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1. Russia in the Arctic Chair: Adapting the Arctic Governance System to Conditions Prevailing in the 2020s

   https://doi.org/10.1017/S0032247421000553  

   Vylegzhanin, Alexander N. ; Young, Oran R. ; Berkman, Paul Arthur ( January 2021 , Polar Record)

   null (Ed.)

   Abstract At the Arctic Council’s Ministerial Meeting in Reykjavik on 20 May 2021, Russia assumed the chairmanship of the council for the second time since its establishment in 1996. Though some Russian analysts and practitioners were skeptical about the usefulness of such a mechanism during the 1980s and 1990s, Russia has become an active contributor to the progress of the Arctic Council (AC). Russia’s first term as chair during 2004–2006 led to the creation of the Arctic Contaminants Action Program as an Arctic Council Working Group. Since then, Russia has served as co-lead of the Task Forces developing the terms of the 2011 agreement on search and rescue, the 2013 agreement on marine oil spill preparedness and response, and the 2017 agreement on enhancing international scientific cooperation. Russia also has participated actively in the creation of related bodies including the Arctic Coast Guard Forum and the Arctic Economic Council whose chairmanships rotate together with the chairmanship of the AC. Now, far-reaching changes in the broader setting are posing growing challenges to the effectiveness of these institutional arrangements. The impacts of climate change in the high latitudes have increased dramatically; the pace of the extraction and shipment of Arctic natural resources has accelerated sharply; great-power politics have returned to the Arctic foregrounding concerns regarding military security. Together, these developments make it clear that a policy of business as usual will not suffice to ensure that the AC remains an important high-level forum for addressing Arctic issues in a global context. The programme Russia has developed for its 2021–2023 chairmanship of the council is ambitious; it proposes a sizeable suite of constructive activities. In this article, however, we go a step further to explore opportunities to adapt the Arctic governance system to the conditions prevailing in the 2020s. We focus on options relating to (i) the AC’s constitutive arrangements, (ii) links between the council and related governance mechanisms, (iii) the role of science diplomacy, and (iv) the treatment of issues involving military security. We conclude with a discussion of the prospect of organising a heads of state/government meeting during the Russian chairmanship as a means of setting the Arctic governance system on a constructive path for the 2020s. 

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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. Juror Scientific Reasoning Skills and Discussion of Scientific Evidence During Deliberation

   McCowan, K. ( March 2020 , Annual Conference of the American Psychology-Law Society.)

