arXiv subjects

Yang He

Publications and source records attributed to Yang He.

At least 109 records · Page 6Linked to original sources

arXiv2022

A Parameter-free Nonconvex Low-rank Tensor Completion Model for Spatiotemporal Traffic Data Recovery

Traffic data chronically suffer from missing and corruption, leading to accuracy and utility reduction in subsequent Intelligent Transportation System (ITS) applications. Noticing the inherent low-rank property of traffic data, numerous studies formulated missing traffic data recovery as a low-rank tensor completion (LRTC) problem. Due to the non-convexity and discreteness of the rank minimization in LRTC, existing methods either replaced rank with convex surrogates that are quite far away from the rank function or approximated rank with nonconvex surrogates involving many parameters. In this study, we proposed a Parameter-Free Non-Convex Tensor Completion model (TC-PFNC) for traffic data recovery, in which a log-based relaxation term was designed to approximate tensor algebraic rank. Moreover, previous studies usually assumed the observations are reliable without any outliers. Therefore, we extended the TC-PFNC to a robust version (RTC-PFNC) by modeling potential traffic data outliers, which can recover the missing value from partial and corrupted observations and remove the anomalies in observations. The numerical solutions of TC-PFNC and RTC-PFNC were elaborated based on the alternating direction multiplier method (ADMM). The extensive experimental results conducted on four real-world traffic data sets demonstrated that the proposed methods outperform other state-of-the-art methods in both missing and corrupted data recovery. The code used in this paper is available at: https://github.com/YoungHe49/T-ITSPFNC.

cs.LG↗

arXiv2022

High-speed tunable microwave-rate soliton microcomb

Microwave signal generation with fast frequency tuning underlies many applications including sensing, imaging, ranging, time keeping, wireless communication, and high-speed electronics. Soliton microcombs are a promising new approach for photonic-based microwave signal synthesis. To date, however, tuning rate has been limited in microcombs (and in frequency combs generally). Here, we demonstrate the first microwave-rate soliton microcomb whose repetition rate can be tuned at a high speed. By integrating an electro-optic tuning/modulation element into a lithium niobate comb microresonator, a modulation bandwidth up to 75 MHz and a continuous frequency modulation rate up to 5.0 * 10^14 Hz/s are achieved, several orders-of-magnitude faster than existing microcomb technology. These features are especially useful for disciplining an optical VCO to a long-term reference such as an optical clock or for tight phase lock in dual-microcomb clocks or synthesizers. Moreover, the microwave signal rate can be set to any X - W band rate. Besides its positive impact on microwave photonics, fast repetition rate control is generally important in all applications of frequency combs.

physics.optics↗

arXiv2022

Self-injection-locked second-harmonic integrated source

High coherence visible and near-visible laser sources are centrally important to the operation of advanced position/navigation/timing systems as well as classical/quantum sensing systems. However, the complexity and size of these bench-top lasers is an impediment to their transitioning beyond the laboratory. Here, a system-on-a-chip that emits high-coherence visible and near-visible lightwaves is demonstrated. The devices rely upon a new approach wherein wavelength conversion and coherence increase by self-injection-locking are combined within in a single nonlinear resonator. This simplified approach is demonstrated in a hybridly-integrated device and provides a short-term linewidth around 10-30 kHz. On-chip, converted optical power over 2 mW is also obtained. Moreover, measurements show that heterogeneous integration can result in conversion efficiency higher than 25% with output power over 11 mW. Because the approach uses mature III-V pump lasers in combination with thin-film lithium niobate, it can be scaled for low-cost manufacturing of high-coherence visible emitters. Also, the coherence generation process can be transferred to other frequency conversion processes including optical parametric oscillation, sum/difference frequency generation, and third-harmonic generation.

physics.optics↗

arXiv2022

Reliable and Broad-range Layer Identification of Au-assisted Exfoliated Large Area MoS$_2$ and WS$_2$ Using Reflection Spectroscopic Fingerprints

