arXiv subjects

Tianyu Zhao

Publications and source records attributed to Tianyu Zhao.

At least 91 records · Page 5Linked to original sources

arXiv2024

DoseGNN: Improving the Performance of Deep Learning Models in Adaptive Dose-Volume Histogram Prediction through Graph Neural Networks

Dose-Volume Histogram (DVH) prediction is fundamental in radiation therapy that facilitate treatment planning, dose evaluation, plan comparison and etc. It helps to increase the ability to deliver precise and effective radiation treatments while managing potential toxicities to healthy tissues as needed to reduce the risk of complications. This paper extends recently disclosed research findings presented on AAPM (AAPM 65th Annual Meeting $\&$ Exhibition) and includes necessary technique details. The objective is to design efficient deep learning models for DVH prediction on general radiotherapy platform equipped with high performance CBCT system, where input CT images and target dose images to predict may have different origins, spacing and sizes. Deep learning models widely-adopted in DVH prediction task are evaluated on the novel radiotherapy platform, and graph neural networks (GNNs) are shown to be the ideal architecture to construct a plug-and-play framework to improve predictive performance of base deep learning models in the adaptive setting.

cs.LG↗

arXiv2023

High-speed image reconstruction for nonlinear structured illumination microscopy

By exploiting the nonlinear responses of the fluorescent probes, the spatial resolution of structured illumination microscopy(SIM) can be further increased. However, due to the complex reconstruction process, the traditional reconstruction method of nonlinear structured illumination microscopy (NL-SIM) is relatively slow, which brings a great challenge to realizing real-time display of super-resolution results. To address these issues, an accelerated NL-SIM reconstruction algorithm was developed by extending a high-speed reconstruction framework, Joint Space and Frequency Reconstruction (JSFR) to NL-SIM. We anticipate that this algorithm will facilitate NL- SIM becoming a routine tool in biomedical laboratories.

physics.optics↗

arXiv2023

Versatile manipulation of low-refractive-index particles using customized optical building blocks

Low-refractive-index (LRI) particles play significant roles in physics, drug delivery, biomedical science, and other fields. However, they have not attained sufficient utilization in active manipulation due to the repulsive effect of light. Here, we demonstrate the establishment of optical building blocks (OBBs) to fulfill the demands of versatile manipulation of LRI particles. The OBBs are generated by assembling generalized perfect optical vortices based on the free lens modulation (FLM) method, by which the beams shape, intensity, and position can be elaborately designed with size independent of topological charge. Using the OBBs with high quality and high efficiency, we realized rotating LRI particles along arbitrary trajectories with controllable speed and parallel manipulation of multiple LRI particles. Importantly, we further achieved the sorting of LRI particles by size with specially structured OBBs. With unprecedented flexibility and quality, OBBs provide tremendous potential in optical trapping, lithography, and biomedicine.

physics.optics↗

arXiv2023

Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework

In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner. Consequently, we develop a novel M ulti-granularity Graph Disentangled Learning framework named MGDL to effectively perform intelligent matching of fund investment products. Benefiting from the well-established fund graph and the attention module, multi-granularity user representations are derived from historical behaviors to separately express personal interest, conformity and risk preference in a fine-grained way. To attain stronger disentangled representations with specific semantics, MGDL explicitly involve two self-supervised signals, i.e., fund type based contrasts and fund popularity. Extensive experiments in offline and online environments verify the effectiveness of MGDL.

cs.LG↗

arXiv2023

FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems

Achieving fairness in sequential-decision making systems within Human-in-the-Loop (HITL) environments is a critical concern, especially when multiple humans with different behavior and expectations are affected by the same adaptation decisions in the system. This human variability factor adds more complexity since policies deemed fair at one point in time may become discriminatory over time due to variations in human preferences resulting from inter- and intra-human variability. This paper addresses the fairness problem from an equity lens, considering human behavior variability, and the changes in human preferences over time. We propose FAIRO, a novel algorithm for fairness-aware sequential-decision making in HITL adaptation, which incorporates these notions into the decision-making process. In particular, FAIRO decomposes this complex fairness task into adaptive sub-tasks based on individual human preferences through leveraging the Options reinforcement learning framework. We design FAIRO to generalize to three types of HITL application setups that have the shared adaptation decision problem. Furthermore, we recognize that fairness-aware policies can sometimes conflict with the application's utility. To address this challenge, we provide a fairness-utility tradeoff in FAIRO, allowing system designers to balance the objectives of fairness and utility based on specific application requirements. Extensive evaluations of FAIRO on the three HITL applications demonstrate its generalizability and effectiveness in promoting fairness while accounting for human variability. On average, FAIRO can improve fairness compared with other methods across all three applications by 35.36%.

