arXiv subjects

Hao Wang

Publications and source records attributed to Hao Wang.

At least 1,585 records · Page 88Linked to original sources

arXiv2019

Thermal Responses of Connected HEVs Engine and Aftertreatment Systems to Eco-Driving

Connected and automated vehicles (CAVs) have been recognized as providing unprecedented opportunities for substantial fuel economy improvement through CAV-based vehicle speed trajectory optimization (eco-driving). At the same time, the implications of the CAV operation on thermal responses, including those of engine and exhaust aftertreatment system, have not been fully investigated. To this end, firstly, a sequential optimization framework for vehicle speed trajectory planning and powertrain control in hybrid electric CAVs is proposed in this paper. Next, the impact of eco-driving and power split optimization on the engine and catalytic converter thermal responses, as well as on the tailpipe emissions is characterized. Despite an average 16% improvement in fuel economy through sequential optimization, this study shows that eco-driving slows down the thermal responses, which could unfavorably affect the tailpipe emissions.

eess.SY↗

arXiv2019

Spectral Perturbation Meets Incomplete Multi-view Data

Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario, referred to as incomplete multi-view clustering, where a number of data instances are missing in certain views. To tackle this problem, we explore spectral perturbation theory. In this work, we show a strong link between perturbation risk bounds and incomplete multi-view clustering. That is, as the similarity matrix fed into spectral clustering is a quantity bounded in magnitude O(1), we transfer the missing problem from data to similarity and tailor a matrix completion method for incomplete similarity matrix. Moreover, we show that the minimization of perturbation risk bounds among different views maximizes the final fusion result across all views. This provides a solid fusion criteria for multi-view data. We motivate and propose a Perturbation-oriented Incomplete multi-view Clustering (PIC) method. Experimental results demonstrate the effectiveness of the proposed method.

cs.LG↗

arXiv2019

Coupling-enhanced Dual ITO Layer Electro-absorption Modulator in Silicon Photonics

Electro-optic signal modulation provides a key functionality in modern technology and information networks. Photonic integration has enabled not only miniaturizing photonic components, but also provided performance improvements due to co-design addressing both electrical and optical device rules. However, the millimeter-to-centimeter large footprint of many foundry-ready photonic electro-optic modulators significantly limits on-chip scaling density. To address these limitations, here we experimentally demonstrate a coupling-enhanced electro-absorption modulator by heterogeneously integrating a novel dual-gated indium-tin-oxide (ITO) phase-shifting tunable absorber placed at a silicon directional coupler region. Our experimental modulator shows a 2 dB extinction ratio for a just 4 um short device at 4 volt bias. Since no material nor optical resonances are deployed, this device shows spectrally broadband operation as demonstrated here across the entire C-band. In conclusion we demonstrate a modulator utilizing strong index-change from both real and imaginary part of active material enabling compact and high-performing modulators using semiconductor foundry-near materials.

physics.app-ph↗

arXiv2019

MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention. Though large efforts have been made, they may fail to describe the multiple aspects of similarity between social users, as only a single vector for one unique aspect has been represented for each node. To that end, in this paper, we propose a novel end-to-end framework named MCNE to learn multiple conditional network representations, so that various preferences for multiple behaviors could be fully captured. Specifically, we first design a binary mask layer to divide the single vector as conditional embeddings for multiple behaviors. Then, we introduce the attention network to model interaction relationship among multiple preferences, and further utilize the adapted message sending and receiving operation of graph neural network, so that multi-aspect preference information from high-order neighbors will be captured. Finally, we utilize Bayesian Personalized Ranking loss function to learn the preference similarity on each behavior, and jointly learn multiple conditional node embeddings via multi-task learning framework. Extensive experiments on public datasets validate that our MCNE framework could significantly outperform several state-of-the-art baselines, and further support the visualization and transfer learning tasks with excellent interpretability and robustness.

