arXiv subjects

Dawei Zhang

Publications and source records attributed to Dawei Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2024

Safe and Stable Teleoperation of Quadrotor UAVs under Haptic Shared Autonomy

We present a novel approach that aims to address both safety and stability of a haptic teleoperation system within a framework of Haptic Shared Autonomy (HSA). We use Control Barrier Functions (CBFs) to generate the control input that follows the user's input as closely as possible while guaranteeing safety. In the context of stability of the human-in-the-loop system, we limit the force feedback perceived by the user via a small $L_2$-gain, which is achieved by limiting the control and the force feedback via a differential constraint. Specifically, with the property of HSA, we propose two pathways to design the control and the force feedback: Sequential Control Force (SCF) and Joint Control Force (JCF). Both designs can achieve safety and stability but with different responses to the user's commands. We conducted experimental simulations to evaluate and investigate the properties of the designed methods. We also tested the proposed method on a physical quadrotor UAV and a haptic interface.

cs.RO↗

arXiv2022

Compositionally Complex Perovskite Oxides as a New Class of Li-Ion Solid Electrolytes

Compositionally complex ceramics (CCCs), including high-entropy ceramics (HECs) as a subclass, offer new opportunities of materials discovery beyond the traditional methodology of searching new stoichiometric compounds. Herein, we establish new strategies of tailoring CCCs via a seamless combination of (1) non-equimolar compositional designs and (2) controlling microstructures and interfaces. Using oxide solid electrolytes for all-solid-state batteries as an exemplar, we validate these new strategies via discovering a new class of compositionally complex perovskite oxides (CCPOs) to show the possibility of improving ionic conductivities beyond the limit of conventional doping. As an example (amongst the 28 CCPOs examined), we demonstrate that the ionic conductivity can be improved by >60% in (Li0.375Sr0.4375)(Ta0.375Nb0.375Zr0.125Hf0.125)O3-δ, in comparison with the state-of-art (Li0.375Sr0.4375)(Ta0.75Zr0.25)O3-δ (LSTZ) baseline, via maintaining comparable electrochemical stability. Furthermore, the ionic conductivity can be improved by another >70% via grain boundary (GB) engineering, achieving >270% of the LSTZ baseline. This work suggests transformative new strategies for designing and tailoring HECs and CCCs, thereby opening a new window for discovering materials for energy storage and many other applications.

cond-mat.mtrl-sci↗

arXiv2022

Compositionally Complex Perovskite Oxides for Solar Thermochemical Water Splitting

Solar thermochemical hydrogen generation (STCH) is a promising approach for eco-friendly H2 production, but conventional STCH redox compounds often suffer from thermodynamic and kinetic limitations with limited tunability. Expanding from the nascent high-entropy ceramics field, this study explores a new class of compositionally complex perovskite oxides (La0.8Sr0.2)(Mn(1-x)/3Fe(1-x)/3CoxAl(1-x)/3)O3 for STCH. In situ X-ray diffraction demonstrates the phase stability during redox cycling and in situ X-ray photoelectron spectroscopy shows preferential redox of Co. The extent of reduction increases, but the intrinsic kinetics decreases, with increased Co content. Consequently, (La0.8Sr0.2)(Mn0.2Fe0.2Co0.4Al0.2)O3-δ achieves an optimal balance between the thermodynamics and kinetics properties. The combination of a moderate enthalpy of reduction, high entropy of reduction, and preferable surface oxygen exchange kinetics enables a maximum H2 yield of 395 +- 11 μmol g-1 in a short 1-hour redox duration. Entropy stabilization expectedly contributes to the structure stability during redox without phase transformation, which enables an exceptional STCH stability for >50 cycles under harsh interrupted conditions. The underlying redox mechanism is further elucidated by the density functional theory based parallel Monte Carlo computation, which represents a new computation paradigm first established here. This study suggests a new class of non-equimolar compositionally complex ceramics for STCH and chemical looping.

cond-mat.mtrl-sci↗

arXiv2022

Roles of scattered and ballistic photons in imaging through scattering media: a deep learning-based study

