First Scarab Host for \u3ci\u3eStrongygaster Triangulifer\u3c/i\u3e (Diptera: Tachinidae): the Dung Beetle, \u3ci\u3eAphodius Fimetarius\u3c/i\u3e (Coleoptera: Scarabaeidae)

We report Strongygaster (=Hyalomyodes ) triangulifer as a solitary primary parasite of the adult introduced dung beetle, Aphodius fimetarius. This is the first record of this tachinid fly parastizing scarab

Alarm Pheromone in a Gregarious Poduromorph Collembolan (Collembola: Hypogastruridae)

We report an alarm pheromone in the gregarious poduromorph collembolan, Hypogastrura pannosa. Cuticular rupture results in emission of a rapidly vaporizing hexane-soluble material with an active space diameter of ca. 1 cm. Conspecifics encountering the vapor front respond with stereotypic aversion and dispersal behaviors. This is the first report on the presence of an alarm pheromone in the order Collembola

The Potential of Erin Brokovitch to Introduce Organizaitonal Behavior Topics

Real organizational behavior is rich, and messy, and emotional, and at times painful, but at other times immensely rewarding. The movie, Erin Brockovich, captures this richness and provides an exciting means to introduce a variety of individual or micro- organizational behavior concepts (such as perception, personality, and motivation) typically covered at the beginning of the Organizational Behavior course. In this paper, we describe the use of the film, including a takehome viewing assignment, an in-class assignment, a description of clips and comments for in-class use, and suggestions for the types of issues to discuss in relation to the major topics and in relation to other potentially relevant areas. The richness of this film and the issues raised also provide the potential for more critical analyses of management and organizational practices

Paquet R pour l'estimation d'un mélange de lois de Student multivariées à échelles multiples

National audienceL'utilisation d'un modèle de mélange de lois est une approche statistique classique en classification non-supervisée. Un mélange fréquemment utilisé pour sa simplicité est le mélange gaussien. Cependant, un tel modèle est sensible aux données atypiques. Pour remédier à cela, nous présentons ici le mélange de lois de Student multivariées à échelles multiples, que nous sommes en train d'incorporer au sein d'un paquet R. Ces lois peuvent gérer des queues de lourdeurs différentes selon les directions alors que les lois gaussiennes et les lois de Student multivariées standards sont contraintes à être symétriques

TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images

The volume of a brain lesion (e.g. infarct or tumor) is a powerful indicator of patient prognosis and can be used to guide the therapeutic strategy. Lesional volume estimation is usually performed by segmentation with deep convolutional neural networks (CNN), currently the state-of-the-art approach. However, to date, few work has been done to equip volume segmentation tools with adequate quantitative predictive intervals, which can hinder their usefulness and acceptation in clinical practice. In this work, we propose TriadNet, a segmentation approach relying on a multi-head CNN architecture, which provides both the lesion volumes and the associated predictive intervals simultaneously, in less than a second. We demonstrate its superiority over other solutions on BraTS 2021, a large-scale MRI glioblastoma image database.Comment: Accepted for presentation at the Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) at MICCAI 202

Multi-layer Aggregation as a key to feature-based OOD detection

Deep Learning models are easily disturbed by variations in the input images that were not observed during the training stage, resulting in unpredictable predictions. Detecting such Out-of-Distribution (OOD) images is particularly crucial in the context of medical image analysis, where the range of possible abnormalities is extremely wide. Recently, a new category of methods has emerged, based on the analysis of the intermediate features of a trained model. These methods can be divided into 2 groups: single-layer methods that consider the feature map obtained at a fixed, carefully chosen layer, and multi-layer methods that consider the ensemble of the feature maps generated by the model. While promising, a proper comparison of these algorithms is still lacking. In this work, we compared various feature-based OOD detection methods on a large spectra of OOD (20 types), representing approximately 7800 3D MRIs. Our experiments shed the light on two phenomenons. First, multi-layer methods consistently outperform single-layer approaches, which tend to have inconsistent behaviour depending on the type of anomaly. Second, the OOD detection performance highly depends on the architecture of the underlying neural network.Comment: Accepted for presentation at the Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging (UNSURE) at MICCAI 202

A deterministic detector for vector vortex states

Encoding information in high-dimensional degrees of freedom of photons has led to new avenues in various quantum protocols such as communication and information processing. Yet to fully benefit from the increase in dimension requires a deterministic detection system, e.g., to reduce dimension dependent photon loss in quantum key distribution. Recently, there has been a growing interest in using vector vortex modes, spatial modes of light with entangled degrees of freedom, as a basis for encoding information. However, there is at present no method to detect these non-separable states in a deterministic manner, negating the benefit of the larger state space. Here we present a method to deterministically detect single photon states in a four dimensional space spanned by vector vortex modes with entangled polarisation and orbital angular momentum degrees of freedom. We demonstrate our detection system with vector vortex modes from the |[Formula: see text]| = 1 and |[Formula: see text]| = 10 subspaces using classical and weak coherent states and find excellent detection fidelities for both pure and superposition vector states. This work opens the possibility to increase the dimensionality of the state-space used for encoding information while maintaining deterministic detection and will be invaluable for long distance classical and quantum communication