Reviewerly/exHarmony huggingface dataset:
Implementation of Actionable Gamification Design Framework in Machining Training

Authors:
raw_author_name: Chao Peng
raw_affiliation_string: Rochester Institute of Technology
raw_author_name: Rui Liu
raw_affiliation_string: Rochester Institute of Technology  (COE)

Abstract: Abstract With the development of Industry 4.0 technologies adopted in machining industry, more and more machine tools and systems become smarter and automatic. However, contrary to our expectations, the machining automation has not much reduced the labor shortage issue in the machining industry, and this demand gap is gradually expanding in recent years. Although this issue is multiaspect and complicated, it is evident that one of the main reasons comes from the conventional training system. By comparing with the outdated training content, which has been realized and emphasized by machining training providers, the problems regarding the training method have not yet attracted sufficient attention. In order to realize the training outcomes efficiently, instructor-led and on-the-job training methods are widely adopted by most of the machining training programs. However, trainee's engagement, i.e., interest and involvement, is often ignored, which is one of the main reasons for downward enrollment and retention rate of many machining related programs from trade schools or community colleges. To engage the younger generation into this career field, more attention should be paid to incorporate more training methods favorite with young students, and the interactive training method with game-based elements has been proved to be a good solution in many similar applications. Gamification, a technique to facilitate training and learning within the playable context, has the potential to enhance training engagement. In a gamified application, game design elements and gameplay mechanics are added to motivate trainees to stay in the loop of repeating necessary equipment operations while mitigating the mental fatigue they might have due to lack of interest and the boreout syndrome at machining workspace. Gamification is promising to improve self-motivation by stimulating the desire to pursue a gaming-inspired goal (i.e., reaching a winning condition in the game or earning a level-up badge to show off on the profile). By connecting the training tasks to a gaming goal, trainees would be more likely to enjoy doing the practice and feel less forced. The objective of this study is to systematically evaluate the conventional machining training under the guidance of the Octalysis Framework with eight core drives of gamification and come up with the Octalysis score to each core drive. In addition, the Octalysis for conventional machining training will be compared with the designs and mechanics of successful skill-learning games to identify the strong and weak aspects of learning engagement. From this study, it is expected to propose a new perspective to evaluate the current machining training method on student attraction and engagement and improve it with appropriate game-based elements.
Reweighting trajectory segments into steady state without knowledge of the sampling distribution

Authors:
raw_author_name: Daniel M. Zuckerman
raw_affiliation_string: Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA

Abstract: Molecular dynamics (MD) and enhanced sampling simulations have become fundamental tools for studying biomolecular events. A significant challenge in these simulations is ensuring that sampled configurations and transitions converge to the stationary distribution of interest, whether equilibrium or nonequilibrium. Lack of convergence constrains the estimation of mechanisms, free energy, and rates of complex molecular events. Here, we introduce the “randomized iterative trajectory reweighting” algorithm to estimate a stationary distribution from unconverged simulation data, applicable to trajectories generated without biasing forces. This method iteratively reweights trajectories based on the stationary solution of a coarse-grained transition matrix, employing a new random clustering in each iteration. The iterative random clustering mitigates the phase-space discretization error inherent in existing trajectory reweighting techniques based on one-shot clustering and ultimately yields numerically unbiased quasi-continuous configuration-space distributions and estimates of observables. We demonstrate this procedure for both equilibrium and nonequilibrium steady states, using synthetic molecular dynamics (synMD) trajectories to enable comparison with exact reference values.
Closed-loop circuit model of coagulation physiology

Authors:
raw_author_name: Philip R. LeDuc
raw_affiliation_string: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
raw_author_name: Phyllis Nelson
raw_affiliation_string: California State Polytechnic University-Pomona, Pomona, CA, USA

Abstract: Clinical anticoagulation therapy is dosed at a standard dose or according to weight, with adjustments made based on laboratory test targets. Patients are at risk of over or under-anticoagulation given lengthy return times for many of these tests. To this end, the authors aim to develop a model of the physiologic coagulative response to anticoagulation administration that reflects real-time dose adjustments. Here, we present our closed-loop multi-element model of physiologic coagulation which is the foundation for this project. The authors have accomplished the basic circuit element model of 64 coupled differential equations describing the human coagulation cascade and feedback paths that incorporate rate kinetics of factor activation. The authors developed a Python script able to convert a large number of equations to a form compatible with SPICE circuit modeling software, which was then used to generate a multi-input circuit model of the differential equations. The equations were converted to a state space representation and used to derive a linearized approximation about the steady state. SPICE simulates internal feedback loops of the coagulation cascade over time with initial conditions representing normal coagulability. The authors are employing the law of conservation of mass to piecewise validate this model by observing species for concentration changes. Our model demonstrates changes in each coagulation factor concentration as an output over a specified time, given initial species concentrations as inputs. These initial parameters may be adjusted to reflect a starting physiologic state of coagulation. The authors observed little change in concentration for components whose eigenvalues equal 0, suggesting initial conditions are very close to steady state values. Coagulation physiology may be simplified into a circuit model that allows for real-time physiologic visualization. This will be useful in timely adjustments of anticoagulation therapy.
Continuous-Variable Quantum State Designs: Theory and Applications

