arXiv subjects

Chen Yang

Publications and source records attributed to Chen Yang.

At least 271 records · Page 15Linked to original sources

arXiv2022

Measuring the tuning curve of spontaneous parameter down-conversion using a comet-tail-like pattern

The comet-tail-like interference patterns are observed using photons from the spontaneous parametric down-conversion (SPDC) process. The patterns are caused by the angular-spectrum-dependent interference and the diffraction of a blazed grating. We present the theoretical explanation and simulation results for these patterns, which are in good agreement with the experimental results. The most significant feature of the patterns is the bright parabolic contour profile, from which, one can deduce the parameter of the parabolic tuning curve of the SPDC process. This method could be helpful in designing experiments based on SPDC.

quant-ph↗

arXiv2022

Federating and querying heterogeneous and distributed Web APIs and triple stores

Today's international corporations such as BASF, a leading company in the crop protection industry, produce and consume more and more data that are often fragmented and accessible through Web APIs. In addition, part of the proprietary and public data of BASF's interest are stored in triple stores and accessible with the SPARQL query language. Homogenizing the data access modes and the underlying semantics of the data without modifying or replicating the original data sources become important requirements to achieve data integration and interoperability. In this work, we propose a federated data integration architecture within an industrial setup, that relies on an ontology-based data access method. Our performance evaluation in terms of query response time showed that most queries can be answered in under 1 second.

cs.DB↗

arXiv2022

Theoretical Study of the Enhancement of Light Saturation Phenomena of Krypton at Critical Ionization Photon Energies

By calculating the correlation between the total photoionization cross-section of the ground state of the Kr atom and photon energy, three particular photon energies close to the near inner orbital energy of 1.75 keV, 1.90 keV, and 14.30 keV are determined in this work. The dynamical simulation under 17.50 keV photon energy in the experimental conditions is achieved by implementing the Monte Carlo method and optimizing the photon flux modeling parameters. As a result, our calculated data are more consistent with the experimental phenomena. The light saturation phenomenon of Kr at 1.75 keV, 1.90 keV, 14.30 keV, and 17.50 keV energies is further calculated and researched using the optimized photon flux model theory. We statistically compare the main ionization paths under those four specific photon energies and calculate the population changes of various hollow atoms. The results demonstrate that the ratio of hollow atoms produced at the critical ionization photon energy is high. Furthermore, the change of position is smooth, showing the significant difference between the generation mode of ions with low photon energy and those with high photon energy, which has important reference significance for studying hollow atoms with medium and high charge states.

physics.atom-ph↗

arXiv2022

Decisions in Continuous Integration and Delivery: An Exploratory Study

In recent years, Continuous Integration (CI) and Continuous Delivery (CD) has been heatedly discussed and widely used in part or all of the software development life cycle as the practices and pipeline to deliver software products in an efficient way. There are many tools, such as Travis CI, that offer various features to support the CI/CD pipeline, but there is a lack of understanding about what decisions are frequently made in CI/CD. In this work, we explored one popular open-source project on GitHub, Budibase, to provide insights on the types of decisions made in CI/CD from a practitioners' perspective. We first explored the GitHub Trending page, conducted a pilot repository extraction, and identified the Budibase repository as the case for our study. We then crawled all the closed issues from the repository and got 1,168 closed issues. Irrelevant issues were filtered out based on certain criteria, and 370 candidate issues that contain decisions were obtained for data extraction. We analyzed the issues using a hybrid approach combining pre-defined types and the Constant Comparison method to get the categories of decisions. The results show that the major type of decisions in the Budibase closed issues is Functional Requirement Decision (67.6%), followed by Architecture Decision (11.1%). Our findings encourage developers to put more effort on the issues and making decisions related to CI/CD, and provide researchers with a reference of decision classification made in CI/CD.

cs.SE↗

arXiv2022

A Unified Description for Polarization-Transfer Mechanisms in Magnetic Resonance in Static Solids: Cross Polarization and DNP

