Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimum description length for selection of models of musical rhythm

We apply the minimum description length (MDL) methodology from information theory in order to select mathematical models for musical rhythm. We consider six models proposed by David Temperley and mathematically formalize them, which allows for an MDL analysis. As a consequence, we find that two of the models are not suitable to apply this methodology, as their codification strategy does not represent all possible rhythm sequences. A slight modification of Temperley's hierarchical model provides an improvement in codification performance, robust across all the four corpora considered in the experiments: three classical corpora commonly used in music studies and one of tango songs recently released. Our study confirms the usefulness of the MDL approach to solve the classical trade-off between model complexity and its ability to fit the data.
A Theoretical Framework for Multimodal Learner Modeling and Performance Analysis in Experiential Learning Environments

This paper presents a theoretical framework for multimodal learner modeling, performance analysis, and feedback generation in personalized experiential learning environments. Effective learning and training programs are crucial in modern workplaces that require complex cognitive and psychomotor skills in individual and team settings. Experiential learning environments have gained popularity for teaching these skills in realistic yet safe contexts. Our framework for assessing learner performance in these environments integrates cognitive task analysis, distributed cognition, and multimodal learning analytics. Drawing on competency-based education theories, the framework introduces a hierarchical learner competency model and Bayesian inferencing techniques to assess individual and team performance. This model provides insights into learner performance at different cognitive levels and tracks performance and behaviors over time. The information derived from multilevel learner modeling is then used to generate explainable and actionable feedback for learners and instructors. This feedback aims to support personalized needs and facilitate improvement.
University of Birmingham Prevalence and clinical correlates of self-injurious behavior in Tourette syndrome

Tourette syndrome (TS) is a neurodevelopmental condition characterized by the presence of multiple tics. In addition to common behavioral co-morbidities such as obsessive-compulsive disorder and attention-deficit and hyperactivity disorder, patients with TS can present with self-injurious behavior (SIB): deliberate, non-accidental, repetitive infliction of self-harm without suicidal intent. In order to determine the prevalence and clinical correlates of SIB in TS, we conducted a systematic literature review in accordance to the methodology described in the MOOSE Guidelines for Meta-Analysis and Systematic Reviews of Observational Studies in Epidemiology. The literature search yielded 20 cohort studies that were suitable for quantitative synthesis. Combined results showed that SIB is present in 35% of patients with TS. Obsessive-compulsive behaviors appear to be associated with SIB in patients with TS, and their presence should alert clinicians to the possible co-occurrence of SIB, particularly if the patient has multiple behavioral co-morbidities. Since the reviewed studies were methodologically heterogeneous and were conducted in specialist clinics, our findings cannot be generalized to patients with TS in the wider community.
The Determinants of Total Factor Productivity in the EU: Insights from Sectoral Data and Common Dynamic Processes

This paper examines the development of total factor productivity (TFP) and the drivers of TFP for a panel of 17 EU countries in the period of 1995-2007. We estimate aggregated and sectoral TFP for 17 EU countries by means of the augmented mean group estimator to control for endogeneity, cross-section dependence and heterogeneous production technology. We find that although wages are the main driver of TFP, ICT, extra-EU trade and human capital also play a role.
Bayesian Logistic Regression with Spatial Correlation: An Application to Tennessee River Pollution

We analyze data (length, weight and location) from a study done by the Army Corps of Engineers along the Tennessee River basin in the summer of 1980. The purpose is to predict the probability that a hypothetical channel catfish at a location studied is toxic and contains 5 ppm or more DDT in its filet. We incorporate spatial information and treate it separetely from other covariates. Ultimately, we want to predict the probability that a catfish from the unobserved location is toxic. In a preliminary analysis, we examine the data for observed locations using frequentist logistic regression, Bayesian logistic regression, and Bayesian logistic regression with random effects. Later we develop a parsimonious extension of Bayesian logistic regression and the corresponding Gibbs sampler for that model to increase computational feasibility and reduce model parameters. Furthermore, we develop a Bayesian model to impute data for locations where catfish were not observed. A comparison is made between results obtained fitting the model to only observed data and data with missing values imputed. Lastly, a complete model is presented which imputes data for missing locations and calculates the probability that a catfish from the unobserved location is toxic at once. We conclude that length and weight of the fish have negligible effect on toxicity. Toxicity of these catfish are mostly explained by location and spatial effects. In particular, the probability that a catfish is toxic decreases as one moves further downstream from the source of pollution. Acknowledgements This thesis draws heavily on past courses and work experience. More importantly, it draws on many people without whom this work would not have been complete. First and foremost, I would like to thank my thesis advisor, Balgobin Nandram. He is a source of enlightenment for me in the technical aspects of the thesis, and has mentored me countless times. I learned much more from him than just Bayesian statistical methodology. I sincerely thank him for all that he has given me. It feels strange that the time I spent helping out the family’s software business is invaluable. I thank my family too for the unconditional love I continue to receive from them. I also thank faculty and staff of the Department of Mathematical Sciences with whom I have interacted over the years. They all have had an impact on this work in some way. Finally, I am greatly indebted to Dr. Jai Won Choi, National Center for Health Statistics, for partial financial support and constant encouragement and praise.
10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’23)

