183 research outputs found
Large Margin Neural Language Model
We propose a large margin criterion for training neural language models.
Conventionally, neural language models are trained by minimizing perplexity
(PPL) on grammatical sentences. However, we demonstrate that PPL may not be the
best metric to optimize in some tasks, and further propose a large margin
formulation. The proposed method aims to enlarge the margin between the "good"
and "bad" sentences in a task-specific sense. It is trained end-to-end and can
be widely applied to tasks that involve re-scoring of generated text. Compared
with minimum-PPL training, our method gains up to 1.1 WER reduction for speech
recognition and 1.0 BLEU increase for machine translation.Comment: 9 pages. Accepted as a long paper in EMNLP201
High-speed in vitro intensity diffraction tomography
We demonstrate a label-free, scan-free intensity diffraction tomography technique utilizing annular illumination (aIDT) to rapidly characterize large-volume three-dimensional (3-D) refractive index distributions in vitro. By optimally matching the illumination geometry to the microscope pupil, our technique reduces the data requirement by 60 times to achieve high-speed 10-Hz volume rates. Using eight intensity images, we recover volumes of ∼350 μm × 100 μm × 20 μm, with near diffraction-limited lateral resolution of ∼ 487 nm and axial resolution of ∼ 3.4 μm. The attained large volume rate and high-resolution enable 3-D quantitative phase imaging of complex living biological samples across multiple length scales. We demonstrate aIDT’s capabilities on unicellular diatom microalgae, epithelial buccal cell clusters with native bacteria, and live Caenorhabditis elegans specimens. Within these samples, we recover macroscale cellular structures, subcellular organelles, and dynamic micro-organism tissues with minimal motion artifacts. Quantifying such features has significant utility in oncology, immunology, and cellular pathophysiology, where these morphological features are evaluated for changes in the presence of disease, parasites, and new drug treatments. Finally, we simulate the aIDT system to highlight the accuracy and sensitivity of the proposed technique. aIDT shows promise as a powerful high-speed, label-free computational microscopy approach for applications where natural imaging is required to evaluate environmental effects on a sample in real time.https://arxiv.org/abs/1904.06004Accepted manuscrip
High-resolution transport-of-intensity quantitative phase microscopy with annular illumination
For quantitative phase imaging (QPI) based on transport-of-intensity equation
(TIE), partially coherent illumination provides speckle-free imaging,
compatibility with brightfield microscopy, and transverse resolution beyond
coherent diffraction limit. Unfortunately, in a conventional microscope with
circular illumination aperture, partial coherence tends to diminish the phase
contrast, exacerbating the inherent noise-to-resolution tradeoff in TIE
imaging, resulting in strong low-frequency artifacts and compromised imaging
resolution. Here, we demonstrate how these issues can be effectively addressed
by replacing the conventional circular illumination aperture with an annular
one. The matched annular illumination not only strongly boosts the phase
contrast for low spatial frequencies, but significantly improves the practical
imaging resolution to near the incoherent diffraction limit. By incorporating
high-numerical aperture (NA) illumination as well as high-NA objective, it is
shown, for the first time, that TIE phase imaging can achieve a transverse
resolution up to 208 nm, corresponding to an effective NA of 2.66. Time-lapse
imaging of in vitro Hela cells revealing cellular morphology and subcellular
dynamics during cells mitosis and apoptosis is exemplified. Given its
capability for high-resolution QPI as well as the compatibility with widely
available brightfield microscopy hardware, the proposed approach is expected to
be adopted by the wider biology and medicine community.Comment: This manuscript was originally submitted on 20 Feb. 201
A Statistical Approach to Estimating Adsorption-Isotherm Parameters in Gradient-Elution Preparative Liquid Chromatography
Determining the adsorption isotherms is an issue of significant importance in
preparative chromatography. A modern technique for estimating adsorption
isotherms is to solve an inverse problem so that the simulated batch separation
coincides with actual experimental results. However, due to the ill-posedness,
the high non-linearity, and the uncertainty quantification of the corresponding
physical model, the existing deterministic inversion methods are usually
inefficient in real-world applications. To overcome these difficulties and
study the uncertainties of the adsorption-isotherm parameters, in this work,
based on the Bayesian sampling framework, we propose a statistical approach for
estimating the adsorption isotherms in various chromatography systems. Two
modified Markov chain Monte Carlo algorithms are developed for a numerical
realization of our statistical approach. Numerical experiments with both
synthetic and real data are conducted and described to show the efficiency of
the proposed new method.Comment: 28 pages, 11 figure
Manifold Fitting
While classical data analysis has addressed observations that are real
numbers or elements of a real vector space, at present many statistical
problems of high interest in the sciences address the analysis of data that
consist of more complex objects, taking values in spaces that are naturally not
(Euclidean) vector spaces but which still feature some geometric structure.
