IRIS observations of magnetic interactions in the solar atmosphere between pre-existing and emerging magnetic fields. II. UV emission properties

Multi-wavelength ultraviolet (UV) observations by the IRIS satellite in active region NOAA 12529 have recently pointed out the presence of long-lasting brightenings, akin to UV bursts, and simultaneous plasma ejections occurring in the upper chromosphere and transition region during secondary flux emergence. These signatures have been interpreted as evidence of small-scale, recurrent magnetic reconnection episodes between the emerging flux region (EFR) and the pre-existing plage field. Here, we characterize the UV emission of these strong, intermittent brightenings and we study the surge activity above the chromospheric arch filament system (AFS) overlying the EFR. We analyze the surges and the cospatial brightenings observed at different wavelengths. We find an asymmetry in the emission between the blue and red wings of the Si IV 1402 \AA{} and Mg II k 2796.3 \AA{} lines, which clearly outlines the dynamics of the structures above the AFS that form during the small-scale eruptive phenomena. We also detect a correlation between the Doppler velocity and skewness of the Si IV 1394 \AA{} and 1402 \AA{} line profiles in the UV burst pixels. Finally, we show that genuine emission in the Fe XII 1349.4 \AA{} line is cospatial to the Si IV brightenings. This definitely reveals a pure coronal counterpart to the reconnection event.Comment: 19 pages, 8 figures + 3 figures in the Appendix; accepted in Ap

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification. However, ImageNet was created during a specific period in time, and as such it is prone to aging, as well as dataset bias issues. Moving beyond fixed training datasets will lead to more robust visual systems, especially when deployed on robots in new environments which must train on the objects they encounter there. To make this possible, it is important to break free from the need for manual annotators. Recent work has begun to investigate how to use the massive amount of images available on the Web in place of manual image annotations. We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy. By combining these two results, we obtain a method for learning powerful deep object models automatically from the Web. We confirm the effectiveness of our approach through object categorization experiments using our Web-derived version of ImageNet on a popular robot vision benchmark database, and on a lifelong object discovery task on a mobile robot.Comment: 8 pages, 7 figures, 3 table

The Narrating Subject: Student Reflection on Witness Narrative

Research Question: Can reflective writing capture the process of thinking when students read or listen to a first person narrative involving a Holocaust Survivor, a combat veteran, a former child soldier, clinical depression, and post-traumatic stress? Is the cognitive/affective capacity made visible as the student becomes a narrating subject his or her self in response to trauma and recovery narrative

Predicting host taxonomic information from viral genomes: a comparison of feature representations

The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus on nucleotide features, ignoring other representations of genomic information. Here we investigate the predictive potential of features generated from four different ‘levels’ of viral genome representation: nucleotide, amino acid, amino acid properties and protein domains. This more fully exploits the biological information present in the virus genomes. Over a hundred and eighty binary datasets for infecting versus non-infecting viruses at all taxonomic ranks of both eukaryote and prokaryote hosts were compiled. The viral genomes were converted into the four different levels of genome representation and twenty feature sets were generated by extracting k-mer compositions and predicted protein domains. We trained and tested Support Vector Machine, SVM, classifiers to compare the predictive capacity of each of these feature sets for each dataset. Our results show that all levels of genome representation are consistently predictive of host taxonomy and that prediction k-mer composition improves with increasing k-mer length for all k-mer based features. Using a phylogenetically aware holdout method, we demonstrate that the predictive feature sets contain signals reflecting both the evolutionary relationship between the viruses infecting related hosts, and host-mimicry. Our results demonstrate that incorporating a range of complementary features, generated purely from virus genome sequences, leads to improved accuracy for a range of virus host prediction tasks enabling computational assignment of host taxonomic information