   Abstract We investigate the link between individual differences in science reasoning skills and mock jurors’ deliberation behavior; specifically, how much they talk about the scientific evidence presented in a complicated, ecologically valid case during deliberation. Consistent with our preregistered hypothesis, mock jurors strong in scientific reasoning discussed the scientific evidence more during deliberation than those with weaker science reasoning skills. Summary With increasing frequency, legal disputes involve complex scientific information (Faigman et al., 2014; Federal Judicial Center, 2011; National Research Council, 2009). Yet people often have trouble consuming scientific information effectively (McAuliff et al., 2009; National Science Board, 2014; Resnick et al., 2016). Individual differences in reasoning styles and skills can affect how people comprehend complex evidence (e.g., Hans, Kaye, Dann, Farley, Alberston, 2011; McAuliff & Kovera, 2008). Recently, scholars have highlighted the importance of studying group deliberation contexts as well as individual decision contexts (Salerno & Diamond, 2010; Kovera, 2017). If individual differences influence how jurors understand scientific evidence, it invites questions about how these individual differences may affect the way jurors discuss science during group deliberations. The purpose of the current study was to examine how individual differences in the way people process scientific information affects the extent to which jurors discuss scientific evidence during deliberations. Methods We preregistered the data collection plan, sample size, and hypotheses on the Open Science Framework. Jury-eligible community participants (303 jurors across 50 juries) from Phoenix, AZ (Mage=37.4, SD=16.9; 58.8% female; 51.5% White, 23.7% Latinx, 9.9% African-American, 4.3% Asian) were paid $55 for a 3-hour mock jury study. Participants completed a set of individual questionnaires related to science reasoning skills and attitudes toward science prior to watching a 45-minute mock armed-robbery trial. The trial included various pieces of evidence and testimony, including forensic experts testifying about mitochondrial DNA evidence (mtDNA; based on Hans et al. 2011 materials). Participants were then given 45 minutes to deliberate. The deliberations were video recorded and transcribed to text for analysis. We analyzed the deliberation content for discussions related to the scientific evidence presented during trial. We hypothesized that those with stronger scientific and numeric reasoning skills, higher need for cognition, and more positive views towards science would discuss scientific evidence more than their counterparts during deliberation. Measures We measured Attitudes Toward Science (ATS) with indices of scientific promise and scientific reservations (Hans et al., 2011; originally developed by the National Science Board, 2004; 2006). We used Drummond and Fischhoff’s (2015) Scientific Reasoning Scale (SRS) to measure scientific reasoning skills. Weller et al.’s (2012) Numeracy Scale (WNS) measured proficiency in reasoning with quantitative information. The NFC-Short Form (Cacioppo et al., 1984) measured need for cognition. Coding We identified verbal utterances related to the scientific evidence presented in court. For instance, references to DNA evidence in general (e.g. nuclear DNA being more conclusive than mtDNA), the database that was used to compare the DNA sample (e.g. the database size, how representative it was), exclusion rates (e.g. how many other people could not be excluded as a possible match), and the forensic DNA experts (e.g. how credible they were perceived). We used word count to operationalize the extent to which each juror discussed scientific information. First we calculated the total word count for each complete jury deliberation transcript. Based on the above coding scheme we determined the number of words each juror spent discussing scientific information. To compare across juries, we wanted to account for the differing length of deliberation; thus, we calculated each juror’s scientific deliberation word count as a proportion of their jury’s total word count. Results On average, jurors discussed the science for about 4% of their total deliberation (SD=4%, range 0-22%). We regressed proportion of the deliberation jurors spend discussing scientific information on the four individual difference measures (i.e., SRS, NFC, WNS, ATS). Using the adjusted R-squared, the measures significantly accounted for 5.5% of the variability in scientific information deliberation discussion, SE=0.04, F(4, 199)=3.93, p=0.004. When controlling for all other variables in the model, the Scientific Reasoning Scale was the only measure that remained significant, b=0.003, SE=0.001, t(203)=2.02, p=0.045. To analyze how much variability each measure accounted for, we performed a stepwise regression, with NFC entered at step 1, ATS entered at step 2, WNS entered at step 3, and SRS entered at step 4. At step 1, NFC accounted for 2.4% of the variability, F(1, 202)=5.95, p=0.02. At step 2, ATS did not significantly account for any additional variability. At step 3, WNS accounted for an additional 2.4% of variability, ΔF(1, 200)=5.02, p=0.03. Finally, at step 4, SRS significantly accounted for an additional 1.9% of variability in scientific information discussion, ΔF(1, 199)=4.06, p=0.045, total adjusted R-squared of 0.055. Discussion This study provides additional support for previous findings that scientific reasoning skills affect the way jurors comprehend and use scientific evidence. It expands on previous findings by suggesting that these individual differences also impact the way scientific evidence is discussed during juror deliberations. In addition, this study advances the literature by identifying Scientific Reasoning Skills as a potentially more robust explanatory individual differences variable than more well-studied constructs like Need for Cognition in jury research. Our next steps for this research, which we plan to present at AP-LS as part of this presentation, incudes further analysis of the deliberation content (e.g., not just the mention of, but the accuracy of the references to scientific evidence in discussion). We are currently coding this data with a software program called Noldus Observer XT, which will allow us to present more sophisticated results from this data during the presentation. Learning Objective: Participants will be able to describe how individual differences in scientific reasoning skills affect how much jurors discuss scientific evidence during deliberation. 

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4. Uncomfortable Conversations with Faculty and Students in Zoom: Experiences with diversity and inclusion spurred by police brutality and racial injustice in the U.S.

   https://doi.org/10.18260/1-2--39149  

   White, Lance ; Hammond, Tracy ; Ray, Samantha ; Jaison, Donna ; Stanley, Christine ( February 2022 , 2022 CoNECD (Collaborative Network for Engineering & Computing Diversity))

