arXiv subjects

Yi Yang

Publications and source records attributed to Yi Yang.

At least 1,081 records · Page 60Linked to original sources

arXiv2017

Reactive infiltration instability amplifies the difference between geometric and reactive surface areas in natural porous materials

Reactive infiltration instability (RII) drives the development of many natural and engineered flow systems. These are encountered e.g. in hydraulic fracturing, geologic carbon storage and well stimulation in enhanced oil recovery. The surface area of the rocks changes as the pore structure evolves. We combined a reactor network model with grey scale tomography to seek the morphological interpretation for differences among geometric, reactive and apparent surface areas of dissolving natural porous materials. The approach allowed us to delineate the experimentally convoluted variables and study independently the effects of initial geometry and macroscopic flowrate. Simulations based on North Sea chalk microstructure showed that geometric surface not only serves as the interface for water-rock interactions but also represents the regional transport heterogeneities that can be amplified indefinitely by dissolutive percolation. Hence, RII leads to channelization of the solid matrix, which results in fluid focusing and an increase in geometric surface area. Fluid focusing reduces the reactive surface area and the residence time of reactants, both of which amplify the differences in question, i.e. they are self-supporting. Our results also suggested that the growing and merging of microchannels near the fluid entrance leads to the macroscopic "fast initial dissolution" of chemically homogeneous materials.

physics.geo-ph↗

arXiv2017

Dissolved CO2 stabilizes dissolution front and increases breakthrough porosity of natural porous materials

When reactive fluids flow through a dissolving porous medium, conductive channels form, leading to fluid breakthrough. This phenomenon is important in geologic carbon storage, where the dissolution of CO2 in water increases the acidity and produce microstructures significantly different from those in an intact reservoir. We demonstrate the controlling mechanism for the dissolution patterns in natural porous materials. This was done using numerical simulations based on high resolution digital models of North Sea chalk. We tested three model scenarios, and found that aqueous CO2 dissolve porous media homogeneously, leading to large breakthrough porosity. In contrast, CO2-free solution develops elongated convective channels in porous media, known as wormholes, and resulting in small breakthrough porosity. We further show that a homogeneous dissolution pattern appears because the sample size is smaller than the theoretical size of a developing wormhole. The result indicates that the presence of dissolved CO2 expands the reactive subvolume of a porous medium, and thus enhances the geochemical alteration of reservoir structures and might undermine the sealing integrity of caprocks when minerals dissolve.

physics.geo-ph↗

arXiv2017

Retraction of dissolution front in natural porous media

The dissolution of porous materials in a flow field shapes the morphologies of many geologic landscapes. Identifying the dissolution front, the interface between the reactive and the unreactive regions in a dissolving medium, is a prerequisite for studying dissolution kinetics. Despite its fundamental importance, the dynamics of a dissolution front in an evolving natural microstructure has never been reported. Here we show an unexpected spontaneous migration of the dissolution front against the pressure gradient of a flow field. This retraction stems from the infiltration instability induced surface generation, which can lead to a reactive surface dramatically greater than the ex situ geometric surface. The results are supported by a very good agreement between observations made with real time X-ray imaging and simulations based on static images of a rock determined by nanoCT. They both show that the in situ specific surface area of natural porous media is dependent on the flow field and reflects a balancing between surface generation and destruction. The reported dynamics challenge many long-held understanding of water-rock interactions and shed light on reconciling the discrepancies between field and laboratory measurements of reaction kinetics.

physics.geo-ph↗

arXiv2017

Approaching Confinement Structure for Light Quarks in a Holographic Soft Wall QCD Model

We study confinement-deconfinement phase transition in a holographic soft-wall QCD model. By solving the Einstein-Maxwell-scalar system analytically, we obtain the phase structure of the black hole backgrounds. We then impose probe open strings in such background to investigate the confinement-deconfinement phase transition from different open string configurations under various temperatures and chemical potentials. Furthermore, we study the Wilson loop by calculating the minimal surface of the probing open string world-sheet and obtain the Cornell potential in confinement phase analytically.

hep-th↗

arXiv2017

Temporal pattern of entropy production induced by reactive infiltration instability in natural porous media