Polarization transfers are crucial building blocks in magnetic resonance experiments, i.e., they can be used to polarize insensitive nuclei and correlate nuclear spins in multidimensional NMR spectroscopy. The polarization can be transferred either across different nuclear spin species or from electron spins to the relatively low-polarized nuclear spins. The former route occurring in solid-state NMR (ssNMR) can be performed via cross polarization (CP), while the latter route is known as dynamic nuclear polarization (DNP). Despite having different operating conditions, we opinionate that both mechanisms are theoretically similar processes in ideal conditions, i.e., the electron is merely another spin-1/2 particle with a much higher gyromagnetic ratio. Here, we show that the CP and DNP processes can be described using a unified theory based on average Hamiltonian theory (AHT) combined with fictitious operators. The intuitive and unified approach has allowed new insights on the cross effect (CE) DNP mechanism, leading to better design of DNP polarizing agents and extending the applications beyond just hyperpolarization. We explore the possibility of exploiting theoretically predicted DNP transients for electron-nucleus distance measurements--like routine dipolar-recoupling experiments in solid-state NMR.

physics.chem-ph↗

arXiv2022

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy ) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets.

cs.CV↗

arXiv2022

Using 5G in Smart Cities: A Systematic Mapping Study

5G is the fifth generation wireless network, with a set of characteristics, e.g., high bandwidth and data rates. The scenarios of using 5G include enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and ultra-Reliable and Low-Latency Communications (uRLLC). 5G is expected to support a wide variety of applications. We conducted a systematic mapping study that covers the literature published between Jan 2012 and Dec 2019 regarding using 5G in smart cities. The scenarios, architecture, technologies, challenges, and lessons learned of using 5G in smart cities are summarized and further analyzed based on 32 selected studies, and the results are that: (1) The studies are distributed over 27 publication venues. 17 studies report results based on academic studies and 13 studies use demonstration or toy examples. Only 2 studies report using 5G in smart cities based on industrial studies. 16 studies include assumptions of 5G network design or smart city scenarios. (2) The most discussed smart city scenario is transportation, followed by public safety, healthcare, city tourism, entertainment, and education. (3) 28 studies propose and/or discuss the architecture of 5G-enabled smart cities, containing smart city architecture (treating 5G as a component), 5G network architecture in smart cities, and business architecture of using 5G in smart cities. (4) The most mentioned 5G-related technologies are radio access technologies, network slicing, and edge computing. (5) Challenges are mainly about complex context, challenging requirements, and network development of using 5G in smart cities. (6) Most of the lessons learned identified are benefits regarding 5G itself or the proposed 5G-related methods in smart cities. This work provides a reflection of the past eight years of the state of the art on using 5G in smart cities, which can benefit both researchers and practitioners.

cs.NI↗

arXiv2022

Detecting systematic anomalies affecting systems when inputs are stationary time series

We develop an anomaly-detection method when systematic anomalies, possibly statistically very similar to genuine inputs, are affecting control systems at the input and/or output stages. The method allows anomaly-free inputs (i.e., those before contamination) to originate from a wide class of random sequences, thus opening up possibilities for diverse applications. To illustrate how the method works on data, and how to interpret its results and make decisions, we analyze several actual time series, which are originally non-stationary but in the process of analysis are converted into stationary. As a further illustration, we provide a controlled experiment with anomaly-free inputs following an ARMA time series model under various contamination scenarios.

stat.ME↗

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

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

Existing anchor-base oriented object detection methods have achieved amazing results, but these methods require some manual preset boxes, which introduces additional hyperparameters and calculations. The existing anchor-free methods usually have complex architectures and are not easy to deploy. Our goal is to propose an algorithm which is simple and easy-to-deploy for aerial image detection. In this paper, we present a one-stage anchor-free rotated object detector (FCOSR) based on FCOS, which can be deployed on most platforms. The FCOSR has a simple architecture consisting of only convolution layers. Our work focuses on the label assignment strategy for the training phase. We use ellipse center sampling method to define a suitable sampling region for oriented bounding box (OBB). The fuzzy sample assignment strategy provides reasonable labels for overlapping objects. To solve the insufficient sampling problem, a multi-level sampling module is designed. These strategies allocate more appropriate labels to training samples. Our algorithm achieves 79.25, 75.41, and 90.15 mAP on DOTA1.0, DOTA1.5, and HRSC2016 datasets, respectively. FCOSR demonstrates superior performance to other methods in single-scale evaluation. We convert a lightweight FCOSR model to TensorRT format, which achieves 73.93 mAP on DOTA1.0 at a speed of 10.68 FPS on Jetson Xavier NX with single scale. The code is available at: https://github.com/lzh420202/FCOSR

cs.CV↗

arXiv2021

The Generalized Occupation-Restricted-Multiple-Active-Space Concept in Multiconfigurational Self-Consistent Field Methods