   The culture within engineering colleges and departments has been historically quiet when considering social justice issues. Often the faculty in those departments are less concerned with social issues and are primarily focused on their disciplines and the concrete ways that they can make impacts academically and professionally in their respective arena’s. However, with the social climate of the United States shifting ever more towards a politically charged climate, and current events, particularly the protests against police brutality in recent years, faculty and students are constantly inundated with news of injustices happening in our society. The murder of George Floyd on May 25th 2020 sent shockwaves across the United States and the world. The video captured of his death shared across the globe brought everyone’s attention to the glaringly ugly problem of police brutality, paired with the COVID-19 pandemic, and US election year, the conditions were just right for a social activist movement to grow to a size that no one could ignore. Emmanuel Acho spoke out, motivated by injustices seen in the George Floyd murder, initially with podcasts and then by writing his book “Uncomfortable Converstations with a Black Man” [1]. In his book he touched on various social justice issues such as: racial terminology (i.e., Black or African American), implicit biases, white privilege, cultural appropriation, stereotypes (e.g., the “angry black man”), racial slurs (particularly the n-word), systemic racism, the myth of reverse racism, the criminal justice system, the struggles faced by black families, interracial families, allyship, and anti-racism. Students and faculty at Anonymous University felt compelled to set aside the time to meet and discuss this book in depth through the video conferencing client Zoom. In these meetings diverse facilitators were tasked with bringing the topics discussed by Acho in his book into conversation and pushing attendees of these meetings to consider those topics critically and personally. In an effort to avoid tasking attendees with reading homework to be able to participate in these discussions, the discussed chapter of the audiobook version of Acho’s book was played at the beginning of each meeting. Each audiobook chapter lasted between fifteen and twenty minutes, after which forty to forty-five minutes were left in the hour-long meetings to discuss the content of the chapter in question. Efforts by students and faculty were made to examine how some of the teachings of the book could be implemented into their lives and at Anonymous University. For broader topics, they would relate the content back to their personal lives (e.g., raising their children to be anti-racist and their experiences with racism in American and international cultures). Each meeting was recorded for posterity in the event that those conversations would be used in a paper such as this. Each meeting had at least one facilitator whose main role was to provide discussion prompts based on the chapter and ensure that the meeting environment was safe and inclusive. Naturally, some chapters address topics that are highly personal to some participants, so it was vital that all participants felt comfortable and supported to share their thoughts and experiences. The facilitator would intervene if the conversation veered in an aggressive direction. For example, if a participant starts an argument with another participant in a non-constructive manner, e.g., arguing over the definition of ethnicity, then the facilitator will interrupt, clear the air to bring the group back to a common ground, and then continue the discussion. Otherwise, participants were allowed to steer the direction of the conversation as new avenues of discussion popped up. These meetings were recorded with the goal of returning to these conversations and analyzing the conversations between attendees. Grounded theory will be used to first assess the most prominent themes of discussion between attendees for each meeting [2]. Attendees will be contacted to expressly ask their permission to have their words and thoughts used in this work, and upon agreement that data will begin to be processed. Select attendees will be asked to participate in focus group discussions, which will also be recorded via Zoom. These discussions will focus around the themes pulled from general discussion and will aim to dive deeper into the impact that this experience has had on them as either students or faculty members. A set of questions will be developed as prompts, but conversation is expected to evolve organically as these focus groups interact. These sessions will be scheduled for an hour, and a set of four focus groups with four participants are expected to participate for a total of sixteen total focus group participants. We hope to uncover how this experience changed the lives of the participants and present a model of how conversations such as this can promote diversity, equity, inclusion, and access activities amongst faculty and students outside of formal programs and strategic plans that are implemented at university, college, or departmental levels. 

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5. Gender Empowerment and Fate Control

   Rozanova-Smith, M. ; Petrov, A. ; Korkina Williams, V. ; Absalonsen, J. ; Alty, R. ; Arnfjord, S. ; Apok, C. ; Biscaye, E. ; Black., J. ; Carothers, C. ; et al ( April 2021 , Gender Equality in the Arctic)

   Oddsdóttir, E.E. ; Ágústsson, H.O. (Ed.)

   The Chapter aims to deepen the understanding of gender equality and empowerment issues in the Arctic region at the national, regional, and local levels, and identify concrete strategies for gender political, economic, and civic empowerment, thereby facilitating sustainable policy-making processes for the New Arctic. The Chapter "Empowerment and Fate Control" is a part of the peer-reviewed Pan-Arctic Report "Gender Equality in the Arctic," undertaken under the auspices of the Arctic Council's Sustainable Development Working Group (SDWG) and the Icelandic Chairmanship Programme 2019-2021. 

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