arXiv subjects

Weimin Li

Publications and source records attributed to Weimin Li.

At least 19 recordsLinked to original sources

arXiv2023

Analytic formulas for the D-mode Robinson instability

The passive superconducting harmonic cavity (PSHC) scheme is adopted by several existing and future synchrotron light source storage rings, as it has a relatively smaller R/Q and a relatively larger quality factor (Q), which can effectively reduce the beam-loading effect and suppress the mode-one instability. Based on the mode-zero Robinson instability equation of uniformly filled rigid bunches and a search algorithm for minimum, we have revealed that the PSHC fundamental mode with a large loaded-Q possibly triggers the D-mode Robinson instability [T. He, et al., Mode-zero Robinson instability in the presence of passive superconducting harmonic cavities, PRAB 26, 064403 (2023)]. This D-mode Robinson instability is unique because it is anti-damped by the radiation-damping effect. In this paper, analytical formulas for the frequency and growth rate of the D-mode Robinson instability are derived with several appropriate approximations. These analytical formulas will facilitate analyzing and understanding the D-mode Robinson instability. Most importantly, useful formulas for the D-mode threshold detuning calculation have finally been found.

physics.acc-ph↗

arXiv2023

Impact of short-range wakefields from radio-frequency cavity resonant modes on bunch lengthening

The high-performance operation of fourth-generation synchrotron light sources critically depends on harmonic cavities (HCs) to alleviate statistical collective effects through bunch lengthening. Active HCs are preferred over passive ones for achieving theoretically optimum bunch lengthening in Timing-mode operation, which is characterized by low average current and high bunch charge. This advantage stems from their ability to control cavity voltages via generator current. However, this study reveals a previously overlooked limitation: the detrimental impact of short-range wakefields from RF cavity resonant modes on bunch lengthening at high bunch charge. Using Hefei Advanced Light Facility parameters, we demonstrate that these wakefields can significantly degrade bunch lengthening. Further analysis with PETRA-IV parameters reveals that fine-tuning the HC voltage can optimize bunch lengthening. Notably, under specific HC settings, there exists two equilibrium bunch distributions. Our findings highlight the critical influence of RF cavity short-range wakefields on beam dynamics at high bunch charge, emphasizing their essential inclusion in the evaluation and optimization of HC performance for new-generation synchrotron light sources.

physics.acc-ph↗

arXiv2023

Near-Linear Scaling Data Parallel Training with Overlapping-Aware Gradient Compression

Existing Data Parallel (DP) trainings for deep neural networks (DNNs) often experience limited scalability in speedup due to substantial communication overheads. While Overlapping technique can mitigate such problem by paralleling communication and computation in DP, its effectiveness is constrained by the high communication-to-computation ratios (CCR) of DP training tasks. Gradient compression (GC) is a promising technique to obtain lower CCR by reducing communication volume directly. However, it is challenging to obtain real performance improvement by applying GC into Overlapping because of (1) severe performance penalties in traditional GCs caused by high compression overhead and (2) decline of Overlapping benefit owing to the possible data dependency in GC schemes. In this paper, we propose COVAP, a novel GC scheme designing a new coarse-grained filter, makes the compression overhead close to zero. COVAP ensures an almost complete overlap of communication and computation by employing adaptive compression ratios and tensor sharding tailored to specific training tasks. COVAP also adopts an improved error feedback mechanism to maintain training accuracy. Experiments are conducted on Alibaba Cloud ECS instances with different DNNs of real-world applications. The results illustrate that COVAP outperforms existent GC schemes in time-to-solution by 1.92x-15.39x and exhibits near-linear scaling. Furthermore, COVAP achieves best scalability under experiments on four different cluster sizes.

