List Of Matches Research Articles

Technical note: The Next Step – a semi-automatic coding and comparison system for forensic footwear impressions

Footwear impression evidence is a key tool in criminal investigations, connecting suspects to the crime scene; in addition, it may provide valuable forensic intelligence linking different crime scenes in the absence of a suspect. This paper presents the development of a computer database and semi-automatic system for shoeprint comparison. The database is based on structured manual coding of elements by CSI (Crime Scene Investigators). The computer algorithm then compares the pattern information of the query footwear impression to the entire database, returning a list of possible matches ranked by pattern similarity and crime scene geographic proximity. Initial results using a database of 284 footwear impressions from real crime scenes revealed that a "match" (i.e., the same shoe model) was found for 30% of the impressions; in most cases, the "match" ranked within the top five places of the "hit" list generated by the algorithm. Our results confirm that this semi-automatic footwear comparison system is simple, cost-effective and efficient, providing great potential for linking crime scenes.

Abstract 5046: DeepTumour: Identify tumor origin from whole genome sequences

Abstract The DeepTumour algorithm predicts the tissue of origin of a tumor based on the pattern of passenger mutations identified by whole genome sequencing. "Passengers" are incidental mutations that accrue in the genome over time due to random mutational processes, and are functionally distinct from the "driver" mutations that are responsible for the cancer's malignant behavior. In adult cancers, passenger mutations typically outnumber drivers by a hundred or thousand-fold; critically, the vast majority of passengers arise in the normal cell lineage that precedes the malignant transformation event and hence reflects mutational processes existing in the cancer's precursor cell and its ancestors. Passenger mutations are not uniformly distributed across the genome, but are concentrated in areas of the genome that have a locally high mutation rate. Mutation rates are highest at places in the genome where chromatin is tightly packed and less accessible to the DNA repair machinery. Each distinct cell type has a different pattern of chromatin packing due to epigenetic modifications. DeepTumour takes advantage of this to infer the chromatin state in the cell of origin from the distribution of passenger mutations in the tumor. Another characteristic of passenger mutations is that the probability of a particular type of mutation occurring (e.g. replacement of C by T) depends on the mutational processes that were active in the cell of origin and its ancestors. Because certain cancers are associated with distinct mutational exposures (e.g. lung cancer and smoking), DeepTumour uses the tumor's distribution of passenger mutation type as well as position on the genome. The DeepTumour algorithm itself is a fully connected, feed-forward neural network which we trained using 28 cohorts representing different tumor types from the Pan-Cancer Analysis of Whole Genomes project. When applied to independent sets of tumors, the algorithm is able to achieve an overall accuracy of 88% on primary tumors and 83% on metastatic tumors for distinguishing the 28 cancer types. Furthermore, DeepTumour provides estimates of the models uncertainty, allowing it to automatically detect rare cancer samples with an accuracy of 93%. The DeepTumour algorithm is now available as a fast, convenient and secure web-based service at https://deeptumour.oicr.on.ca. It accepts uploads of VCF files containing somatic mutations from tumor whole genome sequencing, and returns a ranked list of tumor type matches and their relative probabilities. It can be used to provide leads when evaluating a cancer of uncertain primary, to assist in resolving diagnostic ambiguities, and as a research tool for understanding tumors of intermediate histology. Citation Format: Lincoln David Stein, Wei Jiao, Gurnit Atwal, Quaid Morris. DeepTumour: Identify tumor origin from whole genome sequences [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5046.

Implementation of Active Learning Strategies In Improving Arabic Language Skills for Students of STAI As-Sunnah Tanjung, North Sumatra

This study aims to describe the implementation of active learning strategies to speak Arabic as well as various supporting and inhibiting factors, and also to describe the results obtained after implementing active learning strategies to speak Arabic at STAI As-Sunnah Tanjung Morawa Deli Serdang. The research data obtained from primary data and secondary data. The primary data comes from key informants taken from purposive techniques, the data collection instruments used are interview guides, observation guides and match lists. While secondary data comes from journals, books, or magazines that are still related to the object of research. For check data validation using data triangulation. The results obtained from the study are STAI As-Sunnah using an active learning strategy based on the integration of 4 Arabic skills, namely speaking, listening, writing and reading skills. It was also found that there were supporting and inhibiting factors for active learning strategies. Supporting factors such as various lecturer methods that motivate students to be active in learning, the existence of a code of ethics regarding the use of Arabic as the main language, as well as the availability of facilities such as language laboratories. Meanwhile, the inhibiting factor is that there are still lecturers who do not understand active learning strategies, and the lack of commitment and seriousness of lecturers in implementing active learning strategies. The result of the active learning process itself is that 95% of the Language Preparation Program participants can graduate, there are also those who continue their studies in Arabic-speaking countries, there are students who are able to win the Arabic speech competition.

