WALDATA : Wavelet transform based adversarial learning for the detection of anomalous trading activities

Detecting manipulative activities in stock market trading poses a significant challenge due to the complex temporal correlations inherent to the dynamically changing stock price data. This challenge is further exacerbated by the limited availability of labelled anomalous trading data instances. Stock price manipulations, which consist of infrequent anomalies in stock price trading data, are challenging to capture due to their sporadic occurrence and dynamically evolving nature. This scarcity and inherent complexity significantly complicate the creation of labelled datasets hence hinders the development of robust detection of different stock price manipulation schemes through supervised learning methods. Overcoming these challenges is crucial for enhancing our understanding of market dynamics and implementing robust market surveillance systems. To address these challenges, we introduce a novel stock price manipulation detection approach called WALDATA (Wavelet Transform based Adversarial Learning for the Detection of Anomalous Trading Activities). We leverage the Wavelet Transform (WT) to decompose non-stationary stock price time series into informative features and capture multi-scale dynamics within the data. We encode stock price data by transforming it into scalogram images through the Continuous Wavelet Transform, effectively converting stock price time series data into a 2D image representation. Subsequently, we employ a Generative Adversarial Network (GAN) architecture, originally applied to computer vision, to learn the underlying distribution of normal trading behaviour from the encoded images. We then train the discriminator as an anomaly detector for identifying manipulative trading activities in the stock market. The efficacy of WALDATA is rigorously evaluated on diverse real-world stock datasets using 1-level tick data from the LOBSTER project and the experimental results demonstrate the significant performance of our approach achieving an average AUC of 0.99 while maintaining low false alarm rates across various market conditions. These findings not only validate the effectiveness of the proposed WALDATA approach in accurately identifying stock price manipulations but also provide investors and regulators alike with valuable insights for the development of advanced market surveillance systems. This research demonstrates the promising potential of combining wavelet-based feature extraction and stock price time series to image representation with generative adversarial learning frameworks for anomaly detection in financial time series data. The successful implementation of WALDATA contributes to the development of advanced market surveillance systems and paves the way for further advancements in market surveillance, contributing towards a more efficient and robust financial system and a fair market environment

Génération automatique de curriculum adapté pour l'apprentissage à distance universitaire.

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Artificial ants to extract leaf outlines and primary venation patterns

This paper presents preliminary results on an investigation into using artificial swarms to extract and quantify features in digital images. An ant algorithm has been developed to automatically extract the outlines and primary venation patterns from digital images of living leaf specimens via an edge detection method. A qualitative and quantitative analysis of the results is carried out herein. The artificial swarms are shown to converge onto the edges within the leaf images and statistical accuracy, as measured against ground truth images, is shown to increase in accordance with the swarm convergence. Visual results are promising, however limitations due to background noise need to be addressed for the given application. The findings in this study present potential for increased robustness in using swarm based methods, by exploiting their stigmergic behaviour to reduce the need for parameter fine-tuning with respect to individual image characteristics. © 2008 Springer-Verlag Berlin Heidelberg

La nanophtalmie bilatérale avec plis maculaires et forte hypermétropie : à propos d’un cas

