arXiv subjects

Qi Wu

Publications and source records attributed to Qi Wu.

At least 379 records · Page 21Linked to original sources

arXiv2019

Integrating Temporal and Spatial Attentions for VATEX Video Captioning Challenge 2019

This notebook paper presents our model in the VATEX video captioning challenge. In order to capture multi-level aspects in the video, we propose to integrate both temporal and spatial attentions for video captioning. The temporal attentive module focuses on global action movements while spatial attentive module enables to describe more fine-grained objects. Considering these two types of attentive modules are complementary, we thus fuse them via a late fusion strategy. The proposed model significantly outperforms baselines and achieves 73.4 CIDEr score on the testing set which ranks the second place at the VATEX video captioning challenge leaderboard 2019.

cs.CV↗

arXiv2019

Hall coefficient diagnostics of surface state in pressurized SmB6

In this study, we report the first results of the high-pressure Hall coefficient (RH) measurements in the putative topological Kondo insulator SmB6 up to 37 GPa. Below 10 GPa, our data reveal that RH(T) exhibits a prominent peak upon cooling below 20 K. Remarkably, the temperature at which surface conduction dominates coincides with the temperature of the peak in RH(T). The temperature dependent resistance and Hall coefficient can be well fitted by a two-channel model with contributions from the metallic surface and the thermally activated bulk states. When the bulk of SmB6 becomes metallic and magnetic at ~ 10 GPa, both the RH(T) peak and the resistance plateau disappear simultaneously. Our results indicate that the RH(T) peak is a fingerprint to diagnose the presence of a metallic surface state in SmB6. The high-pressure magnetic state of SmB6 is robust to 180 GPa, and no evidence of superconductivity is observed in the metallic phase.

cond-mat.str-el↗

arXiv2019

Crossover from two-dimensional to three-dimensional superconducting states in bismuth-based cuprate superconductor

To decipher the mechanism of high temperature superconductivity, it is important to know how the superconducting pairing emerges from the unusual normal states of cuprate superconductors, including pseudogap, anomalous Fermi liquid and strange metal (SM). A long-standing issue under debate is how the superconducting pairing is formed and condensed in the SM phase because the superconducting transition temperature is the highest in this phase. Here, we report the first experimental observation of a pressure-induced crossover from two- to three-dimensional superconducting states in the optimally-doped Bi2Sr2CaCu2O8+delta bulk superconductor at a pressure above 2.8 GPa, through state-of-the-art in-situ high-pressure measurements of resistance, magnetoresistance and magnetic susceptibility. By analyzing the temperature dependence of resistance, we find that the two-dimensional (2D) superconducting transition exhibits a Berezinski-Kosterlitz-Thouless-like behavior. The emergence of this 2D superconducting transition provides direct and strong evidence that the SM state is predominantly 2D-like. This is important to a thorough understanding of the phase diagram of cuprate superconductors.

cond-mat.supr-con↗

arXiv2019

Production of $P_c $ states from $Λ_b$ decay

In the present work, we investigate $P_c(4312)$, $P_c(4440)$ and $P_c(4457)$ production from $Λ_b$ decay in a molecular scenario by using an effective Lagrangian approach. We predict the ratio of the branching fraction of $Λ_b \to P_c K$, which is weakly dependent on our model parameter. We also find the ratios of the productions of the branching fractions of $Λ_b \to P_c K$ and $P_c \to J/ψp$ can be well interpreted in the molecular scenario. Moreover, the estimated branching fractions of $Λ_b \to P_c K$ are of order $10^{-6}$, which could be tested by further measurements in LHCb Collaboration.

hep-ph↗

arXiv2019

Neural Learning of Online Consumer Credit Risk

This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers' purchase. The "NeuCredit" model can capture both serial dependences in multi-dimensional time series data when event frequencies in each dimension differ. It also captures nonlinear cross-sectional interactions among different time-evolving features. Also, the predicted default probability is designed to be interpretable such that risks can be decomposed into three components: the subjective risk indicating the consumers' willingness to repay, the objective risk indicating their ability to repay, and the behavioral risk indicating consumers' behavioral differences. Using a unique dataset from one of the largest global e-commerce platforms, we show that the inclusion of shopping behavioral data, besides conventional payment records, requires a deep learning approach to extract the information content of these data, which turns out significantly enhancing forecasting performance than the traditional machine learning methods.