PD30-04 THE FATE OF THE UNMATCHED UROLOGY APPLICANT

You have accessJournal of UrologyCME1 May 2022PD30-04 THE FATE OF THE UNMATCHED UROLOGY APPLICANT Rebecca Takele, Nishant Patel, and Ryan Dobbs Rebecca TakeleRebecca Takele More articles by this author , Nishant PatelNishant Patel More articles by this author , and Ryan DobbsRyan Dobbs More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002578.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Urology remains a competitive surgical sub-specialty with a match process independent of the National Resident Matching Program. Each year a cohort of competitive applicants go unmatched and are faced with the decision to reapply the following cycle while doing a research fellowship or a preliminary internship in the interim or choose a different specialty altogether. We sought to investigate the outcomes and eventual career paths for unmatched applicants in Urology by evaluating a cohort of unmatched applicants. METHODS: Data was obtained from the 2008-2013 American Urological Association urology match lists. Reapplication status was evaluated by comparing urology match lists. Additional information regarding eventual specialty choice, fellowship training, and academic or private practice setting were obtained from publicly available sources. Comparative analysis with univariate and multivariate analysis was performed between unmatched applicants who eventually matched into urology and applicants who chose alternative career paths. RESULTS: 441 unique applicants were identified with at least one unmatched result between 2008-2013. Amongst these applicants, 40.6% (n=179) were reapplicants from another cycle, 69.2% (n=305) were male, 85.3% (n=377) were MDs and 33.9% (n=148) became urologists. Eventual urologists were more likely to be male (73.3%, p=.123) and more likely to be from a Doctor of Osteopathy (DO) degree granting school (p=<0.0001). For eventual urologists, the most common pathway was a preliminary surgery year (43.2% n=64), followed by obtaining a position through the DO match (18.2%, n=27) and research fellowship (12.1%, n=18). Of the 292 applicants who went into other specialties, 9.9% (n= 29) reapplied to Urology before choosing an alternate specialty, such as anesthesiology (18.2%), Internal Medicine (18.8%), General Surgery (18.5%), Radiology (8.9%), Family Medicine (8.6%), Emergency Medicine (5.8%), and OB/GYN (5.8%). CONCLUSIONS: Approximately 1 in 3 unmatched urology applicants will eventually obtain a urology residency position (33.6%, 148/441). DO students were more successful at eventually obtaining a urology position although this may be reflective of separate DO urology residency positions during this study time period. The most common specialty choices for unmatched urology applicants were internal medicine (18.8%), general surgery (18.5%) and anesthesiology (18.2%). Applicants who applied in multiple cycles were likely (83.6%, n=148/177) to be successful in the urology match. Source of Funding: N/A © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e511 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Rebecca Takele More articles by this author Nishant Patel More articles by this author Ryan Dobbs More articles by this author Expand All Advertisement PDF DownloadLoading ...

Empirical Matching-Based Computation Offloading Optimization for 5G and Edge Computing-Integrated EIoT

Electric Internet of things (EIoT) that integrates 5G and edge computing can provide data transmission and processing guarantee for smart grid. However, computation offloading optimization including joint optimization of server selection and computation resource allocation still faces several challenges such as difficulty in tradeoff balance among various quality of service (QoS) parameters, coupling between server selection and computation resource allocation, and multi-device competition. To address these challenges, we propose an empirical matching-based computation offloading optimization algorithm for 5G and edge computing-integrated EIoT. The optimization objective is to minimize the computation offloading delay by jointly optimizing large timescale server selection and small timescale computation resource allocation. We first model the large timescale server selection problem as a many-to-one matching problem, which can be decoupled from small timescale computation resource allocation by establishing a matching preference list based on empirical performance. Then, the large timescale server selection problem is solved by pricing-based matching with a quota algorithm. Furthermore, based on the obtained suboptimal result of large timescale server selection, the small timescale computation resource allocation problem is subsequently solved by Lagrange dual decomposition, the result of which is used to update large timescale empirical performance. Finally, extensive simulations are carried out to demonstrate the superior performance of the proposed algorithm by comparing it with existing algorithms.

