Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Policy Transfer in Large-Scale Traffic Light Control via Multi-Agent Hierarchical Reinforcement Learning

Multi-agent reinforcement learning (MARL) is increasingly being used for traffic light control in traffic networks. However, applying MARL to large-scale traffic light control faces challenges due to the time-consuming and resource-intensive nature of learning in such environments. This paper addresses this challenge by leveraging transfer learning, utilizing policies trained in small-scale environments to effectively control large-scale traffic light systems. To enhance the zero-shot transferability of trained policies, we introduce communication channels and sub-task decomposition, while ensuring transferability by sharing all neural network parameters. In our experiments, we assess the zero-shot transferability of policies trained in different small-scale traffic networks, both with and without sub-task decomposition. The results demonstrate the significance of establishing a hierarchical structure through sub-task decomposition for zero-shot transfer.
Pre-Investment in Infrastructure in Latin America and the Caribbean: Case Studies from Chile, Mexico, Peru, and Uruguay

Evidence suggests that an "infrastructure gap" vis-à-vis other industrial and developing regions opened up in Latin America and the Caribbean (LAC) over the past two decades. The Inter-American Development Bank (IDB) conducted a public consultation process in 2013 during which several participating stakeholders from the public and private sectors analyzed the current situation. The paper concludes that there is room for feasible policy improvements in the pre-investment phase in LAC that would enhance infrastructure delivery, boost growth, and promote development in the region.
Detecting Foreground in Videos via Posterior Regularized Robust Bayesian Tensor Factorization

Foreground detection is a critical step for separating the moving object from the background in video processing. Tensor factorization has been used in foreground detection due to its ability to process complex high-dimensional data, such as color images and videos. However, traditional tensor factorization often lacks the ability for uncertainty quantification. Bayesian tensor factorization can measure the uncertainty by considering the distributions of the tensor factorization model parameters. Besides, domain knowledge is commonly available and could improve the accuracy of foreground detection of the Bayesian tensor factorization model if it can be appropriately incorporated. In this work, a new Bayesian tensor factorization model, named Posterior Regularized Bayesian Robust Tensor Factorization (PR-BRTF), is proposed with incorporating characteristics of dynamic foreground, as a sparsity posterior regularization term. Furthermore, the variational Bayesian inference and $L_{1}$ norm is combined for inducing sparsity with an efficient inference. The experiments in real-world case studies have shown the performance improvement of the proposed model over state-of-art methods.
Building an automated model to predict and detect early onset osteoarthritis

This research targets the need to provide early detection of osteoarthritis to assist the ongoing global effort to make treatment of this disease viable and effective. With regards to the rapid degradation of the condition, it is critical to detect it early enough so that suppressive treatment can reduce symptoms and progression. Using an approach of automation via efficient data analysis, it is possible to detect arthritis quite early in a patient’s diagnostics. This paper aims to provide both a statistical and a more direct approach to the determination of the probability of osteoarthritis at early onset to improve the odds for a patient. The system will take primary variables from a patient's file and look for patterns matching cases in the dataset it was trained against. The dataset for the same has been taken from the NIH archive for academic purposes.
Strictly Decentralized Approaches for Multi-Robot Grasp Coordination

Grasp coordination is crucial for performing cooperative object manipulation tasks. Planning cooperative grasps among a group of decentralized robots is a new line of coordination problem that requires robots to simultaneously handle complex and large shaped and sized objects, a new level of difficulty that is rarely addressed in the literature. In this paper, we propose grasp coordination approaches for a decentralized group of robots facing explicit communication and sensing limitations. In particular, a scenario where robots with incomplete knowledge about each other's embodiments further lose the ability to 1. observe others' grasps occluded by the object's shape 2. exchange direct messages due to potential communication degradation resulting from the real-time planning and execution constraints. To tackle such a scenario, we introduce two baseline and two probabilistic approaches that are specifically designed for strict decentralization. The approaches analyze cooperative grasps using traditional grasp quality metrics and estimate cooperative grasps based on the assigned robot's priority. Simulation experiments demonstrate that the probabilistic approaches exhibit superior performance over the baseline approaches, reaching performance close to optimal for both homogeneous and heterogeneous groups. These approaches provide solutions to simulated multi-robot grasp coordination scenarios that have the potential to translate to real-world environments such as logistics, manufacturing, and services.
Image Segmentation Algorithm in Complex Environment Based on Improved SOLOV2

