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Miguel Angel
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For the first time, researchers have found a person in the United States carrying bacteria resistant to antibiotic of last resort, an alarming development that the top U.S. public health official says could signal "the end of the road" for antibiotics.

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Scientists have created a synthetic organism that possesses only the genes it needs to survive. But they have no idea what roughly a third of those genes do.

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Why don't plants get cancer? Carnegie Institution for Science biologist Dominique Bergmann says it's a mystery.

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This animation guides us through the immune pathways involved in the disease, from the first signs of self-reactive immune cells to joint damage and other symptoms.

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THE INTERCEPT HAS OBTAINED a secret, internal U.S. government catalogue of dozens of cellphone surveillance devices used by the military and by intelligence agencies. The document, thick with previously undisclosed information, also offers rare insight into the spying capabilities of federal law enforcement and local police inside the United States.

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Dunning has now conducted a new study with colleagues Stav Atir and Emily Rosenzweig, finding that expertise has its own pitfalls. In a series of experiments conducted at Cornell University, the researchers found that people with greater knowledge in a particular domain were more likely to claim knowledge that they could not possibly know.

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Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
Jeff Hawkins, Subutai Ahmad

Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple patterns. In this paper we extend this idea by showing that a neuron with several thousand synapses arranged along active dendrites can learn to accurately and robustly recognize hundreds of unique patterns of cellular activity, even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where some of the patterns recognized by a neuron lead to action potentials and define the classic receptive field of the neuron, whereas the majority of the patterns recognized by a neuron act as predictions by slightly depolarizing the neuron without immediately generating an action potential. We then present a network model based on neurons with these properties and show that the network learns a robust model of time-based sequences. Given the similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory is a universal property of neocortical tissue. We further propose that cellular layers in the neocortex implement variations of the same sequence memory algorithm to achieve different aspects of inference and behavior. The neuron and network models we introduce are robust over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. The sequence capacity of the network scales linearly with the number of synapses on each neuron. Thus neurons need thousands of synapses to learn the many temporal patterns in sensory stimuli and motor sequences.

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Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning.

This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

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If the p-value is < .05, then the probability of falsely rejecting the null hypothesis is  <5%, right? That means, a maximum of 5% of all significant results is a false-positive (that’s what we control with the α rate).

Well, no. As you will see in a minute, the “false discovery rate” (aka. false-positive rate), which indicates the probability that a significant p-value actually is a false-positive, usually is much higher than 5%.
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