Assessing the Impact of the COVID-19 Pandemic on Geographic Residency Placement Relative to Medical School Location.

The 2020-2021 residency application cycle was altered to reduce COVID-19 transmission, with moves to all virtual interviews and no away rotations for medical students. These changes may have affected how students ranked residency programs, such as choosing programs near their medical schools. To determine if a larger percentage of medical students matched to residency programs in the same state as their medical schools in 2021 vs 2018-2020. We searched the webpages or emailed student affairs deans of the 155 Liaison Committee on Medical Education accredited MD programs to attain medical school match lists. Differences in the percentage of students matching to residency programs in the same US state as their medical schools in 2021 vs 2018-2020 were compared using chi-square tests. We recorded 36 021 of 79 406 (45%) National Resident Matching Program, 759 of 1720 (44%) ophthalmology, and 586 urology MD residency matches between 2018 and 2021. The percentage of students matching to residency programs in the same state as their medical schools was 35.9% in 2021 versus 34.3% in 2018-2020 (P=.005). Students were more likely to match to programs in the same state as their medical schools in 2021 if they attended a public medical school (40.3% vs 38.5%, P=.009) or applied into specialties where ≥50% of students traditionally perform away rotations (32.2% vs 30.2%, P=.031). There was a small difference in the percentage of medical students matching to residency programs in the same state as their medical schools in 2021 vs 2018-2020.

Examining the Factors Influencing Applicants' Placement on One General Surgery Program's Rank Order List.

Surgery residency applications include variables that determine an individual's rank on a program's match list. We performed this study to determine which residency application variables are the most impactful in creating our program's rank order list. We completed a retrospective examination of all interviewed applicants for the 2019 match. We recorded United States Medical Licensing Examinations (USMLE) step I and II scores, class quartile rank from the Medical Student Performance Evaluation (MSPE), Alpha Omega Alpha (AOA) membership, geographic region, surgery clerkship grade, and grades on other clerkships. The MSPE and letters of recommendation were reviewed by two of the authors and assigned a score of 1 to 3, where 1 was weak and 3 was strong. The same two authors reviewed the assessments from each applicant's interview and assigned a score from 1-5, where 1 was poor and 5 was excellent. Univariate analysis was performed, and the significant variables were used to construct an adjusted multivariate model with significance measured at P < .05. Univariate analysis for all 92 interviewed applicants demonstrated that USMLE step 2 scores (P = .002), class quartile rank (P = .004), AOA status (P = .014), geographic location (P < .001), letters of recommendation (P < .001), and interview rating (P < .001) were significant in predicting an applicant's position on the rank list. On multivariate analysis only USMLE step 2 (P = .018) and interview (P < .001) remained significant. USMLE step 2 and an excellent interview were the most important factors in constructing our rank order list. Applicants with a demonstrated strong clinical fund of knowledge that develop a rapport with our faculty and residents receive the highest level of consideration for our program.

Virtual Interviews for Sports Medicine Fellowship Positions Save Time and Money but Don’t Replace In-Person Meetings

PurposeTo understand the perceptions of program directors (PDs) and fellowship applicants regarding the virtual interview process for orthopaedic surgery sports medicine fellowship programs.MethodsAn anonymous online survey was distributed through the American Orthopaedic Society for Sports Medicine (AOSSM) to applicants and PDs of orthopaedic surgery sports medicine fellowship programs following the 2020-2021 fellowship application cycle.ResultsA total of 40 responses were received from PDs for a response rate of 47% (40 of 85) and 72 responses were received from applicants for a response rate of 27% (72 of 271). All of the surveyed PDs (40/40, 100%) agreed/strongly agreed that the applicant’s interview carries significant weight in determining where an applicant is ranked on the match list. Fifty-eight percent (23 of 40) of PDs agreed/strongly agreed that virtual interviews negatively affected their personal connection with the fellowship interviewee. The presence of virtual interviews allowed 80% (57 of 71) of applicants to go on more interviews. Seventy-three percent (51 of 70) of applicants were able to save greater than $5,000 on travel expenses and 63% (25 of 40) of fellowship programs were able to save greater than $2,500 by conducting virtual interviews.ConclusionsVirtual interviews allowed fellowship programs and applicants to complete more interviews, but both PDs and applicants stated that interviewing in-person was important for applicants to meet faculty and tour the institution where they may be spending a year. In contrast, significant financial savings resulted due to the transition to virtual interviews. Finally, both PDs and applicants were in favor of having the option of interviewing virtually, suggesting that virtual interviews may continue to play a role in future application cycles.Clinical RelevanceThis study may be valuable to fellowship programs that will continue to implement virtual interviews into future application cycles.

