arXiv subjects

Yiwen Chen

Publications and source records attributed to Yiwen Chen.

At least 73 records · Page 4Linked to original sources

arXiv2023

The emergence of global phase coherence from local pairing in underdoped cuprates

In conventional metal superconductors such as aluminum, the large number of weakly bounded Cooper pairs become phase coherent as soon as they start to form. The cuprate high critical temperature ($T_c$) superconductors, in contrast, belong to a distinctively different category. To account for the high $T_c$, the attractive pairing interaction is expected to be strong and the coherence length is short. Being doped Mott insulators, the cuprates are known to have low superfluid density, thus are susceptible to phase fluctuations. It has been proposed that pairing and phase coherence may occur separately in cuprates, and $T_c$ corresponds to the phase coherence temperature controlled by the superfluid density. To elucidate the microscopic processes of pairing and phase ordering in cuprates, here we use scanning tunneling microscopy to image the evolution of electronic states in underdoped $\rm Bi_2La_xSr_{2-x}CuO_{6+δ}$. Even in the insulating sample, we observe a smooth crossover from the Mott insulator to superconductor-type spectra on small islands with chequerboard order and emerging quasiparticle interference patterns following the octet model. Each chequerboard plaquette contains approximately two holes, and exhibits a stripy internal structure that has strong influence on the superconducting features. Across the insulator to superconductor boundary, the local spectra remain qualitatively the same while the quasiparticle interferences become long-ranged. These results suggest that the chequerboard plaquette with internal stripes plays a crucial role on local pairing in cuprates, and the global phase coherence is established once its spatial occupation exceeds a threshold.

cond-mat.supr-con↗

arXiv2023

StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation

The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models. Nevertheless, the limited availability of diverse 3D resources presents significant challenges to learning. In this paper, we present a novel method for generating high-quality, stylized 3D avatars that utilizes pre-trained image-text diffusion models for data generation and a Generative Adversarial Network (GAN)-based 3D generation network for training. Our method leverages the comprehensive priors of appearance and geometry offered by image-text diffusion models to generate multi-view images of avatars in various styles. During data generation, we employ poses extracted from existing 3D models to guide the generation of multi-view images. To address the misalignment between poses and images in data, we investigate view-specific prompts and develop a coarse-to-fine discriminator for GAN training. We also delve into attribute-related prompts to increase the diversity of the generated avatars. Additionally, we develop a latent diffusion model within the style space of StyleGAN to enable the generation of avatars based on image inputs. Our approach demonstrates superior performance over current state-of-the-art methods in terms of visual quality and diversity of the produced avatars.

cs.CV↗

arXiv2023

Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-massive Receive Array

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitter in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple input multiple output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power-iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to gets the final direction estimation through rotational invariance between subarrays. RPI-RI makes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide a good directional measurement result, a rapid power-iterative Polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with polynomial solution method to get the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPIPR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to CRLB. Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance of the computational complexity.

cs.IT↗

arXiv2023

ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model

We present ShapeCrafter, a neural network for recursive text-conditioned 3D shape generation. Existing methods to generate text-conditioned 3D shapes consume an entire text prompt to generate a 3D shape in a single step. However, humans tend to describe shapes recursively-we may start with an initial description and progressively add details based on intermediate results. To capture this recursive process, we introduce a method to generate a 3D shape distribution, conditioned on an initial phrase, that gradually evolves as more phrases are added. Since existing datasets are insufficient for training this approach, we present Text2Shape++, a large dataset of 369K shape-text pairs that supports recursive shape generation. To capture local details that are often used to refine shape descriptions, we build on top of vector-quantized deep implicit functions that generate a distribution of high-quality shapes. Results show that our method can generate shapes consistent with text descriptions, and shapes evolve gradually as more phrases are added. Our method supports shape editing, extrapolation, and can enable new applications in human-machine collaboration for creative design.

cs.CV↗

arXiv2023

StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields

3D style transfer aims to render stylized novel views of a 3D scene with multi-view consistency. However, most existing work suffers from a three-way dilemma over accurate geometry reconstruction, high-quality stylization, and being generalizable to arbitrary new styles. We propose StyleRF (Style Radiance Fields), an innovative 3D style transfer technique that resolves the three-way dilemma by performing style transformation within the feature space of a radiance field. StyleRF employs an explicit grid of high-level features to represent 3D scenes, with which high-fidelity geometry can be reliably restored via volume rendering. In addition, it transforms the grid features according to the reference style which directly leads to high-quality zero-shot style transfer. StyleRF consists of two innovative designs. The first is sampling-invariant content transformation that makes the transformation invariant to the holistic statistics of the sampled 3D points and accordingly ensures multi-view consistency. The second is deferred style transformation of 2D feature maps which is equivalent to the transformation of 3D points but greatly reduces memory footprint without degrading multi-view consistency. Extensive experiments show that StyleRF achieves superior 3D stylization quality with precise geometry reconstruction and it can generalize to various new styles in a zero-shot manner.

