arXiv subjects

Cheng Wang

Publications and source records attributed to Cheng Wang.

At least 451 records · Page 25Linked to original sources

arXiv2018

Monolithic photonic circuits for Kerr frequency comb generation, filtering and modulation

Microresonator Kerr frequency combs, which rely on third-order nonlinearity ($χ^{(3)}$), are of great interest for a wide range of applications including optical clocks, pulse shaping, spectroscopy, telecommunications, light detection and ranging (LiDAR) and quantum information processing. Many of these applications require further spectral and temporal control of the generated frequency comb signal, which is typically accomplished using additional photonic elements with strong second-order nonlinearity ($χ^{(2)}$). To date these functionalities have largely been implemented as discrete off-chip components due to material limitations, which come at the expense of extra system complexity and increased optical losses. Here we demonstrate the generation, filtering and electro-optic modulation of a frequency comb on a single monolithic integrated chip, using a thin-film lithium niobate (LN) photonic platform that simultaneously possesses large $χ^{(2)}$ and $χ^{(3)}$ nonlinearities and low optical losses. We generate broadband Kerr frequency combs using a dispersion-engineered high quality factor LN microresonator, select a single comb line using an electrically programmable add-drop filter, and modulate the intensity of the selected line. Our results pave the way towards monolithic integrated frequency comb solutions for spectroscopy data communication, ranging and quantum photonics.

physics.optics↗

arXiv2018

Electronically Programmable Photonic Molecule

Physical systems with discrete energy levels are ubiquitous in nature and are fundamental building blocks of quantum technology. Realizing controllable artifcial atom- and molecule-like systems for light would allow for coherent and dynamic control of the frequency, amplitude and phase of photons. In this work, we demonstrate a photonic molecule with two distinct energy-levels and control it by external microwave excitation. We show signature two-level dynamics including microwave induced photonic Autler-Townes splitting, Stark shift, Rabi oscillation and Ramsey interference. Leveraging the coherent control of optical energy, we show on-demand photon storage and retrieval in optical microresonators by reconfguring the photonic molecule into a bright-dark mode pair. These results of dynamic control of light in a programmable and scalable electro-optic platform open doors to applications in microwave photonic signal processing, quantum photonics in the frequency domain, optical computing concepts and simulations of complex physical systems.

physics.optics↗

arXiv2018

Broadband electro-optic frequency comb generation in an integrated microring resonator

Optical frequency combs consist of equally spaced discrete optical frequency components and are essential tools for optical communications and for precision metrology, timing and spectroscopy. To date, wide-spanning combs are most often generated by mode-locked lasers or dispersion-engineered resonators with third-order Kerr nonlinearity. An alternative comb generation method uses electro-optic (EO) phase modulation in a resonator with strong second-order nonlinearity, resulting in combs with excellent stability and controllability. Previous EO combs, however, have been limited to narrow widths by a weak EO interaction strength and a lack of dispersion engineering in free-space systems. In this work, we overcome these limitations by realizing an integrated EO comb generator in a thin-film lithium niobate photonic platform that features a large electro-optic response, ultra-low optical loss and highly co-localized microwave and optical felds, while enabling dispersion engineering. Our measured EO frequency comb spans more than the entire telecommunications L-band (over 900 comb lines spaced at ~ 10 GHz), and we show that future dispersion engineering can enable octave-spanning combs. Furthermore, we demonstrate the high tolerance of our comb generator to modulation frequency detuning, with frequency spacing finely controllable over seven orders of magnitude (10 Hz to 100 MHz), and utilize this feature to generate dual frequency combs in a single resonator. Our results show that integrated EO comb generators, capable of generating wide and stable comb spectra, are a powerful complement to integrated Kerr combs, enabling applications ranging from spectroscopy to optical communications.

physics.optics↗

arXiv2018

On the dimension effect of regularized linear discriminant analysis

This paper studies the dimension effect of the linear discriminant analysis (LDA) and the regularized linear discriminant analysis (RLDA) classifiers for large dimensional data where the observation dimension $p$ is of the same order as the sample size $n$. More specifically, built on properties of the Wishart distribution and recent results in random matrix theory, we derive explicit expressions for the asymptotic misclassification errors of LDA and RLDA respectively, from which we gain insights of how dimension affects the performance of classification and in what sense. Motivated by these results, we propose adjusted classifiers by correcting the bias brought by the unequal sample sizes. The bias-corrected LDA and RLDA classifiers are shown to have smaller misclassification rates than LDA and RLDA respectively. Several interesting examples are discussed in detail and the theoretical results on dimension effect are illustrated via extensive simulation studies.

