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Chao Chen

Publications and source records attributed to Chao Chen.

At least 883 records · Page 49Linked to original sources
arXiv2019

Controllable Defect Driven Symmetry Change and Domain Structure Evolution in BiFeO3 with Enhanced Tetragonality

Defect engineering has been a powerful tool to enable the creation of exotic phases and the discovery of intriguing phenomena in ferroelectric oxides. However, accurate control the concentration of defects remains a big challenge. In this work, ion implantation, that can provide controllable point defects, allows us the ability to produce a controlled defect-driven true super-tetragonal (T) phase with enhanced tetragonality in ferroelectric BiFeO3 thin films. This point defect engineering is found to drive the phase transition from the as-grown mixed rhombohedral-like (R) and tetragonal-like (MC) phase to true tetragonal (T) symmetry. By further increasing the injected dose of He ion, we demonstrate an enhanced tetragonality super-tetragonal (super-T) phase with the largest c/a ratio (~ 1.3) that has ever been experimentally achieved in BiFeO3. A combination of morphology change and domain evolution further confirm that the mixed R/MC phase structure transforms to the single-domain-state true tetragonal phase. Moreover, the re-emergence of R phase and in-plane stripe nanodomains after heat treatment reveal the memory effect and reversible phase transition. Our findings demonstrate the control of R-Mc-T-super T symmetry changes and the creation of true T phase BiFeO3 with enhanced tetragonality through controllable defect engineering. This work also provides a pathway to generate large tetragonality (or c/a ratio) that could be extended to other ferroelectric material systems (such as PbTiO3, BaTiO3 and HfO2) which may lead to strong polarization enhancement.

cond-mat.mtrl-sci↗
arXiv2019

Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection

The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented. In many hyperspectral target detection problems, acquiring accurately labeled training data is difficult. Furthermore, each pixel containing target is likely to be a mixture of both target and non-target signatures (i.e., sub-pixel targets), making extracting a pure prototype signature for the target class from the data extremely difficult. The proposed approach addresses these problems by introducing a data mixing model and optimizing the response of the hybrid sub-pixel detector within a multiple instance learning framework. The proposed approach iterates between estimating a set of discriminative target and non-target signatures and solving a sparse unmixing problem. After learning target signatures, a signature based detector can then be applied on test data. Both simulated and real hyperspectral target detection experiments show the proposed algorithm is effective at learning discriminative target signatures and achieves superior performance over state-of-the-art comparison algorithms.

cs.CV↗
arXiv2018

Manipulation of Conductive Domain Walls in Confined Ferroelectric Nano-islands

Conductive ferroelectric domain walls--ultra-narrow and configurable conduction paths, have been considered as essential building blocks for future programmable domain wall electronics. For applications in high density devices, it is imperative to explore the conductive domain walls in small confined systems while earlier investigations have hitherto focused on thin films or bulk single crystals, noting that the size-confined effects will certainly modulate seriously the domain structure and wall transport. Here, we demonstrate an observation and manipulation of conductive domain walls confined within small BiFeO3 nano-islands aligned in high density arrays. Using conductive atomic force microscopy (CAFM), we are able to distinctly visualize various types of conductive domain walls, including the head-to-head charged walls (CDWs), zigzag walls (zigzag-DWs), and typical 71° head-to-tail neutral walls (NDWs). The CDWs exhibit remarkably enhanced metallic conductivity with current of ~ nA order in magnitude and 104 times larger than that inside domains (0.01 ~ 0.1 pA), while the semiconducting NDWs allow also much smaller current ~ 10 pA than the CDWs. The substantially difference in conductivity for dissimilar walls enables additional manipulations of various wall conduction states for individual addressable nano-islands via electrically tuning of their domain structures. A controllable writing of four distinctive states by applying various scanning bias voltages is achieved, offering opportunities for developing multilevel high density memories.

