Style Transfer: From Stitching to Neural Networks

This article compares two style transfer methods in image processing: the traditional method, which synthesizes new images by stitching together small patches from existing images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely to the background. The traditional method excels in creating artistic abstractions but can struggle with seamlessness, whereas the machine learning method preserves the integrity of foreground elements while enhancing the background, offering improved aesthetic quality and computational efficiency. Our study indicates that machine learning-based methods are more suited for real-world applications where detail preservation in foreground elements is essential.

Quantitative Evaluation of the Saliency Map for Alzheimer's Disease Classifier with Anatomical Segmentation

Saliency maps have been widely used to interpret deep learning classifiers for Alzheimer's disease (AD). However, since AD is heterogeneous and has multiple subtypes, the pathological mechanism of AD remains not fully understood and may vary from patient to patient. Due to the lack of such understanding, it is difficult to comprehensively and effectively assess the saliency map of AD classifier. In this paper, we utilize the anatomical segmentation to allocate saliency values into different brain regions. By plotting the distributions of saliency maps corresponding to AD and NC (Normal Control), we can gain a comprehensive view of the model's decisions process. In order to leverage the fact that the brain volume shrinkage happens in AD patients during disease progression, we define a new evaluation metric, brain volume change score (VCS), by computing the average Pearson correlation of the brain volume changes and the saliency values of a model in different brain regions for each patient. Thus, the VCS metric can help us gain some knowledge of how saliency maps resulting from different models relate to the changes of the volumes across different regions in the whole brain. We trained candidate models on the ADNI dataset and tested on three different datasets. Our results indicate: (i) models with higher VCSs tend to demonstrate saliency maps with more details relevant to the AD pathology, (ii) using gradient-based adversarial training strategies such as FGSM and stochastic masking can improve the VCSs of the models.

Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods

We study the matrix denoising problem of estimating the singular vectors of a rank-$1$ signal corrupted by noise with both column and row correlations. Existing works are either unable to pinpoint the exact asymptotic estimation error or, when they do so, the resulting approaches (e.g., based on whitening or singular value shrinkage) remain vastly suboptimal. On top of this, most of the literature has focused on the special case of estimating the left singular vector of the signal when the noise only possesses row correlation (one-sided heteroscedasticity). In contrast, our work establishes the information-theoretic and algorithmic limits of matrix denoising with doubly heteroscedastic noise. We characterize the exact asymptotic minimum mean square error, and design a novel spectral estimator with rigorous optimality guarantees: under a technical condition, it attains positive correlation with the signals whenever information-theoretically possible and, for one-sided heteroscedasticity, it also achieves the Bayes-optimal error. Numerical experiments demonstrate the significant advantage of our theoretically principled method with the state of the art. The proofs draw connections with statistical physics and approximate message passing, departing drastically from standard random matrix theory techniques.

FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction

Mobile devices and the Internet of Things (IoT) devices nowadays generate a large amount of heterogeneous spatial-temporal data. It remains a challenging problem to model the spatial-temporal dynamics under privacy concern. Federated learning (FL) has been proposed as a framework to enable model training across distributed devices without sharing original data which reduce privacy concern. Personalized federated learning (PFL) methods further address data heterogenous problem. However, these methods don't consider natural spatial relations among nodes. For the sake of modeling spatial relations, Graph Neural Netowork (GNN) based FL approach have been proposed. But dynamic spatial-temporal relations among edge nodes are not taken into account. Several approaches model spatial-temporal dynamics in a centralized environment, while less effort has been made under federated setting. To overcome these challeges, we propose a novel Federated Adaptive Spatial-Temporal Attention (FedASTA) framework to model the dynamic spatial-temporal relations. On the client node, FedASTA extracts temporal relations and trend patterns from the decomposed terms of original time series. Then, on the server node, FedASTA utilize trend patterns from clients to construct adaptive temporal-spatial aware graph which captures dynamic correlation between clients. Besides, we design a masked spatial attention module with both static graph and constructed adaptive graph to model spatial dependencies among clients. Extensive experiments on five real-world public traffic flow datasets demonstrate that our method achieves state-of-art performance in federated scenario. In addition, the experiments made in centralized setting show the effectiveness of our novel adaptive graph construction approach compared with other popular dynamic spatial-temporal aware methods.

Application of RESNET50 Convolution Neural Network for the Extraction of Optical Parameters in Scattering Media

Estimation of the optical properties of scattering media such as tissue is important in diagnostics as well as in the development of techniques to image deeper. As light penetrates the sample scattering events occur that alter the propagation direction of the photons in a random manner leading degradation of image quality. The distribution of the scattered light does, however, give a measure of the optical properties such as the reduced scattering coefficient and the absorption coefficient. Unfortunately, inverting scattering patterns to recover the optical properties is not simple, especially in the regime where the light is partially randomized. Machine learning has been proposed by several authors as a means of recovering these properties from either the back scattered or the transmitted light. In the present paper, we train a general purpose convolutional neural network RESNET 50 with simulated data based on Monte Carlo simulations. We show that compared with previous work our approach gives comparable or better reconstruction accuracy with training on a much smaller dataset. Moreover, by training on multiple parameters such as the intensity distribution at multiple planes or the exit angle and spatial distribution one achieves improved performance compared to training on a single input such as the intensity distribution captured at the sample surface. While our approach gives good parameter reconstruction, we identify factors that limit the accuracy of the recovered properties, particularly the absorption coefficient. In the light of these limitations, we suggest how the present approach may be enhanced for even better performance.

