If you have your visa refused or cancelled, you need to get expert advice a soon as possible. Strict time limits apply to drafting submissions and appeals. A visa refusal or cancellation can limit the type or visas you can apply for in the future or even prohibit you from applying for any visa to […]

The post What happens if my visa is refused or cancelled due to my character? appeared first on Visa Australia - Immigration Lawyers & Registered Migration Agents.

The Australian Government has introduced two new regional visas which requires migrants to commit to life in regional Australia for at least three years. This new visa opens to the door to permanent residency for overseas workers from a wider range of occupations than before — including such occupations as real estate agents, call centre […]

The post New regional visas for Australia appeared first on Visa Australia - Immigration Lawyers & Registered Migration Agents.

Australia has introduced a streamlined, priority visa pathway for highly skilled and talented individuals to work and live permanently in Australia. The Government and industry has recognised there is growing competition for talent around the globe and to compete we must have a pathway that leads to certainty for people wishing to come to Australia. […]

The post Australia is recruiting – New Global Talent Visa appeared first on Visa Australia - Immigration Lawyers & Registered Migration Agents.

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.

In late 2019 the Australian Government launched the Global Talent – Independent program which offers a streamlined, priority visa pathway for highly skilled and talented individuals to work and live permanently in Australia. There are two streams. The first is the Global Talent Independent Program (GTI) and the second is the Global Talent Employer Sponsored (GTES). […]

The post Australia’s global talent visa for individuals and businesses 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.

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

'It can happen to you anywhere, at any moment, and just change your world'

Many people think the word “disability” means people who require a wheelchair or walker. In reality, however, there is much more to disability than meets the eye. 

Do you know the difference between a mission and a vision? Or the difference between a purpose and a goal? Don’t worry, you’re not the only one. Let's clarify!

Online marketing with visual content continues to grow and drive tons of traffic. The team at Venngage gathered together the latest data in the 14 Visual Content Marketing Statistics to Know for 2019 infographic and built it using their own tool.

From Nadya Khoja at Venngage:

Two years ago I asked 300 different online marketers to help me figure out how they were using visual content as part of their marketing strategies in 2016 and their predictions for 2017.

This year I wanted to see if there were any changes in how marketers were creating visuals, and what kind of content engagement they were seeing.

I also asked a couple of additional questions to see how the use of various visual formats impacted their blogging strategies.

Conclusion:

The data says it all–visual content isn’t going anywhere any time soon. Not only are more brands leveraging the use for of visuals for various social media platforms, but there is a lot of added benefit when it comes to SEO and organic rankings as well, particularly in Google’s image search results.

And of course, creating engaging visual content is a surefire way to resonate with your audience and communicate your ideas more effectively.

There are a few things to unravel here:

• It’s good survey data, but take it with a grain of salt. Venngage is a visual design tool, sharing data about visual content marketing.

• The infographic is a fantastic format to summarize the survey results and use in social media to draw in readers to the full article.

• The infographic is built using Venngage, so it’s also a great way to showcase what their design tool is capable of. In fact, clicking on the infographic gives you the opportunity to use this design as a template for designing your own infographic.

• Sections 5 & 10 are disappointing visually. There are no data visualizations, just a bunch of percentage values shown in text.

• I’m not a fan of the bright color scheme, and it’s visually distracting from highlighting insights in the data.

• The article still references 2018 data, even though the infographic has been updated with newer data from 2019.

A set of key visuals for Nike Shanghai

Branding and Visual Identity for Potency Design

Join me + bestselling author Vishen Lakhiani LIVE Tuesday April 7 at 6:30pm PST. Vishen Lakhiani is one of today’s most influential minds in the fields of education and human consciousness. He is the founder of Mindvalley University and its 2 million-strong student base and creator of the Quests learning platform: a next-generation method of online learning, which attains an unheard-of 60% completion rate on courses, in an industry where 8% is average. Vishen’s book, The Code of the Extraordinary Mind, made the New York Times Business Best Sellers List, and hit the coveted #1 spot on Amazon five times in 2017. Enjoy! FOLLOW VISHEN: instagram | facebook | website Listen to the Podcast coming soon … Subscribe   This podcast is brought to you by CreativeLive. CreativeLive is the world’s largest hub for online creative education in photo/video, art/design, music/audio, craft/maker, money/life and the ability to make a living in any of those disciplines. They are high quality, highly curated classes taught by the world’s top experts — Pulitzer, Oscar, Grammy Award winners, New York Times best selling authors and the best entrepreneurs of our times.

