arXiv subjects

Wenbin Li

Publications and source records attributed to Wenbin Li.

At least 127 records · Page 7Linked to original sources

arXiv2023

Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning

High altitude balloons have proved useful for ecological aerial surveys, atmospheric monitoring, and communication relays. However, due to weight and power constraints, there is a need to investigate alternate modes of propulsion to navigate in the stratosphere. Very recently, reinforcement learning has been proposed as a control scheme to maintain the balloon in the region of a fixed location, facilitated through diverse opposing wind-fields at different altitudes. Although air-pump based station keeping has been explored, there is no research on the control problem for venting and ballasting actuated balloons, which is commonly used as a low-cost alternative. We show how reinforcement learning can be used for this type of balloon. Specifically, we use the soft actor-critic algorithm, which on average is able to station-keep within 50\;km for 25\% of the flight, consistent with state-of-the-art. Furthermore, we show that the proposed controller effectively minimises the consumption of resources, thereby supporting long duration flights. We frame the controller as a continuous control reinforcement learning problem, which allows for a more diverse range of trajectories, as opposed to current state-of-the-art work, which uses discrete action spaces. Furthermore, through continuous control, we can make use of larger ascent rates which are not possible using air-pumps. The desired ascent-rate is decoupled into desired altitude and time-factor to provide a more transparent policy, compared to low-level control commands used in previous works. Finally, by applying the equations of motion, we establish appropriate thresholds for venting and ballasting to prevent the agent from exploiting the environment. More specifically, we ensure actions are physically feasible by enforcing constraints on venting and ballasting.

cs.RO↗

arXiv2023

Uniformly convex neural networks and non-stationary iterated network Tikhonov (iNETT) method

We propose a non-stationary iterated network Tikhonov (iNETT) method for the solution of ill-posed inverse problems. The iNETT employs deep neural networks to build a data-driven regularizer, and it avoids the difficult task of estimating the optimal regularization parameter. To achieve the theoretical convergence of iNETT, we introduce uniformly convex neural networks to build the data-driven regularizer. Rigorous theories and detailed algorithms are proposed for the construction of convex and uniformly convex neural networks. In particular, given a general neural network architecture, we prescribe sufficient conditions to achieve a trained neural network which is component-wise convex or uniformly convex; moreover, we provide concrete examples of realizing convexity and uniform convexity in the modern U-net architecture. With the tools of convex and uniformly convex neural networks, the iNETT algorithm is developed and a rigorous convergence analysis is provided. Lastly, we show applications of the iNETT algorithm in 2D computerized tomography, where numerical examples illustrate the efficacy of the proposed algorithm.

math.NA↗

arXiv2022

Deterministic synthesis of phase pure 2D perovskites via progressive transformation of layer thickness

Two-dimensional (2D) halide perovskites have emerged as semiconductor platforms for realizing efficient and durable optoelectronic devices. However, the reproducible synthesis of 2D perovskite crystals with desired layer thickness (or n value) greater than 2, has been an enduring challenge due to the lack of kinetic control (temperature, time, stoichiometry) for each layer thickness. Here, we demonstrate a novel method term as the kinetically controlled space confinement (KCSC) for the deterministic growth of phase pure Ruddlesden-Popper (RP) and Dion-Jacobson (DJ) 2D perovskites. The phase-pure growth was achieved by progressively increasing the temperature (fixed time) or the crystallization time (fixed temperature), which allowed for an acute control of the crystallization kinetics. We also observe a systematic transformation from a lower n-value to n=3, 4, 5, 6 in 2D perovskites. In-situ photoluminescence spectroscopy and imaging suggest that the progressive transformation from lower to higher n-value occurs via intercalation of excess precursor ions. These experiments enabled the development of a machine learning assisted multi-parameter phase diagram, which predicts the growth of 2D phase with a specific n-value.

