arXiv subjects

Ying Huang

Publications and source records attributed to Ying Huang.

At least 73 records · Page 4Linked to original sources

arXiv2019

Multi-Level Network for High-Speed Multi-Person Pose Estimation

In multi-person pose estimation, the left/right joint type discrimination is always a hard problem because of the similar appearance. Traditionally, we solve this problem by stacking multiple refinement modules to increase network's receptive fields and capture more global context, which can also increase a great amount of computation. In this paper, we propose a Multi-level Network (MLN) that learns to aggregate features from lower-level (left/right information), upper-level (localization information), joint-limb level (complementary information) and global-level (context) information for discrimination of joint type. Through feature reuse and its intra-relation, MLN can attain comparable performance to other conventional methods while runtime speed retains at 42.2 FPS.

cs.CV↗

arXiv2019

FollowMeUp Sports: New Benchmark for 2D Human Keypoint Recognition

Human pose estimation has made significant advancement in recent years. However, the existing datasets are limited in their coverage of pose variety. In this paper, we introduce a novel benchmark FollowMeUp Sports that makes an important advance in terms of specific postures, self-occlusion and class balance, a contribution that we feel is required for future development in human body models. This comprehensive dataset was collected using an established taxonomy of over 200 standard workout activities with three different shot angles. The collected videos cover a wider variety of specific workout activities than previous datasets including push-up, squat and body moving near the ground with severe self-occlusion or occluded by some sport equipment and outfits. Given these rich images, we perform a detailed analysis of the leading human pose estimation approaches gaining insights for the success and failures of these methods.

cs.CV↗

arXiv2019

Selecting Biomarkers for building optimal treatment selection rules using Kernel Machines

Optimal biomarker combinations for treatment-selection can be derived by minimizing total burden to the population caused by the targeted disease and its treatment. However, when multiple biomarkers are present, including all in the model can be expensive and hurt model performance. To remedy this, we consider feature selection in optimization by minimizing an extended total burden that additionally incorporates biomarker measurement costs. Formulating it as a 0-norm penalized weighted classification, we develop various procedures for estimating linear and nonlinear combinations. Through simulations and a real data example, we demonstrate the importance of incorporating feature-selection and marker cost when deriving treatment-selection rules.

stat.AP↗

arXiv2019

Theoretical investigation of a spectrally pure-state generation from isomorphs of KDP crystal at near-infrared and telecom wavelengths

Spectrally uncorrelated biphoton state generated from the spontaneous nonlinear optical process is an important resource for quantum information. Currently such spectrally uncorrelated biphoton state can only be prepared from limited kinds of nonlinear media, thus limiting their wavelengths. In order to explore wider wavelength range, here we theoretically study the generation of spectrally uncorrelated biphoton state from 14 isomorphs of potassium dihydrogen phosphate (KDP) crystal. We find that 11 crystals from the `KDP family' still maintain similar nonlinear optical properties of KDP, such as KDP, DKDP, ADP, DADP, ADA, DADA, RDA, DRDA, RDP, DRDP and KDA, which satisfy 3 kinds of the group-velocity matching conditions for spectrally uncorrelated biphoton state generation from near-infrared to telecom wavelengths. Based on the uncorrelated biphoton state, we investigate the generation of heralded pure-state single photon by detecting one member of the biphoton state to herald the output of the other. The purity of the heralded single photon is as high as 0.98 without using a narrow-band filter; the Hong-Ou-Mandel interference from independent sources can also achieve a visibility of 98%. This study may provide more and better single-photon sources for quantum information processing at near-infrared and telecom wavelengths.

quant-ph↗

arXiv2018

Optimization Design of Decentralized Control for Complex Decentralized Systems

A new method is developed to deal with the problem that a complex decentralized control system needs to keep centralized control performance. The systematic procedure emphasizes quickly finding the decentralized subcontrollers that matching the closed-loop performance and robustness characteristics of the centralized controller, which is featured by the fact that GA is used to optimize the design of centralized H-infinity controller K(s) and decentralized engine subcontroller KT(s), and that only one interface variable needs to satisfy decentralized control system requirement according to the proposed selection principle. The optimization design is motivated by the implementation issues where it is desirable to reduce the time in trial and error process and accurately find the best decentralized subcontrollers. The method is applied to decentralized control system design for a short takeoff and landing fighter. By comparing the simulation results of the decentralized control system with those of the centralized control system, the target of the decentralized control attains the performance and robustness of centralized control is validated.