The post The Code of the Extraordinary Mind with Vishen Lakhiani appeared first on Chase Jarvis Photography.

How we read pie charts is still an open question: is it angle? Is it area? Is it arc length? In a study I'm presenting as a short paper at the IEEE VIS conference in Vancouver next week, I tried to tease the visual cues apart – using modeling and 3D pie charts. The big […]

Communication has been quite a challenge during the COVID-19 pandemic, and data visualization hasn't been the most helpful given the low quality of the data – see Amanda Makulec's plea to think harder about making another coronavirus chart. A great example of how to do things right is the widely-circulated Flatten the Curve information graphic/cartoon. […]

We show that the complex absorbing potential (CAP) method for computing scattering resonances applies to the case of exponentially decaying potentials. That means that the eigenvalues of $-Delta + V - iepsilon x^2$, $|V(x)|leq e^{-2gamma |x|}$ converge, as $ epsilon o 0+ $, to the poles of the meromorphic continuation of $ ( -Delta + V -lambda^2 )^{-1} $ uniformly on compact subsets of $ extrm{Re},lambda>0$, $ extrm{Im},lambda>-gamma$, $arglambda > pi/8$.

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.

We prove the effectiveness of the canonical bundle of several Hurwitz spaces of degree k covers of the projective line from curves of genus 13<g<20.

Let the group $G = AB$ be the product of subgroups $A$ and $B$, and let $p$ be a prime. We prove that $p$ does not divide the conjugacy class size (index) of each $p$-regular element of prime power order $xin Acup B$ if and only if $G$ is $p$-decomposable, i.e. $G=O_p(G) imes O_{p'}(G)$.

The main aim of this paper is to detect dynamical properties of the Gy"orgyi-Field model of the Belousov-Zhabotinsky chemical reaction. The corresponding three-variable model given as a set of nonlinear ordinary differential equations depends on one parameter, the flow rate. As certain values of this parameter can give rise to chaos, the analysis was performed in order to identify different dynamics regimes. Dynamical properties were qualified and quantified using classical and also new techniques. Namely, phase portraits, bifurcation diagrams, the Fourier spectra analysis, the 0-1 test for chaos, and approximate entropy. The correlation between approximate entropy and the 0-1 test for chaos was observed and described in detail. Moreover, the three-stage system of nested subintervals of flow rates, for which in every level the 0-1 test for chaos and approximate entropy was computed, is showing the same pattern. The study leads to an open problem whether the set of flow rate parameters has Cantor like structure.

We consider a fair division setting where indivisible items are allocated to agents. Each agent in the setting has strictly negative, zero or strictly positive utility for each item. We, thus, make a distinction between items that are good for some agents and bad for other agents (i.e. mixed), good for everyone (i.e. goods) or bad for everyone (i.e. bads). For this model, we study axiomatic concepts of allocations such as jealousy-freeness up to one item, envy-freeness up to one item and Pareto-optimality. We obtain many new possibility and impossibility results in regard to combinations of these properties. We also investigate new computational tasks related to such combinations. Thus, we advance the state-of-the-art in fair division of mixed manna.

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.

This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach.

Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets.

Interaction languages such as MSC are often associated with formal semantics by means of translations into distinct behavioral formalisms such as automatas or Petri nets. In contrast to translational approaches we propose an operational approach. Its principle is to identify which elementary communication actions can be immediately executed, and then to compute, for every such action, a new interaction representing the possible continuations to its execution. We also define an algorithm for checking the validity of execution traces (i.e. whether or not they belong to an interaction's semantics). Algorithms for semantic computation and trace validity are analyzed by means of experiments.

