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Over the past two decades, convolutional neural networks (CNN) have been used to successfully perform classification tasks, such as image recognition, that rely on translation-invariant, locally and spatially correlated data. However, with certain classification problems, such as corporate bankruptcy forecasting, data are made up of unordered columns of numbers, and the distance between two columns is meaningless. Indeed, such data most often represent a firm's financial situation measured at a given point in time, and the variables that can embody this situation are not structured in a way that would make them suitable to be used with a CNN. Since CNN represent a class of classification techniques that is very efficient and as there are virtually no methods that make it possible to adapt accounting data to CNN properties, in this article we propose a technique that aims to transform data into a topological space—a space endowed with a spatial structure—in order to study the ability of CNN to forecast corporate bankruptcy and financial failure. The results of our study show that CNN-based models designed with this technique lead to models that are more accurate than traditional ones. [ABSTRACT FROM AUTHOR]

Most bankruptcy prediction models that have been analyzed in the literature, and that are estismated using ensemble-based techniques, are still not able to fully embody the true diversity of firm bankruptcy situations. Indeed, these models try to assess all bankruptcy situations either mostly using the same set of variables (bagging, boosting), or using the same set of observations (random subspace). In the first case, an ensemble assumes that any symptom of failure has the same origin. In the second case, it assumes that any financial situation that can lead to failure is the same for all firms. However, there are many situations where these two assumptions do not hold and where a state of bankruptcy may be specific to a given subgroup of firms or may be explained by a particular subset of variables. Certain methods, such as random forest or rotation forest, which combine the characteristics of both random subspace and bagging appear as solutions to this issue. However, they do not always perform significantly better than other ensemble models do. This is why we propose a modeling method that attempts to overcome the limitations of the previous models. It is based on a biclustering technique that seeks out groups of firms that are each characterized by a well-defined subset of variables and on an ensemble technique that is used to embody the full diversity of all bankruptcy situations that belong to each bicluster as precisely as possible. We show how the complementarity between these two techniques can improve forecasts. [ABSTRACT FROM AUTHOR]

The optimal forecasting horizon of bankruptcy prediction models is usually one year. Beyond this point, their accuracy decreases as the horizon recedes. However, the ability of models to provide good mid-term forecasts is an essential characteristic for financial institutions due to prudential reasons. This is why we have studied a method of improving their forecasts up to a 5-year horizon. For this purpose, we propose to quantize how firm financial health changes over time, typify these changes and design models that fit each type. Our results show that, whatever the modeling technique used to design prediction models, model accuracy can be significantly improved when the horizon exceeds two years. They also show that when our method is used in combination with ensemble-based models, model accuracy is always improved whatever the forecasting horizon, when compared to traditional models used by financial institutions. The method we propose in this article appears to be a reliable solution that makes it possible to solve a real problem most models are unable to overcome, and it can therefore help financial companies comply with the current recommendations made by the Basel Committee on Banking Supervision. It also provides the scientific community (which is interested in designing reliable failure models) with insights about how the evolution of firms’ financial situations over time can be modeled and efficiently used to make forecasts. [ABSTRACT FROM AUTHOR]

The measurement of the femoral head is usually considered an interesting variable for the sex determination of skeletal remains. To date, there are few published reference measurements of the femoral head in a modern European population for the purpose of sex determination. In this study, 116 femurs from 58 individuals of the South of France (Nice Bone Collection, Nice, France) were studied. Three measurements of the femoral head were taken: the vertical head diameter (VHD), the transversal head diameter (THD) and the head circumference (HC). The results show that: (i) there is no statistical difference between the right and left femurs for each of the three measurements (VHD, THD and HC). Therefore we arbitrarily chose to use the measures from the right femurs (N = 58) to pursue our experiments; (ii) the measurements of the femoral head are similar to those of contemporary American populations; (iii) the dimensions of the femoral head place the measurements of the French population somewhere between Germany or Croatia, and Spain; (iv) there is no significant secular trend (in contrast with the femoral neck diameter); (v) the femoral head measurement as a single variable is useful for sex determination: a 96.5% rate of accuracy was obtained using THD and HC measurements with the artificial neural network; and a 94.8% rate of accuracy using VHD, both with the discriminant analysis and the neural network. [ABSTRACT FROM AUTHOR]

Abstract: We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction. [Copyright &y& Elsevier]

Objective: To study the usefulness of gentamicin-containing sponges in the infected peritoneal cavity in rats. Design: Controlled study. Material: 83 Male Wistar rats, 36 of which were treated by plain sponge, 36 by gentamicin-impregnated sponge, and 11 acted as controls. Interventions: A standard model of intraperitoneal infection was developed by making a 1 cm long incision in the caecum. Main outcome measures: Postoperative mortality, macroscopic and microscopic features of infection, and bacterial concentrations in the abdomen on days 3, 6, and 9. Results: In the respective groups 2, 3 and 2 animals died during operation and 4/34 (11%), 3/33 (9%); and 0/9 died before day 3. There were appreciable reductions in the number of animals with features of infection after 3 days but these were not significant at 6 or 9 days. Conclusion: Gentamicin-containing collagen sponges placed on a septic focus in the abdomen reduce local infection for at least 3 days. [ABSTRACT FROM AUTHOR]