Explaining a Potential Interview Match for Graduate Medical Education

Explaining a Potential Interview Match for Graduate Medical Education

The Professional Instagram Influencer: Residency Recruitment in the Age of COVID

Study Objective Does a residency-specific instagram account help recruit medical students to apply to our program? Design A 10 question survey was sent electronically to the medical students who applied to our residency program. The survey was sent post-match to avoid any NRMP violations. Setting Electronic survey. Patients or Participants 4th year medical students who applied to our OB/GYN residency program. Interventions N/A. Measurements and Main Results We found that 85% of people applying to our residency program utilized our instagram account to learn more about the program, with 55% applicants stating it had an influence on where they ranked us on their match list, and 95% of applicants answering that residency-specific instagram pages are useful in the match process. One shortcoming of our study is our low response rate, which is likely secondary to the survey being sent out post-match to avoid any NRMP violations. Conclusion A residency-specific instagram account is a useful recruiting tool for OB/GYN residency programs. Does a residency-specific instagram account help recruit medical students to apply to our program? A 10 question survey was sent electronically to the medical students who applied to our residency program. The survey was sent post-match to avoid any NRMP violations. Electronic survey. 4th year medical students who applied to our OB/GYN residency program. N/A. We found that 85% of people applying to our residency program utilized our instagram account to learn more about the program, with 55% applicants stating it had an influence on where they ranked us on their match list, and 95% of applicants answering that residency-specific instagram pages are useful in the match process. One shortcoming of our study is our low response rate, which is likely secondary to the survey being sent out post-match to avoid any NRMP violations. A residency-specific instagram account is a useful recruiting tool for OB/GYN residency programs.

Effect of Sign-recognition Performance on the Usability of Sign-language Dictionary Search

Advances in sign-language recognition technology have enabled researchers to investigate various methods that can assist users in searching for an unfamiliar sign in ASL using sign-recognition technology. Users can generate a query by submitting a video of themselves performing the sign they believe they encountered somewhere and obtain a list of possible matches. However, there is disagreement among developers of such technology on how to report the performance of their systems, and prior research has not examined the relationship between the performance of search technology and users’ subjective judgements for this task. We conducted three studies using a Wizard-of-Oz prototype of a webcam-based ASL dictionary search system to investigate the relationship between the performance of such a system and user judgements. We found that, in addition to the position of the desired word in a list of results, the placement of the desired word above or below the fold and the similarity of the other words in the results list affected users’ judgements of the system. We also found that metrics that incorporate the precision of the overall list correlated better with users’ judgements than did metrics currently reported in prior ASL dictionary research.

The Otolaryngology Residency Program Preference Signaling Experience.

ProblemIn the 2021 residency application cycle, the average otolaryngology applicant applied to more than half of programs. Increasing application numbers make it difficult for applicants to stand out to programs of interest and for programs to identify applicants with sincere interest.ApproachAs part of the 2021 Match, otolaryngology applicants could participate in a preference signaling process, signaling up to 5 programs of particular interest at the time of application submission. Programs received a list of applicants who submitted signals to consider during interview offer deliberations. Applicants and program directors completed surveys to evaluate the signaling process and assess the impact of signals on interview offers.OutcomesAll otolaryngology residency programs participated in the signaling process. In total, 611 students submitted applications for otolaryngology residency programs, 559 applicants submitted a Match list including an otolaryngology program, and 558 applicants participated in the signaling process. The survey response rate was 42% for applicants (n = 233) and 52% for program directors (n = 62). The rate of receiving an interview offer was significantly higher from signaled programs (58%) than from both nonsignaled programs (14%; P < .001) and the comparative nonsignal program (23%; P < .001) (i.e., the program an applicant would have signaled given a sixth signal). This impact was consistent across the spectrum of applicant competitiveness. Applicants (178, 77%) and program directors (53, 91%) strongly favored continuing the program.Next StepsMany specialties face high residency application numbers. Programs have difficulty identifying applicants with sincere interest, and applicants face limited opportunities to identify programs of particular interest. Applicants to these specialties may benefit from a preference signaling process like that in otolaryngology. Additional evaluation is needed to determine the impact of signals across racial and demographic lines and to validate these early outcomes.

