arXiv subjects

Zheng Cao

Publications and source records attributed to Zheng Cao.

At least 37 records · Page 2Linked to original sources

arXiv2022

Elevator Optimization: Application of Spatial Process and Gibbs Random Field Approaches for Dumbwaiter Modeling and Multi-Dumbwaiter Systems

This research investigates analytical and quantitative methods for simulating elevator optimizations. To maximize overall elevator usage, we concentrate on creating a multiple-user positive-sum system that is inspired by agent-based game theory. We define and create basic "Dumbwaiter" models by attempting both the Spatial Process Approach and the Gibbs Random Field Approach. These two mathematical techniques approach the problem from different points of view: the spatial process can give an analytical solution in continuous space and the Gibbs Random Field provides a discrete framework to flexibly model the problem on a computer. Starting from the simplest case, we target the assumptions to provide concrete solutions to the models and develop a "Multi-Dumbwaiter System". This paper examines, evaluates, and proves the ultimate success of such implemented strategies to design the basic elevator's optimal policy; consequently, not only do we believe in the results' practicality for industry, but also their potential for application.

math.NA↗

arXiv2022

Application of Deep Q Learning with Simulation Results for Elevator Optimization

This paper presents a methodology for combining programming and mathematics to optimize elevator wait times. Based on simulated user data generated according to the canonical three-peak model of elevator traffic, we first develop a naive model from an intuitive understanding of the logic behind elevators. We take into consideration a general array of features including capacity, acceleration, and maximum wait time thresholds to adequately model realistic circumstances. Using the same evaluation framework, we proceed to develop a Deep Q Learning model in an attempt to match the hard-coded naive approach for elevator control. Throughout the majority of the paper, we work under a Markov Decision Process (MDP) schema, but later explore how the assumption fails to characterize the highly stochastic overall Elevator Group Control System (EGCS).

cs.LG↗

arXiv2022

Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting

This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematical models for the Black-Scholes equation to analyze data for 92,846 companies. We solve the Black-Scholes (BS) equation forwards in time as an ill-posed inverse problem, using the Quasi-Reversibility Method (QRM), to predict option price for the future one day. For each company, we have 13 elements including stock and option daily prices, volatility, minimizer, etc. Because the market is so complicated that there exists no perfect model, we apply ML to train algorithms to make the best prediction. The current stage of research combines QRM with Convolutional Neural Networks (CNN), which learn information across a large number of data points simultaneously. We implement CNN to generate new results by validating and testing on sample market data. We test different ways of applying CNN and compare our CNN models with previous models to see if achieving a higher profit rate is possible.

q-fin.ST↗

arXiv2022

Optimizing Stock Option Forecasting with the Assembly of Machine Learning Models and Improved Trading Strategies

This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the follow-up project of the research "Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting". First, the project included an application of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to provide a novel way of predicting stock option trends. Additionally, it examined the dependence of the ML models by evaluating the experimental method of combining multiple ML models to improve prediction results and decision-making. Lastly, two improved trading strategies and simulated investing results were presented. The Binomial Asset Pricing Model with discrete time stochastic process analysis and portfolio hedging was applied and suggested an optimized investment expectation. These results can be utilized in real-life trading strategies to optimize stock option investment results based on historical data.

q-fin.CP↗

arXiv2022

CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation

Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data. To better solve the above problems, we propose CGoDial, new challenging and comprehensive Chinese benchmark for multi-domain Goal-oriented Dialog evaluation. It contains 96,763 dialog sessions and 574,949 dialog turns totally, covering three datasets with different knowledge sources: 1) a slot-based dialog (SBD) dataset with table-formed knowledge, 2) a flow-based dialog (FBD) dataset with tree-formed knowledge, and a retrieval-based dialog (RBD) dataset with candidate-formed knowledge. To bridge the gap between academic benchmarks and spoken dialog scenarios, we either collect data from real conversations or add spoken features to existing datasets via crowd-sourcing. The proposed experimental settings include the combinations of training with either the entire training set or a few-shot training set, and testing with either the standard test set or a hard test subset, which can assess model capabilities in terms of general prediction, fast adaptability and reliable robustness.

