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Kun Liu

Publications and source records attributed to Kun Liu.

At least 73 records · Page 4Linked to original sources
arXiv2024

Early Risk Assessment Model for ICA Timing Strategy in Unstable Angina Patients Using Multi-Modal Machine Learning

Background: Invasive coronary arteriography (ICA) is recognized as the gold standard for diagnosing cardiovascular diseases, including unstable angina (UA). The challenge lies in determining the optimal timing for ICA in UA patients, balancing the need for revascularization in high-risk patients against the potential complications in low-risk ones. Unlike myocardial infarction, UA does not have specific indicators like ST-segment deviation or cardiac enzymes, making risk assessment complex. Objectives: Our study aims to enhance the early risk assessment for UA patients by utilizing machine learning algorithms. These algorithms can potentially identify patients who would benefit most from ICA by analyzing less specific yet related indicators that are challenging for human physicians to interpret. Methods: We collected data from 640 UA patients at Shanghai General Hospital, including medical history and electrocardiograms (ECG). Machine learning algorithms were trained using multi-modal demographic characteristics including clinical risk factors, symptoms, biomarker levels, and ECG features extracted by pre-trained neural networks. The goal was to stratify patients based on their revascularization risk. Additionally, we translated our models into applicable and explainable look-up tables through discretization for practical clinical use. Results: The study achieved an Area Under the Curve (AUC) of $0.719 \pm 0.065$ in risk stratification, significantly surpassing the widely adopted GRACE score's AUC of $0.579 \pm 0.044$. Conclusions: The results suggest that machine learning can provide superior risk stratification for UA patients. This improved stratification could help in balancing the risks, costs, and complications associated with ICA, indicating a potential shift in clinical assessment practices for unstable angina.

cs.LG↗
arXiv2024

Topological Woodward-Hoffmann classification for cycloadditions in polycyclic aromatic azomethine ylides

The study of cycloaddition mechanisms is central to the fabrication of extended sp2 carbon nanostructures. Reaction modeling in this context has focused mostly on putative, energetically preferred, exothermic products with limited consideration for symmetry allowed or forbidden mechanistic effects. Here, we introduce a scheme for classifying symmetry-forbidden reaction coordinates in Woodward-Hoffmann correlation diagrams. Topological classifiers grant access to the study of reaction pathways and correlation diagrams in the same footing, for the purpose of elucidating mechanisms and products of polycyclic aromatic azomethine ylide (PAMY) cycloadditions with pentacene-yielding polycyclic aromatic hydrocarbons with an isoindole core in the solid-state and on surfaces as characterized by mass spectrometry and scanning tunneling microscopy, respectively. By means of a tight-binding reaction model and density functional theory (DFT) we find topologically-allowed pathways if a product is endothermic, and topologically-forbidden if a product is exothermic. Our work unveils topological classification as a crucial element for reaction modeling for nanographene engineering, and highlights its fundamental role in the design of cycloadditions in on-surface and solid-state chemical reactions, while underscoring that exothermic pathways can be topologically-forbidden.

physics.chem-ph↗
arXiv2024

Design of High-speed readout electronics for the DarkSHINE electromagnetic calorimeter

The DarkSHINE experiment aims to search for dark photons by measuring the energy loss of the electrons recoiled from fixed-target. Its electromagnetic calorimeter is primarily responsible for accurately reconstructing the energy of the recoil electrons and bremsstrahlung photons. The performance of the electromagnetic calorimeter is crucial, as its energy measurement precision directly determines the sensitivity to the search for dark photons. The DarkSHINE electromagnetic calorimeter uses LYSO crystals to form a fully absorptive electromagnetic calorimeter. It utilizes SiPMs to detect scintillation light in the crystals, and its readout electronics system deduces the deposited energy in the crystals by measuring the number of photoelectric signals generated by the SiPMs. The DarkSHINE electromagnetic calorimeter aims to operate at an event rate of 1-10 MHz, detecting energies ranging from 1 MeV to 1 GeV. To meet the requirements of high energy measurement precision, high event rate, and large dynamic range, we have researched and designed a readout electronics system based on dual-channel high-speed ADCs and a customized DAQ. The front-end amplification part of this system uses low-noise trans-impedance amplifiers to achieve high-precision waveform amplification. It successfully achieves a dynamic range up to a thousandfold through a double-gain readout scheme. The digital part uses 1 GSPS high-speed ADCs to achieve non-dead-time, high-precision waveform digitization. The DAQ part uses JESD204B high-speed serial protocol to read out the signal from ADC, and transmit it to PC software for processing and storage. Test results show a signal-to-noise ratio greater than 66 dBFS and an ENOB greater than 10.6 bits. Energy spectra measurements have been conducted using LYSO crystals and SiPMs, and an energy resolution of 5.96% at the 2.6 MeV gamma peak of Th-232 has been achieved.

