The Son Of Neptune Full Text

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Tisham Candella

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Aug 3, 2024, 5:51:57 PM8/3/24

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Neptune integrates with Amazon OpenSearch Service (OpenSearch Service) to support full-text search in both Gremlin and SPARQL queries. This feature is available starting in Neptune engine release 1.0.2.1, although we recommend using it with engine release 1.0.4.2 or higher to take advantage of the latest fixes.

You can use Neptune with an existing OpenSearch Service cluster that has been populated according to the Neptune data model for OpenSearch data. Or, you can create an OpenSearch Service domain linked with Neptune using an AWS CloudFormation stack.

Adding another answer now that it is 2019 to point out that in Apache TinkerPop 3.4 new text predicates were introduced. These include startingWith(), endingWith() and containing() as well as their inverses notStartingWith() etc. Any graph database (including Amazon Neptune) supporting Apache TinkerPop at the 3.4 level or higher should offer these predicates.

Below the has-step makes use of a full text search looking at both the value of the name propertyand the other_name property. Due to the way Neptune data is translated into elasticsearch documentswe need to refer to them as predicates.name.value and predicates.other_name.value

Finally, a sparse matrix is created for the input, out of the frequency of vocabulary words. In this sparse matrix, each row is a sentence vector whose length (the columns of the matrix) is equal to the size of the vocabulary.

This approach was released back in 2013 by Google researchers in this paper, and it took the NLP industry by storm. In a nutshell, this approach uses the power of a simple Neural Network to generate word embeddings.

In Bag of Words and TF-IDF, we saw how every word was treated as an individual entity, and semantics were completely ignored. With the introduction of Word2Vec, the vector representation of words was said to be contextually aware, probably for the first time ever.

Since every word is represented as an n-dimensional vector, one can imagine that all of the words are mapped to this n-dimensional space in such a manner that words having similar meanings exist in close proximity to one another in this hyperspace.

Technically, it predicts the probabilities of a word being a context word for the given target word. The output probabilities coming out of the network will tell us how likely it is to find each vocabulary word near our input word.

CBOW stands for Continuous Bag of Words. In the CBOW approach instead of predicting the context words, we input them into the model and ask the network to predict the current word. The general idea is shown here:

Because of having multiple context words, averaging is done to calculate hidden layer values. After this, it gets similar to our skip-gram model, and learned word embedding comes from the output layer weights instead of hidden layer weights.

For every pair of words (i,j) that might co-occur, we try to minimize the difference between the product of their word embeddings and the log of the co-occurrence count of (i,j). The term f(Pij) makes it a weighted summation and allows us to give lower weights to very frequent word co-occurrences, capping the importance of such pairs.

Again, the performance of this model depends on a lot of factors just like any other model, but if you want to have a quick peek at what the baseline accuracy should be, fasttext could be a very good choice.

Sailing into contested waters has always been a risky venture; in the years following the American Revolution, merchant vessels and whaling ships could carry a sea letter--a standardized document signed by the President and the Secretary of State, to declare their nationality and claim protection under various international agreements. Printed in three or more languages, the boilerplate text appeals to the MOST Serene, Serene, most Puissant, Puissant, High, Illustrious . . . Lords, Emperors, Kings, Republics, . . . Burgomasters, Schepens, Consullors..." to treat the vessel and crew with respect. There are numerous examples of sea letters in the business records collections held at the Library. The documents often provide important information about the destination and purpose of the vessels that carried them. This one is from the Arnold family business records; printed in French, English, and Dutch, it bears the (slightly worm-eaten) signatures of George Washington and Thomas Jefferson, as well as some pencil doodling on the Great Seal.

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Context. Upon discovery, asteroid (309239) 2007 RW10 was considered a Neptune Trojan candidate. The object is currently listed by the Minor Planet Center as a Centaur but it is classified as a scattered disk or trans-Neptunian object by others. Now that its arc-length is 8154 d and has been observed for more than 20 yr, a more robust classification should be possible.

Methods. We perform N-body simulations in both directions of time to investigate the evolution of its orbital elements. In particular, we study the librational properties of the mean longitude.