cs.CL↗

arXiv2022

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing

In this paper, we propose a novel SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing, which leverages contextual information to enrich natural language (NL) utterance and table schema representations for text-to-SQL conversations. Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation. In addition, we construct a high-quality large-scale context-dependent text-to-SQL conversation corpus to pre-train STAR. Extensive experiments show that STAR achieves new state-of-the-art performance on two downstream benchmarks (SParC and CoSQL), significantly outperforming previous pre-training methods and ranking first on the leaderboard. We believe the release of the constructed corpus, codebase and pre-trained STAR checkpoints would push forward the research in this area. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/star.

cs.CL↗

arXiv2022

GW190814: Circumstantial Evidence for Up-Down Quark Star

Within a confining quark matter model which considers phenomenologically the quark confinement and asymptotic freedom as well as the chiral symmetry restoration and quark deconfinement at high baryon density, we find that if the up-down quark matter ($ud$QM) is more stable than nuclear matter and strange quark matter (SQM), the maximum mass of static quark stars with $ud$QM is $2.87M_{\odot}$ under agreement with both the constraints on star tidal deformability from gravitational wave signal GW170817 and the mass-radius of PSR J0030+0451 and PSR J0740+6620 measured by NICER. In contrast, the conventional strange quark star with the SQM that is more stable than nuclear matter while the nuclear matter is more stable than $ud$QM, has a maximum static mass of only $1.87M_{\odot}$ and its radius significantly deviates from NICER's constraint. Our results thus provide circumstantial evidence suggesting the recently reported GW190814's secondary component with a mass of $2.59^{+0.08}_{-0.09}M_\odot$ could be an up-down quark star.

astro-ph.HE↗

arXiv2022

A Game of Simulation: Modeling and Analyzing the Dragons of Game of Thrones

This paper outlines two approaches for mathematical, simulation, modeling, and analysis of hypothetical creatures, in particular, the dragons of HBO's television series Game of Thrones (GOT). Our first approach, the forward model, utilizes quasi-empirical observations of various features of GOT dragons. We then mathematically derive the growth rate, other dimensions, energy consumption, etc. In the backward model, we use projected energy consumption by given ecological impact to model an expected dragon in terms of physical features. We compare and contrast both models to examine the plausibility of a real-world existence for our titular dragons and provide brief analyses of potential impacts on ecology.

math.NA↗

arXiv2022

SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding

Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks. However, current contrastive learning solely considers the self-augmented dialog samples as positive samples and treats all other dialog samples as negative ones, which enforces dissimilar representations even for dialogs that are semantically related. In this paper, we propose SPACE-2, a tree-structured pre-trained conversation model, which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised contrastive pre-training. Concretely, we first define a general semantic tree structure (STS) to unify the inconsistent annotation schema across different dialog datasets, so that the rich structural information stored in all labeled data can be exploited. Then we propose a novel multi-view score function to increase the relevance of all possible dialogs that share similar STSs and only push away other completely different dialogs during supervised contrastive pre-training. To fully exploit unlabeled dialogs, a basic self-supervised contrastive loss is also added to refine the learned representations. Experiments show that our method can achieve new state-of-the-art results on the DialoGLUE benchmark consisting of seven datasets and four popular dialog understanding tasks. For reproducibility, we release the code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/space-2.

cs.CL↗

arXiv2022

Enabling Fast and Flexible Distributed Deep Learning with Programmable Switches

Deep learning has been used in a wide range of areas and made a huge breakthrough. With the ever-increasing model size and train-ing data volume, distributed deep learning emerges which utilizes a cluster to train a model in parallel. Unfortunately, the performance is often far from linear speedup due to the communication overhead between cluster nodes. To address this challenge, this paper designs and implements Libra, a network aggregator, that utilizes in-network computation to optimize the communication for distributed DL training in two aspects: 1) reduce active connections and 2) aggregate exchanged network packets. We implemented our Libra on Intel Tofino switches, customized a lightweight host stack and integrated it into an open-source training framework PS-lite. The experimental result shows that our Libra can achieve 1.5~4 times speedup.

