arXiv subjects

Zihan Li

Publications and source records attributed to Zihan Li.

At least 109 records · Page 6Linked to original sources

arXiv2023

ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge

The primary aim of this research was to address the limitations observed in the medical knowledge of prevalent large language models (LLMs) such as ChatGPT, by creating a specialized language model with enhanced accuracy in medical advice. We achieved this by adapting and refining the large language model meta-AI (LLaMA) using a large dataset of 100,000 patient-doctor dialogues sourced from a widely used online medical consultation platform. These conversations were cleaned and anonymized to respect privacy concerns. In addition to the model refinement, we incorporated a self-directed information retrieval mechanism, allowing the model to access and utilize real-time information from online sources like Wikipedia and data from curated offline medical databases. The fine-tuning of the model with real-world patient-doctor interactions significantly improved the model's ability to understand patient needs and provide informed advice. By equipping the model with self-directed information retrieval from reliable online and offline sources, we observed substantial improvements in the accuracy of its responses. Our proposed ChatDoctor, represents a significant advancement in medical LLMs, demonstrating a significant improvement in understanding patient inquiries and providing accurate advice. Given the high stakes and low error tolerance in the medical field, such enhancements in providing accurate and reliable information are not only beneficial but essential.

cs.CL↗

arXiv2023

Multi-view MERA Subspace Clustering

Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in the self-representation tensor. Current tensor decompositions for MSC suffer from highly unbalanced unfolding matrices or rotation sensitivity, failing to fully explore inter/intra-view information. Using the advanced tensor network, namely, multi-scale entanglement renormalization ansatz (MERA), we propose a low-rank MERA based MSC (MERA-MSC) algorithm, where MERA factorizes a tensor into contractions of one top core factor and the rest orthogonal/semi-orthogonal factors. Benefiting from multiple interactions among orthogonal/semi-orthogonal (low-rank) factors, the low-rank MERA has a strong representation power to capture the complex inter/intra-view information in the self-representation tensor. The alternating direction method of multipliers is adopted to solve the optimization model. Experimental results on five multi-view datasets demonstrate MERA-MSC has superiority against the compared algorithms on six evaluation metrics. Furthermore, we extend MERA-MSC by incorporating anchor learning to develop a scalable low-rank MERA based multi-view clustering method (sMREA-MVC). The effectiveness and efficiency of sMERA-MVC have been validated on three large-scale multi-view datasets. To our knowledge, this is the first work to introduce MERA to the multi-view clustering topic. The codes of MERA-MSC and sMERA-MVC are publicly available at https://github.com/longzhen520/MERA-MSC.

cs.CV↗

arXiv2023

DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

Implicit Discourse Relation Recognition (IDRR) is a sophisticated and challenging task to recognize the discourse relations between the arguments with the absence of discourse connectives. The sense labels for each discourse relation follow a hierarchical classification scheme in the annotation process (Prasad et al., 2008), forming a hierarchy structure. Most existing works do not well incorporate the hierarchy structure but focus on the syntax features and the prior knowledge of connectives in the manner of pure text classification. We argue that it is more effective to predict the paths inside the hierarchical tree (e.g., "Comparison -> Contrast -> however") rather than flat labels (e.g., Contrast) or connectives (e.g., however). We propose a prompt-based path prediction method to utilize the interactive information and intrinsic senses among the hierarchy in IDRR. This is the first work that injects such structure information into pre-trained language models via prompt tuning, and the performance of our solution shows significant and consistent improvement against competitive baselines.

cs.CL↗

arXiv2023

Synchrotron Radiation Dominates the Extremely Bright GRB 221009A

The brightest Gamma-ray burst, GRB 221009A, has spurred numerous theoretical investigations, with particular attention paid to the origins of ultra-high energy TeV photons during the prompt phase. However, analyzing the mechanism of radiation of photons in the $\sim$MeV range has been difficult because the high flux causes pile-up and saturation effects in most GRB detectors. In this letter, we present systematic modeling of the time-resolved spectra of the GRB using unsaturated data obtained from Fermi/GBM (precursor) and SATech-01/GECAM-C (main emission and flare). Our approach incorporates the synchrotron radiation model, which assumes an expanding emission region with relativistic speed and a global magnetic field that decays with radius, and successfully fits such a model to the observational data. Our results indicate that the spectra of the burst are fully in accordance with a synchrotron origin from relativistic electrons accelerated at a large emission radius. The lack of thermal emission in the prompt emission spectra supports a Poynting-flux-dominated jet composition.