cond-mat.mes-hall↗
arXiv2018

An Adaptive Markov Random Field for Structured Compressive Sensing

Exploiting intrinsic structures in sparse signals underpins the recent progress in compressive sensing (CS). The key for exploiting such structures is to achieve two desirable properties: generality (\ie, the ability to fit a wide range of signals with diverse structures) and adaptability (\ie, being adaptive to a specific signal). Most existing approaches, however, often only achieve one of these two properties. In this study, we propose a novel adaptive Markov random field sparsity prior for CS, which not only is able to capture a broad range of sparsity structures, but also can adapt to each sparse signal through refining the parameters of the sparsity prior with respect to the compressed measurements. To maximize the adaptability, we also propose a new sparse signal estimation where the sparse signals, support, noise and signal parameter estimation are unified into a variational optimization problem, which can be effectively solved with an alternative minimization scheme. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed method in recovery accuracy, noise tolerance, and runtime.

eess.SP↗
arXiv2018

A Robust Hierarchical Solver for Ill-conditioned Systems with Applications to Ice Sheet Modeling

A hierarchical solver is proposed for solving sparse ill-conditioned linear systems in parallel. The solver is based on a modification of the LoRaSp method, but employs a deferred-compression technique, which provably reduces the approximation error and significantly improves efficiency. Moreover, the deferred-compression technique introduces minimal overhead and does not affect parallelism. As a result, the new solver achieves linear computational complexity under mild assumptions and excellent parallel scalability. To demonstrate the performance of the new solver, we focus on applying it to solve sparse linear systems arising from ice sheet modeling. The strong anisotropic phenomena associated with the thin structure of ice sheets creates serious challenges for existing solvers. To address the anisotropy, we additionally developed a customized partitioning scheme for the solver, which captures the strong-coupling direction accurately. In general, the partitioning can be computed algebraically with existing software packages, and thus the new solver is generalizable for solving other sparse linear systems. Our results show that ice sheet problems of about 300 million degrees of freedom have been solved in just a few minutes using a thousand processors.

math.NA↗
arXiv2018

Learning to Detect Instantaneous Changes with Retrospective Convolution and Static Sample Synthesis

Change detection has been a challenging visual task due to the dynamic nature of real-world scenes. Good performance of existing methods depends largely on prior background images or a long-term observation. These methods, however, suffer severe degradation when they are applied to detection of instantaneously occurred changes with only a few preceding frames provided. In this paper, we exploit spatio-temporal convolutional networks to address this challenge, and propose a novel retrospective convolution, which features efficient change information extraction between the current frame and frames from historical observation. To address the problem of foreground-specific over-fitting in learning-based methods, we further propose a data augmentation method, named static sample synthesis, to guide the network to focus on learning change-cued information rather than specific spatial features of foreground. Trained end-to-end with complex scenarios, our framework proves to be accurate in detecting instantaneous changes and robust in combating diverse noises. Extensive experiments demonstrate that our proposed method significantly outperforms existing methods.

cs.CV↗
arXiv2018

Collaborative Filtering with Stability

Collaborative filtering (CF) is a popular technique in today's recommender systems, and matrix approximation-based CF methods have achieved great success in both rating prediction and top-N recommendation tasks. However, real-world user-item rating matrices are typically sparse, incomplete and noisy, which introduce challenges to the algorithm stability of matrix approximation, i.e., small changes in the training data may significantly change the models. As a result, existing matrix approximation solutions yield low generalization performance, exhibiting high error variance on the training data, and minimizing the training error may not guarantee error reduction on the test data. This paper investigates the algorithm stability problem of matrix approximation methods and how to achieve stable collaborative filtering via stable matrix approximation. We present a new algorithm design framework, which (1) introduces new optimization objectives to guide stable matrix approximation algorithm design, and (2) solves the optimization problem to obtain stable approximation solutions with good generalization performance. Experimental results on real-world datasets demonstrate that the proposed method can achieve better accuracy compared with state-of-the-art matrix approximation methods and ensemble methods in both rating prediction and top-N recommendation tasks.

cs.LG↗
arXiv2018

Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation

Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove the domain shift. Target domain samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the hyperplane learned from the source domain. To alleviate this issue, we propose to joint domain alignment and discriminative feature learning, which could benefit both domain alignment and final classification. Specifically, an instance-based discriminative feature learning method and a center-based discriminative feature learning method are proposed, both of which guarantee the domain invariant features with better intra-class compactness and inter-class separability. Extensive experiments show that learning the discriminative features in the shared feature space can significantly boost the performance of deep domain adaptation methods.

