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Articles related to "called"


LIGO Black hole merger may have optical confirmation

  • With the help of Caltech's Zwicky Transient Facility (ZTF), funded by the National Science Foundation (NSF) and located at Palomar Observatory near San Diego, the scientists have spotted what might be a flare of light from a pair of coalescing black holes.
  • One flare the survey caught, generated by a distant active supermassive black hole, or quasar, called J1249+3449, was pinpointed to the region of the gravitational-wave event S190521g.
  • The process of merging gave the object a kick that should cause it to enter the supermassive black hole's disk again, producing another flash of light that ZTF should be able to see.
  • The Physical Review Letters paper, titled, "A Candidate Electromagnetic Counterpart to the Binary Black Hole Merger Gravitational Wave Event GW190521g," was funded by the NSF, NASA, the Heising-Simons Foundation, and the GROWTH (Global Relay of Observatories Watching Transients Happen) program.

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Horseshoe crab blood is key to making a COVID-19 vaccine—but the ecosystem may suffer.

  • In 2016, a synthetic alternative to crab lysate, recombinant factor C (rFC), was approved as an alternative in Europe, and a handful of U.S. drug companies also began using it.
  • But on June 1, 2020, the American Pharmacopeia, which sets the scientific standards for drugs and other products in the U.S., declined to place rFC on equal footing with crab lysate, claiming that its safety is still unproven.
  • But she and other conservationists fear that without rFC or other alternatives available, the ongoing burden on horseshoe crab blood for COVID-19 vaccines and related therapeutics may imperil the crabs and the marine ecosystems that depend on them.
  • According to the statement from Lonza, Charles River Laboratories and another lysate maker, Associates of Cape Cod, Inc., raise horseshoe crabs in hatcheries and release them into the ocean.
  • Lonza’s statement says the company would also prefer to use lysate alternatives and has trademarked its own rFC, called PyroGene.

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Probabilistic Programming and Bayesian Inference for Time Series Analysis and Forecasting in Python

  • In this article, I use a small (only 36 data samples) Sales of Shampoo time series dataset from Kaggle [6] to demonstrate how to use probabilistic programming to implement Bayesian analysis and inference for time series analysis and forecasting.
  • In this article, I use PyMC [3][7] as the probabilistic programming language to analyze and forecast the sales of Shampoo [6] for demonstration purpose.
  • This section describes how to use PyMC [7] to program Bayesian analysis and inference for time series forecasting.
  • In other words, we only use the accepted steps after the burn-in period for Bayesian inference.
  • In this article, I used the small Sales of Shampoo [6] time series dataset from Kaggle [6] to how to use PyMC [3][7] as a Python probabilistic programming language to implement Bayesian analysis and inference for time series forecasting.

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Probabilistic Programming and Bayesian Inference for Time Series Analysis and Forecasting

  • In this article, I use a small (only 36 data samples) Sales of Shampoo time series dataset from Kaggle [6] to demonstrate how to use probabilistic programming to implement Bayesian analysis and inference for time series analysis and forecasting.
  • In this article, I use PyMC [3][7] as the probabilistic programming language to analyze and forecast the sales of Shampoo [6] for demonstration purpose.
  • This section describes how to use PyMC [7] to program Bayesian analysis and inference for time series forecasting.
  • In other words, we only use the accepted steps after the burn-in period for Bayesian inference.
  • In this article, I used the small Sales of Shampoo [6] time series dataset from Kaggle [6] to how to use PyMC [3][7] as a Python probabilistic programming language to implement Bayesian analysis and inference for time series forecasting.

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Velodyne becomes latest tech company to go public using a SPAC, eschewing the traditional IPO path

  • Velodyne Lidar, the leading supplier of a sensor widely considered critical to the commercial deployment of autonomous vehicles, said Thursday it has struck a deal to merge with special-purpose acquisition company Graf Industrial Corp., with a market value of $1.8 billion.
  • It has also prompted automakers in the past 18 months to shift more resources and attention toward advanced driver assistance systems in passenger cars, trucks and SUVs. Lidar is perhaps one of the most crowded sub categories in the autonomous vehicle industry.
  • The sensor is considered by most in the self-driving car industry a key piece of technology required to safely deploy robotaxis and other autonomous vehicles.
  • Hall developed the spinning laser lidar and sold the sensors to teams competing in a future autonomous vehicle DARPA competition.