eess.SY↗

arXiv2018

IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation

Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights. In this paper, we present the infrared-to-visual (IR2VI) algorithm, a novel unsupervised thermal-to-visible image translation framework based on generative adversarial networks (GANs). IR2VI is able to learn the intrinsic characteristics from VI images and integrate them into IR images. Since the existing unsupervised GAN-based image translation approaches face several challenges, such as incorrect mapping and lack of fine details, we propose a structure connection module and a region-of-interest (ROI) focal loss method to address the current limitations. Experimental results show the superiority of the IR2VI algorithm over baseline methods.

cs.CV↗

arXiv2018

Test for Incremental Value of New Biomarkers Based on OR Rules

In early detection of disease, a single biomarker often has inadequate classification performance, making it important to identify new biomarkers to combine with the existing marker for improved performance. A biologically natural method to combine biomarkers is to use logic rules, e.g. the OR/AND rules. In our motivating example of early detection of pancreatic cancer, the established biomarker CA19-9 is only present in a subclass of cancer; it is of interest to identify new biomarkers present in the other subclasses and declare disease when either marker is positive. While there has been research on developing biomarker combinations using the OR/AND rules, the inference regarding the incremental value of the new marker within this framework is lacking and challenging due to a statistical non-regularity. In this paper, we aim to answer the inferential question of whether combining the new biomarker achieves better classification performance than using the existing biomarker alone, based on a nonparametrically estimated OR rule that maximizes the weighted average of sensitivity and specificity. We propose and compare various procedures for testing the incremental value of the new biomarker and constructing its confidence interval, using bootstrap, cross-validation, and a novel fuzzy p-value-based technique. We compare the performance of different methods via extensive simulation studies and apply them to the pancreatic cancer example.

stat.AP↗

arXiv2017

Revisiting Graph Construction for Fast Image Segmentation

In this paper, we propose a simple but effective method for fast image segmentation. We re-examine the locality-preserving character of spectral clustering by constructing a graph over image regions with both global and local connections. Our novel approach to build graph connections relies on two key observations: 1) local region pairs that co-occur frequently will have a high probability to reside on a common object; 2) spatially distant regions in a common object often exhibit similar visual saliency, which implies their neighborship in a manifold. We present a novel energy function to efficiently conduct graph partitioning. Based on multiple high quality partitions, we show that the generated eigenvector histogram based representation can automatically drive effective unary potentials for a hierarchical random field model to produce multi-class segmentation. Sufficient experiments, on the BSDS500 benchmark, large-scale PASCAL VOC and COCO datasets, demonstrate the competitive segmentation accuracy and significantly improved efficiency of our proposed method compared with other state of the arts.

cs.CV↗

arXiv2017

Evaluation of Treatment Effect Modification by Biomarkers Measured Pre- and Post-randomization in the Presence of Non-monotone Missingness

In vaccine studies, investigators are often interested in studying effect modifiers of clinical treatment efficacy by biomarker-based principal strata, which is useful for selecting biomarker study endpoints for evaluating treatments in new trials, exploring biological mechanisms of clinical treatment efficacy, and studying mediators of clinical treatment efficacy. However, in trials where participants may enter the study with prior exposure therefore with variable baseline biomarker values, clinical treatment efficacy may depend jointly on a biomarker measured at baseline and measured at a fixed time after vaccination. Therefore, it is of interest to conduct a bivariate effect modification analysis by biomarker-based principal strata and baseline biomarker values. Previous methods allow this assessment if participants who have the biomarker measured at the the fixed time point post randomization would also have the biomarker measured at baseline. However, additional complications in study design could happen in practice. For example, in the Dengue correlates study, baseline biomarker values were only available from a fraction of participants who have biomarkers measured post-randomization. How to conduct the bivariate effect modification analysis in these studies remains an open research question. In this article, we propose an estimated likelihood method to utilize the sub-sampled baseline biomarker in the effect modification analysis and illustrate our method with datasets from two dengue phase 3 vaccine efficacy trials.

stat.ME↗

arXiv2017

Efficient Correlated Topic Modeling with Topic Embedding

Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to linear w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval.