cs.LG↗
arXiv2018

A Topological Regularizer for Classifiers via Persistent Homology

Regularization plays a crucial role in supervised learning. Most existing methods enforce a global regularization in a structure agnostic manner. In this paper, we initiate a new direction and propose to enforce the structural simplicity of the classification boundary by regularizing over its topological complexity. In particular, our measurement of topological complexity incorporates the importance of topological features (e.g., connected components, handles, and so on) in a meaningful manner, and provides a direct control over spurious topological structures. We incorporate the new measurement as a topological penalty in training classifiers. We also pro- pose an efficient algorithm to compute the gradient of such penalty. Our method pro- vides a novel way to topologically simplify the global structure of the model, without having to sacrifice too much of the flexibility of the model. We demonstrate the effectiveness of our new topological regularizer on a range of synthetic and real-world datasets.

cs.LG↗
arXiv2018

Defect-induced exchange bias in a single SrRuO3 layer

Exchange bias stems from the interaction between different magnetic phases and therefore it generally occurs in magnetic multilayers. Here we present a large exchange bias in a single SrRuO3 layer induced by helium ion irradiation. When the fluence increases, the induced defects not only suppress the magnetization and the Curie temperature, but also drive a metal-insulator transition at a low temperature. In particular, a large exchange bias field up to around 0.36 T can be created by the irradiation. This large exchange bias is related to the coexistence of different magnetic and structural phases that are introduced by embedded defects. Our work demonstrates that spintronic properties in complex oxides can be created and enhanced by applying ion irradiation.

cond-mat.mtrl-sci↗
arXiv2018

Parameter Transfer Extreme Learning Machine based on Projective Model

Recent years, transfer learning has attracted much attention in the community of machine learning. In this paper, we mainly focus on the tasks of parameter transfer under the framework of extreme learning machine (ELM). Unlike the existing parameter transfer approaches, which incorporate the source model information into the target by regularizing the di erence between the source and target domain parameters, an intuitively appealing projective-model is proposed to bridge the source and target model parameters. Specifically, we formulate the parameter transfer in the ELM networks by the means of parameter projection, and train the model by optimizing the projection matrix and classifier parameters jointly. Further more, the `L2,1-norm structured sparsity penalty is imposed on the source domain parameters, which encourages the joint feature selection and parameter transfer. To evaluate the e ectiveness of the proposed method, comprehensive experiments on several commonly used domain adaptation datasets are presented. The results show that the proposed method significantly outperforms the non-transfer ELM networks and other classical transfer learning methods.

cs.LG↗
arXiv2018

CrowdExpress: A Probabilistic Framework for On-Time Crowdsourced Package Deliveries

Speed and cost of logistics are two major concerns to on-line shoppers, but they generally conflict with each other in nature. To alleviate the contradiction, we propose to exploit existing taxis that are transporting passengers on the street to relay packages collaboratively, which can simultaneously lower the cost and accelerate the speed. Specifically, we propose a probabilistic framework containing two phases called CrowdExpress for the on-time package express deliveries. In the first phase, we mine the historical taxi GPS trajectory data offline to build the package transport network. In the second phase, we develop an online adaptive taxi scheduling algorithm to find the path with the maximum arriving-on-time probability "on-the-fly" upon real- time requests, and direct the package routing accordingly. Finally, we evaluate the system using the real-world taxi data generated by over 19,000 taxis in a month in the city of New York, US. Results show that around 9,500 packages can be delivered successfully on time per day with the success rate over 94%, moreover, the average computation time is within 25 milliseconds.

cs.OH↗
arXiv2018

DRLGENCERT: Deep Learning-based Automated Testing of Certificate Verification in SSL/TLS Implementations

The Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protocols are the foundation of network security. The certificate verification in SSL/TLS implementations is vital and may become the weak link in the whole network ecosystem. In previous works, some research focused on the automated testing of certificate verification, and the main approaches rely on generating massive certificates through randomly combining parts of seed certificates for fuzzing. Although the generated certificates could meet the semantic constraints, the cost is quite heavy, and the performance is limited due to the randomness. To fill this gap, in this paper, we propose DRLGENCERT, the first framework of applying deep reinforcement learning to the automated testing of certificate verification in SSL/TLS implementations. DRLGENCERT accepts ordinary certificates as input and outputs newly generated certificates which could trigger discrepancies with high efficiency. Benefited by the deep reinforcement learning, when generating certificates, our framework could choose the best next action according to the result of a previous modification, instead of simple random combinations. At the same time, we developed a set of new techniques to support the overall design, like new feature extraction method for X.509 certificates, fine-grained differential testing, and so forth. Also, we implemented a prototype of DRLGENCERT and carried out a series of real-world experiments. The results show DRLGENCERT is quite efficient, and we obtained 84,661 discrepancy-triggering certificates from 181,900 certificate seeds, say around 46.5% effectiveness. Also, we evaluated six popular SSL/TLS implementations, including GnuTLS, MatrixSSL, MbedTLS, NSS, OpenSSL, and wolfSSL. DRLGENCERT successfully discovered 23 serious certificate verification flaws, and most of them were previously unknown.

cs.CR↗
arXiv2018

Crowd-Powered Sensing and Actuation in Smart Cities: Current Issues and Future Directions

With the advent of seamless connection of human, machine, and smart things, there is an emerging trend to leverage the power of crowds (e.g., citizens, mobile devices, and smart things) to monitor what is happening in a city, understand how the city is evolving, and further take actions to enable better quality of life, which is referred to as Crowd-Powered Smart City (CPSC). In this article, we provide a literature review for CPSC and identify future research opportunities. Specifically, we first define the concepts with typical CPSC applications. Then, we present the main characteristics of CPSC and further highlight the research issues. In the end, we point out existing limitations which can inform and guide future research directions.

cs.HC↗
arXiv2018

On Information-Theoretic Characterizations of Markov Random Fields and Subfields

Let $X_i, i \in V$ form a Markov random field (MRF) represented by an undirected graph $G = (V,E)$, and $V'$ be a subset of $V$. We determine the smallest graph that can always represent the subfield $X_i, i \in V'$ as an MRF. Based on this result, we obtain a necessary and sufficient condition for a subfield of a Markov tree to be also a Markov tree. When $G$ is a path so that $X_i, i \in V$ form a Markov chain, it is known that the $I$-Measure is always nonnegative and the information diagram assumes a very special structure Kawabata and Yeung (1992). We prove that Markov chain is essentially the only MRF such that the $I$-Measure is always nonnegative. By applying our characterization of the smallest graph representation of a subfield of an MRF, we develop a recursive approach for constructing information diagrams for MRFs. Our work is built on the set-theoretic characterization of an MRF in Yeung, Lee, and Ye (2002).

cs.DM↗
arXiv2018

18-qubit entanglement with photon's three degrees of freedom

A central theme in quantum information science is to coherently control an increasing number of quantum particles as well as their internal and external degrees of freedom (DoFs), meanwhile maintaining a high level of coherence. The ability to create and verify multiparticle entanglement with individual control and measurement of each qubit serves as an important benchmark for quantum technologies. To this end, genuine multipartite entanglement have been reported up to 14 trapped ions, 10 photons, and 10 superconducting qubits. Here, we experimentally demonstrate an 18-qubit Greenberger-Horne-Zeilinger (GHZ) entanglement by simultaneous exploiting three different DoFs of six photons, including their paths, polarization, and orbital angular momentum (OAM). We develop high-stability interferometers for reversible quantum logic operations between the photon's different DoFs with precision and efficiencies close to unity, enabling simultaneous readout of 262,144 outcome combinations of the 18-qubit state. A state fidelity of 0.708(16) is measured, confirming the genuine entanglement of all the 18 qubits.

quant-ph↗
arXiv2017

A distributed-memory hierarchical solver for general sparse linear systems

We present a parallel hierarchical solver for general sparse linear systems on distributed-memory machines. For large-scale problems, this fully algebraic algorithm is faster and more memory-efficient than sparse direct solvers because it exploits the low-rank structure of fill-in blocks. Depending on the accuracy of low-rank approximations, the hierarchical solver can be used either as a direct solver or as a preconditioner. The parallel algorithm is based on data decomposition and requires only local communication for updating boundary data on every processor. Moreover, the computation-to-communication ratio of the parallel algorithm is approximately the volume-to-surface-area ratio of the subdomain owned by every processor. We present various numerical results to demonstrate the versatility and scalability of the parallel algorithm.

math.NA↗