bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advanced Process Discovery Techniques
Authors: Augusto Adriano , Carmona Josep , Verbeek Eric 
Published: June 2022

AbstractGiven the challenges associated to the process discovery task, more than a hundred research studies addressed the problem over the past two decades. Despite the richness of proposals, many state-of-the-art automated process discovery techniques, especially the oldest ones, struggle to systematically discover accurate and simple process models. In general, when the behavior recorded in the input event log is simple (e.g., exhibiting little parallelism, repetitions, or inclusive choices) or noise free, some basic algorithms such as the alpha miner can output accurate and simple process models. However, as the complexity of the input data increases, the quality of the discovered process models can worsen quickly. Given that oftentimes real-life event logs record very complex and unstructured process behavior containing many repetitions, infrequent traces, and incomplete data, some state-of-the-art techniques turn unreliable and not purposeful. Specifically, they tend to discover process models that either have limited accuracy (i.e., low fitness and/or precision) or are syntactically incorrect. While currently there exists no perfect automated process discovery technique, some are better than others when discovering a process model from event logs recording complex process behavior. In this chapter, we introduce four of such techniques, discussing their underlying approach and algorithmic ideas, reporting their benefits and limitation, and comparing their performance with the algorithms introduced in the previous chapter.
Conformance Checking: Foundations, Milestones and Challenges
Authors: Carmona Josep , van Dongen Boudewijn , Weidlich Matthias 
Published: June 2022

AbstractBy relating observed and modelled behaviour, conformance checking unleashes the full power of process mining. Techniques from this discipline enable the analysis of the quality of a process model discovered from event data, the identification of potential deviations, and the projection of real traces onto process models. This way, the insights gained from the available event data can be transferred to a richer conceptual level, amenable for a human interpretation. The aforementioned functionalities are grounded on the use of conformance checking artefacts that explicit the relation between observed and modelled behaviour. This chapter describes these artefacts, and builds upon them to gain evidence-based insights on the processes of an organization. Moreover, we overview the applications of conformance checking and propose a general framework that incorporates these applications. Finally, milestones and challenges of the field are outlined.
Foundations of Process Event Data
Authors: De Weerdt Jochen , Wynn Moe Thandar 
Published: June 2022

AbstractProcess event data is a fundamental building block for process mining as event logs portray the execution trails of business processes from which knowledge and insights can be extracted. In this Chapter, we discuss the core structure of event logs, in particular the three main requirements in the form of the presence of case IDs, activity labels, and timestamps. Moreover, we introduce fundamental concepts of event log processing and preparation, including data sources, extraction, correlation and abstraction techniques. The chapter is concluded with an imperative section on data quality, arguably the most important determinant of process mining project success.
A Practitioner’s View on Process Mining Adoption, Event Log Engineering and Data Challenges
Authors: Accorsi Rafael , Lebherz Julian 
Published: June 2022

AbstractProcess mining is, today, an essential analytical instrument for data-driven process improvement and steering. While practical literature on how to derive value from process mining exists, less attention haas been paid to how it is being used in different industries, the effort involved in creating an event log and what are the best practices in doing so. Taking a practitioner’s view on process mining, we report on process mining adoption and illustrate the challenges of log contruction by means of the order to cash (i.e. sales) process in an SAP system. By doing so, we collect a set of best practices regarding the data selection, extraction, transformation and data model engineering, which proved themselves handy in large-scale process mining projects.
Process Mining over Multiple Behavioral Dimensions with Event Knowledge Graphs
Authors: Fahland Dirk 
Published: June 2022

AbstractClassical process mining relies on the notion of a unique case identifier, which is used to partition event data into independent sequences of events. In this chapter, we study the shortcomings of this approach for event data over multiple entities. We introduce event knowledge graphs as data structure that allows to naturally model behavior over multiple entities as a network of events. We explore how to construct, query, and aggregate event knowledge graphs to get insights into complex behaviors. We will ultimately show that event knowledge graphs are a very versatile tool that opens the door to process mining analyses in multiple behavioral dimensions at once.
Alternating complexity of counting first-order logic for the subword order
Authors: Kuske Dietrich , Schwarz Christian 
Published: June 2022

