arXiv subjects

Yao Yao

Publications and source records attributed to Yao Yao.

At least 217 records · Page 12Linked to original sources

arXiv2020

BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks

While deep learning has recently achieved great success on multi-view stereo (MVS), limited training data makes the trained model hard to be generalized to unseen scenarios. Compared with other computer vision tasks, it is rather difficult to collect a large-scale MVS dataset as it requires expensive active scanners and labor-intensive process to obtain ground truth 3D structures. In this paper, we introduce BlendedMVS, a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. To create the dataset, we apply a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes. Then, we render these mesh models to color images and depth maps. To introduce the ambient lighting information during training, the rendered color images are further blended with the input images to generate the training input. Our dataset contains over 17k high-resolution images covering a variety of scenes, including cities, architectures, sculptures and small objects. Extensive experiments demonstrate that BlendedMVS endows the trained model with significantly better generalization ability compared with other MVS datasets. The dataset and pretrained models are available at \url{https://github.com/YoYo000/BlendedMVS}.

cs.CV↗

arXiv2020

KFNet: Learning Temporal Camera Relocalization using Kalman Filtering

Temporal camera relocalization estimates the pose with respect to each video frame in sequence, as opposed to one-shot relocalization which focuses on a still image. Even though the time dependency has been taken into account, current temporal relocalization methods still generally underperform the state-of-the-art one-shot approaches in terms of accuracy. In this work, we improve the temporal relocalization method by using a network architecture that incorporates Kalman filtering (KFNet) for online camera relocalization. In particular, KFNet extends the scene coordinate regression problem to the time domain in order to recursively establish 2D and 3D correspondences for the pose determination. The network architecture design and the loss formulation are based on Kalman filtering in the context of Bayesian learning. Extensive experiments on multiple relocalization benchmarks demonstrate the high accuracy of KFNet at the top of both one-shot and temporal relocalization approaches. Our codes are released at https://github.com/zlthinker/KFNet.

cs.CV↗

arXiv2020

Network Cooperation with Progressive Disambiguation for Partial Label Learning

Partial Label Learning (PLL) aims to train a classifier when each training instance is associated with a set of candidate labels, among which only one is correct but is not accessible during the training phase. The common strategy dealing with such ambiguous labeling information is to disambiguate the candidate label sets. Nonetheless, existing methods ignore the disambiguation difficulty of instances and adopt the single-trend training mechanism. The former would lead to the vulnerability of models to the false positive labels and the latter may arouse error accumulation problem. To remedy these two drawbacks, this paper proposes a novel approach termed "Network Cooperation with Progressive Disambiguation" (NCPD) for PLL. Specifically, we devise a progressive disambiguation strategy of which the disambiguation operations are performed on simple instances firstly and then gradually on more complicated ones. Therefore, the negative impacts brought by the false positive labels of complicated instances can be effectively mitigated as the disambiguation ability of the model has been strengthened via learning from the simple instances. Moreover, by employing artificial neural networks as the backbone, we utilize a network cooperation mechanism which trains two networks collaboratively by letting them interact with each other. As two networks have different disambiguation ability, such interaction is beneficial for both networks to reduce their respective disambiguation errors, and thus is much better than the existing algorithms with single-trend training process. Extensive experimental results on various benchmark and practical datasets demonstrate the superiority of our NCPD to other state-of-the-art PLL methods.

cs.LG↗

arXiv2019

Time-dependent Density Matrix Renormalization Group Quantum Dynamics for Realistic Chemical Systems

