arXiv subjects

Yi Chen

Publications and source records attributed to Yi Chen.

At least 217 records · Page 12Linked to original sources

arXiv2023

Nucleon relativistic polarization and magnetization distributions

As a follow up of our work on the electromagnetic four-current, we study for the first time the relativistic polarization and magnetization spatial distributions inside a spin-$\frac{1}{2}$ target within the quantum phase-space approach. While the polarization-magnetization tensor is usually defined in terms of the Gordon decomposition of the electromagnetic four-current, a Breit frame analysis reveals that a physically simpler and more natural picture of the system arises when the polarization-magnetization tensor is instead defined in terms of a Sachs decomposition. Relativistic polarization and magnetization distributions for a moving target are compared with their light-front counterparts. In particular, we show that the genuine light-front magnetization distributions are defined in terms of Fourier transforms of the Sachs magnetic form factor, rather than in terms of the Pauli form factor as suggested earlier in the literature. We finally illustrate our results in the case of a nucleon using the electromagnetic form factors extracted from experimental data.

hep-ph↗

arXiv2023

A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we have developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and the multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, which combined COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from LGA-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding of analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors against the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

stat.AP↗

arXiv2023

High-performance and Scalable Software-based NVMe Virtualization Mechanism with I/O Queues Passthrough

NVMe(Non-Volatile Memory Express) is an industry standard for solid-state drives (SSDs) that has been widely adopted in data centers. NVMe virtualization is crucial in cloud computing as it allows for virtualized NVMe devices to be used by virtual machines (VMs), thereby improving the utilization of storage resources. However, traditional software-based solutions have flexibility benefits but often come at the cost of performance degradation or high CPU overhead. On the other hand, hardware-assisted solutions offer high performance and low CPU usage, but their adoption is often limited by the need for special hardware support or the requirement for new hardware development. In this paper, we propose LightIOV, a novel software-based NVMe virtualization mechanism that achieves high performance and scalability without consuming valuable CPU resources and without requiring special hardware support. LightIOV can support thousands of VMs on each server. The key idea behind LightIOV is NVMe hardware I/O queues passthrough, which enables VMs to directly access I/O queues of NVMe devices, thus eliminating virtualization overhead and providing near-native performance. Results from our experiments show that LightIOV can provide comparable performance to VFIO, with an IOPS of 97.6%-100.2% of VFIO. Furthermore, in high-density VMs environments, LightIOV achieves 31.4% lower latency than SPDK-Vhost when running 200 VMs, and an improvement of 27.1% in OPS performance in real-world applications.

cs.AR↗

arXiv2023

A High-Frequency Focused Network for Lightweight Single Image Super-Resolution

Lightweight neural networks for single-image super-resolution (SISR) tasks have made substantial breakthroughs in recent years. Compared to low-frequency information, high-frequency detail is much more difficult to reconstruct. Most SISR models allocate equal computational resources for low-frequency and high-frequency information, which leads to redundant processing of simple low-frequency information and inadequate recovery of more challenging high-frequency information. We propose a novel High-Frequency Focused Network (HFFN) through High-Frequency Focused Blocks (HFFBs) that selectively enhance high-frequency information while minimizing redundant feature computation of low-frequency information. The HFFB effectively allocates more computational resources to the more challenging reconstruction of high-frequency information. Moreover, we propose a Local Feature Fusion Block (LFFB) effectively fuses features from multiple HFFBs in a local region, utilizing complementary information across layers to enhance feature representativeness and reduce artifacts in reconstructed images. We assess the efficacy of our proposed HFFN on five benchmark datasets and show that it significantly enhances the super-resolution performance of the network. Our experimental results demonstrate state-of-the-art performance in reconstructing high-frequency information while using a low number of parameters.

eess.IV↗

arXiv2023

Multi-Semantic Interactive Learning for Object Detection

Single-branch object detection methods use shared features for localization and classification, yet the shared features are not fit for the two different tasks simultaneously. Multi-branch object detection methods usually use different features for localization and classification separately, ignoring the relevance between different tasks. Therefore, we propose multi-semantic interactive learning (MSIL) to mine the semantic relevance between different branches and extract multi-semantic enhanced features of objects. MSIL first performs semantic alignment of regression and classification branches, then merges the features of different branches by semantic fusion, finally extracts relevant information by semantic separation and passes it back to the regression and classification branches respectively. More importantly, MSIL can be integrated into existing object detection nets as a plug-and-play component. Experiments on the MS COCO, and Pascal VOC datasets show that the integration of MSIL with existing algorithms can utilize the relevant information between semantics of different tasks and achieve better performance.

cs.CV↗

arXiv2023

Class-level Multiple Distributions Representation are Necessary for Semantic Segmentation

