1,254 research outputs found
Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language
We address the problem of efficient acoustic-model refinement (continuous
retraining) using semi-supervised and active learning for a low resource Indian
language, wherein the low resource constraints are having i) a small labeled
corpus from which to train a baseline `seed' acoustic model and ii) a large
training corpus without orthographic labeling or from which to perform a data
selection for manual labeling at low costs. The proposed semi-supervised
learning decodes the unlabeled large training corpus using the seed model and
through various protocols, selects the decoded utterances with high reliability
using confidence levels (that correlate to the WER of the decoded utterances)
and iterative bootstrapping. The proposed active learning protocol uses
confidence level based metric to select the decoded utterances from the large
unlabeled corpus for further labeling. The semi-supervised learning protocols
can offer a WER reduction, from a poorly trained seed model, by as much as 50%
of the best WER-reduction realizable from the seed model's WER, if the large
corpus were labeled and used for acoustic-model training. The active learning
protocols allow that only 60% of the entire training corpus be manually
labeled, to reach the same performance as the entire data
Predicting the dissolution kinetics of silicate glasses using machine learning
Predicting the dissolution rates of silicate glasses in aqueous conditions is
a complex task as the underlying mechanism(s) remain poorly understood and the
dissolution kinetics can depend on a large number of intrinsic and extrinsic
factors. Here, we assess the potential of data-driven models based on machine
learning to predict the dissolution rates of various aluminosilicate glasses
exposed to a wide range of solution pH values, from acidic to caustic
conditions. Four classes of machine learning methods are investigated, namely,
linear regression, support vector machine regression, random forest, and
artificial neural network. We observe that, although linear methods all fail to
describe the dissolution kinetics, the artificial neural network approach
offers excellent predictions, thanks to its inherent ability to handle
non-linear data. Overall, we suggest that a more extensive use of machine
learning approaches could significantly accelerate the design of novel glasses
with tailored properties
PCR-based sex determination of cetaceans and dugong from the Indian seas
A sex-determination technique based on PCR amplifi- cation of genomic DNA extracted from the skin tissue has been standardized in cetaceans and dugong sam-pled from the Indian seas. A Y-chromosome-specific region (SRY or Sex-determining Y-chromosome gene) of 210–224 bp size in the genome has been amplified (only in males) using specific PCR primers. A fragment of the ZFX/ZFY (zinc finger protein genes located both on the X and Y chromosomes respectively) re-gion in the size range 442–445 bp is also amplified (in both sexes) using another pair of primers simultaneously as positive controls for confirmation of sex. Molecular sexing was standardized in spinner dolphin (Stenella longirostris), bridled dolphin (Stenella attenuata), bottlenose dolphin (Tursiops aduncus), Indo-Pacific humpbacked dolphin (Sousa chinensis), Risso’s dolphin (Grampus griseus), finless porpoise (Neopho-caena phocaenoides), sperm whale (Physeter macro-cephalus), blue whale (Balaenoptera musculus), Bryde’s whale (Balaenoptera edeni) and dugong (Dugong du-gon), which are all vulnerable/endangered species pro- tected under the Indian Wildlife Act
On Rotation Distance of Rank Bounded Trees
Computing the rotation distance between two binary trees with internal
nodes efficiently (in time) is a long standing open question in the
study of height balancing in tree data structures. In this paper, we initiate
the study of this problem bounding the rank of the trees given at the input
(defined by Ehrenfeucht and Haussler (1989) in the context of decision trees).
We define the rank-bounded rotation distance between two given binary trees
and (with internal nodes) of rank at most , denoted by
, as the length of the shortest sequence of rotations that
transforms to with the restriction that the intermediate trees must
be of rank at most . We show that the rotation distance problem reduces in
polynomial time to the rank bounded rotation distance problem. This motivates
the study of the problem in the combinatorial and algorithmic frontiers.
Observing that trees with rank coincide exactly with skew trees (binary
trees where every internal node has at least one leaf as a child), we show the
following results in this frontier :
We present an time algorithm for computing . That is,
when the given trees are skew trees (we call this variant as skew rotation
distance problem) - where the intermediate trees are restricted to be skew as
well. In particular, our techniques imply that for any two skew trees
.
We show the following upper bound : for any two trees and of rank
at most and respectively, we have that: where . This bound is asymptotically
tight for .
En route our proof of the above theorems, we associate binary trees to
permutations and bivariate polynomials, and prove several characterizations in
the case of skew trees.Comment: 25 pages, 2 figures, Abstract shortened to meet arxiv requirement
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