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34th Medical Informatics Europe Conference, MIE 2024

作者： Sadeghi, Sina Khodaei, Maryam Hempel, Lars Kirsten, Toralf Department for Medical Data Science Leipzig University Medical Center Leipzig Germany Institute for Medical Informatics Statistics and Epidemiology Leipzig University Leipzig Germany Faculty Applied Computer and Bio Sciences Mittweida University of Applied Sciences Mittweida Germany

ISBN: (纸本)9781643685335

Defacing of brain magnetic resonance imaging (MRI) scans is a crucial process in medical imaging research aimed at preserving patient privacy while maintaining data integrity. However, existing defacing algorithms are prone to errors, potentially compromising patient anonymity. This paper investigates the feasibility and efficacy of automated quality assessment for defaced brain MRIs using machine learning (ML). Our findings demonstrate the promising capability of ML models in accurately distinguishing between properly and inadequately defaced MRI scans. © 2024 The Authors.

关键词： Anonymity

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realSEUDO for real-time calcium imaging analysis 38

realSEUDO for real-time calcium imaging analysis

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dmitrieva, Iuliia Babkin, Sergey Charles, Adam S. Applied Math and Statistics Johns Hopkins University BaltimoreMD21218 United States Microsoft Research RedmondWA United States Biomedical Data Science Center for Imaging Science Mathematical Institute for Data Science Kavli Neuroscience Discovery Institute Johns Hopkins University BaltimoreMD21218 United States

Closed-loop neuroscience experimentation, where recorded neural activity is used to modify the experiment on-the-fly, is critical for deducing causal connections and optimizing experimental time. A critical step in creating a closed-loop experiment is real-time inference of neural activity from streaming recordings. One challenging modality for real-time processing is multi-photon calcium imaging (CI). CI enables the recording of activity in large populations of neurons however, often requires batch processing of the video data to extract single-neuron activity from the fluorescence videos. We use the recently proposed robust time-trace estimator-Sparse Emulation of Unused Dictionary Objects (SEUDO) algorithm-as a basis for a new on-line processing algorithm that simultaneously identifies neurons in the fluorescence video and infers their time traces in a way that is robust to as-yet unidentified neurons. To achieve real-time SEUDO (realSEUDO), we optimize the core estimator via both algorithmic improvements and an fast C-based implementation, and create a new cell finding loop to enable realSEUDO to also identify new cells. We demonstrate comparable performance to offline algorithms (e.g., CNMF), and improved performance over the current on-line approach (OnACID) at speeds of 120 Hz on average. © 2024 Neural information processing systems foundation. All rights reserved.

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Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms 3

Bitcoin price prediction based on financial data, technical ...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Arjmand, Masoud Kazeminia, Saman Sajedi, Hedieh University of Tehran Kish International Campus Department of Computer Engineering Kish Iran NHL Stenden University of Applied Science Department of Computer Vision and Data Science Leeuwarden Netherlands University of Tehran Department of Mathematics Statistics and Computer Science Tehran Iran

ISBN: (纸本)9798350381580

Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model's efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results. © 2024 IEEE.

关键词： Forecasting

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A permutation-free kernel independence test

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 17707-17774页

作者： Shubhanshu Shekhar Ilmun Kim Aaditya Ramdas Department of Statistics and Data Science Carnegie Mellon University Pittsburgh PA Department of Statistics and Data Science Department of Applied Statistics Yonsei University Seodaemun-gu Seoul Republic of Korea Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University Pittsburgh PA

In nonparametric independence testing, we observe i.i.d. data {(Xi, Yi)}ni=1, where X ∈ Χ, Y ∈ Y lie in any general spaces, and we wish to test the null that X is independent of Y. Modern test statistics such as the kernel Hilbert-Schmidt Independence Criterion (HSIC) and Distance Covariance (dCov) have intractable null distributions due to the degeneracy of the underlying U-statistics. Hence, in practice, one often resorts to using permutation testing, which provides a nonasymptotic guarantee at the expense of recalculating the quadratic-time statistics (say) a few hundred times. In this paper, we provide a simple but nontrivial modification of HSIC and dCov (called xHSIC and xdCov, pronounced "cross" HSIC/dCov) so that they have a limiting Gaussian distribution under the null, and thus do not require permutations. We show that our new tests, like the originals, are consistent against fixed alternatives, and minimax rate optimal against smooth local alternatives. Numerical simulations demonstrate that compared to the permutation tests, our variants have the same power within a constant factor, giving practitioners a new option for large problems or data-analysis pipelines where computation, not sample size, could be the bottleneck.