Recommender systems (RSs) have undoubtedly played a significant role in addressing the information overload problem by efficiently filtering and suggesting relevant items to users. These systems use both explicit and implicit user preferences to filter available data and suggest items that might align with the user’s interests. This can range from recommending movies on a streaming platform based on previous views to suggesting products for purchase based on browsing history. In their early stages, RSs focused on enhancing their algorithmic capabilities to provide accurate recommendations. However, the overemphasis on algorithms resulted in neglecting the human aspect of the user experience. Recognizing this limitation, recent trends in RSs have started to shift their attention toward incorporating Symbiotic Human-Machines Decision Making models. These models aim to provide users with dynamic and persuasive interfaces that empower them to understand and engage better with the recommendations. This shift represents an essential step in creating recommender systems that truly resonate with users and create a more enjoyable, trustable, and user-friendly experience. A crucial aspect of recommender systems’ evolution lies in their proactive nature. Early works focused on designing systems that could proactively anticipate user preferences and needs. While this remains a valuable trait, modern RSs also recognize the importance of giving users control and transparency over their recommendations. Striking the right balance between proactivity and user control ensures that the system supports users without being overly intrusive, thus enhancing their overall satisfaction. These aspects are the main discussion topics of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’23. In this summary, we introduce the workshop’s motivation and view, review its history, and discuss the most critical issues that deserve attention for future research directions.
PARALiA: A Performance Aware Runtime for Auto-tuning Linear Algebra on Heterogeneous Systems

Dense linear algebra operations appear very frequently in high-performance computing (HPC) applications, rendering their performance crucial to achieve optimal scalability. As many modern HPC clusters contain multi-GPU nodes, BLAS operations are frequently offloaded on GPUs, necessitating the use of optimized libraries to ensure good performance. Unfortunately, multi-GPU systems are accompanied by two significant optimization challenges: data transfer bottlenecks as well as problem splitting and scheduling in multiple workers (GPUs) with distinct memories. We demonstrate that the current multi-GPU BLAS methods for tackling these challenges target very specific problem and data characteristics, resulting in serious performance degradation for any slightly deviating workload. Additionally, an even more critical decision is omitted because it cannot be addressed using current scheduler-based approaches: the determination of which devices should be used for a certain routine invocation. To address these issues we propose a model-based approach: using performance estimation to provide problem-specific autotuning during runtime. We integrate this autotuning into an end-to-end BLAS framework named PARALiA. This framework couples autotuning with an optimized task scheduler, leading to near-optimal data distribution and performance-aware resource utilization. We evaluate PARALiA in an HPC testbed with 8 NVIDIA-V100 GPUs, improving the average performance of GEMM by 1.7× and energy efficiency by 2.5× over the state-of-the-art in a large and diverse dataset and demonstrating the adaptability of our performance-aware approach to future heterogeneous systems.
An Examination of Estimated BMI and Perceived Wellness among Students at a Midsized Midwest University

Health is complex and the perception of individual wellness can be influenced by various factors. Students at a Midwestern university estimated their BMI, completed the Perceived Wellness Survey, and had their BMI calculated. Measured BMI was significantly higher than estimated [F(2,155) = 62.03, p = 0.001]. Associations existed between the measured constructs of psychological (r = -0.231; p = 0.004), spiritual (r = 0.248; p = 0.002), and estimated BMI. Data indicates that perceived body composition is related to wellness. False perception of body composition could lead to wellness deficiencies.
University of Dundee Enhancing Parent-Child Language Interaction in the Pre-School Years

Citation for published version (APA): Topping, K. (2018). Enhancing Parent-Child Language Interaction in the Pre-School Years. In R. M. Gillies (Ed.), Promoting Academic Talk in Schools: Global Practices and Perspectives (1 ed., pp. 112-127). Routledge Taylor & Francis Group. https://www.routledge. com/Promoting-Academic-Talk-in-Schools-Global-Practices-andPerspectives/Gillies/p/book/9781138305489
Investigating the effects of incremental training on neural ranking models