Existing approaches focus on using class-level features to improve semantic segmentation performance. How to characterize the relationships of intra-class pixels and inter-class pixels is the key to extract the discriminative representative class-level features. In this paper, we introduce for the first time to describe intra-class variations by multiple distributions. Then, multiple distributions representation learning(\textbf{MDRL}) is proposed to augment the pixel representations for semantic segmentation. Meanwhile, we design a class multiple distributions consistency strategy to construct discriminative multiple distribution representations of embedded pixels. Moreover, we put forward a multiple distribution semantic aggregation module to aggregate multiple distributions of the corresponding class to enhance pixel semantic information. Our approach can be seamlessly integrated into popular segmentation frameworks FCN/PSPNet/CCNet and achieve 5.61\%/1.75\%/0.75\% mIoU improvements on ADE20K. Extensive experiments on the Cityscapes, ADE20K datasets have proved that our method can bring significant performance improvement.

cs.CV↗

arXiv2023

Data-driven extraction of the substructure of quark and gluon jets in proton-proton and heavy-ion collisions

The different modifications of quark- and gluon-initiated jets in the quark-gluon plasma (QGP) produced in heavy-ion collisions is a long-standing question that has not yet received a definitive answer from experiments. In particular, the relative sizes of the modification of quark and gluon jets differ between theoretical models. Therefore, a fully data-driven technique is crucial for an unbiased extraction of the quark and gluon jet spectra and substructure. We perform a proof-of-concept study based on proton-proton and heavy-ion collision events from the \textsc{Pyquen} generator with statistics accessible in Run 4 of the Large Hadron Collider. We use a statistical technique called topic modeling to separate quark and gluon contributions to jet observables. We demonstrate that jet substructure observables, such as the jet shape and jet fragmentation function, can be extracted using this data-driven method. These values can then be used to obtain the modification of quark and gluon jet substructures in the QGP. We also perform the topic separation on smeared input data to demonstrate that the approach is robust to fluctuations arising from a QGP background. These results suggest the potential for an experimental determination of quark and gluon jet spectra and their substructure.

hep-ph↗

arXiv2023

THz ISAC: A Physical-Layer Perspective of Terahertz Integrated Sensing and Communication

The Terahertz (0.1-10 THz) band holds enormous potential for supporting unprecedented data rates and millimeter-level accurate sensing thanks to its ultra-broad bandwidth. Terahertz integrated sensing and communication (ISAC) is viewed as a game-changing technology to realize connected intelligence in 6G and beyond systems. In this article, challenges from THz channel and transceiver perspectives, as well as difficulties of ISAC are elaborated. Motivated by these challenges, THz ISAC channels are studied in terms of channel types, measurement and models. Moreover, four key signal processing techniques to unleash the full potential of THz ISAC are investigated, namely, waveform design, receiver processing, narrowbeam management, and localization. Quantitative studies demonstrate the benefits and performance of the state-of-the-art signal processing methods. Finally, open problems and potential solutions are discussed.

eess.SP↗

arXiv2022

Stealthy Peers: Understanding Security Risks of WebRTC-Based Peer-Assisted Video Streaming

As an emerging service for in-browser content delivery, peer-assisted delivery network (PDN) is reported to offload up to 95\% of bandwidth consumption for video streaming, significantly reducing the cost incurred by traditional CDN services. With such benefits, PDN services significantly impact today's video streaming and content delivery model. However, their security implications have never been investigated. In this paper, we report the first effort to address this issue, which is made possible by a suite of methodologies, e.g., an automatic pipeline to discover PDN services and their customers, and a PDN analysis framework to test the potential security and privacy risks of these services. Our study has led to the discovery of 3 representative PDN providers, along with 134 websites and 38 mobile apps as their customers. Most of these PDN customers are prominent video streaming services with millions of monthly visits or app downloads (from Google Play). Also found in our study are another 9 top video/live streaming websites with each equipped with a proprietary PDN solution. Most importantly, our analysis on these PDN services has brought to light a series of security risks, which have never been reported before, including free riding of the public PDN services, video segment pollution, exposure of video viewers' IPs to other peers, and resource squatting. All such risks have been studied through controlled experiments and measurements, under the guidance of our institution's IRB. We have responsibly disclosed these security risks to relevant PDN providers, who have acknowledged our findings, and also discussed the avenues to mitigate these risks.

cs.CR↗

arXiv2022

Pion and nucleon relativistic electromagnetic four-current distributions

The quantum phase-space approach allows one to define relativistic spatial distributions inside a target with arbitrary spin and arbitrary average momentum. We apply this quasiprobabilistic formalism to the whole electromagnetic four-current operator in the case of spin-$0$ and spin-$\frac{1}{2}$ targets, study in detail the frame dependence of the corresponding spatial distributions, and compare our results with those from the light-front formalism. While former works focused on the charge distributions, we extend here the investigations to the current distributions. We clarify the role played by the Wigner rotation and argue that electromagnetic properties are most naturally understood in terms of Sachs form factors, contrary to what the light-front formalism previously suggested. Finally, we illustrate our results using the pion and nucleon electromagnetic form factors extracted from experimental data.