Manifold fitting is a long-standing problem, and has finally been addressed in
recent years by Fefferman et al. (2020, 2021a). We develop a method with a
theory guarantee that fits a -dimensional underlying manifold from noisy
observations sampled in the ambient space . The new approach uses
geometric structures to obtain the manifold estimator in the form of image sets
via a two-step mapping approach. We prove that, under certain mild assumptions
and with a sample size , these estimators are
true -dimensional smooth manifolds whose estimation error, as measured by
the Hausdorff distance, is bounded by
with high probability. Compared with the existing approaches proposed in
Fefferman et al. (2018, 2021b); Genovese et al. (2014); Yao and Xia (2019), our
method exhibits superior efficiency while attaining very low error rates with a
significantly reduced sample size, which scales polynomially in
and exponentially in . Extensive simulations are performed to validate our
theoretical results. Our findings are relevant to various fields involving
high-dimensional data in machine learning. Furthermore, our method opens up new
avenues for existing non-Euclidean statistical methods in the sense that it has
the potential to unify them to analyze data on manifolds in the ambience space
domain.Comment: 60 page
Rational design of dibenzo[a,c]phenazine-derived isomeric thermally activated delayed fluorescence luminophores for efficient orange-red organic light-emitting diodes
It is an immense challenge to develop efficient long-wavelength (orange-to-red) thermally activated delayed fluorescence (TADF) materials due to the increasing nonradiative decay rates following the energy-gap law. Herein, two pairs of asymmetric isomers; DPyPzTPA and TPAPzDPy, and PyPzDTPA and DTPAPzPy based on electron-deficient moieties dibenzo[a,c]phenazine (Pz) and pyridine (Py) combined with electron-donor units of triphenylamine (TPA) were designed and synthesized. Their photophysical properties could be finely modulated by changing the position and number of Py groups as well as TPA fragments onto Pz cores. DPyPzTPA and DTPAPzPy possess much more rigidity and thus less geometry relaxation and non-radiative decay between ground states and excited states than those of PyPzDTPA and TPAPzDPy. Intriguingly, DPyPzTPA exhibits the highest relative photoluminescence quantum yield (ΦPL) and the fastest reverse intersystem crossing (rISC) rate among them owing to relatively stronger rigidity and spin-orbit coupling (SOC) interactions between the lowest singlet (S1) and energetically close-lying excited triplet state and therefore, the device showed the highest maximum external quantum efficiency (EQEmax) of 16.6% (60.9 lm/W, 53.3 cd/A) with Commission Internationale de I'Eclairage (CIE) coordinates of (0.43, 0.55), peak wavelength 556 nm. In stark contrast, due to its lower rigidity and extremely weak delayed fluorescence (DF) characteristic and thus the much lower ΦPL, TPAPzDPy-based devices are only half as efficient (30.8 lm/W, 27.5 cd/A, 8.3% EQE) despite the isomers possessing equal singlet-triplet energy gaps (ΔEST) of 0.43 eV. On the other hand, the device based on DTPAPzPy also demonstrated a strongly enhanced performance (59.1 lm/W, 52.7 cd/A, 16.1% EQE) than its isomer PyPzDTPA-based device (39.5 lm/W, 35.2 cd/A, 10.3% EQE). This work explicitly implicates that the asymmetric and isomeric molecular design is a potential strategy for promoting the development of highly efficient long-wavelength TADF materials
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