Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. The difficulty of scaling search to large datasets largely explains why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine truly massive time series for the first time. We demonstrate the following extremely unintuitive fact; in large datasets we can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms. We demonstrate our work on the largest set of time series experiments ever attempted. In particular, the largest dataset we consider is larger than the combined size of all of the time series datasets considered in all data mining papers ever published. We show that our ideas allow us to solve higher-level time series data mining problem such as motif discovery and clustering at scales that would otherwise be untenable. In addition to mining massive datasets, we will show that our ideas also have implications for real-time monitoring of data streams, allowing us to handle much faster arrival rates and/or use cheaper and lower powered devices than are currently possible.

Autoimmune encephalitis is the most commonly identified cause of new-onset refractory status epilepticus, but half remain cryptogenic. Outcome at discharge is poor but improves during follow-up. Epilepsy develops in most cases. The role of anesthetics and immune therapies warrants further investigation.

Brain plasticity can be conceptualized as nature’s invention to overcome limitations of the genome and adapt to a rapidly changing environment. As such, plasticity is an intrinsic property of the brain across the life-span. However, mechanisms of plasticity may vary with age. The combination of transcranial magnetic stimulation (TMS) with electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) enables clinicians and researchers to directly study local and network cortical plasticity, in humans in vivo, and characterize their changes across the age-span. Parallel, translational studies in animals can provide mechanistic insights. Here, we argue that, for each individual, the efficiency of neuronal plasticity declines throughout the age-span and may do so more or less prominently depending on variable ‘starting-points’ and different ‘slopes of change’ defined by genetic, biological, and environmental factors. Furthermore, aberrant, excessive, insufficient, or mistimed plasticity may represent the proximal pathogenic cause of neurodevelopmental and neurodegenerative disorders such as autism spectrum disorders or Alzheimer’s disease.