Approaches to results-based funding in tertiary education : identifying finance reform options for Chile

Unrealized potential exists for increasing accountability and transparency in Chilean tertiary education by allocating resources based on achieved results rather than historical precedence and political negotiation. Against this background, the authors profile approaches to results-based funding of tertiary education to identify efficacious finance reform options for Chile. International experience shows that financing by results is not a ready-made concept, but a broad label that offers a menu of design options. To decipher results-based funding, the authors cover all phases in designing and implementing a results-based funding system and highlight strengths and weaknesses of concepts, such as taximeter funding, performance contracts, and formula-based allocations.Public Health Promotion,Agricultural Knowledge&Information Systems,Decentralization,Teaching and Learning,Health Monitoring&Evaluation,Agricultural Knowledge&Information Systems,Gender and Education,Health Monitoring&Evaluation,Curriculum&Instruction,Teaching and Learning

Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning

Reinforcement learning (RL) is a flexible and efficient method for programming micro-robots in complex environments. Here we investigate whether reinforcement learning can provide insights into biological systems when trained to perform chemotaxis. Namely, whether we can learn about how intelligent agents process given information in order to swim towards a target. We run simulations covering a range of agent shapes, sizes, and swim speeds to determine if the physical constraints on biological swimmers, namely Brownian motion, lead to regions where reinforcement learners' training fails. We find that the RL agents can perform chemotaxis as soon as it is physically possible and, in some cases, even before the active swimming overpowers the stochastic environment. We study the efficiency of the emergent policy and identify convergence in agent size and swim speeds. Finally, we study the strategy adopted by the reinforcement learning algorithm to explain how the agents perform their tasks. To this end, we identify three emerging dominant strategies and several rare approaches taken. These strategies, whilst producing almost identical trajectories in simulation, are distinct and give insight into the possible mechanisms behind which biological agents explore their environment and respond to changing conditions.Comment: 12 pages, 6 figure

Emergence of Accurate Atomic Energies from Machine Learned Noble Gas Potentials

The quantum theory of atoms in molecules (QTAIM) gives access to well-defined local atomic energies. Due to their locality, these energies are potentially interesting in fitting atomistic machine learning models as they inform about physically relevant properties. However, computationally, quantum-mechanically accurate local energies are notoriously difficult to obtain for large systems. Here, we show that by employing semi-empirical correlations between different components of the total energy, we can obtain well-defined local energies at a moderate cost. We employ this methodology to investigate energetics in noble liquids or argon, krypton, and their mixture. Instead of using these local energies to fit atomistic models, we show how well these local energies are reproduced by machine-learned models trained on the total energies. The results of our investigation suggest that smaller neural networks, trained only on the total energy of an atomistic system, are more likely to reproduce the underlying local energy partitioning faithfully than larger networks. Furthermore, we demonstrate that networks more capable of this energy decomposition are, in turn, capable of transferring to previously unseen systems. Our results are a step towards understanding how much physics can be learned by neural networks and where this can be applied, particularly how a better understanding of physics aids in the transferability of these neural networks.Comment: 13 pages, 10 figure

Towards a Phenomenological Understanding of Neural Networks: Data

A theory of neural networks (NNs) built upon collective variables would provide scientists with the tools to better understand the learning process at every stage. In this work, we introduce two such variables, the entropy and the trace of the empirical neural tangent kernel (NTK) built on the training data passed to the model. We empirically analyze the NN performance in the context of these variables and find that there exists correlation between the starting entropy, the trace of the NTK, and the generalization of the model computed after training is complete. This framework is then applied to the problem of optimal data selection for the training of NNs. To this end, random network distillation (RND) is used as a means of selecting training data which is then compared with random selection of data. It is shown that not only does RND select data-sets capable of outperforming random selection, but that the collective variables associated with the RND data-sets are larger than those of the randomly selected sets. The results of this investigation provide a stable ground from which the selection of data for NN training can be driven by this phenomenological framework.Comment: 13 pages, 7 figure

ZnTrack -- Data as Code

The past decade has seen tremendous breakthroughs in computation and there is no indication that this will slow any time soon. Machine learning, large-scale computing resources, and increased industry focus have resulted in rising investments in computer-driven solutions for data management, simulations, and model generation. However, with this growth in computation has come an even larger expansion of data and with it, complexity in data storage, sharing, and tracking. In this work, we introduce ZnTrack, a Python-driven data versioning tool. ZnTrack builds upon established version control systems to provide a user-friendly and easy-to-use interface for tracking parameters in experiments, designing workflows, and storing and sharing data. From this ability to reduce large datasets to a simple Python script emerges the concept of Data as Code, a core component of the work presented here and an undoubtedly important concept as the age of computation continues to evolve. ZnTrack offers an open-source, FAIR data compatible Python package to enable users to harness these concepts of the future.Comment: 22 pages, 10 figures, 2MB PD

Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning

Multi-Agent Reinforcement Learning (MARL) is a promising candidate for realizing efficient control of microscopic particles, of which micro-robots are a subset. However, the microscopic particles' environment presents unique challenges, such as Brownian motion at sufficiently small length-scales. In this work, we explore the role of temperature in the emergence and efficacy of strategies in MARL systems using particle-based Langevin molecular dynamics simulations as a realistic representation of micro-scale environments. To this end, we perform experiments on two different multi-agent tasks in microscopic environments at different temperatures, detecting the source of a concentration gradient and rotation of a rod. We find that at higher temperatures, the RL agents identify new strategies for achieving these tasks, highlighting the importance of understanding this regime and providing insight into optimal training strategies for bridging the generalization gap between simulation and reality. We also introduce a novel Python package for studying microscopic agents using reinforcement learning (RL) to accompany our results.Comment: 12 pages, 5 figure

Diagnosing coronary artery disease by sound analysis from coronary stenosis induced turbulent blood flow: diagnostic performance in patients with stable angina pectoris

Optimizing risk assessment may reduce use of advanced diagnostic testing in patients with symptoms suggestive of stable coronary artery disease (CAD). Detection of diastolic murmurs from post-stenotic coronary turbulence with an acoustic sensor placed on the chest wall can serve as an easy, safe, and low-cost supplement to assist in the diagnosis of CAD. The aim of this study was to evaluate the diagnostic accuracy of an acoustic test (CAD-score) to detect CAD and compare it to clinical risk stratification and coronary artery calcium score (CACS). We prospectively enrolled patients with symptoms of CAD referred to either coronary computed tomography or invasive coronary angiography (ICA). All patients were tested with the CAD-score system. Obstructive CAD was defined as more than 50 % diameter stenosis diagnosed by quantitative analysis of the ICA. In total, 255 patients were included and obstructive CAD was diagnosed in 63 patients (28 %). Diagnostic accuracy evaluated by receiver operating characteristic curves was 72 % for the CAD-score, which was similar to the Diamond–Forrester clinical risk stratification score, 79 % (p = 0.12), but lower than CACS, 86 % (p < 0.01). Combining the CAD-score and Diamond–Forrester score, AUC increased to 82 %, which was significantly higher than the standalone CAD-score (p < 0.01) and Diamond–Forrester score (p < 0.05). Addition of the CAD-score to the Diamond–Forrester score increased correct reclassification, categorical net-reclassification index = 0.31 (p < 0.01). This study demonstrates the potential use of an acoustic system to identify CAD. The combination of clinical risk scores and an acoustic test seems to optimize patient selection for diagnostic investigation.Danish National Business Innovation Fund and Acarix A/S

Microplastic Contamination in Karst Groundwater Systems

Groundwater in karst aquifers constitutes about 25% of drinking water sources globally. Karst aquifers are open systems, susceptible to contamination by surface‐borne pollutants. In this study, springs and wells from two karst aquifers in Illinois, USA, were found to contain microplastics and other anthropogenic contaminants. All microplastics were fibers, with a maximum concentration of 15.2 particles/L. The presence of microplastic was consistent with other parameters, including phosphate, chloride and triclosan, suggesting septic effluent as a source. More studies are needed on microplastic sources, abundance, and impacts on karst ecosystems

A Stratigraphic Approach to Inferring Depositional Ages From Detrital Geochronology Data

With the increasing use of detrital geochronology data for provenance analyses, we have also developed new constraints on the age of otherwise undateable sedimentary deposits. Because a deposit can be no older than its youngest mineral constituent, the youngest defensible detrital mineral age defines the maximum depositional age of the sampled bed. Defining the youngest “defensible” age in the face of uncertainty (e.g., analytical and geological uncertainty, or sample contamination) is challenging. The current standard practice of finding multiple detrital minerals with indistinguishable ages provides confidence that a given age is not an artifact; however, we show how requiring this overlap reduces the probability of identifying the true youngest component age. Barring unusual complications, the principle of superposition dictates that sedimentary deposits must get younger upsection. This fundamental constraint can be incorporated into the analysis of depositional ages in sedimentary sections through the use of Bayesian statistics, allowing for the inference of bounded estimates of true depositional ages and uncertainties from detrital geochronology so long as some minimum age constraints are present. We present two approaches for constructing a Bayesian model of deposit ages, first solving directly for the ages of deposits with the prior constraint that the ages of units must obey stratigraphic ordering, and second describing the evolution of ages with a curve that represents the sediment accumulation rate. Using synthetic examples we highlight how this method preforms in less-than-ideal circumstances. In an example from the Magallanes Basin of Patagonia, we demonstrate how introducing other age information from the stratigraphic section (e.g., fossil assemblages or radiometric dates) and formalizing the stratigraphic context of samples provides additional constraints on and information regarding depositional ages or derived quantities (e.g., sediment accumulation rates) compared to isolated analysis of individual samples