Miracle-dz/UniFAR-FTUs huggingface dataset:
EDICT: Simulation of Edge Interactions Across IoT-Enhanced Environments

This paper presents EDICT, a tool for simulating Edge interactions in IoT-enhanced environments. Recently, ML and AI-based techniques have gained prominence to solve IoT-related challenges. However, such models require large and diverse datasets to perform well. Finding real-world datasets that capture the performance of IoT systems is a challenging task due to the cost of deploying devices and instrumenting environments, as well as privacy/security concerns. This task becomes more challenging when datasets for specific situations (e.g., overloaded system, emergency scenarios) are needed. EDICT enables IoT systems designers to evaluate the performance of their IoT systems at design time. EDICT is capable of generating performance metrics datasets for specific instances of IoT-enhanced environments under different configuration parameters. To support runtime adaptation of smart environments, EDICT enables rapid performance prediction using ML techniques.
Practical Aspects of Subsalt Tomography Using Reverse Time Migration Based Angle Gathers

Due to the accuracy of the Reverse Time Migration (RTM) algorithm, RTM angle gathers show more coherent and correct moveout subsalt events than Kirchhoff offset gathers, potentially providing a more reliable input for tomography. We have developed a robust, efficient, RTM angle gather generation method. It has the flexibility to incorporate geological knowledge for low signal-to-noise (S/N) areas. Because angle gather tomography targets subsalt areas, it is not necessary to trace rays through salt, thus avoiding the limitations of ray-based tomography, making it a useful tool for subsalt velocity model building.
Deformation across the western Quetico subprovince and adjacent boundarl regions in Minnesota

North- to northwest-directed crustal shortening across the western Quetico subprovince and its boundary regions produced a complex deformation sequence within the Quetico belt and resulted in concentrated zones of dextral ductile shear in the boundary regions within the adjacent greenstone–granite terranes. In this paper, we review and introduce new data on the regional deformation features and their geometries and discuss the history of generation of these features. We attribute the deformation sequence to differential partitioning of shortening and shear strains during dextral transpression associated with oblique convergence and accretion along the southern margin of the Superior Province.The turbiditic wacke in the western Quetico subprovince, now typically amphibolite-facies schist and migmatite, underwent an early deformation stage that included recumbent folding (F1) and the generation of an S1 bedding-parallel foliation. This event is most evident along the northern and southern boundaries of the .
Using meta‐analysis for benefit transfer: In‐sample convergent validity tests of an outdoor recreation database

The application of metaregression analysis models for the purpose of benefit transfer is investigated using in‐sample convergent validity tests on average value transfers. The database on which the metaregression analysis models are developed is composed of empirical outdoor recreation use value studies conducted from 1967 through 1998. Results of the convergent validity tests suggest that the national model is slightly more robust to changes in application than the Census Region models. The results suggest that the application of meta‐analysis for benefit transfers is promising considering limitations imposed by inconsistent data reporting of original studies.
Mean zonal currents below 1500 m near the equator, 159°W

Recent current measurements have shown that the subthermocline equatorial circulation in the central Pacific is considerably more complicated than had been thought. Below 1500 m depth on 159°W there are three currents south of the equator that appear to be permanent. Eastward currents are found at 1700 m and 3100 m, and a westward current is found at 4000 m. The 1700-m current is at the southern edge of the 3°S to 3°N range of current measurements, so its meridional extent and its transport are unknown. The 3100-m and 4000-m currents have 16-month mean transports of about 6 − 7 × 106 m3 s−1. All three of these currents may be important components of the general circulation.
Accelerating the Design of Scientific Workflows with Simulation-Based Rapid Prototyping

