This web page gives detailed information on the equations for various forms of the Menter baseline (BSL) turbulence model.

BSL Fingerspelling V6

## Overview

BSL Fingerspelling V6 is a dataset for object detection tasks - it contains Bsl Alphabet KOpg annotations for 1,572 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

This dataset contains labeled images of British Sign Language (BSL) fingerspelling gestures representing the English alphabet (A–Z). It was created to support the development of lightweight, real-time hand gesture recognition models, particularly for children with hearing impairments.

The images were collected in varied lighting conditions and orientations to simulate real-world webcam input. This makes it ideal for training deep learning models that can generalize well in practical applications like education, accessibility tools, and AI-powered communication aids.

Inspiration: This dataset was curated as part of a university coursework project titled “AI for Countering Language Deprivation in Children with Hearing Impairment.” The goal was to use deep learning to bridge the communication gap for hearing-impaired learners by enabling real-time BSL letter recognition.

This web page gives detailed information on the equations for various forms of the Menter baseline (BSL) turbulence model.

BSL Recognition

## Overview

BSL Recognition is a dataset for object detection tasks - it contains Sign annotations for 2,401 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Since first reported in 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is rapidly acquiring mutations, particularly in the spike protein, that can modulate pathogenicity, transmission and antibody evasion leading to successive waves of COVID19 infections despite an unprecedented mass vaccination necessitating continuous adaptation of therapeutics. Small animal models can facilitate understanding host-pathogen interactions, target selection for therapeutic drugs, and vaccine development, but availability and cost of studies in BSL3 facilities hinder progress. To generate a BSL2-compatible in vivo system that specifically recapitulates spike protein mediated disease we used replication competent, GFP tagged, recombinant Vesicular Stomatitis Virus where the VSV glycoprotein was replaced by the SARS-CoV-2 spike protein (rVSV-SARS2-S). We show that infection requires hACE2 and challenge of neonatal but not adult, K18-hACE2 transgenic mice (hACE2tg) leads to productive infection of the lungs and brains. Although disease progression was faster in SARS-CoV-2 infected mice, infection with both viruses resulted in neuronal infection and encephalitis with increased expression of Interferon-stimulated Irf7, Bst2, Ifi294, as well as CxCL10, CCL5, CLC2, and LILRB4, and both models were uniformly lethal. Further, prophylactic treatment targeting the Spike protein (Receptor Binding Domain) with antibodies resulted in similar levels of protection from lethal infection against rVSV-SARS2-S and SARS-CoV-2 viruses. Strikingly, challenge of neonatal hACE2tg mice with SARS-CoV-2 Variants of Concern (SARS-CoV-2-α, -β, ϒ, or Δ) or the corresponding rVSV-SARS2-S viruses (rVSV-SARS2-Spike-α, rVSV-SARS2-Spike-β, rVSV-SARS2-Spike-ϒ or rVSV-SARS2-Spike-Δ) resulted in increased lethality, suggesting that the Spike protein plays a key role in determining the virulence of each variant. Thus, we propose that rVSV-SARS2-S virus can be used to understand the effect of changes to SARS-CoV-2 spike protein on infection and to evaluate existing or experimental therapeutics targeting spike protein of current or future VOC of SARS-CoV-2 under BSL-2 conditions.

The USGS Geomagnetism Program operates a network of magnetic observatories that collect vector and scalar magnetometer data for use in Earth main-field modeling, geophysics research, space physics research, and space weather hazard assessment and mitigation. Until mid-2011, only 1-minute time resolution magnetic field measurements were archived with the INTERMAGNET consortium following international magnetic observatory standards. 1-second time resolution magnetic field measurements, which had already been collected by all the USGS observatories for up to almost a decade prior, started being archived with INTERMAGNET on June 13, 2011, or July 27, 2012 in the case of the more recently constructed Deadhorse (DED) magnetic observatory. This data release contains 1-second time resolution magnetic field measurements collected up through the end of 2012, after which time 1-second data from USGS magnetic observatories may be obtained from INTERMAGNET. There is some overlap between data in this release and those data archived with INTERMAGNET. Any discrepancies that may exist between these two data sources should resolve in favor of INTERMAGNET. BSL-specific notes: - some filenames originally possessing a ".sec" extension were renamed to ".raw"

