arXiv subjects

Jin Wang

Publications and source records attributed to Jin Wang.

At least 307 records · Page 17Linked to original sources

arXiv2023

Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning

Dataset discovery from data lakes is essential in many real application scenarios. In this paper, we propose Starmie, an end-to-end framework for dataset discovery from data lakes (with table union search as the main use case). Our proposed framework features a contrastive learning method to train column encoders from pre-trained language models in a fully unsupervised manner. The column encoder of Starmie captures the rich contextual semantic information within tables by leveraging a contrastive multi-column pre-training strategy. We utilize the cosine similarity between column embedding vectors as the column unionability score and propose a filter-and-verification framework that allows exploring a variety of design choices to compute the unionability score between two tables accordingly. Empirical evaluation results on real table benchmark datasets show that Starmie outperforms the best-known solutions in the effectiveness of table union search by 6.8 in MAP and recall. Moreover, Starmie is the first to employ the HNSW (Hierarchical Navigable Small World) index for accelerate query processing of table union search which provides a 3,000X performance gain over the linear scan baseline and a 400X performance gain over an LSH index (the state-of-the-art solution for data lake indexing).

cs.DB↗

arXiv2023

Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach

Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge to support resource-intensive applications on mobile devices. As a crucial problem in MEC, service migration needs to decide how to migrate user services for maintaining the Quality-of-Service when users roam between MEC servers with limited coverage and capacity. However, finding an optimal migration policy is intractable due to the dynamic MEC environment and user mobility. Many existing studies make centralized migration decisions based on complete system-level information, which is time-consuming and also lacks desirable scalability. To address these challenges, we propose a novel learning-driven method, which is user-centric and can make effective online migration decisions by utilizing incomplete system-level information. Specifically, the service migration problem is modeled as a Partially Observable Markov Decision Process (POMDP). To solve the POMDP, we design a new encoder network that combines a Long Short-Term Memory (LSTM) and an embedding matrix for effective extraction of hidden information, and further propose a tailored off-policy actor-critic algorithm for efficient training. The extensive experimental results based on real-world mobility traces demonstrate that this new method consistently outperforms both the heuristic and state-of-the-art learning-driven algorithms and can achieve near-optimal results on various MEC scenarios.

cs.NI↗

arXiv2023

Entanglement and work statistics in the driven open system

We study the entanglement and work statistics in a driven two-qubit system. The regulation of periodic driving has much more versatility and universality in contrast to reservoir engineering in static systems. We found the quasi-steady state entanglement can be amplified effectively by the external drive in certain parameter regimes. The drive extends the range of temperatures or temperature differences at which entanglement can emerge. From the view of the effective Hamiltonian, the addition of the driving alters the inter-qubit coupling and system-bath coupling, which are crucial in determining the quasi-steady state. The work statistics are also investigated. The driven system, as a continuous quantum thermal machine, output work continuously and steadily at the quasi-steady state. There is a distinct operation of modes and corresponding performance by changing driving. It can also be understood that the drive changes the effective Hamiltonian, and further the modes of energy exchanges between the system and the baths as well as the work reservoir.

quant-ph↗

arXiv2022

Quantum cosmology of the flat universe via closed real-time path integral

Quantum cosmology is crucial to understand the evolution of the early universe. Despite significant progress, challenges still remain. For example, the role of time in quantum cosmology is unclear. Furthermore, the influence of the environment on the evolution of the quantum universe is challenging. In this work, we studied the evolution of the quantum universe non-perturbatively using the closed real-time path integral. The environments coupled to the quantum universe being considered are the radiation, the non-relativistic matter, or the dark matter. We evaluated the influence functional of the massless scalar field coupled with the flat FRW universe. We studied the evolution of the quantum universe by setting the initial state of spacetime as a Gaussian wave packet. In different scenarios, we show that the classical trajectory of the universe is consistent with the quantum evolution of the wave packet. The coherence, the absolute quantum fluctuation and the Gibbs entropy all monotonically increase with time, yet the relative quantum fluctuation decreases with time. We show that for a given size of the radiation dominated universe, the lower temperature corresponds to a more quantum universe. We find that the minimal coupling of the free massless scalar field with the flat FRW spacetime generally gives rise to the memory characterized via non-Markovian correlations. Finally, we show that under higher radiation temperatures, a small universe has a higher chance of a transition to a bigger universe.

