arXiv subjects

Meng Tian

Publications and source records attributed to Meng Tian.

At least 37 records · Page 2Linked to original sources

arXiv2024

Atmospheric Chemistry of Secondary and Hybrid Atmospheres of Super Earths and Sub-Neptunes

The atmospheres of small exoplanets likely derive from a combination of geochemical outgassing and primordial gases left over from formation. Secondary atmospheres, such as those of Earth, Mars and Venus, are sourced by outgassing. Persistent outgassing into long-lived, primordial, hydrogen-helium envelopes produces hybrid atmospheres of which there are no examples in the Solar System. We construct a unified theoretical framework for calculating the outgassing chemistry of both secondary and hybrid atmospheres, where the input parameters are the surface pressure, oxidation and sulfidation states of the mantle, as well as the primordial atmospheric hydrogen, helium and nitrogen content. Non-ideal gases (quantified by the fugacity coefficient) and non-ideal mixing of gaseous components (quantified by the activity coefficient) are considered. Both secondary and hybrid atmospheres exhibit a rich diversity of chemistries, including hydrogen-dominated atmospheres. The abundance ratio of carbon dioxide to carbon monoxide serves as a powerful diagnostic for the oxygen fugacity of the mantle, which may conceivably be constrained by James Webb Space Telescope spectra in the near future. Methane-dominated atmospheres are difficult to produce and require specific conditions: atmospheric surface pressures exceeding $\sim 10$ bar, a reduced (poorly oxidised) mantle and diminished magma temperatures (compared to modern Earth). Future work should include photochemistry in these calculations and clarify the general role of atmospheric escape. Exoplanet science should quantify the relationship between the mass and oxygen fugacity for a sample of super Earths and sub-Neptunes; such an empirical relationship already exists for Solar System bodies.

astro-ph.EP↗

arXiv2023

Knowledge Augmented Relation Inference for Group Activity Recognition

Most existing group activity recognition methods construct spatial-temporal relations merely based on visual representation. Some methods introduce extra knowledge, such as action labels, to build semantic relations and use them to refine the visual presentation. However, the knowledge they explored just stay at the semantic-level, which is insufficient for pursing notable accuracy. In this paper, we propose to exploit knowledge concretization for the group activity recognition, and develop a novel Knowledge Augmented Relation Inference framework that can effectively use the concretized knowledge to improve the individual representations. Specifically, the framework consists of a Visual Representation Module to extract individual appearance features, a Knowledge Augmented Semantic Relation Module explore semantic representations of individual actions, and a Knowledge-Semantic-Visual Interaction Module aims to integrate visual and semantic information by the knowledge. Benefiting from these modules, the proposed framework can utilize knowledge to enhance the relation inference process and the individual representations, thus improving the performance of group activity recognition. Experimental results on two public datasets show that the proposed framework achieves competitive performance compared with state-of-the-art methods.

cs.CV↗

arXiv2023

Diverse Carbonates in Exoplanet Oceans Promote the Carbon Cycle

Carbonate precipitation in oceans is essential for the carbonate-silicate cycle (inorganic carbon cycle) to maintain temperate climates. By considering the thermodynamics of carbonate chemistry, we demonstrate that the ocean pH decreases by approximately 0.5 for a factor of 10 increase in the atmospheric carbon dioxide content. The upper and lower limits of ocean pH are within 1-4 of each other, where the upper limit is buffered by carbonate precipitation and defines the ocean pH when the carbon cycle operates. If the carbonate compensation depth (CCD) resides above the ocean floor, then carbonate precipitation and the carbon cycle cease to operate. The CCD is deep (>40 km) for high ocean temperature and high atmospheric carbon dioxide content. Key divalent carbonates of magnesium, calcium and iron produce an increasingly wider parameter space of deep CCDs, suggesting that chemical diversity promotes the carbon cycle. The search for life from exoplanets will benefit by including chemically more diverse targets than Earth twins.