cs.DC↗

arXiv2023

A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics

During the diagnostic process, clinicians leverage multimodal information, such as chief complaints, medical images, and laboratory-test results. Deep-learning models for aiding diagnosis have yet to meet this requirement. Here we report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model uses embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and bidirectional blocks with intramodal and intermodal attention to learn a holistic representation of radiographs, the unstructured chief complaint and clinical history, structured clinical information such as laboratory-test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Leveraging unified multimodal Transformer-based models may help streamline triage of patients and facilitate the clinical decision process.

cs.CV↗

arXiv2023

High-sensitivity graphene MEMS force and acceleration sensor based on graphene-induced non-radiative transition

The micro-electromechanical-system (MEMS) force and acceleration sensor utilizing the graphene-induced non-radiative transition was investigated. The graphene-induced non-radiative transition is very sensitive to the distance, and the deflection of the graphene ribbon is highly susceptive to applied force or acceleration. Thus, a high-sensitivity MEMS sensor can be achieved with detecting the graphene ribbon's deflection of 1 nm, the force of 0.1 pN, and the acceleration of 0.1 mg. The MEMS sensor, with a size of only tens of microns, can be charged by light irradiation without connecting power sources. In addition, it allows long-distance detection, i.e., wireless transmitter circuit can be omitted. Therefore, it will have significant application prospects in the fields of micro-smart devices, wearable devices, biomedical systems, and so on.

cond-mat.mes-hall↗

arXiv2021

Robust data analysis and imaging with computational ghost imaging

Nowadays the world has entered into the digital age, in which the data analysis and visualization have become more and more important. In analogy to imaging the real object, we demonstrate that the computational ghost imaging can image the digital data to show their characteristics, such as periodicity. Furthermore, our experimental results show that the use of optical imaging methods to analyse data exhibits unique advantages, especially in anti-interference. The data analysis with computational ghost imaging can be well performed against strong noise, random amplitude and phase changes in the binarized signals. Such robust data data analysis and imaging has an important application prospect in big data analysis, meteorology, astronomy, economics and many other fields.

physics.optics↗

arXiv2021

SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms and systems, and especially the tremendous amount of computation needed are big obstacles for the community to conduct further research in this direction. We propose a deep reinforcement learning agent, StarCraft Commander (SCC). With order of magnitude less computation, it demonstrates top human performance defeating GrandMaster players in test matches and top professional players in a live event. Moreover, it shows strong robustness to various human strategies and discovers novel strategies unseen from human plays. In this paper, we will share the key insights and optimizations on efficient imitation learning and reinforcement learning for StarCraft II full game.

cs.LG↗

arXiv2021

Contention-based Grant-free Transmission with Extremely Sparse Orthogonal Pilot Scheme

Due to the limited number of traditional orthogonal pilots, pilot collision will severely degrade the performance of contention-based grant-free transmission. To alleviate the pilot collision and exploit the spatial degree of freedom as much as possible, an extremely sparse orthogonal pilot scheme is proposed for uplink grant-free transmission. The proposed sparse pilot is used to perform active user detection and estimate the spatial channel. Then, inter-user interference suppression is performed by spatially combining the received data symbols using the estimated spatial channel. After that, the estimation and compensation of wireless channel and time/frequency offset are performed utilizing the geometric characteristics of combined data symbols. The task of pilot is much lightened, so that the extremely sparse orthogonal pilot can occupy minimized resources, and the number of orthogonal pilots can be increased significantly, which greatly reduces the probability of pilot collision. The numerical results show that the proposed extremely sparse orthogonal pilot scheme significantly improves the performance in high-overloading grant-free scenario.

cs.IT↗

arXiv2021

SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation

Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis. In this paper, we propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. To achieve the above idea, we develop a novel adaptive consistency cost function to regularize different components in the predictions. Moreover, we introduce heterogeneous perturbation strategies that work in both feature space and image space, so that the proposed detector is promising to produce powerful image representations and robust predictions. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings. We also demonstrate the strength of each proposed module with comprehensive ablation studies.