cs.LG↗

arXiv2023

FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-System

Ensuring fairness in decision-making systems within Human-Cyber-Physical-Systems (HCPS) is a pressing concern, particularly when diverse individuals, each with varying behaviors and expectations, coexist within the same application space, influenced by a shared set of control actions in the system. The long-term adverse effects of these actions further pose the challenge, as historical experiences and interactions shape individual perceptions of fairness. This paper addresses the challenge of fairness from an equity perspective of adverse effects, taking into account the dynamic nature of human behavior and evolving preferences while recognizing the lasting impact of adverse effects. We formally introduce the concept of Fairness-in-Adverse-Effects (FinA) within the HCPS context. We put forth a comprehensive set of five formulations for FinA, encompassing both the instantaneous and long-term aspects of adverse effects. To empirically validate the effectiveness of our FinA approach, we conducted an evaluation within the domain of smart homes, a pertinent HCPS application. The outcomes of our evaluation demonstrate that the adoption of FinA significantly enhances the overall perception of fairness among individuals, yielding an average improvement of 66.7% when compared to the state-of-the-art method.

cs.AI↗

arXiv2023

Taiji Data Challenge for Exploring Gravitational Wave Universe

The direct observation of gravitational waves (GWs) opens a new window for exploring new physics from quanta to cosmos and provides a new tool for probing the evolution of universe. GWs detection in space covers a broad spectrum ranging over more than four orders of magnitude and enables us to study rich physical and astronomical phenomena. Taiji is a proposed space-based GW detection mission that will be launched in the 2030s. Taiji will be exposed to numerous overlapping and persistent GW signals buried in the foreground and background, posing various data analysis challenges. In order to empower potential scientific discoveries, the Mock LISA Data Challenge and the LISA Data Challenge (LDC) were developed. While LDC provides a baseline framework, the first LDC needs to be updated with more realistic simulations and adjusted detector responses for Taiji's constellation. In this paper, we review the scientific objectives and the roadmap for Taiji, as well as the technical difficulties in data analysis and the data generation strategy, and present the associated data challenges. In contrast to LDC, we utilize second-order Keplerian orbit and second-generation time delay interferometry techniques. Additionally, we employ a new model for the extreme-mass-ratio inspiral waveform and stochastic GW background spectrum, which enables us to test general relativity and measure the non-Gaussianity of curvature perturbations. Furthermore, we present a comprehensive showcase of parameter estimation using a toy dataset. This showcase not only demonstrates the scientific potential of the Taiji Data Challenge but also serves to validate the effectiveness of the pipeline. As the first data challenge for Taiji, we aim to build an open ground for data analysis related to Taiji sources and sciences. More details can be found on the official website at http://taiji-tdc.ictp-ap.org.

gr-qc↗

arXiv2023

Space-based gravitational wave signal detection and extraction with deep neural network

Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios.

gr-qc↗

arXiv2023

Focused Prefix Tuning for Controllable Text Generation

In a controllable text generation dataset, there exist unannotated attributes that could provide irrelevant learning signals to models that use it for training and thus degrade their performance. We propose focused prefix tuning(FPT) to mitigate the problem and to enable the control to focus on the desired attribute. Experimental results show that FPT can achieve better control accuracy and text fluency than baseline models in single-attribute control tasks. In multi-attribute control tasks, FPT achieves comparable control accuracy with the state-of-the-art approach while keeping the flexibility to control new attributes without retraining existing models.

cs.CL↗

arXiv2023

Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems

Ensuring solution feasibility is a key challenge in developing Deep Neural Network (DNN) schemes for solving constrained optimization problems, due to inherent DNN prediction errors. In this paper, we propose a ``preventive learning'' framework to guarantee DNN solution feasibility for problems with convex constraints and general objective functions without post-processing, upon satisfying a mild condition on constraint calibration. Without loss of generality, we focus on problems with only inequality constraints. We systematically calibrate inequality constraints used in DNN training, thereby anticipating prediction errors and ensuring the resulting solutions remain feasible. We characterize the calibration magnitudes and the DNN size sufficient for ensuring universal feasibility. We propose a new Adversarial-Sample Aware training algorithm to improve DNN's optimality performance without sacrificing feasibility guarantee. Overall, the framework provides two DNNs. The first one from characterizing the sufficient DNN size can guarantee universal feasibility while the other from the proposed training algorithm further improves optimality and maintains DNN's universal feasibility simultaneously. We apply the framework to develop DeepOPF+ for solving essential DC optimal power flow problems in grid operation. Simulation results over IEEE test cases show that it outperforms existing strong DNN baselines in ensuring 100% feasibility and attaining consistent optimality loss ($<$0.19%) and speedup (up to $\times$228) in both light-load and heavy-load regimes, as compared to a state-of-the-art solver. We also apply our framework to a non-convex problem and show its performance advantage over existing schemes.