This experimental study examined the lesions produced by a hatchet on human bones (tibiae). A total of 30 lesions were produced and examined macroscopically (naked eye) and by stereomicroscopy. 13 of them were also analyzed using scanning electron microscopy. The general shape of the lesion, both edges, both walls, the kerf floor and the extremities were described. The length and maximum width of the lesions were also recorded. The microscopic analysis of the lesions led to the description of a sharp-blunt mechanism. Specific criteria were identified (lateral pushing back, fragmentation of the upraising, fossa dug laterally to the edge and vertical striae) enabling the forensic expert to conclude that a hacking instrument was used. These criteria are easily identifiable using scanning electron microscopy, but can also be observed with stereomicroscopy. Overall, lateral pushing back and vertical striae visible using stereomicroscopy and scanning electron microscopy signal the use of a hacking tool. [ABSTRACT FROM AUTHOR]

The DSP method (probabilistic sex diagnosis) was applied to100 contemporary coxal bones from elderly individuals of the South of France. Ten variables with a posterior probability greater or equal to a 0.95 threshold were used. There was no statistical difference between right side and left side measurements. There was no mistake for sex assignment but the level of indetermination varied a great deal. It was higher in females than in males. The best combinations were obtained when using all 10 variables, some combination of 9 variables (all except SS or SIS or VEAC) or the first 8 variables. We conclude that the DSP method is of great interest in forensic anthropology, thanks to a very weak possibility of mistake when using the software for sex determination of the coxal bone. The main drawback is the level of indetermination that can be high depending on the available variables. [ABSTRACT FROM AUTHOR]

This paper presents two new clustering techniques based on Extreme Learning Machine (ELM). These clustering techniques can incorporate a priori knowledge (of an expert) to define the optimal structure for the clusters, i.e. the number of points in each cluster. Using ELM, the first proposed clustering problem formulation can be rewritten as a Traveling Salesman Problem and solved by a heuristic optimization method. The second proposed clustering problem formulation includes both a priori knowledge and a self-organization based on a predefined map (or string). The clustering methods are successfully tested on 5 toy examples and 2 real datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for identifying data samples that are likely to be mislabeled in a c -class classification problem (dataset). The methodology relies on an assumption that the generalization error of a model learned from the data decreases if a label of some mislabeled sample is changed to its correct class. A general classification model used in the paper is OP-ELM; it also provides a fast way to estimate the generalization error by PRESS Leave-One-Out. It is tested on two toy datasets, as well as on real life datasets for one of which expert knowledge about the identified potential mislabels has been sought. [ABSTRACT FROM AUTHOR]

Positive (certain, absolute) identification of human remains needs a scientific comparison between ante mortem and post-mortem biologic features, as fingerprint, odontological, radiological or DNA comparisons. X-rays comparison has been extensively used, usually comparing some peculiarities such as outlines of the bones, degenerative evolution or pathological conditions. Trabeculae comparisons are sparsely underlined in the forensic literature. We report on a case of decomposed body where fingerprint, DNA and odontological comparisons were not possible. After dissecting the leg and preparing the bones, comparison of ante mortem and postmortem trabeculae led to a positive identification. It was observed that tens of radiolucencies and radiodensities drawn by the trabeculae were useful for comparison, within a very small part of bone. In the case reported here the positive identity could have been assessed only by the comparison of the first metatarsal. The statement of positive identification needs scientific criteria that will be discussed in this article. [ABSTRACT FROM AUTHOR]

Abstract: Various methods have been published in the literature to estimate endocranial capacity. These are based on mathematical equations using measurements made directly on the skull or indirectly from X-rays, by filling the skull with various materials, by endocasts both physical and virtual (using 3D CT-scan reconstructions). Each method has its advantages, limits and drawbacks. A useful method is one that is simple, time-efficient, cheap, and reproducible. For this study we estimated endocranial capacity by filling the skull with calibrated glass beads. We used a thorough protocol of tamping to ensure results reproducibility. Intra- and inter-observer trials showed no significant differences. The method was applied to 130 recent French (West European) skulls (65 males and 65 females). Sexual dimorphism was studied using logistic regression functions, discriminant analysis, and neural network. The best results were obtained using the neural network, built with two hidden nodes in the hidden layer and one output node, and trained with a steepest descent algorithm. Our tests show that there is obvious sexual dimorphism (p <10−5) of the weight of the skulls (623.44±106.83g in males; 548.07±94.57 in females), the weight of the glass beads used to fill the skulls (2379.83±228.92g in males; 2095.95±145.49 in females), and endocranial capacity (1676.47cm3 ±161.26 in males; 1476.48±102.49 in females), but overlapping between sexes is quite significant. The average ECC in male (1676.5cm3) and in female (1476.5cm3) subjects is higher than those previously published, which deserves some discussion. [Copyright &y& Elsevier]

The DSP method (probabilistic sex diagnosis) was applied to100 contemporary coxal bones from elderly individuals of the South of France. Ten variables with a posterior probability greater or equal to a 0.95 threshold were used. There was no statistical difference between right side and left side measurements. There was no mistake for sex assignment but the level of indetermination varied a great deal. It was higher in females than in males. The best combinations were obtained when using all 10 variables, some combination of 9 variables (all except SS or SIS or VEAC) or the first 8 variables. We conclude that the DSP method is of great interest in forensic anthropology, thanks to a very weak possibility of mistake when using the software for sex determination of the coxal bone. The main drawback is the level of indetermination that can be high depending on the available variables. [ABSTRACT FROM AUTHOR]