Breast cancer is the second most common cancer globally, and is the most commonly diagnosed cancer in Indian women. Of the 685,000 women who die around the world every year because of breast cancer, over 90,000 are in India, where cancer of the breast is the most common cause of cancer-related deaths in India. One of the major reasons for the high mortality rate in India is that most Indian patients present in the later stages of the disease. Population-scale screening with early detection methods, and efforts to increase awareness of breast cancer, could help tackle the disease, improve survival rates and reduce treatment costs. Screening mammography is a widely used method, but its usage in low- and middle-income countries (LMICs) is limited due to equipment cost and the expert skill required for interpretation of mammograms.

Visual question answering (VQA) is a challenging task, which has attracted more and more attention in the field of computer vision and natural language processing. However, the current visual question answering has the problem of language bias, which reduces the robustness of the model and has an adverse impact on the practical application of visual question answering. In this paper, we conduct a comprehensive review and analysis of this field for the first time, and classify the existing methods according to three categories, including enhancing visual information, weakening language priors, data enhancement and training strategies. At the same time, the relevant representative methods are introduced, summarized and analyzed in turn. The causes of language bias are revealed and classified. Secondly, this paper introduces the datasets mainly used for testing, and reports the experimental results of various existing methods. Finally, we discuss the possible future research directions in this field.

A significant obstacle in the development of robust machine learning models is covariate shift, a form of distribution shift that occurs when the input distributions of the training and test sets differ while the conditional label distributions remain the same. Despite the prevalence of covariate shift in real-world applications, a theoretical understanding in the context of modern machine learning has remained lacking. In this work, we examine the exact high-dimensional asymptotics of random feature regression under covariate shift and present a precise characterization of the limiting test error, bias, and variance in this setting. Our results motivate a natural partial order over covariate shifts that provides a sufficient condition for determining when the shift will harm (or even help) test performance. We find that overparameterized models exhibit enhanced robustness to covariate shift, providing one of the first theoretical explanations for this intriguing phenomenon. Additionally, our analysis reveals an exact linear relationship between in-distribution and out-of-distribution generalization performance, offering an explanation for this surprising recent empirical observation.

The four essential chambers of one's heart that lie in the thoracic cavity are crucial for one's survival, yet ironically prove to be the most vulnerable. Cardiovascular disease (CVD) also commonly referred to as heart disease has steadily grown to the leading cause of death amongst humans over the past few decades. Taking this concerning statistic into consideration, it is evident that patients suffering from CVDs need a quick and correct diagnosis in order to facilitate early treatment to lessen the chances of fatality. This paper attempts to utilize the data provided to train classification models such as Logistic Regression, K Nearest Neighbors, Support Vector Machine, Decision Tree, Gaussian Naive Bayes, Random Forest, and Multi-Layer Perceptron (Artificial Neural Network) and eventually using a soft voting ensemble technique in order to attain as many correct diagnoses as possible.

Biometrics adoption is being encouraged in the public sector for digital ID and online government applications, as it continues to rise in the private sector from smartphones, where Fingerprint Cards has announced new wins to airport processes, where NEC technology is being deployed and Vision-Box is positioning for more growth. National digital ID programs are under the microscope, while Thales has signed a major deal in Vietnam, and a debate has broken out on facial recognition ethics between Oosto and Clearview AI. The potential for digital identity to boost national economies is examined by the World Economic Forum in a new white paper. The WEF sees digital ID as benefitting people by easing access to a range of services, helping small and medium-sized businesses with easier access to financing, and help establish robust growth in digital service industries, with China's digital wealth-management market offered as an example. Research ICT Africa has released a series of extensive reports delving into the digital identity systems in 10 African countries.

When Cathy Hackl's son wanted to throw a party for his 9th birthday, he didn't ask for favors for his friends or themed decorations. Instead, he asked if they could hold the celebration on Roblox. On the digital platform, which allows users to play and create a multitude of games, Hackl's son and his friends would attend the party as their virtual avatars. "They hung out and played and they went to other different games together," she says. "Just because it happens in a virtual space doesn't make it less real. The futility of throwing an outdoor pandemic-friendly event in January wasn't the only reason Hackl's son lobbied for a digital event. Roblox might be unknown to many over the age of, say, 25, but the 13-year-old platform is booming. Available on most desktop and mobile platforms, it is simultaneously a venue for free games, a creation engine that allows users to generate new activities of their own, and a marketplace to sell those experiences, as well as side products like ...

Dubai, United Arab Emirates: Alef Education, a leading global education technology provider that empowers 21-st century learning, today announced its participation at the Middle East's premier education event, GESS Dubai 2021, which is taking place November 14-16 at Dubai World Trade Centre. The pandemic underscored the importance of embracing innovative education technologies as the widespread school closures across the globe impacted 1.5 billion students, according to UNESCO. In line with the renewed demand for digital learning, the global education technology market is projected to reach $285.2 billion by 2027, according to business consulting firm Grand View Research. Furthermore, the UAE's education market is expected to touch $7.1 billion by 2023, according to a 2018 report released by the Boston Consulting Group (BCG). Under the theme "Power of AI and Data in transforming education," Alef Education will demonstrate its suite of digital education products.

Satellite imagery of the Sahara desert presents an arid expanse, the endless rolling dunes we know from movies. The thing is, normal satellite images don't show individual trees, but that doesn't necessarily mean they're not there. Researchers from the University of Copenhagen and NASA taught artificial intelligence about trees and had them take another look. It turns out there is lots of vegetation in the Western Sahara: an estimated 1.8 billion trees and shrubs. "We were very surprised to see that quite a few trees actually grow in the Sahara Desert, because up until now, most people thought that virtually none existed," says lead author Martin Brandt of the university's Department of Geosciences and Natural Resource Management.

Automated speech-recognition technology has become more common with the popularity of virtual assistants like Siri, but many of these systems only perform well with the most widely spoken of the world's roughly 7,000 languages. Because these systems largely don't exist for less common languages, the millions of people who speak them are cut off from many technologies that rely on speech, from smart home devices to assistive technologies and translation services. Recent advances have enabled machine learning models that can learn the world's uncommon languages, which lack the large amount of transcribed speech needed to train algorithms. However, these solutions are often too complex and expensive to be applied widely. Researchers at MIT and elsewhere have now tackled this problem by developing a simple technique that reduces the complexity of an advanced speech-learning model, enabling it to run more efficiently and achieve higher performance.

Machine learning models based on temporal point processes are the state of the art in a wide variety of applications involving discrete events in continuous time. However, these models lack the ability to answer counterfactual questions, which are increasingly relevant as these models are being used to inform targeted interventions. In this work, our goal is to fill this gap. To this end, we first develop a causal model of thinning for temporal point processes that builds upon the Gumbel-Max structural causal model. This model satisfies a desirable counterfactual monotonicity condition, which is sufficient to identify counterfactual dynamics in the process of thinning. Then, given an observed realization of a temporal point process with a given intensity function, we develop a sampling algorithm that uses the above causal model of thinning and the superposition theorem to simulate counterfactual realizations of the temporal point process under a given alternative intensity function. Simulation experiments using synthetic and real epidemiological data show that the counterfactual realizations provided by our algorithm may give valuable insights to enhance targeted interventions.