Recommender systems are an essential component of online platforms providing users with personalized experiences. Some recommendation scenarios such as social networks and news are extremely dynamic in nature with user interests changing over time and new items being continuously added due to breaking news and trending events. Incremental training is a popular technique to keep recommender models up-to-date in such dynamic platforms. In this paper, we provide an empirical analysis of a large industry dataset from the Sharechat app MOJ, a social media platform featuring short videos, to answer relevant questions like - How often should I retrain the models? - do different model architectures, features and dataset sizes benefit differently from incremental training? - Does incremental training equally benefit all users and items?
Arctic shipping: a systematic literature review of comparative studies

Following the gradual decline of Arctic sea ice, shipping using Arctic routes increased from 2010. This led to an upsurge in the number of studies investigating the potential of Arctic maritime routes. A systematic literature review was conducted to assess the extant literature from 1980 to 2017 on comparative studies between Arctic and traditional routes. This review also aimed to provide an initial understanding on route choice decision-making factors and to contribute to the literature by providing suggestions for future research and methodological considerations. The competitiveness of Arctic routes is evaluated from both economic and environmental perspectives. Research themes and methodological characteristics are analysed in order to establish an evidence base in Arctic shipping literature. It is identified that analytical research methods and transport cost models are mainly employed. The results indicate that although Arctic routes can be more cost-effective and energy efficient compared to traditional ones, especially in the long-term, they can mainly serve as seasonal alternatives for bulk and specialised shipping in the short-term.
ECONOMIC DISRUPTIONS BY REDOUBT VOLCANO: ASSESSMENT METHODOLOGY AND ANECDOTAL EMPIRICAL EVIDENCE

The eruptions of Redoubt Volcano in 1989-90 imposed significant economic costs on Alaska residents, airlines and other businesses serving Alaska, government agencies, and travelers going to or coming from Alaska. This paper presents a set of principles, or guidelines, for the classification and aggregation of costs and benefits associated with the eruptions. These costs include both market-measured costs and non-market-measured costs, such as waiting time.
Garbage Collection and E ciency in Dynamic Metacircular Runtimes An Experience Report

In dynamic object-oriented languages, low-level mechanisms such as just-in-time compilation, object allocation, garbage collection (GC) and method dispatch are oen handled by virtual machines (VMs). VMs are typically implemented using static languages, allowing only few changes at run time. In such systems, the VM is not part of the language and interfaces to memory management or method dispatch are xed, not allowing for arbitrary adaptation. Furthermore, the implementation can typically not be inspected or debugged with standard tools used to work on application code. is paper reports on our experience building Bee, a dynamic Smalltalk runtime, wrien in Smalltalk. Bee is a Dynamic Metacircular Runtime (DMR) and seamlessly integrates the VM into the application and thereby overcomes many restrictions of classic VMs, for instance by allowing arbitrary code modications of the VM at run time. Furthermore, the approach enables developers to use their standard tools for application code also for the VM, allowing them to inspect, debug, understand, and modify a DMR seamlessly. We detail our experience of implementing GC, compilation, and optimizations in a DMR. We discuss examples where we found that DMRs can improve understanding of the system, provide tighter control of the soware stack, and facilitate research. We also show that in high-level benchmarks the Bee DMR performance is close to that of a widely used Smalltalk VM. CCSConcepts •So ware and its engineering→Object oriented languages; Runtime environments; Garbage collection; Dynamic compilers; DLS’17, Vancouver, Canada © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. is is the author’s version of the work. It is posted here for your personal use. Not for redistribution. e denitive Version of Record was published in Proceedings of 13th ACM SIGPLAN International Symposium on Dynamic Languages, October 25–27, 2017 , hp://dx.doi. org/10.1145/3133841. 3133845.
Journal of Rehabilitation in Civil Engineering

Article history: Received: 09 March 2016 Accepted: 05 September 2017 Corrosion of steel is one of the essential potential dangers and threats in concrete structures. Corrosion of steel embedded in reinforced concrete plays a key role in reducing the strength and durability of reinforced concrete. Several studies have proposed that alternative approaches to enhancing the performance of reinforced concrete and its resistance to corrosion. In this article, the results of steel corrosion embedded in reinforced concrete with silica fume and Nano-silica particles are presented. In the testing phase, samples with a mixture of these particles ranging from 0 to 133 grams were generated, and their performance was compared applying Corrosion Potential (OCP), Electrochemical Impedance Spectroscopy (EIS), Linear Polarization (LPR), and TOEFL polarization tests. The results demonstrate that silica fume is effective in reducing the permeability of the concrete against malicious (adverse) ions, but nano-silica had far satisfying performance in reducing the corrosion rate of steel embedded in concrete.
Machine Learning based sampling of X-Ray images for a computer-aided detection of Tuberculosis