关键词： independence testing kernel-methods permutation-free tests Hilbert-Schmidt independence critrion (HSIC) distance covariance

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KTI-RNN:Recognition of Heart Failure from Clinical Notes

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Tsinghua science and Technology 2023年第1期28卷 117-130页

作者： Dengao Li Huiting Ma Wenjing Li Baofeng Zhao Jumin Zhao Yi Liu Jian Fu College of Data Science Taiyuan University of TechnologyJinzhong 030600China Department of Statistics and Applied Probability University of CaliforniaSanta BarbaraCA 93106USA College of Mining Engineering Taiyuan University of TechnologyTaiyuan 030024China College of Information and Computer Taiyuan University of TechnologyJinzhong 030600China

Although deep learning methods have recently attracted considerable attention in the medical field,analyzing large-scale electronic health record data is still a difficult *** particular,the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment *** study uses data from the Medical Information Mart for Intensive Care *** with structured data,unstructured data contain abundant patient ***,this type of data has unsatisfactory characteristics,e.g.,many colloquial vocabularies and sparse *** solve these problems,we propose the KTI-RNN model for unstructured data *** proposed model overcomes sparse content and obtains good classification *** term frequency-inverse word frequency(TF-IWF)model is used to extract the keyword *** latent dirichlet allocation(LDA)model is adopted to extract the topic word *** models enable the expansion of the medical record text ***,we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network(BiRNN)model and the output *** call it gated-attention-BiRNN(GA-BiRNN)and use it to identify heart failure from extensive medical *** show that the F 1 score of the proposed KTI-RNN model is 85.57%,and the accuracy rate of the proposed KTI-RNN model is 85.59%.

关键词： heart failure diagnosis text classification deep learning

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Empirical Likelihood Inference for the Semiparametric Varying-Coefficient Spatial Autoregressive Model

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Journal of Systems science & Complexity 2021年第6期34卷 2310-2333页

作者： LUO Guowang WU Mixia PANG Zhen College of Big Data Statistics Guizhou University of Finance and EconomicsGuiyang 550025China College of Statistics and Data Science Faculty of ScienceBeijing University of TechnologyBeijing 100124China Department of Applied Mathematics Hong Kong Polytechnic UniversityHong KongChina

In this paper empirical likelihood(EL)-based inference for a semiparametric varyingcoefficient spatial autoregressive model is *** maximum EL estimators for the parametric component and the nonparametric component are ***,asymptotic properties of the proposed estimators and EL ratios are derived,and the corresponding confidence regions/bands are *** finite sample performances are studied via simulation and an example.

关键词： Confidence regions empirical likelihood instrumental variable residual-adjusted Wilks theorem

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A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks 41

A Theory of Non-Linear Feature Learning with One Gradient St...

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41st International Conference on Machine Learning, ICML 2024

作者： Moniri, Behrad Lee, Donghwan Hassani, Hamed Dobriban, Edgar Department of Electrical and Systems Engineering University of Pennsylvania PA United States Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PA United States Department of Statistics and Data Science University of Pennsylvania PA United States

Feature learning is thought to be one of the fundamental reasons for the success of deep neural networks. It is rigorously known that in two-layer fully-connected neural networks under certain conditions, one step of gradient descent on the first layer can lead to feature learning;characterized by the appearance of a separated rank-one component-spike-in the spectrum of the feature matrix. However, with a constant gradient descent step size, this spike only carries information from the linear component of the target function and therefore learning non-linear components is impossible. We show that with a learning rate that grows with the sample size, such training in fact introduces multiple rank-one components, each corresponding to a specific polynomial feature. We further prove that the limiting large-dimensional and large sample training and test errors of the updated neural networks are fully characterized by these spikes. By precisely analyzing the improvement in the training and test errors, we demonstrate that these non-linear features can enhance learning. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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HIGH-DIMENSIONAL SGD ALIGNS WITH EMERGING OUTLIER EIGENSPACES 12

HIGH-DIMENSIONAL SGD ALIGNS WITH EMERGING OUTLIER EIGENSPACE...