astro-ph.HE↗

arXiv2023

Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment

Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19. However, there are few relevant studies due to the lack of detailed information and high-quality annotation in the COVID-19 dataset. To solve the above problem, we propose C2FVL, a Coarse-to-Fine segmentation framework via Vision-Language alignment to merge text information containing the number of lesions and specific locations of image information. The introduction of text information allows the network to achieve better prediction results on challenging datasets. We conduct extensive experiments on two COVID-19 datasets including chest X-ray and CT, and the results demonstrate that our proposed method outperforms other state-of-the-art segmentation methods.

eess.IV↗

arXiv2022

Robust nonparametric integrative analysis to decipher heterogeneity and commonality across subgroups using sparse boosting

In many biomedical problems, data are often heterogeneous, with samples spanning multiple patient subgroups, where different subgroups may have different disease subtypes, stages, or other medical contexts. These subgroups may be related, but they are also expected to have differences with respect to the underlying biology. The heterogeneous data presents a precious opportunity to explore the heterogeneities and commonalities between related subgroups. Unfortunately, effective statistical analysis methods are still lacking. Recently, several novel methods based on integrative analysis have been proposed to tackle this challenging problem. Despite promising results, the existing studies are still limited by ignoring data contamination and making strict assumptions of linear effects of covariates on response. As such, we develop a robust nonparametric integrative analysis approach to identify heterogeneity and commonality, as well as select important covariates and estimate covariate effects. Possible data contamination is accommodated by adopting the Cauchy loss function, and a nonparametric model is built to accommodate nonlinear effects. The proposed approach is based on a sparse boosting technique. The advantages of the proposed approach are demonstrated in extensive simulations. The analysis of The Cancer Genome Atlas data on glioblastoma multiforme and lung adenocarcinoma shows that the proposed approach makes biologically meaningful findings with satisfactory prediction.

stat.ME↗

arXiv2022

SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data

Neural network (NN) based approaches for super-resolution MRI typically require high-SNR high-resolution reference data acquired in many subjects, which is time consuming and a barrier to feasible and accessible implementation. We propose to train NNs for Super-Resolution using Noisy Reference data (SRNR), leveraging the mechanism of the classic NN-based denoising method Noise2Noise. We systematically demonstrate that results from NNs trained using noisy and high-SNR references are similar for both simulated and empirical data. SRNR suggests a smaller number of repetitions of high-resolution reference data can be used to simplify the training data preparation for super-resolution MRI.

eess.IV↗

arXiv2022

Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency

Supervised learning has been widely used for attack categorization, requiring high-quality data and labels. However, the data is often imbalanced and it is difficult to obtain sufficient annotations. Moreover, supervised models are subject to real-world deployment issues, such as defending against unseen artificial attacks. To tackle the challenges, we propose a semi-supervised fine-grained attack categorization framework consisting of an encoder and a two-branch structure and this framework can be generalized to different supervised models. The multilayer perceptron with residual connection is used as the encoder to extract features and reduce the complexity. The Recurrent Prototype Module (RPM) is proposed to train the encoder effectively in a semi-supervised manner. To alleviate the data imbalance problem, we introduce the Weight-Task Consistency (WTC) into the iterative process of RPM by assigning larger weights to classes with fewer samples in the loss function. In addition, to cope with new attacks in real-world deployment, we propose an Active Adaption Resampling (AAR) method, which can better discover the distribution of unseen sample data and adapt the parameters of encoder. Experimental results show that our model outperforms the state-of-the-art semi-supervised attack detection methods with a 3% improvement in classification accuracy and a 90% reduction in training time.