Previous industrial robots combined with vision systems in target grasping tasks have high requirements for the task environment. Once the vision system is confronted with a target object in a complex environment such as stacking, tilting and occlusion, it will be difficult to provide the robot control system with the correct target pose information due to the lack of target feature information, which is not conducive to the dynamic configuration capability of the production system. This study proposes an industrial target image segmentation algorithm based on improved SOLOv2 to solve the problem of low accuracy of industrial target image segmentation caused by small targets as well as occlusion. Building upon the SOLOv2 model, the optimization of fusing multi-layer features within the mask feature branch is deaigned to integrate feature information from the deepest to the shallowest layers, in a layer-by-layer manner; the Mish activation function is selected in the upsampling process to enhance the model's ability to extract small target features in images while improving the generalization ability and convergence speed of the model; to mitigate the impact of occlusion on image segmentation, the weights of the loss function are adaptively adjusted based on the degree of occlusion. This allows for a reduction in the negative influence of occlusion on image segmentation, as the loss function is optimezed to account for varying degrees of occlusion.
Building Contextual Knowledge Graphs for Personalized Learning Recommendations Using Text Mining and Semantic Graph Completion

Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms. This leads to a foundation for personalized recommendations of learning paths. In this paper, the transformation of hierarchical data models into knowledge graph (KG) models of LOs using text mining is introduced and evaluated. We utilize custom text mining pipelines to mine semantic relations between elements of an expert-curated hierarchical model. We evaluate the KG structure and relation extraction using graph quality-control metrics and the comparison of algorithmic semantic-similarities to expert-defined ones. The results show that the relations in the KG are semantically comparable to those defined by domain experts, and that the proposed KG improves representing and linking the contexts of LOs through increasing graph communities and betweenness centrality.
The experiences of women seeking help for vaginismus and its impact on their sense of self: An integrative review

Background: There is low social awareness of vaginismus despite it being a relatively common female sexual dysfunction that makes vaginal penetration painful, difficult, and/or impossible. While existing literature on vaginismus has had a clinical focus on the affected genitalia, there is a lack of research on women’s help-seeking experiences of vaginismus from their perspective. Objectives: This integrative review’s objective was to explore: women’s help-seeking experiences of vaginismus, and how such help-seeking experiences impact their sense of self. Design: Cooper’s five-step integrative review approach was followed to develop a research question, a search strategy, selection criteria, and data evaluation, analysis, and presentation. Data Sources and Methods: A systematic search of the literature was completed in the following seven databases in January 2023: PsycINFO, ProQuest Central, PubMed, Scopus, CINAHL, Cochrane, and Embase. Out of the 373 articles found through database searches and additional citation searching, 22 studies were included in this review for meeting the eligibility criteria of having an empirical design, being written in English, and examining women’s help-seeking experiences for vaginismus and its impact on their sense of self. Results: Thematic analysis was used to summarize the findings from the included studies which were informed by 1671 participants. Help-Seeking Process, Medical Management, Help-Seeking and Sense of Self, and Holistic Care Recommendations from the Findings emerged as four major themes with corresponding subthemes. Conclusion: This review indicates that women continue to face difficulties in seeking and receiving help for vaginismus even through the healthcare system. However, the studies did not explicitly discuss how women’s help-seeking for vaginismus impacted their sense of self. This highlights an epistemological gap on how women’s help-seeking for their vaginismus impacts their sense of self, which can affect their treatment responses. Recommendations are provided for future healthcare and research to improve health outcomes for women with vaginismus.
Mechanics Of Coastal Sediment Transport

The main objective of the book is to describe from a deterministic point of view the sediment transport in the general wave-current situation. For this purpose, the book is divided into two major parts. The first part of the book is related to flow and turbulence in combined wave-current. This part covers the turbulent wave boundary layer, bed friction in combined wave-current motion, turbulence in the surf zone, and wave-driven currents in the long- and cross-shore direction. The second part treats the sediment transport as a result of the wave-current action. This part includes an introduction to basic sediment transport concepts, distribution of suspended sediment in the sheet flow regime, description of bedforms formed by current and waves, and their influence on sediment transport pattern. Finally, the modelling of cross- and long-shore sediment transport is described. This book is useful for students with a background in basic hydrodynamics.
Dynamic Ridges and Valleys in a Strike‐Slip Environment