physics.ins-det↗
arXiv2024

Thermal spin-crossover and temperature-dependent zero-field splitting in magnetic nanographene chains

Nanographene-based magnetism at interfaces offers an avenue to designer quantum materials towards novel phases of matter and atomic-scale applications. Key to spintronics applications at the nanoscale is bistable spin-crossover which however remains to be demonstrated in nanographenes. Here we show that antiaromatic 1,4-disubstituted pyrazine-embedded nanographene derivatives, which promote magnetism through oxidation to a non-aromatic radical are prototypical models for the study of carbon-based thermal spin-crossover. Scanning tunneling spectroscopy studies reveal symmetric spin excitation signals which evolve at Tc to a zero-energy peak, and are assigned to the transition of a S = 3/2 high-spin to a S = 1/2 low-spin state by density functional theory. At temperatures below and close to the spin-crossover Tc, the high-spin S= 3/2 excitations evidence pronouncedly different temperature-dependent excitation energies corresponding to a zero-field splitting in the Hubbard-Kanamori Hamiltonian. The discovery of thermal spin crossover and temperature-dependent zero-field splitting in carbon nanomaterials promises to accelerate quantum information, spintronics and thermometry at the atomic scale.

physics.chem-ph↗
arXiv2024

Building spin-1/2 antiferromagnetic Heisenberg chains with diaza-nanographenes

Understanding and engineering the coupling of spins in nanomaterials is of central importance for designing novel devices. Graphene nanostructures with π-magnetism offer a chemically tunable platform to explore quantum magnetic interactions. However, realizing spin chains bearing controlled odd-even effects with suitable nanographene systems is challenging. Here, we demonstrate the successful on-surface synthesis of spin-1/2 antiferromagnetic Heisenberg chains with parity-dependent magnetization based on antiaromatic diaza-hexa-peri-hexabenzocoronene (diaza-HBC) units. Using distinct synthetic strategies, two types of spin chains with different terminals were synthesized, both exhibiting a robust odd-even effect on the spin coupling along the chain. Combined investigations using scanning tunneling microscopy, non-contact atomic force microscopy, density functional theory calculations, and quantum spin models confirmed the structures of the diaza-HBC chains and revealed their magnetic properties, which has an S = 1/2 spin per unit through electron donation from the diaza-HBC core to the Au(111) substrate. Gapped excitations were observed in even-numbered chains, while enhanced Kondo resonance emerged in odd-numbered units of odd-numbered chains due to the redistribution of the unpaired spin along the chain. Our findings provide an effective strategy to construct nanographene spin chains and unveil the odd-even effect in their magnetic properties, offering potential applications in nanoscale spintronics.

cond-mat.mtrl-sci↗
arXiv2024

Probing spin and pseudospin symmetries in deformed nuclei by the Green's function method

(Pseudo)spin symmetries play vital roles in nuclear physics and have been studied extensively in spherical nuclei. In this work, possible spin and pseudospin symmetries in deformed nuclei are examined by solving a coupled-channel Dirac equation with quadruple deformation. The Green's function method is taken which provides a novel way to exactly determine the single-particle levels and properly describe the spacial density distributions. Taking axially-deformed nucleus $^{154}$Dy as an example, the spin doublets with a combination of Nilsson levels $Λ\pm 1/2[\mathcal{N},n_z, Λ]$ and pseudospin doublets with a combination of $\widetildeΛ\pm 1/2[\widetilde{\mathcal{N},n_z, Λ}]$ are determined. Different behaviors are displayed for the spin and pseudospin doublets. For the spin partners, those with smaller angular momentum $l$ and the third component $Λ$ owns better symmetry such as the $1p$ doublet while good pseudospin symmetry appears in partners locating close to the continuum threshold. By examining the single-particle Nilsson levels $Ω[\mathcal{N},n_z,Λ]$ and the energy splittings between the partners, the conservation and breaking of SS and PSS are examined at different deformations. In the prolate side, the Nilsson levels for the spin and pseudospin doublets are almost parallel and the energy splittings are stable against varying deformations. By examining the density distributions, great similarities have been observed in the upper components for the spin doublets while great similarities in the lower component for the pseudospin doublets. Besides, these similarities maintain well at different deformations.

