A GTD analysis of ogive pedestal

The metal ogive pedestal is claimed to have low radar cross section and low observability features. This study uses the Geometric Theory of Diffraction (GTD) to analyze the pedestal scattering for three cases: direct backscattered field, backscattered field structure, and target/pedestal multiple scattering. This study can be used to evaluate the various ways that the metal conical ogive pedestal can effect the performance of a high quality radar cross section measurement system

Insights into Analogy Completion from the Biomedical Domain

Analogy completion has been a popular task in recent years for evaluating the semantic properties of word embeddings, but the standard methodology makes a number of assumptions about analogies that do not always hold, either in recent benchmark datasets or when expanding into other domains. Through an analysis of analogies in the biomedical domain, we identify three assumptions: that of a Single Answer for any given analogy, that the pairs involved describe the Same Relationship, and that each pair is Informative with respect to the other. We propose modifying the standard methodology to relax these assumptions by allowing for multiple correct answers, reporting MAP and MRR in addition to accuracy, and using multiple example pairs. We further present BMASS, a novel dataset for evaluating linguistic regularities in biomedical embeddings, and demonstrate that the relationships described in the dataset pose significant semantic challenges to current word embedding methods.Comment: Accepted to BioNLP 2017. (10 pages

Jointly Embedding Entities and Text with Distant Supervision

Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.Comment: 12 pages; Accepted to 3rd Workshop on Representation Learning for NLP (Repl4NLP 2018). Code at https://github.com/OSU-slatelab/JE

Smallest small-world network

Efficiency in passage times is an important issue in designing networks, such as transportation or computer networks. The small-world networks have structures that yield high efficiency, while keeping the network highly clustered. We show that among all networks with the small-world structure, the most efficient ones have a single ``center'', from which all shortcuts are connected to uniformly distributed nodes over the network. The networks with several centers and a connected subnetwork of shortcuts are shown to be ``almost'' as efficient. Genetic-algorithm simulations further support our results.Comment: 5 pages, 6 figures, REVTeX

Corsican French questions: is there a prosodic transfer from Corsican to French and how to highlight it?

Poster, 4 pagesInternational audienceThis study investigates whether a prosodic transfer can be highlighted from Corsican (an Italo-Romance language) to French spoken in Corsica, where French is now the dominant language. A corpus of transparent sentences such as la touriste trouve la caserne (French) or a turista trova a caserna (Corsican) was designed and the productions of bilingual speakers, recorded in Corsica, were compared with the French counterparts of Parisian reference speakers. The melody of yes/no questions turns out to contrast Corsican and Corsican French (both with high tones followed by final pitch falls) and standard French (with utterance-final high tones). The former pattern can be interpreted as a prosodic transfer from Corsican to French. Various methods are considered to validate this hypothesis and an experimental paradigm is proposed. Index Terms: prosody in contact, questions, Corsican accent in French, endangered languagesCette étude examine si un transfert prosodique peut être mis en évidence du Corse (une langue Italo-romane) au français parlé en Corse, où le français est maintenant la langue dominante. Un corpus de phrases simples comme touriste la trouve la caserne (français) ou a turista trova a caserna (corse) a été conçu et les productions des locuteurs bilingues, enregistré en Corse, ont été comparé aux homologues françaises de locuteurs de référence Parisiens. La mélodie des yes/no questions s'avèrent contraster le français de corse et le corse (tous deux avec de hauts tons suivis par des chutes de pic final) alors que le français standard connait des pics finaux hauts. Le contour mélogique peut être interprété comme un transfert prosodique du Corse au français. On considère diverses méthodes pour valider cette hypothèse et on propose un paradigme expérimental

How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?

Electronic health records capture patient information using structured controlled vocabularies and unstructured narrative text. While structured data typically encodes lab values, encounters and medication lists, unstructured data captures the physician's interpretation of the patient's condition, prognosis, and response to therapeutic intervention. In this paper, we demonstrate that information extraction from unstructured clinical narratives is essential to most clinical applications. We perform an empirical study to validate the argument and show that structured data alone is insufficient in resolving eligibility criteria for recruiting patients onto clinical trials for chronic lymphocytic leukemia (CLL) and prostate cancer. Unstructured data is essential to solving 59% of the CLL trial criteria and 77% of the prostate cancer trial criteria. More specifically, for resolving eligibility criteria with temporal constraints, we show the need for temporal reasoning and information integration with medical events within and across unstructured clinical narratives and structured data.Comment: AMIA TBI 2014, 6 page