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12th International Conference on Learning Representations, ICLR 2024

作者： Arous, Gérard Ben Gheissari, Reza Huang, Jiaoyang Jagannath, Aukosh Courant Institute New York University New YorkNY United States Department of Mathematics Northwestern University EvanstonIL United States Department of Statistics and Data Science University of Pennsylvania PhiladelphiaPA United States Department of Statistics and Actuarial Science Department of Applied Mathematics Cheriton School of Computer Science University of Waterloo WaterlooON Canada

We rigorously study the joint evolution of training dynamics via stochastic gradient descent (SGD) and the spectra of empirical Hessian and gradient matrices. We prove that in two canonical classification tasks for multi-class high-dimensional mixtures and either 1 or 2-layer neural networks, the SGD trajectory rapidly aligns with emerging low-rank outlier eigenspaces of the Hessian and gradient matrices. Moreover, in multi-layer settings this alignment occurs per layer, with the final layer's outlier eigenspace evolving over the course of training, and exhibiting rank deficiency when the SGD converges to sub-optimal classifiers. This establishes some of the rich predictions that have arisen from extensive numerical studies in the last decade about the spectra of Hessian and information matrices over the course of training in overparametrized networks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： statistics

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Advances in Temporal Point Processes: Bayesian, Deep, and LLM Approaches

arXiv

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arXiv 2025年

作者： Zhou, Feng Kong, Quyu Zhang, Yixuan Center for Applied Statistics School of Statistics Renmin University of China China Alibaba Cloud China School of Statistics and Data Science Southeast University China

Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time. Traditional statistical TPPs have a long-standing history, with numerous models proposed and successfully applied across diverse domains. In recent years, advances in deep learning have spurred the development of neural TPPs, enabling greater flexibility and expressiveness in capturing complex temporal dynamics. The emergence of large language models (LLMs) has further sparked excitement, offering new possibilities for modeling and analyzing event sequences by leveraging their rich contextual understanding. This survey presents a comprehensive review of recent research on TPPs from three perspectives: Bayesian, deep learning, and LLM approaches. We begin with a review of the fundamental concepts of TPPs, followed by an in-depth discussion of model design and parameter estimation techniques in these three frameworks. We also revisit classic application areas of TPPs to highlight their practical relevance. Finally, we outline challenges and promising directions for future research. © 2025, CC BY.

关键词： Stochastic models

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Combining Structural and Unstructured data: A Topic-based Finite Mixture Model for Insurance Claim Prediction

arXiv

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arXiv 2024年

作者： Hou, Yanxi Xia, Xiaolan Gao, Guangyuan School of Data Science Fudan University China Center for Applied Statistics School of Statistics Renmin University of China China

Modeling insurance claim amounts and classifying claims into different risk levels are critical yet challenging tasks. Traditional predictive models for insurance claims often overlook the valuable information embedded in claim descriptions. This paper introduces a novel approach by developing a joint mixture model that integrates both claim descriptions and claim amounts. Our method establishes a probabilistic link between textual descriptions and loss amounts, enhancing the accuracy of claims clustering and prediction. In our proposed model, the latent topic/component indicator serves as a proxy for both the thematic content of the claim description and the component of loss distributions. Specifically, conditioned on the topic/component indicator, the claim description follows a multinomial distribution, while the claim amount follows a component loss distribution. We propose two methods for model calibration: an EM algorithm for maximum a posteriori estimates, and an MH-within-Gibbs sampler algorithm for the posterior distribution. The empirical study demonstrates that the proposed methods work effectively, providing interpretable claims clustering and prediction. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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