Multiple perspectives HMM-based feature engineering for credit card fraud detection

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud? (ii) Is the sequence obtained by fixing the card-holder or the payment terminal? (iii) Is it a sequence of spent amount or of elapsed time between the current and previous transactions? Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sets is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. This multiple perspectives HMM-based approach enables an automatic feature engineering in order to model the sequential properties of the dataset with respect to the classification task. This strategy allows for a 15% increase in the precision-recall AUC compared to the state of the art feature engineering strategy for credit card fraud detection.Comment: Presented as a poster in the conference SAC 2019: 34th ACM/SIGAPP Symposium on Applied Computing in April 201

Effect of rainfall patterns on soil surface CO(2 )efflux, soil moisture, soil temperature and plant growth in a grassland ecosystem of northern Ontario, Canada: implications for climate change

BACKGROUND: The effect of rainfall patterns on soil surface CO(2 )efflux, soil moisture, soil temperature and plant growth was investigated in a grassland ecosystem of northern Ontario, Canada, where climatic change is predicted to introduce new precipitation regimes. Rain shelters were established in a fallow field consisting mainly of Trifolium hybridum L., Trifolium pratense L., and Phleum pratense L. Daytime ambient air temperatures within the shelters increased by an average of 1.9°C similar to predicted future increases in air temperatures for this region. To simulate six precipitation regimes which cover the maximum range to be expected under climate change, a portable irrigation system was designed to modify the frequency of monthly rainfall events with a constant delivery rate of water, while maintaining contemporary average precipitation volumes. Controls consisted of blocks irrigated with frequencies and total monthly precipitation consistent with the 25 year average rainfall for this location. RESULTS: Seasonal soil moisture correlated with soil surface CO(2 )efflux (R = 0.756, P < 0.001) and above ground plant biomass (R = 0.447, P = 0.029). By reducing irrigation frequency, soil surface CO(2 )efflux decreased by 80%, P < 0.001, while soil moisture content decreased by 42%, P < 0.001. CONCLUSIONS: Manipulating the number of precipitation events and inter-rainfall intervals, while maintaining monthly rainfall averages impacted CO(2 )efflux and plant growth. Even with monthly rainfall averages that are similar to contemporary monthly precipitation averages, decreasing the number of monthly rainfall events reduced soil surface CO(2 )efflux and plant growth through soil moisture deficits. Although many have speculated that climate change will increase ecosystem productivity, our results show that a reduction in the number of monthly rainfall events while maintaining monthly averages will limit carbon dynamics

CD40 deficiency mitigates Alzheimer's disease pathology in transgenic mouse models

We have previously shown that transgenic mice carrying a mutant human APP but deficient in CD40L, display a decrease in astrocytosis and microgliosis associated with a lower amount of deposited Aβ. Furthermore, an anti-CD40L treatment causes a diminution of Aβ pathology in the brain and an improved performance in several cognitive tasks in the double transgenic PSAPP mouse model. Although these data suggest a potential role for CD40L in Alzheimer's disease pathology in transgenic mice they do not cast light on whether this effect is due to inhibition of signaling via CD40 or whether it is due to the mitigation of some other unknown role of CD40L. In the present report we have generated APP and PSAPP mouse models with a disrupted CD40 gene and compared the pathological features (such as amyloid burden, astrocytosis and microgliosis that are typical of Alzheimer's disease-like pathology in these transgenic mouse strains) with appropriate controls. We find that all these features are reduced in mouse models deficient for CD40 compared with their littermates where CD40 is present. These data suggest that CD40 signaling is required to allow the full repertoire of AD-like pathology in these mice and that inhibition of the CD40 signaling pathway is a potential therapeutic strategy in Alzheimer's disease

Vasopressin in vasodilatory shock: is the heart in danger?

In patients with hyperdynamic hemodynamics, infusing arginine vasopressin (AVP) in advanced vasodilatory shock is usually accompanied by a decrease in cardiac output and in visceral organ blood flow. Depending on the infusion rate, this vasoconstriction also reduces coronary blood flow despite an increased coronary perfusion pressure. In a porcine model of transitory myocardial ischemia-induced left ventricular dysfunction, Müller and colleagues now report that the AVP-related coronary vaso-constriction may impede diastolic relaxation while systolic contraction remains unaffected. Although any AVP-induced myocardial ischemia undoubtedly is a crucial safety issue, these findings need to be discussed in the context of the model design, the dosing of AVP as well as the complex direct, afterload-independent and systemic, vasoconstriction-related effects on the heart

Automatisation de la planification du chargement d'un avion cargo

peer reviewedThe goal of this paper is the development of a new mixed integer linear pro- gram designed for optimally loading a set of containers and pallets into a compartmentalised cargo aircraft. It is based on real-world problems submitted by a professional partner. This model takes into account strict technical and safety constraints. In addition to the standard goal of optimally positioning the centre of gravity, we also propose a new approach based on the moment of inertia. This double goal implies an increase in aircraft efficiency and a decrease in fuel consumption. Cargo loading generally remains a manual, or at best a computer assisted, and time consuming task. A fully automatic software was developed to quickly compute optimal solutions. Experimental results show that our approach achieves better solutions than manual planning, within only a few seconds

A Generalized Graph Reduction Framework for Interactive Segmentation of Large Images

The speed of graph-based segmentation approaches, such as random walker (RW) and graph cut (GC), depends strongly on image size. For high-resolution images, the time required to compute a segmentation based on user input renders interaction tedious. We propose a novel method, using an approximate contour sketched by the user, to reduce the graph before passing it on to a segmentation algorithm such as RW or GC. This enables a significantly faster feedback loop. The user first draws a rough contour of the object to segment. Then, the pixels of the image are partitioned into “layers” (corresponding to different scales) based on their distance from the contour. The thickness of these layers increases with distance to the contour according to a Fibonacci sequence. An initial segmentation result is rapidly obtained after automatically generating foreground and background labels according to a specifically selected layer; all vertices beyond this layer are eliminated, restricting the segmentation to regions near the drawn contour. Further foreground/background labels can then be added by the user to refine the segmentation. All iterations of the graph-based segmentation benefit from a reduced input graph, while maintaining full resolution near the object boundary. A user study with 16 participants was carried out for RW segmentation of a multi-modal dataset of 22 medical images, using either a standard mouse or a stylus pen to draw the contour. Results reveal that our approach significantly reduces the overall segmentation time compared with the status quo approach (p < 0.01). The study also shows that our approach works well with both input devices. Compared to super-pixel graph reduction, our approach provides full resolution accuracy at similar speed on a high-resolution benchmark image with both RW and GC segmentation methods. However, graph reduction based on super-pixels does not allow interactive correction of clustering errors. Finally, our approach can be combined with super-pixel clustering methods for further graph reduction, resulting in even faster segmentation