Scattering of light in complex media scrambles optical wavefronts and breaks the principles of conventional imaging methods. For decades, researchers have endeavored to conquer the problem by inventing approaches such as adaptive optics, iterative wavefront shaping, and transmission matrix measurement. That said, imaging through/into thick scattering media remains challenging to date. With the rapid development of computing power, deep learning has been introduced and shown potentials to reconstruct target information through complex media or from rough surfaces. But it also fails once coming to optically thick media where ballistic photons become negligible. Here, instead of treating deep learning only as an image extraction method, whose best-selling advantage is to avoid complicate physical models, we exploit it as a tool to explore the underlying physical principles. By adjusting the weights of ballistic and scattered photons through a random phasemask, it is found that although deep learning can extract images from both scattered and ballistic light, the mechanisms are different: scattering may function as an encryption key and decryption from scattered light is key sensitive, while extraction from ballistic light is stable. Based on this finding, it is hypothesized and experimentally confirmed that the foundation of the generalization capability of trained neural networks for different diffusers can trace back to the contribution of ballistic photons, even though their weights of photon counting in detection are not that significant. Moreover, the study may pave an avenue for using deep learning as a probe in exploring the unknown physical principles in various fields.

physics.optics↗

arXiv2022

Different Channels to Transmit Information in a Scattering Medium

A channel should be built to transmit information from one place to another. Imaging is 2 or higher dimensional information communication. Conventionally, an imaging channel comprises a lens and free spaces of its both sides. The transfer function of each part is known; thus, the response of a conventional imaging channel is known as well. Replacing the lens with a scattering layer, the image can still be extracted from the detection plane. That is to say, the scattering medium reconstructs the channel for imaging. Aided by deep learning, we find that different from the lens there are different channels in a scattering medium, i.e., the same scattering medium can construct different channels to match different manners of source encoding. Moreover, we found that without a valid channel the convolution law for a shift-invariant system, i.e., the output is the convolution of its point spread function (PSF) and the input object, is broken, and information cannot be transmitted onto the detection plane. In other words, valid channels are essential to transmit image information through even a shift-invariant system.

physics.optics↗

arXiv2022

Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction

Temporal knowledge prediction is a crucial task for the event early warning that has gained increasing attention in recent years, which aims to predict the future facts by using relevant historical facts on the temporal knowledge graphs. There are two main difficulties in this prediction task. First, from the historical facts point of view, how to model the evolutionary patterns of the facts to predict the query accurately. Second, from the query perspective, how to handle the two cases where the query contains seen and unseen entities in a unified framework. Driven by the two problems, we propose a novel adaptive pseudo-siamese policy network for temporal knowledge prediction based on reinforcement learning. Specifically, we design the policy network in our model as a pseudo-siamese policy network that consists of two sub-policy networks. In sub-policy network I, the agent searches for the answer for the query along the entity-relation paths to capture the static evolutionary patterns. And in sub-policy network II, the agent searches for the answer for the query along the relation-time paths to deal with unseen entities. Moreover, we develop a temporal relation encoder to capture the temporal evolutionary patterns. Finally, we design a gating mechanism to adaptively integrate the results of the two sub-policy networks to help the agent focus on the destination answer. To assess our model performance, we conduct link prediction on four benchmark datasets, the experimental results demonstrate that our method obtains considerable performance compared with existing methods.

cs.AI↗

arXiv2022

Transfer and evolution of structured polarization in a double-V atomic system

We numerically investigate the transfer of optical information from a vector-vortex control beam to an unstructured probe beam, as mediated by an atomic vapour. The right and left circular components of these beams drive the atomic transitions of a double-$V$ system, with the atoms acting as a spatially varying circular birefringent medium. Modelling the propagation of the light fields, we find that, for short distances, the vectorial light structure is transferred from the control field to the probe. However, for larger propagation lengths, diffraction causes the circular components of the probe field to spatially separate. We model this system for the D1 line of cold rubidium atoms. Our investigation is a first step to investigating the coupled dynamics of internal and external degrees of freedom of atoms in four wave mixing.

