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Noisy Recovery in Unlimited Sampling via Adaptive Modulo Representations

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Noisy Recovery in Unlimited Sampling via Adaptive Modulo Rep...

International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa)

作者： Felipe Pagginelli Patricio Paul Catala Felix Krahmer Dept. of Mathematics TU Munich Garching Germany IBMI Helmholtz Munich Neuherberg Germany Dept. of Mathematics&Munich Data Science Institute TU Munich and Munich Center for Machine Learning Garching

ISBN: (数字)9798350365504

ISBN: (纸本)9798350365511

Recent works put forth the Unlimited Sensing Framework (USF), a novel approach to analog-to-digital conversion for high dynamic range sensing. It addresses the saturation phenomenon that commonly arises when physical measurements exceed the dynamic range of a sensor, yielding permanent loss of the input data. However, the USF still has some limitations when dealing with random noise. In the present paper, we propose a novel iterative method to tackle unlimited sensing in a noisy setting. In one step, our approach applies local transformations of the range to remove strong artifacts caused by the noise on local subdivisions of the domain. In the following step, the signal is then approximated via a least squares method. These two types of steps are then alternated. We illustrate the performances of our algorithm in high noise regime.

关键词： Multimodal sensors Conferences Noise Imaging Radar imaging Loss measurement High dynamic range Noise measurement Iterative methods Least mean squares methods

Multi-Scale Clinical-Guided Binocular Fusion Framework for Predicting New-Onset Hypertension Over a Four-Year Period

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Multi-Scale Clinical-Guided Binocular Fusion Framework for P...

IEEE International Symposium on Biomedical Imaging

作者： Haoshen Li Zifan Chen Jie Zhao Heyun Chen Hexin Dong Mingze Yuan Bin Dong Li Zhang Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Hypertension is a major global health concern, linked to various cardiovascular diseases and associated with distinct ocular manifestations. While recent advances in artificial intelligence have enabled accurate diagnosis of current hypertension through fundus images, predicting the future onset of hypertension remains an uncharted domain. In this study, we introduce the multi-scale clinical-guided binocular fusion framework (MCBO), designed to predict the likelihood of developing hypertension within the next four years. MCBO uniquely integrates left and right fundus images and clinical data, utilizing a shared-weight multi-stage Transformer-based encoder. Our multi-scale clinical-guided module (MCM) ensures image feature extraction is clinically contextualized based on clinical information, and our binocular fusion module (BFM) fuses binocular information. Comparative performance against seven baseline models establishes MCBO’s supremacy, with improvements of 6.7% in Area Under Curve (AUC), 6.9% in Accuracy (ACC), 5.1% in Sensitivity (SEN) and 5.5% in Specificity (SPE). This approach offers a promising avenue for proactive hypertension management, underscoring the potential of integrating Deep learning with clinical data for enhanced healthcare outcomes. Our code is available at https://***/HaoshenLi/MCBO.

关键词： Hypertension Deep learning Accuracy Sensitivity Codes Fuses Feature extraction

Audio-based Kinship Verification Using Age Domain Conversion

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arXiv 2024年

作者： Sun, Qiyang Akman, Alican Jing, Xin Milling, Manuel Schuller, Björn W. GLAM Department of Computing Imperial College London United Kingdom MRI Technical University of Munich Germany MDSI – Munich Data Science Institute Munich Germany MCML – Munich Center for Machine Learning Munich Germany

Audio-based kinship verification (AKV) is important in many domains, such as home security monitoring, forensic identification, and social network analysis. A key challenge in the task arises from differences in age across samples from different individuals, which can be interpreted as a domain bias in a cross-domain verification task. To address this issue, we design the notion of an "age-standardised domain" wherein we utilise the optimised CycleGAN-VC3 network to perform age-audio conversion to generate the in-domain audio. The generated audio dataset is employed to extract a range of features, which are then fed into a metric learning architecture to verify kinship. Experiments are conducted on the KAN_AV audio dataset, which contains age and kinship labels. The results demonstrate that the method markedly enhances the accuracy of kinship verification, while also offering novel insights for future kinship verification *** Codes 68T10 © 2024, CC BY-NC-SA.