math.ST↗

arXiv2018

RealPoint3D: Point Cloud Generation from a Single Image with Complex Background

3D point cloud generation by the deep neural network from a single image has been attracting more and more researchers' attention. However, recently-proposed methods require the objects be captured with relatively clean backgrounds, fixed viewpoint, while this highly limits its application in the real environment. To overcome these drawbacks, we proposed to integrate the prior 3D shape knowledge into the network to guide the 3D generation. By taking additional 3D information, the proposed network can handle the 3D object generation from a single real image captured from any viewpoint and complex background. Specifically, giving a query image, we retrieve the nearest shape model from a pre-prepared 3D model database. Then, the image together with the retrieved shape model is fed into the proposed network to generate the fine-grained 3D point cloud. The effectiveness of our proposed framework has been verified on different kinds of datasets. Experimental results show that the proposed framework achieves state-of-the-art accuracy compared to other volumetric-based and point set generation methods. Furthermore, the proposed framework works well for real images in complex backgrounds with various view angles.

cs.CV↗

arXiv2018

LRMM: Learning to Recommend with Missing Modalities

Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.

cs.IR↗

arXiv2018

Influence of chemical kinetics on spontaneous waves and detonation initiation in highly reactive and low reactive mixtures

Understanding the mechanisms of explosions is important for minimizing devastating hazards. Due to the complexity of real chemistry, a single-step reaction mechanism is usually used for theoretical and numerical studies. The purpose of this study is to look more deeply into the influence of chemistry on detonation initiated by a spontaneous wave. Results of high resolution simulations performed for one-step models are compared with simulations for detailed chemical models for highly reactive and low reactive mixtures. The calculated induction times for H2/air and for CH4/air are validated against experimental measurements for a wide range of temperatures and pressures. It is found that the requirements in terms of temperature and size of the hot spots, which produce a spontaneous wave capable to initiate detonation, are quantitatively and qualitatively different for one-step models compared to the detailed chemical models. The impact of detailed chemical model is particularly pronounced for the methane-air mixture. In this case, not only the hot spot size is much greater than that predicted by a one-step model, but even at elevated pressure the initiation of detonation by a temperature gradient is possible only if the temperature outside the gradient is so high, that can ignite thermal explosion. The obtained results suggest that the one-step models do not reproduce correctly the transient and ignition processes, so that interpretation of the simulations performed using a one-step model for understanding mechanisms of flame acceleration, DDT and the origin of explosions must be considered with great caution.

physics.chem-ph↗

arXiv2018

Unsupervised Domain Adaptive Re-Identification: Theory and Practice

We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation. We first extend existing unsupervised domain adaptive classification theories to re-ID tasks. Concretely, we introduce some assumptions on the extracted feature space and then derive several loss functions guided by these assumptions. To optimize them, a novel self-training scheme for unsupervised domain adaptive re-ID tasks is proposed. It iteratively makes guesses for unlabeled target data based on an encoder and trains the encoder based on the guessed labels. Extensive experiments on unsupervised domain adaptive person re-ID and vehicle re-ID tasks with comparisons to the state-of-the-arts confirm the effectiveness of the proposed theories and self-training framework. Our code is available at \url{https://github.com/LcDog/DomainAdaptiveReID}.

cs.CV↗

arXiv2018

Artificial Impostors for Location Privacy Preservation

The progress of location-based services has led to serious concerns on location privacy leakage. For effective and efficient location privacy preservation (LPP), existing methods are still not fully competent. They are often vulnerable under the identification attack with side information, or hard to be implemented due to the high computational complexity. In this paper, we pursue the high protection efficacy and low computational complexity simultaneously. We propose a scalable LPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them through synthesizing artificial impostors (AIs). The AIs refer to the synthesized traces which have similar semantic features to the actual traces, and do not contain any target location. Two dedicated techniques are devised: the sampling-based synthesis method and population-level semantic model. They play significant roles in two critical steps of synthesizing AIs. We conduct experiments on real datasets in two cities (Shanghai, China and Asturias, Spain) to validate the high efficacy and scalability of the proposed method. In these two datasets, the experimental results show that our method achieves the preservation efficacy of $97.65\%$ and $96.12\%$, and its run time of building the generators is only $230.47$ and $215.92$ seconds, respectively. This study would give the research community new insights into improving the practicality of the state-of-the-art LPP paradigm via counterfeiting locations.