cond-mat.mtrl-sci↗

arXiv2022

A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix

In recent years, a variety of contrastive learning based unsupervised visual representation learning methods have been designed and achieved great success in many visual tasks. Generally, these methods can be roughly classified into four categories: (1) standard contrastive methods with an InfoNCE like loss, such as MoCo and SimCLR; (2) non-contrastive methods with only positive pairs, such as BYOL and SimSiam; (3) whitening regularization based methods, such as W-MSE and VICReg; and (4) consistency regularization based methods, such as CO2. In this study, we present a new unified contrastive learning representation framework (named UniCLR) suitable for all the above four kinds of methods from a novel perspective of basic affinity matrix. Moreover, three variants, i.e., SimAffinity, SimWhitening and SimTrace, are presented based on UniCLR. In addition, a simple symmetric loss, as a new consistency regularization term, is proposed based on this framework. By symmetrizing the affinity matrix, we can effectively accelerate the convergence of the training process. Extensive experiments have been conducted to show that (1) the proposed UniCLR framework can achieve superior results on par with and even be better than the state of the art, (2) the proposed symmetric loss can significantly accelerate the convergence of models, and (3) SimTrace can avoid the mode collapse problem by maximizing the trace of a whitened affinity matrix without relying on asymmetry designs or stop-gradients.

cs.CV↗

arXiv2022

Deep object detection for waterbird monitoring using aerial imagery

Monitoring of colonial waterbird nesting islands is essential to tracking waterbird population trends, which are used for evaluating ecosystem health and informing conservation management decisions. Recently, unmanned aerial vehicles, or drones, have emerged as a viable technology to precisely monitor waterbird colonies. However, manually counting waterbirds from hundreds, or potentially thousands, of aerial images is both difficult and time-consuming. In this work, we present a deep learning pipeline that can be used to precisely detect, count, and monitor waterbirds using aerial imagery collected by a commercial drone. By utilizing convolutional neural network-based object detectors, we show that we can detect 16 classes of waterbird species that are commonly found in colonial nesting islands along the Texas coast. Our experiments using Faster R-CNN and RetinaNet object detectors give mean interpolated average precision scores of 67.9% and 63.1% respectively.

cs.CV↗

arXiv2022

Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation

Reinforcement learning (RL) is an agent-based approach for teaching robots to navigate within the physical world. Gathering data for RL is known to be a laborious task, and real-world experiments can be risky. Simulators facilitate the collection of training data in a quicker and more cost-effective manner. However, RL frequently requires a significant number of simulation steps for an agent to become skilful at simple tasks. This is a prevalent issue within the field of RL-based visual quadrotor navigation where state dimensions are typically very large and dynamic models are complex. Furthermore, rendering images and obtaining physical properties of the agent can be computationally expensive. To solve this, we present a simulation framework, built on AirSim, which provides efficient parallel training. Building on this framework, Ape-X is modified to incorporate decentralised training of AirSim environments to make use of numerous networked computers. Through experiments we were able to achieve a reduction in training time from 3.9 hours to 11 minutes using the aforementioned framework and a total of 74 agents and two networked computers. Further details including a github repo and videos about our project, PRL4AirSim, can be found at https://sites.google.com/view/prl4airsim/home

cs.RO↗

arXiv2022

LibFewShot: A Comprehensive Library for Few-shot Learning

Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or ``tricks'', such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, backbone architectures and input image sizes, making fair comparisons difficult and practitioners struggle with reproducibility. To address these situations, we propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few-shot learning methods in a unified framework with the same single codebase in PyTorch. Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmarks with various backbone architectures to evaluate common pitfalls and effects of different training tricks. In addition, with respect to the recent doubts on the necessity of meta- or episodic-training mechanism, our evaluation results confirm that such a mechanism is still necessary especially when combined with pre-training. We hope our work can not only lower the barriers for beginners to enter the area of few-shot learning but also elucidate the effects of nontrivial tricks to facilitate intrinsic research on few-shot learning. The source code is available from https://github.com/RL-VIG/LibFewShot.

cs.CV↗

arXiv2022

Curvature-Controlled Band Alignment Transitions in 1D van der Waals Heterostructures

