Miracle-dz/UniFAR-FTUs huggingface dataset:
Lava Flow Eruption Conditions in the Tharsis Volcanic Province on Mars

Volcanism has played a major role in modifying the Martian surface. The Tharsis volcanic province dominates the western hemisphere of the planet with numerous effusive volcanic constructs and deposits. Here, we present the results of an in‐depth study aimed at characterizing and modeling the emplacement conditions of 40 lava flows in the Tharsis volcanic province. These lava flows display a range of lengths (∼15–310 km), widths (∼0.5–29 km), and thicknesses (∼11–91 m). The volumes and flow masses range from ∼1 to 440 km3 and ∼1011 to 1014 kg, respectively. Using three different models, we calculated a range of eruption rates (0.3–3.5 × 104 m3/s), viscosities (104–107 Pa s), yield strengths (800–104 Pa), and emplacement times (8 h–11 years). While the flow lengths and volumes are typically larger than terrestrial lava flows by an order of magnitude, rheologies and eruption rates are similar based on our findings. Emplacement times suggest that eruptions were active for long periods of time, which implies the presence and persistence of open subsurface pathways. Differences in flow morphology and emplacement conditions across localities within Tharsis highlight different pathways and volumes of available material between the central volcanoes and the plains. The scale of the eruptions suggests there could have been eruption‐driven local, regional, and perhaps, global impacts on the Martian climate. The relatively recent age of the eruptions implies that Mars has retained the capability of producing significant localized volcanism.
Solution-liquid-solid (SLS) growth of crystalline semiconductor fibers and whiskers: A variation on VLS

We recently discovered that crystalline covalent semiconductors (InP, InAs, and GaAs) may be obtained from organometallic reactions conducted in refluxing organic solvents. Such crystallization conditions are unusual for covalent solids. Experiments that support a growth mechanism analogous to the known vapor-liquid-solid (VLS) mechanism, a high-temperature crystal-growth process, will be presented. We have named the new mechanism the solution-liquid-solid (SLS) mechanism, and to our knowledge it affords the lowest temperature syntheses of crystalline III-V semiconductors. The method also affords unusual fiber and whisker morphologies and small crystal dimensions.
The Implementation of Framework for Improvement by Vertical Enhancement Into Energy Exascale Earth System Model

The low cloud bias in global climate models (GCMs) remains an unsolved problem. Coarse vertical resolution in GCMs has been suggested to be a significant cause of low cloud bias because planetary boundary layer parameterizations cannot resolve sharp temperature and moisture gradients often found at the top of subtropical stratocumulus layers. This work aims to ameliorate the low cloud problem by implementing a new computational method, the Framework for Improvement by Vertical Enhancement (FIVE), into the Energy Exascale Earth System Model (E3SM). Three physics schemes representing microphysics, radiation, and turbulence as well as vertical advection are interfaced to vertically enhanced physics (VEP), which allows for these processes to be computed on a higher vertical resolution grid compared to the rest of the E3SM model. We demonstrate the better representation of subtropical boundary layer clouds with FIVE while limiting additional computational cost from the increased number of levels. When the vertical resolution approaches the large eddy simulation‐like vertical resolution in VEP, the climatological low cloud amount shows a significant increase of more than 30% in the southeastern Pacific Ocean. Using FIVE to improve the representation of low‐level clouds does not come with any negative side effects associated with the simulation of mid‐ and high‐level cloud and precipitation, that can occur when running the full model at higher vertical resolution.
Cost-benefit Quantification of Leaf Carbon Economics to Disentangle Responses of Plant Assemblages to Deer Herbivory

