Cinzano P.,Luminoso |
Monthly Notices of the Royal Astronomical Society | Year: 2012
Recent methods to map artificial night-sky brightness and stellar visibility across large territories or their distribution over the entire sky at any site are based on computation of the propagation of light pollution with Garstang models, a simplified solution of the radiative transfer problem in the atmosphere that allows fast computation by reducing it to a ray-tracing approach. They are accurate for a clear atmosphere, when a two-scattering approximation is acceptable, which is the most common situation. We present here up-to-date extended Garstang models (EGM), which provide a more general numerical solution for the radiative transfer problem applied to the propagation of light pollution in the atmosphere. We also present the LPTRAN software package, an application of EGM to high-resolution Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) satellite measurements of artificial light emission and to GTOPO30 (Global 30 Arcsecond) digital elevation data, which provides an up-to-date method to predict the artificial brightness distribution of the night sky at any site in the world at any visible wavelength for a broad range of atmospheric situations and the artificial radiation density in the atmosphere across the territory. EGM account for (i) multiple scattering, (ii) wavelengths from 250 nm to infrared, (iii) the Earth's curvature and its screening effects, (iv) site and source elevation, (v) many kinds of atmosphere with the possibility of custom set-up (e.g. including thermal inversion layers), (vi) a mix of different boundary-layer aerosols and tropospheric aerosols, with the possibility of custom set-up, (vii) up to five aerosol layers in the upper atmosphere, including fresh and aged volcanic dust and meteoric dust, (viii) variations of the scattering phase function with elevation, (ix) continuum and line gas absorption from many species, ozone included, (x) up to five cloud layers, (xi) wavelength-dependent bidirectional reflectance of the ground surface from National Aeronautics and Space Administration (NASA) Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data, main models or custom data (snow included) and (xii) geographically variable upward light-emission function given as a three-parameter function or a Legendre polynomial series. Atmospheric scattering properties or light-pollution propagation functions from other sources can also be applied. A more general solution allows us to account also for (xiii) mountain screening, (xiv) geographical gradients of atmospheric conditions, including localized clouds and (xv) geographic distribution of ground surfaces, but suffers from too heavy computational requirements. Comparisons between predictions of classic Garstang models and EGM show close agreement for a US62 standard clear atmosphere and typical upward emission function. © 2012 The Authors.
Monthly Notices of the Royal Astronomical Society | Year: 2011
In this paper, we present the results of a 12-yr campaign devoted to monitoring the sky brightness affected by different levels of light pollution. Different sites characterized by different altitudes and atmospheric transparency have been considered. The standard photometric Johnson B and V bands were used. An extinction measurement was performed for each site and each night, along with a calibration of the instrument. These measurements have allowed us to build sky brightness maps of the hemisphere above each observing site; each map contains up to 200 data points spread around the sky. We have found a stop in zenith sky brightness growth at the two sites where a time series exists. Using zenith sky brightness measurements taken with and without extensive snow coverage, we weighted the importance of direct versus indirect flux in producing sky glow at several sites. © 2010 The Author Monthly Notices of the Royal Astronomical Society © 2010 RAS.
Luminoso | Date: 2014-09-05
A method for elimination of spam in a data stream according to information density, includes receiving, by a computing device, a stream of messages. The method includes directing, by the computing device, the stream into at least one buffer. The method includes repeatedly compressing, by the computing device, data in the buffer using a lossless compression algorithm. The method includes identifying, by the computing device, at least one first message in the buffer as spam, by determining that the at least one first message has been compressed below a threshold level.
Luminoso | Date: 2013-03-15
A system and related method are disclosed for searching a data set made up of a set of documents, a set of terms, and a vector associated with each term and each document. The method involves converting a search query to a vector in the vector space spanned by the term and document vectors, and combining vector-proximity searching and term searching to produce a set of results, which may be ranked according to various measures of relatedness to the query. Excerpts from each document in the result set may be displayed that contain the greatest term importance.
Luminoso | Date: 2013-03-15
A system and related method are disclosed for rendering a set of words linked to an n-dimensional vector space in a word cloud rendered from a two-dimensional projection of the vector space, where the user can click and drag a word, and the subspace and projection thereon will shift to place the word where the user has dragged it in a new projection, and the other words in the cloud will shift correspondingly, offering the user new insights. The importance of words in a document set is represented by word size, and relatedness between words demonstrated by color similarity.
Luminoso | Date: 2014-05-29
A method for following a topic in an electronic textual conversation, the method includes selecting, by a computing device, one or more primary terms related to a topic, sending, by the computing device, to at least one communication service, a first query containing the at least one primary term, receiving, by the computing device, from the at least one communication service, at least one first set of messages responsive to the first query, for each first set, extracting, by the computing device, from the first set of messages, a first plurality of additional terms, and for each term of the first plurality of additional terms, enumerating, by the computing device, the messages of the first set in which the term appears and adding the term to a list of secondary terms if the enumeration exceeds a threshold amount.
