Stochastic resonance in the recovery of signal from agent price expectations

Contributions that noise can make to the objective of detecting signal in agent expectations for price in financial markets are examined. Although contrary to most assumptions on exogenous noise in financial markets as increasing both risk and uncertainty in the detection of signal, a basis for the contribution that noise can have to agent objectives in signal detection through stochastic resonance (SR) is well-documented across disciplines. After reviewing foundations for the micro-processing of expectations, a multi-component model of networked agents that includes a component of bounded rational processing and a component that has been cited as generating “herding” behavior in financial markets is offered. The signal-to-noise ratios in the proposed models provide a basis to investigate SR in an application to financial markets. Results with both deterministic and stochastic forms of the proposed model support SR as a process in which randomness can contribute to the recovery of signal in agent expectation. Additionally, predictive models that indicate the sensitivity of the occurrence of SR to the parameters of the models of agent expectations were estimated and cross-validated. The discriminative ability of the models is reported through Area Under the Receiver Operating Curve (AUROC) methodology. These results extend the cross-discipline demonstrations of SR to models of price in financial markets.</p

PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system

[EN] Histological remission is evolving as an important treatment target in UC. We aimed to develop a simple histological index, aligned to endoscopy, correlated with clinical outcomes, and suited to apply to an artificial intelligence (AI) system to evaluate inflammatory activity. Methods Using a set of 614 biopsies from 307 patients with UC enrolled into a prospective multicentre study, we developed the Paddington International virtual ChromoendoScopy ScOre (PICaSSO) Histologic Remission Index (PHRI). Agreement with multiple other histological indices and validation for inter-reader reproducibility were assessed. Finally, to implement PHRI into a computer-aided diagnosis system, we trained and tested a novel deep learning strategy based on a CNN architecture to detect neutrophils, calculate PHRI and identify active from quiescent UC using a subset of 138 biopsies. Results PHRI is strongly correlated with endoscopic scores (Mayo Endoscopic Score and UC Endoscopic Index of Severity and PICaSSO) and with clinical outcomes (hospitalisation, colectomy and initiation or changes in medical therapy due to UC flare-up). A PHRI score of 1 could accurately stratify patients' risk of adverse outcomes (hospitalisation, colectomy and treatment optimisation due to flare-up) within 12 months. Our inter-reader agreement was high (intraclass correlation 0.84). Our preliminary AI algorithm differentiated active from quiescent UC with 78% sensitivity, 91.7% specificity and 86% accuracy. Conclusions PHRI is a simple histological index in UC, and it exhibits the highest correlation with endoscopic activity and clinical outcomes. A PHRI-based AI system was accurate in predicting histological remission.MI and SG are funded by the NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham.Gui, X.; Alina Bazarova; Del Amor, R.; Vieth, M.; De Hertogh, G.; Villanacci, V.; Zardo, D.... (2022). PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system. Gut. 71:889-898. https://doi.org/10.1136/gutjnl-2021-3263768898987

Validation of a new optical diagnosis training module to improve dysplasia characterization in inflammatory bowel disease:a multicenter international study

Background and aims Inflammatory bowel disease (IBD) increases risk of dysplasia and colorectal cancer. Advanced endoscopic techniques allow for the detection and characterization of IBD dysplastic lesions, but specialized training is not widely available. We aim to develop and validate an online training platform to improve the detection and characterization of colonic lesions in IBD: OPTIC-IBD. Methods We designed a web-based learning module that includes surveillance principles, optical diagnostic methods, approach to characterization, classifications of colonic lesions, utilizing still images and videos. We invited gastroenterologists from Canada, Italy, and the UK, with a wide range of experience. Participants reviewed 24 educational videos of IBD colonic lesions, predicted histology, and rated their confidence. The primary endpoint was to improve accuracy in detecting dysplastic lesions following training on the platform. Furthermore, participants were randomized 1:1 to get additional training or not, with a final assessment occurring after 60 days. Diagnostic performance for dysplasia and rater confidence were measured. Results One hundred seventeen participants completed the study and were assessed for the primary endpoint. Diagnostic accuracy improved from 70.8% to 75.0% (p 0.002) following training, with the greatest improvements seen in less experienced endoscopists. Improvements in both accuracy and confidence were sustained after 2 months of assessment, although the group randomized to receive additional training did not improve further. Similarly, participants’ confidence in characterizing lesions significantly improved between pre- and post-course (

Strong approximation of lacunary series with random gaps

We investigate the asymptotic behavior of sums (Formula presented.), where f is a mean zero, smooth periodic function on (Formula presented.) and (Formula presented.) is a random sequence such that the gaps (Formula presented.) are i.i.d. Our result shows that, in contrast to the classical Salem–Zygmund theory, the almost sure behavior of lacunary series with random gaps can be described very precisely without any assumption on the size of the gaps. © 2017 Springer-Verlag Wie

RNA contact prediction by data efficient deep learning

On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps") as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction