arXiv subjects

Di Wu

Publications and source records attributed to Di Wu.

At least 649 records · Page 36Linked to original sources

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

Experimental observation of the gate-controlled reversal of the anomalous Hall effect in the intrinsic magnetic topological insulator MnBi2Te4 device

Here we report the reserved anomalous Hall effect (AHE) in the 5-septuple-layer van der Waals device of the intrinsic magnetic topological insulator MnBi2Te4. By employing the top/bottom gate, a negative AHE loop gradually decreases to zero and changes to a reversed sign. The reversed AHE exhibits distinct coercive fields and temperature dependence from the previous AHE. It reaches the maximum inside the gap of the Dirac cone. The newly-seen reversed AHE is attributed to the competition of the intrinsic Berry curvature and the Dirac-gap enhanced extrinsic skew scattering. Its gate-controlled switching contributes a scheme for the topological spin field-effect transistors.

cond-mat.mes-hall↗

arXiv2019

Random Occlusion-recovery for Person Re-identification

As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping multiple cameras or across different time with a single camera. Recently, deep learning-based person re-identification models have achieved great success in many benchmarks. However, these supervised models require a large amount of labeled image data, and the process of manual labeling spends much manpower and time. In this study, we introduce a method to automatically synthesize labeled person images and adopt them to increase the sample number per identity for person re-identification datasets. To be specific, we use block rectangles to randomly occlude pedestrian images. Then, a generative adversarial network (GAN) model is proposed to use paired occluded and original images to synthesize the de-occluded images that similar but not identical to the original image. Afterwards, we annotate the de-occluded images with the same labels of their corresponding raw images and use them to augment the number of samples per identity. Finally, we use the augmented datasets to train baseline model. The experiment results on CUHK03, Market-1501 and DukeMTMC-reID datasets show that the effectiveness of the proposed method.

cs.CV↗

arXiv2019

Visualization of Local Conductance in MoS2/WSe2 Heterostructure Transistors

The vertical stacking of van der Waals (vdW) materials introduces a new degree of freedom to the research of two-dimensional (2D) systems. The interlayer coupling strongly influences the band structure of the heterostructures, resulting in novel properties that can be utilized for electronic and optoelectronic applications. Based on microwave microscopy studies, we report quantitative electrical imaging on gated molybdenum disulfide (MoS2)/tungsten diselenide (WSe2) heterostructure devices, which exhibit an intriguing anti-ambipolar effect in the transfer characteristics. Interestingly, in the region with significant source-drain current, electrons in the n-type MoS2 and holes in the p-type WSe2 segments are nearly balanced, whereas the heterostructure area is depleted of mobile charges. The configuration is analogous to the p-i-n diode, where the injected carriers dominate in the recombination current. The spatial evolution of local conductance can be ascribed to the lateral band bending and formation of depletion regions along the line of MoS2-heterostructure-WSe2. Our work vividly demonstrates the microscopic origin of novel transport behaviors, which is important for the vibrant field of vdW heterojunction research.

physics.app-ph↗

arXiv2018

Omni-directional Feature Learning for Person Re-identification

Person re-identification (PReID) has received increasing attention due to it is an important part in intelligent surveillance. Recently, many state-of-the-art methods on PReID are part-based deep models. Most of them focus on learning the part feature representation of person body in horizontal direction. However, the feature representation of body in vertical direction is usually ignored. Besides, the spatial information between these part features and the different feature channels is not considered. In this study, we introduce a multi-branches deep model for PReID. Specifically, the model consists of five branches. Among the five branches, two of them learn the local feature with spatial information from horizontal or vertical orientations, respectively. The other one aims to learn interdependencies knowledge between different feature channels generated by the last convolution layer. The remains of two other branches are identification and triplet sub-networks, in which the discriminative global feature and a corresponding measurement can be learned simultaneously. All the five branches can improve the representation learning. We conduct extensive comparative experiments on three PReID benchmarks including CUHK03, Market-1501 and DukeMTMC-reID. The proposed deep framework outperforms many state-of-the-art in most cases.