physics.optics↗

arXiv2022

MixKG: Mixing for harder negative samples in knowledge graph

Knowledge graph embedding~(KGE) aims to represent entities and relations into low-dimensional vectors for many real-world applications. The representations of entities and relations are learned via contrasting the positive and negative triplets. Thus, high-quality negative samples are extremely important in KGE. However, the present KGE models either rely on simple negative sampling methods, which makes it difficult to obtain informative negative triplets; or employ complex adversarial methods, which requires more training data and strategies. In addition, these methods can only construct negative triplets using the existing entities, which limits the potential to explore harder negative triplets. To address these issues, we adopt mixing operation in generating harder negative samples for knowledge graphs and introduce an inexpensive but effective method called MixKG. Technically, MixKG first proposes two kinds of criteria to filter hard negative triplets among the sampled negatives: based on scoring function and based on correct entity similarity. Then, MixKG synthesizes harder negative samples via the convex combinations of the paired selected hard negatives. Experiments on two public datasets and four classical KGE methods show MixKG is superior to previous negative sampling algorithms.

cs.AI↗

arXiv2022

21-Component Compositionally Complex Ceramics: Discovery of Ultrahigh-Entropy Weberite and Fergusonite Phases and a Pyrochlore-Weberite Transition

Two new high-entropy ceramics (HECs) in the weberite and fergusonite structures, along with unexpected formation of ordered pyrochlore phases with ultrahigh-entropy compositions and an abrupt pyrochlore-weberite transition, are discovered in a 21-component oxide system. While the Gibbs phase rule allows 21 equilibrium phases, nine out of the 13 compositions examined possess single HEC phases (with ultrahigh ideal configurational entropies: ~2.7kB per cation or higher on one sublattice in most cases). Notably, (15RE1/15)(Nb1/2Ta1/2)O4 possess a single monoclinic fergusonite (C2/c) phase and (15RE1/15)3(Nb1/2Ta1/2)1O7 form a single orthorhombic (C2221) weberite phase, where 15RE1/15 represents Sc1/15Y1/15La1/15Pr1/15Nd1/15Sm1/15Eu1/15Gd1/15Tb1/15Dy1/15Ho1/15Er1/15Tm1/15Yb1/15Lu1/15. Moreover, a series of eight (15RE1/15)2+x(Ti1/4Zr1/4Ce1/4Hf1/4)2-2x(Nb1/2Ta1/2)xO7 specimens all exhibit single phases, where a pyrochlore-weberite transition occurs within 0.75 < x < 0.8125. This cubic-to-orthorhombic transition does not change the temperature-dependent thermal conductivity appreciably, as the amorphous limit may have already been achieved in the ultrahigh-entropy 21-component oxides. These discoveries expand the diversity and complexity of HECs, towards many-component compositionally complex ceramics (CCCs) and ultrahigh-entropy ceramics.

cond-mat.mtrl-sci↗

arXiv2021

Knowledge graph enhanced recommender system

Knowledge Graphs (KGs) have shown great success in recommendation. This is attributed to the rich attribute information contained in KG to improve item and user representations as side information. However, existing knowledge-aware methods leverage attribute information at a coarse-grained level both in item and user side. In this paper, we proposed a novel attentive knowledge graph attribute network(AKGAN) to learn item attributes and user interests via attribute information in KG. Technically, AKGAN adopts a heterogeneous graph neural network framework, which has a different design between the first layer and the latter layer. With one attribute placed in the corresponding range of element-wise positions, AKGAN employs a novel interest-aware attention network, which releases the limitation that the sum of attention weight is 1, to model the complexity and personality of user interests towards attributes. Experimental results on three benchmark datasets show the effectiveness and explainability of AKGAN.

cs.IR↗

arXiv2021

A New Type of Compositionally Complex M5Si3 Silicides: Cation Ordering and Unexpected Phase Stability

A new type of compositionally complex (medium- or high-entropy) M5Si3 silicides is synthesized. Both (V1/5Cr1/5Nb1/5Ta1/5W1/5)5Si3 and (Ti1/5Zr1/5Nb1/5Mo1/5Hf1/5)5Si3 form single-phase homogenous solid solutions. Notably, (V1/5Cr1/5Nb1/5Ta1/5W1/5)5Si3 forms the hexagonal gamma (D88) phase, while all its five constituent binary silicides, V5Si3, Cr5Si3, Nb5Si3, Ta5Si3, and W5Si3, are stable in the tetragonal alpha (D8l) or beta (D8m) phases. Annealing at 1600C demonstrates that this hexagonal gamma phase is stable. Comparison of the experimental and calculated X-ray diffraction patterns, Rietveld refinements, and analysis of aberration-corrected scanning transmission electron microscopy high-angle annular dark-field images suggest cation ordering, which reduces the configurational entropy. This work expands the field of high-entropy and compositional complex ceramics by not only discovering a new compositional complex silicide phase but also demonstrating the cation ordering and unusual phase stability. These compositionally complex silicides can be combined with refractory high-entropy alloys to make the high-entropy counterparts to the Nb-silicide and Mo-Si-B composites.

