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1. Responding to the demands of big data scientific instruments through the development of an international software defined exchange point (SDX)

   Ibarra, J. ; Bezerra, J. ; Lopez, L.F. ; Morgan, L ; Cox, D. ( April 2016 , UbuntuNet Connect-Connect 2016)

   Science is being conducted in an era of information abundance. The rate at which science data is generated is increasing, both in volume and variety. This phenomenon is transforming how science is thought of and practiced. This transformation is being shaped by new scientific instruments that are being designed and deployed that will dramatically increase the need for large, real-time data transfers among scientists throughout the world. One such instrument is the Square Kilometer Array (SKA) being built in South Africa that will transmit approximately 160Gbps of data from each radio dish to a central processor. This paper describes a collaborative effort to respond to the demands of big data scientific instruments through the development of an international software defined exchange point (SDX) that will meet the network provisioning needs for science applications. This paper discusses the challenges of end-to-end path provisioning across multiple research and education networks using OpenFlow/SDN technologies. Furthermore, it refers to the AtlanticWave-SDX, a project at Florida International University and the Georgia Institute of Technology, funded by the US National Science Foundation (NSF), along with support from Brazil’s NREN, Rede Nacional de Ensino e Pesquisa (RNP, and the Academic Network of Sao Paulo (ANSP). Future work explores the feasibility of establishing an SDX in West Africa, in collaboration with regional African RENs, based on the planned availability of submarine cable spectrum for use by research and education communities. 

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2. The Temple University Digital Pathology Corpus: The Breast Tissue Subset

   https://doi.org/10.1109/SPMB52430.2021.9672275  

   Wevodau, Z. ; Doshna, B. ; Jhala, N. ; Akhtar, I. ; Obeid, I. ; Picone, J. ( December 2021 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, I. (Ed.)

