Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Electroweak Symmetry Breaking in the DSSM

We study the theoretical and phenomenological consequences of modifying the Kahler potential of the MSSM two Higgs doublet sector. Such modifications naturally arise when the Higgs sector mixes with a quasi-hidden conformal sector, as in some F-theory GUT models. In the Delta-deformed Supersymmetric Standard Model (DSSM), the Higgs fields are operators with non-trivial scaling dimension 1 < Delta < 2. The Kahler metric is singular at the origin of field space due to the presence of quasi-hidden sector states which get their mass from the Higgs vevs. The presence of these extra states leads to the fact that even as Delta approaches 1, the DSSM does not reduce to the MSSM. In particular, the Higgs can naturally be heavier than the W- and Z-bosons. Perturbative gauge coupling unification, a large top quark Yukawa, and consistency with precision electroweak can all be maintained for Delta close to unity. Moreover, such values of Delta can naturally be obtained in string-motivated constructions. The quasi-hidden sector generically contains states charged under SU(5)_GUT as well as gauge singlets, leading to a rich, albeit model-dependent, collider phenomenology.Comment: v3: 40 pages, 3 figures, references added, typos correcte

Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel deep learning-based approach for this task. Specifically, we present a convolutional neural network (CNN), which performs automatic feature extraction from a given handwritten image, followed by classification of the writer's gender. Also, we introduce a new dataset of labeled handwritten samples, in Hebrew and English, of 405 participants. Comparing the gender classification accuracy on this dataset against human examiners, our results show that the proposed deep learning-based approach is substantially more accurate than that of humans

Effect of Biodiversity Changes in Disease Risk: Exploring Disease Emergence in a Plant-Virus System

The effect of biodiversity on the ability of parasites to infect their host and cause disease (i.e. disease risk) is a major question in pathology, which is central to understand the emergence of infectious diseases, and to develop strategies for their management. Two hypotheses, which can be considered as extremes of a continuum, relate biodiversity to disease risk: One states that biodiversity is positively correlated with disease risk (Amplification Effect), and the second predicts a negative correlation between biodiversity and disease risk (Dilution Effect). Which of them applies better to different host-parasite systems is still a source of debate, due to limited experimental or empirical data. This is especially the case for viral diseases of plants. To address this subject, we have monitored for three years the prevalence of several viruses, and virus-associated symptoms, in populations of wild pepper (chiltepin) under different levels of human management. For each population, we also measured the habitat species diversity, host plant genetic diversity and host plant density. Results indicate that disease and infection risk increased with the level of human management, which was associated with decreased species diversity and host genetic diversity, and with increased host plant density. Importantly, species diversity of the habitat was the primary predictor of disease risk for wild chiltepin populations. This changed in managed populations where host genetic diversity was the primary predictor. Host density was generally a poorer predictor of disease and infection risk. These results support the dilution effect hypothesis, and underline the relevance of different ecological factors in determining disease/infection risk in host plant populations under different levels of anthropic influence. These results are relevant for managing plant diseases and for establishing conservation policies for endangered plant species

Four patients with a history of acute exacerbations of COPD: implementing the CHEST/Canadian Thoracic Society guidelines for preventing exacerbations

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Identification of Lipases Involved in PBAN Stimulated Pheromone Production in Bombyx mori Using the DGE and RNAi Approaches

BACKGROUND: Pheromone biosynthesis activating neuropeptide (PBAN) is a neurohormone that regulates sex pheromone synthesis in female moths. Bombyx mori is a model organism that has been used to explore the signal transduction pattern of PBAN, which is mediated by a G-protein coupled receptor (GPCR). Although significant progress has been made in elucidating PBAN-regulated lipolysis that releases the precursor of the sex pheromone, little is known about the molecular components involved in this step. To better elucidate the molecular mechanisms of PBAN-stimulated lipolysis of cytoplasmic lipid droplets (LDs), the associated lipase genes involved in PBAN- regulated sex pheromone biosynthesis were identified using digital gene expression (DGE) and subsequent RNA interference (RNAi). RESULTS: Three DGE libraries were constructed from pheromone glands (PGs) at different developed stages, namely, 72 hours before eclosion (-72 h), new emergence (0 h) and 72 h after eclosion (72 h), to investigate the gene expression profiles during PG development. The DGE evaluated over 5.6 million clean tags in each PG sample and revealed numerous genes that were differentially expressed at these stages. Most importantly, seven lipases were found to be richly expressed during the key stage of sex pheromone synthesis and release (new emergence). RNAi-mediated knockdown confirmed for the first time that four of these seven lipases play important roles in sex pheromone synthesis. CONCLUSION: This study has identified four lipases directly involved in PBAN-stimulated sex pheromone biosynthesis, which improve our understanding of the lipases involved in releasing bombykol precursors from triacylglycerols (TAGs) within the cytoplasmic LDs