hep-ph↗

arXiv2022

Picking Up Speed: Continuous-Time Lidar-Only Odometry using Doppler Velocity Measurements

Frequency-Modulated Continuous-Wave (FMCW) lidar is a recently emerging technology that additionally enables per-return instantaneous relative radial velocity measurements via the Doppler effect. In this letter, we present the first continuous-time lidar-only odometry algorithm using these Doppler velocity measurements from an FMCW lidar to aid odometry in geometrically degenerate environments. We apply an existing continuous-time framework that efficiently estimates the vehicle trajectory using Gaussian process regression to compensate for motion distortion due to the scanning-while-moving nature of any mechanically actuated lidar (FMCW and non-FMCW). We evaluate our proposed algorithm on several real-world datasets, including publicly available ones and datasets we collected. Our algorithm outperforms the only existing method that also uses Doppler velocity measurements, and we study difficult conditions where including this extra information greatly improves performance. We additionally demonstrate state-of-the-art performance of lidar-only odometry with and without using Doppler velocity measurements in nominal conditions. Code for this project can be found at: https://github.com/utiasASRL/steam_icp.

cs.RO↗

arXiv2022

First measurement of anti-k$_\mathrm{T}$ jet spectra and jet substructure using the archived ALEPH $e^+e^-$ data at 91.2 GeV

We present the first anti-k$_{T}$ jet spectrum and substructure measurements using the archived ALEPH $e^+e^-$ data taken in 1994 at a center of mass energy of $\sqrt{s} = 91.2$ GeV. Jets are reconstructed with the anti-k$_{T}$ algorithm with a resolution parameter of 0.4. It is the cleanest test of jets and QCD without the complication of hadronic initial states. The fixed center-of-mass energy also allows the first direct test of pQCD calculation. We present both the inclusive jet energy spectrum and the leading dijet energy spectra, together with a number of substructure observables. They are compared to predictions from PYTHIA6, PYTHIA8, Sherpa, HERWIG, VINCIA, and PYQUEN. None of the models fully reproduce the data. The data are also compared to two perturbative QCD calculations at NLO and with NLL'+R resummation. The results can also serve as reference measurements to compare to results from hadronic colliders. Future directions, including testing jet clustering algorithms designed for future electron-ion collider experiments, will also be discussed.

hep-ex↗

arXiv2022

Two-particle azimuthal correlations in $e^+e^-$ collisions at 91--209 GeV with archived ALEPH data at LEP-2

We present the first measurement of two-particle angular correlations of charged particles produced in $e^+e^-$ annihilation up to $\sqrt{s}=$ 209 GeV. This analysis utilized the archived hadronic $e^+e^-$ data at center-of-mass energy between 91 and 209 GeV collected with the ALEPH detector at LEP between 1992 and 2000. The angular correlation functions are measured over a broad range of pseudorapidity and full azimuth as a function of charged particle multiplicity for the first time with LEP-2 data. At 91 GeV, no significant long-range correlation is observed in either the beam coordinate analysis or the thrust coordinate analysis, where the latter is sensitive to a medium expanding transverse to the color string between the outgoing $q\bar{q}$ pair from the Z boson decays. Results with $e^+e^-$ data at higher collision energy than 91 GeV, providing higher event multiplicity reach up to around 50, are presented for the first time. The thrust axis analysis shows a long-range near-side excess in the two-particle correlation function. We performed Fourier series decomposition of the two-particle correlation functions. In high multiplicity events with more than 50 particles, the extracted Fourier coefficients $v_2$ and $v_3$ magnitudes in data are larger than the MC reference.

hep-ex↗

arXiv2022

Terahertz Channel Measurement and Analysis on a University Campus Street

Owning abundant bandwidth resource, the Terahertz (0.1-10 THz) band is a promising spectrum to support sixth-generation (6G) and beyond communications. As the foundation of channel study in the spectrum, channel measurement is ongoing in covering representative 6G communication scenarios and promising THz frequency bands. In this paper, a wideband channel measurement in an L-shaped university campus street is conducted at 306-321 GHz and 356-371 GHz. In particular, ten line-of-sight (LoS) and eight non-line-of-sight (NLoS) points are measured at the two frequency bands, respectively. In total, 6480 channel impulse responses (CIRs) are obtained from the measurement, based on which multi-path propagation in the L-shaped roadway in the THz band is elaborated to identify major scatterers of walls, vehicles, etc. in the environment and their impact on multi-path components (MPCs). Furthermore, outdoor THz channel characteristics in the two frequency bands are analyzed, including path losses, shadow fading, cluster parameters, delay spread and angular spread. In contrast with the counterparts in the similar outdoor scenario at lower frequencies, the results verify the sparsity of MPCs at THz frequencies and indicate smaller power spreads in both temporal and spatial domains in the THz band.