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

BSL Blackstone Senior Floating Rate Term Fund Common Shares of Beneficial Interest

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

BSL

## Overview

BSL is a dataset for object detection tasks - it contains Hand Movements annotations for 250 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

BSL YOLO

## Overview

BSL YOLO is a dataset for object detection tasks - it contains Word annotations for 934 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Simulation-based Bayesian inference methods are useful when the statistical model of interest does not possess a computationally tractable likelihood function. One such likelihood-free method is approximate Bayesian computation (ABC), which approximates the likelihood of a carefully chosen summary statistic via model simulation and nonparametric density estimation. ABC is known to suffer a curse of dimensionality with respect to the size of the summary statistic. When the model summary statistic is roughly normally distributed in regions of the parameter space of interest, Bayesian synthetic likelihood (BSL), which uses a normal likelihood approximation for a summary statistic, is a useful method that can be more computationally efficient than ABC. However, BSL requires estimation of the covariance matrix of the summary statistic for each proposed parameter, which requires a large number of simulations to estimate precisely using the sample covariance matrix when the summary statistic is high dimensional. In this article, we propose to use the graphical lasso to provide a sparse estimate of the precision matrix. This approach can estimate the covariance matrix accurately with significantly fewer model simulations. We discuss the nontrivial issue of tuning parameter choice in the context of BSL and demonstrate on several complex applications that our method, which we call BSLasso, provides significant improvements in computational efficiency whilst maintaining the ability to produce similar posterior distributions to BSL. The BSL and BSLasso methods applied to the examples of this article are implemented in the BSL package in R, which is available on the Comprehensive R Archive Network. Supplemental materials for this article are available online.

Bayesian synthetic likelihood (BSL) is now an established method for conducting approximate Bayesian inference in models where, due to the intractability of the likelihood function, exact Bayesian approaches are either infeasible or computationally too demanding. Implicit in the application of BSL is the assumption that the data-generating process (DGP) can produce simulated summary statistics that capture the behaviour of the observed summary statistics. We demonstrate that if this compatibility between the actual and assumed DGP is not satisfied, that is, if the model is misspecified, BSL can yield unreliable parameter inference. To circumvent this issue, we propose a new BSL approach that can detect the presence of model misspecification, and simultaneously deliver useful inferences even under significant model misspecification. Two simulated and two real data examples demonstrate the performance of this new approach to BSL, and document its superior accuracy over standard BSL when the assumed model is misspecified. Supplementary materials for this article are available online.

BSL Jra

## Overview

BSL Jra is a dataset for object detection tasks - it contains Letters annotations for 716 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