astro-ph.EP↗

arXiv2022

Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception

Perceptual quality assessment of the videos acquired in the wilds is of vital importance for quality assurance of video services. The inaccessibility of reference videos with pristine quality and the complexity of authentic distortions pose great challenges for this kind of blind video quality assessment (BVQA) task. Although model-based transfer learning is an effective and efficient paradigm for the BVQA task, it remains to be a challenge to explore what and how to bridge the domain shifts for better video representation. In this work, we propose to transfer knowledge from image quality assessment (IQA) databases with authentic distortions and large-scale action recognition with rich motion patterns. We rely on both groups of data to learn the feature extractor. We train the proposed model on the target VQA databases using a mixed list-wise ranking loss function. Extensive experiments on six databases demonstrate that our method performs very competitively under both individual database and mixed database training settings. We also verify the rationality of each component of the proposed method and explore a simple manner for further improvement.

eess.IV↗

arXiv2022

Weakly Supervised Learning of Keypoints for 6D Object Pose Estimation

State-of-the-art approaches for 6D object pose estimation require large amounts of labeled data to train the deep networks. However, the acquisition of 6D object pose annotations is tedious and labor-intensive in large quantity. To alleviate this problem, we propose a weakly supervised 6D object pose estimation approach based on 2D keypoint detection. Our method trains only on image pairs with known relative transformations between their viewpoints. Specifically, we assign a set of arbitrarily chosen 3D keypoints to represent each unknown target 3D object and learn a network to detect their 2D projections that comply with the relative camera viewpoints. During inference, our network first infers the 2D keypoints from the query image and a given labeled reference image. We then use these 2D keypoints and the arbitrarily chosen 3D keypoints retained from training to infer the 6D object pose. Extensive experiments demonstrate that our approach achieves comparable performance with state-of-the-art fully supervised approaches.

cs.CV↗

arXiv2021

Lithologic Controls on Silicate Weathering Regimes of Temperate Planets

Weathering of silicate rocks at a planetary surface can draw down CO$_2$ from the atmosphere for eventual burial and long-term storage in the planetary interior. This process is thought to provide an essential negative feedback to the carbonate-silicate cycle (carbon cycle) to maintain clement climates on Earth and potentially similar temperate exoplanets. We implement thermodynamics to determine weathering rates as a function of surface lithology (rock type). These rates provide upper limits that allow estimating the maximum rate of weathering in regulating climate. This modeling shows that the weathering of mineral assemblages in a given rock, rather than individual minerals, is crucial to determine weathering rates at planetary surfaces. By implementing a fluid-transport controlled approach, we further mimic chemical kinetics and thermodynamics to determine weathering rates for three types of rocks inspired by the lithologies of Earth's continental and oceanic crust, and its upper mantle. We find that thermodynamic weathering rates of a continental crust-like lithology are about one to two orders of magnitude lower than those of a lithology characteristic of the oceanic crust. We show that when the CO$_2$ partial pressure decreases or surface temperature increases, thermodynamics rather than kinetics exerts a strong control on weathering. The kinetically- and thermodynamically-limited regimes of weathering depend on lithology, whereas, the supply-limited weathering is independent of lithology. Our results imply that the temperature-sensitivity of thermodynamically-limited silicate weathering may instigate a positive feedback to the carbon cycle, in which the weathering rate decreases as the surface temperature increases.

astro-ph.EP↗

arXiv2020

DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation

In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologists to qualitatively show, the proposed AI-based method is effective. With our experimental results, we show that the proposed method is quantitatively and qualitatively effective. Our method is capable of creating meaningful retinal image descriptions and visual explanations that are clinically relevant.

cs.CV↗

arXiv2020

Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation

We present a novel learning approach to recover the 6D poses and sizes of unseen object instances from an RGB-D image. To handle the intra-class shape variation, we propose a deep network to reconstruct the 3D object model by explicitly modeling the deformation from a pre-learned categorical shape prior. Additionally, our network infers the dense correspondences between the depth observation of the object instance and the reconstructed 3D model to jointly estimate the 6D object pose and size. We design an autoencoder that trains on a collection of object models and compute the mean latent embedding for each category to learn the categorical shape priors. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach significantly outperforms the state of the art. Our code is available at https://github.com/mentian/object-deformnet.