A novel concept of multiconfigurational self-consistent field method, the generalized occupation-restricted-multiple-active-space (GORMAS) is presented. GORMAS wave functions are defined by substituting the complete active space (CAS) in ORMAS, given a pre- or a post-restriction. The GORMAS approach shows a flexible selection of active space, reduce the ineffective reference configurations from CAS dramatically. Test calculations in molecule or complex systems, CH$_2$O, (H$_2$O)$_2$ molecule, and oxoMn(salen) are presented. They show the GORMAS wave functions achieve the similar accuracy with under 15\% dimension of reference spaces.

physics.chem-ph↗

arXiv2021

Angular-spectrum-dependent interference

Optical interference is not only a fundamental phenomenon that has enabled new theories of light to be derived but it has also been used in interferometry for the measurement of small displacements, refractive index changes and surface irregularities. In a two-beam interferometer, variations in the interference fringes are used as a diagnostic for anything that causes the optical path difference (OPD) to change; therefore, for a specified OPD, greater variation in the fringes indicates better measurement sensitivity. Here, we introduce and experimentally validate an interesting optical interference phenomenon that uses photons with a structured frequency-angular spectrum, which are generated from a spontaneous parametric down-conversion process in a nonlinear crystal. This interference phenomenon is manifested as interference fringes that vary much more rapidly with increasing OPD than the corresponding fringes for equal-inclination interference; the phenomenon is parameterised using an equivalent wavelength, which under our experimental conditions is 29.38 nm or about 1/27 of the real wavelength. This phenomenon not only enriches the knowledge with regard to optical interference but also offers promise for applications in interferometry.

physics.optics↗

arXiv2021

Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning

Nowadays, deep learning methods with large-scale datasets can produce clinically useful models for computer-aided diagnosis. However, the privacy and ethical concerns are increasingly critical, which make it difficult to collect large quantities of data from multiple institutions. Federated Learning (FL) provides a promising decentralized solution to train model collaboratively by exchanging client models instead of private data. However, the server aggregation of existing FL methods is observed to degrade the model performance in real-world medical FL setting, which is termed as retrogress. To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client. Specifically, we devise a Progressive Fourier Aggregation (PFA) at the server to achieve more stable and effective global knowledge gathering by integrating client models from low-frequency to high-frequency gradually. Moreover, with an introduced deputy model to receive the aggregated server model, we design a Deputy-Enhanced Transfer (DET) strategy at the client and conduct three steps of Recover-Exchange-Sublimate to ameliorate the personalized local model by transferring the global knowledge smoothly. Extensive experiments on real-world dermoscopic FL dataset prove that our personalized retrogress-resilient framework outperforms state-of-the-art FL methods, as well as the generalization on an out-of-distribution cohort. The code and dataset are available at https://github.com/CityU-AIM-Group/PRR-FL.

cs.CV↗

arXiv2021

Interference fringes in a nonlinear Michelson interferometer based on spontaneous parametric down-conversion

Quantum nonlinear interferometers (QNIs) can measure the infrared physical quantities of a sample by detecting visible photons. A QNI with Michelson geometry based on the spontaneous parametric down-conversion in a second-order nonlinear crystal is studied systematically. A simplified theoretical model of the QNI is presented. The interference visibility, coherence length, equal-inclination interference, and equal-thickness interference for the QNI are demonstrated theoretically and experimentally. As an application example of the QNI, the refractive index and the angle between two surfaces of a BBO crystal are measured using equal-inclination interference and equal-thickness interference.

quant-ph↗

arXiv2021

On-chip erbium-doped lithium niobate microring lasers

Lithium niobate on insulator (LNOI), regarded as an important candidate platform for optical integration due to its excellent nonlinear, electro-optic and other physical properties, has become a research hotspot. Light source, as an essential component for integrated optical system, is urgently needed. In this paper, we reported the realization of 1550-nm band on-chip LNOI microlasers based on erbium-doped LNOI ring cavities with loaded quality factors higher than one million, which were fabricated by using electron beam lithography and inductively coupled plasma reactive ion etching processes. These microlasers demonstrated a low pump threshold of ~20 μW and stable performance under the pump of a 980-nm band continuous laser. Comb-like laser spectra spanning from 1510 nm to 1580 nm were observed in high pump power regime, which lays the foundation of the realization of pulsed laser and frequency combs on rare-earth ion doped LNOI platform. This work has effectively promoted the development of on-chip integrated active LNOI devices.