q-fin.RM↗

arXiv2019

Production of $Z_c(3900$) and $Z_c(4020)$ in $B_c$ decay

In the present work, we propose to search the charmonium-like states $Z_c(3900$) and $Z_c(4020)$ in the $B_c$ decay. In an effective Lagrangian approach, the branching ratios of $B_c^+ \to Z_c(3900)^+ π^0$ and $B_c^+\to Z_c (4020)^+ π^0$ are estimated to be of order of $10^{-4}$ and $10^{-7}$, respectively. The large production rate of $Z_c(3900)$ could provide an important source of the production of $Z_c(3900)$ from the semi-exclusive decay of $b$-flavored hadrons reported by D0 Collaboration, which can be tested by the exclusive measurements in LHCb.

hep-ph↗

arXiv2019

You Only Look & Listen Once: Towards Fast and Accurate Visual Grounding

Visual Grounding (VG) aims to locate the most relevant region in an image, based on a flexible natural language query but not a pre-defined label, thus it can be a more useful technique than object detection in practice. Most state-of-the-art methods in VG operate in a two-stage manner, wherein the first stage an object detector is adopted to generate a set of object proposals from the input image and the second stage is simply formulated as a cross-modal matching problem that finds the best match between the language query and all region proposals. This is rather inefficient because there might be hundreds of proposals produced in the first stage that need to be compared in the second stage, not to mention this strategy performs inaccurately. In this paper, we propose an simple, intuitive and much more elegant one-stage detection based method that joints the region proposal and matching stage as a single detection network. The detection is conditioned on the input query with a stack of novel Relation-to-Attention modules that transform the image-to-query relationship to an relation map, which is used to predict the bounding box directly without proposing large numbers of useless region proposals. During the inference, our approach is about 20x ~ 30x faster than previous methods and, remarkably, it achieves 18% ~ 41% absolute performance improvement on top of the state-of-the-art results on several benchmark datasets. We release our code and all the pre-trained models at https://github.com/openblack/rvg.

cs.CV↗

arXiv2019

Production of the $Z_b^{(\prime)}$ states from the $Υ(5S,6S)$ decays

In the present work, we investigate the production mechanism of the $Z_b(10610)$ and $Z_b(10650)$ states from the $Υ(5S,6S)$ decays. Two types of bottom-meson loops are discussed. We show that the loop contributions with all intermediate states being the $S$-wave ground state bottom mesons are negligible, while the loops with one bottom meson being the broad $B_0^\ast$ or $B_1^\prime$ resonance could provide the dominant contributions to the $Υ(5S) \to Z_b^{(\prime)} π$. It is found that such a mechanism is not suppressed by the large width of the $B_0/B_1'$ resonance. In addition, we also estimate the branching ratios for the $Υ(6S) \to Z_b^{(\prime)} π$ which could be tested by future precise measurements at Belle-II.

hep-ph↗

arXiv2019

Record-High Superconductivity in Niobium-Titanium Alloy

Here we report the observation of extraordinary superconductivity in a pressurized commercial niobium-titanium alloy. We find that its zero-resistance superconductivity persists from ambient pressure to the pressure as high as 261.7 GPa, a record high pressure up to which a known superconducting state can continuously survives. Remarkably, at such an ultra-high pressure, although the ambient pressure volume is shrunk by 45% without structural phase transition, the superconducting transition temperature (TC) increases to ~19.1 K from ~9.6 K, and the critical magnetic field (HC2) at 1.8 K has been enhanced to 19 T from 15.4 T. These results set new records for both of the TC and the HC2 among all the known alloy superconductors composed of only transition metal elements. The remarkable high pressure superconducting properties observed in the NbTi alloy not only expand our knowledge on this important commercial superconductor but also are helpful for a better understanding on the superconducting mechanism.

