Context-Aware Embeddings for Automatic Art Analysis

Automatic art analysis aims to classify and retrieve artistic representations from a collection of images by using computer vision and machine learning techniques. In this work, we propose to enhance visual representations from neural networks with contextual artistic information. Whereas visual representations are able to capture information about the content and the style of an artwork, our proposed context-aware embeddings additionally encode relationships between different artistic attributes, such as author, school, or historical period. We design two different approaches for using context in automatic art analysis. In the first one, contextual data is obtained through a multi-task learning model, in which several attributes are trained together to find visual relationships between elements. In the second approach, context is obtained through an art-specific knowledge graph, which encodes relationships between artistic attributes. An exhaustive evaluation of both of our models in several art analysis problems, such as author identification, type classification, or cross-modal retrieval, show that performance is improved by up to 7.3% in art classification and 37.24% in retrieval when context-aware embeddings are used

USING OTOLITH MICROCHEMISTRY TO CLASSIFY YELLOW PERCH AS STOCKED OR NATURALLY PRODUCED

Fisheries managers routinely use stocking to supplement fish populations (Schramm and Piper 1995, Fisher 1996). Stocking eyed-eggs offers substantial cost savings compared to stocking fry and fingerlings (PFBC 2011); however, traditional stocking evaluation using oxytetracycline (OTC) marking of otoliths is ineffective for eyed-eggs of some species (e.g., yellow perch, [Perca fla- vescens]). Thus, there is a need for additional approaches to be able to classify fish as stocked or naturally produced. Fish otoliths are paired calcified structures in the inner ear that permanently deposit trace elements in proportion to water column concentrations (Campana 1999, Campana et al. 2000). Coupled with otolith growth increments (i.e., annuli), elemental accumulation permits retrospective evaluation of environmental history (e.g., natal origins, movement) if water chemistry is spatially heterogeneous and temporally constant (Elsdon et al. 2008). Otolith microchemistry can be used to evaluate stocking contributions (Pracheil et al. 2014) and in the context of eyed-egg stockings, may be useful for classifying fish as stocked or naturally produced. Yellow perch is a popular sport fish species in South Dakota (Gigliotti 2007) and is routinely stocked by fisheries managers to supplement weak year classes (Schoene- beck et al. 2010). The South Dakota Department of Game, Fish and Parks (SDGFP) propagates yellow perch for stocking (e.g., eyed-eggs, fry, fingerlings) and also stocks adult perch through trap and transfer operations (Lott 1991, Fisher 1996). However, the contributions of yellow perch stockings in South Dakota are largely unknown because it is difficult to differentiate stocked fish from resident individuals (Brown and St. Sauver 2002). Our objective was to assess the utility of otolith microchemistry to distinguish hatchery-reared yellow perch stocked at the eyed-egg stage from naturally produced individuals

Genotype Effects and Genotype by Environment Interactions for Traits of Elite Switchgrass Populations

Switchgrass (Panicum virgatum L.) is used as a forage species and has shown potential for use in production of fuel ethanol from cellulosic biomass. Objectives of this research were to determine performance differences between elite switchgrass populations for agronomic, forage quality, and biofuel traits and to determine the magnitude of genotype × environment (G × E) interactions for these traits across midwestern environments.Twenty elite switchgrass populations, consisting of cultivars and advanced breeding populations, were planted in sward trials at Mead, NE, Ames, IA, and West Lafayette, IN, during 1990 and were evaluated in 1991 and 1992. Forage samples were taken at a vegetative growth stage, at heading, and at the end of the season. Plots were harvested for forage yield at heading and at the end of the growing season. Forage composition and in vitro dry matter digestibility was determined using near infrared reflectance spectroscopy. Significant differences (P \u3c 0.05) between populations for forage yield were found at individual locations but not across locations, except at the P = 0.10 probability level, because of G × E interactions. Genotype × environment interactions were significant for hemicellulose plus cellulose (holocellulose) yield, a potentially important biofuel trait. In vitro dry matter digestibility was more stable than both forage yield and holocellulose yield. Despite large G × E interaction effects, a few populations consistently ranked high in forage yield and holocellulose yield. Multiple location, multiple year sward trials will be needed to develop switchgrasses broadly adapted to the midwest

Genotypic Variability and Genotype × Environment Interactions among Switchgrass Accessions from the Midwestern USA