cs.LG↗

arXiv2022

Development of a Full Monte Carlo Therapeutic Dose Calculation Toolkit for Halcyon Using Geant4

Purpose: To develop a Monte Carlo (MC) therapeutic dose calculation toolkit of a recently released ring gantry linac in Geant4 (Version 10.7) for secondary dose validation of radiotherapy plan. Methods: For the Halcyon (Varian Medical Systems), the DSMLC was modeled and radiation transport in DSMLC and patient phantom was simulated using Geant4. Radiation source was sampled from a phase space file for linac head above the DSMLC. The phase space file was obtained using a cloud-based Monte Carlo (MC) simulator, VirtuaLinac (VL) provide by Varian. Dosimetric profiles for different square field widths (2x2, 4x4, 6x6, 8x8, 10x10, 20x20, and 28x28 cm2), i.e., percent depth dose (PDD) curves and lateral profiles are simulated and compared against the experimental profiles. IMRT (intensity modulated radiation therapy) plans in two anatomical sites (prostate and brain) were also calculated using the developed toolkit and compared against the TPS calculated dose (Acuros, Eclipse 15.6). 3D dose difference and 3D gamma analysis were used to evaluate the simulation accuracy compared against the TPS calculated dose. Results: The simulated lateral dose profiles and PDD curves in water phantom match well with the measured ones for all the simulated field sizes with relative difference +-2%. For the prostate and brain IMRT plans, the simulated dose showed a good agreement with the TPS calculated dose. The 3D gamma pass rate (3%/3mm) are 98.08% and 95.4% for the two prostate and brain plans, respectively. Conclusion: The developed full MC dose calculation toolkit for Halcyon performs well in dose calculations in water phantom and patient CT phantom. The developed toolkit shows promising possibility for future secondary dose calculation for IMRT and serve as clinical quality assurance (QA) tool for Halcyon.

physics.med-ph↗

arXiv2022

Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa

Text sentiment analysis, also known as opinion mining, is research on the calculation of people's views, evaluations, attitude and emotions expressed by entities. Text sentiment analysis can be divided into text-level sentiment analysis, sen-tence-level sentiment analysis and aspect-level sentiment analysis. Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the field of sentiment analysis, which aims to predict the polarity of aspects. The research of pre-training neural model has significantly improved the performance of many natural language processing tasks. In recent years, pre training model (PTM) has been applied in ABSA. Therefore, there has been a question, which is whether PTMs contain sufficient syntactic information for ABSA. In this paper, we explored the recent DeBERTa model (Decoding-enhanced BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis problem. DeBERTa is a kind of neural language model based on transformer, which uses self-supervised learning to pre-train on a large number of original text corpora. Based on the Local Context Focus (LCF) mechanism, by integrating DeBERTa model, we purpose a multi-task learning model for aspect-based sentiment analysis. The experiments result on the most commonly used the laptop and restaurant datasets of SemEval-2014 and the ACL twitter dataset show that LCF mechanism with DeBERTa has significant improvement.

cs.CL↗

arXiv2022

DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems

High percentage penetrations of renewable energy generations introduce significant uncertainty into power systems. It requires grid operators to solve alternative current optimal power flow (AC-OPF) problems more frequently for economical and reliable operation in both transmission and distribution grids. In this paper, we develop a Deep Neural Network (DNN) approach, called DeepOPF, for solving AC-OPF problems in a fraction of the time used by conventional solvers. A key difficulty for applying machine learning techniques for solving AC-OPF problems lies in ensuring that the obtained solutions respect the equality and inequality physical and operational constraints. Generalized the 2-stage procedure in [1], [2], DeepOPF first trains a DNN model to predict a set of independent operating variables and then directly compute the remaining dependable ones by solving power flow equations. Such an approach not only preserves the power-flow balance equality constraints but also reduces the number of variables to predict by the DNN, cutting down the number of neurons and training data needed. DeepOPF then employs a penalty approach with a zero-order gradient estimation technique in the training process to preserve the remaining inequality constraints. As another contribution, we drive a condition for tuning the size of the DNN according to the desired approximation accuracy, which measures the DNN generalization capability. It provides theoretical justification for using DNN to solve the AC-OPF problem. Simulation results of IEEE 30/118/300-bus and a synthetic 2000-bus test cases show that DeepOPF speeds up the computing time by up to two orders of magnitude as compared to a state-of-the-art solver, at the expense of $<$0.1% cost difference.

eess.SY↗

arXiv2022

End-to-End Text-to-Speech Based on Latent Representation of Speaking Styles Using Spontaneous Dialogue

