fairdataihub/poster-sentry-training-data huggingface dataset:
CEDAR CENTER FOR EXPANDED DATA ANNOTATION AND RETRIEVAL SAP – a CEDAR-based pipeline for semantic annotation of biomedical metadata Ravi D. Shankar, Marcos Martinez-Romero, Martin J. O’ Connor, John Graybeal, Purvesh Khatri, Mark A. Musen Stanford University, Stanford, CA 94305, U.S.A. metadatacenter.org CEDAR is supported by grant U54 AI117925 awarded by the National Institute of Allergy and Infectious Diseases through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov) 11/2016 Under the umbrella of CEDAR metadata enrichment effort, we are building the Semantic Annotation Pipeline (SAP) that automates the semantic annotation of biomedical data stored in community data repositories. The pipeline has two major segments: 1) transform the metadata stored in the community data repository to CEDAR JSON-LD format, and 2) add semantic annotations to the CEDAR-formatted metadata. Annotated Metadata in CEDAR Repository Metadata in CEDAR Repository Metadata in Community Repository CEDAR Community Template NCBO BioPortal Ontologies MESH LOINC NCIT RXNORM CPT ICD10 PATO SNOMEDCT MEDDRA NCBITAXON DOID UO At the core of the Annotator is the Apache UIMA ConceptMapper, a named- entity recognition tool. The Annotator annotates metadata with ontology terms as speciﬁed in the template. The ontology terms are served by the NCBO BioPortal. The Ingestor transforms the metadata in the community repository to instances of a JSON- encoded CEDAR community template. The CEDAR Community Template encapsulates the metadata speciﬁc to that community. The template includes ontological information on value restrictions on speciﬁc metadata elements. Ingestor Annotator }, "sampleID": { "@value": "GSM1169960" }, "description": { "@value": "IPF" }, "tissue": { "@type": "http://ncicb.nci.nih.gov/ xml/owl/EVS/Thesaurus.owl#C12801", “@value": "Lung" }, "disease": { "@type": "http://ncicb.nci.nih.gov/ xml/owl/EVS/Thesaurus.owl#C7057", “@value": null }, ^SAMPLE = GSM1169960 !Sample_title = IPF23lung !Sample_type = RNA !Sample_source_name_ch1 = IPF !Sample_organism_ch1 = Homo sapiens !Sample_characteristics_ch1 = tissue: Lung !Sample_molecule_ch1 = total RNA !Sample_label_ch1 = biotin !Sample_description = IPF !Sample_label_protocol_ch1 = Biotinylated cRNA were prepared with the Ambion MessageAmp kit for Illumina arrays Ingestor Annotator An Illustration of SAP: Semantic Annotation of GEO Sample Metadata CEDAR GEO Sample Template GEO Sample Metadata in GEO Repository GEO Sample Metadata in CEDAR JSON-LD format Value Restrictions in CEDAR GEO Sample Template Semantically Annotated GEO Sample Metadata in CEDAR JSON-LD format }, "sampleID": { "@value": "GSM1169960" }, "description": { "@value": "IPF" }, "tissue": { "@type": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C12801", "_valueLabel": "Lung Tissue", "@id": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C33024" }, "disease": { "@type": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C7057", "_valueLabel": "Idiopathic Pulmonary Fibrosis", "@id": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C35716" },
Task/Result Queues ML-based drug screening Real-time crystallography You request a function be executed on endpoint A and B Federated Function as a Service ●Modern computing environments are distributed and heterogeneous; modern workloads require specialized hardware, rapid responses, and remote processing (e.g., near data) ●FaaS provides an intuitive interface for users to register and invoke programming functions without regard for underlying infrastructure ●Federated FaaS enables functions to be dispatched to remote endpoints chosen for data locality, security, or other concerns ●funcX allows users to execute Python functions (which may invoke executables, MPI programs, etc.) on arbitrary resources (e.g., CPUs, GPUs) from short to long run times from funcx.sdk.client import FuncXClient fxc = FuncXClient() Instantiate a funcX client Register Python function with input args Execute a function by specifying endpoint and input arguments