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How a Chinese firm with Canadian ties jumped to the front of the coronavirus vaccine race

  • They left top positions at global pharmaceutical companies in Canada to set up a biotechnology firm half a world away in Tianjin, China, hoping to produce vaccines on par with Western countries.
  • A spokesperson for the Chinese company, citing media reports in May, said Canadian Prime Minister Justin Trudeau is supportive of the Canadian researchers working on clinical trials for a coronavirus vaccine with CanSino.
  • In February 2014, about five years after returning to China, Yu licensed a technology from the National Research Council of Canada called HEK 293 cell lines, which is required to produce large quantities of a vaccine reliably.
  • Few Chinese companies had that technology in 2014, when a Chinese army researcher called Chen Wei began looking for viral vector expertise to produce a vaccine amid Africa’s Ebola outbreak.

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Reinforcement Learning with TensorFlow Agents — Tutorial

  • We will also use a wrapper for our environment called TFPyEnvironment — which converts the numpy arrays used for state observations, actions and rewards into TensorFlow tensors.
  • One of the breakthroughs of DQN was experience replay, in which we store the experiences of the agent (state, action, reward) and use it to train the Q network in batches in each step.
  • In order to do this, TF-Agents includes the object TFUniformReplayBuffer, which stores these experiences to re-use them later, so we firstly create this object that we will need later on.
  • In this method, we take an environment, a policy and a buffer, take the current time_step formed by its state observation and reward at that time_step, the action the policy chooses and then the next time_step.

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Reinforcement Learning with TensorFlow Agents — Tutorial

  • We will also use a wrapper for our environment called TFPyEnvironment — which converts the numpy arrays used for state observations, actions and rewards into TensorFlow tensors.
  • One of the breakthroughs of DQN was experience replay, in which we store the experiences of the agent (state, action, reward) and use it to train the Q network in batches in each step.
  • In order to do this, TF-Agents includes the object TFUniformReplayBuffer, which stores these experiences to re-use them later, so we firstly create this object that we will need later on.
  • In this method, we take an environment, a policy and a buffer, take the current time_step formed by its state observation and reward at that time_step, the action the policy chooses and then the next time_step.

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Reinforcement Learning with TensorFlow Agents — Tutorial

  • We will also use a wrapper for our environment called TFPyEnvironment — which converts the numpy arrays used for state observations, actions and rewards into TensorFlow tensors.
  • One of the breakthroughs of DQN was experience replay, in which we store the experiences of the agent (state, action, reward) and use it to train the Q network in batches in each step.
  • In order to do this, TF-Agents includes the object TFUniformReplayBuffer, which stores these experiences to re-use them later, so we firstly create this object that we will need later on.
  • In this method, we take an environment, a policy and a buffer, take the current time_step formed by its state observation and reward at that time_step, the action the policy chooses and then the next time_step.

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Mercury and algal blooms poisoned Maya reservoirs at Tikal

  • A recent study suggests a possible explanation for its decline: mercury and toxic algal blooms poisoned the water sources that should have carried the city through dry seasons.
  • University of Cincinnati biologist David Lentz and his colleagues sampled layers of sediment dating back to the mid-800s, and they found that two of Tikal’s central reservoirs would have been too polluted to drink from.
  • Lentz and his colleagues also found ancient DNA from blue-green algae, or cyanobacteria, which can produce deadly toxins.
  • Lentz and his colleagues found toxic levels of mercury in sediment layers dating from 600 CE to 900 CE, based on radiocarbon dating of bits of organic matter mixed into the sediment.
  • But the reservoirs in Tikal watered the political and ceremonial heart of the city, as they resided next door to a palace complex and major temples.

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