The tendency of irreversible processes to generate entropy is the ultimate driving force for the evolution of nature. In engineering, entropy production is often used as a measure of usable energy losses. In this study we show that the analysis of the entropy production patterns can help understand the vastly diversified experimental observations of water-rock interactions in natural porous media. We first present a numerical scheme for the analysis of entropy production in dissolving porous media. Our scheme uses a greyscale digital model of natural chalk obtained by X-ray nanotomography. Greyscale models preserve structural heterogeneities with very high fidelity, which is essential for simulating a system dominated by infiltration instability. We focus on the coupling between two types of entropy production: the percolative entropy generated by dissipating the kinetic energy of fluid flow and the reactive entropy that originates from the consumption of chemical free energy. Their temporal patterns pinpoint three stages of microstructural evolution. We then show that the regional mixing deteriorates infiltration instability by reducing local variations in reactant distribution. In addition, we show that the microstructural evolution can be particularly sensitive to the initially present transport heterogeneities when the global flowrate is small. This dependence on flowrate indicates that the need to resolve the structural features of a porous system is greater when the residence time of the fluid is long.

physics.geo-ph↗

arXiv2017

An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning

We propose an end-to-end approach to the natural language object retrieval task, which localizes an object within an image according to a natural language description, i.e., referring expression. Previous works divide this problem into two independent stages: first, compute region proposals from the image without the exploration of the language description; second, score the object proposals with regard to the referring expression and choose the top-ranked proposals. The object proposals are generated independently from the referring expression, which makes the proposal generation redundant and even irrelevant to the referred object. In this work, we train an agent with deep reinforcement learning, which learns to move and reshape a bounding box to localize the object according to the referring expression. We incorporate both the spatial and temporal context information into the training procedure. By simultaneously exploiting local visual information, the spatial and temporal context and the referring language a priori, the agent selects an appropriate action to take at each time. A special action is defined to indicate when the agent finds the referred object, and terminate the procedure. We evaluate our model on various datasets, and our algorithm significantly outperforms the compared algorithms. Notably, the accuracy improvement of our method over the recent method GroundeR and SCRC on the ReferItGame dataset are 7.67% and 18.25%, respectively.

cs.CV↗

arXiv2017

Fully Convolutional Attention Networks for Fine-Grained Recognition

Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses. A key to address this problem is to localize discriminative parts to extract pose-invariant features. However, ground-truth part annotations can be expensive to acquire. Moreover, it is hard to define parts for many fine-grained classes. This work introduces Fully Convolutional Attention Networks (FCANs), a reinforcement learning framework to optimally glimpse local discriminative regions adaptive to different fine-grained domains. Compared to previous methods, our approach enjoys three advantages: 1) the weakly-supervised reinforcement learning procedure requires no expensive part annotations; 2) the fully-convolutional architecture speeds up both training and testing; 3) the greedy reward strategy accelerates the convergence of the learning. We demonstrate the effectiveness of our method with extensive experiments on four challenging fine-grained benchmark datasets, including CUB-200-2011, Stanford Dogs, Stanford Cars and Food-101.

cs.CV↗

arXiv2017

Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval

This paper contributes a new large-scale dataset for weakly supervised cross-media retrieval, named Twitter100k. Current datasets, such as Wikipedia, NUS Wide and Flickr30k, have two major limitations. First, these datasets are lacking in content diversity, i.e., only some pre-defined classes are covered. Second, texts in these datasets are written in well-organized language, leading to inconsistency with realistic applications. To overcome these drawbacks, the proposed Twitter100k dataset is characterized by two aspects: 1) it has 100,000 image-text pairs randomly crawled from Twitter and thus has no constraint in the image categories; 2) text in Twitter100k is written in informal language by the users. Since strongly supervised methods leverage the class labels that may be missing in practice, this paper focuses on weakly supervised learning for cross-media retrieval, in which only text-image pairs are exploited during training. We extensively benchmark the performance of four subspace learning methods and three variants of the Correspondence AutoEncoder, along with various text features on Wikipedia, Flickr30k and Twitter100k. Novel insights are provided. As a minor contribution, inspired by the characteristic of Twitter100k, we propose an OCR-based cross-media retrieval method. In experiment, we show that the proposed OCR-based method improves the baseline performance.