Mining virus genomes for host predictive signals

The total dependence of a virus on its host for its survival leads to a fundamental entanglement with its host’s cellular machinery. This drives a coevolutionary relationship that leaves an imprint of the host in viral genomes. The aim of this thesis was to develop machine learning approaches to identify and exploit these host predictive signals. We present methods that use these signals both to build classifiers that can assign putative information to virus genomes and to locate the discriminative features on viral proteins thereby identifying regions that are important in the host relationship. The first step aimed to identify discriminative features that capture the different aspects of the virus host relationship. We generated a range of feature sets from alternative representations of the viral genomes that each aimed to exploit the different levels of biological information present. We used a supervised machine learning approach to compare a range of feature sets for their ability to predict host taxonomic information. Next, we opened these “black box” classifiers and to extract the discriminative information learnt by the model to identify regions of a viral protein that are associated with their host relationship. We used the ‘local’ nature of some of the predictive feature sets to transform an amino acid sequence into host signals. Finally, we developed a multi-view generative mixture model, MVC, to tease apart the complex signals that are embedded in viral genomes via different evolutionary processes. This Bayesian approach uses the clustering of the data defined by labels of interest to guide the features associated with those labels into the "relevant view". The MVC model is able to identify features associated with weak effect in the data

Contested representation: an historical reassessment of the work of art filmmakers in the PRC, 1989-2001

This thesis reconsiders the work of art filmmakers in the People’s Republic of China between 1989 and 2001. These dates bookend the decade of the 1990s, comprising two defining moments in the reform era: the Tiananmen Square political crisis in 1989, and the entry of China into the WTO and the global market economy in 2001. The 1990s is therefore approached in this research as a transitional decade, in which the future direction of China was being decided. The term ‘art film’ is used to identify a distinct mode of film practice, characterised by a peripheral position, a clear directorial voice, and an emphasis on aesthetics. This rubric therefore incorporates films made by a range of auteur directors, rather than solely the ‘independent’ or ‘underground’ works commonly assessed in studies of the decade. By examining the representational modes used by art filmmakers in the 1990s, filmic innovations can be seen to constitute an artistic response to the restrictions placed on representation by the State. This thesis argues that historical reassessment was a key factor in the innovation of cinematic representation in the 1990s. Utilising a cultural history approach, the thesis engages in close textual analysis of seventeen films, identifying and contextualising the representational conventions drawn on by filmmakers. The thesis is structured around five thematic chapters, each dealing with a cluster of films focused on similar content. The first chapter examines filmic reassessments of China’s socialist history, and concludes that the limitations of the official narrative provided opportunities for the assertion of alternative histories. The subsequent chapters develop on the concept of historical reassessment by looking at changing modes of cinematic representation in relation to rural populations, women and gender, urban regeneration, and youth culture. By engaging in a wide-ranging survey of how key themes were represented in art films in the 1990s, the thesis reveals the critical role which historical reassessment played in pushing directors to new levels of artistry and experimentation in their filmmaking. This thesis concludes that by questioning the cinematic forms used historically to represent these issues and social groups, Chinese art filmmakers achieved a new level of artistic independence in their work by the end of the decade

What was Glaucoma Called Before the 20th Century?

Glaucoma involves a characteristic optic neuropathy, often with elevated intraocular pressure. Before 1850, poor vision with a normal eye appearance, as occurs in primary open-angle glaucoma, was termed amaurosis, gutta serena, or black cataract. Few observers noted palpable hardness of the eye in amaurosis. On the other hand, angle-closure glaucoma can produce a green or gray pupil, and therefore was called, variously, glaucoma (derived from the Greek for glaucous, a nonspecific term connoting blue, green, or light gray) and viriditate oculi. Angle closure, with palpable hardness of the eye, mydriasis, and anterior prominence of the lens, was described in greater detail in the 18th and 19th centuries. The introduction of the ophthalmoscope in 1850 permitted the visualization of the excavated optic neuropathy in eyes with a normal or with a dilated greenish-gray pupil. Physicians developed a better appreciation of the role of intraocular pressure in both conditions, which became subsumed under the rubric “glaucoma”