An official website of the United States government
Abstract It is well known that the power spectrum is not able to fully characterize the statistical properties of non-Gaussian density fields. Recently, many different statistics have been proposed to extract information from non-Gaussian cosmological fields that perform better than the power spectrum. The Fisher matrix formalism is commonly used to quantify the accuracy with which a given statistic can constrain the value of the cosmological parameters. However, these calculations typically rely on the assumption that the sampling distribution of the considered statistic follows a multivariate Gaussian distribution. In this work, we follow Sellentin & Heavens and use two different statistical tests to identify non-Gaussianities in different statistics such as the power spectrum, bispectrum, marked power spectrum, and wavelet scattering transform (WST). We remove the non-Gaussian components of the different statistics and perform Fisher matrix calculations with theGaussianizedstatistics using Quijote simulations. We show that constraints on the parameters can change by a factor of ∼2 in some cases. We show with simple examples how statistics that do not follow a multivariate Gaussian distribution can achieve artificially tight bounds on the cosmological parameters when using the Fisher matrix formalism. We think that the non-Gaussian tests used in this work represent a powerful tool to quantify the robustness of Fisher matrix calculations and their underlying assumptions. We release the code used to compute the power spectra, bispectra, and WST that can be run on both CPUs and GPUs.
more »
« less
This content will become publicly available on April 29, 2026
Comparative Analysis of Fixed-Length and Dynamic Segmentation for Feature Extraction from Non-Stationary Spatial Data
This research presents a comparative analysis of non-stationary spatial data segmentation techniques such as fixed-length and dynamic segmentation based feature extraction efficiency. The study utilizes 5 miles of railway track geometry data, a non-stationary spatial dataset, to assess the effectiveness of both segmentation approaches. The profile (vertical alignment) of the track geometry is used for this purpose. For fixed-length segmentation, the track data is divided into segments of 264 feet (1/20th of a mile), resulting in about 102 segments. Dynamic segmentation is performed using an l2 model-based change point detection algorithm, which adapts to natural variations in the signal. Key features such as standard deviation, kurtosis, and energy are extracted from both segmentation methods. Performance is evaluated based on multiple criteria, including the discriminative power of the features for classifying track safety and ride-quality conditions using statistical tests such as the f-test and Fisher score, consistency or signal quality across segments, measured using the variance of the signal-to-noise ratio (SNR), computational efficiency in terms of run-time and memory usage. Results indicate that, features from fixed-length segments have demonstrated better discriminative power between safety and ride quality classes, with higher Fisher scores and f-values showing strong statistical significance (p < 0.05). Additionally, fixed-length segmentation has shown a better performance with lower run-time and stable signal power across segments.
more »
« less
- Award ID(s):
- 2123264
- PAR ID:
- 10632543
- Editor(s):
- Sheeri, Abhay
- Publisher / Repository:
- Pittsburgh Undergraduate Review
- Date Published:
- Journal Name:
- Pittsburgh Undergraduate Review
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2769-724X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)We consider reinforcement learning (RL) in episodic MDPs with adversarial full-information reward feedback and unknown fixed transition kernels. We propose two model-free policy optimization algorithms, POWER and POWER++, and establish guarantees for their dynamic regret. Compared with the classical notion of static regret, dynamic regret is a stronger notion as it explicitly accounts for the non-stationarity of environments. The dynamic regret attained by the proposed algorithms interpolates between different regimes of non-stationarity, and moreover satisfies a notion of adaptive (near-)optimality, in the sense that it matches the (near-)optimal static regret under slow-changing environments. The dynamic regret bound features two components, one arising from exploration, which deals with the uncertainty of transition kernels, and the other arising from adaptation, which deals with non-stationary environments. Specifically, we show that POWER++ improves over POWER on the second component of the dynamic regret by actively adapting to non-stationarity through prediction. To the best of our knowledge, our work is the first dynamic regret analysis of model-free RL algorithms in non-stationary environments.more » « less
-
Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments. In settings where unlabelled speech is the only available resource, such embeddings can be used in "zero-resource" speech search, indexing and discovery systems. Here we propose to train a single supervised embedding model on labelled data from multiple well-resourced languages and then apply it to unseen zero-resource languages. For this transfer learning approach, we consider two multilingual recurrent neural network models: a discriminative classifier trained on the joint vocabularies of all training languages, and a correspondence autoencoder trained to reconstruct word pairs. We test these using a word discrimination task on six target zero-resource languages. When trained on seven well-resourced languages, both models perform similarly and outperform unsupervised models trained on the zero-resource languages. With just a single training language, the second model works better, but performance depends more on the particular training--testing language pair.more » « less
-
One of the promising solutions for energy-efficient CNNs is to break them down into multiple stages that are executed sequentially (MS-CNN). In this paper, we illustrate that unlike deep CNNs, MS-CNNs develop a form of contextual awareness of input data in initial stages, which could be used to dynamically change the structure and connectivity of such networks to reduce their computational complexity, making them a better fit for low-power and real-time systems. We suggest three run-time optimization policies, which are capable of exploring such contextual knowledge, and illustrate how the proposed policies construct a dynamic architecture suitable for a wide range of applications with varied accuracy requirements, resources, and time-budget, without further need for network re-training. Moreover, we propose variable and dynamic bit-length fixed-point conversion to further reduce the memory footprint of the MS-CNNs.more » « less
-
Existing neural cell tracking methods generally use the morphology cell features for data association. However, these features are limited to the quality of cell segmentation and are prone to errors for mitosis determination. To over- come these issues, in this work we propose an online multi- object tracking method that leverages both cell appearance and motion features for data association. In particular, we propose a supervised blob-seed network (BSNet) to predict the cell appearance features and an unsupervised optical flow network (UnFlowNet) for capturing the cell motions. The data association is then solved using the Hungarian al- gorithm. Experimental evaluation shows that our approach achieves better performance than existing neural cell track- ing methods.more » « less