cond-mat.mtrl-sci↗

arXiv2021

Discovery of a reversible redox-induced order-disorder transition in a 10-cation compositionally complex ceramic

This study discovers a reversible order-disorder transition (ODT) in a 10-cation compositionally complex ceramic, (Nd0.15Pr0.15Dy0.8Ho0.8Er0.8Ti0.2Yb0.1Hf0.1Zr0.1Nb0.8)O7-delta, induced via annealing in oxidized vs. reduced environments at 1600C. Notably, the 10-cation oxide remains a homogenous single-phase high-entropy solid solution before and after the ODT in the pyrochlore vs. fluorite structure that can be quenched. In-situ neutron diffraction reveals the oxygen vacancy formation and atomic displacement during this ODT. The temperature dependence of thermal conductivity is altered by the ODT. This study reveals a new pathway to induce ODT via a redox transition to tailor the properties of compositionally complex fluorite-based oxides.

cond-mat.mtrl-sci↗

arXiv2021

SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification

Recently, x-vector has been a successful and popular approach for speaker verification, which employs a time delay neural network (TDNN) and statistics pooling to extract speaker characterizing embedding from variable-length utterances. Improvement upon the x-vector has been an active research area, and enormous neural networks have been elaborately designed based on the x-vector, eg, extended TDNN (E-TDNN), factorized TDNN (F-TDNN), and densely connected TDNN (D-TDNN). In this work, we try to identify the optimal architectures from a TDNN based search space employing neural architecture search (NAS), named SpeechNAS. Leveraging the recent advances in the speaker recognition, such as high-order statistics pooling, multi-branch mechanism, D-TDNN and angular additive margin softmax (AAM) loss with a minimum hyper-spherical energy (MHE), SpeechNAS automatically discovers five network architectures, from SpeechNAS-1 to SpeechNAS-5, of various numbers of parameters and GFLOPs on the large-scale text-independent speaker recognition dataset VoxCeleb1. Our derived best neural network achieves an equal error rate (EER) of 1.02% on the standard test set of VoxCeleb1, which surpasses previous TDNN based state-of-the-art approaches by a large margin. Code and trained weights are in https://github.com/wentaozhu/speechnas.git

cs.SD↗

arXiv2021

Dynamic Multi-scale Convolution for Dialect Identification

Time Delay Neural Networks (TDNN)-based methods are widely used in dialect identification. However, in previous work with TDNN application, subtle variant is being neglected in different feature scales. To address this issue, we propose a new architecture, named dynamic multi-scale convolution, which consists of dynamic kernel convolution, local multi-scale learning, and global multi-scale pooling. Dynamic kernel convolution captures features between short-term and long-term context adaptively. Local multi-scale learning, which represents multi-scale features at a granular level, is able to increase the range of receptive fields for convolution operation. Besides, global multi-scale pooling is applied to aggregate features from different bottleneck layers in order to collect information from multiple aspects. The proposed architecture significantly outperforms state-of-the-art system on the AP20-OLR-dialect-task of oriental language recognition (OLR) challenge 2020, with the best average cost performance (Cavg) of 0.067 and the best equal error rate (EER) of 6.52%. Compared with the known best results, our method achieves 9% of Cavg and 45% of EER relative improvement, respectively. Furthermore, the parameters of proposed model are 91% fewer than the best known model.