gr-qc↗

arXiv2022

GECAM Localization of High Energy Transients and the Systematic Error

Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a pair of microsatellites (i.e. GECAM-A and GECAM-B) dedicated to monitoring gamma-ray transients including gravitational waves high-energy electromagnetic counterparts, Gamma-ray Bursts, Soft Gamma-ray Repeaters, Solar Flares and Terrestrial Gamma-ray Flashes. Since launch in December 2020, GECAM-B has detected hundreds of astronomical and terrestrial events. For these bursts, localization is the key for burst identification and classification as well as follow-up observations in multi-wavelength. Here, we propose a Bayesian localization method with Poisson data with Gaussian background profile likelihood to localize GECAM bursts based on the burst counts distribution in detectors with different orientations. We demonstrate that this method can work well for all kinds of bursts, especially for extremely short ones. In addition, we propose a new method to estimate the systematic error of localization based on a confidence level test, which can overcome some problems of the existing method in literature. We validate this method by Monte Carlo simulations, and then apply it to a burst sample with accurate location and find that the mean value of the systematic error of GECAM-B localization is $\sim 2.5^{\circ}$. By considering this systematic error, we can obtain a reliable localization probability map for GECAM bursts. Our methods can be applied to other gamma-ray monitors.

astro-ph.IM↗

arXiv2022

Full quantum theory of nonequilibrium phonon condensation and phase transition

Frölich condensation is a room-temperature nonequilibrium phenomenon which is expected to occur in many physical and biological systems. Though predicted theoretically a half century ago, the nature of such condensation remains elusive. In this Letter, we derive a full quantum theory of Fröhlich condensation from the Wu-Austin Hamiltonian and present for the first time an analytical proof that a second-order phase transition induced by nonequilibrium and nonlinearity emerges in the large-$D$ limit with and without decorrelation approximation. This critical behavior cannot be witnessed if external sources are treated classically. We show that the phase transition is accompanied by large fluctuations in the statistical distribution of condensate phonons and that the Mandel-Q factor which characterizes fluctuations becomes negative in the limit of excessive external energy input. In contrast with the cold atom equilibrium BEC, the Fröhlich condensate is a result of the nonequilibrium driving where the pump plays a role of setting the number of particles, and the medium plays a role of setting the temperature. Hence, BEC can either arise by reducing the medium temperature at fixed pump (equilibrium case), or by increasing the pump at fixed medium temperature (nonequilibrium case).

quant-ph↗

arXiv2022

Dynamic and Thermodynamic Origins of Motility-Induced Phase Separation

Active matter systems are inherently out of equilibrium and break the detailed balance (DB) at the microscopic scale, exhibiting vital collective phenomena such as motility-induced phase separation (MIPS). Here, we introduce a coarse-grained mapping method to probe DB breaking in the density-energy phase space, which allows us to reveal the dynamic and thermodynamic origins of MIPS based on nonequilibrium potential and flux landscape theory. Hallmarks of nonequilibrium properties are manifested by identifying the visible probability flux in the coarse-grained phase space. Remarkably, the flux for the system with the activity lower than the MIPS threshold tends to ``tear up" the single potential well of the uniform-density phase to create two wells of phases with different densities, presenting directly that the nonequilibrium flux is the dynamic origin of MIPS. Moreover, we find that the obtained entropy production rate (EPR) of the system undergoes a transition from nearly independent of activity to increasing proportionally as activity increases after the single well is "teared up". The transition of EPR's scaling behavior might provide a hint of the thermodynamic origin of MIPS in the coarse-grained space. Our findings propose a new route to explore the nonequilibrium nature of active systems, and provide new insights into dynamic and thermodynamic properties of MIPS.

cond-mat.soft↗

arXiv2022

DeepVulSeeker: A Novel Vulnerability Identification Framework via Code Graph Structure and Pre-training Mechanism

