25,843 research outputs found
Scaling Laws and Similarity Detection in Sequence Alignment with Gaps
We study the problem of similarity detection by sequence alignment with gaps,
using a recently established theoretical framework based on the morphology of
alignment paths. Alignments of sequences without mutual correlations are found
to have scale-invariant statistics. This is the basis for a scaling theory of
alignments of correlated sequences. Using a simple Markov model of evolution,
we generate sequences with well-defined mutual correlations and quantify the
fidelity of an alignment in an unambiguous way. The scaling theory predicts the
dependence of the fidelity on the alignment parameters and on the statistical
evolution parameters characterizing the sequence correlations. Specific
criteria for the optimal choice of alignment parameters emerge from this
theory. The results are verified by extensive numerical simulations.Comment: 25 pages, 11 figure
Regression modeling for digital test of ΣΔ modulators
The cost of Analogue and Mixed-Signal circuit
testing is an important bottleneck in the industry, due to timeconsuming
verification of specifications that require state-ofthe-
art Automatic Test Equipment. In this paper, we apply
the concept of Alternate Test to achieve digital testing of
converters. By training an ensemble of regression models that
maps simple digital defect-oriented signatures onto Signal to
Noise and Distortion Ratio (SNDR), an average error of 1:7%
is achieved. Beyond the inference of functional metrics, we show
that the approach can provide interesting diagnosis information.Ministerio de Educación y Ciencia TEC2007-68072/MICJunta de Andalucía TIC 5386, CT 30
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Hashing based Answer Selection
Answer selection is an important subtask of question answering (QA), where
deep models usually achieve better performance. Most deep models adopt
question-answer interaction mechanisms, such as attention, to get vector
representations for answers. When these interaction based deep models are
deployed for online prediction, the representations of all answers need to be
recalculated for each question. This procedure is time-consuming for deep
models with complex encoders like BERT which usually have better accuracy than
simple encoders. One possible solution is to store the matrix representation
(encoder output) of each answer in memory to avoid recalculation. But this will
bring large memory cost. In this paper, we propose a novel method, called
hashing based answer selection (HAS), to tackle this problem. HAS adopts a
hashing strategy to learn a binary matrix representation for each answer, which
can dramatically reduce the memory cost for storing the matrix representations
of answers. Hence, HAS can adopt complex encoders like BERT in the model, but
the online prediction of HAS is still fast with a low memory cost. Experimental
results on three popular answer selection datasets show that HAS can outperform
existing models to achieve state-of-the-art performance
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