In Silico Enhanced Restriction Enzyme Based Methylation Analysis of the Human Glioblastoma Genome Using Agilent 244K CpG Island Microarrays

Genome wide methylation profiling of gliomas is likely to provide important clues to improving treatment outcomes. Restriction enzyme based approaches have been widely utilized for methylation profiling of cancer genomes and will continue to have importance in combination with higher density microarrays. With the availability of the human genome sequence and microarray probe sequences, these approaches can be readily characterized and optimized via in silico modeling. We adapted the previously described HpaII/MspI based Methylation Sensitive Restriction Enzyme (MSRE) assay for use with two-color Agilent 244K CpG island microarrays. In this assay, fragmented genomic DNA is digested in separate reactions with isoschizomeric HpaII (methylation-sensitive) and MspI (methylation-insensitive) restriction enzymes. Using in silico hybridization, we found that genomic fragmentation with BfaI was superior to MseI, providing a maximum effective coverage of 22,362 CpG islands in the human genome. In addition, we confirmed the presence of an internal control group of fragments lacking HpaII/MspI sites which enable separation of methylated and unmethylated fragments. We used this method on genomic DNA isolated from normal brain, U87MG cells, and a glioblastoma patient tumor sample and confirmed selected differentially methylated CpG islands using bisulfite sequencing. Along with additional validation points, we performed a receiver operating characteristics (ROC) analysis to determine the optimal threshold (p ≤ 0.001). Based on this threshold, we identified ∼2,400 CpG islands common to all three samples and 145 CpG islands unique to glioblastoma. These data provide general guidance to individuals seeking to maximize effective coverage using restriction enzyme based methylation profiling approaches

Diffusion MR Characteristics Following Concurrent Radiochemotherapy Predicts Progression-Free and Overall Survival in Newly Diagnosed Glioblastoma.

The standard of care for newly diagnosed glioblastoma (GBM) is surgery, then radiotherapy (RT) with concurrent temozolomide (TMZ), followed by adjuvant TMZ. We hypothesized patients with low diffusivity measured using apparent diffusion coefficient (ADC) histogram analysis evaluated after RT+TMZ, prior to adjuvant TMZ, would have a significantly shorter progression-free (PFS) and overall survival (OS). To test this hypothesis we evaluated 120 patients with newly diagnosed GBM receiving RT+TMZ followed by adjuvant TMZ. MRI was performed after completion of RT+TMZ, prior to initiation of adjuvant TMZ. A double Gaussian mixed model was used to describe the ADC histograms within the enhancing tumor, where ADCL and ADCH were defined as the mean ADC value of the lower and higher Gaussian distribution, respectively. An ADCL value of 1.0 um2/ms and ADCH value of 1.6 um2/ms were used to stratify patients into high and low risk categories. Results suggest patients with low ADCL had significantly shorter PFS (Cox Hazard Ratio = 0.12, P = 0.0006). OS was significantly shorter with low ADCL tumors, showing a median OS of 407 vs. 644 days (Cox Hazard Ratio = 0.31, P = 0.047). ADCH was not predictive of PFS or OS when accounting for age and ADCL. In summary, newly diagnosed glioblastoma patients with low ADCL after completion of RT+TMZ are likely to progress and die earlier than patients with higher ADCL. Results suggest ADC histogram analysis may be useful for patient risk stratification following completion of RT+TMZ

The Mendeleev-Meyer force project

Here we present the Mendeleev–Meyer Force Project which aims at tabulating all materials and substances in a fashion similar to the periodic table. The goal is to group and tabulate substances using nanoscale force footprints rather than atomic number or electronic configuration as in the periodic table. The process is divided into: (1) acquiring nanoscale force data from materials, (2) parameterizing the raw data into standardized input features to generate a library, (3) feeding the standardized library into an algorithm to generate, enhance or exploit a model to identify a material or property. We propose producing databases mimicking the Materials Genome Initiative, the Medical Literature Analysis and Retrieval System Online (MEDLARS) or the PRoteomics IDEntifications database (PRIDE) and making these searchable online via search engines mimicking Pubmed or the PRIDE web interface. A prototype exploiting deep learning algorithms, i.e. multilayer neural networks, is presented.Award-winningPostprint (author's final draft