cs.NI↗

arXiv2022

TokenPatronus: A Decentralized NFT Anti-theft Mechanism

The emergence of metaverse brings tremendous evolution to Non-Fungible Tokens (NFTs), which could certify the ownership the unique digital asset in the cyber world. The NFT market has garnered unprecedented attention from investors and created billions of dollars in transaction volume. Meanwhile, securing NFT is still a challenging issue. Recently, numerous incidents of NFT theft have been reported, leading to incalculable losses for holders. We propose a decentralized NFT anti-theft mechanism called TokenPatronus, which supports the general ERC-721 standard and provide the holders with strong property protection. TokenPatronus contains pre-event protection, in-event interruption, and post-event replevin enhancements for the complete NFTs transactions stages. Four modules are designed to make up the decentralized anti-theft mechanism, including the decentralized access control (DAC), the decentralized risk management (DRM), the decentralized arbitration system (DAS) and the ERC-721G standard smart contract. TokenPatronus is performing on the Turtlecase NFT project of Ethereum and will support more blockchains in the future.

cs.CR↗

arXiv2022

Gyrohydrodynamics: Relativistic spinful fluid with strong vorticity

We develop a relativistic (quasi-)hydrodynamic framework, dubbed the gyrohydrodynamics, to describe fluid dynamics of many-body systems with spin under strong vorticity based on entropy-current analysis. This framework generalizes the recently-developed spin hydrodynamics to the regime where the spin density is at the leading order in derivatives but suppressed by another small parameter, the Planck constant $\hbar$, due to its quantum nature. Our analysis shows that the complete first-order constitutive relations of gyrohydrodynamics involve seventeen transport coefficients and are highly anisotropic.

hep-th↗

arXiv2022

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks. This paper presents mPLUG, a new vision-language foundation model for both cross-modal understanding and generation. Most existing pre-trained models suffer from the problems of low computational efficiency and information asymmetry brought by the long visual sequence in cross-modal alignment. To address these problems, mPLUG introduces an effective and efficient vision-language architecture with novel cross-modal skip-connections, which creates inter-layer shortcuts that skip a certain number of layers for time-consuming full self-attention on the vision side. mPLUG is pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. It achieves state-of-the-art results on a wide range of vision-language downstream tasks, such as image captioning, image-text retrieval, visual grounding and visual question answering. mPLUG also demonstrates strong zero-shot transferability when directly transferred to multiple video-language tasks.

cs.CL↗

arXiv2022

A Library of Synthetic X-ray Spectra for Fitting Tidal Disruption Events

We present a tabulated version of our slim disk model for fitting tidal disruption events (TDEs). We create a synthetic X-ray spectral library by ray-tracing stationary general relativistic slim disks and including gravitational redshift, Doppler, and lensing effects self-consistently. We introduce the library to reduce computational expense and increase access for fitting future events. Fitting requires interpolation between the library spectra; the interpolation error in the synthetic flux is generally $<10\%$ (it can rise to $40\%$ when the disk is nearly edge-on). We fit the X-ray spectra of the TDEs ASASSN-14li and ASASSN-15oi, successfully reproducing our earlier constraints on black hole mass $M_\bullet$ and spin $a_\bullet$ from full on-the-fly ray-tracing. We use the library to fit mock observational data to explore the degeneracies among parameters, finding that 1) hotter thermal disk and edge-on inclination angle spectra offer tighter constraints on $M_\bullet$ and $a_\bullet$; 2) the constraining power of spectra on $M_\bullet$ and $a_\bullet$ increases as a power-law with the number of X-ray counts, and the index of the power law is higher for hotter thermal disk spectra; 3) multi-epoch X-ray spectra partially break the degeneracy between $M_\bullet$ and $a_\bullet$; 4) the time-dependent level of X-ray absorption can be constrained from spectral fitting. The tabulated model and slim disk model are {\href{https://doi.org/10.25739/hfhz-xn60}{here.}

