251 research outputs found
Live User-guided Intrinsic Video For Static Scenes
We present a novel real-time approach for user-guided intrinsic decomposition of static scenes captured by an RGB-D sensor. In the first step, we acquire a three-dimensional representation of the scene using a dense volumetric reconstruction framework. The obtained reconstruction serves as a proxy to densely fuse reflectance estimates and to store user-provided constraints in three-dimensional space. User constraints, in the form of constant shading and reflectance strokes, can be placed directly on the real-world geometry using an intuitive touch-based interaction metaphor, or using interactive mouse strokes. Fusing the decomposition results and constraints in three-dimensional space allows for robust propagation of this information to novel views by re-projection.We leverage this information to improve on the decomposition quality of existing intrinsic video decomposition techniques by further constraining the ill-posed decomposition problem. In addition to improved decomposition quality, we show a variety of live augmented reality applications such as recoloring of objects, relighting of scenes and editing of material appearance
Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences
Machine learning based Single Image Intrinsic Decomposition (SIID) methods
decompose a captured scene into its albedo and shading images by using the
knowledge of a large set of known and realistic ground truth decompositions.
Collecting and annotating such a dataset is an approach that cannot scale to
sufficient variety and realism. We free ourselves from this limitation by
training on unannotated images.
Our method leverages the observation that two images of the same scene but
with different lighting provide useful information on their intrinsic
properties: by definition, albedo is invariant to lighting conditions, and
cross-combining the estimated albedo of a first image with the estimated
shading of a second one should lead back to the second one's input image. We
transcribe this relationship into a siamese training scheme for a deep
convolutional neural network that decomposes a single image into albedo and
shading. The siamese setting allows us to introduce a new loss function
including such cross-combinations, and to train solely on (time-lapse) images,
discarding the need for any ground truth annotations.
As a result, our method has the good properties of i) taking advantage of the
time-varying information of image sequences in the (pre-computed) training
step, ii) not requiring ground truth data to train on, and iii) being able to
decompose single images of unseen scenes at runtime. To demonstrate and
evaluate our work, we additionally propose a new rendered dataset containing
illumination-varying scenes and a set of quantitative metrics to evaluate SIID
algorithms. Despite its unsupervised nature, our results compete with state of
the art methods, including supervised and non data-driven methods.Comment: To appear in Pacific Graphics 201
A new method for constructing small-bias spaces from Hermitian codes
We propose a new method for constructing small-bias spaces through a
combination of Hermitian codes. For a class of parameters our multisets are
much faster to construct than what can be achieved by use of the traditional
algebraic geometric code construction. So, if speed is important, our
construction is competitive with all other known constructions in that region.
And if speed is not a matter of interest the small-bias spaces of the present
paper still perform better than the ones related to norm-trace codes reported
in [12]
Dyslipidaemia in hypertension - are we treating enough?