cond-mat.supr-con↗

arXiv2018

What's to know? Uncertainty as a Guide to Asking Goal-oriented Questions

One of the core challenges in Visual Dialogue problems is asking the question that will provide the most useful information towards achieving the required objective. Encouraging an agent to ask the right questions is difficult because we don't know a-priori what information the agent will need to achieve its task, and we don't have an explicit model of what it knows already. We propose a solution to this problem based on a Bayesian model of the uncertainty in the implicit model maintained by the visual dialogue agent, and in the function used to select an appropriate output. By selecting the question that minimises the predicted regret with respect to this implicit model the agent actively reduces ambiguity. The Bayesian model of uncertainty also enables a principled method for identifying when enough information has been acquired, and an action should be selected. We evaluate our approach on two goal-oriented dialogue datasets, one for visual-based collaboration task and the other for a negotiation-based task. Our uncertainty-aware information-seeking model outperforms its counterparts in these two challenging problems.

cs.AI↗

arXiv2018

Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks

The task in referring expression comprehension is to localise the object instance in an image described by a referring expression phrased in natural language. As a language-to-vision matching task, the key to this problem is to learn a discriminative object feature that can adapt to the expression used. To avoid ambiguity, the expression normally tends to describe not only the properties of the referent itself, but also its relationships to its neighbourhood. To capture and exploit this important information we propose a graph-based, language-guided attention mechanism. Being composed of node attention component and edge attention component, the proposed graph attention mechanism explicitly represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches. Furthermore, the proposed graph attention mechanism enables the comprehension decision to be visualisable and explainable. Experiments on three referring expression comprehension datasets show the advantage of the proposed approach.

cs.CV↗

arXiv2018

Deep Template Matching for Offline Handwritten Chinese Character Recognition

Just like its remarkable achievements in many computer vision tasks, the convolutional neural networks (CNN) provide an end-to-end solution in handwritten Chinese character recognition (HCCR) with great success. However, the process of learning discriminative features for image recognition is difficult in cases where little data is available. In this paper, we propose a novel method for learning siamese neural network which employ a special structure to predict the similarity between handwritten Chinese characters and template images. The optimization of siamese neural network can be treated as a simple binary classification problem. When the training process has been finished, the powerful discriminative features help us to generalize the predictive power not just to new data, but to entirely new classes that never appear in the training set. Experiments performed on the ICDAR-2013 offline HCCR datasets have shown that the proposed method has a very promising generalization ability to the new classes that never appear in the training set.

cs.CV↗

arXiv2018

Observation of superconductivity in pressurized Weyl semimetal candidate TaIrTe4

Here we report the observation of superconductivity in pressurized type-II Weyl semimetal (WSM) candidate TaIrTe4 by means of complementary high-pressure transport and synchrotron X-ray diffraction measurements. We find that TaIrTe4 shows superconductivity with transition temperature (TC) of 0.57 K at the pressure of ~23.8 GPa. Then, the TC value increases with pressure and reaches ~2.1 K at 65.7 GPa. In situ high-pressure Hall coefficient (RH) measurements at low temperatures demonstrate that the positive RH increases with pressure until the critical pressure of the superconducting transition is reached, but starts to decrease upon further increasing pressure. Above the critical pressure, the positive magnetoresistance effect disappears simultaneously. Our high pressure X-ray diffraction measurements reveal that, at around the critical pressure the lattice of the TaIrTe4 sample is distorted by the application of pressure and its volume is reduced by ~19.2%, the value of which is predicted to result in the change of the electronic structure significantly. We propose that the pressure-induced distortion in TaIrTe4 is responsible for the change of topology of Fermi surface and such a change favors the emergence of superconductivity. Our results clearly demonstrate the correlation among the lattice distortion, topological physics and superconductivity in the WSM.

cond-mat.supr-con↗

arXiv2018

Universal superconductivity phase diagram for pressurized tetradymite topological insulators