MedTransformer: Accurate AD Diagnosis for 3D MRI Images through 2D Vision Transformers

Automated diagnosis of AD in brain images is becoming a clinically important technique to support precision and efficient diagnosis and treatment planning. A few efforts have been made to automatically diagnose AD in magnetic resonance imaging (MRI) using three-dimensional CNNs. However, due to the complexity of 3D models, the performance is still unsatisfactory, both in terms of accuracy and efficiency. To overcome the complexities of 3D images and 3D models, in this study, we aim to attack this problem with 2D vision Transformers. We propose a 2D transformer-based medical image model with various transformer attention encoders to diagnose AD in 3D MRI images, by cutting the 3D images into multiple 2D slices.The model consists of four main components: shared encoders across three dimensions, dimension-specific encoders, attention across images from the same dimension, and attention across three dimensions. It is used to obtain attention relationships among multiple sequences from different dimensions (axial, coronal, and sagittal) and multiple slices. We also propose morphology augmentation, an erosion and dilation based method to increase the structural difference between AD and normal images. In this experiment, we use multiple datasets from ADNI, AIBL, MIRAID, OASIS to show the performance of our model. Our proposed MedTransformer demonstrates a strong ability in diagnosing AD. These results demonstrate the effectiveness of MedTransformer in learning from 3D data using a much smaller model and its capability to generalize among different medical tasks, which provides a possibility to help doctors diagnose AD in a simpler way.

On the Communication Complexity of Decentralized Bilevel Optimization

Decentralized bilevel optimization has been actively studied in the past few years since it has widespread applications in machine learning. However, existing algorithms suffer from large communication complexity caused by the estimation of stochastic hypergradient, limiting their application to real-world tasks. To address this issue, we develop a novel decentralized stochastic bilevel gradient descent algorithm under the heterogeneous setting, which enjoys a small communication cost in each round and small communication rounds. As such, it can achieve a much better communication complexity than existing algorithms. Moreover, we extend our algorithm to the more challenging decentralized multi-level optimization. To the best of our knowledge, this is the first time achieving these theoretical results under the heterogeneous setting. At last, the experimental results confirm the efficacy of our algorithm.

Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing

We consider the problem of parameter estimation from observations given by a generalized linear model. Spectral methods are a simple yet effective approach for estimation: they estimate the parameter via the principal eigenvector of a matrix obtained by suitably preprocessing the observations. Despite their wide use, a rigorous performance characterization of spectral estimators, as well as a principled way to preprocess the data, is available only for unstructured (i.e., i.i.d. Gaussian and Haar) designs. In contrast, real-world design matrices are highly structured and exhibit non-trivial correlations. To address this problem, we consider correlated Gaussian designs which capture the anisotropic nature of the measurements via a feature covariance matrix $\Sigma$. Our main result is a precise asymptotic characterization of the performance of spectral estimators in this setting. This then allows to identify the optimal preprocessing that minimizes the number of samples needed to meaningfully estimate the parameter. Remarkably, such an optimal spectral estimator depends on $\Sigma$ only through its normalized trace, which can be consistently estimated from the data. Numerical results demonstrate the advantage of our principled approach over previous heuristic methods. Existing analyses of spectral estimators crucially rely on the rotational invariance of the design matrix. This key assumption does not hold for correlated Gaussian designs. To circumvent this difficulty, we develop a novel strategy based on designing and analyzing an approximate message passing algorithm whose fixed point coincides with the desired spectral estimator. Our methodology is general, and opens the way to the precise characterization of spiked matrices and of the corresponding spectral methods in a variety of settings.

Can ChatGPT Pass An Introductory Level Functional Language Programming Course?

The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in solving a diverse range of tasks, including language translation, text summarization, and computer programming. Its capability for writing, modifying, and even correcting code together with its ease of use and access is already dramatically impacting computer science education. This paper aims to explore how well ChatGPT can perform in an introductory-level functional language programming course. In our systematic evaluation, we treated ChatGPT as one of our students and demonstrated that it can achieve a grade B- and its rank in the class is 155 out of 314 students overall. Our comprehensive evaluation provides valuable insights into ChatGPT's impact from both student and instructor perspectives. Additionally, we identify several potential benefits that ChatGPT can offer to both groups. Overall, we believe that this study significantly clarifies and advances our understanding of ChatGPT's capabilities and potential impact on computer science education.

Can Decentralized Stochastic Minimax Optimization Algorithms Converge Linearly for Finite-Sum Nonconvex-Nonconcave Problems?

Decentralized minimax optimization has been actively studied in the past few years due to its application in a wide range of machine learning models. However, the current theoretical understanding of its convergence rate is far from satisfactory since existing works only focus on the nonconvex-strongly-concave problem. This motivates us to study decentralized minimax optimization algorithms for the nonconvex-nonconcave problem. To this end, we develop two novel decentralized stochastic variance-reduced gradient descent ascent algorithms for the finite-sum nonconvex-nonconcave problem that satisfies the Polyak-{\L}ojasiewicz (PL) condition. In particular, our theoretical analyses demonstrate how to conduct local updates and perform communication to achieve the linear convergence rate. To the best of our knowledge, this is the first work achieving linear convergence rates for decentralized nonconvex-nonconcave problems. Finally, we verify the performance of our algorithms on both synthetic and real-world datasets. The experimental results confirm the efficacy of our algorithms.