jay Inslee announced a prohibition on gatherings of 250 people or more in three Washington counties (Snohomish, King, Pierce) on Wednesday, and with public health concerns growing over the COVID-19 pandemic, many organizations in Spokane are following suit. The Inlander will be frequently updating its online calendar of events to reflect local cancelations as we hear of them.…

There's one thing the Innovia Foundation can never say: That it hadn't been told.…

We at the Inlander are committed to keeping people informed and connected throughout the coronavirus outbreak. We'll continue to update this page with the latest headlines.…

[IMAGE-1] I started sobbing from the opening moments of A Beautiful Day in the Neighborhood, and I didn't stop crying for two hours. And then after I left the cinema and ran into a fellow film critic who had also just seen it, I literally could not manage a word of discussion without bursting into tears again.…

We could blame the enormous — and justifiable — success of the Lego flicks for the existence of Playmobil: The Movie, but that would be unfair to all the shameless knockoffs and cinematic coattail riders.…

Perhaps you've heard people banging on pans to support health care workers, or howling into the abyss just to let other humans know they were alive. We've gone to some extreme measures to keep ourselves entertained since much of the country went on lockdown to combat COVID-19, and here's another one that can get you out of the house while remaining safely social-distanced and supporting local artists at the same time.…

ON INLANDER.COM WORLD: Roughly two weeks after Canada's deadliest mass shooting, Prime Minister Justin Trudeau introduced an immediate ban on what he called “military-style assault weapons.”…

ON INLANDER.COM NATION: Even as U.S. President Donald Trump urges states to reopen their economies, his own administration projects that the death toll from COVID-19 will spike to 3,000 people per day.…

The latest issue of the Inlander is hitting newsstands today. Find it at your local grocery store and hundreds of other locations; use this map to find a pickup point near you.…

At every level of America's food system, mandated closures and outbreaks of COVID-19 have interrupted the finely tuned network that normally gets food from farmers and food processors to restaurants, grocery stores and food banks.…

If you go to check how many people are hospitalized with COVID-19 in Spokane, the Spokane County Regional Health District website will give you an answer. Right now, it lists eight people as currently hospitalized with COVID-19, and that number has been trending downward.…

Q: I recently read that a combination of the drugs hydroxychloroquine and azithromycin might be effective against COVID-19. I have diabetes and I am at risk for this viral infection.…

A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving an input query, decomposing the input query into a plurality of different subqueries, and conducting a search in one or more data sources to identify at least one candidate answer to each of the subqueries. A ranking function is applied to each of the candidate answers to determine a ranking for each of these candidate answers; and for each of the subqueries, one of the candidate answers to the subquery is selected based on this ranking. A logical synthesis component is applied to synthesize a candidate answer for the input query from the selected the candidate answers to the subqueries. In one embodiment, the procedure applied by the logical synthesis component to synthesize the candidate answer for the input query is determined from the input query.

An information providing apparatus for vehicle has a remaining capacity detecting section 110 that detects a remaining capacity of a battery; a power consumption amount detecting section 130 that detects a power consumption amount of the battery; a power consumption amount history generating section 130 that generates a power consumption amount history on the basis of the power consumption amount detected by the power consumption amount detecting section 130; a charge necessity judgment information generating section 130 that generates, on the basis of the power consumption amount history generated by the power consumption amount history generating section 130, charge necessity judgment information which is information for user's judgment about whether or not charging of the battery is necessary; and a providing section 150 that provides information of the remaining capacity of the battery and the charge necessity judgment information with these information correlated with each other to the user. The information providing apparatus can properly provide the information for user's judgment about whether or not charging of the battery to the user.

Methods, systems, and computer-readable media for providing topical search suggestions are provided. Topical search suggestions allow a user to receive search results related to the designated topic or subject matter. The present invention may generate multiple topics based on search input provided by a user. The search input may be a search prefix that includes one or more words entered into the search query box before the completed search query is submitted to the search engine. A search interface then presents the topics derived from the search prefix to a user before the user submits the query. In another embodiment, the user designates multiple search inputs. The present invention generates search results based on the search inputs and then presents topics extracted from the search results. In one embodiment, the topics are extracted by performing a natural language analysis of search result metadata.

The present invention relates to a method for removing phosphorus-containing compounds from triglyceride-containing compositions.

Provided herein are processes of preparing sulfonated estolide compounds, and the removal of sulfonate residues from those compounds to provide desulfonated estolide base oils. Exemplary sulfonated estolide compounds include those selected from the formula: wherein z is an integer selected from 0 to 15; q is an integer selected from 0 to 15; x is, independently for each occurrence, an integer selected from 0 to 20; y is, independently for each occurrence, an integer selected 0 to 20; n is equal to or greater than 0; R6 is selected from —OH, optionally substituted alkyl, and optionally substituted aryl; and R2 is selected from hydrogen and optionally substituted alkyl that is saturated or unsaturated, and branched or unbranched, wherein each fatty acid chain residue of said compounds is independently optionally substituted.

Methods for removing parasites and in particular ectoparasites of vertebrates, in particular of mammals, and compositions for the implementation of this method.Methods for removing parasites of vertebrates, and in particular arthropods, mainly insects and Arachnida, wherein an effectively parasiticidal amount of a compound of formula (I) ##STR1## in particular of fipronil, is administered to the animal via an administration route which makes possible systemic distribution and good absorption.