Electronic and/or vibronic coherence has been found by recent ultrafast spectroscopy experiments in many chemical, biological and material systems. This indicates that there are strong and complicated interactions between electronic states and vibration modes in realistic chemical systems. Therefore, simulations of quantum dynamics with a large number of electronic and vibrational degrees of freedom are highly desirable. Due to the efficient compression and localized representation of quantum states in the matrix-product state (MPS) formulation, time-evolution methods based on the MPS framework, which we summarily refer to as tDMRG (time-dependent density-matrix renormalization group) methods, are considered to be promising candidates to study the quantum dynamics of realistic chemical systems. In this work, we benchmark the performances of four different tDMRG methods, including global Taylor, global Krylov, local one-site and two-site time-dependent variational principle (1TDVP and 2TDVP), with a comparison to multi-configuration time-dependent Hartree (MCTDH) and experimental results. Two typical chemical systems of internal conversion and singlet fission are investigated, one containing strong and high-order local and non-local electron-vibration couplings, the other exhibiting a continuous phonon bath. The comparison shows that the tDMRG methods (particularly, the 2TDVP method) can describe the full quantum dynamics in large chemical systems accurately and efficiently. Several key parameters in the tDMRG calculation including the truncation error threshold, time interval and ordering of local sites were also investigated to strike the balance between efficiency and accuracy of results.

cond-mat.str-el↗

arXiv2019

Pulsar Candidate Identification with Artificial Intelligence Techniques

Discovering pulsars is a significant and meaningful research topic in the field of radio astronomy. With the advent of astronomical instruments such as he Five-hundred-meter Aperture Spherical Telescope (FAST) in China, data volumes and data rates are exponentially growing. This fact necessitates a focus on artificial intelligence (AI) technologies that can perform the automatic pulsar candidate identification to mine large astronomical data sets. Automatic pulsar candidate identification can be considered as a task of determining potential candidates for further investigation and eliminating noises of radio frequency interferences or other non-pulsar signals. It is very hard to raise the performance of DCNN-based pulsar identification because the limited training samples restrict network structure to be designed deep enough for learning good features as well as the crucial class imbalance problem due to very limited number of real pulsar samples. To address these problems, we proposed a framework which combines deep convolution generative adversarial network (DCGAN) with support vector machine (SVM) to deal with imbalance class problem and to improve pulsar identification accuracy. DCGAN is used as sample generation and feature learning model, and SVM is adopted as the classifier for predicting candidate's labels in the inference stage. The proposed framework is a novel technique which not only can solve imbalance class problem but also can learn discriminative feature representations of pulsar candidates instead of computing hand-crafted features in preprocessing steps too, which makes it more accurate for automatic pulsar candidate selection. Experiments on two pulsar datasets verify the effectiveness and efficiency of our proposed method.

astro-ph.IM↗

arXiv2019

Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency

The self-supervised learning of depth and pose from monocular sequences provides an attractive solution by using the photometric consistency of nearby frames as it depends much less on the ground-truth data. In this paper, we address the issue when previous assumptions of the self-supervised approaches are violated due to the dynamic nature of real-world scenes. Different from handling the noise as uncertainty, our key idea is to incorporate more robust geometric quantities and enforce internal consistency in the temporal image sequence. As demonstrated on commonly used benchmark datasets, the proposed method substantially improves the state-of-the-art methods on both depth and relative pose estimation for monocular image sequences, without adding inference overhead.

cs.CV↗

arXiv2019

Uniqueness and non-uniqueness of steady states of aggregation-diffusion equations

We consider a nonlocal aggregation equation with degenerate diffusion, which describes the mean-field limit of interacting particles driven by nonlocal interactions and localized repulsion. When the interaction potential is attractive, it is previously known that all steady states must be radially decreasing up to a translation, but uniqueness (for a given mass) within the radial class was open, except for some special interaction potentials. For general attractive potentials, we show that the uniqueness/non-uniqueness criteria are determined by the power of the degenerate diffusion, with the critical power being $m = 2$. In the case $m \ge 2$, we show that for any attractive potential the steady state is unique for a fixed mass. In the case $1 < m < 2$, we construct examples of smooth attractive potentials, such that there are infinitely many radially decreasing steady states of the same mass. For the uniqueness proof, we develop a novel interpolation curve between two radially decreasing densities, and the key step is to show that the interaction energy is convex along this curve for any attractive interaction potential, which is of independent interest.

math.AP↗

arXiv2019

Symmetry in stationary and uniformly-rotating solutions of active scalar equations