cs.SI↗

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

Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions

When the performance of a machine learning model varies over groups defined by sensitive attributes (e.g., gender or ethnicity), the performance disparity can be expressed in terms of the probability distributions of the input and output variables over each group. In this paper, we exploit this fact to reduce the disparate impact of a fixed classification model over a population of interest. Given a black-box classifier, we aim to eliminate the performance gap by perturbing the distribution of input variables for the disadvantaged group. We refer to the perturbed distribution as a counterfactual distribution, and characterize its properties for common fairness criteria. We introduce a descent algorithm to learn a counterfactual distribution from data. We then discuss how the estimated distribution can be used to build a data preprocessor that can reduce disparate impact without training a new model. We validate our approach through experiments on real-world datasets, showing that it can repair different forms of disparity without a significant drop in accuracy.

cs.LG↗

arXiv2019

A Priori Analysis of a Higher Order Nonlinear Elasticity Model for an Atomistic Chain with Periodic Boundary Condition

Nonlinear elastic models are widely used to describe the elastic response of crystalline solids, for example, the well-known Cauchy-Born model. While the Cauchy-Born model only depends on the strain, effects of higher order strain gradients are significant and higher order continuum models are preferred, in various applications such as defect dynamics and modeling of carbon nanotubes. In this paper, we rigorously derive a higher order nonlinear elasticity model for crystals from its atomistic description in one dimension. We show that, compared to the second order accuracy of the Cauchy-Born model, the higher order continuum model in this paper is of fourth oder accuracy with respect to the interatomic spacing in the thermal dynamic limit. In addition, we discuss the key issues for the derivation of higher order continuum models in more general cases. The theoretical convergence results are demonstrated by numerical experiments.

math.NA↗

arXiv2019

Structural Color 3D Printing By Shrinking Photonic Crystals

The rings, spots and stripes found on some butterflies, Pachyrhynchus weevils, and many chameleons are notable examples of natural organisms employing photonic crystals to produce colorful patterns. Despite advances in nanotechnology, we still lack the ability to print arbitrary colors and shapes in all three dimensions at this microscopic length scale. Commercial nanoscale 3D printers based on two-photon polymerization are incapable of patterning photonic crystal structures with the requisite ~300 nm lattice constant to achieve photonic stopbands/ bandgaps in the visible spectrum and generate colors. Here, we introduce a means to produce 3D-printed photonic crystals with a 5x reduction in lattice constants (periodicity as small as 280 nm), achieving sub-100-nm features with a full range of colors. The reliability of this process enables us to engineer the bandstructures of woodpile photonic crystals that match experiments, showing that observed colors can be attributed to either slow light modes or stopbands. With these lattice structures as 3D color volumetric elements (voxels), we printed 3D microscopic scale objects, including the first multi-color microscopic model of the Eiffel Tower measuring only 39-microns tall with a color pixel size of 1.45 microns. The technology to print 3D structures in color at the microscopic scale promises the direct patterning and integration of spectrally selective devices, such as photonic crystal-based color filters, onto free-form optical elements and curved surfaces.

physics.optics↗

arXiv2019

Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images

Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle. An important task under the food-computing umbrella is retrieval, which is particularly helpful for health related applications, where we are interested in retrieving important information about food (e.g., ingredients, nutrition, etc.). In this paper, we investigate an open research task of cross-modal retrieval between cooking recipes and food images, and propose a novel framework Adversarial Cross-Modal Embedding (ACME) to resolve the cross-modal retrieval task in food domains. Specifically, the goal is to learn a common embedding feature space between the two modalities, in which our approach consists of several novel ideas: (i) learning by using a new triplet loss scheme together with an effective sampling strategy, (ii) imposing modality alignment using an adversarial learning strategy, and (iii) imposing cross-modal translation consistency such that the embedding of one modality is able to recover some important information of corresponding instances in the other modality. ACME achieves the state-of-the-art performance on the benchmark Recipe1M dataset, validating the efficacy of the proposed technique.

cs.CV↗

arXiv2019

AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions

In the last two decades, the landscape of text generation has undergone tremendous changes and is being reshaped by the success of deep learning. New technologies for text generation ranging from template-based methods to neural network-based methods emerged. Meanwhile, the research objectives have also changed from generating smooth and coherent sentences to infusing personalized traits to enrich the diversification of newly generated content. With the rapid development of text generation solutions, one comprehensive survey is urgent to summarize the achievements and track the state of the arts. In this survey paper, we present the general systematical framework, illustrate the widely utilized models and summarize the classic applications of text generation.