Extreme-scale science requires scientists to combine multiple heterogeneous computational tasks in complex workflows, efficiently managing large amounts of data, and fully exploiting the performance of the entire edge-to-HPC computing continuum. Going from science to workflows requires domain scientists to express their research ideas from a computer science perspective so that the most adapted and efficient tools and techniques can be selected. However, this is a long, error-prone, and tedious process, as there is no “turnkey solution” in such a diverse ecosystem. Therefore, we propose to develop a comprehensive simulation-based framework that will be used by domain scientists to easily prototype their scientific workflows, while expressing all the important information needed by computer scientists to provide them with the most efficient implementation.
Ant Colony Optimization for Retail Based Capacitated Vehicle Routing Problem with Pickup and Delivery for Mobile Robots

Mobile Robots have been the key for automation in various applications including picking and placing items in a retail store. Capacitated Vehicle Routing Problem with Pickup and Delivery(CVRP-PD) is widely used in similar applications like package delivery vehicles and mobile robots in retail, where mobile robots have a capacity limit such as weight and have to pickup and drop multiple items during their tour in an optimized manner. However, Retail application comes with more challenges where there could be multiple fixed deposit locations for particular pickup items such as packing counters and after delivering some items mobile robots can again regain capacity and be able to pickup more items during the same run. In this paper, we consider these constraints for retail applications and optimize retail orders for all mobile robots present in the environment, where the order requests for pickup and delivery of products at various locations while mobile robots have different maximum load capacities and robots can regain their capacity once they have dropped some items at their particular delivery locations. In this paper, we propose a method to solve this retail based CVRP-PD using Ant Colony Optimization(ACO). We take an industrial use-case and test the method with different order sizes and robot parameters. The results have been promising and used to solve the use-case under consideration. In addition, we also evaluate the results and propose future prospects.
Stream temperature modelling using artificial neural networks: application on Catamaran Brook, New Brunswick, Canada

Fish habitat and aquatic life in rivers are highly dependent on water temperature. Therefore, it is important to understand andto be able to predict river water temperatures using models. Such models can increase our knowledge of river thermal regimes as well as provide tools for environmental impact assessments. In this study, artificial neural networks (ANNs) will be used to develop models for predicting both the mean and maximum daily water temperature. The study was conducted within Catamaran Brook, a small drainage basin tributary to the Miramichi River (New Brunswick, Canada). In total, eight ANN models were investigated using a variety of input parameters. Of these models, four predicted mean daily water temperature and four predicted maximum daily water temperature. The best model for mean daily temperature had eight input parameters: minimum, maximum and mean air temperatures of the current day and those of the preceding day, the day of year and the water level. This model had an overall root‐mean‐square error (RMSE) of 0·96 °C, a bias of 0·26 °C and a coefficient of determination R2 = 0·971. The model that best predicted maximum daily water temperature was similar to the first model but excluded mean daily air temperature. Good results were obtained for maximum water temperatures with an overall RMSE of 1·18 °C, a bias of 0·15 °C and R2 = 0·961. The results of ANN models were similar to and/or better than those observed from the literature. The advantages of artificial neural networks models in modelling river water temperature lie in their simplicity of use, their low data requirement and their good performance, as well as their flexibility in allowing many input and output parameters. Copyright © 2008 Crown in the right of Canada and John Wiley & Sons, Ltd.
The Common-directions Method for Regularized Empirical Risk Minimization

State-of-the-art firstand second-order optimization methods are able to achieve either fast global linear convergence rates or quadratic convergence, but not both of them. In this work, we propose an interpolation between firstand second-order methods for regularized empirical risk minimization that exploits the problem structure to efficiently combine multiple update directions. Our method attains both optimal global linear convergence rate for first-order methods, and local quadratic convergence. Experimental results show that our method outperforms state-of-the-art firstand second-order optimization methods in terms of the number of data accesses, while is competitive in training time.
Interplay between folding and ductile shearing in the Proterozoic crust of the Muskoka – Parry Sound region, central Ontario