The emerging Au-assisted exfoliation technique provides a wealth of large-area and high-quality ultrathin two-dimensional (2D) materials compared with traditional tape-based exfoliation. Fast, damage-free, and reliable determination of the layer number of such 2D films is essential to study layer-dependent physics and promote device applications. Here, an optical method has been developed for simple, high throughput, and accurate determination of the layer number for Au-assisted exfoliated MoS$_2$ and WS$_2$ films in a broad thickness range. The method is based on quantitative analysis of layer-dependent white light reflection spectra, revealing that the reflection peak intensity can be used as a clear indicator for determining the layer number. The simple yet robust method will facilitate the fundamental study on layer-dependent optical, electrical, and thermal properties and device applications of 2D materials. The technique can also be readily combined with photoluminescence and Raman spectroscopies to study other layer-dependent physical properties of 2D materials.

cond-mat.mtrl-sci↗

arXiv2022

Observation of biradical spin coupling through hydrogen bonds

Investigation of intermolecular electron spin interaction is of fundamental importance in both science and technology.Here, radical pairs of all-trans retinoic acid molecules on Au(111) are created using an ultra-low temperature scanning tunneling microscope. Antiferromagnetic coupling between two radicals is identified by magnetic-field dependent spectroscopy.The measured exchange energies are from 0.1 to 1.0 meV. The biradical spin coupling is mediated through O-H$\cdots$O hydrogen bonds, as elucidated from analysis combining density functional theory calculation and a modern version of valence bond theory.

cond-mat.mes-hall↗

arXiv2022

CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization

Federated learning is a promising framework to mitigate data privacy and computation concerns. However, the communication cost between the server and clients has become the major bottleneck for successful deployment. Despite notable progress in gradient compression, the existing quantization methods require further improvement when low-bits compression is applied, especially the overall systems often degenerate a lot when quantization are applied in double directions to compress model weights and gradients. In this work, we propose a simple cosine-based nonlinear quantization and achieve impressive results in compressing round-trip communication costs. We are not only able to compress model weights and gradients at higher ratios than previous methods, but also achieve competing model performance at the same time. Further, our approach is highly suitable for federated learning problems since it has low computational complexity and requires only a little additional data to recover the compressed information. Extensive experiments have been conducted on image classification and brain tumor semantic segmentation using the CIFAR-10, and BraTS datasets where we show state-of-the-art effectiveness and impressive communication efficiency.

cs.LG↗

arXiv2022

Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction

The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction. However, the existing methods for exposure sequence modeling bring extensive computational burden and neglect noise problems, resulting in an excessively latency and the limited performance in online recommenders. In this paper, we propose to address the high latency and noise problems via Gating-adapted wavelet multiresolution analysis (Gama), which can effectively denoise the extremely long exposure sequence and adaptively capture the implied multi-dimension user interest with linear computational complexity. This is the first attempt to integrate non-parametric multiresolution analysis technique into deep neural networks to model user exposure sequence. Extensive experiments on large scale benchmark dataset and real production dataset confirm the effectiveness of Gama for exposure sequence modeling, especially in cold-start scenarios. Benefited from its low latency and high effecitveness, Gama has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.

cs.IR↗

arXiv2022

Integrated Pockels Laser

The development of integrated semiconductor lasers has miniaturized traditional bulky laser systems, enabling a wide range of photonic applications. A progression from pure III-V based lasers to III-V/external cavity structures has harnessed low-loss waveguides in different material systems, leading to significant improvements in laser coherence and stability. Despite these successes, however, key functions remain absent. In this work, we address a critical missing function by integrating the Pockels effect into a semiconductor laser. Using a hybrid integrated III-V/Lithium Niobate structure, we demonstrate several essential capabilities that have not existed in previous integrated lasers. These include a record-high frequency modulation speed of 2 exahertz/s (2.0$\times$10$^{18}$ Hz/s) and fast switching at 50 MHz, both of which are made possible by integration of the electro-optic effect. Moreover, the device co-lases at infrared and visible frequencies via the second-harmonic frequency conversion process, the first such integrated multi-color laser. Combined with its narrow linewidth and wide tunability, this new type of integrated laser holds promise for many applications including LiDAR, microwave photonics, atomic physics, and AR/VR.

