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A survey of cross-layer power-reliability tradeoffs in multi and many core systems-on-chip

UC Irvine Previously Published Works (2013)

As systems-on-chip increase in complexity, the underlying technology presents us with significant challenges due to increased power consumption as well as decreased reliability. Today, designers must consider building systems that achieve the requisite functionality and performance using components that may be unreliable. In order to do so, it is crucial to understand the close interplay between the different layers of a system: technology, platform, and application. This will enable the most general tradeoff exploration, reaping the most benefits in power, performance and reliability. This paper surveys various cross layer techniques and approaches for power, performance, and reliability tradeoffs are technology, circuit, architecture and application layers. © 2013 Elsevier B.V. All rights reserved.

Cover page: A survey of cross-layer power-reliability tradeoffs in multi and many core systems-on-chip

Article

Big-Bee: Una iniciativa para promover el conocimiento de las abejas a través de la digitalización de imágenes y datos de rasgos. ID 112.

Posters (2021)

Las abejas son fundamentales para nuestra seguridad alimentaria y la polinización de las plantas silvestres y cultivadas. Sin embargo, algunas poblaciones y especies están en riesgo de desaparecer. Nuestro conocimiento de los factores que causan estas disminuciones es limitado, en parte porque carecemos de datos suficientes sobre la distribución de las especies que nos sirvan para predecir cambios en su rango geográfico bajo escenarios de cambio climático. Además, carecemos de datos adecuados sobre las características morfológicas y comportamentales que podrían influir en la vulnerabilidad de las abejas a los cambios ambientales inducidos por el hombre, como la pérdida de hábitat y el cambio climático. Afortunadamente, se puede extraer una gran cantidad de información a partir de los especímenes depositados en colecciones entomológicas. Acá presentamos este proyecto, el cual incluye 13 instituciones y es financiado por la Fundación Nacional de Ciencias de los EE. UU. (NSF, por sus siglas en Ingles). En el transcurso de tres años, crearemos más de un millón de imágenes (2D y 3D) de alta resolución de especímenes de abejas que representan alrededor de 1⁄4 de la diversidad mundial. También desarrollaremos herramientas para medir los rasgos de las abejas a partir de las imágenes. La información generada estará disponible a través de un portal de datos abierto Symbiota-Light llamado Bee Library. Además, los datos de interacción biótica y asociación de especies se compartirán a través de Global Biotic Interactions. Presented by Victor Hugo Gonzalez at the XII Congreso Mesoamericano de Abejas Nativas, Centro de Investigaciones Apícolas Tropicales (CINAT), Universidad Nacional, Costa Rica, Nov. 20-21, 2021

Cover page: Big-Bee: Una iniciativa para promover el conocimiento de las abejas a través de la digitalización de imágenes y datos de rasgos. ID 112.

Article

Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization.

Posters (2021)

While bees are critical to sustaining a large proportion of global food production, as well as pollinating both wild and cultivated plants, they are decreasing in both numbers and diversity. Our understanding of the factors driving these declines is limited, in part, because we lack sufficient data on the distribution of bee species to predict changes in their geographic range under climate change scenarios. Additionally lacking is adequate data on the behavioral and anatomical traits that may make bees either vulnerable or resilient to human-induced environmental changes, such as habitat loss and climate change. Fortunately, a wealth of associated attributes can be extracted from the specimens deposited in natural history collections for over 100 years.

Extending Anthophila Research Through Image and Trait Digitization (Big-Bee) is a newly funded US National Science Foundation Advancing Digitization of Biodiversity Collections project. Over the course of three years, we will create over one million high-resolution 2D and 3D images of bee specimens (Fig. 1), representing over 5,000 worldwide bee species, including most of the major pollinating species. We will also develop tools to measure bee traits from images and generate comprehensive bee trait and image datasets to measure changes through time. The Big-Bee network of participating institutions includes 13 US institutions (Fig. 2) and partnerships with US government agencies. We will develop novel mechanisms for sharing image datasets and datasets of bee traits that will be available through an open, Symbiota-Light (Gilbert et al. 2020) data portal called the Bee Library. In addition, biotic interaction and species association data will be shared via Global Biotic Interactions (Poelen et al. 2014). The Big-Bee project will engage the public in research through community science via crowdsourcing trait measurements and data transcription from images using Notes from Nature (Hill et al. 2012). Training and professional development for natural history collection staff, researchers, and university students in data science will be provided through the creation and implementation of workshops focusing on bee traits and species identification. We are also planning a short, artistic college radio segment called "the Buzz" to get people excited about bees, biodiversity, and the wonders of our natural world.

This poster was prepared for TDWG 2021 virtual conference.

Cover page: Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization.

Article
Peer Reviewed

Genomic data provide insights into the classification of extant termites.

UC Riverside Previously Published Works (2024)

The higher classification of termites requires substantial revision as the Neoisoptera, the most diverse termite lineage, comprise many paraphyletic and polyphyletic higher taxa. Here, we produce an updated termite classification using genomic-scale analyses. We reconstruct phylogenies under diverse substitution models with ultraconserved elements analyzed as concatenated matrices or within the multi-species coalescence framework. Our classification is further supported by analyses controlling for rogue loci and taxa, and topological tests. We show that the Neoisoptera are composed of seven family-level monophyletic lineages, including the Heterotermitidae Froggatt, Psammotermitidae Holmgren, and Termitogetonidae Holmgren, raised from subfamilial rank. The species-rich Termitidae are composed of 18 subfamily-level monophyletic lineages, including the new subfamilies Crepititermitinae, Cylindrotermitinae, Forficulitermitinae, Neocapritermitinae, Protohamitermitinae, and Promirotermitinae; and the revived Amitermitinae Kemner, Microcerotermitinae Holmgren, and Mirocapritermitinae Kemner. Building an updated taxonomic classification on the foundation of unambiguously supported monophyletic lineages makes it highly resilient to potential destabilization caused by the future availability of novel phylogenetic markers and methods. The taxonomic stability is further guaranteed by the modularity of the new termite classification, designed to accommodate as-yet undescribed species with uncertain affinities to the herein delimited monophyletic lineages in the form of new families or subfamilies.

Cover page: Genomic data provide insights into the classification of extant termites.

Article
Peer Reviewed

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

UC Davis Previously Published Works (2019)

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.