BackgroundT cell immunoglobulin mucin domain-1 (TIM-1) is a phosphatidylserine (PS) receptor, mediating filovirus entry into cells through interactions with PS on virions. TIM-1 expression has been implicated in Ebola virus (EBOV) pathogenesis; however, it remains unclear whether this is due to TIM-1 serving as a filovirus receptor in vivo or, as others have suggested, TIM-1 induces a cytokine storm elicited by T cell/virion interactions. Here, we use a BSL2 model virus that expresses EBOV glycoprotein to demonstrate the importance of TIM-1 as a virus receptor late during in vivo infection.Methodology/Principal findingsInfectious, GFP-expressing recombinant vesicular stomatitis virus encoding either full length EBOV glycoprotein (EBOV GP/rVSV) or mucin domain deleted EBOV glycoprotein (EBOV GPΔO/rVSV) was used to assess the role of TIM-1 during in vivo infection. GFP-expressing rVSV encoding its native glycoprotein G (G/rVSV) served as a control. TIM-1-sufficient or TIM-1-deficient BALB/c interferon α/β receptor-/- mice were challenged with these viruses. While G/rVSV caused profound morbidity and mortality in both mouse strains, TIM-1-deficient mice had significantly better survival than TIM-1-expressing mice following EBOV GP/rVSV or EBOV GPΔO/rVSV challenge. EBOV GP/rVSV or EBOV GPΔO/rVSV in spleen of infected animals was high and unaffected by expression of TIM-1. However, infectious virus in serum, liver, kidney and adrenal gland was reduced late in infection in the TIM-1-deficient mice, suggesting that virus entry via this receptor contributes to virus load. Consistent with higher virus loads, proinflammatory chemokines trended higher in organs from infected TIM-1-sufficient mice compared to the TIM-1-deficient mice, but proinflammatory cytokines were more modestly affected. To assess the role of T cells in EBOV GP/rVSV pathogenesis, T cells were depleted in TIM-1-sufficient and -deficient mice and the mice were challenged with virus. Depletion of T cells did not alter the pathogenic consequences of virus infection.ConclusionsOur studies provide evidence that at late times during EBOV GP/rVSV infection, TIM-1 increased virus load and associated mortality, consistent with an important role of this receptor in virus entry. This work suggests that inhibitors which block TIM-1/virus interaction may serve as effective antivirals, reducing virus load at late times during EBOV infection.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Blackstone's Floating Rate: A Smooth Ride or Bumpy Waters Ahead? (BSL)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset represents the temperature map at 1000m bsl, it is the results of the regional thermal model of Acoculco. The temperature in Acoculco is constrained by 2 deep wells drilled to explore the geothermal system. The model considers the conductive heat transfer and was aimed: - to investigate the effects of varying geometrical parameters of the heat source on the observed thermal anomaly - to investigate the conductive thermal evolution above a intrusive body (or ense...

Dicta-Sign is a three-year EU-funded research project which started in January 2009. It aims to improve online communication for Deaf people by promoting the development of sign language based web applications. It will research and develop recognition and synthesis systems for sign languages at a level of detail necessary for recognising and generating authentic signing. The project deals with four European sign languages: British (BSL), German (DGS), Greek (GSL) and French (LSF). As one of the first steps, a multilingual corpus on the domain „Travel in Europe“ was produced, parallelised for the four target languages as much as is possible for authentic sign language production. This corpus is used within the project to inform progress in areas such as video recognition of signs, formal language models and sign language animation using avatars. These technologies in turn are used to improve sign language technology, e.g. by providing semi-automatic tagging for sign language annotation tools. Aside from this, Dicta-Sign is expected to result in three prototype applications: a sign language- to-sign language terminology translator, a search-by-example tool, and a sign language Wiki.

Table Tennis Ball Model

## Overview

Table Tennis Ball Model is a dataset for object detection tasks - it contains Table Tennis Ball annotations for 305 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains slices at 2000m asl, at 1000m asl, at sea level and at 1000m bsl of the 3D densidy model in Acoculco. The values represents density contrast in g/cm³ with a background density of 2.67 g/cm³. The gravity campaign corresponds to 84 gravity stations measured during the GEMex project, with participation of members of the WP5 "Detection of deep structures", Task 5.3 "Evaluation of other geophysical data".

Context

Sign Language is a communication language just like any other language which is used among deaf community. This dataset is a complete set of gestures which are used in sign language and can be used by other normal people for better understanding of the sign language gestures .

Content

The dataset consists of 37 different hand sign gestures which includes A-Z alphabet gestures, 0-9 number gestures and also a gesture for space which means how the deaf or dumb people represent space between two letter or two words while communicating. The dataset has two parts, that is two folders (1)-Gesture Image Data - which consists of the colored images of the hands for different gestures. Each gesture image is of size 50X50 and is in its specified folder name that is A-Z folders consists of A-Z gestures images and 0-9 folders consists of 0-9 gestures respectively, '_' folder consists of images of the gesture for space. Each gesture has 1500 images, so all together there are 37 gestures which means there 55,500 images for all gestures in the 1st folder and in the 2nd folder that is (2)-Gesture Image Pre-Processed Data which has the same number of folders and same number of images that is 55,500. The difference here is these images are threshold binary converted images for training and testing purpose. Convolutional Neural Network is well suited for this dataset for model training purpose and gesture prediction.

Acknowledgements

I wouldn't be here without the help of others. As this dataset is being created with the help of references of the work done on sign language in data science and also references from the work done on image processing.