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Yu Huang

Publications and source records attributed to Yu Huang.

At least 235 records · Page 13Linked to original sources
arXiv2021

A Vehicles Control Model to Alleviate Traffic Instability

While bringing convenience to people, the growing number of vehicles on road already cause inevitable traffic congestion. Some traffic congestion happen with observable reasons, but others occur without apparent reasons or bottlenecks, which referred to as phantom jams, are caused by traditional vehicle following model. In order to alleviate the traffic instability caused by phantom jam, several models have been proposed with the development of intelligent transportation system (ITS). these have been proved to be able to suppress traffic instability in the ideal situation. But in road scenarios, uncertainties of vehicle state measurements and time delay caused by on-board sensors, inter-vehicle communications and control system of vehicles will affect the performance of the existing models severely, and cannot be ignored. In this paper, a novel predictable bilateral control model-PBCM, which consists of best estimation and state prediction is proposed to determine accurate acceleration values of the host vehicle in traffic flow to alleviate traffic instability. Theoretical analysis and simulation results show that our model could reduce the influence of the measurement errors and the delay caused by communication and control system effectively, control the state of the vehicles in traffic flow accurately, thus achieve the goal of restrain the instability of traffic flow.

eess.SY↗
arXiv2020

Physical rendering of synthetic spaces for topological sound transport

Synthetic dimensions can be rendered in the physical space and this has been achieved with photonics and cold atomic gases, however, little to no work has been succeeded in acoustics because acoustic wave-guides cannot be weakly coupled in a continuous fashion. Here, we establish the theoretical principles and for the first time manufacture acoustic crystals composed of arrays of acoustic cavities strongly coupled through modulated channels to evidence one-dimensional (1D) and two-dimensional (2D) dynamic topological pumpings. In particular, the topological edge-bulkedge and corner-bulk-corner transport are physically illustrated in finite-sized acoustic structures. We delineate the generated 2D and four-dimensional (4D) quantum Hall effects by calculating first and second Chern numbers and demonstrating robustness against the geometrical imperfections. Synthetic dimensions could provide a powerful way for acoustic topological wave steering and open up a new platform to explore higher-order topological matter in dimensions four and higher.

cond-mat.mes-hall↗
arXiv2020

Methodology for In-flight Flat-field Calibration of the Lyman-alpha Solar Telescope (LST)

Flat-field reflects the non-uniformity of the photometric response at the focal plane of an instrument, which uses digital image sensors, such as Charge Coupled Device (CCD) and Complementary Metal-Oxide-Semiconductor (CMOS). This non-uniformity must corrected before being used for scientific research. In this paper, we assess various candidate methods via simulation using available data so as to figure the in-flight flat-field calibration methodology for the Lyman-alpha Solar Telescope (LST). LST is one of the payloads for the Advanced Space-based Solar Observatory (ASO-S) mission and consists of three instruments: a White-light Solar Telescope (WST), a Solar Disk Imager (SDI) and a dual-waveband Solar Corona Imager (SCI). In our simulations, data from the Helioseismic and Magnetic Imager (HMI) and Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) mission are used. Our results show that the normal KLL method is appropriate for in-flight flat-field calibration of WST and implementing a transmissive diffuser is applicable for SCI. For the in-flight flat-field calibration of SDI, we recommend the KLL method with off-pointing images with defocused resolution of around 18", and use the local correlation tracking (LCT) algorithm instead of limb-fitting to determine the relative dis-placements between different images.