astro-ph.HE↗

arXiv2022

GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection

Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and generation tasks while neglecting the exploitation of dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning. Specifically, we introduce a dialog act prediction task for policy optimization during pre-training and employ a consistency regularization term to refine the learned representation with the help of unlabeled dialogs. We also implement a gating mechanism to weigh suitable unlabeled dialog samples. Empirical results show that GALAXY substantially improves the performance of task-oriented dialog systems, and achieves new state-of-the-art results on benchmark datasets: In-Car, MultiWOZ2.0 and MultiWOZ2.1, improving their end-to-end combined scores by 2.5, 5.3 and 5.5 points, respectively. We also show that GALAXY has a stronger few-shot ability than existing models under various low-resource settings.

cs.CL↗

arXiv2022

Linking-Enhanced Pre-Training for Table Semantic Parsing

Recently pre-training models have significantly improved the performance of various NLP tasks by leveraging large-scale text corpora to improve the contextual representation ability of the neural network. The large pre-training language model has also been applied in the area of table semantic parsing. However, existing pre-training approaches have not carefully explored explicit interaction relationships between a question and the corresponding database schema, which is a key ingredient for uncovering their semantic and structural correspondence. Furthermore, the question-aware representation learning in the schema grounding context has received less attention in pre-training objective.To alleviate these issues, this paper designs two novel pre-training objectives to impose the desired inductive bias into the learned representations for table pre-training. We further propose a schema-aware curriculum learning approach to mitigate the impact of noise and learn effectively from the pre-training data in an easy-to-hard manner. We evaluate our pre-trained framework by fine-tuning it on two benchmarks, Spider and SQUALL. The results demonstrate the effectiveness of our pre-training objective and curriculum compared to a variety of baselines.

cs.CL↗

arXiv2021

Achieving Human Parity on Visual Question Answering

The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. It has been a popular research topic with an increasing number of real-world applications in the last decade. This paper describes our recent research of AliceMind-MMU (ALIbaba's Collection of Encoder-decoders from Machine IntelligeNce lab of Damo academy - MultiMedia Understanding) that obtains similar or even slightly better results than human being does on VQA. This is achieved by systematically improving the VQA pipeline including: (1) pre-training with comprehensive visual and textual feature representation; (2) effective cross-modal interaction with learning to attend; and (3) A novel knowledge mining framework with specialized expert modules for the complex VQA task. Treating different types of visual questions with corresponding expertise needed plays an important role in boosting the performance of our VQA architecture up to the human level. An extensive set of experiments and analysis are conducted to demonstrate the effectiveness of the new research work.

cs.CL↗

arXiv2021

Evidence for an expanding corona based on spectral-timing modelling of multiple black hole X-ray binaries

Galactic black hole X-ray binaries (BHXBs) provide excellent laboratories to study accretion, as their relatively quick evolution allows us to monitor large changes in the in-flowing and/or out-flowing material over human timescales. However, the details of how the inflow-outflow coupling evolves during a BHXB outburst remain an area of active debate. In this work we attempt to probe the physical changes underlying the system evolution, by performing a systematic analysis of the multi-wavelength data of three BHXB sources: XTE J1752-223, MAXI J1659-152, and XTE J1650-500, during hard and hard-intermediate states. Using the power spectral hue which characterises the X-ray variability properties, we identify several clusters of BHXB epochs and perform the joint multi-wavelength spectral modelling to test their commonality with a physical jet model. Under the assumption that the corona is related to the base of the jet, we find that the power spectral hue traces the variation of the coronal radius(from ~10Rg - ~40Rg) in multiple BHXBs at hard and hard-intermediate states, and that the data are consistent with moderately truncated accretion discs (<25Rg) during hard-intermediate states. We also find that all epochs of low disc reflection have high hue located near the hard-intermediate to soft-intermediate state transition, indicating that in these states the vertical extent of the corona and/or its bulk speed are increasing. Our results link the geometrical similarity in the corona among multiple BHXB sources to their timing characteristics, and probe the corona responding to the disc-jet interactions at hard and intermediate states during outbursts.

astro-ph.HE↗