arXiv subjects

Fei Ye

Publications and source records attributed to Fei Ye.

At least 73 records · Page 4Linked to original sources

arXiv2020

Attribute Restoration Framework for Anomaly Detection

With the recent advances in deep neural networks, anomaly detection in multimedia has received much attention in the computer vision community. While reconstruction-based methods have recently shown great promise for anomaly detection, the information equivalence among input and supervision for reconstruction tasks can not effectively force the network to learn semantic feature embeddings. We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors. Through forcing the network to restore the original image, the semantic feature embeddings related to the erased attributes are learned by the network. During testing phases, because anomalous data are restored with the attribute learned from the normal data, the restoration error is expected to be large. Extensive experiments have demonstrated that the proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets, especially on ImageNet, increasing the AUROC of the top-performing baseline by 10.1%. We also evaluate our method on a real-world anomaly detection dataset MVTec AD and a video anomaly detection dataset ShanghaiTech.

cs.CV↗

arXiv2020

Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework

Surrogate task based methods have recently shown great promise for unsupervised image anomaly detection. However, there is no guarantee that the surrogate tasks share the consistent optimization direction with anomaly detection. In this paper, we return to a direct objective function for anomaly detection with information theory, which maximizes the distance between normal and anomalous data in terms of the joint distribution of images and their representation. Unfortunately, this objective function is not directly optimizable under the unsupervised setting where no anomalous data is provided during training. Through mathematical analysis of the above objective function, we manage to decompose it into four components. In order to optimize in an unsupervised fashion, we show that, under the assumption that distribution of the normal and anomalous data are separable in the latent space, its lower bound can be considered as a function which weights the trade-off between mutual information and entropy. This objective function is able to explain why the surrogate task based methods are effective for anomaly detection and further point out the potential direction of improvement. Based on this object function we introduce a novel information theoretic framework for unsupervised image anomaly detection. Extensive experiments have demonstrated that the proposed framework significantly outperforms several state-of-the-arts on multiple benchmark data sets.

cs.CV↗

arXiv2020

Dynamic fingerprint of fractionalized excitations in single-crystalline Cu$_3$Zn(OH)$_6$FBr

Quantum spin liquid (QSL) represents a new class of condensed matter states characterized by the long-range many-body entanglement of topological orders. The most prominent feature of the elusive QSL state is the existence of fractionalized spin excitations. Subject to the strong quantum fluctuations, the spin-1/2 antiferromagnetic system on a kagome lattice is the promising candidate for hosting a QSL ground state, but the structurally ideal realization is rare. Here, we report Raman scattering on the single crystalline Cu$_3$Zn(OH)$_6$FBr, and confirm that the ideal kagome structure remains down to low temperatures without any lattice distortion by the angle-resolved polarized Raman responses and second-harmonic-generation measurements. Furthermore, at low temperatures the Raman scattering reveals a continuum of the spin excitations in Cu$_3$Zn(OH)$_6$FBr, in contrast to the sharp magnon peak in the ordered kagome antiferromagnet EuCu$_3$(OH)$_6$Cl$_3$. Such magnetic Raman continuum, in particular, the substantial low-energy one-pair spinon excitation serves as strong evidence for fractionalized spin excitations in Cu$_3$Zn(OH)$_6$FBr.

cond-mat.str-el↗

arXiv2020

Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles

Recent advances in supervised learning and reinforcement learning have provided new opportunities to apply related methodologies to automated driving. However, there are still challenges to achieve automated driving maneuvers in dynamically changing environments. Supervised learning algorithms such as imitation learning can generalize to new environments by training on a large amount of labeled data, however, it can be often impractical or cost-prohibitive to obtain sufficient data for each new environment. Although reinforcement learning methods can mitigate this data-dependency issue by training the agent in a trial-and-error way, they still need to re-train policies from scratch when adapting to new environments. In this paper, we thus propose a meta reinforcement learning (MRL) method to improve the agent's generalization capabilities to make automated lane-changing maneuvers at different traffic environments, which are formulated as different traffic congestion levels. Specifically, we train the model at light to moderate traffic densities and test it at a new heavy traffic density condition. We use both collision rate and success rate to quantify the safety and effectiveness of the proposed model. A benchmark model is developed based on a pretraining method, which uses the same network structure and training tasks as our proposed model for fair comparison. The simulation results shows that the proposed method achieves an overall success rate up to 20% higher than the benchmark model when it is generalized to the new environment of heavy traffic density. The collision rate is also reduced by up to 18% than the benchmark model. Finally, the proposed model shows more stable and efficient generalization capabilities adapting to the new environment, and it can achieve 100% successful rate and 0% collision rate with only a few steps of gradient updates.