astro-ph.SR↗
arXiv2020

Evolutionary Architecture Search for Graph Neural Networks

Automated machine learning (AutoML) has seen a resurgence in interest with the boom of deep learning over the past decade. In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to address grid-like data such as texts and images. However, very litter work has been done about Graph Neural Networks (GNN) learning on unstructured network data. Given the huge number of choices and combinations of components such as aggregator and activation function, determining the suitable GNN structure for a specific problem normally necessitates tremendous expert knowledge and laborious trails. In addition, the slight variation of hyper parameters such as learning rate and dropout rate could dramatically hurt the learning capacity of GNN. In this paper, we propose a novel AutoML framework through the evolution of individual models in a large GNN architecture space involving both neural structures and learning parameters. Instead of optimizing only the model structures with fixed parameter settings as existing work, an alternating evolution process is performed between GNN structures and learning parameters to dynamically find the best fit of each other. To the best of our knowledge, this is the first work to introduce and evaluate evolutionary architecture search for GNN models. Experiments and validations demonstrate that evolutionary NAS is capable of matching existing state-of-the-art reinforcement learning approaches for both the semi-supervised transductive and inductive node representation learning and classification.

cs.NE↗
arXiv2020

Privacy-Aware Data Cleaning-as-a-Service (Extended Version)

Data cleaning is a pervasive problem for organizations as they try to reap value from their data. Recent advances in networking and cloud computing technology have fueled a new computing paradigm called Database-as-a-Service, where data management tasks are outsourced to large service providers. In this paper, we consider a Data Cleaning-as-a-Service model that allows a client to interact with a data cleaning provider who hosts curated, and sensitive data. We present PACAS: a Privacy-Aware data Cleaning-As-a-Service model that facilitates interaction between the parties with client query requests for data, and a service provider using a data pricing scheme that computes prices according to data sensitivity. We propose new extensions to the model to define generalized data repairs that obfuscate sensitive data to allow data sharing between the client and service provider. We present a new semantic distance measure to quantify the utility of such repairs, and we re-define the notion of consistency in the presence of generalized values. The PACAS model uses (X,Y,L)-anonymity that extends existing data publishing techniques to consider the semantics in the data while protecting sensitive values. Our evaluation over real data show that PACAS safeguards semantically related sensitive values, and provides lower repair errors compared to existing privacy-aware cleaning techniques.

cs.DB↗
arXiv2020

Genetic Improvement @ ICSE 2020

Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the 42nd ACM/IEEE International Conference on Software Engineering on Friday 3rd July 2020). Topics included industry take up, human factors, explainabiloity (explainability, justifyability, exploitability) and GI benchmarks. We also contrast various recent online approaches (e.g. SBST 2020) to holding virtual computer science conferences and workshops via the WWW on the Internet without face-2-face interaction. Finally we speculate on how the Coronavirus Covid-19 Pandemic will affect research next year and into the future.

cs.SE↗
arXiv2020

Diversifying Anonymized Data with Diversity Constraints

Recently introduced privacy legislation has aimed to restrict and control the amount of personal data published by companies and shared to third parties. Much of this real data is not only sensitive requiring anonymization, but also contains characteristic details from a variety of individuals. This diversity is desirable in many applications ranging from Web search to drug and product development. Unfortunately, data anonymization techniques have largely ignored diversity in its published result. This inadvertently propagates underlying bias in subsequent data analysis. We study the problem of finding a diverse anonymized data instance where diversity is measured via a set of diversity constraints. We formalize diversity constraints and study their foundations such as implication and satisfiability. We show that determining the existence of a diverse, anonymized instance can be done in PTIME, and we present a clustering-based algorithm. We conduct extensive experiments using real and synthetic data showing the effectiveness of our techniques, and improvement over existing baselines. Our work aligns with recent trends towards responsible data science by coupling diversity with privacy-preserving data publishing.

cs.DB↗
arXiv2020

Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies

Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019. This is a survey of autonomous driving technologies with deep learning methods. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task level respectively, behavior modelling and prediction of vehicle driving and pedestrian trajectories.

cs.CV↗
arXiv2020

Preflare very long-periodic pulsations observed in Halpha emission before the onset of a solar flare