We present a new mouse cursor designed to facilitate the use of the mouse by people with peripheral vision loss. The pointer consists of a collection of converging straight lines covering the whole screen and following the position of the mouse cursor. We measured its positive effects with a group of participants with peripheral vision loss of different kinds and we found that it can reduce by a factor of 7 the time required to complete a targeting task using the mouse. Using eye tracking, we show that this system makes it possible to initiate the movement towards the target without having to precisely locate the mouse pointer. Using Fitts' Law, we compare these performances with those of full visual field users in order to understand the relation between the accuracy of the estimated mouse cursor position and the index of performance obtained with our tool.

Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The saliency and separability of the hidden representations when trained on MNIST dataset is studied using an external classifier, and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.

Visual localization is a useful alternative to standard localization techniques. It works by utilizing cameras. In a typical scenario, features are extracted from captured images and compared with geo-referenced databases. Location information is then inferred from the matching results. Conventional schemes mainly use low-level visual features. These approaches offer good accuracy but suffer from scalability issues. In order to assist localization in large urban areas, this work explores a different path by utilizing high-level semantic information. It is found that object information in a street view can facilitate localization. A novel descriptor scheme called "semantic signature" is proposed to summarize this information. A semantic signature consists of type and angle information of visible objects at a spatial location. Several metrics and protocols are proposed for signature comparison and retrieval. They illustrate different trade-offs between accuracy and complexity. Extensive simulation results confirm the potential of the proposed scheme in large-scale applications. This paper is an extended version of a conference paper in CBMI'18. A more efficient retrieval protocol is presented with additional experiment results.

Previous methods on estimating detailed human depth often require supervised training with `ground truth' depth data. This paper presents a self-supervised method that can be trained on YouTube videos without known depth, which makes training data collection simple and improves the generalization of the learned network. The self-supervised learning is achieved by minimizing a photo-consistency loss, which is evaluated between a video frame and its neighboring frames warped according to the estimated depth and the 3D non-rigid motion of the human body. To solve this non-rigid motion, we first estimate a rough SMPL model at each video frame and compute the non-rigid body motion accordingly, which enables self-supervised learning on estimating the shape details. Experiments demonstrate that our method enjoys better generalization and performs much better on data in the wild.

Correlation alignment (CORAL), a representative domain adaptation (DA) algorithm, decorrelates and aligns a labelled source domain dataset to an unlabelled target domain dataset to minimize the domain shift such that a classifier can be applied to predict the target domain labels. In this paper, we implement the CORAL on quantum devices by two different methods. One method utilizes quantum basic linear algebra subroutines (QBLAS) to implement the CORAL with exponential speedup in the number and dimension of the given data samples. The other method is achieved through a variational hybrid quantum-classical procedure. In addition, the numerical experiments of the CORAL with three different types of data sets, namely the synthetic data, the synthetic-Iris data, the handwritten digit data, are presented to evaluate the performance of our work. The simulation results prove that the variational quantum correlation alignment algorithm (VQCORAL) can achieve competitive performance compared with the classical CORAL.

Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day. Increasingly, we are relying on networked infrastructure and with the advent of IoT, billions of devices will be connected to the internet, providing attackers with more opportunities to exploit. Traditional machine learning methods have been frequently used in the context of network security. However, such methods are more based on statistical features extracted from sources such as binaries, emails, and packet flows.

On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks. At a glance, it is not trivial to see how computer vision methods are related to network security. Nonetheless, there is a significant amount of work that highlighted how methods from computer vision can be applied in network security for detecting attacks or building security solutions. In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection. Next, we review a set of such commercial products for which public information is available and explore how computer vision methods are effectively used in those products. Finally, we discuss existing research gaps and future research directions, especially focusing on how network security research community and the industry can leverage the exponential growth of computer vision methods to build much secure networked systems.

