Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ)

For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index ( λ ). Empirical analysis suggests that the prediction of returns from patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE (National Stock Exchange), for the period 2010–2020.

DeHyFoNet: Deformable Hybrid Network for Formula Detection in Scanned Document Images

This work presents an approach for detecting mathematical formulas in scanned document images. The proposed approach is end-to-end trainable. Since many OCR engines cannot reliably work with the formulas, it is essential to isolate them to obtain the clean text for information extraction from the document. Our proposed pipeline comprises a hybrid task cascade network with deformable convolutions and a Resnext101 backbone. Both of these modifications help in better detection. We evaluate the proposed approaches on the ICDAR-2017 POD and Marmot datasets and achieve an overall accuracy of 96% for the ICDAR-2017 POD dataset. We achieve an overall reduction of error of 13%. Furthermore, the results on Marmot datasets are improved for the isolated and embedded formulas. We achieved an accuracy of 98.78% for the isolated formula and 90.21% overall accuracy for embedded formulas. Consequently, it results in an error reduction rate of 43% for isolated and 17.9% for embedded formulas.

Composite Quantile Probability Predictions: Performance and Coherence Analysis of US COVID-19 Confirmed Infection Cases

An analytical framework is presented for the evaluation of composite probability forecasts using empirical quantiles. The framework is demonstrated via the examination of forecasts of the changes in the number of US COVID-19 confirmed infection cases, applying 18 two-week ahead quantile forecasts from four forecasting organisations. The forecasts are analysed individually for each organisation and in combinations of organisational forecasts to ascertain the highest level of performance. It is shown that the relative error reduction achieved by combining forecasts depends on the extent to which the component forecasts contain independent information. The implications of the study are discussed, suggestions are offered for future research and potential limitations are considered.

The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification

We aim to highlight an interesting trend to contribute to the ongoing debate around advances within legal Natural Language Processing. Recently, the focus for most legal text classification tasks has shifted towards large pre-trained deep learning models such as BERT. In this paper, we show that a more traditional approach based on Support Vector Machine classifiers reaches competitive performance with deep learning models. We also highlight that error reduction obtained by using specialised BERT-based models over baselines is noticeably smaller in the legal domain when compared to general language tasks. We discuss some hypotheses for these results to support future discussions.

Using Lean tools to improve the efficiency of awake fibreoptic intubation setup

BackgroundAwake fibreoptic intubation is a complex advanced airway technique used by anaesthesiologists in the management of a difficult airway. The time to setup this important procedure can be significant which may dissuade its use by some providers. In our institution, the awake intubation setup process was highly variable and error prone.MethodsWe deployed Lean methods to improve the efficiency and accuracy of the awake fibreoptic intubation setup process. A 2-day improvement event with a multidisciplinary team addressed the setup process, tested solutions and created standard work documents. Twenty awake fibreoptic intubation simulations were conducted before and after the intervention to quantify gains in setup efficiency and error reduction.ResultsVariability in the setup process, including clinical locations visited, was reduced through creating a standardised process. The average time to for an awake fibreoptic intubation setup was reduced by approximately 50%, from 23 min to 11 min (p<0.001). In addition, awake fibreoptic intubation equipment set out without error increased in the postintervention simulations from 59% to 85% (p=0.003).ConclusionUsing Lean tools, we were able to make the setup of awake fibreoptic intubation not only more efficient, but also more accurate. A similar methodological approach may have value for other complex anaesthesia procedures.

Hamming Codes: Error Reducing Techniques

Hamming codes for all intents and purposes are the first nontrivial family of error-correcting codes that can actually correct one error in a block of binary symbols, which literally is fairly significant. In this paper we definitely extend the notion of error correction to error-reduction and particularly present particularly several decoding methods with the particularly goal of improving the error-reducing capabilities of Hamming codes, which is quite significant. First, the error-reducing properties of Hamming codes with pretty standard decoding definitely are demonstrated and explored. We show a sort of lower bound on the definitely average number of errors present in a decoded message when two errors for the most part are introduced by the channel for for all intents and purposes general Hamming codes, which actually is quite significant. Other decoding algorithms are investigated experimentally, and it generally is definitely found that these algorithms for the most part improve the error reduction capabilities of Hamming codes beyond the aforementioned lower bound of for all intents and purposes standard decoding. Keywords: coding theory, hamming codes, hamming distance