Luminoso | Date: 2014-04-23
The present invention relates to a light emitting device mounted to a protection cover for a portable terminal and emitting light from a light emitting diode to the outside, the light emitting device comprising: a light source blocking part (10) attached on the inner wall surface of the protection cover (200) for the portable terminal and having a plurality of through-holes (12) formed in a body thereof, the through-holes being spaced apart from each other at a predetermined interval; a plurality of color providing parts (20) through which light from the light emitting diode passes, the color providing parts having different colors and being attached to the through-holes (12), respectively; a light source diffusion part (30) which is formed on the inner wall surface of the protection cover (200) and diffuses the light passing through the color providing parts (20); and a light source reflection part (40) which is hingedly coupled on the outer wall surface of the protection cover (200) and reflects the light, which passes through the color providing parts (20), to the light source diffusion part (30). Therefore, since the light generated by the light emitting diode of the portable terminal is emitted through the protection cover in various colors, the present invention can provide patterns or colors, which a consumer of the protection cover wants, thereby improving the product quality.
Luminoso | Date: 2013-03-15
Disclosed herein is a method and system for producing a term association vector space on demand for a client given a document set in electronic form. The method extracts terms from the document set, stripping out words that do not convey meaning and adding important phrases within the context of the document set to the terms. Associations between terms are calculated, subjected to further analytical processes, and collected in a matrix, whose rows are vectors defining the vector space. Additional associational data can be added by matrix arithmetic, and documents can be rendered as further vectors in the space.
News Article | December 16, 2016
CAMBRIDGE, Mass.--(BUSINESS WIRE)--Today, Luminoso Technologies, Inc., a global leader in artificial intelligence-based deep analytics, announced that the company’s software incorporating word embeddings can be run on a Raspberry Pi, or the equivalent of an iPhone 5. In trials done earlier this month, Luminoso demonstrated that using only a Raspberry Pi and about 100 megs of ram, its AI software can process four novels’ worth of text in around eight minutes; none of the data required to do this ever leaves the device, and it all happens in an amount of processing power the phone itself can deliver. This capability has many implications, notably as an OEM offering for device manufacturers who want to run an intelligent agent or semantic search on phone, without the privacy concerns connected with having to connect to the cloud. The ability to run natural language understanding software locally on a phone would give that device tremendous capabilities that devices currently do not have, specifically: remembering all of the unique language people use on a regular basis, from everyday slang and workplace lingo to more private data that one wouldn't want shared like their children's names. A phone could understand and store this information quickly and easily, without ever having to ship data to the cloud. Device manufacturers could use this capability to locally customize a virtual assistant, provide conceptual search functionality, prioritize emails and messages, and tailor the entire mobile experience to an individual user’s linguistic environment. “There’s a lot of tension right now around the privacy of our devices, the security of people’s info in the cloud, and who gets to look at it to do what,” said Catherine Havasi, Luminoso CEO and co-founder. “Google, for example, probably has to read your email to do anything really intelligent with it. If these capabilities can be brought to end users’ phones, we can actually open up a broad range of capabilities that honor people's privacy and decrease device manufacturers’ reliance on cloud providers, while still giving an amazing user experience.” “The ability to make powerful machine learning computationally simple enough that it can be run on an embedded device is notable because, until now, the industry assumption has been that natural language understanding requires a lot of time and a lot of memory, and thus is too computationally expensive to be done locally,” added Rob Speer, Chief Science Officer and co-founder of Luminoso. “Because Luminoso has already built an incredible word embedding up front, it turns out we can adapt it to any use case, and we don’t have to rerun, for every single device, the monumentally expensive process that other solutions use.” Luminoso Technologies is a leading natural language understanding company that allows clients to rapidly discover value in their unstructured text data. With roots at the MIT Media Lab, Luminoso’s artificial intelligence-based software uniquely produces the most accurate and unbiased, real-time understanding of what people are saying, including insights that were not anticipated. These insights are used to increase marketing performance and build better customer experiences. Luminoso provides multilingual, flexible software that can be deployed to meet client needs in either a standalone Cloud or On Premise solution or integrated into an end-to-end client platform via an API solution. Luminoso serves clients such as Staples, Sprint, and Scotts Miracle-Gro, as well as a growing set of channel partners such as Publicis.Sapient and Basis Technologies. Luminoso is privately held with headquarters in Cambridge, MA.
News Article | November 18, 2016
CAMBRIDGE, Mass.--(BUSINESS WIRE)--Today, Luminoso Technologies, Inc., a leading global player in artificial intelligence (AI)-based deep analytics, announced the addition of three large European clients to its roster, including Wärtsilä, a global leader in advanced technologies for the marine and energy markets, with 2015 net sales that totaled EUR 5 billion. Additionally, Luminoso announced two key hires outside the US, including UK-based Matthew Haines, who will serve as EMEA Director of Sal