147 An Exploratory Study Investigating the Use of a Resident-Driven Virtual Webinar Series for Residency Program Recruitment

Traditional residency program recruitment relies on in-person events such as residency fairs, social events, interviews with faculty and residents, and tours of the hospital and surrounding neighborhoods. The 2020-2021 recruitment season was dramatically impacted by the COVID-19 pandemic and required that all recruitment shift rapidly to a fully virtual model. Applicants were suddenly limited to online exploration only. Both as digital natives and as people intimately familiar with the daily life within the residency program, current residents were uniquely positioned as tech-savvy resources for applicants. Our residency program hosted six resident-driven virtual webinar sessions as a core part of our recruitment strategy. The impact of a fully virtual residency recruitment season is unknown. In this exploratory study we attempt to determine the impact of our resident-driven virtual webinar series (RDVWS) on the attitudes of our incoming intern class about our residency program. This was a mixed-methods exploratory study. Participants were 18 rising interns who just completed the National Resident Matching Program (NRMP) process and were selected for 2021 Emergency Medicine categorical PGY-1 positions at a single urban three-year community-based residency program. The RDVWS was conducted in July-September 2020 prior to the opening of the Electronic Residency Application Service (ERAS) platform. Attending RDVWS was not required to apply to the program. The content of each session was based on topics that applicants are commonly interested in when applying. For instance, the topics ranged from: an alumni panel, the residency curriculum, sub-specialty opportunities, clerkship rotation information, and diversity and inclusion goals. We advertised the RDVWS via social media and through medical schools with a link to our registration page. The residents running the webinar invited faculty, residents and alumni from the program and moderated the sessions. Following the NRMP Match, an anonymous online survey, consisting of multiple choice and free text responses, was distributed to the 18 participants. Descriptive statistics were used to analyze the survey data in order to assess the impact of the RDVWS on rank list preference and value of the RDVWS. Additional qualitative data will be captured through focus groups in July 2021 during intern orientation. 94% (17/18) of matched interns responded to the survey. 59% (10/17) said they watched the RDVWS, while 41% of participants did not watch the VWS. Of the participants who watched the RDVWS, 70% (7/10) said they ranked MMC higher on their rank lists specifically because of the VWS. 24% (4/17) participants stated this affected their decision to apply to the residency program. Representative free text responses included: “Getting to watch how you all interacted… I wanted to know what the family dynamic was.” “The talk about the curriculum, event medicine and international EM month, and... patient population... align with what I want to do.” Preliminary data analysis reveals that students found RDVWS a helpful resource during the NRMP match, and many RDVWS participants cited the webinar as a reason they ranked our program higher on their match lists. We plan to present additional qualitative data by the time of ACEP21. In conclusion, we believe that the VWS is a valuable recruitment tool to supplement the interview experience.

Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction

In construction, master schedules and look-ahead plans are created at different times (monthly vs. weekly), by different personas (planner vs. superintendent), with different software (scheduling solution vs. spreadsheet), and at different levels of granularity (milestones vs. production details). Their full-alignment is essential for project coordination, progress updating, and payment application reviews, and its absence may lead to costly litigation. This paper presents the first attempt to automate linking look-ahead planning tasks to master-schedule activities following an NLP-based multi-stage ranking formulation. Our model employs distance-based matching for candidate generation and a Transformer architecture for final matching.11Access to the code and a sample of the dataset can be granted upon direct request from the authors. Validation results from real-world projects demonstrate that the method helps planners match look-ahead planning tasks to master schedule activities by presenting a list of top-five matches with a precision of 76.5%. We also show that the method helps superintendents create look-ahead plans from a master schedule by generating lists of tasks based on activity descriptions.

An Update on the Accessibility and Quality of Online Information for Pediatric Orthopaedic Surgery Fellowships.