Although the plant carbon cost-benefit balance is known to be related to individual plant growth, reproduction, and population expansion, the association with plant community differences is not well understood. In this study, we examined how the leaf carbon cost-benefit metrics were associated with the assembly process of forest understory plant communities in areas highly affected by deer browsing. We calculated these metrics from plant physiologically parameters for 14 forest floor plant species growing in deer presence/absence site to detect the relationship between species dominance and leaf carbon cost-benefit metrics. As a result, the patterns of interspecific variation in benefit along the plant dominance rank differed in deer presence/absence sites, contributing to the marked differences in species composition and diversity observed at the two sites. In the absence of deer, where competition was the dominant determinant of plant community composition, carbon benefits among species were positively related to the plant dominance rank, indicating that species able to acquire more carbon were at an advantage. On the other hand, under deer herbivory, differences in carbon benefit between species were not strongly apparent and were not related to the plant dominance rank, indicating few differences in reproductive and expansion ability (plant fitness) between species. This process contributes to the high species diversity of plant communities observed in the presence of deer. Our results emphasize the possibility of connecting different fields of studies, physiological ecology, community ecology, and the plant carbon cost-benefit balance of single leaves to explain plant community composition differences.
Experimental Study of the Stability and Phase Relations of Clays at High Temperature in a Thermal Gradient

Clays are involved in a variety of natural and managed processes, and calculation of their stability conditions is important. Such calculations are still fraught with large uncertainties owing to the lack of experimental constraints on the thermodynamic properties of clays, and bracketing of equilibrium reactions at low temperature is barely possible. Experiments aimed at studying the thermal stabilities of, and composition-temperature relations among, smectite, illite, kaolinite, pyrophyllite, mica, and chlorite at different levels of SiO2, K2O, MgO, and Al2O3 species in solution were conducted under a strong thermal gradient in the simple K2O-Al2O3-SiO2-H2O (KASH), MgO-Al2O3-SiO2-H2O (MASH), and KMASH systems. The crystallization series observed in the different experiments match to some degree those observed in active geothermal systems where clay minerals precipitate from oversaturated solutions. Smectite and/or ordered mixed-layer materials, smectite-donbassite, or possibly pyrophyllite-donbassite were observed to crystallize in both KASH and MASH experiments. Similar crystallization sequences and clay composition variations with temperature were observed in most cases when the relative positions of the starting solids were switched. The experimental results were used to refine the thermodynamic properties of K- and Mg-smectite. Stability diagrams calculated by energy minimization and activityactivity diagrams are consistent with the experimental mineral variations, suggesting that smectite is thermodynamically stable at temperatures as high as 300°C in the presence of diluted water and quartz and K-feldspar-free systems.
Formal supervisors' role in stimulating team members' informal leader emergence: Supervisor and member status as critical moderators

Teams can benefit markedly when formal supervisors stimulate their individual members' emergence as informal leaders. Combining insights from informal leadership research and social learning theory, we cast supervisors' role modeling of initiating structure and consideration behaviors as seemingly straightforward means of achiev-ing this — but we suggest that the success of such role modeling critically hinges on supervisors' as well as members' status in the team . Results from a study of 220 nurses across 48 teams showed, accordingly, that a supervisor's initiating structure pro-moted individual members' informal leader emergence by increasing members' respective behavior. This indirect relationship only materialized, however, among relatively high-status supervisors and relatively low-status members. Moreover, although supervisors' and members' consideration were positively related (among relatively high-status supervisors and largely irrespective of a member's status), such behavior did not influence members' emergence as informal leaders. Together, these findings offer novel insights into how , when , and why formal supervisors may aid their team members' attainment of informal leader roles. They shed new light on the complexity of formal – informal leadership linkages, with both supervisors' and members' standing in the team representing crucial, yet heretofore largely unexamined boundary conditions for formal supervisors' respective influence.
Variance-component-based meta-analysis of gene–environment interactions for rare variants