cs.CR↗

arXiv2022

Tightly confining lithium niobate photonic integrated circuits and lasers

Photonic integrated circuits are indispensible for data transmission within modern datacenters and pervade into multiple application spheres traditionally limited for bulk optics, such as LiDAR and biosensing. Of particular interest are ferroelectrics such as Lithium Niobate, which exhibit a large electro-optical Pockels effect enabling ultrafast and efficient modulation, but are difficult to process via dry etching . For this reason, etching tightly confining waveguides - routinely achieved in silicon or silicon nitride - has not been possible. Diamond-like carbon (DLC) was discovered in the 1950s and is a material that exhibits an amorphous phase, excellent hardness, and the ability to be deposited in nano-metric thin films. It has excellent thermal, mechanical, and electrical properties, making it an ideal protective coating. Here we demonstrate that DLC is also a superior material for the manufacturing of next-generation photonic integrated circuits based on ferroelectrics, specifically Lithium Niobate on insulator (LNOI). Using DLC as a hard mask, we demonstrate the fabrication of deeply etched, tightly confining, low loss photonic integrated circuits with losses as low as 5.6 dB/m. In contrast to widely employed ridge waveguides, this approach benefits from a more than 1 order of magnitude higher area integration density while maintaining efficient electro-optical modulation, low loss, and offering a route for efficient optical fiber interfaces. As a proof of concept, we demonstrate a frequency agile hybrid integrated III-V Lithium Niobate based laser with kHz linewidth and tuning rate of 0.7 Peta-Hertz per second with excellent linearity and CMOS-compatible driving voltage. Our approach can herald a new generation of tightly confining ferroelectric photonic integrated circuits.

physics.optics↗

arXiv2022

TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation

Medical image segmentation is one of the most fundamental tasks concerning medical information analysis. Various solutions have been proposed so far, including many deep learning-based techniques, such as U-Net, FC-DenseNet, etc. However, high-precision medical image segmentation remains a highly challenging task due to the existence of inherent magnification and distortion in medical images as well as the presence of lesions with similar density to normal tissues. In this paper, we propose TFCNs (Transformers for Fully Convolutional denseNets) to tackle the problem by introducing ResLinear-Transformer (RL-Transformer) and Convolutional Linear Attention Block (CLAB) to FC-DenseNet. TFCNs is not only able to utilize more latent information from the CT images for feature extraction, but also can capture and disseminate semantic features and filter non-semantic features more effectively through the CLAB module. Our experimental results show that TFCNs can achieve state-of-the-art performance with dice scores of 83.72\% on the Synapse dataset. In addition, we evaluate the robustness of TFCNs for lesion area effects on the COVID-19 public datasets. The Python code will be made publicly available on https://github.com/HUANGLIZI/TFCNs.

eess.IV↗

arXiv2022

Reduced Material Loss in Thin-film Lithium Niobate Waveguides

Thin-film lithium niobate has shown promise for scalable applications ranging from single-photon sources to high-bandwidth data communication systems. Realization of the next generation high-performance classical and quantum devices, however, requires much lower optical losses than the current state of the art ($\sim$10 million). Unfortunately, material limitations of ion-sliced thin-film lithium niobate have not been explored, and therefore it is unclear how high-quality factor can be achieved in this platform. Here we evaluate the material limited quality factor of thin-film lithium niobate photonic platform can be as high as $Q\approx 1.8\times10^{8}$ at telecommunication wavelengths, corresponding to a propagation loss of 0.2 dB/m.

physics.optics↗

arXiv2022

A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits

We consider the sequential optimization of an unknown, continuous, and expensive to evaluate reward function, from noisy and adversarially corrupted observed rewards. When the corruption attacks are subject to a suitable budget $C$ and the function lives in a Reproducing Kernel Hilbert Space (RKHS), the problem can be posed as corrupted Gaussian process (GP) bandit optimization. We propose a novel robust elimination-type algorithm that runs in epochs, combines exploration with infrequent switching to select a small subset of actions, and plays each action for multiple time instants. Our algorithm, Robust GP Phased Elimination (RGP-PE), successfully balances robustness to corruptions with exploration and exploitation such that its performance degrades minimally in the presence (or absence) of adversarial corruptions. When $T$ is the number of samples and $γ_T$ is the maximal information gain, the corruption-dependent term in our regret bound is $O(C γ_T^{3/2})$, which is significantly tighter than the existing $O(C \sqrt{T γ_T})$ for several commonly-considered kernels. We perform the first empirical study of robustness in the corrupted GP bandit setting, and show that our algorithm is robust against a variety of adversarial attacks.

