A 3D cone beam computed tomography study of the styloid process of the temporal bone

Background: To investigate the length and three-dimensional orientation and to detail the morphological variations of the styloid process.Materials and methods: Forty-four patients undergoing temporal bone evaluation for different reasons were randomly selected and included in the present study. The length, angulation in the coronal and sagittal planes, as well as morphological variations of the styloid processes were assessed using conebeam computer tomography. Pearson’s correlation coefficient was used to test possible associations between the length of styloid process and angulations, as well as between angulations. Student’s t-test was used to compare the differencesbetween the sample mean length and angulations in normal and elongated styloid process groups.Results: The sagittal angle showed weak positive correlations with the styloid process length and the transverse angle (r = 0.24, p = 0.02, n = 88). A medium positive correlation was found between the sagittal and transverse angulations in the elongated styloid process group (r = 0.49, p = 0.0015, n = 38).There was a statistical significant difference between the mean sagittal angulation in elongated styloid and normal styloid process groups (p = 0.015). The styloid process morphology also varied in terms of shape, number, and degree of ossification.Conclusions: The morphometric and morphologic variations of the styloid process may be important factors to be taken into account not only from the viewpoint of styloid syndromes, but also in preoperatory planning and during surgery

Progressive Neural Networks

Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features. We evaluate this architecture extensively on a wide variety of reinforcement learning tasks (Atari and 3D maze games), and show that it outperforms common baselines based on pretraining and finetuning. Using a novel sensitivity measure, we demonstrate that transfer occurs at both low-level sensory and high-level control layers of the learned policy

Meta-Learning by the Baldwin Effect

The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan. To this date there has been no demonstration of its necessity in empirically challenging tasks. Here we show that the Baldwin effect is capable of evolving few-shot supervised and reinforcement learning mechanisms, by shaping the hyperparameters and the initial parameters of deep learning algorithms. Furthermore it can genetically accommodate strong learning biases on the same set of problems as a recent machine learning algorithm called MAML "Model Agnostic Meta-Learning" which uses second-order gradients instead of evolution to learn a set of reference parameters (initial weights) that can allow rapid adaptation to tasks sampled from a distribution. Whilst in simple cases MAML is more data efficient than the Baldwin effect, the Baldwin effect is more general in that it does not require gradients to be backpropagated to the reference parameters or hyperparameters, and permits effectively any number of gradient updates in the inner loop. The Baldwin effect learns strong learning dependent biases, rather than purely genetically accommodating fixed behaviours in a learning independent manner

Continual Unsupervised Representation Learning

Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowledge of task labels and boundaries. In this work, we propose an approach (CURL) to tackle a more general problem that we will refer to as unsupervised continual learning. The focus is on learning representations without any knowledge about task identity, and we explore scenarios when there are abrupt changes between tasks, smooth transitions from one task to another, or even when the data is shuffled. The proposed approach performs task inference directly within the model, is able to dynamically expand to capture new concepts over its lifetime, and incorporates additional rehearsal-based techniques to deal with catastrophic forgetting. We demonstrate the efficacy of CURL in an unsupervised learning setting with MNIST and Omniglot, where the lack of labels ensures no information is leaked about the task. Further, we demonstrate strong performance compared to prior art in an i.i.d setting, or when adapting the technique to supervised tasks such as incremental class learning.Comment: NeurIPS 201

NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research

We introduce the Never Ending VIsual-classification Stream (NEVIS'22), a benchmark consisting of a stream of over 100 visual classification tasks, sorted chronologically and extracted from papers sampled uniformly from computer vision proceedings spanning the last three decades. The resulting stream reflects what the research community thought was meaningful at any point in time. Despite being limited to classification, the resulting stream has a rich diversity of tasks from OCR, to texture analysis, crowd counting, scene recognition, and so forth. The diversity is also reflected in the wide range of dataset sizes, spanning over four orders of magnitude. Overall, NEVIS'22 poses an unprecedented challenge for current sequential learning approaches due to the scale and diversity of tasks, yet with a low entry barrier as it is limited to a single modality and each task is a classical supervised learning problem. Moreover, we provide a reference implementation including strong baselines and a simple evaluation protocol to compare methods in terms of their trade-off between accuracy and compute. We hope that NEVIS'22 can be useful to researchers working on continual learning, meta-learning, AutoML and more generally sequential learning, and help these communities join forces towards more robust and efficient models that efficiently adapt to a never ending stream of data. Implementations have been made available at https://github.com/deepmind/dm_nevis

The upgrade of the ALICE TPC with GEMs and continuous readout

The upgrade of the ALICE TPC will allow the experiment to cope with the high interaction rates foreseen for the forthcoming Run 3 and Run 4 at the CERN LHC. In this article, we describe the design of new readout chambers and front-end electronics, which are driven by the goals of the experiment. Gas Electron Multiplier (GEM) detectors arranged in stacks containing four GEMs each, and continuous readout electronics based on the SAMPA chip, an ALICE development, are replacing the previous elements. The construction of these new elements, together with their associated quality control procedures, is explained in detail. Finally, the readout chamber and front-end electronics cards replacement, together with the commissioning of the detector prior to installation in the experimental cavern, are presented. After a nine-year period of R&D, construction, and assembly, the upgrade of the TPC was completed in 2020.publishedVersio

Pressure gradient effect on spin-crossover materials: Experiment vs theory

International audienceWe studied the effect of non-hydrostatic pressure on the hysteretic spin crossover in coordination complexes. By introducing into an Ising-like model a double distribution of the interactions and gap energy, respectively, we were able to generate the major hysteresis loop and the first-order reversal curve (FORC) diagram for spin-crossover systems of 106 hysterons (like-spin domains). We show that, for high pressure gradients around the spin-crossover system, the thermal hysteresis loop takes an asymmetric shape, in good agreement with the experimental data on pressure effect recorded at low temperatures, below the solidification of the pressure transmitting medium. Interestingly, the FORC diagram method seems to be much more sensitive to local changes than the “bulk” parameters, which characterize the major hysteresis loop