cs.LG↗

arXiv2020

Superconductivity in a hole-doped Mott-insulating triangular adatom layer on a silicon surface

Adsorption of one-third monolayer of Sn on an atomically-clean Si(111) substrate produces a two-dimensional triangular adatom lattice with one unpaired electron per site. This dilute adatom reconstruction is an antiferromagnetic Mott insulator; however, the system can be modulation-doped and metallized using heavily-doped p-type Si(111) substrates. Here, we show that the hole-doped dilute adatom layer on a degenerately doped p-type Si(111) wafer is superconducting with a critical temperature of 4.7 +- 0.3 K. While a phonon-mediated coupling scenario would be consistent with the observed TC, Mott correlations in the Sn-derived dangling-bond surface state could suppress the s-wave pairing channel. The latter suggests that the superconductivity in this triangular adatom lattice may be unconventional.

cond-mat.supr-con↗

arXiv2020

Learning latent representations across multiple data domains using Lifelong VAEGAN

The problem of catastrophic forgetting occurs in deep learning models trained on multiple databases in a sequential manner. Recently, generative replay mechanisms (GRM), have been proposed to reproduce previously learned knowledge aiming to reduce the forgetting. However, such approaches lack an appropriate inference model and therefore can not provide latent representations of data. In this paper, we propose a novel lifelong learning approach, namely the Lifelong VAEGAN (L-VAEGAN), which not only induces a powerful generative replay network but also learns meaningful latent representations, benefiting representation learning. L-VAEGAN can allow to automatically embed the information associated with different domains into several clusters in the latent space, while also capturing semantically meaningful shared latent variables, across different data domains. The proposed model supports many downstream tasks that traditional generative replay methods can not, including interpolation and inference across different data domains.

cs.CV↗

arXiv2020

Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning

Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan, overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane change behaviors can be a major cause of traffic flow disruptions and even crashes. While many rule-based methods have been proposed to solve lane change problems for autonomous driving, they tend to exhibit limited performance due to the uncertainty and complexity of the driving environment. Machine learning-based methods offer an alternative approach, as Deep reinforcement learning (DRL) has shown promising success in many application domains including robotic manipulation, navigation, and playing video games. However, applying DRL to autonomous driving still faces many practical challenges in terms of slow learning rates, sample inefficiency, and safety concerns. In this study, we propose an automated lane change strategy using proximal policy optimization-based deep reinforcement learning, which shows great advantages in learning efficiency while still maintaining stable performance. The trained agent is able to learn a smooth, safe, and efficient driving policy to make lane-change decisions (i.e. when and how) in a challenging situation such as dense traffic scenarios. The effectiveness of the proposed policy is validated by using metrics of task success rate and collision rate. The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.

cs.LG↗

arXiv2020

Core-level x-ray photoemission and Raman spectroscopy studies on electronic structures in Mott-Hubbard type nickelate oxide NdNiO$_2$

We perform core-level X-ray photoemission spectroscopy (XPS) and electronic Raman scattering studies of electronic structures and spin fluctuations in the bulk samples of the nickelate oxide NdNiO$_2$. According to Nd $3d$ and O $1s$ XPS spectra, we conclude that NdNiO$_2$ has a large transfer energy. From the analysis of the main line of the Ni $2p_{3/2}$ XPS, we confirm the NiO$_2$ planes in NdNiO$_2$ are of Mott-Hubbard type in the Zaanen-Sawatzky-Allen scheme. The two-magnon peak in the Raman scattering provides direct evidence for the strong spin-fluctuation in NdNiO$_2$. The peak position determines the antiferromagnetic exchange $J=25$~meV. Our experimental results agree well with our previous theoretical results.

cond-mat.supr-con↗

arXiv2020

Using observed bacteria concentration and modeled transit time under an analytical framework to estimate overall removal rate of fecal coliform in an estuary

