arXiv subjects

Cheng Zeng

Publications and source records attributed to Cheng Zeng.

50 records · Page 3Linked to original sources

arXiv2024

Leveraging Large Language Model as Simulated Patients for Clinical Education

Simulated Patients (SPs) play a crucial role in clinical medical education by providing realistic scenarios for student practice. However, the high cost of training and hiring qualified SPs, along with the heavy workload and potential risks they face in consistently portraying actual patients, limit students' access to this type of clinical training. Consequently, the integration of computer program-based simulated patients has emerged as a valuable educational tool in recent years. With the rapid development of Large Language Models (LLMs), their exceptional capabilities in conversational artificial intelligence and role-playing have been demonstrated, making them a feasible option for implementing Virtual Simulated Patient (VSP). In this paper, we present an integrated model-agnostic framework called CureFun that harnesses the potential of LLMs in clinical medical education. This framework facilitates natural conversations between students and simulated patients, evaluates their dialogue, and provides suggestions to enhance students' clinical inquiry skills. Through comprehensive evaluations, our approach demonstrates more authentic and professional SP-scenario dialogue flows compared to other LLM-based chatbots, thus proving its proficiency in simulating patients. Additionally, leveraging CureFun's evaluation ability, we assess several medical LLMs and discuss the possibilities and limitations of using LLMs as virtual doctors from the perspective of their diagnostic abilities.

cs.CL↗

arXiv2024

Machine learning accelerated discovery of corrosion-resistant high-entropy alloys

Corrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional composition and configuration space, making materials designs via experimental trial-and-error or brute-force ab initio calculations almost impossible. Here we develop a physics-informed machine-learning framework to identify corrosion-resistant high-entropy alloys. Three metrics are used to evaluate the corrosion resistance, including single-phase formability, surface energy and Pilling-Bedworth ratios. We used random forest models to predict the single-phase formability, trained on an experimental dataset. Machine learning inter-atomic potentials were employed to calculate surface energies and Pilling-Bedworth ratios, which are trained on first-principles data fast sampled using embedded atom models. A combination of random forest models and high-fidelity machine learning potentials represents the first of its kind to relate chemical compositions to corrosion resistance of high-entropy alloys, paving the way for automatic design of materials with superior corrosion protection. This framework was demonstrated on AlCrFeCoNi high-entropy alloys and we identified composition regions with high corrosion resistance. Machine learning predicted lattice constants and surface energies are consistent with values by first-principles calculations. The predicted single-phase formability and corrosion-resistant compositions of AlCrFeCoNi agree well with experiments. This framework is general in its application and applicable to other materials, enabling high-throughput screening of material candidates and potentially reducing the turnaround time for integrated computational materials engineering.

cond-mat.mtrl-sci↗

arXiv2023

Analyze the Robustness of Classifiers under Label Noise

This study explores the robustness of label noise classifiers, aiming to enhance model resilience against noisy data in complex real-world scenarios. Label noise in supervised learning, characterized by erroneous or imprecise labels, significantly impairs model performance. This research focuses on the increasingly pertinent issue of label noise's impact on practical applications. Addressing the prevalent challenge of inaccurate training data labels, we integrate adversarial machine learning (AML) and importance reweighting techniques. Our approach involves employing convolutional neural networks (CNN) as the foundational model, with an emphasis on parameter adjustment for individual training samples. This strategy is designed to heighten the model's focus on samples critically influencing performance.

cs.LG↗

arXiv2023

Analyze the robustness of three NMF algorithms (Robust NMF with L1 norm, L2-1 norm NMF, L2 NMF)

Non-negative matrix factorization (NMF) and its variants have been widely employed in clustering and classification tasks (Long, & Jian , 2021). However, noises can seriously affect the results of our experiments. Our research is dedicated to investigating the noise robustness of non-negative matrix factorization (NMF) in the face of different types of noise. Specifically, we adopt three different NMF algorithms, namely L1 NMF, L2 NMF, and L21 NMF, and use the ORL and YaleB data sets to simulate a series of experiments with salt-and-pepper noise and Block-occlusion noise separately. In the experiment, we use a variety of evaluation indicators, including root mean square error (RMSE), accuracy (ACC), and normalized mutual information (NMI), to evaluate the performance of different NMF algorithms in noisy environments. Through these indicators, we quantify the resistance of NMF algorithms to noise and gain insights into their feasibility in practical applications.

cs.LG↗

arXiv2023

High Performance Thin-film Lithium Niobate Modulator Applied ITO Composite Electrode with Modulation Efficiency of 1V*cm