Genetic variation for economically important traits in switchgrass (Panicum virgatum L.) is needed to develop improved populations. Objectives of this research were to determine the genotypic variability, and the magnitude of genotype × enviromnent (G × E) interaction for agronomic, forage quality, and biofuel feedstock traits among switchgrass accessions collected from remnant midwestern prairies. A total of 23 accessions and five check strains were evaluated in space planted nurseries at Mead, NE; Ames, IA; and West Lafayette, IN, during 1991 and 1992. Forage quality traits were measured at a vegetative growth stage and at heading. Disease ratings were taken just prior to forage harvest at heading. Forage composition was determined by near infrared reflectance spectroscopy. Across locations and years, significant variation among accessions was observed for forage yield at heading, vegetative in vitro dry matter digestibility (IVDMD), and heading date. Some accessions, such as IA34, were comparable in forage yield at heading to check strains and should be useful genetic sources of variation for this trait. Except for disease rating, G × E interactions were important for all traits. Selection among accessions for forage yield at heading followed by selection for IVDMD within such accessions should be an effective approach in utilizing genetic variation in switchgrasses from remnant prairie sites

Bridging the Climate Information Gap: A Framework for Engaging Knowledge Brokers and Decision Makers in State Climate Assessments

Large-scale analyses like the National Climate Assessment (NCA) contain a wealth of information critical to national and regional responses to climate change but tend to be insufficiently detailed for action at state or local levels. Many states now engage in assessment processes to meet information needs for local authorities. The goals of state climate assessments (SCAs) should be to provide relevant, actionable information to state and local authorities, and to generate primary sources, build networks and inform stakeholders. To communicate local climate impacts to decision makers, SCAs should express credibility, salience and legitimacy. They can provide information (e.g., case studies, data sets) and connect stakeholders to the NCA and its process. Based on our experience in the Vermont Climate Assessment (VCA), we present a framework to engage decision makers in SCAs using a fluid network of scientific experts and knowledge brokers to conduct subject area prioritization, data analysis and writing. The VCA addressed economic, environmental and social impacts of climate change at local scales to increase resiliency and manage risk. Knowledge brokers communicated VCA findings through their own stakeholder networks. We include a qualitative impact evaluation, and believe our framework for interaction among scientists, knowledge brokers and stakeholders to be an effective structure for SCAs and a transformative experience for students

A moving grid finite element method applied to a model biological pattern generator

Many problems in biology involve growth. In numerical simulations it can therefore be very convenient to employ a moving computational grid on a continuously deforming domain. In this paper we present a novel application of the moving grid finite element method to compute solutions of reaction–diffusion systems in two-dimensional continuously deforming Euclidean domains. A numerical software package has been developed as a result of this research that is capable of solving generalised Turing models for morphogenesis

Second cohomology for finite groups of Lie type

Let $G$ be a simple, simply-connected algebraic group defined over $\mathbb{F}_p$. Given a power $q = p^r$ of $p$, let $G(\mathbb{F}_q) \subset G$ be the subgroup of $\mathbb{F}_q$-rational points. Let $L(\lambda)$ be the simple rational $G$-module of highest weight $\lambda$. In this paper we establish sufficient criteria for the restriction map in second cohomology $H^2(G,L(\lambda)) \rightarrow H^2(G(\mathbb{F}_q),L(\lambda))$ to be an isomorphism. In particular, the restriction map is an isomorphism under very mild conditions on $p$ and $q$ provided $\lambda$ is less than or equal to a fundamental dominant weight. Even when the restriction map is not an isomorphism, we are often able to describe $H^2(G(\mathbb{F}_q),L(\lambda))$ in terms of rational cohomology for $G$. We apply our techniques to compute $H^2(G(\mathbb{F}_q),L(\lambda))$ in a wide range of cases, and obtain new examples of nonzero second cohomology for finite groups of Lie type.Comment: 29 pages, GAP code included as an ancillary file. Rewritten to include the adjoint representation in types An, B2, and Cn. Corrections made to Theorem 3.1.3 and subsequent dependent results in Sections 3-4. Additional minor corrections and improvements also implemente

First cohomology for finite groups of Lie type: simple modules with small dominant weights

Let $k$ be an algebraically closed field of characteristic $p > 0$, and let $G$ be a simple, simply connected algebraic group defined over $\mathbb{F}_p$. Given $r \geq 1$, set $q=p^r$, and let $G(\mathbb{F}_q)$ be the corresponding finite Chevalley group. In this paper we investigate the structure of the first cohomology group $H^1(G(\mathbb{F}_q),L(\lambda))$ where $L(\lambda)$ is the simple $G$-module of highest weight $\lambda$. Under certain very mild conditions on $p$ and $q$, we are able to completely describe the first cohomology group when $\lambda$ is less than or equal to a fundamental dominant weight. In particular, in the cases we consider, we show that the first cohomology group has dimension at most one. Our calculations significantly extend, and provide new proofs for, earlier results of Cline, Parshall, Scott, and Jones, who considered the special case when $\lambda$ is a minimal nonzero dominant weight.Comment: 24 pages, 5 figures, 6 tables. Typos corrected and some proofs streamlined over previous versio

Food defense : protecting the food supply from intentional harm (2009)

New 11/09/2M