Abundance of fecal coliform (FC) is widely used to indicate the potential presence of pathogens, the No.1 cause of water impairments in the U.S. Despite extensive monitoring efforts, assessing and modeling FC pollution still faces challenges, largely owing to the uncertainties in estimation of overall removal rate (K). This study proposes an alternative method to estimate in situ K by combining observational data, hydrodynamic simulation, and analytical solution. The method requires the observed spatial distribution of FC concentration along an estuarine channel and the numerically-simulated transit time, and converts the K estimation from a temporal problem into a spatial problem, potentially reducing survey duration, effort, and cost. Application of the method gave an estimation of K = 0.5 d-1 on average for the Nassawadox Creek in Chesapeake Bay. The numerical and analytical model results with the estimated K agreed well with the observation, demonstrating the credibility of the method.

physics.ao-ph↗

arXiv2019

Molecular Beam Epitaxy Grown $Cr_2Te_3$ Thin Films with Tunable Curie Temperatures

Materials with perpendicular magnetic anisotropy (PMA) effect with high Curie temperature ($T_C$) is essential in applications. In this work, $Cr_2Te_3$ thin films showing PMA with $T_C$ ranging from 165 K to 295 K were successfully grown on $Al_2O_3$ by the molecular beam epitaxy (MBE) technique. The structural analysis, magneto-transport and magnetic characterizations were conducted to study the physical origin of the improved $T_C$. In particular, ferromagnetic (FM) and antiferromagnetic (AFM) ordering competition were investigated. A phenomenological model based on the coupling degree between FM and AFM ordering was proposed to explain the observed $T_C$ enhancement. Our findings indicate that the $T_C$ of $Cr_2Te_3$ thin film can be tuned, which make it hold the potential for various magnetic applications.

cond-mat.mtrl-sci↗

arXiv2019

Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders

Most of the data-driven approaches applied to bearing fault diagnosis up to date are established in the supervised learning paradigm, which usually requires a large set of labeled data collected a priori. In practical applications, however, obtaining accurate labels based on real-time bearing conditions can be far more challenging than simply collecting a huge amount of unlabeled data using various sensors. In this paper, we thus propose a semi-supervised learning approach for bearing anomaly detection using variational autoencoder (VAE) based deep generative models, which allows for effective utilization of dataset when only a small subset of data have labels. Finally, a series of experiments is performed using both the Case Western Reserve University (CWRU) bearing dataset and the University of Cincinnati's Center for Intelligent Maintenance Systems (IMS) dataset. The experimental results demonstrate that the proposed semi-supervised learning scheme greatly outperforms two mainstream semi-supervised learning approaches and a baseline supervised convolutional neural network approach, with the overall accuracy improvement ranging between 3% to 30% using different proportions of labeled samples.

cs.LG↗

arXiv2019

Effective Hamiltonian for superconducting Ni oxides Nd$_{1-x}$Sr$_x$NiO$_2$

We derive the effective single-band Hamiltonian in the flat NiO$_2$ planes for nickelate compounds Nd$_{1-x}$Sr$_x$NiO$_2$. We first implement the first-principles calculation to study electronic structures of nickelates using the Heyd-Scuseria-Ernzerhof hybrid density functional and derive a three-band Hubbard model for Ni-O $pdσ$ bands of Ni$^+$ $3d_{x^2-y^2}$ and O$^{2-}$ $2p_{x/y}$ orbitals in the NiO$_2$ planes. To obtain the effective one-band $t$-$t'$-$J$ model Hamiltonian, we perform the exact diagonalization of the three-band Hubbard model for the Ni$_5$O$_{16}$ cluster and map the low-energy spectra onto the effective one-band models. We find that the undoped NiO$_2$ plane is a Hubbard Mott insulator, and the doped holes primarily locate on Ni sites. The physics of the NiO$_2$ plane is a doped Mott insulator, described by the one-band $t$-$t'$-$J$ model with $t=265$~meV, $t'=-21$~meV and $J=28.6$~meV. We also discuss the electronic structure for the "self-doping" effect and heavy fermion behavior of electron pockets of Nd$^{3+}$ $5d$ character in Nd$_{1-x}$Sr$_x$NiO$_2$.

cond-mat.supr-con↗

arXiv2019

The State-of-the-Art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.