In this paper, we study the radial symmetry properties of stationary and uniformly-rotating solutions of the 2D Euler and gSQG equations, both in the smooth setting and the patch setting. For the 2D Euler equation, we show that any smooth stationary solution with compactly supported and nonnegative vorticity must be radial, without any assumptions on the connectedness of the support or the level sets. In the patch setting, for the 2D Euler equation we show that every uniformly-rotating patch $D$ with angular velocity $Ω\leq 0$ or $Ω\geq \frac{1}{2}$ must be radial, where both bounds are sharp. For the gSQG equation we obtain a similar symmetry result for $Ω\leq 0$ or $Ω\geq Ω_α$ (with the bounds being sharp), under the additional assumption that the patch is simply-connected. These results settle several open questions in [T. Hmidi, J. Evol. Equ., 15(4): 801-816, 2015] and [F. de la Hoz, Z. Hassainia, T. Hmidi, and J. Mateu, Anal. PDE, 9(7):1609-1670, 2016] on uniformly-rotating patches. Along the way, we close a question on overdetermined problems for the fractional Laplacian [R. Choksi, R. Neumayer, and I. Topaloglu, Arxiv preprint arXiv:1810.08304, 2018, Remark 1.4], which may be of independent interest. The main new ideas come from a calculus of variations point of view.

math.AP↗

arXiv2019

Discovery of room temperature ferromagnetism in metal-free organic semiconductors

Creating magnetic semiconductors that work at warm circumstance is still a great challenge in the physical sciences. Here, we report the discovery of ferromagnetism in the metal-free perylene diimide semiconductor, whose Curie temperature is higher than 400 Kelvin. A solvothermal approach is used to reduce and dissolve the rigid-backbone perylene diimide crystallites, and radical anion aggregates were fabricated by the subsequent self-assembly and oxidation process. Magnetic measurements exhibit the ferromagnetic ordering with the saturated magnetization of 0.48 $μ_{\rm B}$ per molecule and the appreciable magnetic anisotropy. X-ray magnetic circular dichroism spectra suggest the ferromagnetism stems from $π$ orbitals of radicals. Our findings unambitiously demonstrate the long-range ferromagnetic ordering can survive at room temperature in organic semiconductors, although which are intuitively regarded to be nonmagnetic.

cond-mat.mtrl-sci↗

arXiv2019

Modulating quantum fluctuations of scattered lights in disordered media via wavefront shaping

After multiple scattering of quadrature-squeezed lights in a disordered medium, the quadrature amplitudes of the scattered modes present an excess noise above the shot-noise level [Opt. Expr. 14, 6919 (2006)]. A natural question is raised whether there exists a method of suppressing the quadrature fluctuation of the output mode. The answer is affirmative. In this work, we prove that wavefront shaping is a promising method to reduce the quantum noise of quadrature amplitudes of the scattered modes. This reduction is owing to the destructive interference of quantum noise. Specifically, when the single-mode squeezed states are considered as inputs, the quantum fluctuation can always be reduced, even below the shot-noise level. These results may have potential applications in quantum information processing, for instance, sub-wavelength imaging using the scattering superlens with squeezed-state sources.

quant-ph↗

arXiv2019

Discovering and Understanding the Security Hazards in the Interactions between IoT Devices, Mobile Apps, and Clouds on Smart Home Platforms

A smart home connects tens of home devices to the Internet, where an IoT cloud runs various home automation applications. While bringing unprecedented convenience and accessibility, it also introduces various security hazards to users. Prior research studied smart home security from several aspects. However, we found that the complexity of the interactions among the participating entities (i.e., devices, IoT clouds, and mobile apps) has not yet been systematically investigated. In this work, we conducted an in-depth analysis of five widely-used smart home platforms. Combining firmware analysis, network traffic interception, and blackbox testing, we reverse-engineered the details of the interactions among the participating entities. Based on the details, we inferred three legitimate state transition diagrams for the three entities, respectively. Using these state machines as a reference model, we identified a set of unexpected state transitions. To confirm and trigger the unexpected state transitions, we implemented a set of phantom devices to mimic a real device. By instructing the phantom devices to intervene in the normal entity-entity interactions, we have discovered several new vulnerabilities and a spectrum of attacks against real-world smart home platforms.