Strike‐slip faults have long been known for characteristic near‐fault landforms such as offset rivers and strike‐parallel valleys. In this study, we use a landscape evolution model to investigate the longer‐term, catchment‐wide landscape response to horizontal fault motion. Our results show that strike‐slip faulting induces a persistent state of disequilibrium in the modeled landscapes brought about by river lengthening along the fault alternating with abrupt shortening due to stream capture. The models also predict that, in some cases, ridges oriented perpendicular to the fault migrate laterally in conjunction with fault motion. We find that ridge migration happens when slip rate is slow enough and/or soil creep and river incision are efficient enough that the landscape can respond to the disequilibrium brought about by strike‐slip motion. Regional rock uplift relative to baselevel also plays a role, as topographic relief is required for ridge migration. In models with faster horizontal slip rates, stronger rocks, or less efficient hillslope transport, ridge mobility is limited or arrested despite the continuance of river lengthening and capture. In these cases, prominent steep, fault‐facing facets form along well‐developed fault valleys. Comparison of landscapes adjacent to fast‐slipping (>30 mm/yr) and slower‐slipping (≤1 mm/yr or less) strike‐slip faults in California, USA, reveals features that are consistent with model predictions. Our results highlight a potential suite of geomorphic signatures that can be used as indicators of horizontal crustal motion and geomorphic processes in strike‐slip settings even after river capture has diminished or erased apparent offset along the fault.
Temporal Dynamics of Likes and Retweets Engagement of Indian Politicians on Twitter

A multitude of population utilises social media in today’s era. Social Media provides an efficient platform for the exchange of information and rapid propagation of ideas. Politicians, celebrities, content authors, as well as businesses in the health, real estate, beauty, and fashion industries, among others, greatly benefit from this platform. Hence, its study is of paramount significance and the principal actors of social media are - influencers. Our work is built upon the study of influencers of politics in our country - our very own Indian Politicians. Six Indian politicians have been chosen and their Twitter activity has been monitored around the time when Delhi Municipal Corporation elections (December, 2022) were approaching. On investigation, some crucial facts were found regarding the life of a tweet, the importance of mentions and media content in a tweet and much more. These key findings have been explained in this paper which can help influencers achieve better engagement on their tweets. Along with this, some machine learning models have been trained to predict the engagement on a given tweet and achieved the highest accuracy of 80.1%. To the best of our knowledge, it is the first attempt of conducting such thorough study on the temporal dynamics of engagement for Indian Politicians.
Single-Phase Single-Stage PFC Converter with Soft Switching for EV Applications

For applications involving the battery charging of electric vehicles, there is a constant need for onboard chargers that are reliable, effective, compact, and lightweight. Conventional EV charger uses a two-stage configuration, which employs a PFC stage for input side power factor correction followed by a dc-dc converter for battery side requirement. However, traditional EV chargers have high costs and low power density due to a large number of components. This article presents a high efficiency single stage onboard charger with soft switching characteristics and a compact size. Interleaved topology leads to a reduction in input current ripple and reduces size of passive components. Mathematical modeling, analysis, and controller design for the proposed converter configuration under various operational modes are described in this article. The proposed converter is built in the simscape electrical toolbox of MATLAB software. At last, MATLAB simulation results are used to validate the converter’s steady state and dynamic performance.
Association of temperament and character traits with ADHD and its comorbidities

Abstract Background Increasing evidence suggests that personality traits may be associated with ADHD and comorbid concerns. We investigated temperament and character traits associated with internalizing and externalizing comorbidities in adolescents with or without ADHD. Methods The population consisted of a subsample (n = 457) of the Northern Finland Birth Cohort 1986 (NFBC1986) that comprised 16-year-old adolescents with and without symptoms of ADHD. ADHD and psychiatric comorbidities were assessed using the Kiddie-SADS-PL and temperament and character profiles were determined with TCI-125. Outcomes were examined using Pearson’s chi-square test, Analyses of Covariance and Cohen’s d. Results The adolescents with ADHD endorsed more novelty seeking and less persistence, self-directedness, and cooperativeness than those without. Comparison of the personality traits and psychiatric comorbidities with the ADHD cases showed that comorbidities did not particularly affect the temperament and character profiles, but rather ADHD accounted for the differences between the personality traits. However, the temperament and character traits predicted externalizing and internalizing psychiatric disorders in the control group. Conclusions Our findings reveal unique comorbidity-related temperament factors, which may have implications for adaptive and personalizing intervention efforts.
Biogeochemical controls on methane, nitrous oxide, and carbon dioxide fluxes from deciduous forest soils in eastern Canada