The recent text-to-speech (TTS) has achieved quality comparable to that of humans; however, its application in spoken dialogue has not been widely studied. This study aims to realize a TTS that closely resembles human dialogue. First, we record and transcribe actual spontaneous dialogues. Then, the proposed dialogue TTS is trained in two stages: first stage, variational autoencoder (VAE)-VITS or Gaussian mixture variational autoencoder (GMVAE)-VITS is trained, which introduces an utterance-level latent variable into variational inference with adversarial learning for end-to-end text-to-speech (VITS), a recently proposed end-to-end TTS model. A style encoder that extracts a latent speaking style representation from speech is trained jointly with TTS. In the second stage, a style predictor is trained to predict the speaking style to be synthesized from dialogue history. During inference, by passing the speaking style representation predicted by the style predictor to VAE/GMVAE-VITS, speech can be synthesized in a style appropriate to the context of the dialogue. Subjective evaluation results demonstrate that the proposed method outperforms the original VITS in terms of dialogue-level naturalness.

eess.AS↗

arXiv2022

Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network

Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse architectures and applied scenarios. Besides, in the research community of HGNNs, implementing and evaluating various tasks still need much human effort. To mitigate these issues, we first propose a unified framework covering most HGNNs, consisting of three components: heterogeneous linear transformation, heterogeneous graph transformation, and heterogeneous message passing layer. Then we build a platform Space4HGNN by defining a design space for HGNNs based on the unified framework, which offers modularized components, reproducible implementations, and standardized evaluation for HGNNs. Finally, we conduct experiments to analyze the effect of different designs. With the insights found, we distill a condensed design space and verify its effectiveness.

cs.LG↗

arXiv2022

A Digital Twin Framework for Cyber Security in Cyber-Physical Systems

Currently, most of the research in digital twins focuses on simulation and optimization. Digital twins are especially useful for critical systems. However, digital twins can also be used for safety and cyber security. The idea of this paper is motivated by the limitations of cyber security in Cyber-Physical Systems (CPSs). We introduce an efficient synchronization approach to maintain the state between the virtual environment and the physical environment. In this case, we can receive prompt feedback by conducting security analysis in the virtual domain. Thus, helping to enhance the cyber security of CPSs, we propose a digital twin-based framework. Based on the approach, the security of the CPSs can be protected by the digital twin system. Moreover, the proposed architecture has also been optimized to meet the security requirements and maintain less network burden for CPSs

cs.CR↗

arXiv2022

An adaptive neuro-fuzzy model for attitude estimation and 2 control a 3 DOF system

In recent decades, one of the scientists' main concerns has been to improve the accuracy of satellite attitude, regardless of the expense. The obvious result is that a large number of control strategies have been used to address this problem. In this study, an adaptive neuro-fuzzy integrated (ANFIS) satellite attitude estimation and control system was developed. The controller is trained with the data provided by an optimal controller. A pulse modulator is used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the AN-FIS controller in closed-loop simulation, an ANFIS observer is used to estimate the attitude and angular velocities of the satellite using magnetometer, sun sensor and data gyro data. In addition, a new ANFIS system will be proposed and evaluated that can jointly control and estimate the system. The performance of the ANFIS controller is compared to the optimal PID controller in a Monte Carlo simulation with different initial conditions, disturbance and noise. The results show that the ANFIS controller can surpass the optimal PID controller in several aspects, including time and smoothness. In addition, the ANFIS estimator is examined and the results demonstrate the high ability of this designated observers. Both the control and estimation phases are simulated by a single ANFIS subsystem, taking into account the high capacity of ANFIS, and the results of using the ANFIS model are demonstrated.

eess.SY↗

arXiv2021

Weaving Attention U-net: A Novel Hybrid CNN and Attention-based Method for Organs-at-risk Segmentation in Head and Neck CT Images

In radiotherapy planning, manual contouring is labor-intensive and time-consuming. Accurate and robust automated segmentation models improve the efficiency and treatment outcome. We aim to develop a novel hybrid deep learning approach, combining convolutional neural networks (CNNs) and the self-attention mechanism, for rapid and accurate multi-organ segmentation on head and neck computed tomography (CT) images. Head and neck CT images with manual contours of 115 patients were retrospectively collected and used. We set the training/validation/testing ratio to 81/9/25 and used the 10-fold cross-validation strategy to select the best model parameters. The proposed hybrid model segmented ten organs-at-risk (OARs) altogether for each case. The performance of the model was evaluated by three metrics, i.e., the Dice Similarity Coefficient (DSC), Hausdorff distance 95% (HD95), and mean surface distance (MSD). We also tested the performance of the model on the Head and Neck 2015 challenge dataset and compared it against several state-of-the-art automated segmentation algorithms. The proposed method generated contours that closely resemble the ground truth for ten OARs. Our results of the new Weaving Attention U-net demonstrate superior or similar performance on the segmentation of head and neck CT images.

eess.IV↗