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The world is an ever-changing place, and there is always news happening somewhere. From politics to economics to social issues, there is always something going on that affects people all over the world. One of the most significant news stories that has dominated the headlines in recent times is the ongoing COVID-19 pandemic. The virus, which first emerged in Wuhan, China, in December 2019, has now spread to every corner of the world, causing significant disruptions to daily life, health systems, economies, and social interaction. Despite the development of vaccines, the pandemic is far from over, and new variants continue to emerge. Governments around the world have had to grapple with balancing public health concerns with economic pressures as they seek to bring the pandemic under control. Another major news story is climate change. The issue of global warming has been a significant concern for decades, and it is now beginning to have a visible impact on the environment. Extreme weather events, such as hurricanes, heatwaves, and wildfires, are becoming more frequent and severe, causing significant damage and loss of life. Governments are under pressure to take urgent action to reduce carbon emissions and slow the rate of global warming. Many are investing in renewable energy and green technologies to transition towards a more sustainable future. Political developments have also been making headlines around the world. In the United States, the recent presidential election and the subsequent events that unfolded on Capitol Hill have been significant news stories. Elsewhere, political instability, including coups and protests, have been taking place in countries such as Myanmar and Belarus. Economic news has also been in the spotlight, with the global economy struggling to recover from the impact of the COVID-19 pandemic. The pandemic has caused significant disruptions to supply chains, businesses, and the workforce, leading to job losses and economic uncertainty. Governments have introduced various measures to try and support their economies, including stimulus packages and low-interest rates. Social issues have also been making headlines around the world. The Black Lives Matter movement has gained momentum in many countries, with people protesting against systemic racism and police brutality. The #MeToo movement, which began in 2017, has continued to shed light on sexual harassment and abuse in the workplace and other settings. Technology has also been at the forefront of news stories in recent times. The rise of social media platforms has had a significant impact on the way people consume news and interact with each other. However, concerns around the spread of misinformation and the impact of these platforms on mental health have led to calls for greater regulation. As of February 2023, the world continues to be impacted by a range of significant news events that are affecting people across the globe. From political upheavals to natural disasters, there are many issues that are making headlines and capturing people's attention. In this article, we will take a look at some of the most significant general news stories from around the world. One of the biggest news stories in the world right now is the ongoing COVID-19 pandemic. Despite the development and distribution of several effective vaccines, the virus continues to cause significant disruption and illness in many countries. The emergence of new variants, such as the Omicron variant, has caused concern among health officials and led to renewed calls for caution and vigilance. In many parts of the world, lockdowns and other restrictions remain in place as governments attempt to slow the spread of the virus and protect their citizens. Another major news story in the world is the ongoing political turmoil in several countries. In recent months, there have been widespread protests and unrest in places like Belarus, Myanmar, and Haiti, where citizens are demanding political change and greater democracy. In some cases, these protests have led to violent clashes with police and military forces, and there have been reports of human rights abuses and other violations. In addition, the ongoing conflict in Syria continues to displace millions of people and cause significant humanitarian suffering . The rise of technology and its impact on society is another significant news story that is affecting people around the world. In recent years, we have seen a rapid increase in the use of social media and other online platforms, which have had both positive and negative effects. On the one hand, these platforms have allowed people to connect and communicate with others across the globe, but on the other hand, they have also been linked to issues such as cyberbullying, disinformation, and online harassment. Governments and other organizations are grappling with how best to regulate these technologies and ensure that they are used responsibly. The issue of immigration is also a major news story in many parts of the world. In recent years, we have seen significant numbers of people fleeing conflict, poverty, and persecution in places like Afghanistan, Syria, and Venezuela, and seeking refuge in other countries. However, many governments have responded to this influx of immigrants with policies designed to limit or control immigration, such as building walls, deportations, and other restrictions. This issue has become a point of contention in many political debates and elections. The COVID-19 pandemic has also affected the world of sports, with many events cancelled or postponed.However, some major events like the Olympics and the European Football Championships are still scheduled to take place in 2021.In tennis, Novak Djokovic won his ninth Australian Open title, while Naomi Osaka won her fourth Grand Slam tournament at the same event.In basketball, LeBron James and the Los Angeles Lakers won the 2020 NBA championship. In conclusion, the world is always changing, and there is always something happening that affects people all over the world. From political developments to social issues to economic news, it can be challenging to keep up with everything that is going on. However, staying informed is crucial, as it enables individuals to make informed decisions and engage with the world around them.

Active learning (AL) is a promising ML paradigm that has the potential to parse through large unlabeled data and help reduce annotation cost in domains where labeling data can be prohibitive. Recently proposed neural network based AL methods use different heuristics to accomplish this goal. In this study, we demonstrate that under identical experimental settings, different types of AL algorithms (uncertainty based, diversity based, and committee based) produce an inconsistent gain over random sampling baseline. Through a variety of experiments, controlling for sources of stochasticity, we show that variance in performance metrics achieved by AL algorithms can lead to results that are not consistent with the previously reported results. We also found that under strong regularization, AL methods show marginal or no advantage over the random sampling baseline under a variety of experimental conditions. Finally, we conclude with a set of recommendations on how to assess the results using a new AL algorithm to ensure results are reproducible and robust under changes in experimental conditions. We share our codes to facilitate AL evaluations. We believe our findings and recommendations will help advance reproducible research in AL using neural networks. We open source our code at https://github.com/PrateekMunjal/TorchAL, Comment: Accepted at CVPR 2022; Improved figures and plots for better readability

Semantic segmentation from user inputs has been actively studied to facilitate interactive segmentation for data annotation and other applications. Recent studies have shown that extreme points can be effectively used to encode user inputs. A heat map generated from the extreme points can be appended to the RGB image and input to the model for training. In this study, we present FAIRS -- a new approach to generate object segmentation from user inputs in the form of extreme points and corrective clicks. We propose a novel approach for effectively encoding the user input from extreme points and corrective clicks, in a novel and scalable manner that allows the network to work with a variable number of clicks, including corrective clicks for output refinement. We also integrate a dual attention module with our approach to increase the efficacy of the model in preferentially attending to the objects. We demonstrate that these additions help achieve significant improvements over state-of-the-art in dense object segmentation from user inputs, on multiple large-scale datasets. Through experiments, we demonstrate our method's ability to generate high-quality training data as well as its scalability in incorporating extreme points, guiding clicks, and corrective clicks in a principled manner.

To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data. The learning-based approach uses annotations to train a model that tries to emulate the expert labeling on a new data set. While tremendous progress has been made using such approaches, labeling of medical images remains a time-consuming and expensive task. In this paper, we evaluate the utility of extreme points in learning to segment. Specifically, we propose a novel approach to compute a confidence map from extreme points that quantitatively encodes the priors derived from extreme points. We use the confidence map as a cue to train a deep neural network based on ResNet-101 and PSP module to develop a class-agnostic segmentation model that outperforms state-of-the-art method that employs extreme points as a cue. Further, we evaluate a realistic use-case by using our model to generate training data for supervised learning (U-Net) and observed that U-Net performs comparably when trained with either the generated data or the ground truth data. These findings suggest that models trained using cues can be used to generate reliable training data.

A case of Vogt Koyanagi Harada syndrome is reported. The depigmented macules appeared initially over eyebrows and around both eyes after an episode of fever and then rapidly involved almost the entire body within 6 months.