In my previous article I have dealt with semantic segmentation and its networks. In this paper I will introduce instance segmentation and the network that can be used in the task of instance segmentation. One of the most important things to keep in mind is the apparent difference between semantic and instance segmentation in computer vision. As shown in figure below, in semantic segmentation, every object of the same class is coloured with the same colour. However, in instance segmentation, every object of the same class is coloured in different colours.

In this post, I am mainly interested in the latter 3 architectures. A Convolutional Neural Network is basically a standard neural network that has been extended across space using shared weights. CNN is designed to recognize images by having convolutions inside, which see the edges of an object recognized on the image. A Recurrent Neural Network is basically a standard neural network that has been extended across time by having edges which feed into the next time step instead of into the next layer in the same time step. RNN is designed to recognize sequences, for example, a speech signal or a text.

The power and energy monitoring in carbontracker is limited to a few main components of computational systems. Additional power consumed by the supporting infrastructure, such as that used for cooling or power delivery, is accounted for by multiplying the measured power by the pue of the data center hosting the compute, as suggested by Strubell2019. Previous research has examined pue and its shortcomings (Yuventi2013). These shortcomings may largely be resolved by data centers reporting an average pue instead of a minimum observed value. In our work, we use a pue of 1.58, the global average for data centers in 2018 as reported by Ascierto2018.222Early

Bluware Corp, the digital innovation platform that enables the oil and gas industry to accelerate digital transformation initiatives using deep learning, is pleased to announce a new agreement with BP (NYSE: BP). Bluware's technology will help BP to improve quality and speed when delivering seismic interpretation products. "BP recognizes the significant impact advances in digital technology can bring and we are pleased to implement Bluware InteractivAI, a new and innovative deep learning technology, augmenting our geoscientists' ability to accelerate subsurface data interpretation," says Ahmed Hashmi, Upstream Chief Digital and Technology Officer at BP. Large seismic data sets are difficult to move and use in workflows and time consuming to interpret. InteractivAI, powered by Bluware Volume Data Store (VDS) cloud-native data environment, enables the acceleration of detailed interpretation tasks. With this tool geoscientists can now train and correct deep learning results interactively, significantly improving structural interpretation workflows.

MIM Software Inc., a leading global provider of medical imaging software, announced it has received 510(k) clearance from the US Food and Drug Administration (FDA) for its deep learning auto-contouring software, Contour ProtégéAI . Contour ProtégéAI is an auto-contouring solution that seamlessly integrates into any department's workflow and can be rapidly implemented into virtually any environment. User feedback and a determination to continuously improve auto-segmentation were key drivers in developing the product. "Our customers are under continual pressure to improve their practices while facing escalating time constraints," said Andrew Nelson, Chief Executive Officer of MIM Software Inc. "Our deep learning auto-segmentation product, Contour ProtégéAI, will play a critical role in reducing the burden of contouring." Auto-contouring is an ideal use case for deep learning algorithms because it is one of the most time-consuming clinical tasks.

Deep neural networks achieve state-of-the-art performance in many domains in signal processing. The main practice is getting pairs of examples, input, and its desired output, and then training a network to produce the same outputs with the goal that it will learn how to generalize also to new unseen data, which is indeed the case in many scenarios.

… called on machine learning practitioners to place people who are most impacted by algorithmic systems at the center of development processes.

Everyone despises CAPTCHAs (humans, since bots do not have emotions) -- Those annoying images containing hard to read the text, which you have to type in before you can access or do "something" online. CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart) were developed to prevent automatized programs from being mischievous (filling out online forms, accessing restricted files, accessing a website an incredible amount of times, and others) on the world wide web, by verifying that the end-user is "human" and not a bot. Nevertheless, several attacks on CAPTCHAs have been proposed in the past, but none has been as accurate and fast as the machine learning algorithm presented by a group of researchers from Lancaster University, Northwest University, and Peking University showed below. One of the first known people to break CAPTCHAs was Adrian Rosebrock, who, in his book "Deep Learning for Computer Vision with Python," [4] Adrian goes through how he bypassed the CAPTCHA systems on the E-ZPass New York website using machine learning, where he used deep learning to train his model by downloading a large image dataset of CAPTCHA examples in order to break the CAPTCHA systems. The main difference between Adrian's solution and the solution from the research scientists from Lancaster, Northwest, and Peking, is that the researchers did not need to download a large dataset of images to break the CAPTCHAs system, au contraire, they used the concept of a generative adversarial network (GAN) to create synthesized CAPTCHAs, along with a small dataset of real CAPTCHAs to create an extremely fast and accurate CAPTCHA solver.

We present OtoWorld, an interactive environment in which agents must learn to listen in order to solve navigational tasks. The purpose of OtoWorld is to facilitate reinforcement learning research in computer audition, where agents must learn to listen to the world around them to navigate. OtoWorld is built on three open source libraries: OpenAI Gym for environment and agent interaction, PyRoomAcoustics for ray-tracing and acoustics simulation, and nussl for training deep computer audition models. OtoWorld is the audio analogue of GridWorld, a simple navigation game. OtoWorld can be easily extended to more complex environments and games. To solve one episode of OtoWorld, an agent must move towards each sounding source in the auditory scene and "turn it off". The agent receives no other input than the current sound of the room. The sources are placed randomly within the room and can vary in number. The agent receives a reward for turning off a source. We present preliminary results on the ability of agents to win at OtoWorld. OtoWorld is open-source and available.

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.