Software vulnerabilities can pose severe harms to a computing system. They can lead to system crash, privacy leakage, or even physical damage. Correctly identifying vulnerabilities among enormous software codes in a timely manner is so far the essential prerequisite to patch them. Unfortantely, the current vulnerability identification methods, either the classic ones or the deep-learning-based ones, have several critical drawbacks, making them unable to meet the present-day demands put forward by the software industry. To overcome the drawbacks, in this paper, we propose DeepVulSeeker, a novel fully automated vulnerability identification framework, which leverages both code graph structures and the semantic features with the help of the recently advanced Graph Representation Self-Attention and pre-training mechanisms. Our experiments show that DeepVulSeeker not only reaches an accuracy as high as 0.99 on traditional CWE datasets, but also outperforms all other exisiting methods on two highly-complicated datasets. We also testified DeepVulSeeker based on three case studies, and found that DeepVulSeeker is able to understand the implications of the vulnerbilities. We have fully implemented DeepVulSeeker and open-sourced it for future follow-up research.

cs.CR↗

arXiv2022

Multislice forward modeling of Coherent Surface Scattering Imaging on surface and interfacial structures

To study nanostructures on substrates, surface-sensitive reflection-geometry scattering techniques such as grazing incident small angle x-ray scattering are commonly used to yield an averaged statistical structural information of the surface sample. Grazing incidence geometry can probe the absolute three-dimensional structural morphology of the sample if a highly coherent beam is used. Coherent Surface Scattering Imaging (CSSI) is a powerful yet non-invasive technique similar to Coherent X-ray Diffractive Imaging (CDI) but performed at small angles and grazing-incidence reflection geometry. A challenge with CSSI is that conventional CDI reconstruction techniques cannot be directly applied to CSSI because the Fourier-transform-based forward models cannot reproduce the dynamical scattering phenomenon near the critical angle of total external reflection of the substrate-supported samples. To overcome this challenge, we have developed a multislice forward model which can successfully simulate the dynamical or multi-beam scattering generated from surface structures and the underlying substrate. The forward model is also demonstrated to be able to reconstruct an elongated 3D pattern from a single shot scattering image in the CSSI geometry through fast-performing CUDA-assisted PyTorch optimization with automatic differentiation.

physics.comp-ph↗

arXiv2022

Frictionless nanohighways on crystalline surfaces

The understanding of friction at nano-scales, ruled by the regular arrangement of atoms, is surprisingly incomplete. Here we provide a unified understanding by studying the interlocking potential energy of two infinite contacting surfaces with arbitrary lattice symmetries, and extending it to finite contacts. We categorize, based purely on geometrical features, all possible contacts into three different types: a structurally lubric contact where the monolayer can move isotropically without friction, a corrugated and strongly interlocked contact, and a newly discovered directionally structurally lubric contact where the layer can move frictionlessly along one specific direction and retains finite friction along all other directions. This novel category is energetically stable against rotational perturbations and provides extreme friction anisotropy. The finite-size analysis shows that our categorization applies to a wide range of technologically relevant materials in contact, from adsorbates on crystal surfaces to layered two-dimensional materials and colloidal monolayers.

cond-mat.soft↗

arXiv2022

An Improved Time Feedforward Connections Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel branch was introduced for the hidden state at time t-2 to be directly transferred to time t without the nonlinear transformation at time t-1. This is effective in improving the long-term dependence of RNNs. Then, a novel cell structure named Single Gate Recurrent Unit (SGRU) was presented. This cell structure can reduce the number of parameters for RNNs cell, consequently reducing the computational complexity. Next, applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties. Finally, the performance of our proposed TFC-SGRU was verified through several experiments in terms of long-term memory and anti-interference capabilities. Experimental results demonstrated that our proposed TFC-SGRU model can capture helpful information with time step 1500 and effectively filter out the noise. The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.

