arXiv subjects

Bin Yang

Publications and source records attributed to Bin Yang.

At least 397 records · Page 22Linked to original sources

arXiv2019

MedGAN: Medical Image Translation using GANs

Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.

cs.CV↗

arXiv2019

Temperature dependence of normalized sensitivity of Love wave sensor with unidirectional carbon fiber epoxy composite/Mn-doped 0.24PIN-0.46PMN-0.30PT ternary single crystal configuration

We have derived a general formula for sensitivity optimization of gravimetric sensors and use it to design a high precision and high sensitivity gravimetric sensor using unidirectional carbon fiber epoxy composite (CFEC) guiding layer on single crystal Mn-doped yPb(In1/2Nb1/2)O3-(1-x-y)Pb(Mg1/3Nb2/3)O3-xPbTiO3 (Mn: PIN-PMN-PT) piezoelectric substrate. The normalized maximum sensitivity exhibits a decreasing tendency with temperature up to 55 degrees Celsius. For the CFEC-on-Mn: PIN-PMN-PT sensor configuration with wavelength 24 {mu}m at 25 degrees Celsius, the maximum sensitivity can reach as high as 760.88 cm2/g, which is nearly twice that of traditional SiO2/ST quartz configuration gravimetric sensor.

physics.app-ph↗

arXiv2019

High Resolution Thermal Infrared Imaging of 3200 Phaethon

We present thermal infrared observations of the active asteroid (and Geminid meteoroid stream parent) 3200 Phaethon using the Very Large Telescope. The images, at 10.7 micron wavelength, were taken with Phaethon at its closest approach to Earth (separation 0.07 AU) in 2017 December, at a linear resolution of about 14 km. We probe the Hill sphere (of radius 66 km) for trapped dust and macroscopic bodies, finding neither, and we set limits to the presence of unbound dust. The derived limits to the optical depth of dust near Phaethon depend somewhat on the assumed geometry, but are of order 1e-5. The upper limit to the rate of loss of mass in dust is 14 kg/s. This is about 50 times smaller than the rate needed to sustain the Geminid meteoroid stream in steady state. The observations thus show that the production of the Geminids does not proceed in steady state.

astro-ph.EP↗

arXiv2019

Unsupervised Medical Image Translation Using Cycle-MedGAN

Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. However, for supervised image translation frameworks, co-registered datasets, paired in a pixel-wise sense, are required. This is often difficult to acquire in realistic medical scenarios. On the other hand, unsupervised translation frameworks often result in blurred translated images with unrealistic details. In this work, we propose a new unsupervised translation framework which is titled Cycle-MedGAN. The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images. Qualitative and quantitative comparisons against other unsupervised translation approaches demonstrate the performance of the proposed framework for PET-CT translation and MR motion correction.

cs.CV↗

arXiv2019

PIXOR: Real-time 3D Object Detection from Point Clouds

We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component for safety. Existing approaches are, however, expensive in computation due to high dimensionality of point clouds. We utilize the 3D data more efficiently by representing the scene from the Bird's Eye View (BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs oriented 3D object estimates decoded from pixel-wise neural network predictions. The input representation, network architecture, and model optimization are especially designed to balance high accuracy and real-time efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets we show that the proposed detector surpasses other state-of-the-art methods notably in terms of Average Precision (AP), while still runs at >28 FPS.

cs.CV↗

arXiv2019

Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers

Obtaining a smart surveillance requires a sensing system that can capture accurate and detailed information for the human walking style. The radar micro-Doppler ($\boldsymbolμ$-D) analysis is proved to be a reliable metric for studying human locomotions. Thus, $\boldsymbolμ$-D signatures can be used to identify humans based on their walking styles. Additionally, the signatures contain information about the radar cross section (RCS) of the moving subject. This paper investigates the effect of human body characteristics on human identification based on their $\boldsymbolμ$-D signatures. In our proposed experimental setup, a treadmill is used to collect $\boldsymbolμ$-D signatures of 22 subjects with different genders and body characteristics. Convolutional autoencoders (CAE) are then used to extract the latent space representation from the $\boldsymbolμ$-D signatures. It is then interpreted in two dimensions using t-distributed stochastic neighbor embedding (t-SNE). Our study shows that the body mass index (BMI) has a correlation with the $\boldsymbolμ$-D signature of the walking subject. A 50-layer deep residual network is then trained to identify the walking subject based on the $\boldsymbolμ$-D signature. We achieve an accuracy of 98% on the test set with high signal-to-noise-ratio (SNR) and 84% in case of different SNR levels.

cs.CV↗

arXiv2019

Hadronic Molecular States Composed of Spin-$3\over 2$ Singly Charmed Baryons

We investigate the possible deuteron-like molecules composed of a pair of charmed spin-$\frac{3}{2}$ baryons, or one charmed baryon and one charmed antibaryon within the one-boson-exchange (OBE) model. For the spin singlet and triplet systems, we consider the couple channel effect between systems with different orbital angular momentum. Most of the systems have binding solutions. The couple channel effect plays a significant role in the formation of some loosely bound states. The possible molecular states of $Ω_c^*Ω_c^*$ and $Ω_c^*\barΩ_c^*$ might be stable once produced.

