arXiv subjects

Junhui Li

Publications and source records attributed to Junhui Li.

At least 73 records · Page 4Linked to original sources

arXiv2022

Big Data Meets Metaverse: A Survey

We are living in the era of big data. The Metaverse is an emerging technology in the future, and it has a combination of big data, AI (artificial intelligence), VR (Virtual Reality), AR (Augmented Reality), MR (mixed reality), and other technologies that will diminish the difference between online and real-life interaction. It has the goal of becoming a platform where we can work, go shopping, play around, and socialize. Each user who enters the Metaverse interacts with the virtual world in a data way. With the development and application of the Metaverse, the data will continue to grow, thus forming a big data network, which will bring huge data processing pressure to the digital world. Therefore, big data processing technology is one of the key technologies to implement the Metaverse. In this survey, we provide a comprehensive review of how Metaverse is changing big data. Moreover, we discuss the key security and privacy of Metaverse big data in detail. Finally, we summarize the open problems and opportunities of Metaverse, as well as the future of Metaverse with big data. We hope that this survey will provide researchers with the research direction and prospects of applying big data in the Metaverse.

cs.DB↗

arXiv2022

Droplet impact and Leidenfrost dynamics on a heated post

This study experimentally explores fluid breakup and Leidenfrost dynamics for droplets impacting a heated millimetric post. Using high-speed optical and infrared imaging, we investigate the droplet lifetime, breakup and boiling modes, as well as the cooling performance of different substrates. The post substrate leads to a shorter droplet lifetime and a 20°C higher Leidenfrost temperature compared to a flat substrate, attributed to mixed boiling modes along the height of the post and additional pinning. For temperatures below the Leidenfrost point, in the nucleate boiling regime, the post substrate also provides a larger maximum temperature drop than its flat counterpart. The enhanced cooling capacity can be attributed to better droplet pinning and an enlarged droplet-substrate contact area. The post's superior cooling performance becomes especially clear for impact on an inclined surface, where the post successfully prevents the rolling and bouncing of the droplet, providing a 51% to 180% increase in the maximum local temperature drop. Interestingly, at temperatures slightly above the Leidenfrost point and for a relatively narrow range of Weber numbers, droplets on the post substrate rebound rather than break up due to a complex interplay of inertial, capillary, and bubble (thin film) dynamics. Overall, the findings show that a large-scale structure increases the Leidenfrost temperature and enhances droplet breakup and interfacial heat transfer efficiency during non-isothermal droplet impact.

physics.flu-dyn↗

arXiv2022

Mean-square stability of linear systems over channels with random transmission delays

This work studies the mean-square stability and stabilization problem for networked feedback systems. Data transmission delays in the network channels of the systems are considered. It is assumed that these delays are i.i.d. processes with given probability mass functions (PMFs). A necessary and sufficient condition of mean-square (input-output) stability is studied for the networked feedback systems in terms of the input-output model and state-space model. Furthermore, according to this condition, mean-square stabilization via output feedback is studied for the networked feedback systems.

eess.SY↗

arXiv2021

Data Trading with a Monopoly Social Network: Outcomes are Mostly Privacy Welfare Damaging

This paper argues that data of strategic individuals with heterogeneous privacy valuations in a distributed online social network (e.g., Facebook) will be under-priced, if traded in a monopoly buyer setting, and will lead to diminishing utilitarian welfare. This result, for a certain family of online community data trading problems, is in stark contrast to a popular information economics intuition that increased amounts of end-user data signals in a data market improves its efficiency. Our proposed theory paves the way for a future (counter-intuitive) analysis of data trading oligopoly markets for online social networks (OSNs).

cs.SI↗

arXiv2021

Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation

Intelligent navigation among social crowds is an essential aspect of mobile robotics for applications such as delivery, health care, or assistance. Deep Reinforcement Learning emerged as an alternative planning method to conservative approaches and promises more efficient and flexible navigation. However, in highly dynamic environments employing different kinds of obstacle classes, safe navigation still presents a grand challenge. In this paper, we propose a semantic Deep-reinforcement-learning-based navigation approach that teaches object-specific safety rules by considering high-level obstacle information. In particular, the agent learns object-specific behavior by contemplating the specific danger zones to enhance safety around vulnerable object classes. We tested the approach against a benchmark obstacle avoidance approach and found an increase in safety. Furthermore, we demonstrate that the agent could learn to navigate more safely by keeping an individual safety distance dependent on the semantic information.

cs.RO↗

arXiv2021

Optimal output feedback control of a class of linear systems with quasi-colored control-dependent multiplicative noise

This paper addresses the mean-square optimal control problem for \a class of discrete-time linear systems with a quasi-colored control-dependent multiplicative noise via output feedback. The noise under study is novel and shown to have advantage on modeling a class of network phenomena such as random transmission delays. The optimal output feedback controller is designed using an optimal mean-square state feedback gain and two observer gains, which are determined by the mean-square stabilizing solution to a modified algebraic Riccati equation (MARE), provided that the plant is minimum-phase and left-invertible. A necessary and sufficient condition for the existence of the stabilizing solution to the MARE is explicitly presented. It shows that the separation principle holds in a certain sense for the optimal control design of the work. The result is also applied to the optimal control problems in networked systems with random transmission delays and analog erasure channels, respectively.