cs.CV↗

arXiv2021

Locally symmetric lattices for storage ring light sources

In this paper, a new lattice concept called the locally symmetric lattice is proposed for storage ring light sources. In this new lattice, beta functions are made locally symmetric about two mirror planes of the lattice cell, and the phase advances between the two mirror planes satisfy the condition of nonlinear dynamics cancellation. There are two kinds of locally symmetric lattices, corresponding to two symmetric representations of lattice cell. In a locally symmetric lattice, main nonlinear effects caused by sextupoles can be effectively cancelled within one lattice cell, and generally there can also be many knobs of sextupoles available for further optimizing the nonlinear dynamics. Two kinds of locally symmetric lattices are designed for a 2.2 GeV diffraction-limited storage ring to demonstrate the lattice concept.

physics.acc-ph↗

arXiv2020

PDRS: A Fast Non-iterative Scheme for Massive Grant-free Access in Massive MIMO

Grant-free multiple-input multiple-output (MIMO) usually employs non-orthogonal pilots for joint user detection and channel estimation. However, existing methods are too complex for massive grant-free access in massive MIMO. This letter proposes pilot detection reference signal (PDRS) to greatly reduce the complexity. In PDRS scheme, no iteration is required. Direct weight estimation is also proposed to calculate combining weights without channel estimation. After combining, PDRS recovery errors are used to decide the pilot activity. The simulation results show that the proposed grant-free scheme performs good with a complexity reduced by orders of magnitude.

cs.IT↗

arXiv2020

TRec: Sequential Recommender Based On Latent Item Trend Information

Recommendation system plays an important role in online web applications. Sequential recommender further models user short-term preference through exploiting information from latest user-item interaction history. Most of the sequential recommendation methods neglect the importance of ever-changing item popularity. We propose the model from the intuition that items with most user interactions may be popular in the past but could go out of fashion in recent days. To this end, this paper proposes a novel sequential recommendation approach dubbed TRec, TRec learns item trend information from implicit user interaction history and incorporates item trend information into next item recommendation tasks. Then a self-attention mechanism is used to learn better node representation. Our model is trained via pairwise rank-based optimization. We conduct extensive experiments with seven baseline methods on four benchmark datasets, The empirical result shows our approach outperforms other stateof-the-art methods while maintains a superiorly low runtime cost. Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.

cs.IR↗

arXiv2020

Contention-based Grant-free Transmission with Independent Multi-pilot Scheme

Contention-based grant-free transmission is very promising for future massive machine-type communication (mMTC). In contention-based transmission, the random pilot collision is a challenging problem. To solve this problem, multiple pilots scheme is used to reduce the pilot collision probabliltiy. However, the existing work on multiple pilots relies on the low correlation of spatial channels, limiting its applicability. In this paper, an independent multi-pilot scheme is proposed, which utilizes the diversity of multiple pilots and is not limited by the spatial correlation. The receiver employs interference cancellation for both data symbols and multiple pilots to ensure the performance. The simulation results also show that the proposed independent multi-pilot scheme can significantly improve the BLER performance and increase the number of simultaneous access users.

cs.IT↗

arXiv2018

Blind Receive Beamforming for Autonomous Grant-Free High-Overloading Multiple Access

Massive number of internet of things (IoT) devices are expected to simultaneously connect to the mMTC and beyond future generations of wireless network, posing severe challenge to aspects such as RACH procedure, user equipment detection and channel estimation. Although spatial combining has provided significant gains in conventional grant-based transmission, this technique is stuck in dilemma when it comes to autonomous grant-free transmission tailored for IoT use cases. To address this, blind spatial combining and its incorporation in the data-only MUD are elaborated in this paper answering to both the academic and industry's concern in the overloading potential of autonomous grant-free (AGF) transmission. Blind spatial combining could be interpreted as blind receive beamforming heuristically. Simulation results show that the blind spatial combining enhanced data-only MUD performance for AGF transmission is rather impressive.

