arXiv subjects

Yi Yang

Publications and source records attributed to Yi Yang.

At least 991 records · Page 55Linked to original sources

arXiv2018

Imprints of Early Universe on Gravitational Waves from First-Order Phase Transition in QCD

We study the gravitational wave spectrum from the confinement-deconfinement phase transi-tion in the holographic QCD models for both light quark and heavy quark. We obtain the gravitationalwave spectrum in the deflagration, detonation and runaway cases for different chemical potentials and duration of the phase transition. We find that the gravitational wave spectrum could be observed by EPTA/IPTA and eLISA for large chemical potential and short duartion of phase transtions.

hep-th↗

arXiv2018

Isochronal age-mass discrepancy of young stars: SCExAO/CHARIS integral field spectroscopy of the HIP 79124 triple system

We present SCExAO/CHARIS 1.1--2.4 micron integral field direct spectroscopy of the young HIP 79124 triple system. HIP 79124 is a member of the Scorpius-Centaurus association, consisting of an A0V primary with two low-mass companions at a projected separation of <1 arcsecond. Thanks to the high quality wavefront corrections provided by SCExAO, both companions are decisively detected without the employment of any PSF-subtraction algorithm to eliminate quasi-static noise. The spectrum of the outer C object is very well matched by Upper Scorpius M4 pm 0.5 standard spectra, with a Teff = 2945 pm 100 and a mass of 350 MJup. HIP 79124 B is detected at a separation of only 180 mas in a highly-correlated noise regime, and it falls in the spectral range M6 pm 0.5 with Teff = 2840 pm 190 and 100 MJup. Previous studies of stellar populations in Sco-Cen have highlighted a discrepancy in isochronal ages between the lower-mass and higher-mass populations. This could be explained either by an age spread in the region, or by conventional isochronal models failing to reproduce the evolution of low-mass stars. The HIP 79124 system should be coeval, and therefore it provides an ideal laboratory to test these scenarios. We place the three components in a color-magnitude diagram and find that the models predict a younger age for the two low-mass companions (3 Myr) than for the primary star (6 Myr). These results imply that the omission of magnetic effects in conventional isochronal models inhibit them from reproducing early low-mass stellar evolution, which is further supported by the fact that new models that include such effects provide more consistent ages in the HIP 79124 system.

astro-ph.EP↗

arXiv2018

Experimental observation of superscattering

Superscattering, induced by degenerate resonances, breaks the fundamental single-channel limit of scattering cross section of subwavelength structures; in principle, an arbitrarily large total cross section can be achieved via superscattering. It thus provides a unique way to strengthen the light-matter interaction at the subwavelength scale, and has many potential applications in sensing, energy harvesting, bio-imaging (such as magnetic resonance imaging), communication and optoelectronics. However, the experimental demonstration of superscattering remains an open challenge due to its vulnerability to structural imperfections and intrinsic material losses. Here we report the first experimental evidence for superscattering, by demonstrating the superscattering simultaneously in two different frequency regimes through both the far-field and near-field measurements. The underlying mechanism for the observed superscattering is the degenerate resonances of confined surface waves, by utilizing a subwavelength metasurface-based multilayer structure. Our work paves the way towards practical applications based on superscattering.

physics.class-ph↗

arXiv2018

Personalized Video Recommendation Using Rich Contents from Videos

Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.

cs.IR↗

arXiv2018

DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments

Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing, and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in the real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved by one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments. Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLAM

cs.RO↗

arXiv2018

A solid approach to biopharmaceutical stabilisation

Ensilication is a technology we developed that can physically stabilise proteins in silica without use of a pre-formed particle matrix. Stabilisation is done by tailor fitting individual proteins with a silica coat using a modified sol-gel process. Biopharmaceuticals, for example, liquid-formulated vaccines with adjuvants, have poor thermal stability. Heating or freezing impairs their potency. As a result, there is an increase in the prevalence of vaccine-preventable diseases in low-income countries even when there are means to combat them. One of the root causes lies in the problematic vaccine cold-chain distribution. We believe that ensilication can improve vaccine availability by enabling transportation without refrigeration. Here, we show that ensilication stabilises tetanus toxoid C fragment (TTCF) and demonstrate that this material can be stored and transported at ambient temperature without compromising the immunogenic properties of TTCF in vivo. TTCF is a component of the diphtheria, tetanus and pertussis (DTP) vaccine. To further our understanding of the ensilication process, and its protective effect on proteins we have studied the formation of TTCF-silica nanoparticles via time-resolved Small Angle X-ray Scattering (SAXS). Our results reveal ensilication to be a staged diffusion-limited cluster aggregation (DLCA) type reaction, induced by the presence of TTCF protein at neutral pH. Analysis of scattering data indicates tailor fitting of TTCF protein. The experimental in vivo immunisation data confirms the retention of immunogenicity after release from silica. Our results suggest that we could utilise this technology for multicomponent vaccines, therapeutics or other biopharmaceuticals that are not compatible with lyophilisation.

physics.bio-ph↗

arXiv2018

A Core Decomposition of Compact Sets in the Plane

A Peano continuum means a locally connected continuum. A compact metric space is called a \emph{Peano compactum} if all its components are Peano continua and if for any constant $C>0$ all but finitely many of its components are of diameter less than $C$. Given a compact set $K\subset\mathbb{C}$, there usually exist several upper semi-continuous decompositions of $K$ into subcontinua such that the quotient space, equipped with the quotient topology, is a Peano compactum. We prove that one of these decompositions is finer than all the others and call it the \emph{core decomposition of $K$ with Peano quotient}. This core decomposition gives rise to a metrizable quotient space, called the Peano model of $K$, which is shown to be determined by the topology of $K$ and hence independent of the embedding of $K$ into $\mathbb{C}$. We also construct a concrete continuum $K\subset\mathbb{R}^3$ such that the core decomposition of $K$ with Peano quotient does not exist. For specific choices of $K\subset\mathbb{C}$, the above mentioned core decomposition coincides with two models obtained recently, namely the locally connected model for unshielded planar continua (like connected Julia sets of polynomials) and the finitely Suslinian model for unshielded planar compact sets (like polynomial Julia sets that may not be connected). The study of such a core decomposition provides partial answers to several questions posed by Curry in 2010. These questions are motivated by other works, including those by Curry and his coauthors, that aim at understanding the dynamics of a rational map $f: \hat{\mathbb{C}}\rightarrow\hat{\mathbb{C}}$ restricted to its Julia set.

math.DS↗

arXiv2018

Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound

Contrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It has recently been used to enable imaging with unprecedented subwavelength spatial resolution by relying on super-resolution techniques. A typical preprocessing step in super-resolution ultrasound is to separate the microbubble signal from the cluttering tissue signal. This step has a crucial impact on the final image quality. Here, we propose a new approach to clutter removal based on robust principle component analysis (PCA) and deep learning. We begin by modeling the acquired contrast enhanced ultrasound signal as a combination of a low rank and sparse components. This model is used in robust PCA and was previously suggested in the context of ultrasound Doppler processing and dynamic magnetic resonance imaging. We then illustrate that an iterative algorithm based on this model exhibits improved separation of microbubble signal from the tissue signal over commonly practiced methods. Next, we apply the concept of deep unfolding to suggest a deep network architecture tailored to our clutter filtering problem which exhibits improved convergence speed and accuracy with respect to its iterative counterpart. We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.

cs.LG↗

arXiv2018

Multistable Kirigami for Tunable Architected Materials

In nature, materials such as ferroelastics and multiferroics can switch their microstructure in response to external stimuli, and this reconfiguration causes a simultaneous modulation of its material properties. Rapid prototyping technologies have enabled origami and kirigami-inspired architected materials to provide a means for designing shape-shifting structures, and here we show how multistable structures inspired by kirigami provide novel design criteria for preparing mechanical metamaterials with tunable properties. By changing the geometry of kirigami unit cells, we obtain multistable kirigami lattice structures endowed with a bistable snap-through mechanism. We demonstrate the precise control of material stiffness, along with the ability to tune this property in situ by locally and reversibly switching the unit cell configurations. We anticipate these mechanical metamaterials will provide a platform to achieve in situ tunable electrical, optical, and mechanical properties for a variety of applications in multifunctional materials, two-dimensional materials, and soft robotics.