hep-ph↗

arXiv2019

Active Learning for One-Class Classification Using Two One-Class Classifiers

This paper introduces a novel, generic active learning method for one-class classification. Active learning methods play an important role to reduce the efforts of manual labeling in the field of machine learning. Although many active learning approaches have been proposed during the last years, most of them are restricted on binary or multi-class problems. One-class classifiers use samples from only one class, the so-called target class, during training and hence require special active learning strategies. The few strategies proposed for one-class classification either suffer from their limitation on specific one-class classifiers or their performance depends on particular assumptions about datasets like imbalance. Our proposed method bases on using two one-class classifiers, one for the desired target class and one for the so-called outlier class. It allows to invent new query strategies, to use binary query strategies and to define simple stopping criteria. Based on the new method, two query strategies are proposed. The provided experiments compare the proposed approach with known strategies on various datasets and show improved results in almost all situations.

cs.LG↗

arXiv2019

Performance Analysis of Non-Orthogonal Multicast in Two-tier Heterogeneous Networks

With the explosive growth of mobile services, non-orthogonal broadcast/multicast transmissions can effectively improves spectrum efficiency. Nonorthogonal multiple access (NOMA) represents a paradigm shift from conventional orthogonal multiple-access (OMA) concepts and has been recognized as one of the key enabling technologies for fifth-generation (5G) mobile networks. In this paper, a two-tier heterogeneous network is studied, in which the wireless signal power is partitioned by the NOMA scheme. Moreover, the coverage probability, the average rate and the average QoE are derived to evaluate network performance. Simulation results show that compared with the OMA method, non-orthogonal broadcast/multicast method improve both the average user rate and QoE in the two-tier heterogeneous network.

eess.SP↗

arXiv2018

Macro pose based non-invasive thermal comfort perception for energy efficiency

Individual thermal comfort perception gives important feedback signals for energy efficient control of smart buildings. However, there is no effective method to measure real-time thermal comfort status of individual occupant until now. For overcoming this challenge, a novel macro posed based non-invasive perception method for thermal comfort (NIMAP) was presented. The occupant pose images were captured by normal phone camera (computer or cell phone) and the corresponding 2D coordinates can be obtained. Based on this, a novel pose recognition algorithm for thermal comfort, including 12 sub-algorithms, was presented. The 12 thermal comfort related macro poses can be recognized. Further, based on Fanger theory, 369 subjects were invited for subjective questionnaire survey. 3 human occupants participated in the validation of the proposed method and massive data were collected. All the 12 thermal comfort related poses can be recognized effectively.

cs.HC↗

arXiv2018

Non-invasive measuring method of skin temperature based on skin sensitivity index and deep learning

In human-centered intelligent building, real-time measurements of human thermal comfort play critical roles and supply feedback control signals for building heating, ventilation, and air conditioning (HVAC) systems. Due to the challenges of intra- and inter-individual differences and skin subtleness variations, there is no satisfactory solution for thermal comfort measurements until now. In this paper, a non-invasive measuring method based on skin sensitivity index and deep learning (NISDL) was proposed to measure real-time skin temperature. A new evaluating index, named skin sensitivity index (SSI), was defined to overcome individual differences and skin subtleness variations. To illustrate the effectiveness of SSI proposed, two multi-layers deep learning framework (NISDL method I and II) was designed and the DenseNet201 was used for extracting features from skin images. The partly personal saturation temperature (NIPST) algorithm was use for algorithm comparisons. Another deep learning algorithm without SSI (DL) was also generated for algorithm comparisons. Finally, a total of 1.44 million image data was used for algorithm validation. The results show that 55.6180% and 52.2472% error values (NISDL method I, II) are scattered at [0, 0.25), and the same error intervals distribution of NIPST is 35.3933%.

cs.CV↗

arXiv2018

Recurrent Multi-Graph Neural Networks for Travel Cost Prediction

Origin-destination (OD) matrices are often used in urban planning, where a city is partitioned into regions and an element (i, j) in an OD matrix records the cost (e.g., travel time, fuel consumption, or travel speed) from region i to region j. In this paper, we partition a day into multiple intervals, e.g., 96 15-min intervals and each interval is associated with an OD matrix which represents the costs in the interval; and we consider sparse and stochastic OD matrices, where the elements represent stochastic but not deterministic costs and some elements are missing due to lack of data between two regions. We solve the sparse, stochastic OD matrix forecasting problem. Given a sequence of historical OD matrices that are sparse, we aim at predicting future OD matrices with no empty elements. We propose a generic learning framework to solve the problem by dealing with sparse matrices via matrix factorization and two graph convolutional neural networks and capturing temporal dynamics via recurrent neural network. Empirical studies using two taxi datasets from different countries verify the effectiveness of the proposed framework.

cs.LG↗

arXiv2018

Non-invasive thermal comfort perception based on subtleness magnification and deep learning for energy efficiency