cs.SI↗

arXiv2018

Numerical methods for Porous Medium Equation by an Energetic Variational Approach

We study numerical methods for porous media equation (PME). There are two important characteristics: the finite speed propagation of the free boundary and the potential waiting time, which make the problem not easy to handle. Based on different dissipative energy laws, we develop two numerical schemes by an energetic variational approach. Firstly, based on $f \log f$ as the total energy form of the dissipative law, we obtain the trajectory equation, and then construct a fully discrete scheme. It is proved that the scheme is uniquely solvable on an admissible convex set by taking the advantage of the singularity of the total energy. Next, based on $\frac{1}{2 f}$ as the total energy form of the dissipation law, we construct a linear numerical scheme for the corresponding trajectory equation. Both schemes preserve the corresponding discrete dissipation law. Meanwhile, under some smoothness assumption, it is proved, by a higher order expansion technique, that both schemes are second-order convergent in space and first-order convergent in time. Each scheme yields a good approximation for the solution and the free boundary. No oscillation is observed for the numerical solution around the free boundary. Furthermore, the waiting time problem could be naturally treated, which has been a well-known difficult issue for all the existence methods. Due to its linear nature, the second scheme is more efficient.

math.NA↗

arXiv2018

Completely Distributed Guaranteed-performance Consensualization for High-order Multiagent Systems with Switching Topologies

The guaranteed-performance consensualization for high-order linear and nonlinear multiagent systems with switching topologies is respectively realized in a completely distributed manner in the sense that consensus design criteria are independent of interaction topologies and switching motions. The current paper firstly proposes an adaptive consensus protocol with guaranteed-performance constraints and switching topologies, where interaction weights among neighboring agents are adaptively adjusted and state errors among all agents can be regulated. Then, a new translation-adaptive strategy is shown to realize completely distributed guaranteed-performance consensus control and an adaptive guaranteed-performance consensualization criterion is given on the basis of the Riccati inequality. Furthermore, an approach to regulate the consensus control gain and the guaranteed-performance cost is proposed in terms of linear matrix inequalities. Moreover, main conclusions for linear multiagent systems are extended to Lipschitz nonlinear cases. Finally, two numerical examples are provided to demonstrate theoretical results.

eess.SY↗

arXiv2018

Dark matter direct search sensitivity of the PandaX-4T experiment

The PandaX-4T experiment, a four-ton scale dark matter direct detection experiment, is being planned at the China Jinping Underground Laboratory. In this paper we present a simulation study of the expected background in this experiment. In a 2.8-ton fiducial mass and the signal region between 1 to 10 keV electron equivalent energy, the total electron recoil background is found to be 4.9x10^{-5} /(kg day keV). The nuclear recoil background in the same region is 2.8x10^{-7}/(kg day keV). With an exposure of 5.6 ton-years, the sensitivity of PandaX-4T could reach a minimum spin-independent dark matter-nucleon cross section of 6x10^{-48} cm^{2} at a dark matter mass of 40 GeV/c^{2}.

physics.ins-det↗

arXiv2018

Numerical Complete Solution for Random Genetic Drift by Energetic Variational Approach

In this paper, we focus on numerical solutions for random genetic drift problem, which is governed by a degenerated convection-dominated parabolic equation. Due to the fixation phenomenon of genes, Dirac delta singularities will develop at boundary points as time evolves. Based on an energetic variational approach (EnVarA), a balance between the maximal dissipation principle (MDP) and least action principle (LAP), we obtain the trajectory equation. In turn, a numerical scheme is proposed using a convex splitting technique, with the unique solvability (on a convex set) and the energy decay property (in time) justified at a theoretical level. Numerical examples are presented for cases of pure drift and drift with semi-selection. The remarkable advantage of this method is its ability to catch the Dirac delta singularity close to machine precision over any equidistant grid.

math.NA↗

arXiv2018

Dynamic Output Feedback Guaranteed-Cost Synchronization for Multiagent Networks with Given Cost Budgets