IntroductionThe internet is an important tool for applicants seeking information on pediatric orthopaedic surgery fellowship programs. Previous analysis of pediatric orthopaedic surgery fellowship websites demonstrated they were often inaccessible and incomplete. As such, the purpose of this study was to (1) perform an updated assessment of the accessibility and content of pediatric orthopaedic fellowship program websites and (2) compare the results to the previous study to discern temporal trends in website accessibility and quality. MethodsA list of pediatric orthopaedic fellowship programs was compiled from the San Francisco Match (SF Match) and the Pediatric Orthopaedic Society of North America (POSNA) online databases. All identified websites were evaluated for (1) accessibility and (2) the presence of 12 education and 12 recruitment criteria. These criteria were determined by prior fellowship website analyses and the needs of current fellowship applicants. Website accessibility and quality were compared with previously reported metrics.ResultsApproximately 91% of pediatric orthopaedic surgery fellowship programs had a functioning website. While the SF Match and POSNA databases listed nearly identical programs, there were discrepancies in the information provided by the two databases, and individual program website links provided on both databases were often nonfunctional. Fellowship program websites contained an average of 15.1 ± 3.9 total education and recruitment criteria (range: 3 - 21). The most common education criteria featured on program websites included information about research, affiliated hospital information, and rotations. The most common recruitment criteria featured on program websites included program descriptions, contact information, and social media links. There was an increased frequency in nearly all education and recruitment criteria evaluated when compared with 2014 metrics. DiscussionAlthough website accessibility and content have improved since 2014, information on pediatric orthopaedic fellowship program websites remains incomplete, with many websites failing to provide information on criteria deemed important by fellowship applicants. In addition, many discrepancies exist between the SF Match and POSNA databases, the two primary sources of information for pediatric orthopaedic fellowship applicants. Increased consistency on pediatric orthopaedic fellowship websites and both the SF Match and POSNA databases may help applicants to better assess which programs to apply to and which programs to rank highly on their match list.

MP12-06 CHARACTERISTICS AND OUTCOMES OF SUCCESSFUL APPLICANTS IN THE 2021 UROLOGY MATCH

You have accessJournal of UrologyEducation Research II (MP12)1 Sep 2021MP12-06 CHARACTERISTICS AND OUTCOMES OF SUCCESSFUL APPLICANTS IN THE 2021 UROLOGY MATCH Trevor C. Hunt, Spencer H. Bell, Alberto A. Castro Bigalli, and Joshua Randolph Trevor C. HuntTrevor C. Hunt More articles by this author , Spencer H. BellSpencer H. Bell More articles by this author , Alberto A. Castro BigalliAlberto A. Castro Bigalli More articles by this author , and Joshua RandolphJoshua Randolph More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001985.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Urology is historically a highly selective residency, and the 2021 urology match rate of 74% was the lowest in 5 years. It is unclear whether this is a result of COVID-related disruptions or is due to other factors such as increased interest in urology. Only 34% of applicants surveyed preferred virtual interviews, and many expressed concerns about match outcomes in the months prior to it. Unfortunately, applicant data are scarce compared to those published by other specialties. Thus, our aim was to describe the characteristics and outcomes of recently matched urology applicants. METHODS: The popular Urology Match Google spreadsheet was accessed in February 2021 and reviewed to summarize anonymous, crowdsourced, self-reported data for matched applicants, including a complete match list for all residency programs. Results were compared to historical data published by the AUA. RESULTS: Of 198 matched applicants with data, 64 (18%) matched at their home program while 32 (9%) matched where they completed an away rotation. Six of 143 (4%) programs matched all home students. Of 83 matched applicants with data, 73 (88%) were US allopathic seniors, 8 (10%) were osteopaths, 2 were foreign grads, and 20% had no home residency program. Additional data are displayed in the Table. USMLE scores were consistent with prior reports and >70% sent Step 2 CK scores to programs. Most matched applicants were in the top class quartile, with 39% earning AOA status. On average, matched applicants had 4 published articles and 9 other research items. They completed 2 urology sub-internships at home or away sites and applied to 80 programs. Matched applicants received 19 interviews, 2-3 being from the waitlist, and attended and ranked 15. Over half matched in their top 2 choices with 83% in their top 5. 11% matched from a waitlist interview and 21% matched at a program ranked lower than their home/away rotation sites. CONCLUSIONS: In an unprecedented academic year and application cycle, matched urology applicants remained highly competitive and obtained their desired programs even while completing fewer away rotations. Interview metrics were not notably higher than in prior years, despite concerns of virtual interviews leading to inflation and hoarding. A significant minority matched from the waitlist, lending support to the new interview invitation process’s utility. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e190-e190 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Trevor C. Hunt More articles by this author Spencer H. Bell More articles by this author Alberto A. Castro Bigalli More articles by this author Joshua Randolph More articles by this author Expand All Advertisement Loading ...