cs.IT↗

arXiv2017

Blind Multi-user Detection for Autonomous Grant-free High-Overloading MA without Reference Signal

In this paper, a novel blind multi-user detection(MUD) framework for autonomous grant-free high-overloading non-orthogonal multiple access is introduced in detail aimed at fulfilling the requirements of fifth-generation massive Machine Type Communications. From the perspective of the transmitter side, pros and cons regarding diverse types of emerging grant-free transmission, particularly autonomous grant-free, are elaborated and presented in a comparative manner. In the receiver end,code word-level successive interference cancellation (CL-SIC) is revealed as the main framework to perform MUD. In addition, underpinning state-of-art blind ideas such as blind activation detection taking advantage of the statistical metric of the aggregate signals, blind equalization based on the constellation's simple geometric character of low order modulation symbols, and blind channel estimation employing solely the successfully decoded code words are explained.

cs.IT↗

arXiv2015

Broadband Impedance Calculations and Single Bunch Instabilities Estimations of the HLS-II storage ring

The upgrade project of Hefei Light Source storage ring is under way. In this paper, the broadband impedances of resistive wall and coated ceramic vacuum chamber were calculated using the analytic formula, the wake fields and impedances of other designed vacuum chambers have been simulated by the CST code, and then the broadband impedances model was obtained. Using the theoretical formula, longitudinal and transverse single bunch instabilities were discussed. With the carefully-designed vacuum chamber, we find that the thresholds of the beam instabilities are higher than the beam current goal.

physics.acc-ph↗

arXiv2014

A Multi-length Bunches Design for Electron Storage Rings with Odd Buckets

A scheme with two superconducting RF cavities (sc-cavities) is designed to upgrade electron storage rings with odd buckets into multi-length bunches. In this paper, Hefei Light Source II (HLS II) is given as an example for odd buckets. In accordance with 45 buckets, which is multiples of 3, three different length of bunches generated simultaneously is proposed in the presently applied user optics. The final result is to, without low-alpha optics, fill HLS II with long bunches of 50 ps length, medium bunches of 23 ps and short bunches of 6 ps. Each third buckets can be filled with short bunches, of which the current limit is up to 6.6 mA, more than 60 times the value of low-alpha mode. Moreover, particles tracking about beam dynamics performed by ELEGANT and calculations about beam instabilities are presented in this paper.

physics.acc-ph↗

arXiv2014

Induced radioactivity analysis for the NSRL Linac in China using Monte Carlo simulations and gamma-spectroscopy

The 200-MeV electron linac of the National Synchrotron Radiation Laboratory (NSRL) located in Hefei is one of the earliest high-energy electron linear accelerators in China. The electrons are accelerated to 200 MeV by five acceleration tubes and are collimated by scrapers. The scraper aperture is smaller than the acceleration tube one, so some electrons hit the materials when passing through them. These lost electrons cause induced radioactivity mainly due to bremsstrahlung and photonuclear reaction. This paper describes a study of induced radioactivity for the NSRL Linac using FLUKA simulations and gamma-spectroscopy. The measurements showed that electrons were lost mainly at the scraper. So the induced radioactivity of the NSRL Linac is mainly produced here. The radionuclide types were simulated using the FLUKA Monte Carlo code and the results were compared against measurements made with a High Purity Germanium (HPGe) gamma spectrometer. The NSRL linac had been retired because of upgrading last year. The removed components were used to study induced radioactivity. The radionuclides confirmed by the measurement are: $^{57}$Ni, $^{52}$Mn, $^{51}$Cr, $^{58}$Co, $^{56}$Co, $^{57}$Co, $^{54}$Mn, $^{60}$Co and $^{22}$Na, the first eight nuclides of which are predicted by FLUKA simulation. The research will provide the theoretical basis for the similar accelerator decommissioning plan, and is significant for accelerator structure design, material selection and radiation protection design.

physics.acc-ph↗