Elon Musk, a man not known for thinking small, has taken to the stage at an energy conference to announce his goals for the rest of the year. The Tesla chief said that he hopes his company's self-driving cars will be'in wide release' in the US and Europe by the end of 2022. Speaking at Offshore Northern Seas 2022 in Norway yesterday, he claimed that the launch of the autonomous electric vehicles depends on regulatory approval. The billionaire also said that he was focusing on the SpaceX Starship spacecraft, that had an orbital flight scheduled for this summer that has now been delayed. SpaceX was granted a license for this flight by the U.S. Federal Communications Commission earlier this month, and is now targeting a six-month window that opens on September 1. Musk told the audience: 'The two technologies I am focused on, trying to ideally get done before the end of the year, are getting our Starship into orbit ... and then having Tesla cars to be able to do self-driving.

The liver is one of the most critical metabolic organs in vertebrates due to its vital functions in the human body, such as detoxification of the blood from waste products and medications. Liver diseases due to liver tumors are one of the most common mortality reasons around the globe. Hence, detecting liver tumors in the early stages of tumor development is highly required as a critical part of medical treatment. Many imaging modalities can be used as aiding tools to detect liver tumors. Computed tomography (CT) is the most used imaging modality for soft tissue organs such as the liver. This is because it is an invasive modality that can be captured relatively quickly. This paper proposed an efficient automatic liver segmentation framework to detect and segment the liver out of CT abdomen scans using the 3D CNN DeepMedic network model. Segmenting the liver region accurately and then using the segmented liver region as input to tumors segmentation method is adopted by many studies as it reduces the false rates resulted from segmenting abdomen organs as tumors. The proposed 3D CNN DeepMedic model has two pathways of input rather than one pathway, as in the original 3D CNN model. In this paper, the network was supplied with multiple abdomen CT versions, which helped improve the segmentation quality. The proposed model achieved 94.36%, 94.57%, 91.86%, and 93.14% for accuracy, sensitivity, specificity, and Dice similarity score, respectively. The experimental results indicate the applicability of the proposed method.

The development of domain ontological models, though being a mature research arena backed by well-established methodologies, still suffer from two key shortcomings. Firstly, the issues concerning the semantic persistency of ontology concepts and their flexible reuse in domain development employing existing approaches. Secondly, due to the difficulty in understanding and reusing top-level concepts in existing foundational ontologies, the obfuscation regarding the semantic nature of domain representations. The paper grounds the aforementioned shortcomings in representation diversity and proposes a three-fold solution - (i) a pipeline for rendering concepts reuse-ready, (ii) a first characterization of a minimalistic foundational knowledge model, named foundational teleology, semantically explicating foundational distinctions enforcing the static as well as dynamic nature of domain representations, and (iii) a flexible, reuse-native methodology for diversity-aware domain development exploiting solutions (i) and (ii). The preliminary work reported validates the potentiality of the solution components.

Multiresolution deep learning approaches, such as the U-Net architecture, have achieved high performance in classifying and segmenting images. However, these approaches do not provide a latent image representation and cannot be used to decompose, denoise, and reconstruct image data. The U-Net and other convolutional neural network (CNNs) architectures commonly use pooling to enlarge the receptive field, which usually results in irreversible information loss. This study proposes to include a Riesz-Quincunx (RQ) wavelet transform, which combines 1) higher-order Riesz wavelet transform and 2) orthogonal Quincunx wavelets (which have both been used to reduce blur in medical images) inside the U-net architecture, to reduce noise in satellite images and their time-series. In the transformed feature space, we propose a variational approach to understand how random perturbations of the features affect the image to further reduce noise. Combining both approaches, we introduce a hybrid RQUNet-VAE scheme for image and time series decomposition used to reduce noise in satellite imagery. We present qualitative and quantitative experimental results that demonstrate that our proposed RQUNet-VAE was more effective at reducing noise in satellite imagery compared to other state-of-the-art methods. We also apply our scheme to several applications for multi-band satellite images, including: image denoising, image and time-series decomposition by diffusion and image segmentation.