Introduction: The coexistence of dyslipidaemia and hypertension results in enhanced atherosclerosis. Adequate treatment of dyslipidaemia in hypertensive patients is thus essential for reducing the burden of cardiovascular diseases.Objective: To determine the prevalence of dyslipidaemia among hypertensives and evaluate lipid treatment status of patients with dyslipidaemia in a tertiary hospital in Nigeria.Methods: This cross-sectional comparative study was done between May, 2015 and June, 2016 in a tertiary hospital in Nigeria. The serum lipid levels of adult patients with hypertension and controls without hypertension were determined. Lipid treatment status of patients with dyslipidaemia were also reviewed. Serum lipid levels were analyzed using spectrophotometric methods.Results: The study included 200 adult hypertensive patients and 100 control participants. The mean age (SD) was 56.3 (6.9) years and 54.9 (8.3) years with range 41-68 and 44-69 years for patients and controls respectively. Eighty-eight (44.0%) hypertensive patients and 23(23.5%) of the control group were found to have dyslipidaemia. Out of the 60(68.2%) patients with elevated LDL-C, 32(53.3%) had LDL-C >4.1mmol/L, out of which only 8(25%) were on antilipid medication.Conclusion: Over one-third of studied hypertensive patients had dyslipidaemia and only a quarter of those who needed antilipids were on the medication. Greater awareness is needed both in the medical and patient communities in order to effectively manage dyslipidaemic hypertension, and hence aid in ameliorating the burden of cardiovascular diseases
Composition of uroliths in a tertiary hospital in South East Nigeria
Background: Urolithiasis affects primarily the urinary tract and complications as debilitating as renal failure may develop. Determining the chemical composition of uroliths can aid management and prevention of recurrence in patients.Objective: To determine the chemical composition and anatomical distribution of uroliths in Nigeria.Methods: This descriptive cross-sectional study was conducted between March 2014 and February 2016, in a tertiary hospital in Nigeria. We reviewed the outcomes of uroliths of adult patients sent to our laboratory for chemical analyses. Samples were analyzed using simple qualitative tests.Results: 52 adult patients were included with a mean age (SD) of 46.6 (12.6) years. Males (76.9%) were more affected than females (23.1%). For both sexes, highest occurrence of stones was in bladder (85.7%). Calcium-containing stones had the highest occurrence (85.2%) and predominated in the renal, ureter and urethra, followed by struvite stones (59.5%). In the bladder, struvite stones were predominant (85.8%), with calcium-containing stones accounting for 71.4%.Conclusion: This study showed that struvite and calcium phosphate-containing stones constitute majority of uroliths in our setting with low occurrence of calcium oxalate stones. This indicates that urinary tract infection most likely plays a substantial role in the formation of uroliths in Nigerians. Modern methods of stone analysis is advocated to further define management options.Keywords: Uroliths, calcium oxalate, chemical composition, struvite, stone, calculi
Fast hashing with Strong Concentration Bounds
Previous work on tabulation hashing by Patrascu and Thorup from STOC'11 on
simple tabulation and from SODA'13 on twisted tabulation offered Chernoff-style
concentration bounds on hash based sums, e.g., the number of balls/keys hashing
to a given bin, but under some quite severe restrictions on the expected values
of these sums. The basic idea in tabulation hashing is to view a key as
consisting of characters, e.g., a 64-bit key as characters of
8-bits. The character domain should be small enough that character
tables of size fit in fast cache. The schemes then use tables
of this size, so the space of tabulation hashing is . However, the
concentration bounds by Patrascu and Thorup only apply if the expected sums are
.
To see the problem, consider the very simple case where we use tabulation
hashing to throw balls into bins and want to analyse the number of
balls in a given bin. With their concentration bounds, we are fine if ,
for then the expected value is . However, if , as when tossing
unbiased coins, the expected value is for large data sets,
e.g., data sets that do not fit in fast cache.