One-dimensional (1D) van der Waals (vdW) heterostructures, formed between coaxial nanotubes of transition metal dichalcogenides (TMDCs), have emerged as a new area of endeavor in nanoscience. A key to designing and engineering the properties of such 1D vdW heterostructures lies on understanding the band alignment of coaxial nanotubes in the heterostructures. However, how curvature, tube diameters, and intertube coupling affect the band-edge levels and band alignment of TMDC nanotubes in 1D vdW heterostructures remains unknown. Here, through comprehensive first-principles calculations and analyses, we establish a complete framework of band alignment in 1D vdW heterostructures of TMDC nanotubes. We reveal that, as the diameter of a TMDC nanotube decreases, the combined effects of curvature-induced flexoelectricity and intrinsic circumferential tensile strain cause a rapid and continuous lowering of the conduction band minimum (CBM), whereas the valence band maximum (VBM) exhibits an initial lowering before rising, which originates from a change in the orbital character of the VBM. The transition in the orbital character of VBM also leads to direct-to-indirect bandgap transition in small-diameter armchair and chiral nanotubes, as well as photoluminescence quenching in zigzag nanotubes. As individual TMDC nanotubes form coaxial 1D vdW heterostructures, the effect of intertube coupling via flexovoltage effect can result in a transition of intertube band alignment from Type II to Type I in multiple heterostructural systems, including large-diameter MoSe$_2$@WS$_2$, MoTe$_2$@MoSe$_2$, and MoTe$_2$@WS$_2$ heterostructures. These results lay down a foundation for the rational design of 1D vdW heterostructures.

cond-mat.mtrl-sci↗

arXiv2022

Dense RGB-D-Inertial SLAM with Map Deformations

While dense visual SLAM methods are capable of estimating dense reconstructions of the environment, they suffer from a lack of robustness in their tracking step, especially when the optimisation is poorly initialised. Sparse visual SLAM systems have attained high levels of accuracy and robustness through the inclusion of inertial measurements in a tightly-coupled fusion. Inspired by this performance, we propose the first tightly-coupled dense RGB-D-inertial SLAM system. Our system has real-time capability while running on a GPU. It jointly optimises for the camera pose, velocity, IMU biases and gravity direction while building up a globally consistent, fully dense surfel-based 3D reconstruction of the environment. Through a series of experiments on both synthetic and real world datasets, we show that our dense visual-inertial SLAM system is more robust to fast motions and periods of low texture and low geometric variation than a related RGB-D-only SLAM system.

cs.RO↗

arXiv2022

Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation

In this paper, we mainly focus on the problem of how to learn additional feature representations for few-shot image classification through pretext tasks (e.g., rotation or color permutation and so on). This additional knowledge generated by pretext tasks can further improve the performance of few-shot learning (FSL) as it differs from human-annotated supervision (i.e., class labels of FSL tasks). To solve this problem, we present a plug-in Hierarchical Tree Structure-aware (HTS) method, which not only learns the relationship of FSL and pretext tasks, but more importantly, can adaptively select and aggregate feature representations generated by pretext tasks to maximize the performance of FSL tasks. A hierarchical tree constructing component and a gated selection aggregating component is introduced to construct the tree structure and find richer transferable knowledge that can rapidly adapt to novel classes with a few labeled images. Extensive experiments show that our HTS can significantly enhance multiple few-shot methods to achieve new state-of-the-art performance on four benchmark datasets. The code is available at: https://github.com/remiMZ/HTS-ECCV22.

cs.CV↗

arXiv2022

Playing Lottery Tickets in Style Transfer Models

Style transfer has achieved great success and attracted a wide range of attention from both academic and industrial communities due to its flexible application scenarios. However, the dependence on a pretty large VGG-based autoencoder leads to existing style transfer models having high parameter complexities, which limits their applications on resource-constrained devices. Compared with many other tasks, the compression of style transfer models has been less explored. Recently, the lottery ticket hypothesis (LTH) has shown great potential in finding extremely sparse matching subnetworks which can achieve on par or even better performance than the original full networks when trained in isolation. In this work, we for the first time perform an empirical study to verify whether such trainable matching subnetworks also exist in style transfer models. Specifically, we take two most popular style transfer models, i.e., AdaIN and SANet, as the main testbeds, which represent global and local transformation based style transfer methods respectively. We carry out extensive experiments and comprehensive analysis, and draw the following conclusions. (1) Compared with fixing the VGG encoder, style transfer models can benefit more from training the whole network together. (2) Using iterative magnitude pruning, we find the matching subnetworks at 89.2% sparsity in AdaIN and 73.7% sparsity in SANet, which demonstrates that style transfer models can play lottery tickets too. (3) The feature transformation module should also be pruned to obtain a much sparser model without affecting the existence and quality of the matching subnetworks. (4) Besides AdaIN and SANet, other models such as LST, MANet, AdaAttN and MCCNet can also play lottery tickets, which shows that LTH can be generalized to various style transfer models.