cs.NE↗

arXiv2022

On-chip picosecond synchrotron pulse shaper

Synchrotrons are powerful and productive in revealing the spatiotemporal complexities in matter. However, X-ray pulses produced by the synchrotrons are predetermined in specific patterns and widths, limiting their operational flexibility and temporal resolution. Here, we introduce the on-chip picosecond synchrotron pulse shaper that shapes the sub-nm-wavelength hard X-ray pulses at individual beamlines, flexibly and efficiently beyond the synchrotron pulse limit. The pulse shaper is developed using the widely available silicon-on-insulator technology, oscillates in torsional motion at the same frequency or at harmonics of the storage ring, and manipulates X-ray pulses through the narrow Bragg peak of the crystalline silicon. Stable pulse manipulation is achieved by synchronizing the shaper timing to the X-ray timing using electrostatic closed-loop control. Tunable shaping windows down to 40 $ps$ are demonstrated, allowing X-ray pulse picking, streaking, and slicing in the majority of worldwide synchrotrons. The compact, on-chip shaper offers a simple but versatile approach to boost synchrotron operating flexibility and to investigate structural dynamics from condensed matter to biological systems beyond the current synchrotron-source limit.

physics.acc-ph↗

arXiv2022

LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge

This paper describes LeVoice automatic speech recognition systems to track2 of intelligent cockpit speech recognition challenge 2022. Track2 is a speech recognition task without limits on the scope of model size. Our main points include deep learning based speech enhancement, text-to-speech based speech generation, training data augmentation via various techniques and speech recognition model fusion. We compared and fused the hybrid architecture and two kinds of end-to-end architecture. For end-to-end modeling, we used models based on connectionist temporal classification/attention-based encoder-decoder architecture and recurrent neural network transducer/attention-based encoder-decoder architecture. The performance of these models is evaluated with an additional language model to improve word error rates. As a result, our system achieved 10.2\% character error rate on the challenge test set data and ranked third place among the submitted systems in the challenge.

eess.AS↗

arXiv2022

Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation

Machine learning (ML) is playing an increasingly important role in data management tasks, particularly in Data Integration and Preparation (DI&P). The success of ML-based approaches, however, heavily relies on the availability of large-scale, high-quality labeled datasets for different tasks. Moreover, the wide variety of DI&P tasks and pipelines oftentimes requires customizing ML solutions which can incur a significant cost for model engineering and experimentation. These factors inevitably hold back the adoption of ML-based approaches to new domains and tasks. In this paper, we propose Sudowoodo, a multi-purpose DI&P framework based on contrastive representation learning. Sudowoodo features a unified, matching-based problem definition capturing a wide range of DI&P tasks including Entity Matching (EM) in data integration, error correction in data cleaning, semantic type detection in data discovery, and more. Contrastive learning enables Sudowoodo to learn similarity-aware data representations from a large corpus of data items (e.g., entity entries, table columns) without using any labels. The learned representations can later be either directly used or facilitate fine-tuning with only a few labels to support different DI&P tasks. Our experiment results show that Sudowoodo achieves multiple state-of-the-art results on different levels of supervision and outperforms previous best specialized blocking or matching solutions for EM. Sudowoodo also achieves promising results in data cleaning and semantic type detection tasks showing its versatility in DI&P applications.

cs.DB↗

arXiv2022

Deep Baseline Network for Time Series Modeling and Anomaly Detection

Deep learning has seen increasing applications in time series in recent years. For time series anomaly detection scenarios, such as in finance, Internet of Things, data center operations, etc., time series usually show very flexible baselines depending on various external factors. Anomalies unveil themselves by lying far away from the baseline. However, the detection is not always easy due to some challenges including baseline shifting, lacking of labels, noise interference, real time detection in streaming data, result interpretability, etc. In this paper, we develop a novel deep architecture to properly extract the baseline from time series, namely Deep Baseline Network (DBLN). By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result. Empirical evaluation on both synthetic and public real-world datasets shows that our purely unsupervised algorithm achieves superior performance compared with state-of-art methods and has good practical applications.