Visualization-oriented natural language interfaces (V-NLIs) have been explored and developed in recent years. One challenge faced by V-NLIs is in the formation of effective design decisions that usually requires a deep understanding of user queries. Learning-based approaches have shown potential in V-NLIs and reached state-of-the-art performance in various NLP tasks. However, because of the lack of sufficient training samples that cater to visual data analytics, cutting-edge techniques have rarely been employed to facilitate the development of V-NLIs. We present a new dataset, called Quda, to help V-NLIs understand free-form natural language. Our dataset contains 14;035 diverse user queries annotated with 10 low-level analytic tasks that assist in the deployment of state-of-the-art techniques for parsing complex human language. We achieve this goal by first gathering seed queries with data analysts who are target users of V-NLIs. Then we employ extensive crowd force for paraphrase generation and validation. We demonstrate the usefulness of Quda in building V-NLIs by creating a prototype that makes effective design decisions for free-form user queries. We also show that Quda can be beneficial for a wide range of applications in the visualization community by analyzing the design tasks described in academic publications.

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.

Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation.

Public key encryption with equality test (PKEET) allows testing whether two ciphertexts are generated by the same message or not. PKEET is a potential candidate for many practical applications like efficient data management on encrypted databases. Potential applicability of PKEET leads to intensive research from its first instantiation by Yang et al. (CT-RSA 2010). Most of the followup constructions are secure in the random oracle model. Moreover, the security of all the concrete constructions is based on number-theoretic hardness assumptions which are vulnerable in the post-quantum era. Recently, Lee et al. (ePrint 2016) proposed a generic construction of PKEET schemes in the standard model and hence it is possible to yield the first instantiation of PKEET schemes based on lattices. Their method is to use a $2$-level hierarchical identity-based encryption (HIBE) scheme together with a one-time signature scheme. In this paper, we propose, for the first time, a direct construction of a PKEET scheme based on the hardness assumption of lattices in the standard model. More specifically, the security of the proposed scheme is reduces to the hardness of the Learning With Errors problem.

Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising yet under-explored in unsupervised multimodal MT (MMT). In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT. Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision. The experimental results on the widely used Multi30K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when the images are not available at the testing time.

Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. These step-by-step navigational instructions are crucial when the agent is navigating new environments about which it has no prior knowledge. Most recent works that study VLN observe a significant performance drop when tested on unseen environments (i.e., environments not used in training), indicating that the neural agent models are highly biased towards training environments. Although this issue is considered as one of the major challenges in VLN research, it is still under-studied and needs a clearer explanation. In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias. We observe that neither the language nor the underlying navigational graph, but the low-level visual appearance conveyed by ResNet features directly affects the agent model and contributes to this environment bias in results. According to this observation, we explore several kinds of semantic representations that contain less low-level visual information, hence the agent learned with these features could be better generalized to unseen testing environments. Without modifying the baseline agent model and its training method, our explored semantic features significantly decrease the performance gaps between seen and unseen on multiple datasets (i.e. R2R, R4R, and CVDN) and achieve competitive unseen results to previous state-of-the-art models. Our code and features are available at: https://github.com/zhangybzbo/EnvBiasVLN

Dialogue engines that incorporate different types of agents to converse with humans are popular.

However, conversations are dynamic in the sense that a selected response will change the conversation on-the-fly, influencing the subsequent utterances in the conversation, which makes the response selection a challenging problem.

We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents by taking into account the conversational history, and propose a emph{Neural Response Selection} method.

The proposed method is trained to predict a coherent set of responses within a single conversation, considering its own predictions via a curriculum training mechanism.

Our experimental results show that the proposed method can accurately select the most appropriate responses, thereby significantly improving the user experience in dialogue systems.

Need some inspiration to build a high converting website? Websites that convert persuade visitors to become customers. These websites drive more revenue, so if you want to increase your site’s revenue, use these examples of websites that convert as inspiration! We’ll go over what makes for the best converting websites and five examples of websites […]

The post 5 Magnificent Examples of Websites That Convert Visitors into Customers appeared first on WebFX Blog.

Summary

One feature that Go does not offer that is really useful in visitor patterns is overriding methods. The basic idea is to write a concrete class that contains all the VisitX methods with empty implementations, and a subclass can choose to only override the methods it cares about, ignoring the rest.

We'll see an example of how to implement this pattern in idiomatic Go code.

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

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The Buzz Bin HERE'S THE BEEF…

[IMAGE-1] The days of Post Falls being merely an exit you point out to your restless kids to assure them you're almost to Coeur d'Alene are over. In the next 20 years, the little town is projected to explode to 100,000 people.…