Fire-and-forget execution Outsource the challenging aspects of remote execution funcX manages authentication, serialization of functions and data, reliable execution optionally in containers, and delivery of results back to requesting users Transform resources into FaaS endpoints Easily manage execution across distributed resources The funcX endpoint software can be deployed on laptops, clouds, clusters, and supercomputers. It provisions resources elastically based on workload. High performance Launch millions of tasks funcX supports batch submission and monitoring, asynchronous callbacks, container warming, automated resource scaling, fault tolerance, prefetching, and memoization Portable serverless computing to enable scalable data science def hello_world(): return "Hello World!" func_uuid = fxc.register_function(hello_world) ep_id = '4b116d3c-1703-4f8f-9f6f-39921e5864df' result = fxc.run(endpoint_id=ep_id, function_id=func_uuid) fxc.get_result(result) Retrieve results (and exceptions) asynchronously $ pip install funcx_endpoint $ funcx-endpoint configure $ funcx-endpoint start <ENDPOINT_NAME> Install and conﬁgure a funcX endpoint def compute(args): # do something return results (1) Registration (function + container) F(ep1,1) F(ep1, 2) F(ep1, 3) F(ep1, 4) F(ep1, 5) F(ep1, 6) (2) Execution (function, endpoint, args) A A 1 3 2 funcX manages the reliable and secure execution on those endpoints funcX notifies you when the function is complete funcX development has been supported by NSF 2004894/2004932 and an Argonne Lab Directed Research and Development award Try on Binder https://funcx.org/binder HEP fitting as a service Application examples Functions Endpoints Yadu Babuji*, Josh Bryan*⚑, Kyle Chard*⚑, Ryan Chard*⚑, Ben Clifford*, Ian Foster*⚑, Ben Galewsky°, Daniel S. Katz°, Kevin Hunter Kesling*, Zhuozhao Li*, Kirill Nagairtsev*, Stephen Rosen*, Tyler Skluzacek* *University of Chicago & Argonne National Laboratory; ⚑Globus; °National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
Field plots were established in Knoxville, TN (latitude 35.971438, longitude -83.854094) on the East TN Research and Education Center, Holston River Unit. Differing levels of weed populations were created in a 4.5 ha field by herbicide application in year 1. Primary weeds were Xanthium strumarium and Urochloa platyphylla Variable rate applications (VRAs) of atrazine preemergence (PRE) followed by dicamba postemergence (POST) were investigated for the reduction of herbicide inputs and their resulting impact on weed control and corn yield. VRAs of atrazine were on the basis of weed density data collected in Year 1. VRAs of dicamba (Banvel) were based upon common cocklebur density evaluations within the field. Technical support provided by John B Wilkerson and William E Hart was appreciated. This research was conducted on research farms associated with the Tennessee Agricultural Experiment Station, a division of the University of Tennessee Institute of Agriculture. 1.Compared with conventional applications, atrazine usage was decreased by 43 and 32% in the site-specific application treatments in Year 2 and Year 3, respectively. 2.VRAs of dicamba reduced herbicide inputs by greater than 45% for Year 2 and Year 3. 3.Corn yields were similar for the conventional and site- specific treatments in both years. This research compared site- specific weed control using variable rate herbicide dosing to broadcast applications under field conditions in a maize production field in Tennessee, USA. On the basis of these data, site- specific herbicide applications have the greatest potential and least risk for managing weeds when POST or PRE + POST variable rate herbicide applications are used. Yields in site-specific plots were equal to using broadcast sprays. Site-specific weed management can increase crop production efficiency by minimizing herbicide input costs without compromising crop yields. A reduction in herbicide inputs resulting from site-specific weed management may also decrease the probability of nonpoint pollution compared with conventional herbicide applications.