To handle expectations that go beyond the limits of our small space, we need
a much more advanced analysis of simple tabulation, plus a new tabulation
technique that we call \emph{tabulation-permutation} hashing which is at most
twice as slow as simple tabulation. No other hashing scheme of comparable speed
offers similar Chernoff-style concentration bounds.Comment: 54 pages, 3 figures. An extended abstract appeared at the 52nd Annual
ACM Symposium on Theory of Computing (STOC20
Self-supervised Outdoor Scene Relighting
Outdoor scene relighting is a challenging problem that requires good
understanding of the scene geometry, illumination and albedo. Current
techniques are completely supervised, requiring high quality synthetic
renderings to train a solution. Such renderings are synthesized using priors
learned from limited data. In contrast, we propose a self-supervised approach
for relighting. Our approach is trained only on corpora of images collected
from the internet without any user-supervision. This virtually endless source
of training data allows training a general relighting solution. Our approach
first decomposes an image into its albedo, geometry and illumination. A novel
relighting is then produced by modifying the illumination parameters. Our
solution capture shadow using a dedicated shadow prediction map, and does not
rely on accurate geometry estimation. We evaluate our technique subjectively
and objectively using a new dataset with ground-truth relighting. Results show
the ability of our technique to produce photo-realistic and physically
plausible results, that generalizes to unseen scenes.Comment: Published in ECCV '20,
http://gvv.mpi-inf.mpg.de/projects/SelfRelight
Fast Differentially Private Matrix Factorization
Differentially private collaborative filtering is a challenging task, both in
terms of accuracy and speed. We present a simple algorithm that is provably
differentially private, while offering good performance, using a novel
connection of differential privacy to Bayesian posterior sampling via
Stochastic Gradient Langevin Dynamics. Due to its simplicity the algorithm
lends itself to efficient implementation. By careful systems design and by
exploiting the power law behavior of the data to maximize CPU cache bandwidth
we are able to generate 1024 dimensional models at a rate of 8.5 million
recommendations per second on a single PC
Preparation and in vitro characterization of non-effervescent floating drug delivery system of poorly soluble drug, carvedilol phosphate
The objective of the study was to enhance the solubility of carvedilol phosphate and to formulate it into non-effervescent floating tablets using swellable polymers. Solid dispersions (SD)of carvedilol were prepared with hydrophilic carriers such as polyvinylpyrrolidone and poloxamer to enhance solubility. Non-effervescent floating tablets were prepared with a combination of optimized solid dispersions and release retarding polymers/swellable polymers such as xanthan gum and polyethylene oxide. Tablets were evaluated for physicochemical properties such as hardness, thickness and buoyancy. SD prepared with the drug to poloxamer ratio of 1:4 by melt granulation showed higher dissolution rate than all other dispersions. Formulations containing 40 mg of polyethylene oxide (C-P40) and 50 mg xanthan gum (C-X50) were found to be best, with the drug retardation up to 12 hours. Optimized formulations were characterized using FTIR and DSC and no drug and excipient interactions were detected
Genetic divergence in common bean genotypes from the IRAD gene bank: morpho-agronomic characteristics, fungal and bacterial disease resistance, and opportunities for genetic improvement
For successful plant breeding in any crop species, the importance of diversity in the available germplasm population is known and established. Thirty-two common bean (Phaseolus vulgaris) genotypes from the IRAD gene bank in Cameroon were evaluated for divergence in terms of their morpho-agronomic traits, fungal disease resistance, and bacterial disease resistance to assess the opportunity for genetic improvement of the crop. The trait associations were estimated using correlation coefficients and genotypes were classified into groups using cluster and principal component analyses. Seven qualitative and 16 quantitative traits comprising growth, phenological, yield, and disease variables were evaluated in this study. The qualitative markers revealed the degree of polymorphism among the 32 common bean genotypes. The number of phenotypic classes per character (Na) ranged from 2 to 18, with an average of 5.14. The expected gene diversity (He) ranged from 0.37 to 0.93 (mean = 0.56). The number of effective phenotypic classes (Ne) ranged from 1.82 to 14.22, with a mean of 3.85. An extensive range of variation was evident for the majority of traits, highlighting their utility for characterizing common bean germplasm. Many qualitative traits, including seed coat color, seed shape, and seed size, and also some quantitative traits of economic importance including seed yield, were found to be highly variable within the collection, with the MAC55 genotype displaying the highest yield (32.65 g per plant). Four genotypes, namely MAC55, BOA-5-1M6, FEB 192, and Banguem showed resistance to the two main common bean diseases, angular leaf spot and common blight. We detected highly significant correlations among several traits related to yield. A high broad-sense heritability was found for most of the quantitative traits. We carried out two-dimensional principal component analysis and used hierarchical clustering to group the analyzed germplasm according to their phenotypic similitudes. The evidence of agro-morphological diversity in the present collection and the identification of discriminant characters between the available germplasm through the use of PCA analysis have significant implications for establishing breeding schemes in common bean
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