arXiv subjects

Jian Xu

Publications and source records attributed to Jian Xu.

At least 289 records · Page 16Linked to original sources

arXiv2019

Self-limited Growth of an Oxyhydroxide Phase at the Fe3O4(001) Surface in Liquid and Ambient Pressure Water

Atomic-scale investigations of metal oxide surfaces exposed to aqueous environments are vital to understand degradation phenomena (e.g. dissolution and corrosion) as well as the performance of these materials in applications. Here, we utilize a new experimental setup for the UHV-compatible dosing of liquids to explore the stability of the Fe3O4(001)-c(2x2) surface following exposure to liquid and ambient pressure water. X-ray photoelectron spectroscopy (XPS) and low energy electron diffraction (LEED) data show that extensive hydroxylation causes the surface to revert to a bulk-like (1x1) termination. However, scanning tunnelling microscopy (STM) images reveal a more complex situation, with the slow growth of an oxyhydroxide phase, which ultimately saturates at approximately 40% coverage. We conclude that the new material contains OH groups from dissociated water coordinated to Fe cations extracted from subsurface layers, and that the surface passivates once the surface oxygen lattice is saturated with H because no further dissociation can take place. The resemblance of the STM images to those acquired in previous electrochemical STM (EC-STM) studies lead us to believe a similar structure exists at the solid-electrolyte interface during immersion at pH 7.

physics.chem-ph↗

arXiv2019

Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds

Most e-commerce product feeds provide blended results of advertised products and recommended products to consumers. The underlying advertising and recommendation platforms share similar if not exactly the same set of candidate products. Consumers' behaviors on the advertised results constitute part of the recommendation model's training data and therefore can influence the recommended results. We refer to this process as Leverage. Considering this mechanism, we propose a novel perspective that advertisers can strategically bid through the advertising platform to optimize their recommended organic traffic. By analyzing the real-world data, we first explain the principles of Leverage mechanism, i.e., the dynamic models of Leverage. Then we introduce a novel Leverage optimization problem and formulate it with a Markov Decision Process. To deal with the sample complexity challenge in model-free reinforcement learning, we propose a novel Hybrid Training Leverage Bidding (HTLB) algorithm which combines the real-world samples and the emulator-generated samples to boost the learning speed and stability. Our offline experiments as well as the results from the online deployment demonstrate the superior performance of our approach.

stat.ML↗

arXiv2019

Induced entanglement entropy of harmonic oscillators in noncommutative phase space

We study the entanglement entropy of harmonic oscillators in noncommutative phase space. We propose a new definition of quantum Rényi entropy based on Wigner functions in noncommutative phase space. Using the Rényi entropy, we calculate the entanglement entropy of the ground state of the 2D isotropic harmonic oscillators. We find that for some values of the noncommutative parameters, the harmonic oscillators can be entangled in noncommutative phase space. This is a new entanglement-like effect caused by the noncommutativity of the phase space.

math-ph↗

arXiv2019

Biomimetic Polymer Film with Brilliant Brightness Using a One-Step Water Vapor-Induced Phase Separation Method

The scales of the white Cyphochilus beetles are endowed with unusual whiteness arising from the exceptional scattering efficiency of their disordered ultrastructure optimized through millions of years of evolution. Here, a simple, one-step method based on water vapor-induced phase separation (VIPS) is developed to prepare ultra-thin polystyrene (PS) films with similar microstructure and comparable optical performance. A typical biomimetic 3.5 um PS film exhibits a diffuse reflectance of 61% at 500 nm, which translates into a transport mean free path below 1 um. A complete optical characterization through Monte Carlo simulations reveals how such scattering performance arises from the scattering coefficient and scattering anisotropy, whose interplay provides insight into the morphological properties of the material. The potential of bright-white coatings as smart sensors or wearable devices is highlighted using a treated ultra-thin film as a real-time sensor for human exhalation.

physics.app-ph↗

arXiv2019

Bid Optimization by Multivariable Control in Display Advertising

Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices.

cs.GT↗

arXiv2019

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.

cs.IR↗

arXiv2019

Miniature Multi-Level Optical Memristive Switch Using Phase Change Material

The optical memristive switches are electrically activated optical switches that can memorize the current state. They can be used as optical latching switches in which the switching state is changed only by applying an electrical Write/Erase pulse and maintained without external power supply. We demonstrate an optical memristive switch based on a silicon MMI structure covered with nanoscale-size Ge2Sb2Te5 (GST) material on top. The phase change of GST is triggered by resistive heating of the silicon layer beneath GST with an electrical pulse. Experimental results reveal that the optical transmissivity can be tuned in a controllable and repeatable manner with the maximum transmission contrast exceeding 20 dB. Partial crystallization of GST is obtained by controlling the width and amplitude of the electric pulses. Crucially, we also demonstrate that both Erase and Write operations, to and from any intermediate level, are possible with accurate control of the electrical pulses. Our work marks a significant step forward towards realizing photonic memristive switches without static power consumption, which are highly demanded in high-density large-scale integrated photonics.