eess.SY↗

arXiv2019

Magnetic Raman continuum in single crystalline H$_3$LiIr$_2$O$_6$

Recently H$_3$LiIr$_2$O$_6$ has been reported as a spin-orbital entangled quantum spin liquid (QSL) [K. Kitagawa et al., Nature {\bf 554}, 341 (2018)], albeit its connection to Kitaev QSL has not been yet identified. To unveil the related Kitaev physics, we perform the first Raman spectroscopy studies on single crystalline H$_3$LiIr$_2$O$_6$ samples. We implement a soft chemical replacement of Li$^+$ with H$^+$ from $α$-Li$_2$IrO$_3$ single crystals to synthesize the single crystal samples of the iridate second generation H$_3$LiIr$_2$O$_6$. The Raman spectroscopy can be used to diagnose the QSL state since the magnetic Raman continuum arises from a process involving pairs of fractionalized Majorana fermionic excitation in a pure Kitaev model. We observe a broad dome-shaped magnetic continuum in H$_3$LiIr$_2$O$_6$, in line with theoretical expectations for the two-spin process in the Kitaev QSL. Our results establish the close connection to the Kitaev QSL physics in H$_3$LiIr$_2$O$_6$.

cond-mat.str-el↗

arXiv2019

Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problems

Recent researches have shown that deep forest ensemble achieves a considerable increase in classification accuracy compared with the general ensemble learning methods, especially when the training set is small. In this paper, we take advantage of deep forest ensemble and introduce the Dense Adaptive Cascade Forest (daForest). Our model has a better performance than the original Cascade Forest with three major features: first, we apply SAMME.R boosting algorithm to improve the performance of the model. It guarantees the improvement as the number of layers increases. Second, our model connects each layer to the subsequent ones in a feed-forward fashion, which enhances the capability of the model to resist performance degeneration. Third, we add a hyper-parameters optimization layer before the first classification layer, making our model spend less time to set up and find the optimal hyper-parameters. Experimental results show that daForest performs significantly well, and in some cases, even outperforms neural networks and achieves state-of-the-art results.

cs.LG↗

arXiv2019

Interfacial superconductivity induced by single-quintuple-layer Bi$_2$Te$_3$ on top of FeTe forming van-der-Waals heterostructure

We report the first clear observation of interfacial superconductivity on top of FeTe(FT) covered by one quintuple-layer Bi$_2$Te$_3$(BT) forming van-der-Waals heterojunction. Both transport and scanning tunneling spectroscopy measurements confirm the occurrence of superconductivity at a transition temperature T$_c$ = 13~K, when a single-quintuple-layer BT is deposited on the non-superconducting FT surface. The superconductivity gap decays exponentially with the thickness of BT, suggesting it occurs at the BT-FT interface and the proximity length is above 5-6~nm. We also measure the work function's dependence on the thickness of BT, implying a charge transfer may occur at the BT/FT interface to introduce hole doping into the FT layer, which may serve as a possible candidate for the superconducting mechanism. Our BT/FT heterojunction provides a clean system to study the unconventional interfacial superconductivity.

cond-mat.supr-con↗

arXiv2019

Topological Hall effect in bulk ferromagnet Cr$_2$Te$_3$ embedded with black-phosphorus-like bismuth nanosheets

We implement the molecular beam epitaxy method to embed the black-phosphorus-like bismuth nanosheets into the bulk ferromagnet Cr$_2$Te$_3$. As a typical surfactant, bismuth lowers the surface tensions and mediates the layer-by-layer growth of Cr$_2$Te$_3$. Meanwhile, the bismuth atoms precipitate into black-phosphorus-like nanosheets with the lateral size of several tens of nanometers. In Cr$_2$Te$_3$ embedded with Bi-nanosheets, we observe simultaneously a large topological Hall effect together with the magnetic susceptibility plateau and magnetoresistivity anomaly. As a control experiment, none of these signals is observed in the pristine Cr$_2$Te$_3$ samples. Therefore, the Bi-nanosheets serve as seeds of topological Hall effect induced by non-coplanar magnetic textures planted into Cr$_2$Te$_3$. Our experiments demonstrate a new method to generates a large topological Hall effect by planting strong spin-orbit couplings into the traditional ferromagnet, which may have potential applications in spintronics.

cond-mat.mtrl-sci↗

arXiv2018

On the competition between the Kondo effect and the exchange interaction in a parallel double quantum dot system

We study the competition between the Kondo effect and the exchange interaction in the parallel double quantum dot(DQDs) system within an effective action field theory. The strong on-site Coulomb interactions in DQDs are treated by using the Hubbard-Stratonovich transformation and the introduction of scalar potential fields. We show that a self-consistent perturbation approach, which takes into account the statistical properties of the potential fields acting on the electrons in DQDs, describes well the crossover from the Kondo regime to the spin-singlet state in this system. The linear conductance and the intradot/interdot spin excitation spectra of this system are obtain

cond-mat.mes-hall↗