A novel bioactive derivative of eicosapentaenoic acid (EPA) suppresses intestinal tumor development in ApcΔ14/+ mice

Familial adenomatous polyposis (FAP) is a genetic disorder characterized by the development of hundreds of polyps throughout the colon. Without prophylactic colectomy, most individuals with FAP develop colorectal cancer at an early age. Treatment with EPA in the free fatty acid form (EPA-FFA) has been shown to reduce polyp burden in FAP patients. Since high-purity EPA-FFA is subject to rapid oxidation, a stable form of EPA compound has been developed in the form of magnesium l-lysinate bis-eicosapentaenoate (TP-252). We assessed the chemopreventive efficacy of TP-252 on intestinal tumor formation using ApcΔ14/+ mice and compared it with EPA-FFA. TP-252 was supplemented in a modified AIN-93G diet at 1, 2 or 4% and EPA-FFA at 2.5% by weight and administered to mice for 11 weeks. We found that administration of TP-252 significantly reduced tumor number and size in the small intestine and colon in a dose-related manner and as effectively as EPA-FFA. To gain further insight into the cancer protection afforded to the colon, we performed a comprehensive lipidomic analysis of total fatty acid composition and eicosanoid metabolites. Treatment with TP-252 significantly decreased the levels of arachidonic acid (AA) and increased EPA concentrations within the colonic mucosa. Furthermore, a classification and regression tree (CART) analysis revealed that a subset of fatty acids, including EPA and docosahexaenoic acid (DHA), and their downstream metabolites, including PGE3 and 14-hydroxy-docosahexaenoic acid (HDoHE), were strongly associated with antineoplastic activity. These results indicate that TP-252 warrants further clinical development as a potential strategy for delaying colectomy in adolescent FAP patients

Analysis Method and Experimental Conditions Affect Computed Circadian Phase from Melatonin Data

Accurate determination of circadian phase is necessary for research and clinical purposes because of the influence of the master circadian pacemaker on multiple physiologic functions. Melatonin is presently the most accurate marker of the activity of the human circadian pacemaker. Current methods of analyzing the plasma melatonin rhythm can be grouped into three categories: curve-fitting, threshold-based and physiologically-based linear differential equations. To determine which method provides the most accurate assessment of circadian phase, we compared the ability to fit the data and the variability of phase estimates for seventeen different markers of melatonin phase derived from these methodological categories. We used data from three experimental conditions under which circadian rhythms - and therefore calculated melatonin phase - were expected to remain constant or progress uniformly. Melatonin profiles from older subjects and subjects with lower melatonin amplitude were less likely to be fit by all analysis methods. When circadian drift over multiple study days was algebraically removed, there were no significant differences between analysis methods of melatonin onsets (P = 0.57), but there were significant differences between those of melatonin offsets (P<0.0001). For a subset of phase assessment methods, we also examined the effects of data loss on variability of phase estimates by systematically removing data in 2-hour segments. Data loss near onset of melatonin secretion differentially affected phase estimates from the methods, with some methods incorrectly assigning phases too early while other methods assigning phases too late; missing data at other times did not affect analyses of the melatonin profile. We conclude that melatonin data set characteristics, including amplitude and completeness of data collection, differentially affect the results depending on the melatonin analysis method used

Structure in 6D and 4D N=1 supergravity theories from F-theory

We explore some aspects of 4D supergravity theories and F-theory vacua that are parallel to structures in the space of 6D theories. The spectrum and topological terms in 4D supergravity theories correspond to topological data of F-theory geometry, just as in six dimensions. In particular, topological axion-curvature squared couplings appear in 4D theories; these couplings are characterized by vectors in the dual to the lattice of axion shift symmetries associated with string charges. These terms are analogous to the Green-Schwarz terms of 6D supergravity theories, though in 4D the terms are not generally linked with anomalies. We outline the correspondence between F-theory topology and data of the corresponding 4D supergravity theories. The correspondence of geometry with structure in the low-energy action illuminates topological aspects of heterotic-F-theory duality in 4D as well as in 6D. The existence of an F-theory realization also places geometrical constraints on the 4D supergravity theory in the large-volume limit.Comment: 63 page