cs.CV↗

arXiv2018

DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks

Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability. Results: We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally-annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC$_{50}$ within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead. Availability: Data and source codes are available at https://github.com/Shen-Lab/DeepAffinity Supplementary Information: Supplementary data are available at http://shen-lab.github.io/deep-affinity-bioinf18-supp-rev.pdf

q-bio.BM↗

arXiv2018

Analyzing and provably improving fixed budget ranking and selection algorithms

This paper studies the fixed budget formulation of the Ranking and Selection (R&S) problem with independent normal samples, where the goal is to investigate different algorithms' convergence rate in terms of their resulting probability of false selection (PFS). First, we reveal that for the well-known Optimal Computing Budget Allocation (OCBA) algorithm and its two variants, a constant initial sample size (independent of the total budget) only amounts to a sub-exponential (or even polynomial) convergence rate. After that, a modification is proposed to achieve an exponential convergence rate, where the improvement is shown by a finite-sample bound on the PFS as well as numerical results. Finally, we focus on a more tractable two-design case and explicitly characterize the large deviations rate of PFS for some simplified algorithms. Our analysis not only develops insights into the algorithms' properties, but also highlights several useful techniques for analyzing the convergence rate of fixed budget R\&S algorithms.

math.OC↗

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

Defect-Fluorite Gd2Zr2O7 Ceramics under Helium Irradiation: Amorphization, Cell Volume Expansion, and Multi-stage Bubble Formation

Here, we report a study on the radiation resistance enhancement of Gd2Zr2O7 nanograin ceramics, in which amorphization, cell volume expansion and multi-stage helium (He) bubble formation are investigated and discussed. Gd2Zr2O7 ceramics with a series of grain sizes (55-221 nm) were synthesized and irradiated by 190 keV He ion beam up to a fluence of 5x10^17 ions/cm2. Both the degree of post irradiation cell volume expansion and the amorphization fraction appear to be size dependent. As the average grain size evolves from 55 to 221 nm, the degree of post irradiation cell volume expansion increases from 0.56 to 1.02 %, and the amorphization fraction increases from 6.8 to 11.1 %. Additionally, the threshold He concentrations (at. %) of bubbles at different formation stages and locations, including (1) bubbles at grain boundary, (2) bubble-chains and (3) ribbon-like bubbles within the grain, are all found to be much higher in the nanograin ceramic (55 nm) compared with that of the submicron sample (221 nm). We conclude that grain boundary plays a critical role in minimizing the structural defects, and inhibiting the multi-stage He bubble formation process.

cond-mat.mtrl-sci↗

arXiv2018

Energy-resolved Photoconductivity Mapping in a Monolayer-bilayer WSe2 Lateral Heterostructure

Vertical and lateral heterostructures of van der Waals materials provide tremendous flexibility for band structure engineering. Since electronic bands are sensitively affected by defects, strain, and interlayer coupling, the edge and heterojunction of these two-dimensional (2D) systems may exhibit novel physical properties, which can be fully revealed only by spatially resolved probes. Here, we report the spatial mapping of photoconductivity in a monolayer-bilayer WSe2 lateral heterostructure under multiple excitation lasers. As the photon energy increases, the light-induced conductivity detected by microwave impedance microscopy first appears along the hetero-interface and bilayer edge, then along the monolayer edge, inside the bilayer area, and finally in the interior of the monolayer region. The sequential emergence of mobile carriers in different sections of the sample is consistent with the theoretical calculation of local energy gaps. Quantitative analysis of the microscopy and transport data also reveals the linear dependence of photoconductivity on the laser intensity and the influence of interlayer coupling on carrier recombination. Combining theoretical modeling, atomic scale imaging, mesoscale impedance microscopy, and device-level characterization, our work suggests an exciting perspective to control the intrinsic band-gap variation in 2D heterostructures down to the few-nanometer regime.