physics.app-ph↗

arXiv2018

3D Pose Estimation for Fine-Grained Object Categories

Existing object pose estimation datasets are related to generic object types and there is so far no dataset for fine-grained object categories. In this work, we introduce a new large dataset to benchmark pose estimation for fine-grained objects, thanks to the availability of both 2D and 3D fine-grained data recently. Specifically, we augment two popular fine-grained recognition datasets (StanfordCars and CompCars) by finding a fine-grained 3D CAD model for each sub-category and manually annotating each object in images with 3D pose. We show that, with enough training data, a full perspective model with continuous parameters can be estimated using 2D appearance information alone. We achieve this via a framework based on Faster/Mask R-CNN. This goes beyond previous works on category-level pose estimation, which only estimate discrete/continuous viewpoint angles or recover rotation matrices often with the help of key points. Furthermore, with fine-grained 3D models available, we incorporate a dense 3D representation named as location field into the CNN-based pose estimation framework to further improve the performance. The new dataset is available at www.umiacs.umd.edu/~wym/3dpose.html

cs.CV↗

arXiv2018

Zero-Shot Transfer VQA Dataset

Acquiring a large vocabulary is an important aspect of human intelligence. Onecommon approach for human to populating vocabulary is to learn words duringreading or listening, and then use them in writing or speaking. This ability totransfer from input to output is natural for human, but it is difficult for machines.Human spontaneously performs this knowledge transfer in complicated multimodaltasks, such as Visual Question Answering (VQA). In order to approach human-levelArtificial Intelligence, we hope to equip machines with such ability. Therefore, toaccelerate this research, we propose a newzero-shot transfer VQA(ZST-VQA)dataset by reorganizing the existing VQA v1.0 dataset in the way that duringtraining, some words appear only in one module (i.e. questions) but not in theother (i.e. answers). In this setting, an intelligent model should understand andlearn the concepts from one module (i.e. questions), and at test time, transfer themto the other (i.e. predict the concepts as answers). We conduct evaluation on thisnew dataset using three existing state-of-the-art VQA neural models. Experimentalresults show a significant drop in performance on this dataset, indicating existingmethods do not address the zero-shot transfer problem. Besides, our analysis findsthat this may be caused by the implicit bias learned during training.

cs.AI↗

arXiv2018

Few-Example Object Detection with Model Communication

In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-example object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07, MS COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.

cs.CV↗

arXiv2018

SCExAO/CHARIS Near-IR High-Contrast Imaging and Integral Field Spectroscopy of the HIP 79977 Debris Disk

We present new, near-infrared (1.1--2.4 $μm$) high-contrast imaging of the bright debris disk surrounding HIP 79977 with the Subaru Coronagraphic Extreme Adaptive Optics system (SCExAO) coupled with the CHARIS integral field spectrograph. SCExAO/CHARIS resolves the disk down to smaller angular separations of (0.11"; $r \sim 14$ au) and at a higher significance than previously achieved at the same wavelengths. The disk exhibits a marginally significant east-west brightness asymmetry in $H$ band that requires confirmation. Geometrical modeling suggests a nearly edge-on disk viewed at a position angle of $\sim$ 114.6$^{o}$ east of north. The disk is best-fit by scattered-light models assuming strongly forward-scattering grains ($g$ $\sim$ 0.5--0.65) confined to a torus with a peak density at $r_{0}$ $\sim$ 53--75 au. We find that a shallow outer density power law of $α_{out}=$-1-- -3 and flare index of $β= 1$ are preferred. Other disk parameters (e.g.~inner density power law and vertical scale height) are more poorly constrained. The disk has a slightly blue intrinsic color and its profile is broadly consistent with predictions from birth ring models applied to other debris disks. While HIP 79977's disk appears to be more strongly forward-scattering than most resolved disks surrounding 5--30 Myr-old stars, this difference may be due to observational biases favoring forward-scattering models for inclined disks vs. lower inclination, ostensibly neutral-scattering disks like HR 4796A's. Deeper, higher signal-to-noise SCExAO/CHARIS data can better constrain the disk's dust composition.