cs.CL↗

arXiv2019

The Wu-Yang potential of Magnetic Skyrmion from SU(2) Flat Connection

The theoretical research of the origin of magnetic skyrmion is very interesting. By using decomposition theory of gauge potential and the gauge parallel condition of local bases of $su(2)$ Lie algebra, its $SU(2)$ gauge potential is expressed as flat connection. As an example of application, we obtain the inner topological structure of second Chern number by $SU(2)$ flat connection method. It's well known that if magnetic monopole exists in electrodynamics, its Wu-Yang potential is indispensable in $U(1)$ invariant electromagnetic field. In $2$-dim magnetic materials, we prove that if magnetic skyrmion exists, its integral kernel must be $U(1)$ Wu-Yang curvature, where its $U(1)$ Wu-Yang potential is the projection of $SU(2)$ flat connection on $su(2)$ local Cartan subalgebra. The magnetic skyrmion can be created by performing concrete $SU(2)$ local gauge transformation to $su(2)$ Cartan subalgebra $σ_3$. The components of the $U(1)$ Wu-Yang curvature correspond to the emergent electromagnetic field of magnetic skyrmion

hep-th↗

arXiv2019

A robust DOA estimation method for a linear microphone array under reverberant and noisy environments

A robust method for linear array is proposed to address the difficulty of direction-of-arrival (DOA) estimation in reverberant and noisy environments. A direct-path dominance test based on the onset detection is utilized to extract time-frequency bins containing the direct propagation of the speech. The influence of the transient noise, which severely contaminates the onset test, is mitigated by a proper transient noise determination scheme. Then for voice features, a two-stage procedure is designed based on the extracted bins and an effective dereverberation method, with robust but possibly biased estimation from middle frequency bins followed by further refinement in higher frequency bins. The proposed method effectively alleviates the estimation bias caused by the linear arrangement of microphones, and has stable performance under noisy and reverberant environments. Experimental evaluation using a 4-element microphone array demonstrates the efficacy of the proposed method.

eess.AS↗

arXiv2019

Decorrelated Adversarial Learning for Age-Invariant Face Recognition

There has been an increasing research interest in age-invariant face recognition. However, matching faces with big age gaps remains a challenging problem, primarily due to the significant discrepancy of face appearances caused by aging. To reduce such a discrepancy, in this paper we propose a novel algorithm to remove age-related components from features mixed with both identity and age information. Specifically, we factorize a mixed face feature into two uncorrelated components: identity-dependent component and age-dependent component, where the identity-dependent component includes information that is useful for face recognition. To implement this idea, we propose the Decorrelated Adversarial Learning (DAL) algorithm, where a Canonical Mapping Module (CMM) is introduced to find the maximum correlation between the paired features generated by a backbone network, while the backbone network and the factorization module are trained to generate features reducing the correlation. Thus, the proposed model learns the decomposed features of age and identity whose correlation is significantly reduced. Simultaneously, the identity-dependent feature and the age-dependent feature are respectively supervised by ID and age preserving signals to ensure that they both contain the correct information. Extensive experiments are conducted on popular public-domain face aging datasets (FG-NET, MORPH Album 2, and CACD-VS) to demonstrate the effectiveness of the proposed approach.

cs.CV↗

arXiv2019

Towards Motion Invariant Authentication for On-Body IoT Devices

As the rapid proliferation of on-body Internet of Things (IoT) devices, their security vulnerabilities have raised serious privacy and safety issues. Traditional efforts to secure these devices against impersonation attacks mainly rely on either dedicated sensors or specified user motions, impeding their wide-scale adoption. This paper transcends these limitations with a general security solution by leveraging ubiquitous wireless chips available in IoT devices. Particularly, representative time and frequency features are first extracted from received signal strengths (RSSs) to characterize radio propagation profiles. Then, an adversarial multi-player network is developed to recognize underlying radio propagation patterns and facilitate on-body device authentication. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances. We build a prototype of our system using universal software radio peripheral (USRP) devices and conduct extensive experiments with both static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 90.4%, with a high area under the receiver operating characteristic curve (AUROC) of 0.958 and better generalization performance in comparison with the conventional non-adversarial-based approach.