cs.CR↗

arXiv2019

Quantum coherence fraction

As an analogy of fully entangled fraction in the framework of entanglement theory, we have introduced the notion of quantum coherence fraction $C_{\mathcal{F}}$, which quantifies the closeness between a given state and the set of maximally coherent states. By providing an alternative formulation of the robustness of coherence $C_{\mathcal{R}}$, we have elucidated the relationship between quantum coherence fraction and the normalized version of $C_{\mathcal{R}}$ (i.e., $\overline{C}_{\mathcal{R}}$), where the role of genuinely incoherent operations (GIO) is highlighted. Numerical simulation shows that though as expected $C_{\mathcal{F}}$ is upper bounded by $\overline{C}_{\mathcal{R}}$, $C_{\mathcal{F}}$ constitutes a good approximation to $\overline{C}_{\mathcal{R}}$ especially in low-dimensional Hilbert spaces. Even more intriguingly, we can analytically prove that $C_{\mathcal{F}}$ is exactly equivalent to $\overline{C}_{\mathcal{R}}$ for qubit and qutrit states. Moreover, some intuitive properties and implications of $C_{\mathcal{F}}$ are also indicated.

quant-ph↗

arXiv2019

Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures

Multiple mechanisms have been proposed to explain how greenery enhances their mental wellbeing. Mediation studies, however, focus on a limited number of mechanisms and rely on remotely sensed greenery measures, which do not accurately capture how neighborhood greenery is perceived on the ground. To examine: 1) how streetscape and remote sensing-based greenery affect people's mental wellbeing in Guangzhou, China; 2) whether and, if so, to what extent the associations are mediated by physical activity, stress, air quality and noise, and social cohesion; and 3) whether differences in the mediation across the streetscape greenery and NDVI exposure metrics occurred. Mental wellbeing was quantified by the WHO-5 wellbeing index. Greenery measures were extracted at the neighborhood level: 1) streetscape greenery from street view data via a convolutional neural network, and 2) the NDVI remote sensing images. Single and multiple mediation analyses with multilevel regressions were conducted. Streetscape and NDVI greenery were weakly and positively, but not significantly, correlated. Our regression results revealed that streetscape greenery and NDVI were, individually and jointly, positively associated with mental wellbeing. Significant partial mediators for the streetscape greenery were physical activity, stress, air quality and noise, and social cohesion; together, they explained 62% of the association. For NDVI, only physical activity and social cohesion were significant partial mediators, accounting for 22% of the association. Mental health and wellbeing and both streetscape and satellite-derived greenery seem to be both directly correlated and indirectly mediated. Our findings signify that both greenery measures capture different aspects of natural environments and may contribute to people's wellbeing by means of different mechanisms.

stat.AP↗

arXiv2019

Statistical distribution of quantum correlation induced by multiple scattering in the disordered medium

For the quantum correlations between scattered modes in the disordered media, the previous works focus mainly on the cases where the inputs are non-superposed states, for instance, products of Fock states [Phys. Rev. Letts. 105 (2010) 090501]. A natural question that arises is how the superpositions affect the quantum correlations. Following this trail, the comparison between superpositions and products of Fock states is performed. It is found an interesting phenomenon that for the superposition and the corresponding product of Fock state (non-Gaussian states), their averaged quantum correlations are nearly same, whereas the distributions of their quantum correlations might be different. Therefore, superpositions may affect the distributions of the quantum correlations. In addition, to examine how the Gaussian states affect the quantum correlations, we compare the typical Gaussian states with the non-Gaussian states (superpositions and products of Fock states). It is discovered that the non-Gaussian-state input could result in the quantum correlation that is either positive or negative, depending on the number of the input modes and the number of the photons in each mode, whereas the Gaussian-state input always leads to the non-negative quantum correlation. Besides, it is demonstrated that with the increase of the disorder strength, the mean strength of the quantum correlation increases for multi-mode-state inputs (except for multi-mode-coherent-state inputs). These results may be useful to control and adjust the quantum properties of scattered modes after the quantized lights propagating through the disordered medium.