Representation learning has proven to be a powerful methodology in a wide variety of machine learning applications. For atmospheric dynamics, however, it has so far not been considered, arguably due to the lack of large-scale, labeled datasets that could be used for training. In this work, we show that the difficulty is benign and introduce a self-supervised learning task that defines a categorical loss for a wide variety of unlabeled atmospheric datasets. Specifically, we train a neural network on the simple yet intricate task of predicting the temporal distance between atmospheric fields from distinct but nearby times. We demonstrate that training with this task on ERA5 reanalysis leads to internal representations capturing intrinsic aspects of atmospheric dynamics. We do so by introducing a data-driven distance metric for atmospheric states. When employed as a loss function in other machine learning applications, this Atmodist distance leads to improved results compared to the classical $\ell_2$-loss. For example, for downscaling one obtains higher resolution fields that match the true statistics more closely than previous approaches and for the interpolation of missing or occluded data the AtmoDist distance leads to results that contain more realistic fine scale features. Since it is derived from observational data, AtmoDist also provides a novel perspective on atmospheric predictability.

Atmospheric pollution remains one of the major public health threat worldwide with an estimated 7 millions deaths annually. In Africa, rapid urbanization and poor transport infrastructure are worsening the problem. In this paper, we have analysed spatio-temporal variations of PM2.5 across different geographical regions in Africa. The West African region remains the most affected by the high levels of pollution with a daily average of 40.856 $\mu g/m^3$ in some cities like Lagos, Abuja and Bamako. In East Africa, Uganda is reporting the highest pollution level with a daily average concentration of 56.14 $\mu g/m^3$ and 38.65 $\mu g/m^3$ for Kigali. In countries located in the central region of Africa, the highest daily average concentration of PM2.5 of 90.075 $\mu g/m^3$ was recorded in N'Djamena. We compare three data driven models in predicting future trends of pollution levels. Neural network is outperforming Gaussian processes and ARIMA models.

The attack on the Black Sea fleet in Sevastopol is the latest in a string of strikes against Russia.

A drone has been shot down over the headquarters of Russia's Black Sea Fleet in annexed Crimea, a local official said, in the second attempted strike on the command in Sevastopol in less than a month. "The drone was shot down just above the fleet headquarters" in the city of Sevastopol, city Governor Mikhail Razvojaev wrote on Telegram on Saturday, blaming the attempt on Ukrainian forces. "It fell on the roof and caught fire," he said, adding that there was no significant damage or victim. The first reported attack came on July 31, when a presumed Ukrainian drone attacked the Black Sea Fleet on Russia's Navy Day, wounding five people. Russia also reported Ukrainian drone attacks late on Friday.

Fox News correspondent Alex Hogan reports from Kyiv, Ukraine on Russian attacks on civilian areas in the Donbas region this week on'America Reports.' Russia's naval headquarters for its Black Sea fleet in Ukraine's occupied Crimea was hit by a drone Saturday, a Russian official said. The Moscow installed governor of Sevastopol, Mikhail Razvozhayev, took to Telegram to confirm the hit and said a drone crashed into the roof of the building. There were no reported casualities. Razvozhayev first said the drone "flew into the roof" of the building and noted that Russian forces had not been able to down the strike. FILE - Russian Navy ships are docked in the Sevastopol bay on March 4, 2014.

A rare giant squid was discovered dead on a beach in Cape Town, South Africa, months after another washed up six miles away. Twitter user Tim Dee, who found the strange-looking sea creature on Scarborough Beach on Tuesday, shared photos and videos online that show the colorful squid's gigantic eye. 'Giant squid species wrecked on Scarborough beach this morning,' he wrote. Twitter user Tim Dee, who found the strange-looking sea creature (above) on Scarborough Beach on Tuesday, shared photos and videos online that show the colorful squid's gigantic eye Dee's video shows a marine biologist pulling back flesh to reveal the squid's huge beak that it uses for hunting and fishing. The sea creature, which looks like something Salvador Dali would have painted, is also known for having a very large eye - usually up to 11 inches in diameter with a 3.5 inch pupil.