cs.CV↗

arXiv2022

Direct visualization of ultrafast lattice ordering triggered by an electron-hole plasma in 2D perovskites

Direct visualization of ultrafast coupling between charge carriers and lattice degrees of freedom in photo-excited semiconductors has remained a long-standing challenge and is critical for understanding the light-induced physical behavior of materials under extreme non-equilibrium conditions. Here, by monitoring the evolution of the wave-vector resolved ultrafast electron diffraction intensity following above-bandgap photo-excitation, we obtain a direct visual of the structural dynamics in monocrystalline 2D perovskites. Analysis reveals a surprising, light-induced ultrafast lattice ordering resulting from a strong interaction between hot-carriers and the perovskite lattice, which induces an in-plane octahedra rotation, towards a more symmetric phase. Correlated ultrafast spectroscopy performed at the same carrier density as ultrafast electron diffraction reveals that the creation of a hot and dense electron-hole plasma triggers lattice ordering at short timescales by modulating the crystal cohesive energy. Finally, we show that the interaction between the carrier gas and the lattice can be altered by tailoring the rigidity of the 2D perovskite by choosing the appropriate organic spacer layer.

cond-mat.mtrl-sci↗

arXiv2022

Giant modulation of the electron mobility in semiconductor Bi$_2$O$_2$Se via incipient ferroelectric phase transition

High-mobility layered semiconductors have the potential to enable the next-generation electronics and computing. This paper demonstrates that the ultrahigh electron mobility observed in the layered semiconductor Bi$_2$O$_2$Se originates from an incipient ferroelectric transition that endows the material with a robust protection against mobility degradation by Coulomb scattering. Based on first-principles calculations of electron-phonon interaction and ionized impurity scattering, it is shown that the electron mobility of Bi$_2$O$_2$Se can reach 10$^4$ to 10$^6$ cm$^2$V$^{-1}$s$^{-1}$ over a wide range of realistic doping concentrations. Furthermore, a small elastic strain of 1.7% can drive the material toward a unique interlayer ferroelectric transition, resulting in a large increase in the dielectric permittivity and a giant enhancement of the low-temperature electron mobility by more than an order of magnitude. These results establish a new route to realize high-mobility layered semiconductors via phase and dielectric engineering.

cond-mat.mtrl-sci↗

arXiv2022

Keeping Minimal Experience to Achieve Efficient Interpretable Policy Distillation

Although deep reinforcement learning has become a universal solution for complex control tasks, its real-world applicability is still limited because lacking security guarantees for policies. To address this problem, we propose Boundary Characterization via the Minimum Experience Retention (BCMER), an end-to-end Interpretable Policy Distillation (IPD) framework. Unlike previous IPD approaches, BCMER distinguishes the importance of experiences and keeps a minimal but critical experience pool with almost no loss of policy similarity. Specifically, the proposed BCMER contains two basic steps. Firstly, we propose a novel multidimensional hyperspheres intersection (MHI) approach to divide experience points into boundary points and internal points, and reserve the crucial boundary points. Secondly, we develop a nearest-neighbor-based model to generate robust and interpretable decision rules based on the boundary points. Extensive experiments show that the proposed BCMER is able to reduce the amount of experience to 1.4%~19.1% (when the count of the naive experiences is 10k) and maintain high IPD performance. In general, the proposed BCMER is more suitable for the experience storage limited regime because it discovers the critical experience and eliminates redundant experience.

cs.LG↗

arXiv2022

A stochastic gradient descent approach with partitioned-truncated singular value decomposition for large-scale inverse problems of magnetic modulus data

We propose a stochastic gradient descent approach with partitioned-truncated singular value decomposition for large-scale inverse problems of magnetic modulus data. Motivated by a uniqueness theorem in gravity inverse problem and realizing the similarity between gravity and magnetic inverse problems, we propose to solve the level-set function modeling the volume susceptibility distribution from the nonlinear magnetic modulus data. To deal with large-scale data, we employ a mini-batch stochastic gradient descent approach with random reshuffling when solving the optimization problem of the inverse problem. We propose a stepsize rule for the stochastic gradient descent according to the Courant-Friedrichs-Lewy condition of the evolution equation. In addition, we develop a partitioned-truncated singular value decomposition algorithm for the linear part of the inverse problem in the context of stochastic gradient descent. Numerical examples illustrate the efficacy of the proposed method, which turns out to have the capability of efficiently processing large-scale measurement data for the magnetic inverse problem. A possible generalization to the inverse problem of deep neural network is discussed at the end.