Thin film lithium niobate (TFLN) based electro-optic modulator is widely applied in the field of broadband optical communications due to its advantages such as large bandwidth, high extinction ratio, and low optical loss, bringing new possibilities for the next generation of high-performance electro-optic modulators. However, the modulation efficiency of TFLN modulators is still relatively low when compared with Silicon and Indium-Phosphide (InP) based competitors. Due to the restriction of the trade-off between half-wave voltage and modulation length, it is difficult to simultaneously obtain low driving voltage and large modulating bandwidth. Here, we break this limitation by introducing Transparent Conductive Oxide (TCO) film, resulting in an ultra-high modulation efficiency of 1.02 V*cm in O-Band. The fabricated composite electrode not only achieves high modulation efficiency but also maintains a high electro-optic bandwidth, as demonstrated by the 3 dB roll-off at 108 GHz and the transmission of PAM-4 signals at 224 Gbit/s. Our device presents new solutions for the next generation of low-cost high-performance electro-optic modulators. Additionally, it paves the way for downsizing TFLN-based multi-channel optical transmitter chips.

physics.optics↗

arXiv2023

The phase stability of large-size nanoparticle alloy catalysts at ab initio quality using a nearsighted force-training approach

CoPt nanoparticle catalysts are integral to commercial fuel cells. Such systems are prohibitive to fully characterize with electronic structure calculations. Machine-learned potentials offer a scalable solution; however, such potentials are only reliable if representative training data can be employed, which typically requires large electronic structure calculations. Here, we use the nearsighted-force training approach to make high-fidelity machine-learned predictions on large nanoparticles with $>$5,000 atoms using only systematically generated small structures ranging from 38-168 atoms. The resulting ensemble model shows good accuracy and transferability in describing relative energetics for CoPt nanoparticles with various shapes, sizes and Co compositions. It is found that the fcc(100) surface is more likely to form a L1$_0$ ordered structure than the fcc(111) surface. The energy convex hull of the icosahedron shows the most stable particles have Pt-rich skins and Co-rich underlayers. Although the truncated octahedron is the most stable shape across all sizes of Pt nanoparticles, a crossover to icosahedron exists due to a large downshift of surface energy for CoPt nanoparticle alloys. The downshift can be attributed to strain release on the icosahedron surface due to Co alloying. We introduced a simple empirical model to describe the role of Co alloying in the crossover for CoPt nanoparticles. With Monte-Carlo simulations we additionally searched for the most stable atomic arrangement for a truncated octahedron with equal Pt and Co compositions, and also we studied its order-disorder phase transition. We validated the most stable configurations with a new highly scalable density functional theory code called SPARC. Lastly, the order-disorder phase transition for a CoPt nanoparticle exhibits a lower transition temperature and a smoother transition, compared to the bulk CoPt alloy.

cond-mat.mtrl-sci↗

arXiv2023

Depth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization

Recently, methods for neural surface representation and rendering, for example NeuS, have shown that learning neural implicit surfaces through volume rendering is becoming increasingly popular and making good progress. However, these methods still face some challenges. Existing methods lack a direct representation of depth information, which makes object reconstruction unrestricted by geometric features, resulting in poor reconstruction of objects with texture and color features. This is because existing methods only use surface normals to represent implicit surfaces without using depth information. Therefore, these methods cannot model the detailed surface features of objects well. To address this problem, we propose a neural implicit surface learning method called Depth-NeuS based on depth information optimization for multi-view reconstruction. In this paper, we introduce depth loss to explicitly constrain SDF regression and introduce geometric consistency loss to optimize for low-texture areas. Specific experiments show that Depth-NeuS outperforms existing technologies in multiple scenarios and achieves high-quality surface reconstruction in multiple scenarios.

cs.CV↗

arXiv2023

Consistent Model-based Clustering: using the Quasi-Bernoulli Stick-Breaking Process

In mixture modeling and clustering applications, the number of components and clusters is often not known. A stick-breaking mixture model, such as the Dirichlet process mixture model, is an appealing construction that assumes infinitely many components, while shrinking the weights of most of the unused components to near zero. However, it is well-known that this shrinkage is inadequate: even when the component distribution is correctly specified, spurious weights appear and give an inconsistent estimate of the number of clusters. In this article, we propose a simple solution: when breaking each mixture weight stick into two pieces, the length of the second piece is multiplied by a quasi-Bernoulli random variable, taking value one or a small constant close to zero. This effectively creates a soft-truncation and further shrinks the unused weights. Asymptotically, we show that as long as this small constant diminishes to zero at a rate faster than $o(1/n^2)$, with $n$ the sample size, the posterior distribution will converge to the true number of clusters. In comparison, we rigorously explore Dirichlet process mixture models using a concentration parameter that is either constant or rapidly diminishes to zero -- both of which lead to inconsistency for the number of clusters. Our proposed model is easy to implement, requiring only a small modification of a standard Gibbs sampler for mixture models. In simulations and a data application of clustering brain networks, our proposed method recovers the ground-truth number of clusters, and leads to a small number of clusters.

