Basics: What is a Gleason Score? One important component of staging your cancer is the grade of the cancer. While the stage of your cancer looks at where the cancer is present in your body -- how it is behaving at the macro level -- the grade describes what the actual cancer cells look like under a microscope -- how they are behaving on a micro level. Traditionally, prostate cancer grades were described according to the Gleason Score, a system named for the pathologist who developed it in the 1960s. Dr. Donald Gleason realized that cancerous cells fall into 5 distinct patterns as they change from normal cells to tumor cells.

Video analytics represents something of a holy grail to those in the security industry. Computers have long been able to scan text and even audio for keywords or phrases, but analyzing video -- especially in real time -- is considerably more challenging. In recent years, however, major improvements to artificial intelligence (AI), machine learning and deep learning capabilities have given rise to impressive new tools capable of analyzing video with minimal input from security personnel. As companies look to invest in these new technologies, it's important to establish a baseline understanding of what terms like artificial intelligence, machine learning and deep learning actually mean -- and what these technologies are capable of. Education will be increasingly critical as we move away from relying on human-based security and lean more on technology to identify and alert us to anomalous or troubling behavior.

The novel coronavirus has been circulating among humans for barely three months, but several bio-tech firms have already created drugs that target the COVID-19 disease. One of the secret weapons for the fast response is artificial intelligence. The Chinese government initially was criticized for downplaying the severity of the coronavirus outbreak that originated in Wuhan last December. However, researchers around the world applauded the quick work of Chinese scientists in decoding the genetic sequence of the virus, dubbed SARS-CoV-2, and posting the results in a public database on January 10. Researchers quickly went to work.

In this very engaging and hugely interesting chat, Chandini talks about her FinTech journey, about machine learning and deep learning in the fund management space, and has some great top tips for entrepreneurs. If you enjoy this podcast, subscribe to the Rise FinTech Podcast channel via your favourite podcast app, and be the first to hear new episodes.

Python is one of the most popular languages used by data scientists and software developers. In this article, you'll see a line-up of the most important Python libraries for data science tasks, covering areas such as data processing, modeling, and visualization. Python is one of the most popular languages used by data scientists and software developers alike for data science tasks. It can be used to predict outcomes, automate tasks, streamline processes, and offer business intelligence insights. It's possible to work with data in vanilla Python, but there are quite a few open-source libraries that make Python data tasks much, much easier.

It'll take hours to train! I don't even have a good enough machine." I've heard this countless times from aspiring data scientists who shy away from building deep learning models on their own machines. Deep learning is a vast field so we'll narrow our focus a bit and take up the challenge of solving an Image Classification project. Additionally, we'll be using a very simple deep learning architecture to achieve a pretty impressive accuracy score.

Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS). A knowledge graph is capable of encoding high-order relations that connect two objects with one or multiple related attributes. With the help of the emerging Graph Neural Networks (GNN), it is possible to extract both object characteristics and relations from KG, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of the GNN-based knowledge-aware deep recommender systems. Specifically, we discuss the state-of-the-art frameworks with a focus on their core component, i.e., the graph embedding module, and how they address practical recommendation issues such as scalability, cold-start and so on. We further summarize the commonly-used benchmark datasets, evaluation metrics as well as open-source codes. Finally, we conclude the survey and propose potential research directions in this rapidly growing field.

Raven's Progressive Matrices are a benchmark originally designed to test the cognitive abilities of humans. It has recently been adapted to test relational reasoning in machine learning systems. For this purpose the so-called Procedurally Generated Matrices dataset was set up, which is so far one of the most difficult relational reasoning benchmarks. Here we show that deep neural networks are capable of solving this benchmark, reaching an accuracy of 98.0 percent over the previous state-of-the-art of 62.6 percent by combining Wild Relation Networks with Multi-Layer Relation Networks and introducing Magnitude Encoding, an encoding scheme designed for late fusion architectures.

The global degradation of water resources is a matter of great concern, especially for the survival of humanity. The effective monitoring and management of existing water resources is necessary to achieve and maintain optimal water quality. The prediction of the quality of water resources will aid in the timely identification of possible problem areas and thus increase the efficiency of water management. The purpose of this research is to develop a water quality prediction model based on water quality parameters through the application of a specialised recurrent neural network (RNN), Long Short-Term Memory (LSTM) and the use of historical water quality data over several years. Both multivariate single and multiple step LSTM models were developed, using a Rectified Linear Unit (ReLU) activation function and a Root Mean Square Propagation (RMSprop) optimiser was developed. The single step model attained an error of 0.01 mg/L, whilst the multiple step model achieved a Root Mean Squared Error (RMSE) of 0.227 mg/L.

We introduce a new task, Video-and-Language Inference, for joint multimodal understanding of video and text. Given a video clip with aligned subtitles as premise, paired with a natural language hypothesis based on the video content, a model needs to infer whether the hypothesis is entailed or contradicted by the given video clip. A new large-scale dataset, named Violin (VIdeO-and-Language INference), is introduced for this task, which consists of 95,322 video-hypothesis pairs from 15,887 video clips, spanning over 582 hours of video. These video clips contain rich content with diverse temporal dynamics, event shifts, and people interactions, collected from two sources: (i) popular TV shows, and (ii) movie clips from YouTube channels. In order to address our new multimodal inference task, a model is required to possess sophisticated reasoning skills, from surface-level grounding (e.g., identifying objects and characters in the video) to in-depth commonsense reasoning (e.g., inferring causal relations of events in the video). We present a detailed analysis of the dataset and an extensive evaluation over many strong baselines, providing valuable insights on the challenges of this new task.