6,475 research outputs found
Supervised and Unsupervised Transfer Learning for Question Answering
Although transfer learning has been shown to be successful for tasks like
object and speech recognition, its applicability to question answering (QA) has
yet to be well-studied. In this paper, we conduct extensive experiments to
investigate the transferability of knowledge learned from a source QA dataset
to a target dataset using two QA models. The performance of both models on a
TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson
et al., 2013) is significantly improved via a simple transfer learning
technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models
achieves the state-of-the-art on all target datasets; for the TOEFL listening
comprehension test, it outperforms the previous best model by 7%. Finally, we
show that transfer learning is helpful even in unsupervised scenarios when
correct answers for target QA dataset examples are not available.Comment: To appear in NAACL HLT 2018 (long paper
Probing gravitational non-minimal coupling with dark energy surveys
We investigate observational constraints on a specific one-parameter
extension to the minimal quintessence model, where the quintessence field
acquires a quadratic coupling to the scalar curvature through a coupling
constant . The value of is highly suppressed in typical tracker
models if the late-time cosmic acceleration is driven at some field values near
the Planck scale. We test in a second class of models in which the field
value today becomes a free model parameter. We use the combined data from
type-Ia supernovae, cosmic microwave background, baryon acoustic oscillations
and matter power spectrum, to weak lensing measurements and find a best-fit
value where is excluded outside the 95 per cent
confidence region. The effective gravitational constant subject
to the hint of a non-zero is constrained to at the same confidence level on cosmological scales, and can be narrowed
down to when combining with Solar
System tests.Comment: Context extended, figures and references added, title changed to
match with accepted version for publicatio
Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data
It is well known that recognizers personalized to each user are much more
effective than user-independent recognizers. With the popularity of smartphones
today, although it is not difficult to collect a large set of audio data for
each user, it is difficult to transcribe it. However, it is now possible to
automatically discover acoustic tokens from unlabeled personal data in an
unsupervised way. We therefore propose a multi-task deep learning framework
called a phoneme-token deep neural network (PTDNN), jointly trained from
unsupervised acoustic tokens discovered from unlabeled data and very limited
transcribed data for personalized acoustic modeling. We term this scenario
"weakly supervised". The underlying intuition is that the high degree of
similarity between the HMM states of acoustic token models and phoneme models
may help them learn from each other in this multi-task learning framework.
Initial experiments performed over a personalized audio data set recorded from
Facebook posts demonstrated that very good improvements can be achieved in both
frame accuracy and word accuracy over popularly-considered baselines such as
fDLR, speaker code and lightly supervised adaptation. This approach complements
existing speaker adaptation approaches and can be used jointly with such
techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201
"Teleparallel" Dark Energy
Using the "teleparallel" equivalent of General Relativity as the
gravitational sector, which is based on torsion instead of curvature, we add a
canonical scalar field, allowing for a nonminimal coupling with gravity.
Although the minimal case is completely equivalent to standard quintessence,
the nonminimal scenario has a richer structure, exhibiting quintessence-like or
phantom-like behavior, or experiencing the phantom-divide crossing. The richer
structure is manifested in the absence of a conformal transformation to an
equivalent minimally-coupled model.Comment: 5 pages, 1 figure, Version published in PLB704 (2011) 384-38
Acute generalized exanthematous pustulosis: A retrospective study of 51 cases in Taiwan
AbstractBackground/ObjectiveAcute generalized exanthematous pustulosis (AGEP) is a severe cutaneous adverse drug reaction characterized by fever and numerous sterile non-follicular pustules. It is mainly attributed to drugs, although other factors have been implicated. The objective of this study was to evaluate the clinical and histological features of AGEP in a Taiwanese population.MethodsIn this retrospective study, we reviewed patients diagnosed with AGEP with a EuroSCAR (RegiSCAR) validation score more than 4 (>4, probable to definite cases), between 1992 and 2012 at the Chang Gung Memorial Hospital in Taiwan. Demographic, clinical and laboratory data, pathologic findings, and disease causality were analyzed.ResultsA total of 51 patients were included in this study, with 34 (66.7%) patients being diagnosed with AGEP with drug causality, and 17 (33.3%) patients being diagnosed with AGEP without drug causality. Cases of AGEP with drug causality showed an older average age, and a significantly higher rate of previous drug hypersensitivity history compared to cases of AGEP without drug causality (p = 0.0018). None of the patients had a history of psoriasis or had developed psoriasis at the 1-year follow-up. A total of 12 cases (23.5%) had systemic involvement, including liver and kidneys. Penicillin or aminopenicillin (17.6%) and cephalosporins (17.6%) were the most common causative drug groups related to AGEP. In AGEP patients without drug causality, three cases of pathogen infections were identified (1 case of mycoplasma, Coxsackie virus, and Epstein-Barr virus, respectively).ConclusionWe found that beta-lactam antibiotics were the major drug class responsible for inducing AGEP in a Taiwanese population, but that some infectious pathogens may also contribute to AGEP development
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