physics.optics↗

arXiv2021

Photoacoustic Silk Scaffolds for Neural stimulation and Regeneration

Neural interfaces using biocompatible scaffolds provide crucial properties for the functional repair of nerve injuries and neurodegenerative diseases, including cell adhesion, structural support, and mass transport. Neural stimulation has also been found to be effective in promoting neural regeneration. This work provides a new strategy to integrate photoacoustic (PA) neural stimulation into hydrogel scaffolds using a nanocomposite hydrogel approach. Specifically, polyethylene glycol (PEG)-functionalized carbon nanotubes (CNT), highly efficient photoacoustic agents, are embedded into silk fibroin to form biocompatible and soft photoacoustic materials. We show that these photoacoustic functional scaffolds enable non-genetic activation of neurons with a spatial precision defined by the area of light illumination, promoting neuron regeneration. These CNT/silk scaffolds offered reliable and repeatable photoacoustic neural stimulation. 94% of photoacoustic stimulated neurons exhibit a fluorescence change larger than 10% in calcium imaging in the light illuminated area. The on-demand photoacoustic stimulation increased neurite outgrowth by 1.74-fold in a dorsal root ganglion model, when compared to the unstimulated group. We also confirmed that photoacoustic neural stimulation promoted neurite outgrowth by impacting the brain-derived neurotrophic factor (BDNF) pathway. As a multifunctional neural scaffold, CNT/silk scaffolds demonstrated non-genetic PA neural stimulation functions and promoted neurite outgrowth, providing a new method for non-pharmacological neural regeneration.

q-bio.NC↗

arXiv2021

Enhanced Remote Astronomical Archive System Based on the File-Level Unlimited Sliding-Window Technique

Data archiving is one of the most critical issues for modern astronomical observations. With the development of a new generation of radio telescopes, the transfer and archiving of massive remote data have become urgent problems to be solved. Herein, we present a practical and robust file-level flow-control approach, called the Unlimited Sliding-Window (USW), by referring to the classic flow-control method in TCP protocol. Basing on the USW and the Next Generation Archive System (NGAS) developed for the Murchison Widefield Array telescope, we further implemented an enhanced archive system (ENGAS) using ZeroMQ middleware. The ENGAS substantially improves the transfer performance and ensures the integrity of transferred files. In the tests, the ENGAS is approximately three to twelve times faster than the NGAS and can fully utilize the bandwidth of network links. Thus, for archiving radio observation data, the ENGAS reduces the communication time, improves the bandwidth utilization, and solves the remote synchronous archiving of data from observatories such as Mingantu spectral radioheliograph. It also provides a better reference for the future construction of the Square Kilometer Array (SKA) Science Regional Center.

astro-ph.IM↗

arXiv2021

CHECKED: Chinese COVID-19 Fake News Dataset

COVID-19 has impacted all lives. To maintain social distancing and avoiding exposure, works and lives have gradually moved online. Under this trend, social media usage to obtain COVID-19 news has increased. Also, misinformation on COVID-19 is frequently spread on social media. In this work, we develop CHECKED, the first Chinese dataset on COVID-19 misinformation. CHECKED provides a total 2,104 verified microblogs related to COVID-19 from December 2019 to August 2020, identified by using a specific list of keywords. Correspondingly, CHECKED includes 1,868,175 reposts, 1,185,702 comments, and 56,852,736 likes that reveal how these verified microblogs are spread and reacted on Weibo. The dataset contains a rich set of multimedia information for each microblog including ground-truth label, textual, visual, temporal, and network information. Extensive experiments have been conducted to analyze CHECKED data and to provide benchmark results for well-established methods when predicting fake news using CHECKED. We hope that CHECKED can facilitate studies that target misinformation on coronavirus. The dataset is available at https://github.com/cyang03/CHECKED.

cs.SI↗