physics.optics↗

arXiv2022

Hamiltonian Particle-in-Cell methods for Vlasov-Poisson equations

In this paper, Particle-in-Cell algorithms for the Vlasov-Poisson system are presented based on its Poisson bracket structure. The Poisson equation is solved by finite element methods, in which the appropriate finite element spaces are taken to guarantee that the semi-discretized system possesses a well defined discrete Poisson bracket structure. Then, splitting methods are applied to the semi-discretized system by decomposing the Hamiltonian function. The resulting discretizations are proved to be Poisson bracket preserving. Moreover, the conservative quantities of the system are also well preserved. In numerical experiments, we use the presented numerical methods to simulate various physical phenomena. Due to the huge computational effort of the practical computations, we employ the strategy of parallel computing. The numerical results verify the efficiency of the new derived numerical discretizations.

math.NA↗

arXiv2022

Filter Pruning by Switching to Neighboring CNNs with Good Attributes

Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity and achieve better performance. However, during the iterative pruning process, even if the network weights are updated to new values, the pruning criterion remains the same. In addition, when evaluating the filter importance, only the magnitude information of the filters is considered. However, in neural networks, filters do not work individually, but they would affect other filters. As a result, the magnitude information of each filter, which merely reflects the information of an individual filter itself, is not enough to judge the filter importance. To solve the above problems, we propose Meta-attribute-based Filter Pruning (MFP). First, to expand the existing magnitude information based pruning criteria, we introduce a new set of criteria to consider the geometric distance of filters. Additionally, to explicitly assess the current state of the network, we adaptively select the most suitable criteria for pruning via a meta-attribute, a property of the neural network at the current state. Experiments on two image classification benchmarks validate our method. For ResNet-50 on ILSVRC-2012, we could reduce more than 50% FLOPs with only 0.44% top-5 accuracy loss.

cs.CV↗

arXiv2022

PYTHIA 8 underlying event tune For RHIC energies

We report an underlying event tune for the PYTHIA 8 Monte Carlo event generator that is applicable for hadron collisions primarily at $\sqrt{s}$ ranges available at the Relativistic Heavy-Ion Collider (RHIC). We compare our new PYTHIA 8 tuned predictions to mid-rapidity inclusive $π^{\pm}$ spectra, jet sub-structure, Drell-Yan production, and underlying event measurements from RHIC and the Tevatron, as well as underlying event data from the Large Hadron Collider. With respect to the default PYTHIA 8 Monash Tune, the new `Detroit' tune shows significant improvements in the description of the experimental data. Additionally, we explore the validity of PYTHIA 8 predictions for forward rapidity $π$ in $\sqrt{s}$ = 200 GeV collisions, where neither tune is able to sufficiently describe the data. We advocate for the new tune to be used for PYTHIA 8 studies at current and future RHIC experiments, and discuss future tuning exercises at lower center-of-mass energies, where forward/backward kinematics are essential at the upcoming Electron-Ion collider.

hep-ph↗

arXiv2022

Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework

We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alleviating over-fits caused by data-sparsity in two ways: learning probabilistic embedding, and incorporating trainable and regularized priors which utilize the rich side information of cold start users and advertisements (Ads). The two techniques are naturally integrated into a variational inference framework, forming an end-to-end training process. Abundant empirical tests on benchmark datasets well demonstrate the advantages of our proposed VELF. Besides, extended experiments confirmed that our parameterized and regularized priors provide more generalization capability than traditional fixed priors.

cs.IR↗

arXiv2021

Automated Deepfake Detection

In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection. This is the first time to employ automated machine learning for deepfake detection. Based on our explored search space, our proposed method achieves competitive prediction accuracy compared to previous methods. To improve the generalizability of our method, especially when training data and testing data are manipulated by different methods, we propose a simple yet effective strategy in our network learning process: making it to estimate potential manipulation regions besides predicting the real/fake labels. Unlike previous works manually design neural networks, our method can relieve us from the high labor cost in network construction. More than that, compared to previous works, our method depends much less on prior knowledge, e.g., which manipulation method is utilized or where exactly the fake image is manipulated. Extensive experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method for deepfake detection.

cs.CV↗

arXiv2021

Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis

The rapid advances in deep generative models over the past years have led to highly {realistic media, known as deepfakes,} that are commonly indistinguishable from real to human eyes. These advances make assessing the authenticity of visual data increasingly difficult and pose a misinformation threat to the trustworthiness of visual content in general. Although recent work has shown strong detection accuracy of such deepfakes, the success largely relies on identifying frequency artifacts in the generated images, which will not yield a sustainable detection approach as generative models continue evolving and closing the gap to real images. In order to overcome this issue, we propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection. The re-synthesis procedure is flexible, allowing us to incorporate a series of visual tasks - we adopt super-resolution, denoising and colorization as the re-synthesis. We demonstrate the improved effectiveness, cross-GAN generalization, and robustness against perturbations of our approach in a variety of detection scenarios involving multiple generators over CelebA-HQ, FFHQ, and LSUN datasets. Source code is available at https://github.com/SSAW14/BeyondtheSpectrum.