   The Neural Engineering Data Consortium (NEDC) is developing the Temple University Digital Pathology Corpus (TUDP), an open source database of high-resolution images from scanned pathology samples [1], as part of its National Science Foundation-funded Major Research Instrumentation grant titled “MRI: High Performance Digital Pathology Using Big Data and Machine Learning” [2]. The long-term goal of this project is to release one million images. We have currently scanned over 100,000 images and are in the process of annotating breast tissue data for our first official corpus release, v1.0.0. This release contains 3,505 annotated images of breast tissue including 74 patients with cancerous diagnoses (out of a total of 296 patients). In this poster, we will present an analysis of this corpus and discuss the challenges we have faced in efficiently producing high quality annotations of breast tissue. It is well known that state of the art algorithms in machine learning require vast amounts of data. Fields such as speech recognition [3], image recognition [4] and text processing [5] are able to deliver impressive performance with complex deep learning models because they have developed large corpora to support training of extremely high-dimensional models (e.g., billions of parameters). Other fields that do not have access to such data resources must rely on techniques in which existing models can be adapted to new datasets [6]. A preliminary version of this breast corpus release was tested in a pilot study using a baseline machine learning system, ResNet18 [7], that leverages several open-source Python tools. The pilot corpus was divided into three sets: train, development, and evaluation. Portions of these slides were manually annotated [1] using the nine labels in Table 1 [8] to identify five to ten examples of pathological features on each slide. Not every pathological feature is annotated, meaning excluded areas can include focuses particular to these labels that are not used for training. A summary of the number of patches within each label is given in Table 2. To maintain a balanced training set, 1,000 patches of each label were used to train the machine learning model. Throughout all sets, only annotated patches were involved in model development. The performance of this model in identifying all the patches in the evaluation set can be seen in the confusion matrix of classification accuracy in Table 3. The highest performing labels were background, 97% correct identification, and artifact, 76% correct identification. A correlation exists between labels with more than 6,000 development patches and accurate performance on the evaluation set. Additionally, these results indicated a need to further refine the annotation of invasive ductal carcinoma (“indc”), inflammation (“infl”), nonneoplastic features (“nneo”), normal (“norm”) and suspicious (“susp”). This pilot experiment motivated changes to the corpus that will be discussed in detail in this poster presentation. To increase the accuracy of the machine learning model, we modified how we addressed underperforming labels. One common source of error arose with how non-background labels were converted into patches. Large areas of background within other labels were isolated within a patch resulting in connective tissue misrepresenting a non-background label. In response, the annotation overlay margins were revised to exclude benign connective tissue in non-background labels. Corresponding patient reports and supporting immunohistochemical stains further guided annotation reviews. The microscopic diagnoses given by the primary pathologist in these reports detail the pathological findings within each tissue site, but not within each specific slide. The microscopic diagnoses informed revisions specifically targeting annotated regions classified as cancerous, ensuring that the labels “indc” and “dcis” were used only in situations where a micropathologist diagnosed it as such. Further differentiation of cancerous and precancerous labels, as well as the location of their focus on a slide, could be accomplished with supplemental immunohistochemically (IHC) stained slides. When distinguishing whether a focus is a nonneoplastic feature versus a cancerous growth, pathologists employ antigen targeting stains to the tissue in question to confirm the diagnosis. For example, a nonneoplastic feature of usual ductal hyperplasia will display diffuse staining for cytokeratin 5 (CK5) and no diffuse staining for estrogen receptor (ER), while a cancerous growth of ductal carcinoma in situ will have negative or focally positive staining for CK5 and diffuse staining for ER [9]. Many tissue samples contain cancerous and non-cancerous features with morphological overlaps that cause variability between annotators. The informative fields IHC slides provide could play an integral role in machine model pathology diagnostics. Following the revisions made on all the annotations, a second experiment was run using ResNet18. Compared to the pilot study, an increase of model prediction accuracy was seen for the labels indc, infl, nneo, norm, and null. This increase is correlated with an increase in annotated area and annotation accuracy. Model performance in identifying the suspicious label decreased by 25% due to the decrease of 57% in the total annotated area described by this label. A summary of the model performance is given in Table 4, which shows the new prediction accuracy and the absolute change in error rate compared to Table 3. The breast tissue subset we are developing includes 3,505 annotated breast pathology slides from 296 patients. The average size of a scanned SVS file is 363 MB. The annotations are stored in an XML format. A CSV version of the annotation file is also available which provides a flat, or simple, annotation that is easy for machine learning researchers to access and interface to their systems. Each patient is identified by an anonymized medical reference number. Within each patient’s directory, one or more sessions are identified, also anonymized to the first of the month in which the sample was taken. These sessions are broken into groupings of tissue taken on that date (in this case, breast tissue). A deidentified patient report stored as a flat text file is also available. Within these slides there are a total of 16,971 total annotated regions with an average of 4.84 annotations per slide. Among those annotations, 8,035 are non-cancerous (normal, background, null, and artifact,) 6,222 are carcinogenic signs (inflammation, nonneoplastic and suspicious,) and 2,714 are cancerous labels (ductal carcinoma in situ and invasive ductal carcinoma in situ.) The individual patients are split up into three sets: train, development, and evaluation. Of the 74 cancerous patients, 20 were allotted for both the development and evaluation sets, while the remain 34 were allotted for train. The remaining 222 patients were split up to preserve the overall distribution of labels within the corpus. This was done in hope of creating control sets for comparable studies. Overall, the development and evaluation sets each have 80 patients, while the training set has 136 patients. In a related component of this project, slides from the Fox Chase Cancer Center (FCCC) Biosample Repository (https://www.foxchase.org/research/facilities/genetic-research-facilities/biosample-repository -facility) are being digitized in addition to slides provided by Temple University Hospital. This data includes 18 different types of tissue including approximately 38.5% urinary tissue and 16.5% gynecological tissue. These slides and the metadata provided with them are already anonymized and include diagnoses in a spreadsheet with sample and patient ID. We plan to release over 13,000 unannotated slides from the FCCC Corpus simultaneously with v1.0.0 of TUDP. Details of this release will also be discussed in this poster. Few digitally annotated databases of pathology samples like TUDP exist due to the extensive data collection and processing required. The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases. ACKNOWLEDGMENTS This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. REFERENCES [1] N. Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432. [2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/. [3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015. [4] C.-J. Wu et al., “Machine Learning at Facebook: Understanding Inference at the Edge,” in Proceedings of the IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 331–344. https://ieeexplore.ieee.org/document/8675201. [5] I. Caswell and B. Liang, “Recent Advances in Google Translate,” Google AI Blog: The latest from Google Research, 2020. [Online]. Available: https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html. [Accessed: 01-Aug-2021]. [6] V. Khalkhali, N. Shawki, V. Shah, M. Golmohammadi, I. Obeid, and J. Picone, “Low Latency Real-Time Seizure Detection Using Transfer Deep Learning,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2021, pp. 1 7. https://www.isip. piconepress.com/publications/conference_proceedings/2021/ieee_spmb/eeg_transfer_learning/. [7] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning,” Philadelphia, Pennsylvania, USA, 2020. https://www.isip.piconepress.com/publications/reports/2020/nsf/mri_dpath/. [8] I. Hunt, S. Husain, J. Simons, I. Obeid, and J. Picone, “Recent Advances in the Temple University Digital Pathology Corpus,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2019, pp. 1–4. https://ieeexplore.ieee.org/document/9037859. [9] A. P. Martinez, C. Cohen, K. Z. Hanley, and X. (Bill) Li, “Estrogen Receptor and Cytokeratin 5 Are Reliable Markers to Separate Usual Ductal Hyperplasia From Atypical Ductal Hyperplasia and Low-Grade Ductal Carcinoma In Situ,” Arch. Pathol. Lab. Med., vol. 140, no. 7, pp. 686–689, Apr. 2016. https://doi.org/10.5858/arpa.2015-0238-OA. 

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3. The population of merging compact binaries inferred using gravitational waves through GWTC-3 - Data release

   https://doi.org/10.5281/zenodo.5650061  

   Scientific, LIGO Collaboration ( January 2023 , Zenodo)

   Data associated with Figures, Tables, and population parameter samples associated with 
   The population of merging compact binaries inferred using gravitational waves through GWTC-3 ,
   LIGO DCC, arXiv, PRX. 
   This is v2, superseding v1. Please see the README.md for more information.

   LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. The construction and operation of KAGRA are funded by Ministry of Education, Culture, Sports, Science and Technology (MEXT), and Japan Society for the Promotion of Science (JSPS), National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS) and the Ministry of Science and Technology (MoST) in Taiwan. 

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4. Named Data Networking for Genomics Data Management and Integrated Workflows

   https://doi.org/10.3389/fdata.2021.582468  

   Ogle, Cameron ; Reddick, David ; McKnight, Coleman ; Biggs, Tyler ; Pauly, Rini ; Ficklin, Stephen P. ; Feltus, F. Alex ; Shannigrahi, Susmit ( February 2021 , Frontiers in Big Data)

   Advanced imaging and DNA sequencing technologies now enable the diverse biology community to routinely generate and analyze terabytes of high resolution biological data. The community is rapidly heading toward the petascale in single investigator laboratory settings. As evidence, the single NCBI SRA central DNA sequence repository contains over 45 petabytes of biological data. Given the geometric growth of this and other genomics repositories, an exabyte of mineable biological data is imminent. The challenges of effectively utilizing these datasets are enormous as they are not only large in the size but also stored in geographically distributed repositories in various repositories such as National Center for Biotechnology Information (NCBI), DNA Data Bank of Japan (DDBJ), European Bioinformatics Institute (EBI), and NASA’s GeneLab. In this work, we first systematically point out the data-management challenges of the genomics community. We then introduce Named Data Networking (NDN), a novel but well-researched Internet architecture, is capable of solving these challenges at the network layer. NDN performs all operations such as forwarding requests to data sources, content discovery, access, and retrieval using content names (that are similar to traditional filenames or filepaths) and eliminates the need for a location layer (the IP address) for data management. Utilizing NDN for genomics workflows simplifies data discovery, speeds up data retrieval using in-network caching of popular datasets, and allows the community to create infrastructure that supports operations such as creating federation of content repositories, retrieval from multiple sources, remote data subsetting, and others. Named based operations also streamlines deployment and integration of workflows with various cloud platforms. Our contributions in this work are as follows 1) we enumerate the cyberinfrastructure challenges of the genomics community that NDN can alleviate, and 2) we describe our efforts in applying NDN for a contemporary genomics workflow (GEMmaker) and quantify the improvements. The preliminary evaluation shows a sixfold speed up in data insertion into the workflow. 3) As a pilot, we have used an NDN naming scheme (agreed upon by the community and discussed in Section 4 ) to publish data from broadly used data repositories including the NCBI SRA. We have loaded the NDN testbed with these pre-processed genomes that can be accessed over NDN and used by anyone interested in those datasets. Finally, we discuss our continued effort in integrating NDN with cloud computing platforms, such as the Pacific Research Platform (PRP). The reader should note that the goal of this paper is to introduce NDN to the genomics community and discuss NDN’s properties that can benefit the genomics community. We do not present an extensive performance evaluation of NDN—we are working on extending and evaluating our pilot deployment and will present systematic results in a future work. 

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5. All-in-One Urban Mobility Mapping Application with Optional Routing Capabilities

   Thompson, R. ; Stovall, J. ; Valasquez, D. ; Anne, V. ; Samoylov, A. V. ; & Sartipi, M. ( December 2018 , Big data)

   To create safer and less congested traffic operating environments researchers at the University of Tennessee at Chattanooga (UTC) and the Georgia Tech Research Institute (GTRI) have fostered a vision of cooperative sensing and cooperative mobility. This vision is realized in a mobile application that combines visual data extracted from cameras on roadway infrastructure with a user’s coordinates via a GPS-enabled device to create a visual representation of the driving or walking environment surrounding the application user. By merging the concepts of computer vision, object detection, and mono-vision image depth calculation, this application is able to gather absolute Global Positioning System (GPS) coordinates from a user’s mobile device and combine them with relative GPS coordinates determined by the infrastructure cameras and determine the position of vehicles and pedestrians without the knowledge of their absolute GPS coordinates. The joined data is then used by an iOS mobile application to display a map showing the location of other entities such as vehicles, pedestrians, and obstacles creating a real-time visual representation of the surrounding area prior to the area appearing in the user’s visual perspective. Furthermore, a feature was implemented to display routing by using the results of a traffic scenario that was analyzed by rerouting algorithms in a simulated environment. By displaying where proximal entities are concentrated and showing recommended optional routes, users have the ability to be more informed and aware when making traffic decisions helping ensure a higher level of overall safety on our roadways. This vision would not be possible without high speed gigabit network infrastructure installed in Chattanooga, Tennessee and UTC’s wireless testbed, which was used to test many functions of this application. This network was required to reduce the latency of the massive amount of data generated by the infrastructure and vehicles that utilize the testbed; having results from this data come back in real-time is a critical component. 

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