cond-mat.mtrl-sci↗

arXiv2018

Topological quantum properties of chiral crystals

Chiral crystals are materials whose lattice structure has a well-defined handedness due to the lack of inversion, mirror, or other roto-inversion symmetries. These crystals represent a broad, important class of quantum materials; their structural chirality has been found to allow for a wide range of phenomena in condensed matter physics, including skyrmions in chiral magnets, unconventional pairing in chiral superconductors, nonlocal transport and unique magnetoelectric effects in chiral metals, as well as enantioselective photoresponse. Nevertheless, while these phenomena have been intensely investigated, the topological electronic properties of chiral crystals have still remained largely uncharacterized. While recent theoretical advances have shown that the presence of crystalline symmetries can protect novel band crossings in 2D and 3D systems, we present a new class of Weyl fermions enforced by the absence of particular crystal symmetries. These "Kramers-Weyl" fermions are a universal topological electronic property of all nonmagnetic chiral crystals with spin-orbit coupling (SOC); they are guaranteed by lattice translation, structural chirality, and time-reversal symmetry, and unlike conventional Weyl fermions, appear at time-reversal-invariant momenta (TRIMs). We cement this finding by identifying representative chiral materials in the majority of the 65 chiral space groups in which Kramers-Weyl fermions are relevant to low-energy physics. By combining our analysis with the results of previous works, we determine that all point-like nodal degeneracies in nonmagnetic chiral crystals with relevant SOC carry nontrivial Chern numbers. We further show that, beyond the previous phenomena allowed by structural chirality, Kramers-Weyl fermions enable unusual phenomena, such as a novel electron spin texture, chiral bulk Fermi surfaces over large energy windows.

cond-mat.mes-hall↗

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

Wideband Hybrid Precoding for Next-Generation Backhaul/Fronthaul Based on mmWave FD-MIMO

Millimeter-wave (mmWave) communication is considered as an indispensable technique for the next-generation backhaul/fronthaul network thanks to its large transmission bandwidth. Especially for heterogeneous network (HetNet), the mmWave full-dimension (FD)-MIMO is exploited to establish the backhaul/fronthaul link between phantom-cell base stations (BSs) and macro-cell BSs, where an efficient precoding is prerequisite. Against this background, this paper proposes a principle component analysis (PCA)-based hybrid precoding for wideband mmWave MIMO backhaul/fronthaul channels. We first propose an optimal hybrid precoder by exploiting principal component analysis (PCA), whereby the optimal high dimensional frequency-selective precoder are projected to the low-dimensional frequency-flat precoder. Moreover, the combiner is designed by leveraging the weighted PCA, where the covariance of received signal is taken into account as weight to the optimal minimum mean square error (MMSE) fully-digital combiner for further improved performance. Simulations have confirmed that the proposed scheme outperforms conventional schemes in spectral efficiency (SE) and bit-error-rate (BER) performance.

cs.IT↗

arXiv2018

Machine Learning Based Hybrid Precoding for MmWave MIMO-OFDM with Dynamic Subarray

Hybrid precoding design can be challenging for broadband millimeter-wave (mmWave) massive MIMO due to the frequency-flat analog precoder in radio frequency (RF). Prior broadband hybrid precoding work usually focuses on fully-connected array (FCA), while seldom considers the energy-efficient partially-connected subarray (PCS) including the fixed subarray (FS) and dynamic subarray (DS). Against this background, this paper proposes a machine learning based broadband hybrid precoding for mmWave massive MIMO with DS. Specifically, we first propose an optimal hybrid precoder based on principal component analysis (PCA) for the FS, whereby the frequency-flat RF precoder for each subarray is extracted from the principle component of the optimal frequency-selective precoders for fully-digital MIMO. Moreover, we extend the PCA-based hybrid precoding to DS, where a shared agglomerative hierarchical clustering (AHC) algorithm developed from machine learning is proposed to group the DS for improved spectral efficiency (SE). Finally, we investigate the energy efficiency (EE) of the proposed scheme for both passive and active antennas. Simulations have confirmed that the proposed scheme outperforms conventional schemes in both SE and EE.

