The result of univariate power curve modeling of different models.

Traditional subspace feature selection methods typically rely on a fixed distance to compute residuals between the original and feature reconstruction spaces. However, this approach struggles to adapt to diverse datasets and often fails to handle noise and outliers effectively. In this paper, we propose an unsupervised feature selection method named unsupervised feature selection algorithm based on -norm feature reconstruction (NFRFS). Employing a flexible norm to represent both the original space and the spatial distance of feature reconstruction, enhances adaptability and broadens its applicability by adjusting p. Additionally, adaptive graph learning is integrated into the feature selection process to preserve the local geometric structure of the data. Features exhibiting sparsity and low redundancy are selected through the regularization constraint of the inner product in the feature selection matrix. To demonstrate the effectiveness of the method, numerical studies were conducted on 14 benchmark datasets. Our results indicate that the method outperforms 10 unsupervised feature selection algorithms in terms of clustering performance.

This dataset builds upon "Financial Statement Data Sets" by incorporating several key improvements to enhance the accuracy and usability of US-GAAP financial data from SEC filings of U.S. exchange-listed companies. Drawing on submissions from January 2009 onward, the enhanced dataset aims to provide analysts with a cleaner, more consistent dataset by addressing common challenges found in the original data.

Key Enhancements:

1. Outlier Detection and Correction: Outliers in the original dataset have been systematically identified and corrected, providing more reliable financial figures.
2. Amendment Adjustments: In cases where SEC rules allow amendment filings to only include delta figures, full figures from the original submissions have been carried over for consistency, facilitating more straightforward analysis.
3. Missing Figure Estimation: Using calculation arcs from the US-GAAP taxonomy, missing financial figures have been computed where possible, ensuring greater completeness.
4. Data Structuring: Financial figures that previously appeared as separate rows have been consolidated into single rows with new columns, offering a cleaner structure.

Scope:

• Data Scope: The dataset is restricted to figures reported under US-GAAP standards, with the exception of EntityCommonStockSharesOutstanding and EntityPublicFloat.
• Currency and Units: The dataset exclusively includes figures reported in USD or shares, ensuring uniformity and comparability. It excludes ratios and non-financial metrics to maintain focus on financial data.
• Company Selection: The dataset is limited to companies with U.S. exchange tickers, providing a concentrated analysis of publicly traded firms within the United States.
• Submission Types: The dataset only incorporates data from 10-Q, 10-K, 10-Q/A, and 10-K/A filings, ensuring consistency in the type of financial reports analyzed.

Dataset Features:

• Refined Financial Data: Accurate and consistent figures by addressing reporting issues, corrections for outliers, and data consolidation.
• Enhanced Usability: By handling amendment submissions and leveraging GAAP taxonomies, the dataset offers a more analysis-friendly structure.
• Improved Completeness: Where original submissions had gaps in reporting, this dataset fills those gaps using calculated figures based on accounting principles.

The source code for data extraction is available here

Skewness of error distributions of MARS, GBR, KNN, and RFR for both datasets and both cases.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in United States Is Outlier in Tax Trends in Advanced and Large Emerging Economies, PIIE Policy Brief 17-29. If you use the data, please cite as: Djankov, Simeon. (2017). United States Is Outlier in Tax Trends in Advanced and Large Emerging Economies. PIIE Policy Brief 17-29. Peterson Institute for International Economics.

The H1B Sponsorship Trends linear chart shows the number of H1B cases filed by Outlier Org from 2020 to 2023, providing a clear view of filing trends over time. Alongside, the horizontal bar chart titled Distribution of Job Fields Receiving H1B Sponsorship breaks down which roles and industries are most commonly sponsored.

Ranking of performance of different models for univariate case.

The changes in posts by pro-Russian disinformation profiles on Twitter in Poland were analyzed in comparison with the entire period from January 2022 to January 2023. In general, the posts were negative, but the graph represents the extent to which there were positive and negative outliers and polarization. In addition, the negative intensity increased after the war began in February 2022. What can be observed is as soon as there were increased positive outliers in a given month, there were simultaneously increased negative outliers. This was particularly noticeable in January and July 2022.

Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student's t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply.