nucl-th↗
arXiv2024

Experimental quantum computational chemistry with optimised unitary coupled cluster ansatz

Quantum computational chemistry has emerged as an important application of quantum computing. Hybrid quantum-classical computing methods, such as variational quantum eigensolvers (VQE), have been designed as promising solutions to quantum chemistry problems, yet challenges due to theoretical complexity and experimental imperfections hinder progress in achieving reliable and accurate results. Experimental works for solving electronic structures are consequently still restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors. The experimental realisation of scalable and high-precision quantum chemistry simulation remains elusive. Here, we address the critical challenges {associated with} solving molecular electronic structures using noisy quantum processors. Our protocol presents significant improvements in the circuit depth and running time, key metrics for chemistry simulation. Through systematic hardware enhancements and the integration of error mitigation techniques, we push forward the limit of experimental quantum computational chemistry and successfully scale up the implementation of VQE with an optimised unitary coupled-cluster ansatz to 12 qubits. We produce high-precision results of the ground-state energy for molecules with error suppression by around two orders of magnitude. We achieve chemical accuracy for H$_2$ at all bond distances and LiH at small bond distances in the experiment, even beyond the two recent concurrent works. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.

quant-ph↗
arXiv2024

Privacy and Security Trade-off in Interconnected Systems with Known or Unknown Privacy Noise Covariance

This paper is concerned with the security problem for interconnected systems, where each subsystem is required to detect local attacks using locally available information and the information received from its neighboring subsystems. Moreover, we consider that there exists an additional eavesdropper being able to infer the private information by eavesdropping transmitted data between subsystems. Then, a privacy-preserving method is employed by adding privacy noise to transmitted data, and the privacy level is measured by mutual information. Nevertheless, adding privacy noise to transmitted data may affect the detection performance metrics such as detection probability and false alarm probability. Thus, we theoretically analyze the trade-off between the privacy and the detection performance. An optimization problem with maximizing both the degree of privacy preservation and the detection probability is established to obtain the covariance of the privacy noise. In addition, the attack detector of each subsystem may not obtain all information about the privacy noise. We further theoretically analyze the trade-off between the privacy and the false alarm probability when the attack detector has no knowledge of the privacy noise covariance. An optimization problem with maximizing the degree of privacy preservation with guaranteeing a bound of false alarm distortion level is established to obtain {\color{black}{the covariance of the privacy noise}}. Moreover, to analyze the effect of the privacy noise on the detection probability, we consider that each subsystem can estimate the unknown privacy noise covariance by the secondary data. Based on the estimated covariance, we construct another attack detector and analyze how the privacy noise affects its detection performance. Finally, a numerical example is provided to verify the effectiveness of theoretical results.

eess.SY↗
arXiv2024

Distributed Adaptive Gradient Algorithm with Gradient Tracking for Stochastic Non-Convex Optimization

This paper considers a distributed stochastic non-convex optimization problem, where the nodes in a network cooperatively minimize a sum of $L$-smooth local cost functions with sparse gradients. By adaptively adjusting the stepsizes according to the historical (possibly sparse) gradients, a distributed adaptive gradient algorithm is proposed, in which a gradient tracking estimator is used to handle the heterogeneity between different local cost functions. We establish an upper bound on the optimality gap, which indicates that our proposed algorithm can reach a first-order stationary solution dependent on the upper bound on the variance of the stochastic gradients. Finally, numerical examples are presented to illustrate the effectiveness of the algorithm.

math.OC↗
arXiv2024

BioDrone: A Bionic Drone-based Single Object Tracking Benchmark for Robust Vision

Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone -- the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack KeepTrack) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://biodrone.aitestunion.com.

cs.CV↗
arXiv2023

Collective octahedral tilting in ultrathin Ruddlesden-Popper perovskite under terahertz light

Perovskites have been applied in a wide range of fields such as solar cells and non-volatile memories due to their multiferroic nature and excellent photo-electric conversion capabilities. Recently, two-dimensional (2D) perovskites with a few atomic layers have been successfully synthesized, attracting significant attention for potential applications. In this work, we perform first-principles calculations to investigate an ultrathin prototypical Ruddlesden-Popper phase, $\mathrm{Bi}_2\mathrm{FeO}_4$, with its thickness down to one unit cell. We show that this compound could exist in two (meta-)stable octahedral tilting phases, belonging to $P2_1/c$ and $C2/m$ space groups, respectively. Using the optomechanical theory, we suggest that reversible and non-volatile phase switching can be triggered using non-destructive terahertz light. In addition, the two phases show distinct optical reflectance spectrum in the visible light range, which can be used as an optical probe for phase transformation. This enables both "write" and "read" in an all-optical route.