Grenville gneiss of the central Georgian Bay region was subjected to ductile deformation that produced narrow mylonite zones as well as three sets of superimposed folds differing greatly in structural signature, size, and orientation. Some mylonite zones are concordant to gneissosity and are repeatedly folded, others cut gneissosity and postdate the folding. Gneissosity was generated as a regionally subhorizontal feature, either by crustal thinning or, like the early mylonite zones, by low-angle reverse shearing. An attempt is made to account for the initially subhorizontal gneissosity, the mylonite zones, and the folds in a regime of large-scale reverse shearing that strikes parallel to the Grenville Front.Upright northwest–southwest to north–south buckle folds dominate the map pattern and are subperpendicular to the reverse Grenville Front boundary fault. These set-2 folds cannot be attributed to reverse simple shearing but require a large component of east–west compression. Such stress could have been .
Glaucoma Diagnosis and Prediction Based on Multi-Scale Feature Coding

Digital health has attracted a lot of attention as a result of the development of artificial intelligence (AI), big data, and fifth-generation (5G) telecommunications networks. AI, the Internet of Things (IoT), e-health, telemedicine, and big data analysis and application are all overlapping subfields of digital health. The World Health Organization (WHO) has a wealth of innovative opportunities in the field of digital medicine, and earlier this year issued a series of guidelines advising on how to leverage technology to provide evidence-based treatments for patients in practical situations. In order to promote the combination of artificial intelligence technology and practical application industry, especially clinical medicine, this paper conducts a series of studies on the diagnosis and prediction of glaucoma through deep convolutional neural network. The final results have certain clinical application value. Using AlexNet and LeNet two full convolutional neural network models, the Batch Normalization layer is added to the original model for normalization processing to speed up the convergence speed of the training model. The obtained glaucoma fundus retinal images are trained and compared. At the data set level, the original data is enhanced and expanded, and the model effects before and after expansion are compared again. The final experimental results show that the LeNet model has a better processing effect on simple images. The correct rate of the training set is 98 %, and the correct rate of the verification set is close to 100 %.
Heat Stress Mitigation Strategies in Feedyards: Use, Perceptions, and Experiences of Industry Stakeholders

Simple Summary In the United States, cattle in feedyards can experience heat stress due to extreme climatic events negatively affecting welfare and productivity. Various heat mitigation strategies have been studied in feedyard settings, but information on the current adoption of those strategies and stakeholder perceptions of strategies and their benefits is limited. This study utilized an online survey to ask feedyard operators, veterinarians, and nutritionists about their perception and use of heat mitigation strategies. Generally, respondents agreed that heat stress impacts cattle performance, health, and welfare. The majority of respondents utilized or recommended some type of heat mitigation such as changing feed delivery time and feed composition, utilizing sprinklers and/or changing processing and handling times. Respondents used a variety of indicators and monitoring tools to aid in identifying when to implement protocols and strategies. Respondents perceived pen infrastructure (i.e., shade, fans) and water/feed management as important considerations for better mitigating the negative effects of heat stress on cattle in the face of another extreme heat event. This survey highlighted the need for future research to evaluate the cost:benefit of different mitigation strategies, the effectiveness of mitigation strategies in different geographical areas, and development of more in-depth heat stress forecasting and monitoring tools. Abstract The purpose of this study was to: (1) understand heat mitigation strategies currently used and recommended by feedyard operators, veterinarians, and nutritionists, (2) understand their perceptions of heat mitigation strategies related to cattle health, performance, welfare, and carcass quality, (3) quantify the frequency of extreme heat events, and (4) understand industry needs associated with heat stress mitigation strategies. An online survey was shared via 11 industry association listservs. Descriptive statistics were performed on 56 responses (n = 22 operators, 26 veterinarians and eight nutritionists). Thematic analysis was performed on free-response questions. Sixteen (72.7%) operators, 23 (88.5%) veterinarians and eight (100%) nutritionists utilized at least one heat mitigation strategy. “Changing processing and shipping hours” (n = 42, 75%) had the most “strongly agree” responses when asked about strategy effectiveness. The majority of respondents agreed that heat stress negatively impacts cattle health, performance, and welfare (Mean ± SD; ≥7.8 ± 2.6 for all roles). Forty-two (75%) respondents experienced cattle death loss from extreme heat events. Thematic analysis indicated that respondents perceived pen infrastructure and water/feed management as important considerations for better mitigating heat stress impacts. When asked what resources would be helpful, respondents indicated research and data regarding the effectiveness of various strategies.
On the Workflow Satisfiability Problem with Class-Independent Constraints for Hierarchical Organizations

