Pre-trained language model (PTM) has been shown to yield powerful text representations for dense passage retrieval task. The Masked Language Modeling (MLM) is a major sub-task of the pre-training process. However, we found that the conventional random masking strategy tend to select a large number of tokens that have limited effect on the passage retrieval task (e,g. stop-words and punctuation). By noticing the term importance weight can provide valuable information for passage retrieval, we hereby propose alternative retrieval oriented masking (dubbed as ROM) strategy where more important tokens will have a higher probability of being masked out, to capture this straightforward yet essential information to facilitate the language model pre-training process. Notably, the proposed new token masking method will not change the architecture and learning objective of original PTM. Our experiments verify that the proposed ROM enables term importance information to help language model pre-training thus achieving better performance on multiple passage retrieval benchmarks.

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section.

Several approaches have been developed to mitigate algorithmic bias stemming from health data poverty, where minority groups are underrepresented in training datasets. Augmenting the minority class using resampling (such as SMOTE) is a widely used approach due to the simplicity of the algorithms. However, these algorithms decrease data variability and may introduce correlations between samples, giving rise to the use of generative approaches based on GAN. Generation of high-dimensional, time-series, authentic data that provides a wide distribution coverage of the real data, remains a challenging task for both resampling and GAN-based approaches. In this work we propose CA-GAN architecture that addresses some of the shortcomings of the current approaches, where we provide a detailed comparison with both SMOTE and WGAN-GP*, using a high-dimensional, time-series, real dataset of 3343 hypotensive Caucasian and Black patients. We show that our approach is better at both generating authentic data of the minority class and remaining within the original distribution of the real data.

In the previous tutorial, we learned how to import the Dataset and import the libraries. Now, we're finally going to start preparing the data so that our machine learning models run correctly. In most cases, you are going to have to deal with the problem of dealing with missing data. In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. It happens really often so you need to know to take care of missing data.

We've learned how to install R and RStudio, import the dataset, and take care of missing data using the R language. Now I'm going you show you how to encode categorical data in R. If you take a look at our dataset, you'll see that we have two categorical variables. We have the county variables – Nairobi, Kisumu, and Mombasa – and we have the Purchased variables – Yes and No. They're categorical variables, obviously because they have categories. Since machine learning models are based on mathematical/numerical equations, keeping the text in the categorical variables would definitely cause us some problems. We want to have'numbers only' in our equations.

As a society, living in a technological age, we have become incredibly used to rapid change. Sometimes it feelslike the one constant we can rely on is that everything will change. For millennia humankind lived in caves, scrawling drawings on the walls. The Stone Age was 2.5 million years long, then came the Bronze Age and, with it, urbanisation, which lasted 1,500 years. The first Industrial Revolution lasted just 80 years (1760 – 1840). Before we reached our current, digital age, the Wright Brothers perfected powered flight and just 66 years later, our species had escaped Earth's gravity, traversed the vacuum of space and landed on the moon.

The race to remove CO2 from our atmosphere is on. In an effort to draw down carbon at a meaningful scale, people are looking to the ground. The top meter of the world's soil holds over three times the amount of carbon currently in our atmosphere--and if we treat our land better, it could suck up even more. This is good news for farmers. Companies and individuals desperate to offset their emissions by purchasing carbon credits are willing to pay farmers to use sustainable agricultural practices and sequester carbon in their fields.

Enhancing the player experience is the ultimate goal of artificial intelligence in gaming. Given that game developers create games for a variety of platforms, it is imperative. The option between a console and a desktop PC gaming has become obsolete. Thanks to AI, developers can now create console-like experiences for several device kinds. AI games come in several formats every year. Some experts argue that the less obvious uses of AI in games are the most potent. AI is becoming more prevalent in games, which has significant economic advantages for companies.

In recent months there has been talk about how artificial intelligence can create images from textual prompts. Therefore, when one associates the words artificial intelligence and art, one immediately thinks of DALL-E, Stable Diffusion, and other algorithms. In this article, instead, I want to discuss why artworks are often less safe than we think, and how artificial intelligence can help preserve them. "Every act of creation is first of all an act of destruction." It is a mistake to think that cultural heritage is safe. Many of humanity's most valuable works are also among the most fragile. Throughout history, only a fraction of works of art has managed to survive over time. For example, during wars, cultural heritage is often damaged.

Lots of businesses utilize large information to improve their operations. E-commerce businesses employ qualitative and probabilistic procedures to venture off cybersecurity risks while mining huge amounts of customer information to construct recommendation engines. Targeted marketing campaigns geared toward providing a personalized customer experience. But since the usage cases for information science grow more complicated, a few innovative startups are currently relying on artificial intelligence and machine learning to their core product offering or business model–and in doing this, attaining things that would not be possible without information. More than 12,000 startups recorded on Crunchbase rely upon machine learning due to their primary and ancillary services and products.