stat.ME↗

arXiv2022

FedTADBench: Federated Time-Series Anomaly Detection Benchmark

Time series anomaly detection strives to uncover potential abnormal behaviors and patterns from temporal data, and has fundamental significance in diverse application scenarios. Constructing an effective detection model usually requires adequate training data stored in a centralized manner, however, this requirement sometimes could not be satisfied in realistic scenarios. As a prevailing approach to address the above problem, federated learning has demonstrated its power to cooperate with the distributed data available while protecting the privacy of data providers. However, it is still unclear that how existing time series anomaly detection algorithms perform with decentralized data storage and privacy protection through federated learning. To study this, we conduct a federated time series anomaly detection benchmark, named FedTADBench, which involves five representative time series anomaly detection algorithms and four popular federated learning methods. We would like to answer the following questions: (1)How is the performance of time series anomaly detection algorithms when meeting federated learning? (2) Which federated learning method is the most appropriate one for time series anomaly detection? (3) How do federated time series anomaly detection approaches perform on different partitions of data in clients? Numbers of results as well as corresponding analysis are provided from extensive experiments with various settings. The source code of our benchmark is publicly available at https://github.com/fanxingliu2020/FedTADBench.

cs.LG↗

arXiv2022

Thin-Film Lithium Niobate based Dual-Polarization IQ modulator for Single-Carrier 1.6 Tb/s Transmission

We successfully demonstrate a monolithic integrated dual-polarization (DP) IQ modulator based on thin-film lithium niobate (TFLN) platform with a silicon substrate, which consists of IQ modulators, spot-size converters (SSCs) and a polarization rotator combiner (PRC). After coupled with polarization maintaining fibers, the measured insertion loss of the modulator is 12 dB. In addition, we experimentally achieve a single-carrier 1.6 Tb/s net bitrate transmission.

physics.app-ph↗

arXiv2020

High-efficient and polarization independent edge coupler for thin-film lithium niobite waveguide devices

Lithium niobate (LN) devices have been widely used in optical communication and nonlinear optics due to its attractive optical properties. The emergence of thin-film lithium niobate on insulator (LNOI) improves performances of LN-based devices greatly. However, a high-efficient fiber-chip optical coupler is still necessary for the LNOI-based devices for practical applications. In this paper, we demonstrate a highly efficient and polarization-independent edge coupler based on LNOI. The coupler, fabricated by standard semiconductor process, shows a low fiber-chip coupling loss of 0.54 dB/0.59 dB per facet at 1550 nm for TE/TM light respectively, when coupled with ultra-high numerical aperture fiber (UHNAF) of which mode field diameter is about 3.2 micrometers. The coupling loss is lower than 1dB/facet for both TE and TM light at wavelengths longer than 1527nm. A relatively large tolerance for optical misalignment is also proved. The coupler shows a promising stability in high optical power and temperature variation.

physics.optics↗

arXiv2019

Many could be better than all: A novel instance-oriented algorithm for Multi-modal Multi-label problem

With the emergence of diverse data collection techniques, objects in real applications can be represented as multi-modal features. What's more, objects may have multiple semantic meanings. Multi-modal and Multi-label (MMML) problem becomes a universal phenomenon. The quality of data collected from different channels are inconsistent and some of them may not benefit for prediction. In real life, not all the modalities are needed for prediction. As a result, we propose a novel instance-oriented Multi-modal Classifier Chains (MCC) algorithm for MMML problem, which can make convince prediction with partial modalities. MCC extracts different modalities for different instances in the testing phase. Extensive experiments are performed on one real-world herbs dataset and two public datasets to validate our proposed algorithm, which reveals that it may be better to extract many instead of all of the modalities at hand.

cs.LG↗

arXiv2018

A Logarithmic Barrier Method For Proximal Policy Optimization

Proximal policy optimization(PPO) has been proposed as a first-order optimization method for reinforcement learning. We should notice that an exterior penalty method is used in it. Often, the minimizers of the exterior penalty functions approach feasibility only in the limits as the penalty parameter grows increasingly large. Therefore, it may result in the low level of sampling efficiency. This method, which we call proximal policy optimization with barrier method (PPO-B), keeps almost all advantageous spheres of PPO such as easy implementation and good generalization. Specifically, a new surrogate objective with interior penalty method is proposed to avoid the defect arose from exterior penalty method. Conclusions can be draw that PPO-B is able to outperform PPO in terms of sampling efficiency since PPO-B achieved clearly better performance on Atari and Mujoco environment than PPO.

cs.LG↗

arXiv2017

Contact Mechanics of a Small Icosahedral Virus

Virus binding to a surface results at least locally, at the contact area, in stress and potential structural perturbation of the virus cage. Here we address the question of the role of substrate-induced deformation in the overall virus mechanical response to the adsorption event. This question may be especially important for the broad category of viruses that have their shells stabilized by weak, non-covalent interactions. We utilize atomic force microscopy to measure the height change distributions of the brome mosaic virus upon adsorption from liquid on atomically flat substrates and present a continuum model which captures well the behavior. Height data fitting according the model provides, without recourse to indentation, estimates of virus elastic properties and of the interfacial energy.

q-bio.QM↗