A workflow specification defines a set of steps, a set of users, and an access control policy. The policy determines which steps a user is authorized to perform and imposes constraints on which sets of users can perform which sets of steps. The workflow satisfiability problem (WSP) is the problem of determining whether there exists an assignment of users to workflow steps that satisfies the policy. Given the computational hardness of WSP and its importance in the context of workflow management systems, it is important to develop algorithms that are as efficient as possible to solve WSP. In this article, we study the fixed-parameter tractability of WSP in the presence of class-independent constraints, which enable us to (1) model security requirements based on the groups to which users belong and (2) generalize the notion of a user-independent constraint. Class-independent constraints are defined in terms of equivalence relations over the set of users. We consider sets of nested equivalence relations because this enables us to model security requirements in hierarchical organizations. We prove that WSP is fixed-parameter tractable (FPT) for class-independent constraints defined over nested equivalence relations and develop an FPT algorithm to solve WSP instances incorporating such constraints. We perform experiments to evaluate the performance of our algorithm and compare it with that of SAT4J, an off-the-shelf pseudo-Boolean SAT solver. The results of these experiments demonstrate that our algorithm significantly outperforms SAT4J for many instances of WSP.
Particulate formation due to freezing humic waters

Water samples were collected from a soft water highly humic pond, and all particulates of >0.15-μm nominal diameter were removed by continuous flow centrifugation. Freezing caused the formation of brown particulates which could be removed by centrifugation. The particulates can be dispersed by sonification, but the size distribution of organic fractions is permanently altered by freezing.
Social APF-RL: Safe Mapless Navigation in Unknown & Human-Populated Environments

Safe mapless navigation of mobile robots in unknown and human-populated areas is integral for increasing their usage in our daily lives. In this paper, we consider how such a behavior can be exhibited by a mobile robot and introduce Social APF-RL (Artificial Potential Functions with Reinforcement Learning). Social APF-RL extends our previously presented approach APF-RL in which the strengths of artificial potential functions (APF) with deep reinforcement learning are combined so that the robot learns how to adjust the input parameters of the APF controller. With Social APF-RL, the model is extended to accommodate the presence of humans and to respect their comfort zones while navigating. Our experimental results including both simulation and real-life scenarios demonstrate that differing from the classical navigation methods or social navigation methods, the robot can navigate successfully on its own even in complex scenarios with moving entities while maintaining social distance to humans encountered. Hence, it has better applicability in real-life scenarios. For future work, we plan to use the proposed approach in human following while adhering to social distance norms.
A Method for Constructing Research Data Provenance in High-Performance Computing Systems

Research must be reproducible to be verifiable. Provenance, which describes how data was produced, is one of the metadata that can improve reproducibility. In this paper, we propose a method to construct the provenance of research data produced in high-performance computing (HPC) systems. Our method can construct a high-level and user-perspective provenance by integrating information available in HPC systems, such as a workload manager, with low-level data about running programs' behavior captured in an operating system kernel. The method enables users of HPC systems to collect the provenance without modifying assets such as programs and scripts.
Performance of the Clemson Instrumented Shank in Coastal Plain Soils