Results. Its mean longitude currently librates around the value of the mean longitude of Neptune with an amplitude of nearly 50 and a period of about 7.5 kyr. Our calculations show that it has been in its present dynamical state for about 12.5 kyr and it will stay there for another 12.5 kyr. Therefore, its current state is relatively short-lived. Due to its chaotic behaviour, the object may have remained in the 1:1 mean motion resonance with Neptune for several 100 kyr at most, undergoing transitions between the various resonant states.

Conclusions. (309239) 2007 RW10 is currently a quasi-satellite, the first object of this dynamical class to be discovered around Neptune. With a diameter of about 250 km, it is the largest known co-orbital in the solar system. Although it is not a Centaur now, it may become one in the future as it appears to move in an unstable region. Its significant eccentricity (0.30) and inclination (36), strongly suggest that it did not form in situ but was captured, likely from beyond Neptune. With an apparent magnitude of 21.1 at opposition (October), it is well suited for spectroscopic observations that may provide information on its composition and hence eventually its origin.

Minor planet (309239) 2007 RW10 was discovered by the Palomar Distant Solar System Survey on September 9, 2007 (Schwamb et al. 2007; Schwamb et al. 2010) and reobserved multiple times soon after (Schwamb et al. 2007; Parker et al. 2008). In addition, a number of precovery images of the objectwere unveiled: it first appears in images obtained as part of the Digitized Sky Survey (DSS) on June 1988 and 1990 from Palomar Mountain and it was pictured again in October 2001 and September 2002 on behalf of the Near-Earth Asteroid Tracking (NEAT) project at Palomar. All this observational material enabled the computation of a very reliable orbit characterized by both significant eccentricity (0.30) and inclination (36). Herschel-PACS observations indicate that the albedo of the object is 8.3% and its absolute magnitude is Hv = 6.39 0.61 which translates into a diameter of 247 30 km (Santos-Sanz et al. 2012). The dynamical status of this object remains controversial. Upon discovery, it was considered a Neptune Trojan candidate1 but it was reclassified as Centaur shortly afterwards2. It is currently listed by the Minor Planet Center (MPC) as a Centaur but it has been classified as a scattered disk object by Santos-Sanz et al. (2012) and the JPL Solar System Dynamics portal includes this asteroid among the trans-Neptunian objects. Now that its arc-length is 8154 d and the object has been observed for more than 20 yr, a more robust dynamical classification should be possible.

In this Letter, we use N-body simulations to study the librational properties of the principal resonant angle of (309239) 2007 RW10 with Neptune in order to understand its current dynamical status. The numerical model is described in Sect. 2; the results are presented in Sect. 3 and the long-term orbit behaviour is studied in Sect. 4. Our conclusions are summarized in Sect. 5 after the corresponding discussion.

For accurate initial positions and velocities we used the Heliocentric ecliptic Keplerian elements and their uncertainties provided by the JPL3 and the AstDyS-2 portal4 (see Table 1) and initial positions and velocities based on the DE405 planetary orbital ephemerides (Standish 1998)5 referred to the barycentre of the solar system. The numerical simulations were completed using a Hermite integration scheme (Makino 1991; Aarseth 2003); more details can be found in de la Fuente Marcos & de la Fuente Marcos (2012). Additional calculations were performed using the time-symmetric Hermite method described by Kokubo et al. (1998) but it was found that, for the problem studied here, its overall performance was lower and the results almost identical. The standard versions of these direct N-body codes are publicly available from the IoA web site6. These versions have been modified in order to study the orbital evolution of (309239) 2007 RW10. Our calculations include the perturbations by eight major planets and treat the Earth and the Moon as two separate objects, they also include the three largest asteroids and the barycentre of the dwarf planet Pluto-Charon system. Orbits are calculated forward and backward in time. In addition to the calculations completed using the nominal orbital elements in Table 1 we have performed 100 control simulations using sets of orbital elements sprinkled from the nominal ones within the accepted uncertainties (3σ) following a Monte Carlo approach.