AbstractThis paper considers the structure consisting of the set of all words over a given alphabet together with the subword relation, regular predicates, and constants for every word. We are interested in the counting extension of first-order logic by threshold counting quantifiers. The main result shows that the two-variable fragment of this logic can be decided in twofold exponential alternating time with linearly many alternations (and therefore in particular in twofold exponential space as announced in the conference version (Kuske and Schwarz, in: MFCS’20, Leibniz International Proceedings in Informatics (LIPIcs) vol. 170, pp 56:1–56:13. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020) of this paper) provided the regular predicates are restricted to piecewise testable ones. This result improves prior insights by Karandikar and Schnoebelen by extending the logic and saving one exponent in the space bound. Its proof consists of two main parts: First, we provide a quantifier elimination procedure that results in a formula with constants of bounded length (this generalises the procedure by Karandikar and Schnoebelen for first-order logic). From this, it follows that quantification in formulas can be restricted to words of bounded length, i.e., the second part of the proof is an adaptation of the method by Ferrante and Rackoff to counting logic and deviates significantly from the path of reasoning by Karandikar and Schnoebelen.
Reallocation of Hospital Resources During COVID‐19 Pandemic and Effect on Trauma Outcomes in a Resource‐Limited Setting
Authors: Gallaher Jared R. , Yohann Avital , Kajombo Chifundo , Schneider Andrew , Purcell Laura , Charles Anthony 
Published: June 2022

AbstractBackgroundThe COVID‐19 pandemic has caused unprecedented disruptions to surgical care worldwide, particularly in low‐resource countries. We sought to characterize the association between pre‐and intra‐pandemic trauma clinical outcomes at a busy tertiary hospital in Malawi.MethodsWe analyzed trauma patients that presented to Kamuzu Central Hospital in Lilongwe, Malawi, from 2011 through July 2021. Burn patients were excluded. We compared patients based on whether they presented before or during the pandemic (defined as starting March 11, 2020, the date of the official WHO designation). We used logistic regression modeling to estimate the adjusted odds ratio of death based on presentation.ResultsA total of 137,867 patients presented during the study period, with 13,526 patients during the pandemic. During the pandemic, patients were more likely to be older (mean 28 vs. 25 years, p < 0.001), male (79 vs. 74%, p < 0.001), and suffer a traumatic brain injury (TBI) as their primary injury (9.7 vs. 4.9%, p < 0.001). Crude trauma‐associated mortality was higher during the pandemic at 3.7% vs. 2.1% (p < 0.001). The odds ratio of mortality during the pandemic compared to pre‐pandemic presentation was 1.28 (95% CI 1.06, 1.53) adjusted for age, sex, initial AVPU score, transfer status, injury type, and mechanism.ConclusionsDuring the pandemic, adjusted trauma‐associated mortality significantly increased at a tertiary trauma center in a low‐resource setting despite decreasing patient volume. Further research is urgently needed to prepare for future pandemics. Potential targets for improvement include improving pre‐hospital care and transportation, planning for intensive care utilization, and addressing nursing shortages.
Roles of climate feedback and ocean vertical mixing in modulating global warming rate
Authors: Yang Haijun , Zhou Xiangying , Yang Qianzi , Li Yang 
Published: June 2022

AbstractDespite the rapid increase of greenhouse gases (GHGs) in the atmosphere during the past 50 years, observed global mean surface temperature (GMST) showed a pause in the warming trend during the first decade of the twenty-first century. This is referred to as the global warming “hiatus”. A dominant hypothesis emphasizes that the superimposition of the cold phase of the Pacific decadal variability and the global warming trend can lead to the hiatus. Using simply energy balance models, we explore two potential mechanisms that may supress the GMST warming trend: enhanced negative climate feedback and downward heat mixing. Forced by linearly increasing heating, a stronger negative climate feedback can reduce the GMST warming rate, but cannot result in a warming hiatus. Downward mixing of heat can cause a short-lived hiatus of surface warming rate due to enhanced nonlinear ocean heat uptake by the lower ocean, but the surface warming would be accelerated in the long run due to the decline of downward heat mixing rate. This study provides further evidence, both theoretically and numerically, that in the long run, the only route to contain the global warming effectively is to reduce GHG emissions.
On the Influence of Sampling Scale on the In Situ Block Size Distribution
Authors: Bernardini Maurizio , Paredes Carlos , Sanchidrián José A. , Segarra Pablo , Gómez Santiago 
Published: June 2022