cs.LG↗

arXiv2016

Text Network Exploration via Heterogeneous Web of Topics

A text network refers to a data type that each vertex is associated with a text document and the relationship between documents is represented by edges. The proliferation of text networks such as hyperlinked webpages and academic citation networks has led to an increasing demand for quickly developing a general sense of a new text network, namely text network exploration. In this paper, we address the problem of text network exploration through constructing a heterogeneous web of topics, which allows people to investigate a text network associating word level with document level. To achieve this, a probabilistic generative model for text and links is proposed, where three different relationships in the heterogeneous topic web are quantified. We also develop a prototype demo system named TopicAtlas to exhibit such heterogeneous topic web, and demonstrate how this system can facilitate the task of text network exploration. Extensive qualitative analyses are included to verify the effectiveness of this heterogeneous topic web. Besides, we validate our model on real-life text networks, showing that it preserves good performance on objective evaluation metrics.

cs.SI↗

arXiv2016

Multi-Agent System for Groundwater Depletion Using Game Theory

Groundwater is one of the most vital of all common pool resources throughout the world. More than half of groundwater is used to grow crops. This research models groundwater depletion patterns within a multi-agent system framework. Irrigators are modeled as agents in the multi-agent system. The irrigation strategies adopted by the agents are investigated using game theory. A set of five irrigators, growing three crops: corn, sorghum and wheat, have been considered in this study. To allow groundwater flow, these agents are assumed to be located in adjoining farm lands. Irrigators are modeled selfish agents that strategize their irrigation patterns in order to maximize their own utilities, i.e. the difference between the total revenue obtained from crop sales and the costs incurred, including groundwater extraction costs. Due to groundwater flow, and have no incentive to conserve groundwater. This leads to unsustainable depletion of the resource under Nash equilibrium, when no irrigator can increase its utility by unilaterally changing its strategy. All parameters in this research are representative of Kansas. Recorded environmental and economic data of the region, along with the DSSAT software, have been used to obtain these futuristic projections. One of the emergent phenomena of the simulations is the adoption of crop rotation patterns by the irrigators to conserve groundwater. The irrigators grow corn, which is a more profitable yet water intensive crop in one year, and in the next, conserve water by growing sorghum instead. Another emergent outcome of this research is the viability of LEMAs. When the irrigators are subject to LEMA-level limits on groundwater use, there is a slight increase in the aggregate utility of the LEMA.

cs.GT↗

arXiv2016

A Comparative Study of Object Trackers for Infrared Flying Bird Tracking

Bird strikes present a huge risk for aircraft, especially since traditional airport bird surveillance is mainly dependent on inefficient human observation. Computer vision based technology has been proposed to automatically detect birds, determine bird flying trajectories, and predict aircraft takeoff delays. However, the characteristics of bird flight using imagery and the performance of existing methods applied to flying bird task are not well known. Therefore, we perform infrared flying bird tracking experiments using 12 state-of-the-art algorithms on a real BIRDSITE-IR dataset to obtain useful clues and recommend feature analysis. We also develop a Struck-scale method to demonstrate the effectiveness of multiple scale sampling adaption in handling the object of flying bird with varying shape and scale. The general analysis can be used to develop specialized bird tracking methods for airport safety, wildness and urban bird population studies.

cs.CV↗

arXiv2015

Genetic structure of Sino-Tibetan populations revealed by forensic STR loci

The origin and diversification of Sino-Tibetan populations have been a long-standing hot debate. However, the limited genetic information of Tibetan populations keeps this topic far from clear. In the present study, we genotyped 15 forensic autosomal STRs from 803 unrelated Tibetan individuals from Gansu Province (635 from Gannan and 168 from Tianzhu). We combined these data with published dataset to infer a detailed population affinities and admixture of Sino-Tibetan populations. Our results revealed that the genetic structure of Sino-Tibetan populations was strongly correlated with linguistic affiliations. Although the among-population variances are relatively small, the genetic components for Tibetan, Lolo-Burmese, and Han Chinese were quite distinctive, especially for the Deng, Nu, and Derung of Lolo-Burmese. Southern indigenous populations, such as Tai-Kadai and Hmong-Mien populations might have made substantial genetic contribution to Han Chinese and Altaic populations, but not to Tibetans. Likewise, Han Chinese but not Tibetan shared very similar genetic makeups with Altaic populations, which did not support the North Asian origin of Tibetan populations. The dataset generated here are also valuable for forensic identification.