cs.CV↗

arXiv2023

Critical resolved shear stresses for slip and twinning in Mg-Y-Ca alloys and their effect on the ductility

The deformation mechanisms of an extruded Mg-5Y-0.08Ca (wt. %) alloy were analyzed by means of micropillar compression tests on single crystals along different orientations -- selected to activate specific deformation modes -- as well as slip trace analysis, transmission electron microscopy and transmission Kikuchi diffraction. The polycrystalline alloy presented a remarkable ductility in tension (~32%) and negligible differences in the yield strength between tension and compression. It was found that the presence of Y and Ca in solid solution led to a huge increase in the CRSS for basal slip (29 $\pm$ 5 MPa), pyramidal slip (203 $\pm$ 7 MPa) and tensile twin nucleation (above 148 MPa), while the CRSS for prismatic slip only increases up to 105 $\pm$ 4 MPa. The changes in the CRSS for slip and tensile twinning in Mg-Y-Ca alloys expectedly modify the dominant deformation mechanisms in polycrystals. In particular, tensile twinning is replaced by prismatic slip during compressive deformation along the a-axis. The reduction of twinning (which generally induces strong anisotropy in the plastic deformation in textured alloys), and the activation of prismatic slip (which provides an additional plastic deformation mechanism with limited hardening) were responsible for the large tensile ductility of the alloy.

cond-mat.mtrl-sci↗

arXiv2023

Deep-learning-aided Low-complexity DOA Estimators for Ultra-Massive MIMO Overlapped Receive Array

Massive multiple input multiple output(MIMO)-based fully-digital receive antenna arrays bring huge amount of complexity to both traditional direction of arrival(DOA) estimation algorithms and neural network training, which is difficult to satisfy high-precision and low-latency applications in future wireless communications. To address this challenge, two estimators called OPSC and OSAP-CBAM-CNN are proposed in this paper. The computational complexity of the traditional DOA algorithm is first considered to be reduced by dividing the total set of antennas into multiple overlapped subarrays uniformly, each subarray crosses each other proportionally and performs DOA estimation to generate coarse angles, and all angles are coherently combined to get the better estimation, the final DOA estimation can given by maximum likelihood alternating projection(ML-AP) in a very small range, which has a better performance than the direct partitioning of subarrays. To further reduce the complexity of traditional estimation algorithms, deep neural networks(DNN) are utilized to offline train the relationship between the received signal covariance matrix and the estimated angles. Due to the high complexity of the training network based on large-scale arrays, in the OSAP-CBAM-CNN method, the complex network is divided into several smaller networks based on the overlapped subarray to give rough DOA estimations, followed by coherent combining and AP algorithm to get the final DOA estimation. Simulation results show that as the number of antennas goes to large-scale, the proposed methods can achieve a remarkable complexity reduction over conventional ML-AP algorithm.

eess.SP↗

arXiv2022

Electronic Origin of High-Tc Maximization and Persistence in Trilayer Cuprate Superconductors

In high temperature cuprate superconductors, it was found that the superconducting transition temperature Tc depends on the number of CuO2 planes (n) in the structural unit and the maximum Tc is realized in the trilayer system (n=3). It was also found that the trilayer superconductors exhibit an unusual phase diagram that Tc keeps nearly constant in the overdoped region which is in strong contrast to the Tc decrease usually found in other cuprate superconductors. The electronic origin of the Tc maximization in the trilayer superconductors and its high Tc persistence in the overdoped region remains unclear. By taking high resolution laser-based angle resolved photoemission (ARPES) measurements, here we report our revelation of the microscopic origin of the unusual superconducting properties in the trilayer superconductors. For the first time we have observed the trilayer splitting in Bi2Sr2Ca2Cu3O10+d (Bi2223) superconductor. The observed Fermi surface, band structures, superconducting gap and the selective Bogoliubov band hybridizations can be well described by a three-layer interaction model. Quantitative information of the microscopic processes involving intra- and interlayer hoppings and pairings are extracted. The electronic origin of the maximum Tc in Bi2223 and the persistence of the high Tc in the overdoped region is revealed. These results provide key insights in understanding high Tc superconductivity and pave a way to further enhance Tc in the cuprate superconductors.

