arXiv subjects

Bowen Li

Publications and source records attributed to Bowen Li.

244 records · Page 14Linked to original sources

arXiv2019

Designing compact training sets for data-driven molecular property prediction

In this paper, we consider the problem of designing a training set using the most informative molecules from a specified library to build data-driven molecular property models. Specifically, we use (i) sparse generalized group additivity and (ii) kernel ridge regression as two representative classes of models, we propose a method combining rigorous model-based design of experiments and cheminformatics-based diversity-maximizing subset selection within the epsilon--greedy framework to systematically minimize the amount of data needed to train these models. We demonstrate the effectiveness of the algorithm on subsets of various databases, including QM7, NIST, and a catalysis dataset. For sparse group additive models, a balance between exploration (diversity-maximizing selection) and exploitation (D-optimality selection) leads to learning with a fraction (sometimes as little as 15%) of the data to achieve similar accuracy as five-fold cross validation on the entire set. On the other hand, kernel ridge regression prefers diversity-maximizing selections.

physics.data-an↗

arXiv2019

An Imitation Learning Approach to Unsupervised Parsing

Recently, there has been an increasing interest in unsupervised parsers that optimize semantically oriented objectives, typically using reinforcement learning. Unfortunately, the learned trees often do not match actual syntax trees well. Shen et al. (2018) propose a structured attention mechanism for language modeling (PRPN), which induces better syntactic structures but relies on ad hoc heuristics. Also, their model lacks interpretability as it is not grounded in parsing actions. In our work, we propose an imitation learning approach to unsupervised parsing, where we transfer the syntactic knowledge induced by the PRPN to a Tree-LSTM model with discrete parsing actions. Its policy is then refined by Gumbel-Softmax training towards a semantically oriented objective. We evaluate our approach on the All Natural Language Inference dataset and show that it achieves a new state of the art in terms of parsing $F$-score, outperforming our base models, including the PRPN.

cs.CL↗

arXiv2019

Gate-Controlled VO2 Phase Transition for High-Performance Smart Window

VO2 material is promising for developing energy-saving "smart window", owing to its thermochromic property induced by metal-insulator transition (MIT). However, its practical application is greatly limited by the relatively high critical transition temperature (~68oC), low luminous transmittance (<60%) and poor solar energy regulation ability (<15%). Here we developed a reversible and non-volatile electric-field control on the MIT of monoclinic VO2 film. With a solid electrolyte layer assisted gating treatment, we modulated the insertion/extraction of hydrogens into/from VO2 lattice at room temperature, causing tri-state phase transitions accompanied with controllable transmission adjustment. The dramatic increase of visible/infrared transmittance during the phase transition from the metallic (lightly H-doping) to insulating (heavily H-doping) phase leads to an increased solar energy regulation ability up to 26.5%, while keep 70.8% visible-luminous transmittance. These results beat all previous records and even exceeded the theoretical limit for traditional VO2 smart window, removing intrinsic disadvantages of VO2 for energy-saving utilizations. Our findings not only demonstrated an electric-field controlled phase modulation strategy, but also open the door for high-performance VO2-based smart window applications.

physics.app-ph↗

arXiv2019

SoReC: A Social-Relation Based Centrality Measure in Mobile Social Networks

Mobile Social Networks (MSNs) have been evolving and enabling various fields in recent years. Recent advances in mobile edge computing, caching, and device-to-device communications, can have significant impacts on 5G systems. In those settings, identifying central users is crucial. It can provide important insights into designing and deploying diverse services and applications. However, it is challenging to evaluate the centrality of nodes in MSNs with dynamic environments. In this paper, we propose a Social-Relation based Centrality (SoReC) measure, in which social network information is used to quantify the influence of each user in MSNs. We first introduce a new metric to estimate direct social relations among users via direct contacts, and then extend the metric to explore indirect social relations among users bridging by the third parties. Based on direct and indirect social relations, we detect the influence spheres of users and quantify their influence in the networks. Simulations on real-world networks show that the proposed measure can perform well in identifying future influential users in MSNs.

cs.SI↗

arXiv2018

Dependency Grammar Induction with a Neural Variational Transition-based Parser

Dependency grammar induction is the task of learning dependency syntax without annotated training data. Traditional graph-based models with global inference achieve state-of-the-art results on this task but they require $O(n^3)$ run time. Transition-based models enable faster inference with $O(n)$ time complexity, but their performance still lags behind. In this work, we propose a neural transition-based parser for dependency grammar induction, whose inference procedure utilizes rich neural features with $O(n)$ time complexity. We train the parser with an integration of variational inference, posterior regularization and variance reduction techniques. The resulting framework outperforms previous unsupervised transition-based dependency parsers and achieves performance comparable to graph-based models, both on the English Penn Treebank and on the Universal Dependency Treebank. In an empirical comparison, we show that our approach substantially increases parsing speed over graph-based models.

cs.CL↗

arXiv2018

Distributionally Robust Chance Constrained Optimal Power Flow Assuming Unimodal Distributions with Misspecified Modes