日本語における対のある自他動詞の習得 ―中国人学習者の場合― 王冠亮（南山大学大学院生）・坂本正（南山大学） 従来の対のある自他動詞に関す る習得研究の結論の一つは、学習 者にとって自動詞の習得が難しく、 特に結果状態が注目される自動詞 文の習得が難しいということである （守屋1994;小林･直井1996;小林 1996;王2012;伊藤2012; ）。 はじめに 先行研究の問題点 1 結果状態が注目される自動詞 文に関する問題項目が少ない。 2 パーセンテージ（％）が主な手 法で、結果は一般化しにくい。信頼性がどれぐらいある？母集団については？ 本研究の研究課題 結果状態が注目される自動詞文 の習得は果たして難しいか 本研究の研究仮説先行研究の結果を検証しなおす とともに、三つの仮説を立てる H1 動作主特定･非特定は自他 動詞の選択に影響がある。 H2 文の肯定・否定は学習者の 自他動詞の選択に影響があ る。 H3 自他の３つの派生パターン は自他動詞の選択に影響が ある。 H1を立てる理由 守屋（1994）と杉村（2013）などの 研究において，対のある自他動詞 の習得難易度には，「動作主の特 定・不特定」が一つの目安、要因と して取り上げられている。 H2を立てる理由 部分の研究は対のある自動詞で 成り立つ「結果（無標）可能表現」が 学習者に難しいと主張している。 しかし、これらの研究で使われる 調査問題は100％否定文である。 H3を立てる理由 一般的に日本語の自他交替を自 動化・他動化・両極化が取り上げら れてきたが，自他交替に三つのパ ターンがあることが学習者の習得 に影響を及ぼすのか。これ、本当？肯定文、やさしい？検証する！ 調査 調査の実施 調査期間：2016年4月25日—29日・ 9月26日—10月07日 調査場所：中国安徽省にあるS大学 とN大学，並びに，日本南山大学 調査対象： S大学三年生（S3）26人 N大学二年生（N2）31人 N大学三年生（N3）35人，計92人 （回収有効調査票は85部） 日本人大学生（JP）30人 調査の設計 1 学習者の既習語彙を使用 2 研究課題と仮説を中心に24問を 用意 3 五つの選択肢のある多肢選択問 題を使う 4 文脈情報を学習者の母語で提供 グループ 平均正答率 mean 標準偏差 sd S3 62.86 19.74 N2 56.95 20.94 N3 52.34 16.53 JP 94.86 10.36 調査の結果（記述統計量） 各組の正答率の箱ひげ図 結論 研究課題 Kruskal-Wallis検定で4群を比べる。 H(3)=62.04,η2=.54,p＝2.152e-13 四群に有意差があり、 続いて多重比較 学習者の間に有意差がない。 JP－S3: r=.74, p=4.0e-08 JP－N2: r=.73, p=7.7e-09 JP－N3: r=.82, p=6.5e-11 結果状態が注目される自動詞文が難しい！ 研究仮説１ N2:t(29)=0.57, d=.10 ,p=.57 N3: V=212.5, r=.03 , p=.84→ノンパラ S3: t(22)=0.32, d=.07 , p=.75 三群いずれも有意差が見られな かったので、動作主特定･不特定が 学習者に影響を与えるとは言えない！ 研究仮説２ N2:t(29)=.84,d=.16, p=.40 N3: t(31)=.90,d=.16,p=.37 S3: V=130,r=.01,p=.92→ノンパラ 三群いずれも有意差が見られな かったので、文の肯定･否定が学習 者に影響を与えるとは言えない！ 研究仮説３ N2:F(2,87)=0.285,MS=178.8 η2＝.01 ,p=.75, N3: χ2（2）＝0.39,p=.82→ノンパラ S3: χ2（2）＝4.963,p=.08→ノンパラ 三群いずれも有意差が見られな かったので、派生パターンが学習者 に影響を与えるとは言えない！ 第27回第二言語習得研究会全国大会 2016年12月18日於九州大学 参考文献（予稿集にない部分） （1）伊藤秀明（2012b）「相対自動詞･他動詞選択判断の要因―中国人大学生の場合―」『国際交流基金日本語教 育紀要』8,7-21.独立行政法人国際交流基金. （2）大友賢二･中村洋一（2002）『テストで言語能力は測れるか－言語テストデータ分析入門－』桐原書店 （3）奥津敬一郎（1967）「自動化・他動化および両極化転形―自・他動詞の対応―」須賀一好・早津恵美子［編］ （1995）『動詞の自他』ひつじ書房 （4）王冠华（2012）「中国日语学习者有对自动词习得状况的实证研究」『日语学习与研究』5，86-94,对外 经济贸易大学. （5）王恰韓（2012）「中国人学習者における日本語無標可能表現の習得に関する研究: この役はあの新人俳優 にはつとまらない」『日本語研究』32，1-14,首都大学東京・東京都立大学日本語・日本語教育研究会. （6）関承（2014）『中国語母語話者における日本語自・他動詞の習得研究―中国語で可能標識が使われる表現を 中心に―』広島大学未公刊博士学位論文 （7）小林典子（1996）「相対自動詞による結果・状態表現―日本語学習者の習得状況― 」 『文芸言語研究・言語篇』29, 41-56,筑波大学. （8）封小芹（2007）「可能の意味を含む有対自動詞の受容能力の習得について- 中国人日 本語学習者の場合- 」『ククロス:国際コミュニケーション論集』4，49-63,名古屋大学. （9）張威（1992）「『可能表現の本質』考：無標の可能表現へのアプローチ」『中京大学教養 論叢』32(4)，1351-1373, 中京大学.
§ Expanded Illinois Natural History Survey host database with additional records from 2010-2020 from Google Scholar literature search § Compiled host database into usable data § Wrote R script to generate summary data for various measures, using t-tests to compare 2 groups and ANOVA for over two Methods § Both the IUCN and federal status rankings showed that, on average, mussels listed as endangered used significantly fewer host families than non-threatened mussels (IUCN, 1.94 and 3.25 respectively; p<.01. Federal, 2.00 and 3.21 respectively; p<.01). § Anodontini was significantly different in host family numbers compared to Quadrulini(p<.01), Pleuroblemini (p<.001), and Lampsilini (p < .001) Mussels are commonly thought of as distinct groups of host-specialists and host-generalists (Haag, 2012). However, this research supports the idea that this division is less of a dichotomy and more of a continuum (Fig. 1). The mussels that fall into the threatened categories of the conservation assessments tended to be more specialized than those that were non-threatened (Fig. 2). Some taxa also display more specialized host use than others (Fig. 3). These insights provide predictive power that can be used to help prioritize taxon-based conservation efforts. Many thanks to the Smithsonian Institution and the National Museum of Natural History, especially to Elizabeth Cottrell, Gene Hunt, and Virginia Power. Also, a thank you to Sarah Douglass and all others involved in the creation & maintenance of the Illinois Natural History Survey Freshwater Mussel Host Database. Funding provided by the NSF. Photo by Robert Aguilar, Smithsonian Environmental Research Center. Haag, W.R., 2012. North American freshwater mussels: natural history, ecology, and conservation. Cambridge University Press Results Conclusion Acknowledgements Using Freshwater Mussel Host Use to Examine Conservation-Relevant Factors David H. Nichols1,2, John Pfeiffer1 1Smithsonian National Museum of Natural History, Department of Invertebrate Zoology, 2Hendrix College Background Freshwater mussels are some of the most endangered animals in the US (Haag, 2012). Federal and state conservation efforts are complicated by a complex life history that requires parasitizing a host. While some freshwater mussel species can parasitize a broad range of host fish species, other mussels can only complete their life cycle by parasitizing a single species. Understanding more about how these factors influence their conservation status is an essential part of influencing their conservation going forward. Big Questions § Does host number impact the level of endangerment for a mussel species? § Do different taxa of mussels have different trends in host numbers? § Based on this information, where should we focus conservation efforts in the future? Fig. 1. Frequency distribution of host number by taxon Fig. 3. Plots showing the number of host families for species across different taxa Fig. 2. Distribution of host number between mussels of various conservation status
2.Energy Response Model of Detector Global Fitting result Potential of geo-neutrino measurements at JUNO and the Local 3D model Ran Han On behalf of the JUNO collaboration (Poster no.) The Local Crust Geochemical Model Poster Wall #204 The Jiangmen Underground Neutrino Observatory （JUNO） 20 kton LS detector 3%/sqrt（E）energy resolution A multiple-purpose neutrino experiment: Reactor neutrinos, Supernova neutrinos,Geo-neutrinos, Solar neutrinos, Sterile neutrinos, Atmospheric neutrinos, Exotic searches Taishan NPP Guang Zhou Daya Bay Hong Kong JUNO JUNO Location Geo-neutrinos: a new probe of Earth's interior Open questions about natural radioactivity in the Earth 1 - What is the radiogenic contribution to terrestrial heat production? 2 - How much U and Th are in the crust and particularly in the mantle? 3 – A global check of the standard geochemical model (BSE)? 