cond-mat.mtrl-sci↗
arXiv2023

Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challenges

Surveillance videos are an essential component of daily life with various critical applications, particularly in public security. However, current surveillance video tasks mainly focus on classifying and localizing anomalous events. Existing methods are limited to detecting and classifying the predefined events with unsatisfactory semantic understanding, although they have obtained considerable performance. To address this issue, we propose a new research direction of surveillance video-and-language understanding, and construct the first multimodal surveillance video dataset. We manually annotate the real-world surveillance dataset UCF-Crime with fine-grained event content and timing. Our newly annotated dataset, UCA (UCF-Crime Annotation), contains 23,542 sentences, with an average length of 20 words, and its annotated videos are as long as 110.7 hours. Furthermore, we benchmark SOTA models for four multimodal tasks on this newly created dataset, which serve as new baselines for surveillance video-and-language understanding. Through our experiments, we find that mainstream models used in previously publicly available datasets perform poorly on surveillance video, which demonstrates the new challenges in surveillance video-and-language understanding. To validate the effectiveness of our UCA, we conducted experiments on multimodal anomaly detection. The results demonstrate that our multimodal surveillance learning can improve the performance of conventional anomaly detection tasks. All the experiments highlight the necessity of constructing this dataset to advance surveillance AI. The link to our dataset is provided at: https://xuange923.github.io/Surveillance-Video-Understanding.

cs.CV↗
arXiv2023

Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network

Ball mills play a critical role in modern mining operations, making their bearing failures a significant concern due to the potential loss of production efficiency and economic consequences. This paper presents an anomaly detection method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for addressing the issue of ball mill bearing fault detection. The proposed approach leverages vibration data collected during normal operation for training, overcoming challenges such as labeling issues and data imbalance often encountered in supervised learning methods. DCAN includes the modules of convolutional feature extraction and transposed convolutional feature reconstruction, demonstrating exceptional capabilities in signal processing and feature extraction. Additionally, the paper describes the practical deployment of the DCAN-based anomaly detection model for bearing fault detection, utilizing data from the ball mill bearings of Wuhan Iron & Steel Resources Group and fault data from NASA's bearing vibration dataset. Experimental results validate the DCAN model's reliability in recognizing fault vibration patterns. This method holds promise for enhancing bearing fault detection efficiency, reducing production interruptions, and lowering maintenance costs.

cs.LG↗
arXiv2023

Probing Electroweak Phase Transition in the Singlet Standard Model via $bbγγ$ and 4$l$ channels

We investigate the prospects for resonant di-Higgs and heavy Higgs production searches at the 14 TeV HL-LHC in the combination of $bbγγ$ and $4l$ channels, as a probe of a possible first order electroweak phase transition in real singlet scalar extension of the Standard Model. Event selection follows those utilized in the $bbγγ$ and $4l$ searches by the ATLAS Collaboration, applied to simulation using benchmark parameters that realize a strong first order electroweak phase transition. The output of discriminant analysis is implemented by numerical calculation, optimised by the joint restriction from the two channels. The prospective reach for $bbγγ$/$4l$ channel could be more competitive in probing the electroweak phase transition at lower/higher resonance masses. With 3 $ab^{-1}$ integrated luminosity, the combination of the $bbγγ$ and $4l$ channels can discover/exclude a significant portion of %\mrmC{isn't it more accurate to say "a significant portion of" ?} of the viable parameter space that realizes a strong first order phase transition when the resonance mass is heavier than 500 GeV.

hep-ph↗
arXiv2023

Performance of AC-LGAD strip sensor designed for the DarkSHINE experiment

AC-coupled Low Gain Avalanche Detector (AC-LGAD) is a new precise detector technology developed in recent years. Based on the standard Low Gain Avalanche Detector (LGAD) technology, AC-LGAD sensors can provide excellent timing performance and spatial resolution. This paper presents the design and performance of several prototype AC-LGAD strip sensors for the DarkSHINE tracking system, as well as the electrical characteristics and spatial resolution of the prototype sensors from two batches of wafers with different $n^+$ dose.The range of spatial resolutions of 6.5$\mathrm{μm}$ $\sim$ 8.2$\mathrm{μm}$ and 8.8$\mathrm{μm}$ $\sim$ 12.3$\mathrm{μm}$ are achieved by the AC-LGAD sensors with 100$μm$ pitch size.