cs.CR↗

arXiv2019

Deep learning inter-atomic potential model for accurate irradiation damage simulations

We propose a hybrid scheme that interpolates smoothly the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a newly developed deep learning potential energy model. The resulting DP-ZBL model can not only provide overall good performance on the predictions of near-equilibrium material properties but also capture the right physics when atoms are extremely close to each other, an event that frequently happens in computational simulations of irradiation damage events. We applied this scheme to the simulation of the irradiation damage processes in the face-centered-cubic aluminium system, and found better descriptions in terms of the defect formation energy, evolution of collision cascades, displacement threshold energy, and residual point defects, than the widely-adopted ZBL modified embedded atom method potentials and its variants. Our work provides a reliable and feasible scheme to accurately simulate the irradiation damage processes and opens up new opportunities to solve the predicament of lacking accurate potentials for enormous newly-discovered materials in the irradiation effect field.

physics.comp-ph↗

arXiv2019

Rethinking Knowledge Graph Propagation for Zero-Shot Learning

Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning. These models are highly sample efficient as related concepts in the graph structure share statistical strength allowing generalization to new classes when faced with a lack of data. However, multi-layer architectures, which are required to propagate knowledge to distant nodes in the graph, dilute the knowledge by performing extensive Laplacian smoothing at each layer and thereby consequently decrease performance. In order to still enjoy the benefit brought by the graph structure while preventing dilution of knowledge from distant nodes, we propose a Dense Graph Propagation (DGP) module with carefully designed direct links among distant nodes. DGP allows us to exploit the hierarchical graph structure of the knowledge graph through additional connections. These connections are added based on a node's relationship to its ancestors and descendants. A weighting scheme is further used to weigh their contribution depending on the distance to the node to improve information propagation in the graph. Combined with finetuning of the representations in a two-stage training approach our method outperforms state-of-the-art zero-shot learning approaches.

cs.CV↗

arXiv2019

Sequential Optimization of Speed, Thermal Load, and Power Split in Connected HEVs

The emergence of connected and automated vehicles (CAVs) provides an unprecedented opportunity to capitalize on these technologies well beyond their original designed intents. While abundant evidence has been accumulated showing substantial fuel economy improvement benefits achieved through advanced powertrain control, the implications of the CAV operation on power and thermal management have not been fully investigated. In this paper, in order to explore the opportunities for the coordination between the onboard thermal management and the power split control, we present a sequential optimization solution for eco-driving speed trajectory planning, air conditioning (A/C) thermal load planning (eco-cooling), and powertrain control in hybrid electric CAVs to evaluate the individual as well as the collective energy savings through proactive usage of traffic data for vehicle speed prediction. Simulation results over a real-world driving cycle show that compared to a baseline non-CAV, 11.9%, 14.2%, and 18.8% energy savings can be accumulated sequentially through speed, thermal load, and power split optimizations, respectively.

eess.SY↗

arXiv2019

Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification

Two-stream architecture have shown strong performance in video classification task. The key idea is to learn spatio-temporal features by fusing convolutional networks spatially and temporally. However, there are some problems within such architecture. First, it relies on optical flow to model temporal information, which are often expensive to compute and store. Second, it has limited ability to capture details and local context information for video data. Third, it lacks explicit semantic guidance that greatly decrease the classification performance. In this paper, we proposed a new two-stream based deep framework for video classification to discover spatial and temporal information only from RGB frames, moreover, the multi-scale pyramid attention (MPA) layer and the semantic adversarial learning (SAL) module is introduced and integrated in our framework. The MPA enables the network capturing global and local feature to generate a comprehensive representation for video, and the SAL can make this representation gradually approximate to the real video semantics in an adversarial manner. Experimental results on two public benchmarks demonstrate our proposed methods achieves state-of-the-art results on standard video datasets.

cs.CV↗