physics.optics↗

arXiv2019

Excavate Condition-invariant Space by Intrinsic Encoder

As the human, we can recognize the places across a wide range of changing environmental conditions such as those caused by weathers, seasons, and day-night cycles. We excavate and memorize the stable semantic structure of different places and scenes. For example, we can recognize tree whether the bare tree in winter or lush tree in summer. Therefore, the intrinsic features that are corresponding to specific semantic contents and condition-invariant of appearance changes can be employed to improve the performance of long-term place recognition significantly. In this paper, we propose a novel intrinsic encoder that excavates the condition-invariant latent space of different places under drastic appearance changes. Our method excavates the space of intrinsic structure and semantic information by proposed self-supervised encoder loss. Different from previous learning based place recognition methods that need paired training data of each place with appearance changes, we employ the weakly-supervised strategy to utilize unpaired set-based training data of different environmental conditions. We conduct comprehensive experiments and show that our semi-supervised intrinsic encoder achieves remarkable performance for place recognition under drastic appearance changes. The proposed intrinsic encoder outperforms the state-of-the-art image-level place recognition methods on standard benchmark Nordland.

cs.CV↗

arXiv2019

Adversarial Soft-detection-based Aggregation Network for Image Retrieval

In recent year, the compact representations based on activations of Convolutional Neural Network (CNN) achieve remarkable performance in image retrieval. However, retrieval of some interested object that only takes up a small part of the whole image is still a challenging problem. Therefore, it is significant to extract the discriminative representations that contain regional information of the pivotal small object. In this paper, we propose a novel adversarial soft-detection-based aggregation (ASDA) method free from bounding box annotations for image retrieval, based on adversarial detector and soft region proposal layer. Our trainable adversarial detector generates semantic maps based on adversarial erasing strategy to preserve more discriminative and detailed information. Computed based on semantic maps corresponding to various discriminative patterns and semantic contents, our soft region proposal is arbitrary shape rather than only rectangle and it reflects the significance of objects. The aggregation based on trainable soft region proposal highlights discriminative semantic contents and suppresses the noise of background. We conduct comprehensive experiments on standard image retrieval datasets. Our weakly supervised ASDA method achieves state-of-the-art performance on most datasets. The results demonstrate that the proposed ASDA method is effective for image retrieval.

cs.CV↗

arXiv2019

Coherent population transfer via state-independent quasi-adiabatic dynamics

High-fidelity and robust coherent population transfer is a major challenge in coherent quantum control. Different from the well known adiabatic condition, we present a rigorous adiabatic condition that is inspired by the idea of the Landau-Zener tunneling. Based on this, we propose a coherent population transfer approach, which just needs only one control parameter and depends on the eigenvalues of the systems. Compared to other approaches, such as fast quasiadiabatic dynamics, shortcut to adiabatic passage, we numerically demonstrate that our approach can provide a more high-fidelity and more robustness coherent population transfer without affecting the speed. In short, our approach opens a new way to further increase the fidelity and the robustness of coherent population transfer. Moreover, it may be generalized to complex quantum systems where the exact expressions of eigenstates are difficult to obtain or the paremeters of systems are difficult to simultaneously drive.

quant-ph↗

arXiv2018

Polarization-insensitive space-selective etching in fused silica induced by picosecond laser irradiation

It is well known that when the fused silica is irradiated with focused femtosecond laser beams, space selective chemical etching can be achieved. The etching rate depends sensitively on the polarization of the laser. Surprisingly, we observe that by chirping the Fourier-transform-limited femtosecond laser pulses to picosecond pulses, the polarization dependence of the etching rate disappears, whereas an efficient etching rate can still be maintained. Observation with a scanning electron microscope reveals that the chirped pulses can induce interconnected nanocracks in the irradiated areas which facilitates efficient introduction of the etchant into the microchannel. The reported technology is of great use for fabrication of three-dimensional (3D) microfluidic systems and glass-based 3D printing.

physics.optics↗

arXiv2018

Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising

Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.

cs.AI↗

arXiv2018

Low-loss Lithium Niobate on Insulator (LNOI) Waveguides of a 10 cm-length and a Sub-nanometer Surface Roughness

