Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning

This project intends to study a cardiovascular disease risk early warning model based on one-dimensional convolutional neural networks. First, the missing values of 13 physiological and symptom indicators such as patient age, blood glucose, cholesterol, and chest pain were filled and Z-score was standardized. The convolutional neural network is converted into a 2D matrix, the convolution function of 1,3, and 5 is used for the first-order convolution operation, and the Max Pooling algorithm is adopted for dimension reduction. Set the learning rate and output rate. It is optimized by the Adam algorithm. The result of classification is output by a soft classifier. This study was conducted based on Statlog in the UCI database and heart disease database respectively. The empirical data indicate that the forecasting precision of this technique has been enhanced by 11.2%, relative to conventional approaches, while there is a significant improvement in the logarithmic curve fitting. The efficacy and applicability of the novel approach are corroborated through the examination employing a one-dimensional convolutional neural network.

Model X-ray:Detect Backdoored Models via Decision Boundary

Deep neural networks (DNNs) have revolutionized various industries, leading to the rise of Machine Learning as a Service (MLaaS). In this paradigm, well-trained models are typically deployed through APIs. However, DNNs are susceptible to backdoor attacks, which pose significant risks to their applications. This vulnerability necessitates a method for users to ascertain whether an API is compromised before usage. Although many backdoor detection methods have been developed, they often operate under the assumption that the defender has access to specific information such as details of the attack, soft predictions from the model API, and even the knowledge of the model parameters, limiting their practicality in MLaaS scenarios. To address it, in this paper, we begin by presenting an intriguing observation: the decision boundary of the backdoored model exhibits a greater degree of closeness than that of the clean model. Simultaneously, if only one single label is infected, a larger portion of the regions will be dominated by the attacked label. Building upon this observation, we propose Model X-ray, a novel backdoor detection approach for MLaaS through the analysis of decision boundaries. Model X-ray can not only identify whether the target API is infected by backdoor attacks but also determine the target attacked label under the all-to-one attack strategy. Importantly, it accomplishes this solely by the hard prediction of clean inputs, regardless of any assumptions about attacks and prior knowledge of the training details of the model. Extensive experiments demonstrated that Model X-ray can be effective for MLaaS across diverse backdoor attacks, datasets, and architectures.

Neural-Optic Co-Designed Polarization-Multiplexed Metalens for Compact Computational Spectral Imaging

As the realm of spectral imaging applications extends its reach into the domains of mobile technology and augmented reality, the demands for compact yet high-fidelity systems become increasingly pronounced. Conventional methodologies, exemplified by coded aperture snapshot spectral imaging systems, are significantly limited by their cumbersome physical dimensions and form factors. To address this inherent challenge, diffractive optical elements (DOEs) have been repeatedly employed as a means to mitigate issues related to the bulky nature of these systems. Nonetheless, it's essential to note that the capabilities of DOEs primarily revolve around the modulation of the phase of light. Here, we introduce an end-to-end computational spectral imaging framework based on a polarization-multiplexed metalens. A distinguishing feature of this approach lies in its capacity to simultaneously modulate orthogonal polarization channels. When harnessed in conjunction with a neural network, it facilitates the attainment of high-fidelity spectral reconstruction. Importantly, the framework is intrinsically fully differentiable, a feature that permits the joint optimization of both the metalens structure and the parameters governing the neural network. The experimental results presented herein validate the exceptional spatial-spectral reconstruction performance, underscoring the efficacy of this system in practical, real-world scenarios. This innovative approach transcends the traditional boundaries separating hardware and software in the realm of computational imaging and holds the promise of substantially propelling the miniaturization of spectral imaging systems.

FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) has achieved promising results while relying on sufficient annotation data in a specific domain. However, it is infeasible to annotate data for each individual domain. We propose to explore ASTE in the cross-domain setting, which transfers knowledge from a resource-rich source domain to a resource-poor target domain, thereby alleviating the reliance on labeled data in the target domain. To effectively transfer the knowledge across domains and extract the sentiment triplets accurately, we propose a method named Fine-grained cOntrAstive Learning (FOAL) to reduce the domain discrepancy and preserve the discriminability of each category. Experiments on six transfer pairs show that FOAL achieves 6% performance gains and reduces the domain discrepancy significantly compared with strong baselines. Our code will be publicly available once accepted.

C-Silicon-based metasurfaces for aperture-robust spectrometer/imaging with angle integration

Compared with conventional grating-based spectrometers, reconstructive spectrometers based on spectrally engineered filtering have the advantage of miniaturization because of the less demand for dispersive optics and free propagation space. However, available reconstructive spectrometers fail to balance the performance on operational bandwidth, spectral diversity and angular stability. In this work, we proposed a compact silicon metasurfaces based spectrometer/camera. After angle integration, the spectral response of the system is robust to angle/aperture within a wide working bandwidth from 400nm to 800nm. It is experimentally demonstrated that the proposed method could maintain the spectral consistency from F/1.8 to F/4 (The corresponding angle of incident light ranges from 7{\deg} to 16{\deg}) and the incident hyperspectral signal could be accurately reconstructed with a fidelity exceeding 99%. Additionally, a spectral imaging system with 400x400 pixels is also established in this work. The accurate reconstructed hyperspectral image indicates that the proposed aperture-robust spectrometer has the potential to be extended as a high-resolution broadband hyperspectral camera.

Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is widely used in various applications. However, existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering the advancement of research in this area. In this paper, we introduce a new dataset, named DMASTE, which is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews for the task. The dataset includes various lengths, diverse expressions, more aspect types, and more domains than existing datasets. We conduct extensive experiments on DMASTE in multiple settings to evaluate previous ASTE approaches. Empirical results demonstrate that DMASTE is a more challenging ASTE dataset. Further analyses of in-domain and cross-domain settings provide promising directions for future research. Our code and dataset are available at https://github.com/NJUNLP/DMASTE.

Continuous Trajectory Optimization via B-splines for Multi-jointed Robotic Systems

Continuous formulations of trajectory planning problems have two main benefits. First, constraints are guaranteed to be satisfied at all times. Secondly, dynamic obstacles can be naturally considered with time. This paper introduces a novel B-spline based trajectory optimization method for multi-jointed robots that provides a continuous trajectory with guaranteed continuous constraints satisfaction. At the core of this method, B-spline basic operations, like addition, multiplication, and derivative, are rigorously defined and applied for problem formulation. B-spline unique characteristics, such as the convex hull and smooth curves properties, are utilized to reformulate the original continuous optimization problem into a finite-dimensional problem. Collision avoidance with static obstacles is achieved using the signed distance field, while that with dynamic obstacles is accomplished via constructing time-varying separating hyperplanes. Simulation results on various robots validate the effectiveness of the algorithm. In addition, this paper provides experimental validations with a 6-link FANUC robot avoiding static and moving obstacles.

A Consistent Independence Test for Multivariate Time-Series

A fundamental problem in statistical data analysis is testing whether two phenomena are related. When the phenomena in question are time series, many challenges emerge. The first is defining a dependence measure between time series at the population level, as well as a sample level test statistic. The second is computing or estimating the distribution of this test statistic under the null, as the permutation test procedure is invalid for most time series structures. This work aims to address these challenges by combining distance correlation and multiscale graph correlation (MGC) from independence testing literature and block permutation testing from time series analysis. Two hypothesis tests for testing the independence of time series are proposed. These procedures also characterize whether the dependence relationship between the series is linear or nonlinear, and the time lag at which this dependence is maximized. For strictly stationary auto-regressive moving average (ARMA) processes, the proposed independence tests are proven valid and consistent. Finally, neural connectivity in the brain is analyzed using fMRI data, revealing linear dependence of signals within the visual network and default mode network, and nonlinear relationships in other regions. This work opens up new theoretical and practical directions for many modern time series analysis problems.