[1] The exchange of the important trace gases, methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2), between forested soils and the atmosphere can show great temporal and spatial variability. We measured the flux of these three gases over 2 years along catenas at two forested sites, to determine the important controls. Well-drained soils consumed atmospheric CH4, while poorly drained swamp soils embedded in depressions were a source. CH4 fluxes could be predicted primarily by temperature and moisture, and tree cover exerted an influence mainly through the creation of large soil porosity, leading to increased consumption rates. In contrast, there were very poor relationships between N2O fluxes and environmental variables, reflecting the complex interactions of microbial, edaphic, and N cycling processes, such as nitrification in well-drained soils and denitrification in poorly drained soils, which led to N2O production (or consumption) in soils and hence larger variability. At the broad temporal and spatial scale, soil C:N ratio was a good predictor of N2O emission rates, through its influence upon N cycling processes. Soil CO2 emission rates showed less spatial and temporal variability, and were controlled by temperature and moisture. The source strength, in global warming potential of CH4 and N2O fluxes in CO2 equivalents, was reduced markedly when trace gas fluxes from 5 to 15% poorly drained soils were included in the net global warming potential calculation of whole forested watersheds. Soils drainage class integrates many of the biogeochemical processes controlling the flux of these gases providing a framework for extrapolating results.
USV Path Planning Under Marine Environment Simulation Using DWA and Safe Reinforcement Learning

It is a challenge for USV navigation due to uncertainties and disturbances in the complex marine environment, which may lead to tilting or collision. However, current path planning methods for USVs lack dynamic environment adaptivity and timeliness. We formulate this problem as a Markov Decision Process (MDP) and combine the advantages of Dynamic Window Approach (DWA) and Safe Reinforcement Learning (RL) to solve it. First, the state encoding output by DWA is employed as observation to reduce the state dimension. Second, a safety-ensured reward function is designed to avoid collision areas. Then, the actor-critic network based on RL algorithm is used. Along with the RL training, the parameters in DWA is adaptively adjusted. Simulation experiments with dynamic waves and ocean currents demonstrate the adaptivity of our method, with avg. trajectory length decreased by 10%, avg. time cost decreased by 17%, and avg. speed increased by 18%, compared to the case using only DWA.
Headland Turn Automation Concept for Tractor-Trailer System with Deep Reinforcement Learning

Navigation of agricultural vehicles along predefined paths using control and path planning strategies has been extensively studied, but some cases require challenging maneuvers that cannot be manually predefined. An example of such cases in agriculture is the headland turning process, which heavily depends on the field geometry, tractor, and implement. Therefore, automating this process can improve time efficiency, optimize land use, and reduce the cognitive burden on semi-autonomous tractor drivers. Reinforcement Learning (RL) is a powerful framework for learning complex policies in high-dimensional environments, making it widely used in autonomous driving. In this paper, we investigate the effectiveness of recent model-free RL algorithms, specifically policy optimization and Q-learning types, for headland turning of a tractor-trailer combination. The findings show that reinforcement learning-based approaches are effective for learning a headland turn maneuver and provide insights into which state-of-the-art method has a superior performance in learning a headland turn maneuver.
Porosity Calibration of Neutron Logs, SACROC Unit (includes associated papers 15799 and 15846 )

A major field study has been initiated at the Scurry Area Canyon Reef Operators Committee (SACROC) unit to provide a detailed geologic description of the reservoir. To supply porosity information to the digital database, a new technique has been developed that transforms neutron deflection logs into accurate neutron porosity logs. The technique uses computer programs to analyze statistically the neutron log to be rescaled as well as nearby modern porosity logs. An equations is derived that transforms neutron log deflection into porosity. Once the porosity response of a particular tool is determined in areas of good porosity control, that response can be applied successfully in areas of little or no control.
Warm core ring dynamics derived from satellite imagery

A reinterpretation of the life history of warm core ring 82-B is constructed from drifter trajectories, hydrographie profiles, and satellite-derived sea surface temperature fields. An analysis of the observations strongly suggests this ring is fundamentally a dipole structure for much of its existence. The dipole has unequal strength vortices, with the cyclone being the weaker component. Quantification of the dipole indicates the anticyclone is oval shaped and rotates at approximately 7.7° per day, while the cyclone revolves around the anticyclone at about 14.5° per day, yielding an orbit period of approximately 25 days for the cyclone. The elusive part of the dipole explanation is in interpreting its surface signature as a function of time, as the cyclone appears to disappear when in proximity to the steep continental slope. Vortex modeling suggests that when the weaker cyclonic eddy is placed close to a boundary, the cyclone is drawn into a filament as it is advected through the gap between the anticyclone and the boundary. This means the cyclone is either in a filamentation state or undergoing the straining associated with filamentation approximately three fourths of the time, which is in complete agreement with the satellite imagery. In addition, the 82-B dipole configuration is sufficiently robust to survive continuous interaction with the continental slope and the onset of a Gulf Stream interaction. Although only warm core ring 82-B is analyzed in detail (and found to be a vortex pair), dipole configurations are found in the warm core ring 81-F time series as well.