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Principles for effective risk data aggregation and risk reporting
This article is written by basel committee and referred by certified FRM (Financial Risk Manager) exam. It mentions the principles of risk data aggregation: accuracy and integrity, completeness, timeliness and adaptability.
basel  risk-reporting  risk  data  aggregation 
8 days ago by peng.zhang
[1604.04660] Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like
The concept of "task" is at the core of artificial intelligence (AI): Tasks are used for training and evaluating AI systems, which are built in order to perform and automatize tasks we deem useful. In other fields of engineering theoretical foundations allow thorough evaluation of designs by methodical manipulation of well understood parameters with a known role and importance; this allows an aeronautics engineer, for instance, to systematically assess the effects of wind speed on an airplane's performance and stability. No framework exists in AI that allows this kind of methodical manipulation: Performance results on the few tasks in current use (cf. board games, question-answering) cannot be easily compared, however similar or different. The issue is even more acute with respect to artificial *general* intelligence systems, which must handle unanticipated tasks whose specifics cannot be known beforehand. A *task theory* would enable addressing tasks at the *class* level, bypassing their specifics, providing the appropriate formalization and classification of tasks, environments, and their parameters, resulting in more rigorous ways of measuring, comparing, and evaluating intelligent behavior. Even modest improvements in this direction would surpass the current ad-hoc nature of machine learning and AI evaluation. Here we discuss the main elements of the argument for a task theory and present an outline of what it might look like for physical tasks.
artificial-intelligence  philosophy-of-engineering  rather-interesting  representation  approximation  aggregation  nudge-targets  consider:looking-to-see  consider:performance-space-analysis 
10 days ago by Vaguery
todayEVERY - Latest World News
news aggregation site - scrapes original content, redisplays it here, with some categorization.
news  scraping  aggregation 
12 days ago by tswaterman
Barfing Up Balls of Trouble Keeps Some Neurons Healthy | ALZFORUM
> As reported in the February 9 Nature, neurons pack damaged mitochondria and aggregated proteins into giant vesicles the researchers call exophers. The neurons then hurl these bundles of detritus out into the extracellular space. ... Exophers add to a growing list of tools cells use to rid themselves of waste by passing it off to other cells. Exosomes have been proposed to ferry misfolded proteins away from neurons to their neighbors, as have thread-like structures called tunneling nanotubes that connect distant cells (Dec 2016 news; Gerdes et al., 2013). 
vesicle  aggregation  protein 
13 days ago by porejide
OK Log
OK Log is a distributed and coördination-free log management system for big ol’ clusters.
I built it from first principles, to teach myself the gory details of shuffling logs around.
This is the story of the prototype.
distributed  golang  logging  aggregation  log  logger 
5 weeks ago by newtonapple
A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means. The t-digest algorithm is also very parallel friendly making it useful in map-reduce and parallel streaming applications.

The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to product a data structure that is related to the Q-digest. This t-digest data structure can be used to estimate quantiles or compute other rank statistics. The advantage of the t-digest over the Q-digest is that the t-digest can handle floating point values while the Q-digest is limited to integers. With small changes, the t-digest can handle any values from any ordered set that has something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by Q-digests in spite of the fact that t-digests are more compact when stored on disk.

Super-nice feature is that it's mergeable, so amenable to parallel usage across multiple hosts if required. Java implementation, ASL licensing.
data-structures  algorithms  java  t-digest  statistics  quantiles  percentiles  aggregation  digests  estimation  ranking 
10 weeks ago by jm

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