NASA’s New Mars Rover ‘Perseverance’ Launched, Seeks Signs of Life

NASA’s next-generation Mars rover Perseverance blasted off from Florida’s Cape Canaveral on Thursday atop an Atlas 5 rocket on a $2.4 billion (roughly Rs. 17,974 crores) mission to search for traces of potential past life on Earth’s planetary neighbor.

The next-generation robotic rover – a car-sized six-wheeled scientific vehicle – also is scheduled to deploy a mini helicopter on Mars and test out equipment for future human missions to the fourth planet from the sun. It is expected to reach Mars next February.

 

 

It soared into the sky under clear, sunny, and warm conditions carried by an Atlas 5 rocket from the Boeing-Lockheed joint venture United Launch Alliance. The launch took place after the Jet Propulsion Laboratory in California where its mission engineers were located was rattled by an earthquake.

This marked NASA’s ninth journey to the Martian surface.

Perseverance is due to land at the base of an 820-foot-deep (250 meters) crater called Jezero, a former lake from 3.5 billion years ago that scientists suspect could bear evidence of potential past microbial life on Mars.

Scientists have long debated whether Mars – once a much more hospitable place than it is today – ever harbored life. Water is considered

Future-Guided Incremental Transformer for Simultaneous Translation

Simultaneous translation is the type of machine translation, where output is generated while reading source sentences. It can be used in the live subtitle or simultaneous interpretation.

However, the current policies have low computational speed and lack guidance from future source information. Those two weaknesses are overcome by a recently suggested method called Future-Guided Incremental Transformer.

Image credit: Pxhere, CC0 Public Domain

Image credit: Pxhere, CC0 Public Domain

It uses the average embedding layer to summarize the consumed source information and avoid time-consuming recalculation. The predictive ability is enhanced by embedding some future information through knowledge distillation. The results show that training speed is accelerated about 28 times compared to currently used models. Improved translation quality was also achieved on the Chinese-English and German-English simultaneous translation tasks.

Simultaneous translation (ST) starts translations synchronously while reading source sentences, and is used in many online scenarios. The previous wait-k policy is concise and achieved good results in ST. However, wait-k policy faces two weaknesses: low training speed caused by the recalculation of hidden states and lack of future source information to guide training. For the low training speed, we propose an incremental Transformer with an average embedding layer (AEL) to accelerate the speed of calculation of the