Human thermal comfort measurement plays a critical role in giving feedback signals for building energy efficiency. A non-invasive measuring method based on subtleness magnification and deep learning (NIDL) was designed to achieve a comfortable, energy efficient built environment. The method relies on skin feature data, e.g., subtle motion and texture variation, and a 315-layer deep neural network for constructing the relationship between skin features and skin temperature. A physiological experiment was conducted for collecting feature data (1.44 million) and algorithm validation. The non-invasive measurement algorithm based on a partly-personalized saturation temperature model (NIPST) was used for algorithm performance comparisons. The results show that the mean error and median error of the NIDL are 0.4834 Celsius and 0.3464 Celsius which is equivalent to accuracy improvements of 16.28% and 4.28%, respectively.

cs.HC↗

arXiv2018

Deep Neural Network inference with reduced word length

Deep neural networks (DNN) are powerful models for many pattern recognition tasks, yet their high computational complexity and memory requirement limit them to applications on high-performance computing platforms. In this paper, we propose a new method to evaluate DNNs trained with 32bit floating point (float32) accuracy using only low precision integer arithmetics in combination with binary shift and clipping operations. Because hardware implementation of these operations is much simpler than high precision floating point calculation, our method can be used for an efficient DNN inference on dedicated hardware. In experiments on MNIST, we demonstrate that DNNs trained with float32 can be evaluated using a combination of 2bit integer arithmetics and a few float32 calculations in each layer or only 3bit integer arithmetics in combination with binary shift and clipping without significant performance degradation.

cs.LG↗

arXiv2018

Adversarial Inpainting of Medical Image Modalities

Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of medical images via Generative Adversarial Networks (GANs). The proposed framework incorporates two patch-based discriminator networks with additional style and perceptual losses for the inpainting of missing information in realistically detailed and contextually consistent manner. The proposed framework outperformed other natural image inpainting techniques both qualitatively and quantitatively on two different medical modalities.

cs.CV↗

arXiv2018

Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs

Motion artifacts are a primary source of magnetic resonance (MR) image quality deterioration with strong repercussions on diagnostic performance. Currently, MR motion correction is carried out either prospectively, with the help of motion tracking systems, or retrospectively by mainly utilizing computationally expensive iterative algorithms. In this paper, we utilize a new adversarial framework, titled MedGAN, for the joint retrospective correction of rigid and non-rigid motion artifacts in different body regions and without the need for a reference image. MedGAN utilizes a unique combination of non-adversarial losses and a new generator architecture to capture the textures and fine-detailed structures of the desired artifact-free MR images. Quantitative and qualitative comparisons with other adversarial techniques have illustrated the proposed model performance.

cs.CV↗

arXiv2018

Correlated Time Series Forecasting using Deep Neural Networks: A Summary of Results

Cyber-physical systems often consist of entities that interact with each other over time. Meanwhile, as part of the continued digitization of industrial processes, various sensor technologies are deployed that enable us to record time-varying attributes (a.k.a., time series) of such entities, thus producing correlated time series. To enable accurate forecasting on such correlated time series, this paper proposes two models that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first model employs a CNN on each individual time series, combines the convoluted features, and then applies an RNN on top of the convoluted features in the end to enable forecasting. The second model adds additional auto-encoders into the individual CNNs, making the second model a multi-task learning model, which provides accurate and robust forecasting. Experiments on two real-world correlated time series data set suggest that the proposed two models are effective and outperform baselines in most settings. This report extends the paper "Correlated Time Series Forecasting using Multi-Task Deep Neural Networks," to appear in ACM CIKM 2018, by providing additional experimental results.

cs.LG↗

arXiv2018

Icy Grains from the Nucleus of Comet C/2013 US10 (Catalina)

We present IRTF/SpeX and NEOWISE observations of the dynamically new comet C/2013 US$_{10}$ (Catalina), hereafter US10, from 5.8 au inbound, to near perihelion at 1.3 au, and back to 5.0 au outbound. We detect water ice in the coma of US10, assess and monitor the physical properties of the ice as insolation varies with heliocentric distance, and investigate the relationship between water ice and CO$_{2}$. This set of measurements is unique in orbital coverage and can be used to infer the physical evolution of the ice and, potentially, the nucleus composition. We report (1) nearly identical near-infrared spectroscopic measurements of the coma at $-$5.8 au, $-$5.0 au, +3.9 au (where $<$0 au indicates pre-perihelion epochs), all presenting evidence of water-ice grains, (2) a dust-dominated coma at 1.3 au and 2.3 au and, (3) an increasing CO$_{2}$/$Afρ$ ratio from $-$4.9 au to 1.8 au. We propose that sublimation of the hyper-volatile CO$_{2}$ is responsible for dragging water-ice grains into the coma throughout the orbit. Once in the coma, the observability of the water-ice grains is controlled by the ice grain sublimation lifetime, which seems to require some small dust contaminant (i.e., non-pure ice grains). At |R$_{h}$|>=3.9 au, the ice grains are long-lived and may be unchanged since leaving the comet nucleus. We find the nucleus of comet US10 is made of, among other components, $\sim$1-micron water-ice grains containing up to 1% refractory materials.

astro-ph.EP↗