cs.CL↗

arXiv2021

Multi-Level Graph Contrastive Learning

Graph representation learning has attracted a surge of interest recently, whose target at learning discriminant embedding for each node in the graph. Most of these representation methods focus on supervised learning and heavily depend on label information. However, annotating graphs are expensive to obtain in the real world, especially in specialized domains (i.e. biology), as it needs the annotator to have the domain knowledge to label the graph. To approach this problem, self-supervised learning provides a feasible solution for graph representation learning. In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs. Specifically, we introduce a novel contrastive view - topological and feature space views. The original graph is first-order approximation structure and contains uncertainty or error, while the $k$NN graph generated by encoding features preserves high-order proximity. Thus $k$NN graph generated by encoding features not only provide a complementary view, but is more suitable to GNN encoder to extract discriminant representation. Furthermore, we develop a multi-level contrastive mode to preserve the local similarity and semantic similarity of graph-structured data simultaneously. Extensive experiments indicate MLGCL achieves promising results compared with the existing state-of-the-art graph representation learning methods on seven datasets.

cs.LG↗

arXiv2021

Short-Range Order and Origin of the Low Thermal Conductivity in Compositionally Complex Rare-Earth Niobates and Tantalates

Rare-earth niobates and tantalates possess low thermal conductivities, which can be further reduced in high-entropy compositions. Here, a large number of 40 compositions are synthesized to investigate the origin of low thermal conductivity. Amongst, 29 possess single (nominally cubic) fluorite phases and most of them are new compositionally complex (medium- or high-entropy) compositions. One new finding is that doping 2 % of light element cations can further reduce thermal conductivity. This large data set enables the discovery of a negative correlation between the thermal conductivity and averaged radius ratio of the 3+/5+ cations. While this ratio is still below the threshold for forming long-range ordered weberite phases, this correlation suggests the reduced thermal conductivity is related to short-range weberite order, which is indeed revealed by diffuse scattering in X-ray and neutron diffraction. Specifically, neutron diffraction is used characterize five selected specimens. A better fit to a weberite structure is found at nanoscale (~1 nm). The characteristic length (domain size) is smaller but with stronger short-range ordering in more insulative materials. As it approaches the Ioffe-Regel limit, the phonon limit breaks down and "diffusons" give rise to the observed amorphous-like thermal conductivity. Disordered oxygen sublattices are also confirmed by neutron diffraction.

cond-mat.mtrl-sci↗

arXiv2021

Stable Haptic Teleoperation of UAVs via Small $L_2$ Gain and Control Barrier Functions

We present a novel haptic teleoperation approach that considers not only the safety but also the stability of a teleoperation system. Specifically, we build upon previous work on haptic shared control, which uses control barrier functions (CBFs) to generate a reference haptic feedback that informs the human operator on the internal state of the system, helping them to safely navigate the robot without taking away their control authority. Crucially, in this approach the force rendered to the user is not directly reflected in the motion of the robot (which is still directly controlled by the user); however, previous work in the area neglected to consider the feedback loop through the user, possibly resulting in unstable closed trajectories. In this paper we introduce a differential constraint on the rendered force that makes the system finite-gain $L_2$ stable; the constraint results in a Quadratically Constrained Quadratic Program (QCQP), for which we provide a closed-form solution. Our constraint is related to but less restrictive than the typical passivity constraint used in previous literature. We conducted an experimental simulation in which a human operator flies a UAV near an obstacle to evaluate the proposed method.

cs.RO↗

arXiv2021

Haptic Feedback Improves Human-Robot Agreement and User Satisfaction in Shared-Autonomy Teleoperation

Shared autonomy teleoperation can guarantee safety, but does so by reducing the human operator's control authority, which can lead to reduced levels of human-robot agreement and user satisfaction. This paper presents a novel haptic shared autonomy teleoperation paradigm that uses haptic feedback to inform the user about the inner state of a shared autonomy paradigm, while still guaranteeing safety. This differs from haptic shared control, which uses haptic feedback to inform the user's actions, but gives the human operator full control over the robot's actions. We conducted a user study in which twelve users flew a simulated UAV in a search-and-rescue task with no assistance or assistance provided by haptic shared control, shared autonomy, or haptic shared autonomy. All assistive teleoperation methods use control barrier functions to find a control command that is both safe and as close as possible to the human-generated control command. For assistive teleoperation conditions with haptic feedback, we apply a force to the user that is proportional to the difference between the human-generated control and the safe control. We find that haptic shared autonomy improves the user's task performance and satisfaction. We also find that haptic feedback in assistive teleoperation can improve the user's situational awareness. Finally, results show that adding haptic feedback to shared-autonomy teleoperation can improve human-robot agreement.

cs.RO↗