This paper presents an algorithm that relies on a series of dense and deep neural networks for passive microwave retrieval of precipitation. The neural networks learn from coincidences of brightness temperatures from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) with the active precipitating retrievals from the Dual-frequency Precipitation Radar (DPR) onboard GPM as well as those from the {CloudSat} Profiling Radar (CPR). The algorithm first detects the precipitation occurrence and phase and then estimates its rate, while conditioning the results to some key ancillary information including parameters related to cloud microphysical properties. The results indicate that we can reconstruct the DPR rainfall and CPR snowfall with a detection probability of more than 0.95 while the probability of a false alarm remains below 0.08 and 0.03, respectively. Conditioned to the occurrence of precipitation, the unbiased root mean squared error in estimation of rainfall (snowfall) rate using DPR (CPR) data is less than 0.8 (0.1) mm/hr over oceans and land. Beyond methodological developments, comparing the results with ERA5 reanalysis and official GPM products demonstrates that the uncertainty in global satellite snowfall retrievals continues to be large while there is a good agreement among rainfall products. Moreover, the results indicate that CPR active snowfall data can improve passive microwave estimates of global snowfall while the current CPR rainfall retrievals should only be used for detection and not estimation of rates.

Time series forecasting is a long-standing challenge due to the real-world information is in various scenario (e.g., energy, weather, traffic, economics, earthquake warning). However some mainstream forecasting model forecasting result is derailed dramatically from ground truth. We believe it's the reason that model's lacking ability of capturing frequency information which richly contains in real world datasets. At present, the mainstream frequency information extraction methods are Fourier transform(FT) based. However, use of FT is problematic due to Gibbs phenomenon. If the values on both sides of sequences differ significantly, oscillatory approximations are observed around both sides and high frequency noise will be introduced. Therefore We propose a novel frequency enhanced channel attention that adaptively modelling frequency interdependencies between channels based on Discrete Cosine Transform which would intrinsically avoid high frequency noise caused by problematic periodity during Fourier Transform, which is defined as Gibbs Phenomenon. We show that this network generalize extremely effectively across six real-world datasets and achieve state-of-the-art performance, we further demonstrate that frequency enhanced channel attention mechanism module can be flexibly applied to different networks. This module can improve the prediction ability of existing mainstream networks, which reduces 35.99% MSE on LSTM, 10.01% on Reformer, 8.71% on Informer, 8.29% on Autoformer, 8.06% on Transformer, etc., at a slight computational cost ,with just a few line of code. Our codes and data are available at https://github.com/Zero-coder/FECAM.

According to a 2020 survey of data scientists conducted by Anaconda, data preparation is one of the critical steps in machine learning (ML) and data analytics workflows, and often very time consuming for data scientists. Data scientists spend about 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), and visualizing data (21%). Amazon SageMaker Studio is the first fully integrated development environment (IDE) for ML. With a single click, data scientists and developers can quickly spin up Studio notebooks to explore datasets and build models. If you prefer a GUI-based and interactive interface, you can use Amazon SageMaker Data Wrangler, with over 300 built in visualizations, analyses, and transformations to efficiently process data backed by Spark without writing a single line of code.

Inc (AMZN.O) is planning to roll out warning cards for software sold by its cloud-computing division, in light of ongoing concern that artificially intelligent systems can discriminate against different groups, the company told Reuters. Akin to lengthy nutrition labels, Amazon's so-called AI Service Cards will be public so its business customers can see the limitations of certain cloud services, such as facial recognition and audio transcription. The goal would be to prevent mistaken use of its technology, explain how its systems work and manage privacy, Amazon said. The company is not the first to publish such warnings. International Business Machines Corp (IBM.N), a smaller player in the cloud, did so years ago.

San Francisco leaders voted to allow the city's police department to use potentially lethal robots in emergency situations. "Under this policy, SFPD is authorized to use these robots to carry out deadly force in extremely limited situations when risk to loss of life to members of the public or officers is imminent and outweighs any other force option available," City Supervisor Rafael Mandelman wrote on Twitter. San Francisco leaders voted 8-3 on Tuesday in support of the new policy. The San Francisco Police Department has 17 robots, but none are armed with guns, and the department has no plans to do so. The department could deploy robots equipped with explosive charges "to contact, incapacitate, or disorient violent, armed, or dangerous suspect" during emergency situations when lives are at risk, according to a police department spokesperson.

Officials in San Francisco have voted to give the city's police the power to use potentially lethal, remote-controlled robots in emergency situations. The 8-3 vote in favour of the move followed an emotionally charged two-hour debate and came despite strong objections from civil liberties and other police oversight groups in the city on the west coast of the United States. Supervisor Connie Chan, a member of the committee that forwarded the proposal to the full board, said she understood concerns over use of force but that "according to state law, we are required to approve the use of these equipments. So here we are, and it's definitely not an easy discussion." The San Francisco Police Department (SFPD) has said it does not have pre-armed robots and has no plans to arm robots with guns.

Police in San Francisco will be allowed to deploy potentially lethal, remote-controlled robots in emergency situations. The controversial policy was approved after weeks of scrutiny and a heated debate among the city's board of supervisors during their meeting on Tuesday. Police oversight groups, the ACLU and San Francisco's public defender had urged the 11-member body to reject the police's use of equipment proposal. Opponents of the policy said it would lead to further militarization of a police force already too aggressive with underserved communities. They said the parameters under which use would be allowed were too vague.

We introduce a novel gated recurrent unit (GRU) with a weighted time-delay feedback mechanism in order to improve the modeling of long-term dependencies in sequential data. This model is a discretized version of a continuous-time formulation of a recurrent unit, where the dynamics are governed by delay differential equations (DDEs). By considering a suitable time-discretization scheme, we propose $\tau$-GRU, a discrete-time gated recurrent unit with delay. We prove the existence and uniqueness of solutions for the continuous-time model, and we demonstrate that the proposed feedback mechanism can help improve the modeling of long-term dependencies. Our empirical results show that $\tau$-GRU can converge faster and generalize better than state-of-the-art recurrent units and gated recurrent architectures on a range of tasks, including time-series classification, human activity recognition, and speech recognition.

NASA's Artemis programme is already breaking records, less than two weeks after its very first spaceflight launched. The agency has confirmed its Artemis 1 Orion capsule smashed the record for the furthest distance travelled from Earth by any craft designed to carry humans. At 08:40 EST (13:40 GMT) on Saturday (November 26), Orion reached 248,655 miles from Earth, beating the record set by Apollo 13 in April 1970. Then, at 16:06 EST (21:06 GMT) on Saturday, it reached the farthest point in its orbit – a maximum distance of 268,553 miles. Artemis 1 is an uncrewed test flight for NASA's Artemis programme, comprising the Orion spacecraft, Space Launch System (SLS) rocket.

Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction. Diverse traditional and deep learning models have been applied to discover the nonlinear relationships and recognize the complex patterns in these types of data. However, existing methods usually ignore the negative influence of imbalanced data, or severe events, on model training. Moreover, methods are usually evaluated on a small number of generally well-behaved time series, which does not show their ability to generalize. To tackle these issues, we propose a novel probability-enhanced neural network model, called NEC+, which concurrently learns extreme and normal prediction functions and a way to choose among them via selective back propagation. We evaluate the proposed model on the difficult 3-day ahead hourly water level prediction task applied to 9 reservoirs in California. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines and exhibits superior generalization ability on data with diverse distributions.