cs.CV↗

arXiv2020

Vulnerability Assessment of Load Frequency Control Considering Cyber Security

Security is one of the biggest concern in power system operation. Recently, the emerging cyber security threats to operational functions of power systems arouse high public attention, and cybersecurity vulnerability thus become an emerging topic to evaluate compromised operational performance under cyber attack. In this paper, vulnerability of cyber security of load frequency control (LFC) system, which is the key component in energy manage system (EMS), is assessed by exploiting the system response to attacks on LFC variables/parameters. Two types of attacks: 1) injection attack and 2) scale attack are considered for evaluation. Two evaluation criteria reflecting the damage on system stability and power generation are used to quantify system loss under cyber attacks. Through a sensitivity-based method and attack tree models, the vulnerability of different LFC components is ranked. In addition, a post-intrusion cyber attack detection scheme is proposed. Classification-based schemes using typical classification algorithms are studied and compared to identify different attack scenarios.

eess.SY↗

arXiv2020

Robust 6D Object Pose Estimation by Learning RGB-D Features

Accurate 6D object pose estimation is fundamental to robotic manipulation and grasping. Previous methods follow a local optimization approach which minimizes the distance between closest point pairs to handle the rotation ambiguity of symmetric objects. In this work, we propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Additionally, the object location is detected by aggregating point-wise vectors pointing to the 3D center. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches. Our code is available at https://github.com/mentian/object-posenet.

cs.CV↗

arXiv2019

Devolatilization of Subducting Slabs, Part I: Thermodynamic Parameterization and Open System Effects

The amount of H$_2$O and CO$_2$ that is carried into deep mantle by subduction beyond subarc depths is of fundamental importance to the deep volatile cycle but remains debated. Given the large uncertainties surrounding the spatio-temporal pattern of fluid flow and the equilibrium state within subducting slabs, a model of H$_2$O and CO$_2$ transport in slabs should be balanced between model simplicity and capability. We construct such a model in a two-part contribution. In this Part I of our contribution, thermodynamic parameterization is performed for the devolatilization of representative subducting materials---sediments, basalts, gabbros, peridotites. The parameterization avoids reproducing the details of specific devolatilization reactions, but instead captures the overall behaviors of coupled (de)hydration and (de)carbonation. Two general, leading-order features of devolatilization are captured: (1) the released volatiles are H$_2$O-rich near the onset of devolatilization; (2) increase of the ratio of bulk CO$_2$ over H$_2$O inhibits overall devolatilization and thus lessens decarbonation. These two features play an important role in simulation of volatile fractionation and infiltration in thermodynamically open systems. When constructing the reactive fluid flow model of slab H$_2$O and CO$_2$ transport in the companion paper Part II, this parameterization can be incorporated to efficiently account for the open-system effects of H$_2$O and CO$_2$ transport.

physics.geo-ph↗

arXiv2019

Devolatilization of Subducting Slabs, Part II: Volatile Fluxes and Storage

Subduction is a crucial part of the long-term water and carbon cycling between Earth's exosphere and interior. However, there is broad disagreement over how much water and carbon is liberated from subducting slabs to the mantle wedge and transported to island-arc volcanoes. In the companion paper Part I, we parameterize the metamorphic reactions involving H$_2$O and CO$_2$ for representative subducting lithologies. On this basis, a two-dimensional reactive transport model is constructed in this Part II. We assess the various controlling factors of CO$_2$ and H$_2$O release from subducting slabs. Model results show that up-slab fluid flow directions produce a flux peak of CO$_2$ and H$_2$O at subarc depths. Moreover, infiltration of H$_2$O-rich fluids sourced from hydrated slab mantle enhances decarbonation or carbonation at lithological interfaces, increases slab surface fluxes, and redistributes CO$_2$ from basalt and gabbro layers to the overlying sedimentary layer. As a result, removal of the cap sediments (by diapirism or off-scraping) leads to elevated slab surface CO$_2$ and H$_2$O fluxes. The modelled subduction efficiency (the percentage of initially subducted volatiles retained until $\sim$200 km deep) of H$_2$O and CO$_2$ is increased by open-system effects due to fractionation within the interior of lithological layers.

physics.geo-ph↗

arXiv2019

Level shift and decay dynamics of a quantum emitter around plasmonic nanostructure