q-bio.PE↗

arXiv2013

Logistic regression analysis with standardized markers

Two different approaches to analysis of data from diagnostic biomarker studies are commonly employed. Logistic regression is used to fit models for probability of disease given marker values, while ROC curves and risk distributions are used to evaluate classification performance. In this paper we present a method that simultaneously accomplishes both tasks. The key step is to standardize markers relative to the nondiseased population before including them in the logistic regression model. Among the advantages of this method are the following: (i) ensuring that results from regression and performance assessments are consistent with each other; (ii) allowing covariate adjustment and covariate effects on ROC curves to be handled in a familiar way, and (iii) providing a mechanism to incorporate important assumptions about structure in the ROC curve into the fitted risk model. We develop the method in detail for the problem of combining biomarker data sets derived from multiple studies, populations or biomarker measurement platforms, when ROC curves are similar across data sources. The methods are applicable to both cohort and case-control sampling designs. The data set motivating this application concerns Prostate Cancer Antigen 3 (PCA3) for diagnosis of prostate cancer in patients with or without previous negative biopsy where the ROC curves for PCA3 are found to be the same in the two populations. The estimated constrained maximum likelihood and empirical likelihood estimators are derived. The estimators are compared in simulation studies and the methods are illustrated with the PCA3 data set.

stat.AP↗

arXiv2013

Rabi Oscillations between Atomic and Molecular Condensates Driven with Coherent One-Color Photoassociation

We demonstrate coherent one-color photoassociation of a Bose-Einstein condensate, which results in Rabi oscillations between atomic and molecular condensates. We attain atom-molecule Rabi frequencies that are comparable to decoherence rates by driving photoassociation of atoms in an $^{88}$Sr condensate to a weakly-bound level of the metastable $^{1}S_{0}+^{3}P_{1}$ molecular potential, which has a long lifetime and large Franck-Condon overlap integral with the ground scattering state. Transient shifts and broadenings of the excitation spectrum are clearly seen at short times, and they create an asymmetric excitation profile that only displays Rabi oscillations for blue detuning from resonance.

physics.atom-ph↗

arXiv2013

Preparation and characterization of Bismaleimide resin/titania nanocomposites via sol-gel process

Bismaleimide (BMI) resin/ titania nanocomposites were synthesized from allylated-phenolic modified bismaleimide resin and TiO2 via the sol-gel process of tetrabutyltitanate (Ti(OnBu)4, TBT). These nanocomposite materials were characterized by FT-IR, XRD, FE-SEM, TGA and DMA. It was found that the nano-scale TiO2 particles were formed in the AP-BMI resin matrix, and the average primary particle size of the dispersed phase in the nanocomposites was less than 100nm, but the particle aggregates with bigger size existed. Obvious improvements of glass transition temperature and heat resistance properties of the AP-BMI resins were achieved by the introduction of nano-sized TiO2 inorganic phase, and the modulus of the material was also improved.

cond-mat.mtrl-sci↗

arXiv2013

Novel Bismaleimide Resin/Silsesquioxane and Titania Nanocomposites by the Sol-Gel Process: the Preparation, Morphology, Thermal and Thermo-mechanical Properties

Bismaleimide(BMI) resin/silsesquioxane or titania nanocomposites were synthesized from bismaleimide resin and SiO3/2 or TiO2 via the sol-gel process of N-γ-triethoxylsilylpropyl-maleamic acid (TESPMA) or tetrabutyltitanate (Ti(OnBu)4, TBT), respectively, in the presence of the AP-BMI prepolymers. These nanocomposite materials were characterized by FT-IR, FE-SEM, TGA and DMA. It was found that the nano-scale SiO3/2 or TiO2 particles were formed in the AP-BMI resin matrix and the average original particle size of the dispersed phase in the nanocomposites was less than 100nm, but the particle aggregates with bigger size existed. Obvious improvements of Tg and the heat resistance of the AP-BMI resin were achieved by introduction of the nano-sized SiO3/2 inorganic phase, and the modulus at high temperatures was improved too. The incorporation of nano-scale TiO2 particles into the AP-BMI resin improved the Tg of the polymer, but lowered the thermal resistance of the material, and improved the modulus of the material at lower temperatures, but lowered the modulus at higher temperatures.

cond-mat.mtrl-sci↗