Abstract Complex diseases are often caused by interplay between genetic and environmental factors. Existing gene–environment interaction (G × E) tests for rare variants largely focus on detecting gene-based G × E effects in a single study; thus, their statistical power is limited by the sample size of the study. Meta-analysis methods that synthesize summary statistics of G × E effects from multiple studies for rare variants are still limited. Based on variance component models, we propose four meta-analysis methods of testing G × E effects for rare variants: HOM-INT-FIX, HET-INT-FIX, HOM-INT-RAN, and HET-INT-RAN. Our methods consider homogeneous or heterogeneous G × E effects across studies and treat the main genetic effect as either fixed or random. Through simulations, we show that the empirical distributions of the four meta-statistics under the null hypothesis align with their expected theoretical distributions. When the interaction effect is homogeneous across studies, HOM-INT-FIX and HOM-INT-RAN have as much statistical power as a pooled analysis conducted on a single interaction test with individual-level data from all studies. When the interaction effect is heterogeneous across studies, HET-INT-FIX and HET-INT-RAN provide higher power than pooled analysis. Our methods are further validated via testing 12 candidate gene–age interactions in blood pressure traits using whole-exome sequencing data from UK Biobank.
Stereo Superpixel Segmentation Via Dual-Attention Fusion Networks

Stereo image pairs can improve performance of many tasks benefiting from the additional information obtained from a second viewpoint when compared with single images. Existing superpixel segmentation algorithms for stereo images mostly adopt single images as input, and neglect the correspondence between the left and right views. In this work, we consider to exploit the depth information between stereo image pairs, and propose an end-to-end dual-attention fusion network for stereo images to generate parallax-consistency superpixels. We first utilize a deep convolution network to extract the deep features of stereo images. Then, to effectively utilize the additional information from the other view, features of the left and right views is integrated by a parallax attention and channel attention mechanism. Finally, the stereo superpixels are generated by a differentiable clustering algorithm, which is end-to-end trainable with deep learning networks. Comprehensive experimental results demonstrate that our method can outperform the state-of-the-art performance on the KITTI2015 and Cityscapes dataset.
Decentralized geoprivacy: leveraging social trust on the distributed web

ABSTRACT Despite several high-profile data breaches and business models that commercialize user data, participation in social media networks continues to require users to trust corporations to safeguard their personal data. Since these data increasingly contain geographic references that allude to individuals’ locations and movements, the need for new approaches to geoprivacy and data sovereignty has grown. We develop a geoprivacy framework that couples two emerging technologies – decentralized data storage and discrete global grid systems – to facilitate fine-grained user control over the ownership of, access to and map-based representation of their data. The framework is illustrated with a dynamic k-anonymity model that links the geographic precision of shared data to social trust within in a social network. In this framework, users’ spatio-temporal data are shared through a decentralized system and are represented on a discrete global grid data model at spatial resolutions that correspond to varying degrees of trust between individuals who are exchanging information. Our framework has several advantages over centralized geoprivacy approaches, namely trust in a third-party entity is not required and geoprivacy is dynamic and context-dependent with users maintaining autonomy. As the distributed web begins to emerge, so too can the next generation of geographic information sharing tools.
A cold-start-free reinforcement learning approach for traffic signal control

Abstract Typical reinforcement learning (RL) requires a huge amount of data before achieving an acceptable result, and its performance can be rather poor during initial interacting process. Sample inefficiency and cold-start phenomenon of RL limits its feasibility in a range of real-world applications such as traffic signal control (TSC). On the other hand, a large amount of data on TSC can be accumulated by various model-based controllers (MBCs) rooted in traffic engineering. In this context, we propose a new RL approach which can avoid the appearance of cold starts by taking advantage of MBC experiences. First, three frameworks of joint utilization of RL and MBC are summarized for TSC, and staged framework is considered to have the edge over the other two. Then, a staged noisy-net prioritized dueling double deep Q-network (NPDD-DQN) is described in detail for TSC, where MBC experiences are used in both pre-training and online training processes. Experimental evaluation demonstrates that staged NPDD-DQN can achieve a boost in initial performance as compared to pure NPDD-DQN that does not utilize any control experiences, and learn to improve final performance beyond the underlying MBC. The effectiveness of the proposed method opens up the possibility of real implementation of RL in TSC.
The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model