Authors:
raw_author_name: Joseph T. Iosue
raw_affiliation_string: Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, Maryland 20742, USA; Joint Quantum Institute, NIST/University of Maryland, College Park, Maryland 20742, USA
raw_author_name: Kunal Sharma
raw_affiliation_string: IBM Quantum, IBM T. J. Watson Research Center, Yorktown Heights, New York 10598, USA; Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, Maryland 20742, USA
raw_author_name: Michael J. Gullans
raw_affiliation_string: Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, Maryland 20742, USA
raw_author_name: Victor V. Albert
raw_affiliation_string: Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, Maryland 20742, USA

Abstract: We generalize the notion of quantum state designs to infinite-dimensional spaces. We first prove that, under the definition of continuous-variable (CV) state t-designs from [Blume-Kohout et al., Commun. Math. Phys. 326, 755 (2014)], no state designs exist for t≥2. Similarly, we prove that no CV unitary t-designs exist for t≥2. We propose an alternative definition for CV state designs, which we call rigged t-designs, and provide explicit constructions for t=2. As an application of rigged designs, we develop a design-based shadow-tomography protocol for CV states. Using energy-constrained versions of rigged designs, we define an average fidelity for CV quantum channels and relate this fidelity to the CV entanglement fidelity. As an additional result of independent interest, we establish a connection between torus 2-designs and complete sets of mutually unbiased bases.Received 13 December 2022Revised 25 October 2023Accepted 12 December 2023DOI:https://doi.org/10.1103/PhysRevX.14.011013Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum harmonic oscillatorQuantum information processingQuantum information processing with continuous variablesQuantum information theoryQuantum measurementsQuantum Information, Science & Technology
Visualisations for Bayesian Additive Regression Trees

Authors:
raw_author_name: Andrew Parnell
raw_affiliation_string: Maynooth University
raw_author_name: Catherine Hurley
raw_affiliation_string: Maynooth University

Abstract: Tree-based regression and classification has become a standard tool in modern data science. Bayesian Additive Regression Trees (BART) has in particular gained wide popularity due its flexibility in dealing with interactions and non-linear effects. BART is a Bayesian tree-based machine learning method that can be applied to both regression and classification problems and yields competitive or superior results when compared to other predictive models. As a Bayesian model, BART allows the practitioner to explore the uncertainty around predictions through the posterior distribution. In this paper, we present new Visualisation techniques for exploring BART models. We construct conventional plots to analyse a model’s performance and stability as well as create new tree-based plots to analyse variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our new Visualisations are designed to work with the most popular BART R packages available, namely BART, dbarts, and bartMachine. Our approach is implemented in the R package bartMan (BART Model ANalysis).
Full Paper: Integrating the iPad Into the Engineering Classroom

Authors:
raw_author_name: Michael Cross
raw_affiliation_string: Norwich University

Abstract: Abstract Beginning in 2016, the University embarked on the Digital Citizen Initiative with the goal of including an iPad as standard issue for all students and faculty. From 2016 to 2019, pilot programs were conducted to test the use of the iPad in both classroom and laboratory settings. In the Fall of 2019, all incoming first-year students at the University were issued an iPad along with an Apple pencil and protective cover. Laptop-, tablet-, or Chromebook-per-child programs are becoming more and more prevalent in P-12 environments. As today's students matriculate to college, they expect a technology-rich learning environment that is an improvement from what they experienced in their primary and secondary education. Mobile computing has become more commonplace in many industry and professional settings. As we work to educate future professionals, it is important that the on-campus experience is preparing students for life after the University. The file management improvements to the iPad combined with the creation of an active stylus, the Apple pencil, have created a powerful mobile productivity platform that can be useful in the traditional ECE classroom. The authors performed a pilot study on the use of the iPad in multiple ECE courses, including Circuits and Electronics. Initially, the iPads were used to perform homework digitally, allowing for easy integration with the University's learning management system (LMS). Based on the results of this pilot, the use of the iPad has been expanded to include the use of the iPad for circuit simulation, hand sketching, and digital lab notebooks. The authors have also used the iPad for assignment grading, again allowing for improved assignment feedback, modernized grading workflows, and efficient integration with the University's LMS. In this paper, insights into the use of the iPad in the ECE classroom will be discussed, including student perceptions from the pilot program. Example student work will be included to compare work completed on the iPad to work traditionally composed on paper. The authors' thoughts on further integration possibilities will also be discussed. Ultimately, the iPad has been seen as a value-additive augmentation to the ECE classroom by many students.
Promoting quality STEM Education in the time of Social Distancing