cs.LG↗

arXiv2022

Accelerating Federated Learning with a Global Biased Optimiser

Federated Learning (FL) is a recent development in distributed machine learning that collaboratively trains models without training data leaving client devices, preserving data privacy. In real-world FL, the training set is distributed over clients in a highly non-Independent and Identically Distributed (non-IID) fashion, harming model convergence speed and final performance. To address this challenge, we propose a novel, generalised approach for incorporating adaptive optimisation into FL with the Federated Global Biased Optimiser (FedGBO) algorithm. FedGBO accelerates FL by employing a set of global biased optimiser values during training, reducing 'client-drift' from non-IID data whilst benefiting from adaptive optimisation. We show that in FedGBO, updates to the global model can be reformulated as centralised training using biased gradients and optimiser updates, and apply this framework to prove FedGBO's convergence on nonconvex objectives when using the momentum-SGD (SGDm) optimiser. We also conduct extensive experiments using 4 FL benchmark datasets (CIFAR100, Sent140, FEMNIST, Shakespeare) and 3 popular optimisers (SGDm, RMSProp, Adam) to compare FedGBO against six state-of-the-art FL algorithms. The results demonstrate that FedGBO displays superior or competitive performance across the datasets whilst having low data-upload and computational costs, and provide practical insights into the trade-offs associated with different adaptive-FL algorithms and optimisers.

cs.LG↗

arXiv2022

Quantum correlations of a two-qubit system and the Aubry-André chain in bosonic environments

In this research, we analyze two models using the tensor network algorithm. The quantum correlations of a two-qubit system are first studied in different bosonic reservoirs. Both equilibrium and nonequilibrium scenarios are discussed. Non-Markovian effects can improve the survival time of the quantum correlations significantly and weaken the decoherence effect. Non-Markovian dynamics with existing memory can lead to entanglement rebirth in specific scenarios instead of the eventual entanglement decay or death seen in memoryless Markovian cases. The system reaches a steady state quickest in sub-Ohmic reservoirs and shows the most apparent non-Markovian behavior in super-Ohmic reservoirs. We not only study the impact of the environment on quantum correlations but also how to protect quantum correlations. Starting from a state in which the two ends are maximally entangled, a one-dimensional AA chain model is also studied. We identify distinct phases by monitoring the imbalance dynamics. When the chain is closed, the imbalance dynamics behave differently in various phases, and so does the entanglement evolution between the chain's ends. When the first site couples to a bath, we found the imbalance dynamics can still be an effective indicator to differentiate various phases in an early evolution stage since the imbalance dynamics is only remarkably affected at relatively high temperatures. The distribution of the eigenenergy of the system can account for it. The entanglement of the chain ends decays rapidly in all phases due to one of the ends being coupled to the bath directly. However, the entanglement of the chain ends will persist for a perceptible amount of time in the localization phase if the bath is coupled to the middle site of the chain. Our research shows that one can utilize the disordered environment as a buffer to protect quantum correlations.

quant-ph↗

arXiv2022

Supporting GNSS Baseband Using Smartphone IMU and Ultra-Tight Integration

A great surge in the development of global navigation satellite systems (GNSS) excavates the potential for prosperity in many state-of-the-art technologies, e.g., autonomous ground vehicle navigation. Nevertheless, the GNSS is vulnerable to various ground interferences, which significantly break down the continuity of the navigation system. Meanwhile, the GNSS-based next-generation navigation devices are being developed to be smaller, more low-cost, and lightweight, as the commercial market forecasts. This work aims to answer whether the smartphone inertial measurement unit (IMU) is sufficient to support the GNSS baseband. Thus, a cascaded ultra-tightly coupled GNSS/inertial navigation system (INS) technique, where consumer-level smartphone sensors are used, is applied to improve the baseband of GNSS software-defined radios (SDRs). A Doppler value is predicted based on an integrated extended Kalman filter (EKF) navigator where the pseudorange-state-based measurements of GNSS and INS are fused. It is used to assist numerically controlled oscillators (NCOs) in the GNSS baseband. Then, an ultra-tight integration platform is built with the upgraded GNSS SDR, of which baseband processing is integrated with INS mechanization. Finally, tracking and carrier-based positioning performances are assessed in the proposed platform for the smartphone-IMU-aided GNSS baseband via kinematic field tests. The experimental results prove that extra hardware with only a few dollars instead of more expensive ones can improve the GNSS baseband efficiently.

eess.SY↗