Computer Aided Detection software relies on an annotated data set of X-rays to be developed. The annotation task requires extensive know-how and it is very time-consuming. This work presents a sampling method to select the most relevant images which will be annotated for the development of Tuberculosis screening platform based on machine learning algorithms. The sampling task optimizes the annotation process by reducing the number of images to be analyzed without compromising the diversity and the significance power of the images in the dataset. In this context, the image relevance is based on similarity and dissimilarity measurements. The experiment consisted in a deep learning feature engineering step, followed by topological analysis based on Self-Organizing Map and K-Means.
INDOMETHACIN & SARS-COV-2: PROSPECTS FOR CLINICAL MANAGEMENT OF THE INFLAMMATORY STATE. Review

Actuality. Severe acute respiratory syndrome- (SARS) is a pandemic (called as SARS-CoV-2 or COVID-19), severely affected by transmission and fatal disease caused by unknown coronavirus family of RNA virus (SARS-COV). The phenotype can range from asymptomatic to fulminate cytokine storm which leads to multiorgan failure resulting in death. Still, the world is eagerly waiting for antiviral drugs to stop the coronavirus infection. Previous studies found that indomethacin had the ability to inhibit the RNA and DNA virus replication. Objectives. Discuss the possibility of clinical treatment of inflammation caused by SARS-COV-2 with indomethacin. Materials and methods. Analysis of scientific publications in the international electronic scientometric database PubMed by keywords for the period 1991-2020. Comparison with clinical cases. Results. Interleukins, Interferons, and metabolites formed by the enzyme cyclooxygenase (COX 1/COX 2) are active against some RNA viruses. Experts have divided the SARS infection into three phases (phase-1, 2, 3) based on the severity of the infection. In phase-3 there was “cytokine storm” due to exuberant inflammation observed which can damage organs and even fatal. We investigated the effect of indomethacin on the COX inhibitor on coronavirus replication and cytokine storm in reducing the hyperinflammatory state. Indomethacin is a potent inhibitor of SARS CoV-2. Conclusion. Indomethacin can be considered safe and effective for the prevention and treatment of coronavirus infection also antiviral activity.
Comparación de modelos de calidad, factores y métricas

There are different approaches to software development, most of them prioritize the quality of the process and the product obtained. It is important to use quality models appropriate to each methodology. These quality models present factors and indicators that describe the characteristics of the software and its relationships. They can be adaptations of others or created based on existing standards. This paper describes and analyzes some quality models developed for the methodologies of software development oriented to objects, components, aspects and agile methods. It makes a comparison according to the criteria, factors and characteristics that compose it, levels of abstraction and metrics to carry out the measurement.
Prognostic value of the lymph node ratio in surgically treated stage III colorectal cancer patients with high numbers of harvested lymph nodes

Abstract Background The lymph node ratio (LNR), defined as the number of positive lymph nodes (LN) divided by the total number of harvested LN, has been demonstrated to be an independent factor in the prognosis of surgically treated colorectal cancer (CRC) patients. This study aims to establish the prognostic value of LNR in stage III CRC patients with high numbers of LN removed. Methods Stage III CRC patients who underwent curative resections over an 8-year period were included to the study. Demographics, clinicopathological features, surgical as well as recurrence and survival outcomes were recorded and statistically analyzed. Calculations for LNR were carried out as a function of percentage rates and Cox proportional hazards regression analyses were performed to determine its effect on disease-free and overall survival. Results Among a total of 493 surgically treated CRC cases, 104 patients were included to the study consisting of 68 (65.4%) men and 36 (34.6%) women with a median age of 64 (inter-quartile range: 55–74) years. The mean number of harvested LN was 31.6 ± 21.0 (range: 12–103). Multivariate Cox regression analyses proved LNR to be a significant factor in both disease-free and overall survival (p = 0.007 and p = 0.003, respectively). Forward elimination analyses showed that a 1% increase in LNR resulted with a 2% increase in both the risks of recurrence and mortality. Conclusions The LNR may be assessed as an adjunct to the current staging systems for the prediction of oncological outcomes and survival of surgically treated stage III CRC patients.
The NiuTrans Machine Translation Systems for WMT20

This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks. We participated in Japanese<->English, English->Chinese, Inuktitut->English and Tamil->English total five tasks and rank first in Japanese<->English both sides. We mainly utilized iterative back-translation, different depth and widen model architectures, iterative knowledge distillation and iterative fine-tuning. And we find that adequately widened and deepened the model simultaneously, the performance will significantly improve. Also, iterative fine-tuning strategy we implemented is effective during adapting domain. For Inuktitut->English and Tamil->English tasks, we built multilingual models separately and employed pretraining word embedding to obtain better performance.