82,079 research outputs found
Energy efficient mining on a quantum-enabled blockchain using light
We outline a quantum-enabled blockchain architecture based on a consortium of
quantum servers. The network is hybridised, utilising digital systems for
sharing and processing classical information combined with a fibre--optic
infrastructure and quantum devices for transmitting and processing quantum
information. We deliver an energy efficient interactive mining protocol enacted
between clients and servers which uses quantum information encoded in light and
removes the need for trust in network infrastructure. Instead, clients on the
network need only trust the transparent network code, and that their devices
adhere to the rules of quantum physics. To demonstrate the energy efficiency of
the mining protocol, we elaborate upon the results of two previous experiments
(one performed over 1km of optical fibre) as applied to this work. Finally, we
address some key vulnerabilities, explore open questions, and observe
forward--compatibility with the quantum internet and quantum computing
technologies.Comment: 25 pages, 5 figure
Data Mining and Machine Learning in Astronomy
We review the current state of data mining and machine learning in astronomy.
'Data Mining' can have a somewhat mixed connotation from the point of view of a
researcher in this field. If used correctly, it can be a powerful approach,
holding the potential to fully exploit the exponentially increasing amount of
available data, promising great scientific advance. However, if misused, it can
be little more than the black-box application of complex computing algorithms
that may give little physical insight, and provide questionable results. Here,
we give an overview of the entire data mining process, from data collection
through to the interpretation of results. We cover common machine learning
algorithms, such as artificial neural networks and support vector machines,
applications from a broad range of astronomy, emphasizing those where data
mining techniques directly resulted in improved science, and important current
and future directions, including probability density functions, parallel
algorithms, petascale computing, and the time domain. We conclude that, so long
as one carefully selects an appropriate algorithm, and is guided by the
astronomical problem at hand, data mining can be very much the powerful tool,
and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra
figures, some minor additions to the tex
Grouping Method Of Image Fragments Of Adjacent Dislocation Etch Pits Of The Semiconductor Wafer
An increase in production volumes of gallium arsenide semiconductor devices determines the need for better control of dislocations of semiconductor wafer.The grouping method of image fragments of adjacent dislocation etch pits of the semiconductor wafer is proposed in the article. Adjacent fragments will be allocated in the pre-binarized image of wafer surface, which contains adjacent fragments of etch pits of dislocation loops after treatment by the described method. Improved methods for determining the loop line width determines the edge line width of etch pits of suspected dislocations, given the variability of their display in the binarized image. The current loop line width is compared to the reference line width of the dislocation loop.The grouping method of image fragments of adjacent dislocation etch pits of the semiconductor wafer defines recovery of loop lines branching, takes into account various options of line adjacency and determines the direction of further recovery of loop line of dislocation etch pits. A step by step description of the method is given
Mining Knowledge in Astrophysical Massive Data Sets
Modern scientific data mainly consist of huge datasets gathered by a very
large number of techniques and stored in very diversified and often
incompatible data repositories. More in general, in the e-science environment,
it is considered as a critical and urgent requirement to integrate services
across distributed, heterogeneous, dynamic "virtual organizations" formed by
different resources within a single enterprise. In the last decade, Astronomy
has become an immensely data rich field due to the evolution of detectors
(plates to digital to mosaics), telescopes and space instruments. The Virtual
Observatory approach consists into the federation under common standards of all
astronomical archives available worldwide, as well as data analysis, data
mining and data exploration applications. The main drive behind such effort
being that once the infrastructure will be completed, it will allow a new type
of multi-wavelength, multi-epoch science which can only be barely imagined.
Data Mining, or Knowledge Discovery in Databases, while being the main
methodology to extract the scientific information contained in such MDS
(Massive Data Sets), poses crucial problems since it has to orchestrate complex
problems posed by transparent access to different computing environments,
scalability of algorithms, reusability of resources, etc. In the present paper
we summarize the present status of the MDS in the Virtual Observatory and what
is currently done and planned to bring advanced Data Mining methodologies in
the case of the DAME (DAta Mining & Exploration) project.Comment: Pages 845-849 1rs International Conference on Frontiers in
Diagnostics Technologie
Mining the Web for Lexical Knowledge to Improve Keyphrase Extraction: Learning from Labeled and Unlabeled Data.
A journal article is often accompanied by a list of keyphrases, composed of about five to fifteen important words and phrases that capture the articleÂ’s main topics. Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. Good performance on this task has been obtained by approaching it as a supervised learning problem. An input document is treated as a set of candidate phrases that must be classified as either keyphrases or non-keyphrases. To classify a candidate phrase as a keyphrase, the most important features (attributes) appear to be the frequency and location of the candidate phrase in the document. Recent work has demonstrated that it is also useful to know the frequency of the candidate phrase as a manually assigned keyphrase for other documents in the same domain as the given document (e.g., the domain of computer science). Unfortunately, this keyphrase-frequency feature is domain-specific (the learning process must be repeated for each new domain) and training-intensive (good performance requires a relatively large number of training documents in the given domain, with manually assigned keyphrases). The aim of the work described here is to remove these limitations. In this paper, I introduce new features that are conceptually related to keyphrase-frequency and I present experiments that show that the new features result in improved keyphrase extraction, although they are neither domain-specific nor training-intensive. The new features are generated by issuing queries to a Web search engine, based on the candidate phrases in the input document. The feature values are calculated from the number of hits for the queries (the number of matching Web pages). In essence, these new features are derived by mining lexical knowledge from a very large collection of unlabeled data, consisting of approximately 350 million Web pages without manually assigned keyphrases
SciTech News Volume 71, No. 1 (2017)
Columns and Reports From the Editor 3
Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11
Reviews Sci-Tech Book News Reviews 12
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