Evaluating MPI resource usage summary statistics

The Message Passing Interface (MPI) remains the dominant programming model for scientific applications running on today’s high-performance computing (HPC) systems. This dominance stems from MPI’s powerful semantics for inter-process communication that has enabled scientists to write applications for simulating important physical phenomena. MPI does not, however, specify how messages and synchronization should be carried out. Those details are typically dependent on low-level architecture details and the message characteristics of the application. Therefore, analyzing an application’s MPI resource usage is critical to tuning MPI’s performance on a particular platform. The result of this analysis is typically a discussion of the mean message sizes, queue search lengths and message arrival times for a workload or set of workloads. While a discussion of the arithmetic mean in MPI resource usage might be the most intuitive summary statistic, it is not always the most accurate in terms of representing the underlying data. In this paper, we analyze MPI resource usage for a number of key MPI workloads using an existing MPI trace collector and discrete-event simulator. Our analysis demonstrates that the average, while easy and efficient to calculate, is a useful metric for characterizing latency and bandwidth measurements, but may not be a good representation of application message sizes, match list search depths, or MPI inter-operation times. Additionally, we show that the median and mode are superior choices in many cases. We also observe that the arithmetic mean is not the best representation of central tendency for data that are drawn from distributions that are multi-modal or have heavy tails. The results and analysis of our work provide valuable guidance on how we, as a community, should discuss and analyze MPI resource usage data for scientific applications.

Trust Hardware Based Secured Privacy Preserving Computation System for Three-Dimensional Data

Three-dimensional (3D) data are easily collected in an unconscious way and are sensitive to lead biological characteristics exposure. Privacy and ownership have become important disputed issues for the 3D data application field. In this paper, we design a privacy-preserving computation system (SPPCS) for sensitive data protection, based on distributed storage, trusted execution environment (TEE) and blockchain technology. The SPPCS separates a storage and analysis calculation from consensus to build a hierarchical computation architecture. Based on a similarity computation of graph structures, the SPPCS finds data requirement matching lists to avoid invalid transactions. With TEE technology, the SPPCS implements a dual hybrid isolation model to restrict access to raw data and obscure the connections among transaction parties. To validate confidential performance, we implement a prototype of SPPCS with Ethereum and Intel Software Guard Extensions (SGX). The evaluation results derived from test datasets show that (1) the enhanced security and increased time consumption (490 ms in this paper) of multiple SGX nodes need to be balanced; (2) for a single SGX node to enhance data security and preserve privacy, an increased time consumption of about 260 ms is acceptable; (3) the transaction relationship cannot be inferred from records on-chain. The proposed SPPCS implements data privacy and security protection with high performance.

Not all applications have boring communication patterns: Profiling message matching with BMM

SummaryMessage matching within MPI is an important performance consideration for applications that utilize two‐sided semantics. In this work, we present an instrumentation of the CrayMPI library that allows the collection of detailed message‐matching statistics as well as an implementation of hashed matching in software. We use this functionality to profile key DOE applications with complex communication patterns to determine under what circumstances an application might benefit from hardware offload capabilities within the NIC to accelerate message matching. We find that there are several applications and libraries that exhibit sufficiently long match list lengths to motivate a Binned Message Matching approach.

Enhancing deep-learning training for phase identification in powder X-ray diffractograms.

Within the domain of analyzing powder X-ray diffraction (XRD) scans, manual examination of the recorded data is still the most popular method, but it requires some expertise and is time consuming. The usual workflow for the phase-identification task involves software for searching databases of known compounds and matching lists of d spacings and related intensities to the measured data. Most automated approaches apply some iterative procedure for the search/match process but fail to be generally reliable yet without the manual validation step of an expert. Recent advances in the field of machine and deep learning have led to the development of algorithms for use with diffraction patterns and are producing promising results in some applications. A limitation, however, is that thousands of training samples are required for the model to achieve a reliable performance and not enough measured samples are available. Accordingly, a framework for the efficient generation of thousands of synthetic XRD scans is presented which considers typical effects in realistic measurements and thus simulates realistic patterns for the training of machine- or deep-learning models. The generated data set can be applied to any machine- or deep-learning structure as training data so that the models learn to analyze measured XRD data based on synthetic diffraction patterns. Consequently, we train a convolutional neural network with the simulated diffraction patterns for application with iron ores or cements compounds and prove robustness against varying unit-cell parameters, preferred orientation and crystallite size in synthetic, as well as measured, XRD scans.