Very long-periodic pulsations during preflare phases (preflare-VLPs) have been detected in the full-disk solar soft X-Ray (SXR) flux (see Tan et al. 2016). They may be regarded as precursors to solar flares and may help us better understand the trigger mechanism of solar flares. In this letter, we report a preflare-VLP event before the onset of an M1.1 circular-ribbon flare on 2015 October 16. It was simultaneously observed in Halpha, SXR, and extreme ultraviolet (EUV) wavelengths, which were recorded by the NVST, GOES, EVE, and AIA respectively. The preflare-VLP is identified as the repeat and quasi-periodic pulses in light curves during preflare phase, which might be modulated by LRC-circuit oscillation in the current-carrying plasma loop. The quasi-periodicity can be determined from the Fourier power spectrum with Markov chain Monte Carlo (MCMC)-based Bayesian (e.g., Liang et al. 2020), such as ~9.3 minutes. We present the first report of a preflare-VLP event in the local Halpha line and EUV wavelength, which could be considered a precursor of a solar flare. This finding should therefore prove useful for the prediction of solar flares, especially for powerful flares.

astro-ph.SR↗
arXiv2020

Fine-grained Analysis on Fast Implementations of Distributed Multi-writer Atomic Registers

Distributed multi-writer atomic registers are at the heart of a large number of distributed algorithms. While enjoying the benefits of atomicity, researchers further explore fast implementations of atomic reigsters which are optimal in terms of data access latency. Though it is proved that multi-writer atomic register implementations are impossible when both read and write are required to be fast, it is still open whether implementations are impossible when only write or read is required to be fast. This work proves the impossibility of fast write implementations based on a series of chain arguments among indistiguishable executions. We also show the necessary and sufficient condition for fast read implementations by extending the results in the single-writer case. This work concludes a series of studies on fast implementations of distributed atomic registers.

cs.DC↗
arXiv2020

A class of multiplicative splitting iterations for solving the continuous Sylvester equation

For solving the continuous Sylvester equation, a class of the multiplicative splitting iteration method is presented. We consider two symmetric positive definite splittings for each coefficient matrix of the continuous Sylvester equations and it can be equivalently written as two multiplicative splitting matrix equations. When both coefficient matrices in the continuous Sylvester equation are (non-symmetric) positive semi-definite, and at least one of them is positive definite; we can choose Hermitian and skew-Hermitian (HS) splittings of matrices $A$ and $B$, in the first equation, and the splitting of the Jacobi iterations for matrices $A$ and $B$, in the second equation in the multiplicative splitting iteration method. Convergence conditions of this method are studied and numerical experiments show the efficiency of this method.

math.NA↗
arXiv2020

GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries

The automatic identification system (AIS), an automatic vessel-tracking system, has been widely adopted to perform intelligent traffic management and collision avoidance services in maritime Internet of Things (IoT) industries. With the rapid development of maritime transportation, tremendous numbers of AIS-based vessel trajectory data have been collected, which make trajectory data compression imperative and challenging. This paper mainly focuses on the compression and visualization of large-scale vessel trajectories and their Graphics Processing Unit (GPU)-accelerated implementations. The visualization was implemented to investigate the influence of compression on vessel trajectory data quality. In particular, the Douglas-Peucker (DP) and Kernel Density Estimation (KDE) algorithms, respectively utilized for trajectory compression and visualization, were significantly accelerated through the massively parallel computation capabilities of GPU architecture. Comprehensive experiments on trajectory compression and visualization have been conducted on large-scale AIS data of recording ship movements collected from 3 different water areas, i.e., the South Channel of Yangtze River Estuary, the Chengshan Jiao Promontory, and the Zhoushan Islands. Experimental results illustrated that (1) the proposed GPU-based parallel implementation frameworks could significantly reduce the computational time for both trajectory compression and visualization; (2) the influence of compressed vessel trajectories on trajectory visualization could be negligible if the compression threshold was selected suitably; (3) the Gaussian kernel was capable of generating more appropriate KDE-based visualization performance by comparing with other seven kernel functions.