Most upland sandy soils of the southeastern coastal plain have a compacted zone or hardpan which limits root penetration below the plowing depth, reducing yields, and making plants more susceptible to drought stress. There is a great amount of variability in depth and thickness of hardpan layers in this region. A real-time, sensor-based, site-specific tillage could achieve significant savings in tillage energy and increase crop yields.
Tectono‐metamorphic evolution of the Cretaceous Shimanto accretionary complex, central Japan: Constraints from a fluid inclusion analysis of syn‐tectonic veins

Abstract Micro‐thermometry of water‐rich fluid inclusions from two syn‐tectonic veins sets (D1 and D2 veins) in the Otaki Group, part of the Cretaceous Shimanto accretionary complex of the Kanto Mountains, central Japan reveals the following tectono‐metamorphic evolution. Combining the results of microthermometric analyses of fluid inclusions from D1 veins with an assumed geothermal gradient of 20–50°C/km indicates that the temperature and fluid pressure conditions during D1 were 270–300°C and 140–190 MPa, respectively. Peak metamorphic conditions during the development of D2 slaty cleavage involved temperatures in excess of 300°C and fluid pressures greater than 270 MPa, based on analyses of microthermometry of water‐rich fluid inclusions from the D2 vein and illite crystallinity. The estimated fluid pressure increased by approximately 80 MPa from D1 accretionary processes to metamorphism and slaty cleavage development during D2. Assuming that fluid pressure reached lithostatic pressure, the observed increase in fluid pressure can be accounted for by thrusting of the Jurassic Chichibu accretionary complex over the Cretaceous Shimanto accretionary complex. Following thrusting, both accretionary complexes were subjected to metamorphism during the latest Cretaceous.
EliScholar – A Digital Platform for EliScholar – A Digital Platform for Scholarly Publishing at Yale Scholarly Publishing at Yale

This paper investigates the effect of the “First Financial Restructuring” (FFR) on the operating efficiency of commercial banks in Taiwan. Applying data envelopment analysis (DEA) to operations data for 40 commercial banks over the five year period 2000-2004, we find that while the banks had lower operating efficiency on average during the reform period (2002-2003) compared to the pre-reform period (2000-2001), improved operating efficiency was reflected in the post-reform period (2004). Our results remain unchanged even after controlling for the nonperforming loan ratio, capital adequacy ratio, bank ownership and size. Overall, our results indicate that the improved efficiency in the post-reform period was possibly due to the reduction of nonperforming loans rather than the boosting of capital adequacy in the reform period.
Emulating Hydrodynamics from Dark Matter 3D Density Fields

In this work, we propose an efficient methodology for emulating hydrodynamic gas structures in Dark Matter Nbody simulations. We perform fast emulations of 3D gas density cubes from the Dark Matter counterparts, comparing different machine learning approaches, namely Principal Component Analysis + Random Forest ($\text{PCA}+\text{RF}$) and a Convolutional Neural Network (CNN). The method provides a gain of 5 orders of magnitude in CPU run times compared to running the full hydrodynamic N-body simulation in the same computer system. Using $\text{PCA}+\text{RF}$, the method achieves 98% accuracy in the matter power spectrum compared to the full hydrodynamic simulation throughout most of the k domain $(k < 1.0\ Mpc\ h^{-1})$. Finally, CNNs offer increased accuracy (∼ 99%), showing a striking improvement in performance at the extremes of the k domain.
Passive image interferometry and seasonal variations of seismic velocities at Merapi Volcano, Indonesia

We propose passive image interferometry as a technique for seismology that allows to continuously monitor small temporal changes of seismic velocities in the subsurface. The technique is independent of sources in the classical sense and requires just one or two permanent seismic stations. We retrieve the Green's functions that we use for interferometry from ambient seismic noise. Applying passive image interferometry to data from Merapi volcano we show that velocity variations can be measured with an accuracy of 0.1% with a temporal resolution of a single day. At Mt. Merapi the velocity variations show a strong seasonal influence and we present a depth dependent hydrological model that describes our observations solely based on precipitation.