cs.CV↗

arXiv2021

High Order Explicit Lorentz Invariant Volume-preserving Algorithms for Relativistic Dynamics of Charged Particles

Lorentz invariant structure-preserving algorithms possess reference-independent secular stability, which is vital for simulating relativistic multi-scale dynamical processes. The splitting method has been widely used to construct structure-preserving algorithms, but without exquisite considerations, it can easily break the Lorentz invariance of continuous systems. In this paper, we introduce a Lorentz invariant splitting technique to construct high order explicit Lorentz invariant volume-preserving algorithms (LIVPAs) for simulating charged particle dynamics. Using this method, long-term stable explicit LIVPAs with different orders are developed and their performances of Lorentz invariance and long-term stability are analyzed in detail.

physics.plasm-ph↗

arXiv2021

Ultra-broadband Entangled Photons on a Nanophotonic Chip

Nanophotonic entangled-photon sources are a critical building block of chip-scale quantum photonic architecture and have seen significant development over the past two decades. These sources generate photon pairs that typically span over a narrow frequency bandwidth. Generating entanglement over a wide spectral region has proven to be useful in a wide variety of applications including quantum metrology, spectroscopy and sensing, and optical communication. However, generation of broadband photon pairs with temporal coherence approaching an optical cycle on a chip is yet to be seen. Here we demonstrate generation of ultra-broadband entangled photons using spontaneous parametric down-conversion in a periodically-poled lithium niobate nanophotonic waveguide. We employ dispersion engineering to achieve a bandwidth of 100 THz (1.2 - 2 $μ$m), at a high efficiency of 13 GHz/mW. The photons show strong temporal correlations and purity with the coincidence-to-accidental ratio exceeding $10^5$ and $>$ 98\% two-photon interference visibility. These properties together with the piezo-electric and electro-optic control and reconfigurability, make thin-film lithium niobate an excellent platform for a controllable entanglement source for quantum communication and computing, and open a path towards femtosecond metrology and spectroscopy with non-classical light on a nanophotonic chip.

physics.optics↗

arXiv2020

NaturalCC: A Toolkit to Naturalize the Source Code Corpus

We present NaturalCC, an efficient and extensible toolkit to bridge the gap between natural language and programming language, and facilitate the research on big code analysis. Using NaturalCC, researchers both from natural language or programming language communities can quickly and easily reproduce the state-of-the-art baselines and implement their approach. NaturalCC is built upon Fairseq and PyTorch, providing (1) an efficient computation with multi-GPU and mixed-precision data processing for fast model training, (2) a modular and extensible framework that makes it easy to reproduce or implement an approach for big code analysis, and (3) a command line interface and a graphical user interface to demonstrate each model's performance. Currently, we have included several state-of-the-art baselines across different tasks (e.g., code completion, code comment generation, and code retrieval) for demonstration. The video of this demo is available at https://www.youtube.com/watch?v=q4W5VSI-u3E&t=25s.

cs.SE↗

arXiv2020

Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation

Today's success of state of the art methods for semantic segmentation is driven by large datasets. Data is considered an important asset that needs to be protected, as the collection and annotation of such datasets comes at significant efforts and associated costs. In addition, visual data might contain private or sensitive information, that makes it equally unsuited for public release. Unfortunately, recent work on membership inference in the broader area of adversarial machine learning and inference attacks on machine learning models has shown that even black box classifiers leak information on the dataset that they were trained on. We show that such membership inference attacks can be successfully carried out on complex, state of the art models for semantic segmentation. In order to mitigate the associated risks, we also study a series of defenses against such membership inference attacks and find effective counter measures against the existing risks with little effect on the utility of the segmentation method. Finally, we extensively evaluate our attacks and defenses on a range of relevant real-world datasets: Cityscapes, BDD100K, and Mapillary Vistas.

cs.CV↗