eess.SP↗
arXiv2019

Attributed Rhetorical Structure Grammar for Domain Text Summarization

This paper presents a new approach of automatic text summarization which combines domain oriented text analysis (DoTA) and rhetorical structure theory (RST) in a grammar form: the attributed rhetorical structure grammar (ARSG), where the non-terminal symbols are domain keywords, called domain relations, while the rhetorical relations serve as attributes. We developed machine learning algorithms for learning such a grammar from a corpus of sample domain texts, as well as parsing algorithms for the learned grammar, together with adjustable text summarization algorithms for generating domain specific summaries. Our practical experiments have shown that with support of domain knowledge the drawback of missing very large training data set can be effectively compensated. We have also shown that the knowledge based approach may be made more powerful by introducing grammar parsing and RST as inference engine. For checking the feasibility of model transfer, we introduced a technique for mapping a grammar from one domain to others with acceptable cost. We have also made a comprehensive comparison of our approach with some others.

cs.CL↗
arXiv2019

The State and Future of Genetic Improvement

We report the discussion session at the sixth international Genetic Improvement workshop, GI-2019 @ ICSE, which was held as part of the 41st ACM/IEEE International Conference on Software Engineering on Tuesday 28th May 2019. Topics included GI representations, the maintainability of evolved code, automated software testing, future areas of GI research, such as co-evolution, and existing GI tools and benchmarks.

cs.NE↗
arXiv2019

Inversion-based Measurement of Data Consistency for Read/Write Registers

Both providers and consumers of distributed storage services benefit from the quantification of the severity of consistency violations. However, existing methods fail to capture a typical pattern of violation - the disorder among operations with reference to the ideally strong atomic execution. Such disorder are often seen in Internet applications based on distributed storage services, such as instant messaging. To this end, we use inversions in a permutation with reference to the totally-ordered permutation to measure the consistency of data. The $i$-atomicity model is proposed and a (pseudo-)polynomial verification algorithm for a restricted type of histories is proposed. The basic idea of the verification algorithm is brute-force search of the permutation with less than $i$ inversions, which requires exponential cost. Reasonable assumptions abstracted from applications scenarios can be leveraged to prune the search space, which yields an efficient polynomial verification algorithm.

cs.DC↗
arXiv2018

Specification and Implementation of Replicated List: The Jupiter Protocol Revisited

The replicated list object is frequently used to model the core functionality of replicated collaborative text editing systems. Since 1989, the convergence property has been a common specification of a replicated list object. Recently, Attiya et al. proposed the strong/weak list specification and conjectured that the well-known Jupiter protocol satisfies the weak list specification. The major obstacle to proving this conjecture is the mismatch between the global property on all replica states prescribed by the specification and the local view each replica maintains in Jupiter using data structures like 1D buffer or 2D state space. To address this issue, we propose CJupiter (Compact Jupiter) based on a novel data structure called $n$-ary ordered state space for a replicated client/server system with $n$ clients. At a high level, CJupiter maintains only a single $n$-ary ordered state space which encompasses exactly all states of each replica. We prove that CJupiter and Jupiter are equivalent and that CJupiter satisfies the weak list specification, thus solving the conjecture above.

cs.DC↗
arXiv2018

Spatial Modulation for Molecular Communication

In this paper, we propose an energy-efficient spatial modulation based molecular communication (SM-MC) scheme, in which a transmitted symbol is composed of two parts, i.e., a space derived symbol and a concentration derived symbol. The space symbol is transmitted by embedding the information into the index of a single activated transmitter nanomachine. The concentration symbol is drawn according to the conventional concentration shift keying (CSK) constellation. Befitting from a single active transmitter during each symbol transmission period, SM-MC can avoid the inter-link interference problem existing in the current multiple-input multiple-output (MIMO) MC schemes, which hence enables low-complexity symbol detection and performance improvement. Specifically, in our low-complexity scheme, the space symbol is first detected by energy comparison, and then the concentration symbol is detected by the equal gain combining assisted CSK demodulation. In this paper, we analyze the symbol error rate (SER) of the SM-MC and its special case, namely the space shift keying based MC (SSK-MC), where only space symbol is transmitted and no CSK modulation is invoked. Finally, the analytical results are validated by computer simulations, and our studies demonstrate that both the SM-MC and SSK-MC are capable of achieving better SER performance than the conventional MIMO-MC and single-input single-output MC (SISO-MC) when the same symbol rate is assumed.

cs.ET↗