stat.ML↗

arXiv2022

Gaussian Process Bandit Optimization with Few Batches

In this paper, we consider the problem of black-box optimization using Gaussian Process (GP) bandit optimization with a small number of batches. Assuming the unknown function has a low norm in the Reproducing Kernel Hilbert Space (RKHS), we introduce a batch algorithm inspired by batched finite-arm bandit algorithms, and show that it achieves the cumulative regret upper bound $O^\ast(\sqrt{Tγ_T})$ using $O(\log\log T)$ batches within time horizon $T$, where the $O^\ast(\cdot)$ notation hides dimension-independent logarithmic factors and $γ_T$ is the maximum information gain associated with the kernel. This bound is near-optimal for several kernels of interest and improves on the typical $O^\ast(\sqrt{T}γ_T)$ bound, and our approach is arguably the simplest among algorithms attaining this improvement. In addition, in the case of a constant number of batches (not depending on $T$), we propose a modified version of our algorithm, and characterize how the regret is impacted by the number of batches, focusing on the squared exponential and Matérn kernels. The algorithmic upper bounds are shown to be nearly minimax optimal via analogous algorithm-independent lower bounds.

stat.ML↗

arXiv2021

Gate-tunable Intrinsic Anomalous Hall Effect in Epitaxial MnBi2Te4 Films

Anomalous Hall effect (AHE) is an important transport signature revealing topological properties of magnetic materials and their spin textures. Recently, antiferromagnetic MnBi2Te4 has been demonstrated to be an intrinsic magnetic topological insulator that exhibits quantum AHE in exfoliated nanoflakes. However, its complicated AHE behaviors may offer an opportunity for the unexplored correlation between magnetism and band structure. Here, we show the Berry curvature dominated intrinsic AHE in wafer-scale MnBi2Te4 thin films. By utilizing a high-dielectric SrTiO3 as the back-gate, we unveil an ambipolar conduction and electron-hole carrier (n-p) transition in ~7 septuple layer MnBi2Te4. A quadratic relation between the saturated AHE resistance and longitudinal resistance suggests its intrinsic AHE mechanism. For ~3 septuple layer MnBi2Te4, however, the AHE reverses its sign from pristine negative to positive under the electric-gating. The first-principles calculations demonstrate that such behavior is due to the competing Berry curvature between polarized spin-minority-dominated surface states and spin-majority-dominated inner-bands. Our results shed light on the physical mechanism of the gate-tunable intrinsic AHE in MnBi2Te4 thin films and provide a feasible approach to engineering its AHE.

cond-mat.mtrl-sci↗

arXiv2021

Real-to-Sim Registration of Deformable Soft Tissue with Position-Based Dynamics for Surgical Robot Autonomy

Autonomy in robotic surgery is very challenging in unstructured environments, especially when interacting with deformable soft tissues. The main difficulty is to generate model-based control methods that account for deformation dynamics during tissue manipulation. Previous works in vision-based perception can capture the geometric changes within the scene, however, model-based controllers integrated with dynamic properties, a more accurate and safe approach, has not been studied before. Considering the mechanic coupling between the robot and the environment, it is crucial to develop a registered, simulated dynamical model. In this work, we propose an online, continuous, real-to-sim registration method to bridge 3D visual perception with position-based dynamics (PBD) modeling of tissues. The PBD method is employed to simulate soft tissue dynamics as well as rigid tool interactions for model-based control. Meanwhile, a vision-based strategy is used to generate 3D reconstructed point cloud surfaces based on real-world manipulation, so as to register and update the simulation. To verify this real-to-sim approach, tissue experiments have been conducted on the da Vinci Research Kit. Our real-to-sim approach successfully reduces registration error online, which is especially important for safety during autonomous control. Moreover, it achieves higher accuracy in occluded areas than fusion-based reconstruction.