cs.CV↗

arXiv2017

A New Evaluation Protocol and Benchmarking Results for Extendable Cross-media Retrieval

This paper proposes a new evaluation protocol for cross-media retrieval which better fits the real-word applications. Both image-text and text-image retrieval modes are considered. Traditionally, class labels in the training and testing sets are identical. That is, it is usually assumed that the query falls into some pre-defined classes. However, in practice, the content of a query image/text may vary extensively, and the retrieval system does not necessarily know in advance the class label of a query. Considering the inconsistency between the real-world applications and laboratory assumptions, we think that the existing protocol that works under identical train/test classes can be modified and improved. This work is dedicated to addressing this problem by considering the protocol under an extendable scenario, \ie, the training and testing classes do not overlap. We provide extensive benchmarking results obtained by the existing protocol and the proposed new protocol on several commonly used datasets. We demonstrate a noticeable performance drop when the testing classes are unseen during training. Additionally, a trivial solution, \ie, directly using the predicted class label for cross-media retrieval, is tested. We show that the trivial solution is very competitive in traditional non-extendable retrieval, but becomes less so under the new settings. The train/test split, evaluation code, and benchmarking results are publicly available on our website.

cs.CV↗

arXiv2017

A Discriminatively Learned CNN Embedding for Person Re-identification

We revisit two popular convolutional neural networks (CNN) in person re-identification (re-ID), i.e, verification and classification models. The two models have their respective advantages and limitations due to different loss functions. In this paper, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a new siamese network that simultaneously computes identification loss and verification loss. Given a pair of training images, the network predicts the identities of the two images and whether they belong to the same identity. Our network learns a discriminative embedding and a similarity measurement at the same time, thus making full usage of the annotations. Albeit simple, the learned embedding improves the state-of-the-art performance on two public person re-ID benchmarks. Further, we show our architecture can also be applied in image retrieval.

cs.CV↗

arXiv2017

The Sizes and Depletions of the Dust and Gas Cavities in the Transitional Disk J160421.7-213028

We report ALMA Cycle 2 observations of 230 GHz (1.3 mm) dust continuum emission, and $^{12}$CO, $^{13}$CO, and C$^{18}$O J = 2-1 line emission, from the Upper Scorpius transitional disk [PZ99] J160421.7-213028, with an angular resolution of ~0".25 (35 AU). Armed with these data and existing H-band scattered light observations, we measure the size and depth of the disk's central cavity, and the sharpness of its outer edge, in three components: sub-$μ$m-sized "small" dust traced by scattered light, millimeter-sized "big" dust traced by the millimeter continuum, and gas traced by line emission. Both dust populations feature a cavity of radius $\sim$70 AU that is depleted by factors of at least 1000 relative to the dust density just outside. The millimeter continuum data are well explained by a cavity with a sharp edge. Scattered light observations can be fitted with a cavity in small dust that has either a sharp edge at 60 AU, or an edge that transitions smoothly over an annular width of 10 AU near 60 AU. In gas, the data are consistent with a cavity that is smaller, about 15 AU in radius, and whose surface density at 15 AU is $10^{3\pm1}$ times smaller than the surface density at 70 AU; the gas density grades smoothly between these two radii. The CO isotopologue observations rule out a sharp drop in gas surface density at 30 AU or a double-drop model as found by previous modeling. Future observations are needed to assess the nature of these gas and dust cavities, e.g., whether they are opened by multiple as-yet-unseen planets or photoevaporation.

astro-ph.SR↗

arXiv2017

Pose Invariant Embedding for Deep Person Re-identification

Pedestrian misalignment, which mainly arises from detector errors and pose variations, is a critical problem for a robust person re-identification (re-ID) system. With bad alignment, the background noise will significantly compromise the feature learning and matching process. To address this problem, this paper introduces the pose invariant embedding (PIE) as a pedestrian descriptor. First, in order to align pedestrians to a standard pose, the PoseBox structure is introduced, which is generated through pose estimation followed by affine transformations. Second, to reduce the impact of pose estimation errors and information loss during PoseBox construction, we design a PoseBox fusion (PBF) CNN architecture that takes the original image, the PoseBox, and the pose estimation confidence as input. The proposed PIE descriptor is thus defined as the fully connected layer of the PBF network for the retrieval task. Experiments are conducted on the Market-1501, CUHK03, and VIPeR datasets. We show that PoseBox alone yields decent re-ID accuracy and that when integrated in the PBF network, the learned PIE descriptor produces competitive performance compared with the state-of-the-art approaches.