We develop a technique for realizing lithium niobate on insulator (LNOI) waveguides of a multi-centimeter-length with a propagation loss as low as 0.027 dB/cm. Our technique relies on patterning a chromium (Cr) thin film coated on the top surface of LNOI into a hard mask with a femtosecond laser followed by the chemo-mechanical polishing for structuring the LNOI into the waveguides. The surface roughness on the waveguides is determined to be 0.452 nm with an atomic force microscope (AFM). The approach is compatible with other surface patterning technologies such as optical and electron beam lithographies or laser direct writing, enabling high-throughput manufacturing of large-scale LNOI-based photonic integrated circuits.

physics.app-ph↗

arXiv2018

A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising

In online display advertising, guaranteed contracts and real-time bidding (RTB) are two major ways to sell impressions for a publisher. Despite the increasing popularity of RTB, there is still half of online display advertising revenue generated from guaranteed contracts. Therefore, simultaneously selling impressions through both guaranteed contracts and RTB is a straightforward choice for a publisher to maximize its yield. However, deriving the optimal strategy to allocate impressions is not a trivial task, especially when the environment is unstable in real-world applications. In this paper, we formulate the impression allocation problem as an auction problem where each contract can submit virtual bids for individual impressions. With this formulation, we derive the optimal impression allocation strategy by solving the optimal bidding functions for contracts. Since the bids from contracts are decided by the publisher, we propose a multi-agent reinforcement learning (MARL) approach to derive cooperative policies for the publisher to maximize its yield in an unstable environment. The proposed approach also resolves the common challenges in MARL such as input dimension explosion, reward credit assignment, and non-stationary environment. Experimental evaluations on large-scale real datasets demonstrate the effectiveness of our approach.

cs.AI↗

arXiv2018

Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification

Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label classification. The J-Play learns high-level and semantically meaningful feature representation from high-dimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multi-coupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.

cs.LG↗

arXiv2018

Long-time asymptotics for the Nonlocal mKdV equation

In this paper, we study the Cauchy problem with decaying initial data for the nonlocal modified Korteweg-de Vries equation (nonlocal mKdV) \[q_t(x,t)+q_{xxx}(x,t)-6q(x,t)q(-x,-t)q_x(x,t)=0,\] which can be viewed as a generalization of the local classical mKdV equation. We first formulate the Riemann-Hilbert problem associated with the Cauchy problem of the nonlocal mKdV equation. Then we apply the Deift-Zhou nonlinear steepest-descent method to analyze the long-time asymptotics for the solution of the nonlocal mKdV equation. In contrast with the classical mKdV equation, we find some new and different results on long-time asymptotics for the nonlocal mKdV equation and some additional assumptions about the scattering data are made in our main results.

nlin.SI↗

arXiv2018

Locally Adaptive Learning Loss for Semantic Image Segmentation

We propose a novel locally adaptive learning estimator for enhancing the inter- and intra- discriminative capabilities of Deep Neural Networks, which can be used as improved loss layer for semantic image segmentation tasks. Most loss layers compute pixel-wise cost between feature maps and ground truths, ignoring spatial layouts and interactions between neighboring pixels with same object category, and thus networks cannot be effectively sensitive to intra-class connections. Stride by stride, our method firstly conducts adaptive pooling filter operating over predicted feature maps, aiming to merge predicted distributions over a small group of neighboring pixels with same category, and then it computes cost between the merged distribution vector and their category label. Such design can make groups of neighboring predictions from same category involved into estimations on predicting correctness with respect to their category, and hence train networks to be more sensitive to regional connections between adjacent pixels based on their categories. In the experiments on Pascal VOC 2012 segmentation datasets, the consistently improved results show that our proposed approach achieves better segmentation masks against previous counterparts.

cs.CV↗

arXiv2018

Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval

Existing manifold learning methods are not appropriate for image retrieval task, because most of them are unable to process query image and they have much additional computational cost especially for large scale database. Therefore, we propose the iterative manifold embedding (IME) layer, of which the weights are learned off-line by unsupervised strategy, to explore the intrinsic manifolds by incomplete data. On the large scale database that contains 27000 images, IME layer is more than 120 times faster than other manifold learning methods to embed the original representations at query time. We embed the original descriptors of database images which lie on manifold in a high dimensional space into manifold-based representations iteratively to generate the IME representations in off-line learning stage. According to the original descriptors and the IME representations of database images, we estimate the weights of IME layer by ridge regression. In on-line retrieval stage, we employ the IME layer to map the original representation of query image with ignorable time cost (2 milliseconds). We experiment on five public standard datasets for image retrieval. The proposed IME layer significantly outperforms related dimension reduction methods and manifold learning methods. Without post-processing, Our IME layer achieves a boost in performance of state-of-the-art image retrieval methods with post-processing on most datasets, and needs less computational cost.

cs.CV↗