关键词： Economic and social effects

Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition

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arXiv 2024年

作者： Triantafyllopoulos, Andreas Schuller, Björn MRI Technical University of Munich Germany MCML - Munich Center for Machine Learning Germany MDSI - Munich Data Science Institute Germany GLAM - Group on Language Audio & Music Imperial College London United Kingdom

The expression of emotion is highly individualistic. However, contemporary speech emotion recognition (SER) systems typically rely on population-level models that adopt a 'one-size-fits-all' approach for predicting emotion. Moreover, standard evaluation practices measure performance also on the population level, thus failing to characterise how models work across different speakers. In the present contribution, we present a new method for capitalising on individual differences to adapt an SER model to each new speaker using a minimal set of enrolment utterances. In addition, we present novel evaluation schemes for measuring fairness across different speakers. Our findings show that aggregated evaluation metrics may obfuscate fairness issues on the individual-level, which are uncovered by our evaluation, and that our proposed method can improve performance both in aggregated and disaggregated terms. © 2024, CC BY-NC-ND.

关键词： Emotion Recognition

A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification

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arXiv 2024年

作者： Marks, Markus Knott, Manuel Kondapaneni, Neehar Cole, Elijah Defraeye, Thijs Perez-Cruz, Fernando Perona, Pietro California Institute of Technology United States ETH Zurich Institute for Machine Learning Department of Computer Science Switzerland Swiss Data Science Center ETH Zurich and EPFL Switzerland Empa Swiss Federal Laboratories for Materials Science and Technology Switzerland Altos Labs Switzerland

Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data's inherent structure or context by solving a pretext task. With SSL, models can learn from abundant and cheap unlabeled data, significantly reducing the cost of training models where labels are expensive or inaccessible. In Computer Vision, SSL is widely used as pre-training followed by a downstream task, such as supervised transfer, few-shot learning on smaller labeled data sets, and/or unsupervised clustering. Unfortunately, it is infeasible to evaluate SSL methods on all possible downstream tasks and objectively measure the quality of the learned representation. Instead, SSL methods are evaluated using in-domain evaluation protocols, such as fine-tuning, linear probing, and k-nearest neighbors (kNN). However, it is not well understood how well these evaluation protocols estimate the representation quality of a pre-trained model for different downstream tasks under different conditions, such as dataset, metric, and model architecture. In this work, we study how classification-based evaluation protocols for SSL correlate and how well they predict downstream performance on different dataset types. Our study includes eleven common image datasets and 26 models that were pre-trained with different SSL methods or have different model backbones. We find that in-domain linear/kNN probing protocols are, on average, the best general predictors for out-of-domain performance. We further investigate the importance of batch normalization for the various protocols and evaluate how robust correlations are for different kinds of dataset domain shifts. In addition, we challenge assumptions about the relationship between discriminative and generative self-supervised methods, finding that most of their performance differences can be explained by changes to model backbones. Copyright © 2024, The

关键词： Nearest neighbor search

The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks

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arXiv 2024年

作者： Bonfanti, Andrea Bruno, Giuseppe Cipriani, Cristina BMW AG Basque Center for Applied Mathematics University of the Basque Country Digital Campus Munich Spain BMW AG Digital Campus Munich Germany Technical University of Munich Munich Center for Machine Learning Munich Data Science Institute Germany

The Neural Tangent Kernel (NTK) viewpoint is widely employed to analyze the training dynamics of overparameterized Physics-Informed Neural Networks (PINNs). However, unlike the case of linear Partial Differential Equations (PDEs), we show how the NTK perspective falls short in the nonlinear scenario. Specifically, we establish that the NTK yields a random matrix at initialization that is not constant during training, contrary to conventional belief. Another significant difference from the linear regime is that, even in the idealistic infinite-width limit, the Hessian does not vanish and hence it cannot be disregarded during training. This motivates the adoption of second-order optimization methods. We explore the convergence guarantees of such methods in both linear and nonlinear cases, addressing challenges such as spectral bias and slow convergence. Every theoretical result is supported by numerical examples with both linear and nonlinear PDEs, and we highlight the benefits of second-order methods in benchmark test cases. © 2024, CC BY.