cs.CV↗

arXiv2017

Subaru/SCExAO First-Light Direct Imaging of a Young Debris Disk around HD 36546

We present $H$-band scattered light imaging of a bright debris disk around the A0 star HD 36546 obtained from the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) system with data recorded by the HiCIAO camera using the vector vortex coronagraph. SCExAO traces the disk from $r$ $\sim$ 0.3" to $r$ $\sim$ 1" (34--114 au). The disk is oriented in a near east-west direction (PA $\sim$ 75$^{o}$), is inclined by $i$ $\sim$ 70--75$^{o}$ and is strongly forward-scattering ($g$ $>$ 0.5). It is an extended disk rather than a sharp ring; a second, diffuse dust population extends from the disk's eastern side. While HD 36546 intrinsic properties are consistent with a wide age range ($t$ $\sim$ 1--250 $Myr$), its kinematics and analysis of coeval stars suggest a young age (3--10 $Myr$) and a possible connection to Taurus-Auriga's star formation history. SCExAO's planet-to-star contrast ratios are comparable to the first-light Gemini Planet Imager contrasts; for an age of 10 $Myr$, we rule out planets with masses comparable to HR 8799 b beyond a projected separation of 23 au. A massive icy planetesimal disk or an unseen superjovian planet at $r$ $>$ 20 au may explain the disk's visibility. The HD 36546 debris disk may be the youngest debris disk yet imaged, is the first newly-identified object from the now-operational SCExAO extreme AO system, is ideally suited for spectroscopic follow up with SCExAO/CHARIS in 2017, and may be a key probe of icy planet formation and planet-disk interactions.

astro-ph.EP↗

arXiv2016

Low-loss Plasmonic Dielectric Nanoresonators

Material losses in metals are a central bottleneck in plasmonics for many applications. Here we propose and theoretically demonstrate that metal losses can be successfully mitigated with dielectric particles on metallic films, giving rise to hybrid dielectric--metal resonances. In the far field, they yield strong and efficient scattering, beyond even the theoretical limits of all-metal and all-dielectric structures. In the near field, they offer high-Purcell-factor (${>}5000$), high-quantum-efficiency (${>}90\%$), and highly directional emission at visible and infrared wavelengths. Their quality factors can be readily tailored from plasmonic-like (${\sim}10$) to dielectric-like (${\sim}10^3$), with wide control over the individual resonant coupling to photon, plasmon, and dissipative channels. Compared with conventional plasmonic nanostructures, such resonances show robustness against detrimental nonlocal effects and provide higher field enhancement at extreme nanoscopic sizes and spacings. These hybrid resonances equip plasmonics with high efficiency, which has been the predominant goal since the field's inception.

physics.optics↗

arXiv2016

Review on High energy String Scattering Amplitudes and Symmetries of String Theory

We review high energy symmetries of string theory at both the fixed angle or Gross regime (GR) and the fixed momentum transfer or Regge regime (RR). We calculated in details high energy string scattering amplitudes at arbitrary mass levels for both regimes. We discovered infinite linear relations among fixed angle string amplitudes conjectured by Gross in 1988 from decoupling of high energy zero-norm states (ZNS), and infinite recurrence relations among Regge string amplitudes from Kummer function U and Appell function F_1. However, the linear relations we obtained in the GR corrected [27-32] the saddle point calculations of Gross, Gross and Mende and Gross and Manes [1-5]. Our results were consistent with the decoupling of high energy ZNS or unitarity of the theory while those of them were not. In addition, for the case of high energy closed string scatterings, our results [36] differ from theirs by an oscillating prefactor which was crucial to recover the KLT relation valid for all energies. In the GR/RR regime, all high energy string amplitudes can be solved by these linear/recurrence relations so that all GR/RR string amplitudes can be expressed in terms of one single GR/RR string amplitude. In addition, we found an interesting link between string amplitudes of the two regimes, and discovered that at each mass level the ratios among fixed angle amplitudes can be extracted from Regge string scattering amplitudes. This result enables us to argue that the known SL(5,C) dynamical symmetry of the Appell function F_1 is crucial to probe high energy spacetime symmetry of string theory.