We show that two different superconducting phases exist at high pressures in the optimized tetradymite topological insulators Bi2Te2Se (BTS) and Bi1.1Sb0.9Te2S (BSTS). The superconducting phases emerge at structural phase transitions, the first at 8.4 GPa for BTS and 12.4 GPa for BSTS, and the second at 13.6 GPa for BTS and 20.4 GPa, for BSTS. Electronic structure calculations show that these phases do not have topological character. Comparison of our results with prior work on Bi2Se3, Bi2Te3 and (Bi,Sb)2(Se,Te)3 allows us to uncover a universal phase diagram for pressure-induced superconductivity in tetradymites, providing a basis for understanding the relationships between topological behavior, crystal structure, and superconductivity for these materials.

cond-mat.supr-con↗

arXiv2018

Independence of topological surface state and bulk conductances in three-dimensional topological insulators

The archetypical 3D topological insulators Bi2Se3, Bi2Te3 and Sb2Te3 commonly exhibit high bulk conductivities, hindering the characterization of the surface state charge transport. The optimally doped topological insulators Bi2Te2Se and Bi2-xSbxTe2S, however, allow for such characterizations to be made. Here we report the first experimental comparison of the topological surface states and bulk conductances of Bi2Te2Se and Bi1.1Sb0.9Te2S, based on temperature-dependent high-pressure measurements. We find that the surface state conductance at low temperatures remains constant in the face of orders of magnitude increase in the bulk state conductance, revealing in a straightforward way that the topological surface states and bulk states are decoupled at low temperatures, consistent with theoretical models, and confirming topological insulators to be an excellent venue for studying charge transport in 2D Dirac electron systems.

cond-mat.str-el↗

arXiv2018

Topological Data Analysis Made Easy with the Topology ToolKit

This tutorial presents topological methods for the analysis and visualization of scientific data from a user's perspective, with the Topology ToolKit (TTK), a recently released open-source library for topological data analysis. Topological methods have gained considerably in popularity and maturity over the last twenty years and success stories of established methods have been documented in a wide range of applications (combustion, chemistry, astrophysics, material sciences, etc.) with both acquired and simulated data, in both post-hoc and in-situ contexts. While reference textbooks have been published on the topic, no tutorial at IEEE VIS has covered this area in recent years, and never at a software level and from a user's point-of-view. This tutorial fills this gap by providing a beginner's introduction to topological methods for practitioners, researchers, students, and lecturers. In particular, instead of focusing on theoretical aspects and algorithmic details, this tutorial focuses on how topological methods can be useful in practice for concrete data analysis tasks such as segmentation, feature extraction or tracking. The tutorial describes in detail how to achieve these tasks with TTK. First, after an introduction to topological methods and their application in data analysis, a brief overview of TTK's main entry point for end users, namely ParaView, will be presented. Second, an overview of TTK's main features will be given. A running example will be described in detail, showcasing how to access TTK's features via ParaView, Python, VTK/C++, and C++. Third, hands-on sessions will concretely show how to use TTK in ParaView for multiple, representative data analysis tasks. Fourth, the usage of TTK will be presented for developers, in particular by describing several examples of visualization and data analysis projects that were built on top of TTK. Finally, some feedback regarding the usage of TTK as a teaching platform for topological analysis will be given. Presenters of this tutorial include experts in topological methods, core authors of TTK as well as active users, coming from academia, labs, or industry. A large part of the tutorial will be dedicated to hands-on exercises and a rich material package (including TTK pre-installs in virtual machines, code, data, demos, video tutorials, etc.) will be provided to the participants. This tutorial mostly targets students, practitioners and researchers who are not experts in topological methods but who are interested in using them in their daily tasks. We also target researchers already familiar to topological methods and who are interested in using or contributing to TTK.

cs.DM↗

arXiv2018

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matterport3D Simulator -- a large-scale reinforcement learning environment based on real imagery. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings -- the Room-to-Room (R2R) dataset.

cs.CV↗

arXiv2018

Visual Question Answering with Memory-Augmented Networks

In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set. The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. We show that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting. Experimental results on two large-scale benchmark datasets show the favorable performance of the proposed algorithm with a comparison to state of the art.

cs.CV↗