The current paper addresses the distributed guaranteed-cost synchronization problems for general high-order linear multiagent networks. Existing works on the guaranteed-cost synchronization usually require all state information of neighboring agents and cannot give the cost budget previously. For both leaderless and leader-following interaction topologies, the current paper firstly proposes a dynamic output feedback synchronization protocol with guaranteed-cost constraints, which can realize the tradeoff design between the energy consumption and the synchronization regulation performance with the given cost budget. Then, according to different structure features of interaction topologies, leaderless and leader-following guaranteed-cost synchronization analysis and design criteria are presented, respectively, and an algorithm is proposed to deal with the impacts of nonlinear terms by using both synchronization analysis and design criteria. Especially, an explicit expression of the synchronization function is shown for leaderless cases, which is independent of protocol states and the given cost budget. Finally, numerical examples are presented to demonstrate theoretical results.

eess.SY↗

arXiv2018

Distinct differences in the nanoscale behaviors of the twist-bend liquid crystal phase of a flexible linear trimer and homologous dimer

We synthesized the liquid crystal dimer and trimer members of a series of flexible linear oligomers and characterized their microscopic and nanoscopic properties using resonant soft x-ray scattering and a number of other experimental techniques. On the microscopic scale, the twist-bend phases of the dimer and trimer appear essentially identical. However, while the liquid crystal dimer exhibits a temperature-dependent variation of its twist-bend helical pitch varying from 100 - 170 Å on heating, the trimer exhibits an essentially temperature-independent pitch of 66 Å, significantly shorter than those reported for other twist-bend forming materials in the literature. We attribute this to a specific combination of intrinsic conformational bend of the trimer molecules and a sterically favorable intercalation of the trimers over a commensurate fraction (two-thirds) of the molecular length. We develop a geometric model of the twist-bend phase for these materials with the molecules arranging into helical chain structures, and we fully determine their respective geometric parameters.

cond-mat.soft↗

arXiv2018

Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks

In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we find that there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. To deeply exploit such a complementary effect, we propose a joint model to capture both online and offline features of a user's composite behavior. We evaluate the proposed joint model by comparing with some typical models on two real-world datasets: Foursquare and Yelp. In the widely-used setting of theft simulation (simulating thefts via behavioral replacement), the experimental results show that our model outperforms the existing ones, with the AUC values $0.956$ in Foursquare and $0.947$ in Yelp, respectively. Particularly, the recall (True Positive Rate) can reach up to $65.3\%$ in Foursquare and $72.2\%$ in Yelp with the corresponding disturbance rate (False Positive Rate) below $1\%$. It is worth mentioning that these performances can be achieved by examining only one composite behavior (visiting a place and posting a tip online simultaneously) per authentication, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how a real-time online identity authentication can be improved via modeling users' composite behavioral patterns.

cs.SI↗

arXiv2018

Laser guided ionic wind

We report on a method to experimentally generate ionic wind by coupling an external high voltage electric field with an intense femtosecond laser induced air plasma filament. The measured ionic wind velocity could be as strong as >4 m/s. It could be optimized by changing the applied electric field and the laser induced plasma channel. The experimental observation was qualitatively confirmed by a numerical simulation of spatial distribution of the electric field. This technique is robust and free from sharp metallic electrodes for coronas; it opens a way to optically generate ionic wind at a distance.

physics.plasm-ph↗

arXiv2017

Risk-Based Distributionally Robust Optimal Power Flow With Dynamic Line Rating

In this paper, we propose a risk-based data-driven approach to optimal power flow (DROPF) with dynamic line rating. The risk terms, including penalties for load shedding, wind generation curtailment and line overload, are embedded into the objective function. To hedge against the uncertainties on wind generation data and line rating data, we consider a distributionally robust approach. The ambiguity set is based on second-order moment and Wasserstein distance, which captures the correlations between wind generation outputs and line ratings, and is robust to data perturbation. We show that the proposed DROPF model can be reformulated as a conic program. Considering the relatively large number of constraints involved, an approximation of the proposed DROPF model is suggested, which significantly reduces the computational costs. A Wasserstein distance constrained DROPF and its tractable reformulation are also provided for practical large-scale test systems. Simulation results on the 5-bus, the IEEE 118-bus and the Polish 2736-bus test systems validate the effectiveness of the proposed models.

math.OC↗