hep-th↗

arXiv2016

Few-Shot Object Recognition from Machine-Labeled Web Images

With the tremendous advances of Convolutional Neural Networks (ConvNets) on object recognition, we can now obtain reliable enough machine-labeled annotations easily by predictions from off-the-shelf ConvNets. In this work, we present an abstraction memory based framework for few-shot learning, building upon machine-labeled image annotations. Our method takes some large-scale machine-annotated datasets (e.g., OpenImages) as an external memory bank. In the external memory bank, the information is stored in the memory slots with the form of key-value, where image feature is regarded as key and label embedding serves as value. When queried by the few-shot examples, our model selects visually similar data from the external memory bank, and writes the useful information obtained from related external data into another memory bank, i.e., abstraction memory. Long Short-Term Memory (LSTM) controllers and attention mechanisms are utilized to guarantee the data written to the abstraction memory is correlated to the query example. The abstraction memory concentrates information from the external memory bank, so that it makes the few-shot recognition effective. In the experiments, we firstly confirm that our model can learn to conduct few-shot object recognition on clean human-labeled data from ImageNet dataset. Then, we demonstrate that with our model, machine-labeled image annotations are very effective and abundant resources to perform object recognition on novel categories. Experimental results show that our proposed model with machine-labeled annotations achieves great performance, only with a gap of 1% between of the one with human-labeled annotations.

cs.CV↗

arXiv2016

Bidirectional Multirate Reconstruction for Temporal Modeling in Videos

Despite the recent success of neural networks in image feature learning, a major problem in the video domain is the lack of sufficient labeled data for learning to model temporal information. In this paper, we propose an unsupervised temporal modeling method that learns from untrimmed videos. The speed of motion varies constantly, e.g., a man may run quickly or slowly. We therefore train a Multirate Visual Recurrent Model (MVRM) by encoding frames of a clip with different intervals. This learning process makes the learned model more capable of dealing with motion speed variance. Given a clip sampled from a video, we use its past and future neighboring clips as the temporal context, and reconstruct the two temporal transitions, i.e., present$\rightarrow$past transition and present$\rightarrow$future transition, reflecting the temporal information in different views. The proposed method exploits the two transitions simultaneously by incorporating a bidirectional reconstruction which consists of a backward reconstruction and a forward reconstruction. We apply the proposed method to two challenging video tasks, i.e., complex event detection and video captioning, in which it achieves state-of-the-art performance. Notably, our method generates the best single feature for event detection with a relative improvement of 10.4% on the MEDTest-13 dataset and achieves the best performance in video captioning across all evaluation metrics on the YouTube2Text dataset.

cs.CV↗

arXiv2016

Near-Infrared Imaging Polarimetry of Inner Region of GG Tau A Disk

By performing non-masked polarization imaging with Subaru/HiCIAO, polarized scattered light from the inner region of the disk around the GG Tau A system was successfully detected in the $H$ band with a spatial resolution of approximately 0.07$\arcsec$, revealing the complicated inner disk structures around this young binary. This paper reports the observation of an arc-like structure to the north of GG Tau Ab and part of a circumstellar structure that is noticeable around GG Tau Aa extending to a distance of approximately 28 AU from the primary star. The speckle noise around GG Tau Ab constrains its disk radius to <13 AU. Based on the size of the circumbinary ring and the circumstellar disk around GG Tau Aa, the semi-major axis of the binary's orbit is likely to be 62 AU. A comparison of the present observations with previous ALMA and near-infrared (NIR) H$_2$ emission observations suggests that the north arc could be part of a large streamer flowing from the circumbinary ring to sustain the circumstellar disks. According to the previous studies, the circumstellar disk around GG Tau Aa has enough mass and can sustain itself for a duration sufficient for planet formation; thus, our study indicates that planets can form within close (separation $\lesssim$ 100 AU) young binary systems.

astro-ph.EP↗