AbstractThe modelling of discontinuities in rock mass is undertaken with different measurement techniques and used to determine the in situ block size distribution (IBSD). Two monitoring techniques are employed: televiewer logging of boreholes and photogrammetry of highwall faces in a quarry bench; televiewer performs at the borehole diameter scale, while photogrammetry surveys at the entire bench scale. Ground sampling distances were, respectively, about 1 and 8.5 mm. The discontinuities are modelled as a stochastic discrete fracture network (DFN), with the number of discontinuities used in the simulation calibrated by the intensity per unit length (P10) on the televiewer data, or by the fracture density (P21) on the photogrammetry data, leading to different fracture networks. From the discontinuity network models, the IBSDs are calculated and discussed as function of the sampling scale (i.e. televiewer or photogrammetry data source) and of the fracture density. The goal is to compare the results from both techniques for rock mass structural characterization, to assess their limitations and shortcomings, and to show their potential complementarity at different sampling scales. The televiewer data provides smaller block sizes than the photogrammetry, following the higher number of fractures observed in the former. All volumetric distributions obtained are extremely well represented by Gamma with a power law tail distribution. Despite different location parameters, it is particularly remarkable that all distributions present very similar Gamma shape parameters. The constant log–log slopes of the tails provide evidence of multi-scale validity and a scaling invariant structure (more than two orders of magnitude) of discontinuities of the rock mass. The IBSDs and the scale effect are discussed in the light of the fragment size distributions from blasts carried out in the area characterized.
A short- and long-term follow-up study of intersphincteric NASHA Dx implants for fecal incontinence
Authors: Ezra E. , Danielsson J. M. , Graf W. 
Published: June 2022

Abstract
Background
The bulking agent NASHA Dx injected into the submucosal layer is effective in the treatment of fecal incontinence (FI) at short-and medium-term follow-up but efficacy after injection in the intersphincteric location is unknown. The aim of this study was to determine the short- and long-term efficacy and safety of NASHA Dx injected into the intersphincteric location for FI.

Methods
Patients were recruited from referrals to our Department for treatment of FI in November 2008–January 2010. Eligible patients were injected with 8 ml of NASHA Dx. Patients with a subtotal treatment effect were retreated after 2–4 weeks. The change in number of fecal incontinence episodes, the proportion of responders defined as at least 50% decrease in number of FI episodes and side effects were the main outcome measures.

Results
Sixteen patients, 15 women and 1 man with a median age of 68, 5 (range 44–80) years and a median CCFIS of 15 (range 10–19) were included in the study. The median number of incontinence episodes decreased from 21.5 (range 8–61) at baseline to 10 (range 0–30) at 6 months (p = 0.003) and 6 (range 0–44) at 12 months (p = 0.05). The median number of incontinence episodes in the 11 patients completing the 10-year follow-up was 26.5 (range 0–68). The percentage of responders at 12 months and 10 years were 56% and 27%, respectively. Mild to moderate pain at the injection site was described by 69%. There was one case of mild infection, successfully treated with antibiotics and one implant had to be removed due to dislocation.

Conclusions
NASHA Dx as an intersphincteric implant improves incontinence symptoms in the short term with moderate side effects and can be used alone or as an adjunct to other treatment modalities. Long-term efficacy was observed in 27%.
Site- and Zone-Dependent Changes in Proteoglycan Content and Biomechanical Properties of Bluntly and Sharply Grooved Equine Articular Cartilage
Authors: Mohammadi Ali , te Moller Nikae C. R. , Ebrahimi Mohammadhossein , Plomp Saskia , Brommer Harold , van Weeren P. René , Mäkelä Janne T. A. , Töyräs Juha , Korhonen Rami K. 
Published: June 2022

AbstractIn this study, we mapped and quantified changes of proteoglycan (PG) content and biomechanical properties in articular cartilage in which either blunt or sharp grooves had been made, both close to the groove and more remote of it, and at the opposing joint surface (kissing site) in equine carpal joints. In nine adult Shetland ponies, standardized blunt and sharp grooves were surgically made in the radiocarpal and middle carpal joints of a randomly chosen front limb. The contralateral control limb was sham-operated. At 39 weeks after surgery, ponies were euthanized. In 10 regions of interest (ROIs) (six remote from the grooves and four directly around the grooves), PG content as a function of tissue-depth and distance-to-groove was estimated using digital densitometry. Biomechanical properties of the cartilage were evaluated in the six ROIs remote from the grooves. Compared to control joints, whole tissue depth PG loss was found in sites adjacent to sharp and, to a larger extent, blunt grooves. Also, superficial PG loss of the surgically untouched kissing cartilage layers was observed. Significant PG loss was observed up to 300 µm (sharp) and at 500 µm (blunt) from the groove into the surrounding tissue. Equilibrium modulus was lower in grooved cartilage than in controls. Grooves, in particular blunt grooves, gave rise to severe PG loss close to the grooved sites and to mild degeneration more remote from the grooves in both sharply and bluntly grooved cartilage and at the kissing sites, resulting in loss of mechanical strength over the 9-month period.
Relationships between preadmission variables and academic outcomes for postbaccalaureate students in medical school
Authors: Schneid Stephen D. , Kelly Carolyn J. , Brandl Katharina 
Published: June 2022