4 - What is hidden in the Earth’s core? (geo-reactor, 40K, …) Geo-neutrinos: anti-neutrinos from the Earth. U Th and 40K in the Earth release heat together with anti-neutrinos in a well fixed ratio. They bring to Earth’s surface information about the chemical composition of the whole planet. Geo-neutrino Spectrum Earth’s surface heat flow JUNO Potential in Measuring Geo-neutrinos • 400events/year, much larger than existing experiments • With 10 years: total uncertainty reach 5%（2TNU) • Comparison of the global reference model (18% crust) and a benchmark accuracy of the local model(8% crust) The energy spectrum geoneutrino signal vs radiogenic heat the precision of the geo-neutrino measurement The chi2 distribution of Mantle geo JUNO JUNO Data collection (U & Th abundances),3000 points Preliminary surface model (0.5º x 0.5º) Preliminary vertical model (4 layers) The Local Crust Geophysical Model Why Local 3D model The 3D density distribution （one line） The crust thickness around JUNO. Results from the inversion of Bouguer gravity anomalies, constraint by the seismic sounding The 3D Vs structure R. Han, Y.F.Li et al.Chin.Phys.C(2016) Zhi-Wei LI (IGGWH, CAS) Ruo-Han GAO (CUGB) • Near continental margin, affected by the geology evolution of South China Block and the South China Sea • Paleogeographic evolution of South China fold belt during Permian-Jurassic time and proposed flat-slab subduction model for propagating • Local geology, including the geometry of strata, distribution rocks play important role in geo-ν flux estimation What’s the special of JUNO Geology Significant uncertainties exist in current global models. High-resolution local crust model are needed Global CRUST1.0 model has low resolution (1°× 1°) Global abundance model has no spatial resolution and large uncertainties Continuous Seismic Waveforms, from 450 Stations in South China Geophysical data source Local Neutrino Geo-Science Working Group
Cyber Training: Pilot -- Breaking the Compute Barrier, Upskilling Agri-Food Researchers to Utilize HPC Resources Kevin Silverstein, Benjamin Lynch, Alison Joglekar NSF Award Number: 2320769 MOTIVATION The era of “Big Data in Agriculture” is rapidly moving well beyond genomics data to encompass environmental, management and socio-economic data sourced from satellites, unmanned aerial vehicles (UAV), stationary and robot-enabled ground-based sensors, and ever more data-enabled agri-food machinery (Kamilaris et al. 2017; Shekhar et al. 2017). Leveraging high-performance computing (HPC) assets into the agri-food domain space is key to realizing a big data revolution in agriculture (EU 2018; Georgiou et al. 2020). However, there is a dearth of scientists with expertise in the agri-food and natural resource domains that have compute-to-scale capabilities enabled by HPC environments. Low adoption of HPC capabilities among agri-food researchers can be largely attributed to the real (or perceived) complexity of using HPC. OBJECTIVE Onboard and upskill the agri-food workforce so they can eﬀectively and eﬃciently tap the analytical horsepower of cyberinfrastructure (CI), speciﬁcally HPC, via the creation of a tiered multi-module learning curriculum tailored to CI-applications in the agri-food sciences. CURRICULUM HPC and Cloud Concepts 05 ● Divide and conquer ● Parallelization ● Containerization ● Storage types Computer Science for the Agri-Food Researcher 04 ● Algorithm design ● Data structures ● Computational complexity (Big-O) ● Proﬁling Expanding HPC Skills 03 ● Machine Learning primer ● Data leakage ● Overtraining HPC Basics 02 ● UNIX primer ● Working remotely and scheduling jobs using SLURM ● Syncing your work with the community Computer Hardware and Resources 06 ● Processor types ● Memory ● Networking speed Future trends 07 ● GPUs continue to soar in usage ● Generative AI models are rapidly increasing in complexity 01 ● Agricultural research ● Computers in agricultural research Intro to HPC Computing for Ag Researchers COURSE MATERIAL EXAMPLES Easy-to-Learn Jupyter Notebooks Easy-to-Navigate Canvas-Based Materials CHECK OUT OUR NEXT OFFERINGS Fall 2024 – Free (U of Minnesota Aﬃliates Only) Spring 2025 – Free for Everyone Tomato Image Source: https://aeroponic.gr/en/knowledge/post/diataraches-t hrepsis-futon-trofopenies-toxikotites1.html