quant-ph↗

arXiv2019

ContextDesc: Local Descriptor Augmentation with Cross-Modality Context

Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations. In this paper, we go beyond the local detail representation by introducing context awareness to augment off-the-shelf local feature descriptors. Specifically, we propose a unified learning framework that leverages and aggregates the cross-modality contextual information, including (i) visual context from high-level image representation, and (ii) geometric context from 2D keypoint distribution. Moreover, we propose an effective N-pair loss that eschews the empirical hyper-parameter search and improves the convergence. The proposed augmentation scheme is lightweight compared with the raw local feature description, meanwhile improves remarkably on several large-scale benchmarks with diversified scenes, which demonstrates both strong practicality and generalization ability in geometric matching applications.

cs.CV↗

arXiv2019

Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference

Deep learning has recently demonstrated its excellent performance for multi-view stereo (MVS). However, one major limitation of current learned MVS approaches is the scalability: the memory-consuming cost volume regularization makes the learned MVS hard to be applied to high-resolution scenes. In this paper, we introduce a scalable multi-view stereo framework based on the recurrent neural network. Instead of regularizing the entire 3D cost volume in one go, the proposed Recurrent Multi-view Stereo Network (R-MVSNet) sequentially regularizes the 2D cost maps along the depth direction via the gated recurrent unit (GRU). This reduces dramatically the memory consumption and makes high-resolution reconstruction feasible. We first show the state-of-the-art performance achieved by the proposed R-MVSNet on the recent MVS benchmarks. Then, we further demonstrate the scalability of the proposed method on several large-scale scenarios, where previous learned approaches often fail due to the memory constraint. Code is available at https://github.com/YoYo000/MVSNet.

cs.CV↗

arXiv2019

Aggregating Large-Scale Generalized Energy Storages to Participate in Energy Market and Regulation Market

This paper proposes a concept of generalized energy storage (GES) to facilitate the integration of large-scale heterogeneous flexible resources with electric/thermal energy storage capacity to participate in multiple markets. First, a generalized state variable referred to as degree of satisfaction (DoS) is defined, and dynamic models with a unified form are derived for different types of GESs. Second, a real-time market-based coordination framework is proposed to facilitate control, and ensure user privacy and device security. Demand curves of different GESs are then developed based on DoS to express their demand urgencies as well as flexibilities. Furthermore, a low-dimensional aggregate dynamic model of a GES cluster is derived thanks to the DoS-equality control feature provided by the design of demand curve. At last, an optimization model for a large-scale GESs to participate in both the energy market and regulation market is established based on the aggregate model. Simulations results demonstrate that the optimization algorithm could effectively reduce the total cost of an aggregator. Additionally, the proposed coordination method has high tracking accuracy and could well satisfy users' diversified power demand.

eess.SY↗

arXiv2018

Aggregation-diffusion equations: dynamics, asymptotics, and singular limits

Given a large ensemble of interacting particles, driven by nonlocal interactions and localized repulsion, the mean-field limit leads to a class of nonlocal, nonlinear partial differential equations known as aggregation-diffusion equations. Over the past fifteen years, aggregation-diffusion equations have become widespread in biological applications and have also attracted significant mathematical interest, due to their competing forces at different length scales. These competing forces lead to rich dynamics, including symmetrization, stabilization, and metastability, as well as sharp dichotomies separating well-posedness from finite time blowup. In the present work, we review known analytical results for aggregation-diffusion equations and consider singular limits of these equations, including the slow diffusion limit, which leads to the constrained aggregation equation, as well as localized aggregation and vanishing diffusion limits, which lead to metastability behavior. We also review the range of numerical methods available for simulating solutions, with special attention devoted to recent advances in deterministic particle methods. We close by applying such a method -- the blob method for diffusion -- to showcase key properties of the dynamics of aggregation-diffusion equations and related singular limits.

math.AP↗