cond-mat.supr-con↗

arXiv2022

A Versatile Agent for Fast Learning from Human Instructors

In recent years, a myriad of superlative works on intelligent robotics policies have been done, thanks to advances in machine learning. However, inefficiency and lack of transfer ability hindered algorithms from pragmatic applications, especially in human-robot collaboration, when few-shot fast learning and high flexibility become a wherewithal. To surmount this obstacle, we refer to a "Policy Pool", containing pre-trained skills that can be easily accessed and reused. An agent is employed to govern the "Policy Pool" by unfolding requisite skills in a flexible sequence, contingent on task specific predilection. This predilection can be automatically interpreted from one or few human expert demonstrations. Under this hierarchical setting, our algorithm is able to pick up a sparse-reward, multi-stage knack with only one demonstration in a Mini-Grid environment, showing the potential for instantly mastering complex robotics skills from human instructors. Additionally, the innate quality of our algorithm also allows for lifelong learning, making it a versatile agent.

cs.AI↗

arXiv2022

Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning

Precision robotic manipulation tasks (insertion, screwing, precisely pick, precisely place) are required in many scenarios. Previous methods achieved good performance on such manipulation tasks. However, such methods typically require tedious calibration or expensive sensors. 3D/RGB-D cameras and torque/force sensors add to the cost of the robotic application and may not always be economical. In this work, we aim to solve these but using only weak-calibrated and low-cost webcams. We propose Binocular Alignment Learning (BAL), which could automatically learn the eye-hand coordination and points alignment capabilities to solve the four tasks. Our work focuses on working with unknown eye-hand coordination and proposes different ways of performing eye-in-hand camera calibration automatically. The algorithm was trained in simulation and used a practical pipeline to achieve sim2real and test it on the real robot. Our method achieves a competitively good result with minimal cost on the four tasks.

cs.RO↗

arXiv2022

Two Low-complexity DOA Estimators for Massive/Ultra-massive MIMO Receive Array

Eigen-decomposition-based direction finding methods of using large-scale/ultra-large-scale fully-digital receive antenna arrays lead to a high or ultra-high complexity. To address the complexity dilemma, in this paper, three low-complexity estimators are proposed: partitioned subarray auto-correlation combining (PSAC), partitioned subarray cross-correlation combining (PSCC) and power iteration max correlation successive convex approximation (PI-Max-CSCA). Compared with the conventional no-partitioned direction finding method like root multiple signal classification (Root-MUSIC), in the PSAC method, the total set of antennas are equally partitioned into subsets of antennas, called subarrays, each subarray performs independent DOA estimation, and all DOA estimates are coherently combined to give the final estimation. For a better performance, the cross-correlation among sub-arrays is further exploited in the PSCC method to achieve the near-Cramer-Rao lower bound (CRLB) performance with the help of auto-correlation. To further reduce the complexity, in the PI-Max-CSCA method, using a fraction of all subarrays to make an initial coarse direction measurement (ICDM), the power iterative method is adopted to compute the more precise steering vector (SV) by exploiting the total array, and a more accurate DOA value is found using ICDM and SV through the maximum correlation method solved by successive convex approximation. Simulation results show that as the number of antennas goes to large-scale, the proposed three methods can achieve a dramatic complexity reduction over conventional Root-MUISC. Particularly, the PSCC and PI-Max-CSCA can reach the CRLB while the PSAC shows a substantial performance loss.

eess.SP↗

arXiv2022

Conservation of the particle-hole symmetry in the pseudogap state in optimally-doped Bi2Sr2CuO6+δ superconductor

The pseudogap state is one of the most enigmatic characteristics in the anomalous normal state properties of the high temperature cuprate superconductors. A central issue is to reveal whether there is a symmetry breaking and which symmetries are broken across the pseudogap transition. By performing high resolution laser-based angle-resolved photoemission measurements on the optimally-doped Bi2Sr1.6La0.4CuO6+δ superconductor, we report the observations of the particle-hole symmetry conservation in both the superconducting state and the pseudogap state along the entire Fermi surface. These results provide key insights in understanding the nature of the pseudogap and its relation with high temperature superconductivity.