Authors:
raw_author_name: Marcelo Caplan
raw_affiliation_string: Columbia College

Abstract: Abstract Promoting quality STEM Education in the time of Social Distancing The pandemic produced by COVID-19 has forced a radical change in the strategies and methodologies used to share and transmit knowledge. With the closure of the Schools / Universities, the educational process has been radically transformed from one day to the next. STEM education, based on collaborative work, inquiry, experimentation, problem-solving, and project generation, encounters many obstacles in the present situation: students do not have access to laboratories, materials, and other essential supplies to implement an educational process of quality. The Institution has developed alternative ways to promote quality STEM education for our students when learning from their homes with the present limitations. These activities allow students to explore phenomena through "virtual" labs (not simulations), project generation using a supply of pre-existing materials (constraints that any project has at any time), and in particular, the development of projects based in easy to find at home materials. In this paper, the author will present three strategies to promote STEM education through remote learning: 1) Laboratory activities for college-level students 2) Hands-on activities for high-school students through informal education settings, and 3) Activities for the public at large through social media (Facebook live and YouTube) and sponsored by public institutions. The author implemented these strategies successfully in a university setting, in Out of School Time (OST) programs in the City for nonprivileged student groups, and with multiple public institutions partners in different countries.
Mixed Reality Game for Active Geotechnical Engineering Learning

Authors:
raw_author_name: Ying Tang
raw_affiliation_string: Rowan University
raw_author_name: Cheng Zhu
raw_affiliation_string: Rowan University
raw_author_name: Ryan Hare
raw_affiliation_string: Rowan University

Abstract: Abstract Our fully-integrated mixed reality game system, called multiphysics enriched mixed reality for integrated geotechnical education (MERGE), focuses on improving student education in the context of geotechnical engineering. This work allows students to participate in online learning in a more inclusive way while playing a game. The game is meant as an "integrated geotechnical learning experience". By providing students visualization, collaboration, and simulation tools, we hope to promote problem-solving and improved learning. A big part of that is leveraging mixed reality technology to improve student access to lab equipment. By gamifying the learning, we also focus on improving student interest in geotechnical engineering. Due to the virtual nature of the game, everyone will have access to the same lab equipment and the same opportunity to experience the material. They are also able to progress through the game at their own pace, which helps keep the interest of the students while they are learning. Learning by playing games is not a new concept, but the potential has not been reached yet. Our goal is to provide a fulfilling learning experience while keeping the students engaged in a fun game. The MERGE platform is inspired by popular mixed reality games, allowing students to see computer-generated models superimposed into the world through the camera on their mobile device. Building on recent advancements in cyberinfrastructure, MERGE is meant to provide an attractive and motivating environment to support students' geotechnical engineering skills. Throughout gameplay, students' strategic thinking will be tested through carefully designed games that relate to geotechnical problems. Additionally, the game uses their GPS to create a map of the real world around them, giving players the ability to explore their local environment to advance their game progress. Initial gameplay mainly focuses on a lab assignment involved with the study, construction, and implementation of geothermal piles, which are special foundations that utilize the earth's temperature to heat and cool the associated building. The main contribution of this work is a discussion of the educational technology and processes behind implementing a mixed reality educational game. We provide developmental insights and educational background as well as results from preliminary game testing to inform researchers who seek to develop similar games.
Neighbor list artifacts in molecular dynamics simulations

Authors:
raw_author_name: Gerhard Hummer
raw_affiliation_string: Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany

Abstract: Molecular dynamics (MD) simulations are widely used in biophysical research. To aid non-expert users, most simulation packages provide default values for key input parameters. In MD simulations using the GROMACS package with default parameters, we found large membranes to deform under the action of a semi-isotropically coupled barostat. As the primary cause, we identified overly short outer cutoffs and infrequent neighbor list updates that result in missed long-range non-bonded interactions. Small but systematic imbalances in the apparent pressure tensor then induce unphysical asymmetric box deformations that crumple the membrane. We also observed rapid oscillations in averages of the instantaneous pressure tensor components and traced these to the use of a dual pair list with dynamic pruning. We confirmed that similar effects are present in MD simulations of neat water in atomistic and coarse-grained representations. Whereas the slight pressure imbalances likely have minimal impact in most current atomistic MD simulations, we expect their impact to grow in studies of ever-larger systems with coarse-grained representation, in particular in combination with anisotropic pressure coupling. We present measures to diagnose problems with missed interactions and guidelines for practitioners to avoid them, including estimates for appropriate values for the outer cutoff r_l and the number of time steps nstlist between neighbor list updates.
Assessing the relative importance of dry-season incoming solar radiation and water storage dynamics during the 2005, 2010 and 2015 southern Amazon droughts: Not all droughts are created equal

Authors:
raw_author_name: Tim R McVicar
raw_affiliation_string: Environment, CSIRO, Black Mountain, Canberra, Australian Capital Territory, 2601, AUSTRALIA
raw_author_name: Xin Cao
raw_affiliation_string: School of Computer Science and Engineering, University of New South Wales, Computer Science Building (K17), Engineering Rd, UNSW Sydney, Sydney, New South Wales, 2052, AUSTRALIA

Abstract: Abstract Three severe droughts impacted the Amazon in 2005, 2010, and 2015, leading to widespread above-average land surface temperature (LST) (i.e., positive thermal anomalies) over the southern Amazon in the dry season (Aug–Sep) of these years. Below-average dry-season incoming solar radiation (SW↓) and terrestrial water storage (TWSA) were simultaneously observed in 2005 and 2010, whereas the opposite was observed in 2015. We found that anomalies in precipitation (P), SW↓, and TWSA combined can well explain dry-season thermal anomalies during these droughts (average R 2 ~0.51). We investigated the causes for opposing anomalies in dry-season SW↓ and TWSA, and found different hydro-meteorological conditions preceding the drought-year dry seasons. In 2005 and 2010, P was considerably below average during the wet-to-dry transition season (May–Jul), causing below-average TWSA in dry season that was favourable for fires. Increased atmospheric aerosols resulting from fires reduced solar radiation reaching the ground. In 2015, although below-average dry-season P was observed, it was above the average during the wet-to-dry transition season, leading to reduced fires and aerosols, and increased dry-season SW↓. To further examine the impact of opposite hydro-meteorological processes on the drought severity, we compared dry-season LST during droughts with the maximum LST during non-drought years (i.e., LST max ) for all grid cells, and a similar analysis was conducted for TWSA with the minimum TWSA (i.e., TWSA min ). Accordingly, the regions that suffered from concurrent thermal and water stress (i.e., LST &gt; LST max and TWSA &lt; TWSA min ) were identified. These regions are mainly observed over the southeast in 2005 and southern Amazon in 2010. In 2015, large-scale dry-season thermal stress was found over central and southeast Amazon with little water stress. This study underlines the complex interactions of different hydrological components and the importance of understanding the evolution of droughts to better predict their possible impacts on the Amazon rainforest.
Maximum entropy inverse reinforcement learning-based trajectory planning for autonomous driving

Authors:
raw_author_name: Shumin Xie
raw_affiliation_string: Northwestern Polytechnical University (China)
raw_author_name: XiaoYa Lv
raw_affiliation_string: Beijing University of Science and Technology (China)
raw_author_name: Zuodong Zhong
raw_affiliation_string: Beijing University of Science and Technology (China)
raw_author_name: Qing Li
raw_affiliation_string: Beijing University of Science and Technology (China); Key Laboratory of Knowledge Automation for Industrial Processes (China)

Abstract: With the rapid advancement of autonomous driving technology, effective trajectory planning has become crucial for ensuring road safety and driving efficiency. Traditional trajectory planning methods often rely on preset rules and models, making them ill-suited for the complex and dynamic traffic environment. To address this, a Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) based trajectory planning method is proposed in this paper, aiming at learning from expert driving behaviors to infer an efficient reward function, which in turn guides decision-making and path planning for autonomous vehicles. This study begins by analyzing expert driving data to extract key state and action features. Then, the MaxEnt IRL algorithm is applied to learn the reward function underlying these features, reflecting the decision-making logic of expert drivers. The learned reward function is subsequently used to guide the trajectory planning of the autonomous driving system, generating safe and efficient driving paths. A series of experiments conducted in a simulated environment demonstrate that the MaxEnt IRL-based method proposed in this paper exhibits higher adaptability and efficiency in handling complex traffic scenarios compared to traditional trajectory planning methods.