Detecting Visual Relationships with Deep Relational Networks

Relationships among objects play a crucial role in image understanding. Despite the great success of deep learning techniques in recognizing individual objects, reasoning about the relationships among objects remains a challenging task. Previous methods often treat this as a classification problem, considering each type of relationship (e.g. "ride") or each distinct visual phrase (e.g. "person-ride-horse") as a category. Such approaches are faced with significant difficulties caused by the high diversity of visual appearance for each kind of relationships or the large number of distinct visual phrases. We propose an integrated framework to tackle this problem. At the heart of this framework is the Deep Relational Network, a novel formulation designed specifically for exploiting the statistical dependencies between objects and their relationships. On two large datasets, the proposed method achieves substantial improvement over state-of-the-art.Comment: To be appeared in CVPR 2017 as an oral pape

A mechanism for the latitudinal dependence of peak-spectrum sea surface height variability

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 119 (2014): 1431–1444, doi:10.1002/2013JC009642.Previous studies have shown that the power spectrum of satellite-observed sea surface height (SSH) variability peaks at a certain frequency (or a wave number) band at a given latitude. Lin et al. (2008) attributed this latitudinal dependence to the critical frequency of the first baroclinic mode Rossby waves in the tropical and subtropical oceans. Their study was based on the linear Rossby wave theory and focused on SSH variability in the tropical and subtropical oceans since the altimetry data do not adequately resolve lengths of baroclinic Rossby waves at and near the critical frequency in high latitudes. In this study, we expand their analysis to high-latitude oceanic basins and to include nonlinear eddy effects, by using a linear wave model and a high-resolution model output from the OGCM for the Earth Simulator (OFES). It is found that the linear wave mechanism by and large remains valid in the tropical and subtropical oceans. In higher latitudes as well as in some regions in the western tropical and subtropical oceans, other mechanisms, like nonlinear eddy, play more important role in determining the SSH variability.This work was supported by the China’s National Basic Research Priorities Programmer (2013CB956202), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA11010103), the Natural Science Foundation of China (41222037 and 41221063), the project of Global Change and Air-Sea interaction (GASI-03-01-01–02), the Ministry of Education’s 111 Project (B07036), the National Natural Science Foundation of Shandong (JQ201111), and the National Special Research Fund for Non-Profit Marine Sector (201205018). J. Y. is supported by US NSF (OCE 0927017 and OCE 1028739).2014-08-2

Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space

Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods build face graphs mainly according to kNN relations in the feature space, which may lead to a lot of noise edges connecting two faces of different classes. The face features will be polluted when messages pass along these noise edges, thus degrading the performance of GCNs. In this paper, a novel algorithm named Ada-NETS is proposed to cluster faces by constructing clean graphs for GCNs. In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images. Then, an adaptive neighbour discovery strategy is proposed to determine a proper number of edges connecting to each face image. It significantly reduces the noise edges while maintaining the good ones to build a graph with clean yet rich edges for GCNs to cluster faces. Experiments on multiple public clustering datasets show that Ada-NETS significantly outperforms current state-of-the-art methods, proving its superiority and generalization. Code is available at https://github.com/damo-cv/Ada-NETS

Electron dynamics in topological insulator based semiconductor-metal interfaces (topological p-n interface based on Bi2Se3 class)

Single-Dirac-cone topological insulators (TI) are the first experimentally discovered class of three dimensional topologically ordered electronic systems, and feature robust, massless spin-helical conducting surface states that appear at any interface between a topological insulator and normal matter that lacks the topological insulator ordering. This topologically defined surface environment has been theoretically identified as a promising platform for observing a wide range of new physical phenomena, and possesses ideal properties for advanced electronics such as spin-polarized conductivity and suppressed scattering. A key missing step in enabling these applications is to understand how topologically ordered electrons respond to the interfaces and surface structures that constitute a device. Here we explore this question by using the surface deposition of cathode (Cu/In/Fe) and anode materials (NO$_2$) and control of bulk doping in Bi$_2$Se$_3$ from P-type to N-type charge transport regimes to generate a range of topological insulator interface scenarios that are fundamental to device development. The interplay of conventional semiconductor junction physics and three dimensional topological electronic order is observed to generate novel junction behaviors that go beyond the doped-insulator paradigm of conventional semiconductor devices and greatly alter the known spin-orbit interface phenomenon of Rashba splitting. Our measurements for the first time reveal new classes of diode-like configurations that can create a gap in the interface electron density near a topological Dirac point and systematically modify the topological surface state Dirac velocity, allowing far reaching control of spin-textured helical Dirac electrons inside the interface and creating advantages for TI superconductors as a Majorana fermion platform over spin-orbit semiconductors.Comment: 14 pages, 4 Figure

Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation

The capability to transfer mastered skills to accomplish a range of similar yet novel tasks is crucial for intelligent robots. In this work, we introduce $\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics simulation to efficiently transfer robotic skills. Specifically, $\textit{Diff-Transfer}$ discovers a feasible path within the task space that brings the source task to the target task. At each pair of adjacent points along this task path, which is two sub-tasks, $\textit{Diff-Transfer}$ adapts known actions from one sub-task to tackle the other sub-task successfully. The adaptation is guided by the gradient information from differentiable physics simulations. We propose a novel path-planning method to generate sub-tasks, leveraging $Q$-learning with a task-level state and reward. We implement our framework in simulation experiments and execute four challenging transfer tasks on robotic manipulation, demonstrating the efficacy of $\textit{Diff-Transfer}$ through comprehensive experiments. Supplementary and Videos are on the website https://sites.google.com/view/difftransfe

Identification and Simultaneous Determination of the Main Toxical Pyrrolizidine Alkaloids in a Compound Prescription of Traditional Chinese Medicine: Qianbai Biyan Tablet

Qianbai biyan tablet (QT) is a compound prescription of traditional Chinese medicine which is used to treat nasal congestion, rhinitis, and nasosinusitis, with Senecio scandens as its main plant material. Several pyrrolizidine alkaloids (PAs) were reported in Senecio scandens and others of Senecio species. Although Senecio scandens is assigned as the legal plant material of QT, whether replaced use of it by other Senecio plants can bring toxicity is unknown because of the lack of quantitative data about toxic PAs between different Senecio species. In the present study, adonifoline, senkirkine, and another PA presumed as emiline have been identified in QT; however, there was no senecionine detected in all tablets. PA contents in QTs varied in different companies and different batches. Adonifoline existed only in Senecio scandens, and senecionine was detected in all eight Senecio plants investigated in the present study. Data showed that replaced use of Senecio scandens with a low level of senecionine by other Senecio plants such as Senecio vulgaris containing a high level of senecionine is advertised to be forbidden. Data of the present study may be used as a reference to make new drug quality regularity and recommendation guideline for the safety of QT

Effects of Wettability and Minerals on Residual Oil Distributions Based on Digital Rock and Machine Learning

AbstractThe wettability of mineral surfaces has significant impacts on transport mechanisms of two-phase flow, distribution characteristics of fluids, and the formation mechanisms of residual oil during water flooding. However, few studies have investigated such effects of mineral type and its surface wettability on rock properties in the literature. To unravel the dependence of hydrodynamics on wettability and minerals distribution, we designed a new experimental procedure that combined the multiphase flow experiments with a CT scan and QEMSCAN to obtain 3D digital models with multiple minerals and fluids. With the aid of QEMSCAN, six mineral components and two fluids in sandstones were segmented from the CT data based on the histogram threshold and watershed methods. Then, a mineral surface analysis algorithm was proposed to extract the mineral surface and classify its mineral categories. The in situ contact angle and pore occupancy were calculated to reveal the wettability variation of mineral surface and distribution characteristics of fluids. According to the shape features of the oil phase, the self-organizing map (SOM) method, one of the machine learning methods, was used to classify the residual oil into five types, namely, network, cluster, film, isolated, and droplet oil. The results indicate that each mineral’s contribution to the mineral surface is not proportional to its relative content. Feldspar, quartz, and clay are the main minerals in the studied sandstones and play a controlling role in the wettability variation. Different wettability samples show various characteristics of pore occupancy. The water flooding front of the weakly water-wet to intermediate-wet sample is uniform, and oil is effectively displaced in all pores with a long oil production period. The water-wet sample demonstrates severe fingering, with a high pore occupancy change rate in large pores and a short oil production period. The residual oil patterns gradually evolve from networks to clusters, isolated, and films due to the effects of snap-off and wettability inversion. This paper reveals the effects of wettability of mineral surface on the distribution characteristics and formation mechanisms of residual oil, which offers us an in-deep understanding of the impacts of wettability and minerals on multiphase flow and helps us make good schemes to improve oil recovery