88,045 research outputs found
Efficient image retrieval using multi neural hash codes and bloom filters
This paper aims to deliver an efficient and modified approach for image
retrieval using multiple neural hash codes and limiting the number of queries
using bloom filters by identifying false positives beforehand. Traditional
approaches involving neural networks for image retrieval tasks tend to use
higher layers for feature extraction. But it has been seen that the activations
of lower layers have proven to be more effective in a number of scenarios. In
our approach, we have leveraged the use of local deep convolutional neural
networks which combines the powers of both the features of lower and higher
layers for creating feature maps which are then compressed using PCA and fed to
a bloom filter after binary sequencing using a modified multi k-means approach.
The feature maps obtained are further used in the image retrieval process in a
hierarchical coarse-to-fine manner by first comparing the images in the higher
layers for semantically similar images and then gradually moving towards the
lower layers searching for structural similarities. While searching, the neural
hashes for the query image are again calculated and queried in the bloom filter
which tells us whether the query image is absent in the set or maybe present.
If the bloom filter doesn't necessarily rule out the query, then it goes into
the image retrieval process. This approach can be particularly helpful in cases
where the image store is distributed since the approach supports parallel
querying.Comment: 2020 IEEE International Conference for Innovation in Technology.
Asian Journal for Convergence in Technology(AJCT) Volume VI Issue II
The Case for Learned Index Structures
Indexes are models: a B-Tree-Index can be seen as a model to map a key to the
position of a record within a sorted array, a Hash-Index as a model to map a
key to a position of a record within an unsorted array, and a BitMap-Index as a
model to indicate if a data record exists or not. In this exploratory research
paper, we start from this premise and posit that all existing index structures
can be replaced with other types of models, including deep-learning models,
which we term learned indexes. The key idea is that a model can learn the sort
order or structure of lookup keys and use this signal to effectively predict
the position or existence of records. We theoretically analyze under which
conditions learned indexes outperform traditional index structures and describe
the main challenges in designing learned index structures. Our initial results
show, that by using neural nets we are able to outperform cache-optimized
B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over
several real-world data sets. More importantly though, we believe that the idea
of replacing core components of a data management system through learned models
has far reaching implications for future systems designs and that this work
just provides a glimpse of what might be possible
The SAMI Galaxy Survey: The Low-Redshift Stellar Mass Tully-Fisher Relation
We investigate the Tully-Fisher Relation (TFR) for a morphologically and
kine- matically diverse sample of galaxies from the SAMI Galaxy Survey using 2
dimensional spatially resolved Halpha velocity maps and find a well defined
relation across the stellar mass range of 8.0 < log(M*) < 11.5. We use an
adaptation of kinemetry to parametrise the kinematic Halpha asymmetry of all
galaxies in the sample, and find a correlation between scatter (i.e. residuals
off the TFR) and asymmetry. This effect is pronounced at low stellar mass,
corresponding to the inverse relationship between stellar mass and kinematic
asymmetry found in previous work. For galaxies with log(M*) < 9.5, 25 +/- 3%
are scattered below the root mean square (RMS) of the TFR, whereas for galaxies
with log(M*) > 9.5 the fraction is 10 +/- 1% We use 'simulated slits' to
directly compare our results with those from long slit spectroscopy and find
that aligning slits with the photometric, rather than the kinematic, position
angle, increases global scatter below the TFR. Further, kinematic asymmetry is
correlated with misalignment between the photometric and kinematic position
angles. This work demonstrates the value of 2D spatially resolved kinematics
for accurate TFR studies; integral field spectroscopy reduces the
underestimation of rotation velocity that can occur from slit positioning off
the kinematic axis
Randomized Maximum Entropy Language Models
Abstract—We address the memory problem of maximum entropy language models(MELM) with very large feature sets. Randomized techniques are employed to remove all large, exact data structures in MELM implementations. To avoid the dictionary structure that maps each feature to its corresponding weight, the feature hashing trick [1] [2] can be used. We also replace the explicit storage of features with a Bloom filter. We show with extensive experiments that false positive errors of Bloom filters and random hash collisions do not degrade model performance. Both perplexity and WER improvements are demonstrated by building MELM that would otherwise be prohibitively large to estimate or store. I
The SAMI Galaxy Survey: gas content and interaction as the drivers of kinematic asymmetry
In order to determine the causes of kinematic asymmetry in the H gas
in the SAMI Galaxy Survey sample, we investigate the comparative influences of
environment and intrinsic properties of galaxies on perturbation. We use
spatially resolved H velocity fields from the SAMI Galaxy Survey to
quantify kinematic asymmetry () in nearby galaxies and
environmental and stellar mass data from the GAMA survey.
{We find that local environment, measured as distance to nearest neighbour,
is inversely correlated with kinematic asymmetry for galaxies with
, but there is no significant correlation for
galaxies with . Moreover, low mass galaxies
() have greater kinematic asymmetry at all
separations, suggesting a different physical source of asymmetry is important
in low mass galaxies.}
We propose that secular effects derived from gas fraction and gas mass may be
the primary causes of asymmetry in low mass galaxies. High gas fraction is
linked to high (where is H velocity
dispersion and the rotation velocity), which is strongly correlated with
, and galaxies with have offset
from the rest of the sample. Further,
asymmetry as a fraction of dispersion decreases for galaxies with
. Gas mass and asymmetry are also inversely correlated
in our sample. We propose that low gas masses in dwarf galaxies may lead to
asymmetric distribution of gas clouds, leading to increased relative
turbulence.Comment: 15 pages, 20 figure
“Constructing selfhood and otherness in the East-West context"
Since his debut in 2002, Gary Shteyngart, a Russian-American author of Jewish extraction has not only garnered popularity among readers, but also inspired critical interest from reviewers and scholars. While Shteyngart’s talent for satire and his idiosyncratic, fast-pace style of writing undoubtedly account for his popular success, the critics are invariably drawn to the thematic threads that drive his first three novels and bloom in his latest autobiographical work. Among these, there is construction of immigrant identity on the threshold of three cultures, the search for and the development of the writerly voice, and the representation of selfhood and otherness within the East-West context. Accordingly, in this paper I will address these main threads within Shteyngart’s works, focusing particularly on the second one. Drawing on imagology, I will situate Shteyngart’s body of work at the intersections between identity and culture, in order to analyse the role of emotional geographies and cultural maps in his development as a three-culture writer.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec
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