cond-mat.supr-con↗

arXiv2022

Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation

Reinforcement learning has shown a wide usage in robotics tasks, such as insertion and grasping. However, without a practical sim2real strategy, the policy trained in simulation could fail on the real task. There are also wide researches in the sim2real strategies, but most of those methods rely on heavy image rendering, domain randomization training, or tuning. In this work, we solve the insertion task using a pure visual reinforcement learning solution with minimum infrastructure requirement. We also propose a novel sim2real strategy, Real2Sim, which provides a novel and easier solution in policy adaptation. We discuss the advantage of Real2Sim compared with Sim2Real.

cs.RO↗

arXiv2022

Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array

For a sub-connected hybrid multiple-input multiple-output (MIMO) receiver with $K$ subarrays and $N$ antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. First, a DOA estimator of maximizing received power (Max-RP) is proposed to find the maximum value of $K$-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation (Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of Max-RP. Finally, to achieve the CRLB, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low-computational-complexities. In particular, the proposed Root-MUSIC plus Max-RP-QI scheme can reach the CRLB, and the proposed Max-RP and Max-RP-QI are still some performance losses $2dB\thicksim4dB$ compared to the CRLB.

eess.SP↗

arXiv2021

Error Analysis of Surrogate Models Constructed through Operations on Sub-models

Model-based methods are popular in derivative-free optimization (DFO). In most of them, a single model function is built to approximate the objective function. This is generally based on the assumption that the objective function is one blackbox. However, some real-life and theoretical problems show that the objective function may consist of several blackboxes. In those problems, the information provided by each blackbox may not be equal. In this situation, one could build multiple sub-models that are then combined to become a final model. In this paper, we analyze the relation between the accuracy of those sub-models and the model constructed through their operations. We develop a broad framework that can be used as a theoretical tool in model error analysis and future research in DFO algorithms design.

math.OC↗

arXiv2021

Improving Learning from Demonstrations by Learning from Experience

How to make imitation learning more general when demonstrations are relatively limited has been a persistent problem in reinforcement learning (RL). Poor demonstrations lead to narrow and biased date distribution, non-Markovian human expert demonstration makes it difficult for the agent to learn, and over-reliance on sub-optimal trajectories can make it hard for the agent to improve its performance. To solve these problems we propose a new algorithm named TD3fG that can smoothly transition from learning from experts to learning from experience. Our algorithm achieves good performance in the MUJOCO environment with limited and sub-optimal demonstrations. We use behavior cloning to train the network as a reference action generator and utilize it in terms of both loss function and exploration noise. This innovation can help agents extract a priori knowledge from demonstrations while reducing the detrimental effects of the poor Markovian properties of the demonstrations. It has a better performance compared to the BC+ fine-tuning and DDPGfD approach, especially when the demonstrations are relatively limited. We call our method TD3fG meaning TD3 from a generator.

cs.AI↗

arXiv2021

Canoe : A System for Collaborative Learning for Neural Nets

For highly distributed environments such as edge computing, collaborative learning approaches eschew the dependence on a global, shared model, in favor of models tailored for each location. Creating tailored models for individual learning contexts reduces the amount of data transfer, while collaboration among peers provides acceptable model performance. Collaboration assumes, however, the availability of knowledge transfer mechanisms, which are not trivial for deep learning models where knowledge isn't easily attributed to precise model slices. We present Canoe - a framework that facilitates knowledge transfer for neural networks. Canoe provides new system support for dynamically extracting significant parameters from a helper node's neural network and uses this with a multi-model boosting-based approach to improve the predictive performance of the target node. The evaluation of Canoe with different PyTorch and TensorFlow neural network models demonstrates that the knowledge transfer mechanism improves the model's adaptiveness to changes up to 3.5X compared to learning in isolation, while affording several magnitudes reduction in data movement costs compared to federated learning.

cs.LG↗

arXiv2021

Theoretical Study and Comparison of SPSA and RDSA Algorithms

Stochastic approximation (SA) algorithms are widely used in system optimization problems when only noisy measurements of the system are available. This paper studies two types of SA algorithms in a multivariate Kiefer-Wolfowitz setting: random-direction SA (RDSA) and simultaneous-perturbation SA (SPSA), and then describes the bias term, convergence, and asymptotic normality of RDSA algorithms. The gradient estimations in RDSA and SPSA have different forms and, consequently, use different types of random perturbations. This paper looks at various valid distributions for perturbations in RDSA and SPSA and then compares the two algorithms using mean-square errors computed from asymptotic distribution. From both a theoretical and numerical point of view, we find that SPSA generally outperforms RDSA.

math.OC↗