For the past two decades, there have been significant efforts to develop methods for object detection in Remote Sensing (RS) images. In most cases, the datasets for small object detection in remote sensing images are inadequate. Many researchers used scene classification datasets for object detection, which has its limitations; for example, the large-sized objects outnumber the small objects in object categories. Thus, they lack diversity; this further affects the detection performance of small object detectors in RS images. This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images. We also propose a large-scale, publicly available benchmark Remote Sensing Super-resolution Object Detection (RSSOD) dataset. The RSSOD dataset consists of 1,759 hand-annotated images with 22,091 instances of very high resolution (VHR) images with a spatial resolution of ~0.05 m. There are five classes with varying frequencies of labels per class. The image patches are extracted from satellite images, including real image distortions such as tangential scale distortion and skew distortion. We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolution-based object detection and compare with the existing state-of-the-art methods based on image super-resolution (SR). The proposed MCGR achieved state-of-the-art performance for image SR with an improvement of 1.2dB PSNR compared to the current state-of-the-art NLSN method. MCGR achieved best object detection mAPs of 0.758, 0.881, 0.841, and 0.983, respectively, for five-class, four-class, two-class, and single classes, respectively surpassing the performance of the state-of-the-art object detectors YOLOv5, EfficientDet, Faster RCNN, SSD, and RetinaNet.

In this paper, we propose a new end-to-end methodology to optimize the energy performance as well as comfort and air quality in large buildings without any renovation work. We introduce a metamodel based on recurrent neural networks and trained to predict the behavior of a general class of buildings using a database sampled from a simulation program. This metamodel is then deployed in different frameworks and its parameters are calibrated using the specific data of two real buildings. Parameters are estimated by comparing the predictions of the metamodel with real data obtained from sensors using the CMA-ES algorithm, a derivative free optimization procedure. Then, energy consumptions are optimized while maintaining a target thermal comfort and air quality, using the NSGA-II multi-objective optimization procedure. The numerical experiments illustrate how this metamodel ensures a significant gain in energy efficiency, up to almost 10%, while being computationally much more appealing than numerical models and flexible enough to be adapted to several types of buildings.

Microsoft announced yesterday it will begin offering an updated version of the AI natural language program (NLP) GPT-3 to business customers as part of its Azure cloud platform. Why it matters: The move puts what is likely the most powerful AI writing and reading algorithm at the fingertips of large businesses that will be able to use it to automatically analyze and generate new written content. Driving the news: While OpenAI -- the artificial general intelligence research company that created GPT-3 -- has and will continue selling access to the model through its own API, Microsoft will offer a version for corporate clients that emphasizes "safety and security," says Eric Boyd, corporate vice president of Azure AI at Microsoft. How it works: GPT-3 is a natural language transformer program that was trained on half a trillion words on the internet, making it the largest such model in the world when it was released last summer. The catch: Like all NLP models, GPT-3 can incorporate bias found in its training set, producing text that can be marked with sexism, Islamophobia and other very human ills that could expose corporate users to legal and reputational risk.

Last year, Alphabet's DeepMind announced its AlphaFold 2 AI showed it could predict how certain proteins would fold in a way that was competitive with experimental data. The news was met with enthusiasm by the scientific community, but it wasn't clear at the time what the breakthrough would mean in practical terms. Now we have a better idea with Alphabet announcing the creation of a new subsidiary called Isomorphic Labs. The company states its goal is to "reimagine" the process of developing new drugs with an AI-first approach. "We believe that the foundational use of cutting edge computational and AI methods can help scientists take their work to the next level, and massively accelerate the drug discovery process," Demis Hassabis, the founder and CEO of Isomorphic Labs said.

The field of drug discovery has been supercharged by the capabilities of AI, which several companies have applied in various ways to turn an enormous practical problem into a tractable information problem. The latest to do so is Google parent company Alphabet, which has established Isomorphic Labs, under DeepMind head Demis Hassabis, to take its shot at the promising new field. Very little was revealed about the company in its debut blog post and a very general accompanying FAQ. The aim of the company is to "build a computational platform to understand biological systems from first principles to discover new ways to treat disease." There are, of course, a few assumptions baked into that founding statement, most prominently that it's possible to computationally simulate biological systems in a matter conducive to drug discovery.

Luca Arrotta is Ph.D. student at the Department of Computer Science of the University of Milan. His interests are Machine Learning, Data Analysis, IoT, Mobile Programming, and Indoor Positioning. His research currently focuses on Pervasive Computing, Context-awareness, Explainable AI, and Human Activity Recognition in smart environments.

Model speed can be a deal breaker on large datasets. Leveraging an empirical study, we will look at two dimension reduction techniques and how they can be applied to a Gaussian Processes. Regarding implementation of the method, anyone familiar with the basics of conditional probability can develop a Gaussian Process model. However, to fully leverage the capabilities of the framework, a fair amount of in-depth knowledge is required. Gaussian processes also are not very computationally efficient, but their flexibility is makes them a common choice for niche regression problems.

Persistent observations of large underwater animals are difficult to achieve without the help of electronic, multi-sensor tags. Data obtained from these sensors provide important insight into the biomechanics, activity patterns, energy expenditure, diving and mating behaviors of these animals, which are otherwise "foreign" to the scientists who study them. In particular, there has been little work done on large reef fish such as the Atlantic goliath grouper (Epinephelus itajara), whose behaviors have been poorly described despite being a common inhabitant of many of Florida's offshore reefs and wrecks. Researchers from Florida Atlantic University's Harbor Branch Oceanographic Institute and College of Engineering and Computer Science are the first to reveal detailed behavior of this grouper species, which can reach lengths of 8 feet and weigh more than 800 pounds. To accomplish this task, they developed a novel multi-sensor tag that includes a three axis accelerometer, gyroscope and magnetometer (collectively referred to as an inertial measurement unit or IMU) as well as a temperature, pressure and light sensor, a video camera and a hydrophone for monitoring underwater sound.

Ballyhooed artificial-intelligence technique known as "deep learning" revives 70-year-old idea. In the past 10 years, the best-performing artificial-intelligence systems -- such as the speech recognizers on smartphones or Google's latest automatic translator -- have resulted from a technique called "deep learning." Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years. Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what's sometimes called the first cognitive science department. Neural nets were a major area of research in both neuroscience and computer science until 1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who a year later would become co-directors of the new MIT Artificial Intelligence Laboratory. Most applications of deep learning use "convolutional" neural networks, in which the nodes of each layer are clustered, the clusters overlap, and each cluster feeds data to multiple nodes (orange and green) of the next layer.

International Law and Health Related International Standard Setting Instruments play an important role in evolution and development of International Health Law. Conventional International Law is the primary International Legal Instrument through which International Organisations can extend International Cooperation for improving the Global Health Status as also reducing the Global Burden of Diseases. In the recent times, there has been an increase in the Inter-Governmental Organisations in the domain of Health Care. Let us take the instance of the growing diversity of International Law relating to Public Health wherein a broad array of Inter-Governmental Organisations including United Nations and its agencies and other related bodies are contributing to the development of International Health Law. The International Health Law is therefore emerging in a fragmented and amorphous manner.