  RESUME :L’objectif  : Décrire les signes cliniques et para cliniques  chez un enfant atteint de nanophtalmie .observation : garçon  de 10 ans , l’examen ophtalmologique trouve  une micro cornée ,une chambre antérieure réduite en ODG ,des angles étroits à la gonioscopie  , et au FO des disques comblés avec des marges floues et  des plis maculaires horizontaux émanant de la fovéa et s'étendant jusqu’ au disque optique, l’échographie oculaire  objective  une longueur axiale de 14,5 mm  en OD et de  16 mm  en OG , un épaississement scléral , une  chambre réduite  avec un cristallin de taille normale .l’OCT maculaire confirme  la présence d’un pli maculaire de la rétine neurosensorielle épargnant l’ épithélium pigmentaire et la choroïde   . Discussion :  La nanophtalmie est une forme  de microphtalmie congénitale rare dans laquelle les segments antérieurs et postérieurs sont anormalement petits, mais le cristallin a des dimensions normales, ce qui crée un rapport volume / cristallin  élevé, associé à des anomalie  du segment postérieur y compris les plis maculopapillaire rapporté dans notre cas . Conclusion :   La nanophtalmie est une pathologie rare cécitante par ses complications si elle n'est pas prise en charge précocement .Les diagnostics génétiques faciliteront le conseil génétique pour les formes familiales de cette anomalie  et peut aider à diminuer l’amblyopie de l'hypermétropie non corrigée,  et à améliorer la surveillance pour minimiser le glaucome et les complications rétiniennes causées par la  nanophtalmie. Mots clés : Nanophtalmie ,forte hypermetropie , plis maculaire , OCT maculaire , échographie oculaire .ABSTRACT :The purpose : Describe the clinical and para-clinical signs in a child with nanophthalmia. Observation: 10-year-old boy, the ophthalmological examination finds a micro cornea, an anterior chamber reduced in ODG, narrow angles to gonioscopy, and in  fundus exam  find a   filled discs with fuzzy margins and horizontal macular folds emanating from the fovea and extending up to the optical disc, ocular ultrasound objective an axial length of 14.5 mm in OD and 16 mm in OG, a scleral thickening, a reduced chamber with a normal-sized lens. The macular OCT confirms the presence of a macular fold in the neurosensory retina sparing the pigment epithelium and the choroid. Discussion: Nanophthalmia is a rare form of congenital microphthalmia in which the anterior and posterior segments are abnormally small, but the lens has normal dimensions, which creates a high volume / lens ratio, associated with abnormalities of the posterior segment including maculopapillary folds reported in our case. Conclusion: Nanophthalmia is a rare blinding pathology due to its complications if it is not taken care of early. Genetic diagnostics will facilitate genetic counseling for familial forms of this anomaly and can help reduce amblyopia due to farsightedness. corrected, and to improve monitoring to minimize glaucoma and retinal complications caused by nanophthalmia.Key words: nanophthalmia, high hyperopia, macular folds, macular OCT, ocular ultrasound

On the stress–force–fabric relationship for granular materials

This paper employed the theory of directional statistics to study the stress state of granular materials from the particle scale. The work was inspired by the stress–force–fabric relationship proposed by Rothenburg and Bathurst (1989), which represents a fundamental effort to establish analytical macro–micro relationship in granular mechanics. The micro-structural expression of the stress tensor rij ¼ 1 V P c2Vvci f c j , where f c i is the contact force and vci is the contact vector, was transformed into directional integration by grouping the terms with respect to their contact normal directions. The directional statistical theory was then employed to investigate the statistical features of contact vectors and contact forces. By approximating the directional distributions of contact normal density, mean contact force and mean contact vector with polynomial expansions in unit direction vector n, the directional dependences were characterized by the coefficients of the polynomial functions, i.e., the direction tensors. With such approximations, the directional integration was achieved by means of tensor multiplication, leading to an explicit expression of the stress tensor in terms of the direction tensors. Following the terminology used in Rothenburg and Bathurst (1989), the expression was referred to as the stress–force–fabric (SFF) relationship. Directional statistical analyses were carried out based on the particle-scale information obtained from discrete element simulations. The result demonstrated a small but isotropic statistical dependence between contact forces and contact vectors. It has also been shown that the directional distributions of contact normal density, mean contact forces and mean contact vectors can be approximated sufficiently by polynomial expansions in direction n up to 2nd, 3rd and 1st ranks, respectively. By incorporating these observations and revoking the symmetry of the Cauchy stress tensor, the stress–force–fabric relationship was further simplified, while its capacity of providing nearly identical predictions of the stresses was maintained. The derived SFF relationship predicts the complete stress information, including the mean normal stress, the deviatoric stress ratio as well as the principal stress directions. The main benefits of deriving the stress–force–fabric relationship based on the directional statistical theory are: (1) the method does not involve space subdivision and does not require a large number of directional data; (2) the statistical and directional characteristics of particle-scale directional data can be systematically investigated; (3) the directional integration can be converted into and achieved by tensor multiplication, an attractive feature to conduct computer program aided analyses