eess.SY↗

arXiv2021

Mean-Square Input-Output Stability and Stabilizability of a Networked Control System with Random Channel Induced Delays

This work mainly investigates the mean-square stability and stabilizability for a single-input single-output networked linear feedback system. The control signal in the networked system is transmitted over an unreliable channel. In this unreliable channel, the data transmission times, referred to as channel induced delays, are random values and the transmitted data could also be dropout with certain probability. The channel induced delays and packet dropout are modeled by an independent and identically distributed stochastic process with a fixed probability mass function. At the channel terminal, a linear combination of data received at one sampling time is applied to the plant of the networked feedback system as a new control signal. To describe the uncertainty in the channel, a concept so called frequency response of variation is introduced for the unreliable channel. With the given linear receiving strategy, a mean-square stability criterion is established in terms of the frequency response of variation of the unreliable channel for the networked feedback system. It is shown by this criterion that the mean-square stability is determined by the interaction between the frequency response of variation and the nominal feedback system. The role played by the random channel induced delays is the same as that played by a colored additive noise in an additive noise channel with a signal-to-noise ratio constraint. Moreover, the mean-square input-output stabilizability via output feedback is studied for the networked system. When the plant in the networked feedback system is minimum phase, an analytic necessary and sufficient condition is presented for its mean-square input-output stabilizability. It turns out that the stabilizability is only determined by the interaction between the frequency response of variation of the channel and unstable poles of the plant.

eess.SY↗

arXiv2020

Improving AMR Parsing with Sequence-to-Sequence Pre-training

In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance. To alleviate such data size restriction, pre-trained models have been drawing more and more attention in AMR parsing. However, previous pre-trained models, like BERT, are implemented for general purpose which may not work as expected for the specific task of AMR parsing. In this paper, we focus on sequence-to-sequence (seq2seq) AMR parsing and propose a seq2seq pre-training approach to build pre-trained models in both single and joint way on three relevant tasks, i.e., machine translation, syntactic parsing, and AMR parsing itself. Moreover, we extend the vanilla fine-tuning method to a multi-task learning fine-tuning method that optimizes for the performance of AMR parsing while endeavors to preserve the response of pre-trained models. Extensive experimental results on two English benchmark datasets show that both the single and joint pre-trained models significantly improve the performance (e.g., from 71.5 to 80.2 on AMR 2.0), which reaches the state of the art. The result is very encouraging since we achieve this with seq2seq models rather than complex models. We make our code and model available at https://github.com/xdqkid/S2S-AMR-Parser.

cs.CL↗

arXiv2019

Evaporation of sessile water droplets on horizontal and vertical bi-phobic patterned surfaces

This paper presents an experimental study on thermal transport to single water droplets evaporating on heated bi-phobic surfaces consisting of a superhydrophobic matrix with a circular hydrophobic pattern with strong contact line pinning. A single water droplet of 8 ul volume is placed on a preheated surface and allowed to evaporate in an open laboratory environment. We investigate the influence of substrate orientation (horizontal and vertical) on evaporation dynamics. Using optical and infrared imaging, we report droplet fluid dynamics and heat transfer characteristics of the evaporating droplet. Overall, evaporation is more efficient on the vertical surface, exhibiting higher total heat transfer rates and up to 10% shorter evaporation times. Counterintuitively, on the vertical surface, the substrate-droplet interfacial heat flux was higher near the lower contact line than in the upper region, despite a high contact angle and an expected wedge effect at the bottom. At the same time, the temperature is colder in the lower part of the droplet. We attribute this apparent anomaly to the competition between sensible heating and evaporation, and a modified convective flow signature (both within the droplet and the gas phase) compared to a horizontal surface. We also show that the thermal signature becomes uniform once the contact angles at the upper and lower contact lines become equal towards the end of the evaporation process. Insights from this work can guide the design of spray cooling devices, or be used to alter particle deposition patterns during evaporation-based fabrication techniques and ink-jet printing.

physics.flu-dyn↗

arXiv2019

A Discrete CVAE for Response Generation on Short-Text Conversation

Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder(CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable. With different sampled la-tent variables, the model is expected to generate diverse responses. Although the CVAE-based models have shown tremendous potential, their improvement of generating high-quality responses is still unsatisfactory. In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation. A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables. Accordingly, we pro-pose a two-stage sampling approach to enable efficient diverse variable selection from a large latent space assumed in the short-text conversation task. Experimental results indicate that our model outperforms various kinds of generation models under both automatic and human evaluations and generates more diverse and in-formative responses.

cs.CL↗

arXiv2019

Modeling Graph Structure in Transformer for Better AMR-to-Text Generation

Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence. Graph structures are further modeled into the seq2seq framework in order to utilize the structural information in the AMR graphs. However, previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. In this paper we eliminate such a strong limitation and propose a novel structure-aware self-attention approach to better modeling the relations between indirectly connected concepts in the state-of-the-art seq2seq model, i.e., the Transformer. In particular, a few different methods are explored to learn structural representations between two concepts. Experimental results on English AMR benchmark datasets show that our approach significantly outperforms the state of the art with 29.66 and 31.82 BLEU scores on LDC2015E86 and LDC2017T10, respectively. To the best of our knowledge, these are the best results achieved so far by supervised models on the benchmarks.

cs.CL↗

arXiv2019

Generating Multiple Diverse Responses for Short-Text Conversation

Neural generative models have become popular and achieved promising performance on short-text conversation tasks. They are generally trained to build a 1-to-1 mapping from the input post to its output response. However, a given post is often associated with multiple replies simultaneously in real applications. Previous research on this task mainly focuses on improving the relevance and informativeness of the top one generated response for each post. Very few works study generating multiple accurate and diverse responses for the same post. In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously. A reinforcement learning algorithm is designed to solve our model. Experiments on two short-text conversation tasks validate that the multiple responses generated by our model obtain higher quality and larger diversity compared with various state-of-the-art generative models.

cs.CL↗

arXiv2018

Electricity consumption forecasting method based on MPSO-BP neural network model

This paper deals with the problem of the electricity consumption forecasting method. An MPSO-BP (modified particle swarm optimization-back propagation) neural network model is constructed based on the history data of a mineral company of Anshan in China. The simulation showed that the convergence of the algorithm and forecasting accuracy using the obtained model are better than those of other traditional ones, such as BP, PSO, fuzzy neural network and so on. Then we predict the electricity consumption of each month in 2017 based on the MPSO-BP neural network model.

cs.NE↗

arXiv2018

Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings

In neural machine translation, a source sequence of words is encoded into a vector from which a target sequence is generated in the decoding phase. Differently from statistical machine translation, the associations between source words and their possible target counterparts are not explicitly stored. Source and target words are at the two ends of a long information processing procedure, mediated by hidden states at both the source encoding and the target decoding phases. This makes it possible that a source word is incorrectly translated into a target word that is not any of its admissible equivalent counterparts in the target language. In this paper, we seek to somewhat shorten the distance between source and target words in that procedure, and thus strengthen their association, by means of a method we term bridging source and target word embeddings. We experiment with three strategies: (1) a source-side bridging model, where source word embeddings are moved one step closer to the output target sequence; (2) a target-side bridging model, which explores the more relevant source word embeddings for the prediction of the target sequence; and (3) a direct bridging model, which directly connects source and target word embeddings seeking to minimize errors in the translation of ones by the others. Experiments and analysis presented in this paper demonstrate that the proposed bridging models are able to significantly improve quality of both sentence translation, in general, and alignment and translation of individual source words with target words, in particular.

cs.CL↗

arXiv2017

Learning When to Attend for Neural Machine Translation

In the past few years, attention mechanisms have become an indispensable component of end-to-end neural machine translation models. However, previous attention models always refer to some source words when predicting a target word, which contradicts with the fact that some target words have no corresponding source words. Motivated by this observation, we propose a novel attention model that has the capability of determining when a decoder should attend to source words and when it should not. Experimental results on NIST Chinese-English translation tasks show that the new model achieves an improvement of 0.8 BLEU score over a state-of-the-art baseline.

cs.CL↗

arXiv2017

Modeling Source Syntax for Neural Machine Translation

Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements. Specifically, we linearize parse trees of source sentences to obtain structural label sequences. On the basis, we propose three different sorts of encoders to incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word and label annotation vectors parallelly; 2) Hierarchical RNN encoder that learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed RNN encoder that stitchingly learns word and label annotation vectors over sequences where words and labels are mixed. Experimentation on Chinese-to-English translation demonstrates that all the three proposed syntactic encoders are able to improve translation accuracy. It is interesting to note that the simplest RNN encoder, i.e., Mixed RNN encoder yields the best performance with an significant improvement of 1.4 BLEU points. Moreover, an in-depth analysis from several perspectives is provided to reveal how source syntax benefits NMT.

cs.CL↗

arXiv2017

Negative exponential behavior of image mutual information for pseudo-thermal light ghost imaging: Observation, modeling, and verification

When use the image mutual information to assess the quality of reconstructed image in pseudo-thermal light ghost imaging, a negative exponential behavior with respect to the measurement number is observed. Based on information theory and a few simple and verifiable assumptions, semi-quantitative model of image mutual information under varying measurement numbers is established. It is the Gaussian characteristics of the bucket detector output probability distribution that leads to this negative exponential behavior. Designed experiments verify the model.

physics.optics↗

arXiv2017

Binary sampling ghost imaging: add random noise to fight quantization caused image quality decline

When the sampling data of ghost imaging is recorded with less bits, i.e., experiencing quantization, decline of image quality is observed. The less bits used, the worse image one gets. Dithering, which adds suitable random noise to the raw data before quantization, is proved to be capable of compensating image quality decline effectively, even for the extreme binary sampling case. A brief explanation and parameter optimization of dithering are given.

physics.optics↗