A Task-Interdependency Model of Complex Collaboration Towards Human-Centered Crowd Work

Models of crowdsourcing and human computation often assume that individuals independently carry out small, modular tasks. However, while these models have successfully shown how crowds can accomplish significant objectives, they can inadvertently advance a less than human view of crowd workers and fail to capture the unique human capacity for complex collaborative work. We present a model centered on interdependencies -- a phenomenon well understood to be at the core of collaboration -- that allows one to formally reason about diverse challenges to complex collaboration. Our model represents tasks as an interdependent collection of subtasks, formalized as a task graph. We use it to explain challenges to scaling complex collaborative work, underscore the importance of expert workers, reveal critical factors for learning on the job, and explore the relationship between coordination intensity and occupational wages. Using data from O*NET and the Bureau of Labor Statistics, we introduce an index of occupational coordination intensity to validate our theoretical predictions. We present preliminary evidence that occupations with greater coordination intensity are less exposed to displacement by AI, and discuss opportunities for models that emphasize the collaborative capacities of human workers, bridge models of crowd work and traditional work, and promote AI in roles augmenting human collaboration

A Time-varying Shockwave Speed Model for Trajectory Reconstruction using Lagrangian and Eulerian Observations

Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from probe vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, energy consumption estimation is largely ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction. The approach introduces a calibration algorithm for time-varying shockwave speed. The calibrated shockwave speed of the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionaly, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption

High-Temperature Activated AB2 Nanopowders for Metal Hydride Hydrogen Compression

A reliable process for compressing hydrogen and for removing all contaminants is that of the metal hydride thermal compression. The use of metal hydride technology in hydrogen compression applications though, requires thorough structural characterization of the alloys and investigation of their sorption properties. The samples have been synthesized by induction - levitation melting and characterized by Rietveld analysis of the X-Ray diffraction (XRD) patterns. Volumetric PCI (Pressure-Composition Isotherm) measurements have been conducted at 20, 60 and 90 oC, in order to investigate the maximum pressure that can be reached from the selected alloys using water of 90oC. Experimental evidence shows that the maximum hydrogen uptake is low since all the alloys are consisted of Laves phases, but it is of minor importance if they have fast kinetics, given a constant volumetric hydrogen flow. Hysteresis is almost absent while all the alloys release nearly all the absorbed hydrogen during desorption. Due to hardware restrictions, the maximum hydrogen pressure for the measurements was limited at 100 bars. Practically, the maximum pressure that can be reached from the last alloy is more than 150 bars.Comment: 9 figures. arXiv admin note: text overlap with arXiv:1207.354

Time-to-Green predictions for fully-actuated signal control systems with supervised learning

Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications

Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning

The optimal operation of transportation networks is often susceptible to unexpected disruptions, such as traffic incidents and social events. Many established control strategies rely on mathematical models that struggle to cope with real-world uncertainties, leading to a significant decline in effectiveness when faced with substantial disruptions. While previous research works have dedicated efforts to improving the robustness or resilience of transportation systems against disruptions, this paper applies the cutting-edge concept of antifragility to better design a traffic control strategy for urban road networks. Antifragility sets itself apart from robustness and resilience as it represents a system's ability to not only withstand stressors, shocks, and volatility but also thrive and enhance performance in the presence of such adversarial events. Hence, modern transportation systems call for solutions that are antifragile. In this work, we propose a model-free deep Reinforcement Learning (RL) scheme to control a two-region urban traffic perimeter network. The system exploits the learning capability of RL under disruptions to achieve antifragility. By monitoring the change rate and curvature of the traffic state with the RL framework, the proposed algorithm anticipates imminent disruptions. An additional term is also integrated into the RL algorithm as redundancy to improve the performance under disruption scenarios. When compared to a state-of-the-art model predictive control approach and a state-of-the-art RL algorithm, our proposed method demonstrates two antifragility-related properties: (a) gradual performance improvement under disruptions of constant magnitude; and (b) increasingly superior performance under growing disruptions.Comment: 32 pages, 13 figure

On the Evaluation of the Hyperthermic Efficiency of Magnetic Scaffolds

Goal: Deep-seated tumors (DST) can be treated using thermoseeds exposed to a radiofrequency magnetic field for performing local interstitial hyperthermia treatment (HT). Several research efforts were oriented to the manufacturing of novel biocompatible magnetic nanostructured thermo-seeds, called magnetic scaffolds (MagS). Several iron-doped bioceramics or magnetic polymers in various formulations are available. However, the crucial evaluation of their heating potential has been carried out with significantly different, lab specific, variable experimental conditions and protocols often ignoring the several error sources and inaccuracies estimation. Methods: This work comments and provides a perspective analysis of an experimental protocol for the estimation methodology of the specific absorption rate (SAR) of MagS for DST HT. Numerical multiphysics simultions have been performed to outline the theoretical framework. After the in silico analysis, an experimental case is considered and tested. Results: From the simulations, we found that large overestimation in the SAR values can be found, due to the axial misplacement in the radiofrequency coil, while the radial misplacement has a lower impact on the estimated SAR value. Conclusions: The averaging of multiple temperature records is needed to reliably and effectively estimate the SAR of MagS for DST HT