关键词： Numerical methods

Nonparametric inference of higher order interaction patterns in networks

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arXiv 2024年

作者： Wegner, Anatol E. Olhede, Sofia C. Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science University of Würzburg Würzburg97070 Germany Institute of Mathematics École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland

We propose a method for obtaining parsimonious decompositions of networks into higher order interactions which can take the form of arbitrary motifs. The method is based on a class of analytically solvable generative models, where vertices are connected via explicit copies of motifs, which in combination with non-parametric priors allow us to infer higher order interactions from dyadic graph data without any prior knowledge on the types or frequencies of such interactions. Crucially, we also consider’degree–corrected’ models that correctly reflect the degree distribution of the network and consequently prove to be a better fit for many real world–networks compared to non-degree corrected models. We test the presented approach on simulated data for which we recover the set of underlying higher order interactions to a high degree of accuracy. For empirical networks the method identifies concise sets of atomic subgraphs from within thousands of candidates that cover a large fraction of edges and include higher order interactions of known structural and functional significance. The method not only produces an explicit higher order representation of the network but also a fit of the network to analytically tractable models opening new avenues for the systematic study of higher order network structures. Copyright © 2024, The Authors. All rights reserved.

关键词： machine learning

CENTRAL LIMIT THEOREMS FOR SMOOTH OPTIMAL TRANSPORT MAPS

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arXiv 2023年

作者： Manole, Tudor Balakrishnan, Sivaraman Niles-Weed, Jonathan Wasserman, Larry Statistics and Data Science Center Massachusetts Institute of Technology United States Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Center for Data Science New York University United States Courant Institute of Mathematical Sciences New York University United States

One of the central objects in the theory of optimal transport is the Brenier map: the unique monotone transformation which pushes forward an absolutely continuous probability law onto any other given law. A line of recent work has analyzed L2 convergence rates of plugin estimators of Brenier maps, which are defined as the Brenier map between density estimators of the underlying distributions. In this work, we show that such estimators satisfy a pointwise central limit theorem when the underlying laws are supported on the flat torus of dimension d ≥ 3. We also derive a negative result, showing that these estimators do not converge weakly in L2 when the dimension is sufficiently large. Our proofs hinge upon a quantitative linearization of the Monge-Ampère equation, which may be of independent interest. This result allows us to reduce our problem to that of deriving limit laws for the solution of a uniformly elliptic partial differential equation with a stochastic right-hand side, subject to periodic boundary conditions. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multimodal data

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Signal Transduction and Targeted Therapy 2024年第9期9卷 4137-4148页

作者： Zifan Chen Yang Chen Yu Sun Lei Tang Li Zhang Yajie Hu Meng He Zhiwei Li Siyuan Cheng Jiajia Yuan Zhenghang Wang Yakun Wang Jie Zhao Jifang Gong Liying Zhao Baoshan Cao Guoxin Li Xiaotian Zhang Bin Dong Lin Shen Center for Data Science Peking UniversityBeijingChina Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Pathology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Department of General Surgery Nanfang HospitalSouthern Medical UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor GuangzhouChina Department of Medical Oncology and Radiation Sickness Peking University Third HospitalBeijingChina National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Beijing International Center for Mathematical Research(BICMR) Peking UniversityBeijingChina Center for Machine Learning Research Peking UniversityBeijingChina

The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.

关键词： immunotherapy HER2 cancer