math.NA↗

arXiv2021

Commensurate stacking phase transitions in an intercalated transition metal dichalcogenide

Intercalation and stacking order modulation are two active ways in manipulating the interlayer interaction of transition metal dichalcogenides (TMDCs), which lead to a variety of emergent phases and allow for engineering material properties. Herein, the growth of Pb intercalated TMDCs--Pb(Ta$_{1+x}$Se$_2$)$_2$, the first 124-phase, is reported. Pb(Ta$_{1+x}$Se$_2$)$_2$ exhibits a unique two-step first-order structural phase transition at around 230 K. The transitions are solely associated with the stacking degree of freedom, evolving from a high temperature phase with ABC stacking and symmetry $R3m$ to an intermediate phase with AB stacking and $P3m1$, and finally to a low temperature phase with again symmetry $R3m$, but with ACB stacking. Each step involves a rigid slide of building blocks by a vector [1/3, 2/3, 0]. Intriguingly, gigantic lattice contractions occur at the transitions on warming. At low temperature, bulk superconductivity with $T_\textrm{c}\approx$ 1.8 K is observed. The underlying physics of the structural phase transitions are discussed from first-principle calculations. The symmetry analysis reveals topological nodal lines in the band structure. Our results demonstrate the possibility to realize higher order metal intercalated phases of TMDCs, advance our knowledge of polymorphic transitions and may inspire stacking order engineering in TMDCs and beyond.

cond-mat.mtrl-sci↗

arXiv2021

Manifold Alignment for Semantically Aligned Style Transfer

Most existing style transfer methods follow the assumption that styles can be represented with global statistics (e.g., Gram matrices or covariance matrices), and thus address the problem by forcing the output and style images to have similar global statistics. An alternative is the assumption of local style patterns, where algorithms are designed to swap similar local features of content and style images. However, the limitation of these existing methods is that they neglect the semantic structure of the content image which may lead to corrupted content structure in the output. In this paper, we make a new assumption that image features from the same semantic region form a manifold and an image with multiple semantic regions follows a multi-manifold distribution. Based on this assumption, the style transfer problem is formulated as aligning two multi-manifold distributions and a Manifold Alignment based Style Transfer (MAST) framework is proposed. The proposed framework allows semantically similar regions between the output and the style image share similar style patterns. Moreover, the proposed manifold alignment method is flexible to allow user editing or using semantic segmentation maps as guidance for style transfer. To allow the method to be applicable to photorealistic style transfer, we propose a new adaptive weight skip connection network structure to preserve the content details. Extensive experiments verify the effectiveness of the proposed framework for both artistic and photorealistic style transfer. Code is available at https://github.com/NJUHuoJing/MAST.

cs.CV↗

arXiv2020

CariMe: Unpaired Caricature Generation with Multiple Exaggerations

Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from the generic image-to-image translation, drawing a caricature automatically is a more challenging task due to the existence of various spacial deformations. Previous caricature generation methods are obsessed with predicting definite image warping from a given photo while ignoring the intrinsic representation and distribution for exaggerations in caricatures. This limits their ability on diverse exaggeration generation. In this paper, we generalize the caricature generation problem from instance-level warping prediction to distribution-level deformation modeling. Based on this assumption, we present the first exploration for unpaired CARIcature generation with Multiple Exaggerations (CariMe). Technically, we propose a Multi-exaggeration Warper network to learn the distribution-level mapping from photo to facial exaggerations. This makes it possible to generate diverse and reasonable exaggerations from randomly sampled warp codes given one input photo. To better represent the facial exaggeration and produce fine-grained warping, a deformation-field-based warping method is also proposed, which helps us to capture more detailed exaggerations than other point-based warping methods. Experiments and two perceptual studies prove the superiority of our method comparing with other state-of-the-art methods, showing the improvement of our work on caricature generation.

cs.CV↗