Chance constrained optimal power flow (CC-OPF) formulations have been proposed to minimize operational costs while controlling the risk arising from uncertainties like renewable generation and load consumption. To solve CC-OPF, we often need access to the (true) joint probability distribution of all uncertainties, which is rarely known in practice. A solution based on a biased estimate of the distribution can result in poor reliability. To overcome this challenge, recent work has explored distributionally robust chance constraints, in which the chance constraints are satisfied over a family of distributions called the ambiguity set. Commonly, ambiguity sets are only based on moment information (e.g., mean and covariance) of the random variables; however, specifying additional characteristics of the random variables reduces conservatism and cost. Here, we consider ambiguity sets that additionally incorporate unimodality information. In practice, it is difficult to estimate the mode location from the data and so we allow it to be potentially misspecified. We formulate the problem and derive a separation-based algorithm to efficiently solve it. Finally, we evaluate the performance of the proposed approach on a modified IEEE-30 bus network with wind uncertainty and compare with other distributionally robust approaches. We find that a misspecified mode significantly affects the reliability of the solution and the proposed model demonstrates a good trade-off between cost and reliability.

math.OC↗

arXiv2018

A Design of FPGA Based Small Animal PET Real Time Digital Signal Processing and Correction Logic

Small animal Positron Emission Tomography (PET) is dedicated to small animal imaging. Animals used in experiments, such as rats and monkeys, are often much smaller than human bodies, which requires higher position and energy precision of the PET imaging system. Besides, Flexibility, high efficiency are also the major demands of a practical PET system. These requires a high-quality analog front-end and a digital signal processing logic with high efficiency and compatibility of multiple data processing modes. The digital signal processing logic of the small animal PET system presented in this paper implements 32-channel signal processing in a single Xilinx Artix-7 family of Field-Programmable Gate Array (FPGA). The logic is designed to support three online modes which are regular package mode, flood map and energy spectrum histogram. Several functions are integrated, including two-dimensional (2D) raw position calculation, crystal identification, events filtering, etc. Besides, a series of online corrections are also integrated, such as photon peak correction to 511 keV and timing offset correction with crystal granularity. A Gigabit Ethernet interface is utilized for data transfer, Look-Up Tables (LUTs) configuration and commands issuing. The pipe-line logic processes the signals at 125 MHz with a rate of 1,000,000 events/s. A series of initial tests are conducted. The results indicate that the digital processing logic achieves the expectations.

eess.SP↗

arXiv2017

Nonparametric Marginal Analysis of Recurrent Events Data under Competing Risks

This project was motivated by a dialysis study in northern Taiwan. Dialysis patients, after shunt implantation, may experience two types ("acute" or "non-acute") of shunt thrombosis, both of which may recur. We formulate the problem under the framework of recurrent events data in the presence of competing risks. In particular we focus on marginal inference for the gap time variable of specific type. The functions of interest are the cumulative incidence function and cause-specific hazard function. The major challenge of nonparametric inference is the problem of induced dependent censoring. We apply the technique of inverse probability of censoring weighting (IPCW) to adjust for the selection bias. Besides point estimation, we apply the bootstrap re-sampling method for further inference. Large sample properties of the proposed estimators are derived. Simulations are performed to examine the finite-sample performances of the proposed methods. Finally we apply the proposed methodology to analyze the dialysis data.

stat.ME↗

arXiv2017

Panoramic-reconstruction temporal imaging for seamless measurements of slowly-evolved femtosecond pulse dynamics

Single-shot real-time characterization of optical waveforms with sub-picosecond resolution is essential for investigating various ultrafast optical dynamics. However, the finite temporal recording length of current techniques hinders comprehensive understanding of many intriguing ultrafast optical phenomena that evolve over a time scale much longer than their fine temporal details. Inspired by the space-time duality and by stitching of multiple microscopic images to achieve a larger field of view in the spatial domain, here a panoramic-reconstruction temporal imaging (PARTI) system is devised to scale up the temporal recording length without sacrificing the resolution. As a proof-of-concept demonstration, the PARTI system is applied to study the dynamic waveforms of slowly-evolved dissipative Kerr solitons in an ultrahigh-Q microresonator. Two 1.5-ns-long comprehensive evolution portraits are reconstructed with 740-fs resolution and dissipative Kerr soliton transition dynamics, in which a multiplet soliton state evolves into stable singlet soliton state, are depicted.

physics.optics↗

arXiv2016

Active control of surface plasmon resonance in MoS2-Ag hybrid nanostructures

Molybdenum disulfide (MoS2) monolayers have attracted much attention for their novel optical properties and efficient light-matter interactions. When excited by incident laser, the optical response of MoS2 monolayers was effectively modified by elementary photo-excited excitons owing to their large exciton binding energy, which can be facilitated for the optical-controllable exciton-plasmon interactions. Inspired by this concept, we experimentally investigated active light control of surface plasmon resonance (SPR) in MoS2-Ag hybrid nanostructures. The white light spectra of SPR were gradually red-shifted by increasing laser power, which was distinctly different from the one of bare Ag nanostructure. This spectroscopic tunability can be further controlled by near-field coupling strength and polarization state of light, and selectively applied to the control of plasmonic dark mode. An analytical Lorentz model for photo-excited excitons induced modulation of MoS2 dielectric function was developed to explain the insight physics of this SPR tunability. Our study opens new possibilities to the development of all-optical controlled nanophotonic devices based on 2D materials.

physics.optics↗