astro-ph.EP↗

arXiv2018

Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories

Existing 3D pose datasets of object categories are limited to generic object types and lack of fine-grained information. In this work, we introduce a new large-scale dataset that consists of 409 fine-grained categories and 31,881 images with accurate 3D pose annotation. Specifically, we augment three existing fine-grained object recognition datasets (StanfordCars, CompCars and FGVC-Aircraft) by finding a specific 3D model for each sub-category from ShapeNet and manually annotating each 2D image by adjusting a full set of 7 continuous perspective parameters. Since the fine-grained shapes allow 3D models to better fit the images, we further improve the annotation quality by initializing from the human annotation and conducting local search of the pose parameters with the objective of maximizing the IoUs between the projected mask and the segmentation reference estimated from state-of-the-art deep Convolutional Neural Networks (CNNs). We provide full statistics of the annotations with qualitative and quantitative comparisons suggesting that our dataset can be a complementary source for studying 3D pose estimation. The dataset can be downloaded at http://users.umiacs.umd.edu/~wym/3dpose.html.

cs.CV↗

arXiv2018

Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos

Learning depth and optical flow via deep neural networks by watching videos has made significant progress recently. In this paper, we jointly solve the two tasks by exploiting the underlying geometric rules within stereo videos. Specifically, given two consecutive stereo image pairs from a video, we first estimate depth, camera ego-motion and optical flow from three neural networks. Then the whole scene is decomposed into moving foreground and static background by compar- ing the estimated optical flow and rigid flow derived from the depth and ego-motion. We propose a novel consistency loss to let the optical flow learn from the more accurate rigid flow in static regions. We also design a rigid alignment module which helps refine ego-motion estimation by using the estimated depth and optical flow. Experiments on the KITTI dataset show that our results significantly outperform other state-of- the-art algorithms. Source codes can be found at https: //github.com/baidu-research/UnDepthflow

cs.CV↗

arXiv2018

Learning Discriminators as Energy Networks in Adversarial Learning

We propose a novel framework for structured prediction via adversarial learning. Existing adversarial learning methods involve two separate networks, i.e., the structured prediction models and the discriminative models, in the training. The information captured by discriminative models complements that in the structured prediction models, but few existing researches have studied on utilizing such information to improve structured prediction models at the inference stage. In this work, we propose to refine the predictions of structured prediction models by effectively integrating discriminative models into the prediction. Discriminative models are treated as energy-based models. Similar to the adversarial learning, discriminative models are trained to estimate scores which measure the quality of predicted outputs, while structured prediction models are trained to predict contrastive outputs with maximal energy scores. In this way, the gradient vanishing problem is ameliorated, and thus we are able to perform inference by following the ascent gradient directions of discriminative models to refine structured prediction models. The proposed method is able to handle a range of tasks, e.g., multi-label classification and image segmentation. Empirical results on these two tasks validate the effectiveness of our learning method.

cs.CV↗

arXiv2018

Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning

Graph convolutional network (GCN) provides a powerful means for graph-based semi-supervised tasks. However, as a localized first-order approximation of spectral graph convolution, the classic GCN can not take full advantage of unlabeled data, especially when the unlabeled node is far from labeled ones. To capitalize on the information from unlabeled nodes to boost the training for GCN, we propose a novel framework named Self-Ensembling GCN (SEGCN), which marries GCN with Mean Teacher - another powerful model in semi-supervised learning. SEGCN contains a student model and a teacher model. As a student, it not only learns to correctly classify the labeled nodes, but also tries to be consistent with the teacher on unlabeled nodes in more challenging situations, such as a high dropout rate and graph collapse. As a teacher, it averages the student model weights and generates more accurate predictions to lead the student. In such a mutual-promoting process, both labeled and unlabeled samples can be fully utilized for backpropagating effective gradients to train GCN. In three article classification tasks, i.e. Citeseer, Cora and Pubmed, we validate that the proposed method matches the state of the arts in the classification accuracy.

cs.LG↗

arXiv2018

Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics

Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 90% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first implementation reported of a multidomain prognostic model based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness. We therefore suggest that this novel prognostic model is accurate, robust, and interpretable.

q-bio.NC↗