AbstractThere is currently little guidance for medical school admissions committees regarding how to weigh postbaccalaureate program grades relative to undergraduate grades. This study was designed to address this issue. Admissions data, preclerkship course performance and United States Medical Licensing Exam (USMLE) Step 1 results were analyzed over three years for University of California, San Diego (UCSD) postbaccalaureate premedical (PBPM) students (n = 25), students who participated in other postbaccalaureate programs (n = 34), and for the remainder of the medical students who did not participate in any postbaccalaureate programs (n = 329). UCSD PBPM program alumni did not significantly differ in their cumulative academic performance on exams in preclerkship courses and USMLE Step 1 pass rates compared to the rest of the class despite their significantly lower GPA, lower Biology, Chemistry, Physics and Math (BCPM) GPA, and Medical College Admissions Test (MCAT) percentiles. For students who participated in the PBPM programs, PBPM program GPA was a significant predictor of preclerkship academic performance and USMLE Step 1 performance. When assessing academic readiness of applicants who have completed postbaccalaureate programs, admissions committees might closely consider the postbaccalaureate program GPA in addition to other academic metrices such as BCPM GPA and MCAT score.
An architecture-level analysis on deep learning models for low-impact computations
Authors: Li Hengyi , Wang Zhichen , Yue Xuebin , Wang Wenwen , Tomiyama Hiroyuki , Meng Lin 
Published: June 2022

AbstractDeep neural networks (DNNs) have made significant achievements in a wide variety of domains. For the deep learning tasks, multiple excellent hardware platforms provide efficient solutions, including graphics processing units (GPUs), central processing units (CPUs), field programmable gate arrays (FPGAs), and application-specific integrated circuit (ASIC). Nonetheless, CPUs outperform other solutions including GPUs in many cases for the inference workload of DNNs with the support of various techniques, such as the high-performance libraries being the basic building blocks for DNNs. Thus, CPUs have been a preferred choice for DNN inference applications, particularly in the low-latency demand scenarios. However, the DNN inference efficiency remains a critical issue, especially when low latency is required under conditions with limited hardware resources, such as embedded systems. At the same time, the hardware features have not been fully exploited for DNNs and there is much room for improvement. To this end, this paper conducts a series of experiments to make a thorough study for the inference workload of prominent state-of-the-art DNN architectures on a single-instruction-multiple-data (SIMD) CPU platform, as well as with widely applicable scopes for multiple hardware platforms. The study goes into depth in DNNs: the CPU kernel-instruction level performance characteristics of DNNs including branches, branch prediction misses, cache misses, etc, and the underlying convolutional computing mechanism at the SIMD level; The thorough layer-wise time consumption details with potential time-cost bottlenecks; And the exhaustive dynamic activation sparsity with exact details on the redundancy of DNNs. The research provides researchers with comprehensive and insightful details, as well as crucial target areas for optimising and improving the efficiency of DNNs at both the hardware and software levels.
In Defence of the Normative Account of Ignorance
Authors: Meylan Anne 
Published: June 2022

AbstractThe standard view of ignorance is that it consists in the mere lack of knowledge or true belief. Duncan Pritchard has recently argued, against the standard view, that ignorance is the lack of knowledge/true belief that is due to an improper inquiry. I shall call, Pritchard’s alternative account the Normative Account. The purpose of this article is to strengthen the Normative Account by providing an independent vargument supporting it.
In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning
Authors: Gaikwad Aniruddha , Chang Tammy , Giera Brian , Watkins Nicholas , Mukherjee Saptarshi , Pascall Andrew , Stobbe David , Rao Prahalada 
Published: June 2022

AbstractIn droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.