FORK-TO-FARM AGENT-BASED SIMULATION TOOL AUGMENTING BIODIVERSITY IN THE AGRI-FOOD VALUE CHAIN This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101000499 The BIOVALUE tool is a dynamic, and modular agent-based simulation tool of the agri-food value chain with the aim to analyse experimentally the introduction of marginal traditional varieties starting with novel food dish recipes and processed food products and closing the value chain with an extensive breeding program throughout Europe. The proposed tool will be applied to real-world decision-making to extract and augment the value of so far underutilised and genetically diverse crops in Europe. Furthermore, environmental impacts of future water availability and climate change scenarios will be incorporated modularly into the BIOVALUE tool. The main aim of the BIOVALUE project is to develop a dynamic and customisable tool that will analyse the link among biodiversity, the agri-food value chain, the environment and consumer’s preferences and health. To this end, a demand driven approach (fork-to-farm) is adopted. The ultimate outcome of the research are novel food dish recipes and processed food products from the underutilised, genetically diverse crops resulting from the extensive breeding programme foreseen within the project. BioValue is divided into 11 Work Packages, 3 devoted to the ethical aspects (WP1), the dissemination & exploitation activities (WP10) and the management (WP11), and 8 core research Work Packages (WP2-9), ensuring that scientific excellence is accomplished throughout the project (Figure 2). www.biovalue-project.eu info@biovalue-project.eu # B I O V A L U E © COPYRIGHT AXIA Innovation Figure 2: BioValue’s Research related WPs Figure 1: BioValue’s Concept
ROOT I/O data compression improvements for HEP analysis Oksana Shadura (CMS/UNL), Zhe Zhang (CMS/UNL), Brian Paul Bockelman (CMS/Morgridge Institute for Research), Philippe Canal (CMS/FNAL), Danilo Piparo (CMS/CERN) oksana.shadura@cern.ch,bbockelman@morgridge.org Introduction ZSTD - a dictionary-type algorithm (LZ77) with large search window and fast implementations of entropy coding stage, using either very fast Finite State Entropy (tANS) or Huﬀman coding. [https://github.com/facebook/zstd.git] AVAILABLE IN ROOT 6.20! ZSTD for NanoAOD (compression levels) →Better compression ratio and 2x faster decompression then ZLIB; 6x faster comparing to LZMA; ﬁle compressed with ZSTD is only 20 % bigger! NanoAOD 2019 compression ratio comparison *ZSTD-1 means ZSTD with compression level 1 →Better compression ratio then ZLIB Data ﬁle with oﬀset arrays →ZSTD has no problems with compression of data that contains the byte oﬀset of each event in the branch data (vs LZ4) CMSSW NanoAOD and MiniAOD (including LZ4) →NanoAOD - using ZSTD could be a better compromise between size of ﬁle on a disk and decompression speed for a faster analysis!; →MiniAOD - time spend in decompressing on readback is 15x less vs LZMA, while size of ﬁle with ZSTD is only 10% bigger! (thanks to David Lange for MiniAOD measurements) LHCB compression speed vs compression ratio →For the very simple ntuples with a simple structure the best choice could be LZ4: 10x time faster read speed TTree::kOnlyFlushAtCluster: faster decompression →TTrees can be forced to only create new baskets at event cluster boundaries, it simpliﬁes ﬁle layout and I/O at the cost of memory (NanoAOD 2017 size diﬀerence was 3.6 %). Recommended for simple ﬁle formats such as ntuples but not for more complex data types. tree->SetBit(TTree::kOnlyFlushAtCluster); Acknowledgements This work was supported by the National Science Foundation under Grant ACI-1450323.