Abstract:

Building health management is an important part in running an efficient and cost-effective building. Many problems in a building’s system can go undetected for long periods of time, leading to expensive repairs or wasted resources. This project aims to help detect and diagnose the building‘s health with data driven methods throughout the day. Orca and IMS are two state of the art algorithms that observe an array of building health sensors and provide feedback on the overall system’s health as well as localize the problem to one, or possibly two, components. With this level of feedback the hope is to quickly identify problems and provide appropriate maintenance while reducing the number of complaints and service calls.

Introduction:

To prepare these technologies for the new installation, the proposed methods are being tested on a current system that behaves similarly to the future green building. Building 241 was determined to best resemble the proposed building 232 and therefore was chosen for this study. Building 241 is currently outfitted with 34 sensors that monitor the heating & cooling temperatures for the air and water systems as well as other various subsystem states. The daily sensor recordings were logged and sent to the IDU group for analysis. The period of analysis was focused from July 1st through August 10th 2009.

Methodology:

The two algorithms used for analysis were Orca and IMS. Both methods look for anomalies using a distanced based scoring approach. Orca has the ability to use a single data set and find outliers within that data set. This tactic was applied to each day. After scoring each time sample throughout a given day the Orca score profiles were compared by computing the correlation against all other days. Days with high overall correlations were considered normal however days with lower overall correlations were more anomalous. IMS, on the other hand, needs a normal set of data to build a model, which can be applied to a set of test data to asses how anomaly the particular data set is. The typical days identified by Orca were used as the reference/training set for IMS, while all the other days were passed through IMS resulting in an anomaly score profile for each day. The mean of the IMS score profile was then calculated for each day to produce a summary IMS score. These summary scores were ranked and the top outliers were identified (see Figure 1). Once the anomalies were identified the contributing parameters were then ranked by the algorithm.

Analysis:

The contributing parameters identified by IMS were localized to the return air temperature duct system.

-7/03/09 (Figure 2 & 3) AHU-1 Return Air Temperature (RAT) Calculated Average Return Air Temperature -7/19/09 (Figure 3 & 4) AHU-2 Return Air Temperature (RAT) Calculated Average Return Air Temperature

IMS identified significantly higher temperatures compared to other days during the month of July and August.

Conclusion:

The proposed algorithms Orca and IMS have shown that they were able to pick up significant anomalies in the building system as well as diagnose the anomaly by identifying the sensor values that were anomalous. In the future these methods can be used on live streaming data and produce a real time anomaly score to help building maintenance with detection and diagnosis of problems.

Addition-point OLS matrix, B.

The crcc T2 Revised statistics.

The leftmost figure is the initial point sets with 98 points in the model point set (blue pluses) and 196 points in the scene point set (red circles). The right six figures are the correspondence and the distribution of the outliers in the iterations of 1, 3, 5, 10, 20 and 50 times of our method. The corresponding point pairs are connected by green lines, and the other points are the outliers.

The result of multivariate power curve modeling of different models.

Ranking of performance of different models for multivariate case.

VCF containing SNPs located in the MDS outlier region identified using lostruct. PCA in figure 2d was generated using the accompanying R script.

The crcc, RMVE, RMCD, and classical Hotelling’s T2 statistics.

Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. We leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. J. Chem. Phys. 159, 024120 (2023)] to train three machine learning (ML) models for transition temperature (T1/2) prediction using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set RF and recursive feature addition RF models perform best, achieving moderate correlation to experimental T1/2 values. We then compare ML T1/2 predictions to those from three previously identified best-performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict T1/2 more accurately than the best-performing DFAs. In addition, we study ML model predictions for a set of 18 SCO complexes for which only estimated T1/2 values are available. Upon excluding outliers from this set, the RF-RFA RF model shows a strong correlation to estimated T1/2 values with a Pearson’s r of 0.82. In contrast, DFA-predicted T1/2 values have large errors and show no correlation to estimated T1/2 values over the same set of complexes. Overall, our study demonstrates slightly superior performance of ML models in comparison with some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training.

The simulated control limits for TRMVE,i2 statistic under various combinations of n and p at an overall fixed false alarm rate of 0.05.

ARLs of control charts when trained with samples obtained with the wild-bootstrap method, where ηt ∼ N(0, 1), and no additive outliers are present.