Reforming Hellenization into a Two-Way Street: the dialectic of colonization between Greeks and Sikels in eastern Sicily

Extensive colonization was a key feature of Greek-speaking societies of the ancient Mediterranean. Diffusion of colonizers likewise led to a diffusion of the colonized, ramifications of which pepper extant literature. Rather than acknowledging these groups’ multi-vocality, Classical scholarship traditionally discusses their relationship employing the one-sided term, “Hellenization.” Even those interested in the experiences of the colonized often employ concepts such as appropriation and assimilation in their discussions. Rejecting these approaches, this paper employs a case study of Greek colonization in eastern Sicily to seek, instead, a dialectic, a lens to account for the nuances of pluralism inherent in these interactions

Exploiting citation networks for large-scale author name disambiguation

We present a novel algorithm and validation method for disambiguating author names in very large bibliographic data sets and apply it to the full Web of Science (WoS) citation index. Our algorithm relies only upon the author and citation graphs available for the whole period covered by the WoS. A pair-wise publication similarity metric, which is based on common co-authors, self-citations, shared references and citations, is established to perform a two-step agglomerative clustering that first connects individual papers and then merges similar clusters. This parameterized model is optimized using an h-index based recall measure, favoring the correct assignment of well-cited publications, and a name-initials-based precision using WoS metadata and cross-referenced Google Scholar profiles. Despite the use of limited metadata, we reach a recall of 87% and a precision of 88% with a preference for researchers with high h-index values. 47 million articles of WoS can be disambiguated on a single machine in less than a day. We develop an h-index distribution model, confirming that the prediction is in excellent agreement with the empirical data, and yielding insight into the utility of the h-index in real academic ranking scenarios.Comment: 14 pages, 5 figure

Towards sustainability in ex situ populations

Um künftig Zuchtmethoden etablieren zu können, behandelt die vorliegende Dissertation die drei essentiellen Schritte in der sexuellen Vermehrung von Korallen: Das Planulationsverhalten, die Ansiedlung sowie die ersten Jugendstadien wurden anhand von Modelarten in ex situ Populationen studiert. Die vorliegende Arbeit umfasst praktische, anwendungsorientierte und wichtige grundlagenwissenschaftliche Aspekte bezüglich der Fortpflanzungsökologie, als auch der Marikultur und Aquakultur von riffbildenden Steinkorallen. In der vorliegenden Dissertation wurden zahlreiche Techniken entwickelt, um die nötige Basis zu einer umfangreich ex situ Zucht zu bilden. Dies umfasst Transporttechniken für adulte Brutkolonien, Korallenlarven und Primärpolypen. Ferner wurden neuartige Siedlungssubstrate zur räumlichen Steuerung der Ansiedlung, sowie der effektiven Handhabung der Primärpolypen-Substrateinheit entwickelt. Unter Verwendung dieser Substrate wurde der Einfluss des Biofilms auf die Ansiedlung verschiedener Korallenarten untersucht. Weiterhin wurden die ersten Lebensstadien unter verschiedenen Lichtbedingungen mit und ohne Herbivore studiert. Die entwickelten Techniken wurden schließlich in einer Fallstudie in Kooperation mit 5 europäischen Zooaquarien hinsichtlich ihrer praktischen Anwendbarkeit untersucht

Towards Error Handling in a DSL for Robot Assembly Tasks

This work-in-progress paper presents our work with a domain specific language (DSL) for tackling the issue of programming robots for small-sized batch production. We observe that as the complexity of assembly increases so does the likelihood of errors, and these errors need to be addressed. Nevertheless, it is essential that programming and setting up the assembly remains fast, allows quick changeovers, easy adjustments and reconfigurations. In this paper we present an initial design and implementation of extending an existing DSL for assembly operations with error specification, error handling and advanced move commands incorporating error tolerance. The DSL is used as part of a framework that aims at tackling uncertainties through a probabilistic approach.Comment: Presented at DSLRob 2014 (arXiv:cs/1411.7148

Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus

Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS

Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus

Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively

Restoration of critically endangered elkhorn coral (Acropora palmata) populations using larvae reared from wild-caught gametes

AbstractElkhorn coral (Acropora palmata) populations provide important ecological functions on shallow Caribbean reefs, many of which were lost when a disease reduced their abundance by more than 95% beginning in the mid-1970s. Since then, a lack of significant recovery has prompted rehabilitation initiatives throughout the Caribbean. Here, we report the first successful outplanting and long-term survival of A. palmata settlers reared from gametes collected in the field. A. palmata larvae were settled on clay substrates (substrate units) and either outplanted on the reef two weeks after settlement or kept in a land-based nursery. After 2.5 years, the survival rate of A. palmata settlers outplanted two weeks after settlement was 6.8 times higher (3.4%) than that of settlers kept in a land-based nursery (0.5%). Furthermore, 32% of the substrate units on the reef still harbored one or more well-developed recruit compared to 3% for substrate units kept in the nursery. In addition to increasing survival, outplanting A. palmata settlers shortly after settlement reduced the costs to produce at least one 2.5-year-old A. palmata individual from $325 to$13 USD. Thus, this study not only highlights the first successful long-term rearing of this critically endangered coral species, but also shows that early outplanting of sexually reared coral settlers can be more cost-effective than the traditional approach of nursery rearing for restoration efforts aimed at rehabilitating coral populations