Pathologies of the Brauer-Manin obstruction

We construct a conic bundle over an elliptic curve over a real quadratic field that is a counterexample to the Hasse principle not explained by the \'etale Brauer-Manin obstruction. We also give simple examples of threefolds with the same property that are families of 2-dimensional quadrics, and discuss some other examples and general properties of the Brauer-Manin obstruction.Comment: 22 pages, to appear in Mathematische Zeitschrif

Pathologies in Lovelock AdS Black Branes and AdS/CFT

We study the pathologies in AdS black branes in Lovelock theory. More precisely, we examine the conditions that AdS black branes have the naked singularity, the ghost instability and the dynamical instability. From the point of view of the AdS/CFT correspondence, the pathologies in AdS black branes indicate the pathologies in the corresponding CFT. Hence, we need to be careful when we apply AdS/CFT in Lovelock theory to various phenomena such as the shear viscosity to entropy ratio in strongly coupled quantum filed theory.Comment: 11 pages, 5 figure

A framework for pathologies of message sequence charts

This is the post-print version of the final paper published in Information Software and Technology. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.Context - It is known that a Message Sequence Chart (MSC) specification can contain different types of pathology. However, definitions of different types of pathology and the problems caused by pathologies are unclear, let alone the relationships between them. In this circumstance, it can be problematic for software engineers to accurately predict the possible problems that may exist in implementations of MSC specifications and to trace back to the design problems in MSC specifications from the observed problems of an implementation. Objective - We focus on generating a clearer view on MSC pathologies and building formal relationships between pathologies and the problems that they may cause. Method - By concentrating on the problems caused by pathologies, a categorisation of problems that a distributed system may suffer is first introduced. We investigate the different types of problems and map them to categories of pathologies. Thus, existing concepts related to pathology are refined and necessary concepts in the pathology framework are identified. Finally, we formally prove the relationships between the concepts in the framework. Results - A pathology framework is established as desired based on a restriction that considers problematic scenarios with a single undesirable event. In this framework, we define disjoint categories of both pathologies and the problems caused; the identified types of pathology are successfully mapped to the problems that they may cause. Conclusion - The framework achieved in this paper introduces taxonomies into and clarifies relationships between concepts in research on MSC pathologies. The taxonomies and relationships in the framework can help software engineers to predict problems and verify MSC specifications. The single undesirable event restriction not only enables a categorisation of pathological scenarios, but also has the potential practical benefit that a software engineer can concentrate on key problematic scenarios. This may make it easier to either remove pathologies from an MSC specification MM or test an implementation developed from MM for potential problems resulting from such pathologies

On the Origin of Western Diet Pathologies

The ratio of the two sulfur-containing amino acids, methionine (Met) and cysteine (Cys), may be a determining factor for which foods contribute to longevity and health. It is shown here that substantially more Met than Cys is consistently found in foods, such as dairy and meat products, thought to contribute to pathologies associated with the Western Diet

Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach

Brain extraction from images is a common pre-processing step. Many approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images with strong pathologies, for example, the presence of a tumor or of a traumatic brain injury, is challenging. In such cases, tissue appearance may deviate from normal tissue and violates algorithmic assumptions for these approaches; hence, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis, (2) pathologies are captured via a total variation term, and (3) non-brain tissue is captured by a sparse term. Decomposition and image registration steps are alternated to allow statistical modeling in a fixed atlas space. As a beneficial side effect, the model allows for the identification of potential pathologies and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our method on four datasets: the IBSR and LPBA40 datasets which show normal images, the BRATS dataset containing images with brain tumors and a dataset containing clinical TBI images. We compare the performance with other popular models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing methods on all four datasets. Specifically, our model achieves the best median (97.11) and mean (96.88) Dice scores over all datasets. The two best performing competitors, ROBEX and MASS, achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach is an effective method for high quality brain extraction on a wide variety of images

Calcium in the initiation, progression and as an effector of Alzheimer's disease pathology.

The cause(s) of sporadic Alzheimer's disease (sAD) are complex and currently poorly understood. They likely result from a combination of genetic, environmental, proteomic and lipidomic factors that crucially occur only in the aged brain. Age-related changes in calcium levels and dynamics have the potential to increase the production and accumulation of both amyloid-beta peptide (Abeta) and tau pathologies in the AD brain, although these two pathologies themselves can induce calcium dyshomeostasis, particularly at synaptic membranes. This review discuses the evidence for a role for calcium dyshomeostasis in the initiation of pathology, as well as the evidence for these pathologies themselves disrupting normal calcium homeostasis, which lead to synaptic and neuronal dysfunction, synaptotoxicity and neuronal loss, underlying the dementia associated with the disease

Pathologies of Neural Models Make Interpretations Difficult

One way to interpret neural model predictions is to highlight the most important input features---for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word's importance is determined by either input perturbation---measuring the decrease in model confidence when that word is removed---or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction without accuracy loss on regular examples.Comment: EMNLP 2018 camera read