physics.ins-det↗
arXiv2023

Simultaneous 3D Construction and Imaging of Plant Cells Using Plasmonic Nanoprobe Assisted Multimodal Nonlinear Optical Microscopy

Nonlinear optical (NLO) imaging has emerged as a promising plant cell imaging technique due to its large optical penetration, inherent 3D spatial resolution, and reduced photodamage, meanwhile exogenous nanoprobes are usually needed for non-signal target cell analysis. Here, we report in-vivo, simultaneous 3D labeling and imaging of potato cell structures using plasmonic nanoprobe-assisted multimodal NLO microscopy. Experimental results show that the complete cell structure could be imaged by the combination of second-harmonic generation (SHG) and two-photon luminescence (TPL) when noble metal silver or gold ions are added. In contrast, without noble metal ion solution, no NLO signals from the cell wall could be acquired. The mechanism can be attributed to noble metal nanoprobes with strong nonlinear optical responses formed along the cell walls via a femtosecond laser scan. During the SHG-TPL imaging process, noble metal ions that cross the cell wall could be rapidly reduced to plasmonic nanoparticles by fs laser and selectively anchored onto both sides of the cell wall, thereby leading to simultaneous 3D labeling and imaging of potato cells. Compared with traditional labeling technique that needs in-vitro nanoprobe fabrication and cell labeling, our approach allows for one-step, in-vivo labeling of plant cells, thus providing a rapid, cost-effective way for cellular structure construction and imaging.

physics.optics↗
arXiv2023

Enabling High Performance Debugging for Variational Quantum Algorithms using Compressed Sensing

Variational quantum algorithms (VQAs) can potentially solve practical problems using contemporary Noisy Intermediate Scale Quantum (NISQ) computers. VQAs find near-optimal solutions in the presence of qubit errors by classically optimizing a loss function computed by parameterized quantum circuits. However, developing and testing VQAs is challenging due to the limited availability of quantum hardware, their high error rates, and the significant overhead of classical simulations. Furthermore, VQA researchers must pick the right initialization for circuit parameters, utilize suitable classical optimizer configurations, and deploy appropriate error mitigation methods. Unfortunately, these tasks are done in an ad-hoc manner today, as there are no software tools to configure and tune the VQA hyperparameters. In this paper, we present OSCAR (cOmpressed Sensing based Cost lAndscape Reconstruction) to help configure: 1) correct initialization, 2) noise mitigation techniques, and 3) classical optimizers to maximize the quality of the solution on NISQ hardware. OSCAR enables efficient debugging and performance tuning by providing users with the loss function landscape without running thousands of quantum circuits as required by the grid search. Using OSCAR, we can accurately reconstruct the complete cost landscape with up to 100X speedup. Furthermore, OSCAR can compute an optimizer function query in an instant by interpolating a computed landscape, thus enabling the trial run of a VQA configuration with considerably reduced overhead.

quant-ph↗
arXiv2023

FETNet: Feature Erasing and Transferring Network for Scene Text Removal

The scene text removal (STR) task aims to remove text regions and recover the background smoothly in images for private information protection. Most existing STR methods adopt encoder-decoder-based CNNs, with direct copies of the features in the skip connections. However, the encoded features contain both text texture and structure information. The insufficient utilization of text features hampers the performance of background reconstruction in text removal regions. To tackle these problems, we propose a novel Feature Erasing and Transferring (FET) mechanism to reconfigure the encoded features for STR in this paper. In FET, a Feature Erasing Module (FEM) is designed to erase text features. An attention module is responsible for generating the feature similarity guidance. The Feature Transferring Module (FTM) is introduced to transfer the corresponding features in different layers based on the attention guidance. With this mechanism, a one-stage, end-to-end trainable network called FETNet is constructed for scene text removal. In addition, to facilitate research on both scene text removal and segmentation tasks, we introduce a novel dataset, Flickr-ST, with multi-category annotations. A sufficient number of experiments and ablation studies are conducted on the public datasets and Flickr-ST. Our proposed method achieves state-of-the-art performance using most metrics, with remarkably higher quality scene text removal results. The source code of our work is available at: \href{https://github.com/GuangtaoLyu/FETNet}{https://github.com/GuangtaoLyu/FETNet.

cs.CV↗