cs.IT↗

arXiv2022

300 GHz Channel Measurement and Characterization in the Atrium of a Building

With abundant bandwidth resource, the Terahertz band (0.1~THz to 10~THz) is envisioned as a key technology to realize ultra-high data rates in the 6G and beyond mobile communication systems. However, moving to the THz band, existing channel models dedicated for microwave or millimeter-wave bands are ineffective. To fill this research gap, extensive channel measurement campaigns and characterizations are necessary. In this paper, using a frequency-domain Vector Network Analyzer (VNA)-based sounder, a measurement campaign is conducted in the outdoor atrium of a building in 306-321 GHz band. The measured data are further processed to obtain the channel transfer functions (CTFs), parameters of multipath components (MPCs), as well as clustering results. Based on the MPC parameters, the channel characteristics, such as path loss, shadow fading, K-factor, etc., are calculated and analyzed. The extracted channel characteristics and numerology are helpful to study channel modeling and guide system design for THz communications.

cs.IT↗

arXiv2022

Joint Optimization of Sensing and Computation for Status Update in Mobile Edge Computing Systems

IoT devices recently are utilized to detect the state transition in the surrounding environment and then transmit the status updates to the base station for future system operations. To satisfy the stringent timeliness requirement of the status updates for the accurate system control, age of information (AoI) is introduced to quantify the freshness of the sensory data. Due to the limited computing resources, the status update can be offloaded to the mobile edge computing (MEC) server for execution to ensure the information freshness. Since the status updates generated by insufficient sensing operations may be invalid and cause additional processing time, the data sensing and processing operations need to be considered simultaneously. In this work, we formulate the joint data sensing and processing optimization problem to ensure the freshness of the status updates and reduce the energy consumption of IoT devices. Then, the formulated NP-hard problem is decomposed into the sampling, sensing and computation offloading optimization problems. Afterwards, we propose a multi-variable iterative system cost minimization algorithm to optimize the system overhead. Simulation results show the efficiency of our method in decreasing the system cost and dominance of sensing and processing under different scenarios.

cs.NI↗

arXiv2022

Understanding Impacts of Task Similarity on Backdoor Attack and Detection

With extensive studies on backdoor attack and detection, still fundamental questions are left unanswered regarding the limits in the adversary's capability to attack and the defender's capability to detect. We believe that answers to these questions can be found through an in-depth understanding of the relations between the primary task that a benign model is supposed to accomplish and the backdoor task that a backdoored model actually performs. For this purpose, we leverage similarity metrics in multi-task learning to formally define the backdoor distance (similarity) between the primary task and the backdoor task, and analyze existing stealthy backdoor attacks, revealing that most of them fail to effectively reduce the backdoor distance and even for those that do, still much room is left to further improve their stealthiness. So we further design a new method, called TSA attack, to automatically generate a backdoor model under a given distance constraint, and demonstrate that our new attack indeed outperforms existing attacks, making a step closer to understanding the attacker's limits. Most importantly, we provide both theoretic results and experimental evidence on various datasets for the positive correlation between the backdoor distance and backdoor detectability, demonstrating that indeed our task similarity analysis help us better understand backdoor risks and has the potential to identify more effective mitigations.

cs.CR↗

arXiv2022

A novel $\sqrt{19}\times\sqrt{19}$ superstructure in epitaxially grown 1T-TaTe$_2$

The spontaneous formation of electronic orders is a crucial element for understanding complex quantum states and engineering heterostructures in two-dimensional materials. We report a novel $\sqrt{19}\times\sqrt{19}$ charge order in few-layer thick 1T-TaTe$_2$ transition metal dichalcogenide films grown by molecular beam epitaxy, which has not been realized. Our photoemission and scanning probe measurements demonstrate that monolayer 1T-TaTe$_2$ exhibits a variety of metastable charge density wave orders, including the $\sqrt{19}\times\sqrt{19}$ superstructure, which can be selectively stabilized by controlling the post-growth annealing temperature. Moreover, we find that only the $\sqrt{19}\times\sqrt{19}$ order persists in 1T-TaTe$_2$ films thicker than a monolayer, up to 8 layers. Our findings identify the previously unrealized novel electronic order in a much-studied transition metal dichalcogenide and provide a viable route to control it within the epitaxial growth process.

cond-mat.mtrl-sci↗