cs.RO↗

arXiv2021

Giant nonlinear anomalous Hall effect induced by spin-dependent band structure evolution

Anomalous Hall effect (AHE) is the key transport signature unlocking topological properties of magnetic materials. While AHE is usually proportional to the magnetization, the nonlinearity suggests the existence of complex magnetic and electron orders. Nonlinear AHE includes the topological Hall effect (THE) that has been widely used to identify the presence of spin chirality in real space. But it can in principle be induced by band structure evolution via Berry curvatures in the reciprocal space. This effect has been largely overlooked due to the intertwined mechanisms in both real and reciprocal spaces. Here, we observed a giant nonlinear AHE with the resistivity up to 383.5 uohm cm, contributing unprecedentedly 97% of the total Hall response in EuCd2As2. Moreover, it can be further enhanced by tilting the magnetic field 30° away from [001] direction, reaching a large anomalous Hall angle up to 21%. Although it shows exactly the same double-peak feature as THE, our scaling analysis and first-principles calculations reveal that the Berry phase is extremely sensitive to the spin canting, and nonlinear AHE is a consequence of band structure evolution under the external magnetic fields. When the spins gradually tilt from the in-plane antiferromagnetic ground state to out-of-plane direction, band crossing and band inversion occur, introducing a bandgap at Γ point at a canting angle of 45°. That contributes to the enhancement of Berry curvature and consequently a large intrinsic Hall conductivity. Our results unequivocally reveal the sensitive dependence of band structures on spin tilting process under external magnetic fields and its pronounced influence on the transport properties, which also need to be taken into consideration in other magnetic materials.

cond-mat.mtrl-sci↗

arXiv2021

EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks

Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and a multi-object GT image set. The EMDS-5 dataset has 21 types of EMs, each of which contains 20 original EM images, 20 single-object GT images and 20 multi-object GT images. EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. In order to prove the effectiveness of EMDS-5, for each function, we select the most representative algorithms and price indicators for testing and evaluation. The image preprocessing functions contain two parts: image denoising and image edge detection. Image denoising uses nine kinds of filters to denoise 13 kinds of noises, respectively. In the aspect of edge detection, six edge detection operators are used to detect the edges of the images, and two evaluation indicators, peak-signal to noise ratio and mean structural similarity, are used for evaluation. Image segmentation includes single-object image segmentation and multi-object image segmentation. Six methods are used for single-object image segmentation, while k-means and U-net are used for multi-object segmentation.We extract nine features from the images in EMDS-5 and use the Support Vector Machine classifier for testing. In terms of image classification, we select the VGG16 feature to test different classifiers. We test two types of retrieval approaches: texture feature retrieval and deep learning feature retrieval. We select the last layer of features of these two deep learning networks as feature vectors. We use mean average precision as the evaluation index for retrieval.

cs.CV↗

arXiv2021

Van der Waals Ferromagnetic Josephson Junctions

Superconductor-ferromagnet (S-F) interfaces in two-dimensional (2D) heterostructures present a unique opportunity to study the interplay between superconductivity and ferromagnetism. The realization of such nanoscale heterostructures in van der Waals (vdW) crystals remains largely unexplored due to the challenge of making an atomically-sharp interface from their layered structures. Here, we build a vdW ferromagnetic Josephson junction (JJ) by inserting a few-layer ferromagnetic insulator Cr2Ge2Te6 into two layers of superconductor NbSe2. Owing to the remanent magnetic moment of the barrier, the critical current and the corresponding junction resistance exhibit a hysteretic and oscillatory behavior against in-plane magnetic fields, manifesting itself as a strong Josephson coupling state. Through the control of this hysteresis, we can effectively trace the magnetic properties of atomic Cr2Ge2Te6 in response to the external magnetic field. Also, we observe a central minimum of critical current in some thick JJ devices, evidencing the coexistence of 0 and π phase coupling in the junction region. Our study paves the way to exploring the sensitive probes of weak magnetism and multifunctional building blocks for phase-related superconducting circuits with the use of vdW heterostructures.

cond-mat.mes-hall↗