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 $d$-dimensional underlying manifold from noisy observations sampled in the ambient space $\mathbb{R}^D$. 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 $N=\mathcal{O}(\sigma^{(-d+3)})$, these estimators are true $d$-dimensional smooth manifolds whose estimation error, as measured by the Hausdorff distance, is bounded by $\mathcal{O}(\sigma^2\log(1/\sigma))$ 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 $\sigma^{-1}$ and exponentially in $d$. 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