cs.IT↗

arXiv2018

On the Multilinear Fractional Integral Operators with Correlation Kernels

In this paper, we study a class of multilinear fractional integral operators which have correlation kernels $\prod_{1\leq i<j \leq k}|x_i-x_j|^{-α_{ij}}$. The necessary and sufficient conditions are obtained under which these oprators are bounded from $L^{p_1}\times \cdots \times L^{p_k}$ into $L^q$. As a consequence, we also get the endpoint estimates from $L^{p_1}\times \cdots \times L^{p_k}$ to $BMO$ of these operators.

math.CA↗

arXiv2018

Microwave Conductivity of Ferroelectric Domains and Domain Walls in Hexagonal Rare-earth Ferrite

We report the nanoscale electrical imaging results in hexagonal $Lu_{0.6}Sc_{0.4}FeO_3$ single crystals using conductive atomic force microscopy (C-AFM) and scanning microwave impedance microscopy (MIM). While the dc and ac response of the ferroelectric domains can be explained by the surface band bending, the drastic enhancement of domain wall (DW) ac conductivity is clearly dominated by the dielectric loss due to DW vibration rather than mobile-carrier conduction. Our work provides a unified physical picture to describe the local conductivity of ferroelectric domains and domain walls, which will be important for future incorporation of electrical conduction, structural dynamics, and multiferroicity into high-frequency nano-devices.

cond-mat.mtrl-sci↗

arXiv2018

Imaging Quantum Spin Hall Edges in Monolayer WTe2

A two-dimensional (2D) topological insulator (TI) exhibits the quantum spin Hall (QSH) effect, in which topologically protected spin-polarized conducting channels exist at the sample edges. Experimental signatures of the QSH effect have recently been reported for the first time in an atomically thin material, monolayer WTe2. Electrical transport measurements on exfoliated samples and scanning tunneling spectroscopy on epitaxially grown monolayer islands signal the existence of edge modes with conductance approaching the quantized value. Here, we directly image the local conductivity of monolayer WTe2 devices using microwave impedance microscopy, establishing beyond doubt that conduction is indeed strongly localized to the physical edges at temperatures up to 77 K and above. The edge conductivity shows no gap as a function of gate voltage, ruling out trivial conduction due to band bending or in-gap states, and is suppressed by magnetic field as expected. Interestingly, we observe additional conducting lines and rings within most samples which can be explained by edge states following boundaries between topologically trivial and non-trivial regions. These observations will be critical for interpreting and improving the properties of devices incorporating WTe2 or other air-sensitive 2D materials. At the same time, they reveal the robustness of the QSH channels and the potential to engineer and pattern them by chemical or mechanical means in the monolayer material platform.

cond-mat.mes-hall↗

arXiv2018

Visualization of Surface-acoustic-wave Potential by Transmission-mode Microwave Impedance Microscopy

Elastic waves propagating in piezoelectric materials are accompanied by a time-varying electric potential, which is of critical importance for acousto-electronic applications. The spatial mapping of such a potential at microwave frequencies is challenging since the characteristic length scale is determined by the acoustic wavelength of several micrometers. In this work, we report the visualization of surface acoustic waves (SAWs) on ferroelectric samples by transmission-mode microwave impedance microscopy (T-MIM). The SAW potential launched by the interdigital transducer is detected by the tip and demodulated by the microwave electronics as time-independent spatial patterns. Wave phenomena such as interference and diffraction are imaged and the results are in excellent agreement with the theoretical analysis. Our work opens up a new avenue to study various electromechanical systems in a spatially resolved manner.

cond-mat.mes-hall↗