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In this paper, we introduce a new adaptive data analysis method to study trend and instantaneous frequency of nonlinear and non-stationary data. This method is inspired by the Empirical Mode Decomposition method (EMD) and the recently…

Numerical Analysis · Mathematics 2012-02-28 Thomas Y. hou , Zuoqiang Shi

Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called to rescue. In this setting, analytics tasks become very…

Databases · Computer Science 2020-03-17 Fotis Savva , Christos Anagnostopoulos , Peter Triantafillou

We consider the problem of modeling high-speed flows using machine learning methods. While most prior studies focus on low-speed fluid flows in which uniform time-stepping is practical, flows approaching and exceeding the speed of sound…

Stochastic resetting, the procedure of stopping and re-initializing random processes, has recently emerged as a powerful tool for accelerating processes ranging from queuing systems to molecular simulations. However, its usefulness is…

Statistical Mechanics · Physics 2025-03-18 Tommer D. Keidar , Ofir Blumer , Barak Hirshberg , Shlomi Reuveni

Training a neural network (NN) typically relies on some type of curve-following method, such as gradient descent (GD) (and stochastic gradient descent (SGD)), ADADELTA, ADAM or limited memory algorithms. Convergence for these algorithms…

Machine Learning · Computer Science 2023-05-08 Michael A Kouritzin , Stephen Styles , Beatrice-Helen Vritsiou

Firm disclosures about future prospects are crucial for corporate valuation and compliance with global regulations, such as the EU's MAR and the US's SEC Rule 10b-5 and RegFD. To comply with disclosure obligations, issuers must identify…

Statistical Finance · Quantitative Finance 2023-11-21 Moritz Scherrmann , Ralf Elsas

Mouse-tracking recording techniques are becoming very attractive in experimental psychology. They provide an effective means of enhancing the measurement of some real-time cognitive processes involved in categorization, decision-making, and…

Applications · Statistics 2019-12-18 Antonio Calcagnì , Luigi Lombardi , Marco D'Alessandro

It is not an exaggeration to say that the recent progress in artificial intelligence technology depends on large-scale and high-quality data. Simultaneously, a prevalent issue exists everywhere: the budget for data labeling is constrained.…

Machine Learning · Computer Science 2023-08-22 Yujin Hwang , Won Jo , Juyoung Hong , Yukyung Choi

First-order optimization methods are crucial for solving large-scale data processing problems, particularly those involving convex non-smooth composite objectives. For such problems with convex non-smooth composite objectives, we introduce…

Optimization and Control · Mathematics 2025-10-06 Endrit Dosti , Sergiy A. Vorobyov , Themistoklis Charalambous

We follow up on the study of correlations between GDP's of rich countries. We analyze web-downloaded data on GDP that we use as individual wealth signatures of the country economical state. We calculate the yearly fluctuations of the GDP.…

Physics and Society · Physics 2012-09-25 M. Ausloos , R. Lambiotte

Neural networks are prone to learn easy solutions from superficial statistics in the data, namely shortcut learning, which impairs generalization and robustness of models. We propose a data augmentation strategy, named DFM-X, that leverages…

Computer Vision and Pattern Recognition · Computer Science 2023-08-15 Shunxin Wang , Christoph Brune , Raymond Veldhuis , Nicola Strisciuglio

Current approaches for activity recognition often ignore constraints on computational resources: 1) they rely on extensive feature computation to obtain rich descriptors on all frames, and 2) they assume batch-mode access to the entire test…

Computer Vision and Pattern Recognition · Computer Science 2016-04-05 Yu-Chuan Su , Kristen Grauman

The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks…

Methodology · Statistics 2023-11-10 Anna Malinovskaya , Pavlo Mozharovskyi , Philipp Otto

Deep learning models often require large amounts of data for training, leading to increased costs. It is particularly challenging in medical imaging, i.e., gathering distributed data for centralized training, and meanwhile, obtaining…

Computer Vision and Pattern Recognition · Computer Science 2023-06-27 Zhenyu Tang , Shaoting Zhang , Xiaosong Wang

When we can not assume a large amount of annotated data , active learning is a good strategy. It consists in learning a model on a small amount of annotated data (annotation budget) and in choosing the best set of points to annotate in…

Computer Vision and Pattern Recognition · Computer Science 2022-01-19 Umang Aggarwal , Adrian Popescu , Céline Hudelot

The field of multi-object tracking has recently seen a renewed interest in the good old schema of tracking-by-detection, as its simplicity and strong priors spare it from the complex design and painful babysitting of tracking-by-attention…

Computer Vision and Pattern Recognition · Computer Science 2023-08-23 Gianluca Mancusi , Aniello Panariello , Angelo Porrello , Matteo Fabbri , Simone Calderara , Rita Cucchiara

We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing.…

Machine Learning · Computer Science 2015-11-05 Andrew M. Dai , Quoc V. Le

Big data analytics has opened new avenues in economic research, but the challenge of analyzing datasets with tens of millions of observations is substantial. Conventional econometric methods based on extreme estimators require large amounts…

Econometrics · Economics 2023-11-02 Sokbae Lee , Yuan Liao , Myung Hwan Seo , Youngki Shin

Timely information about the state of regional economies can be essential for planning, implementing and evaluating locally targeted economic policies. However, European regional accounts for output are published at an annual frequency and…

This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We augment and…

Econometrics · Economics 2022-05-17 David Kohns , Arnab Bhattacharjee