We put forward a general approach for calculating the quantum energy level shift for emitter in arbitrary nanostructures, in which the energy level shift is expressed by the sum of the real part of the scattering photon Green function (GF) and a simple integral about the imaginary part of the photon GF in the real frequency range without principle value. Compared with the method of direct principal value integral over the positive frequency axis and the method by transferring into the imaginary axis, this method avoids the principle value integral and the calculation of the scattering GF with imaginary frequency. In addition, a much narrower frequency range about the scattering photon GF in enough to get a convergent result. It is numerically demonstrated in the case for a quantum emitter (QE) located around a nanosphere and in a gap plasmonic nanocavity. Quantum dynamics of the emitter is calculated by the time domain method through solving Schrödinger equation in the form of Volterra integral of the second kind and by the frequency domain method based on the Green's function expression for the evolution operator. It is found that the frequency domain method needs information of the scattering GF over a much narrower frequency range. In addition, reversible dynamics is observed. These findings are instructive in the fields of coherent light-matter interactions.

quant-ph↗

arXiv2019

Synthesizing New Retinal Symptom Images by Multiple Generative Models

Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease which may result in loss of vision. There is limited access to high-quality relevant retinal images and poor understanding of the features defining sub-classes of this disease. Motivated by recent advances in machine learning we specifically explore the potential of generative modeling, using Generative Adversarial Networks (GANs) and style transferring, to facilitate clinical diagnosis and disease understanding by feature extraction. We design an analytic pipeline which first generates synthetic retinal images from clinical images; a subsequent verification step is applied. In the synthesizing step we merge GANs (DCGANs and WGANs architectures) and style transferring for the image generation, whereas the verified step controls the accuracy of the generated images. We find that the generated images contain sufficient pathological details to facilitate ophthalmologists' task of disease classification and in discovery of disease relevant features. In particular, our system predicts the drusen and geographic atrophy sub-classes of AMD. Furthermore, the performance using CFP images for GANs outperforms the classification based on using only the original clinical dataset. Our results are evaluated using existing classifier of retinal diseases and class activated maps, supporting the predictive power of the synthetic images and their utility for feature extraction. Our code examples are available online.

cs.CV↗

arXiv2019

Finite element method for obtaining the regularized photon Green function in lossy material

Photon Green function (GF) is the vital and most decisive factor in the field of quantum light-matter interaction. It is divergent with two equal space arguments in arbitrary-shaped lossy structure and should be regularized. We introduce a finite element method for calculating the regularized GF. It is expressed by the averaged radiation electric field over the finite-size of the photon emitter. For emitter located in homogeneous lossy material, excellent agreement with the analytical results is found for both real cavity model and virtual cavity model. For emitter located in a metal nano-sphere, the regularized scattered GF, which is the difference between the regularized GF and the analytical regularized one in homogeneous space, agrees well with the analytical scattered GF.

physics.optics↗

arXiv2018

Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine (SVM) and deep neural networks (DNNs). Furthermore, we present a new clinical retina dataset, called EyeNet2, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet2, our model achieves 90.43\% diagnosis accuracy, and the model performance is comparable to the professional ophthalmologists.

cs.CV↗

arXiv2018

Ambient Hidden Space of Generative Adversarial Networks

Generative adversarial models are powerful tools to model structure in complex distributions for a variety of tasks. Current techniques for learning generative models require an access to samples which have high quality, and advanced generative models are applied to generate samples from noisy training data through ambient modules. However, the modules are only practical for the output space of the generator, and their application in the hidden space is not well studied. In this paper, we extend the ambient module to the hidden space of the generator, and provide the uniqueness condition and the corresponding strategy for the ambient hidden generator in the adversarial training process. We report the practicality of the proposed method on the benchmark dataset.

cs.LG↗

arXiv2018

Towards Adversarial Training with Moderate Performance Improvement for Neural Network Classification

It has been demonstrated that deep neural networks are prone to noisy examples particular adversarial samples during inference process. The gap between robust deep learning systems in real world applications and vulnerable neural networks is still large. Current adversarial training strategies improve the robustness against adversarial samples. However, these methods lead to accuracy reduction when the input examples are clean thus hinders the practicability. In this paper, we investigate an approach that protects the neural network classification from the adversarial samples and improves its accuracy when the input examples are clean. We demonstrate the versatility and effectiveness of our proposed approach on a variety of different networks and datasets.

cs.LG↗