As global competition has intensified in the automotive industry, there is a strong need for management teams to develop methods that allow accurate and objective assessments of plant productivity and to identify productivity improvement opportunities for the best manufacturing practices. Stochastic frontier analysis (SFA) models have been used as a statistical benchmarking tool to provide a bird’s-eye view of an industrial sector. SFA models can also be adapted for plant productivity assessment. However, owing to the problem of multicollinearity, the general form of SFA is difficult to apply to the assessment of complex manufacturing systems in the automotive industry, which is characterized by many control and external factors that are intercorrelated to each other. This study proposes a method for applying SFA to vehicle manufacturing plants with a focus on gaining high accuracy in model parameter estimation, by decomposing a plant into components (i.e., shops), building an SFA model for each shop, and reintegrating the general plant system through the appropriate combination of shop-level inefficiency distributions. In particular, this study focuses on documenting the derivation of a new probability density function that integrates three different inefficiency distributions. For illustration of the proposed approach, hypothetical vehicle assembly plants are assessed as examples, where the total labor hours are split into Bodyshop, Paintshop, and General Assembly, exclusively and collectively. Finally, this study offers a solution process to clarify the reasons for underperforming plants in terms of labor productivity and identify the course of actions to cure the issues with some managerial insights emphasizing the balanced approach, incorporating people, process and technology.
Machine Learning-Based Rate Distortion Modeling for VVC/H.266 Intra-Frame

Rate-distortion (R-D) optimization has been widely adopted to improve the coding efficiency on the video encoder side. However, there are few studies related to modeling the R-D characteristics of the latest Versatile Video Coding (VVC) reference software. In this paper, we investigate the R-D modeling of the intra-frame on the VVC encoder by utilizing four traditional machine learning algorithms. We extract four highly descriptive features to capture the relationship between the video content and the R-D model. Moreover, it is applied to the initial intra-frame rate control of VVC. Experimental results show that our method outperforms VTM-7.0, which improves the accuracy by up to 8.65% with affordable computational complexity increase1.
The impact of cognitive and noncognitive factors on odor discrimination performance

This study aimed to investigate which components among a range of cognitive and noncognitive factors contribute to odor discrimination and to which extent. One hun-dred sixty participants were assessed for olfactory discrimination considering (a) the sequence of presentation of the detection target and (b) response time taken to per-form the discrimination task. Participants were tested with several cognitive tests covering executive functioning, and semantic memory and noncognitive tests measuring optimism, mindset, general self-efficacy, and self-esteem. Hierarchical regression analyses conducted in classic and Bayesian ways demonstrate a strong impact of semantic memory and age on odor discrimination performance. Conversely, executive functions together with noncognitive factors and response time were rather weak predictors of odor discrimination performance. Additionally, the positioning of the target odor, when presented last, facilitated the correct choice in odor discrimination. In conclusion, odor discrimination is related to semantic memory, while noncognitive factors exert only minor influences on odor discrimination scores. we found the when presented as the last of the three pens, facilitated the correct choice in process. Even though, the influence of the target odor position of several trials, the amount of time taken to respond when performing odor discrimination task did not contribute strongly to the final outcome. Thus, controlling for the response time in this kind of task appears not to be highly significant.
Using DNA metabarcoding to detect burrowing seabirds in a remote landscape

Species inventories and biodiversity assessments are critical to conservation. Yet cryptic species or recolonizing species can be challenging to detect. DNA metabarcoding provides an alternative tool to identify species that can be difficult to observe during field surveys. We test the efficacy of DNA analysis to identify burrowing petrel species in a rapidly changing landscape, on a remote sub‐Antarctic island following pest eradication. Discarded feathers and scats provided high quality DNA for species identification, assisting in detection of new species arrivals and new breeding sites across Macquarie Island. We highlight how DNA metabarcoding informs species inventories and is a valuable tool to complement seabird field surveys.
MProve+: Privacy Enhancing Proof of Reserves Protocol for Monero

Proof of reserves protocols enable cryptocurrency exchanges to prove solvency, i.e. prove that they have enough reserves to meet their liabilities towards their customers. <inline-formula> <tex-math notation="LaTeX">$\textsf{MProve}$ </tex-math></inline-formula> (EuroS&PW, 2019) was the first proof of reserves protocol for Monero which provided some privacy to the exchanges’ addresses. As the key images and the addresses are inherently linked in the <inline-formula> <tex-math notation="LaTeX">$\textsf{MProve}$ </tex-math></inline-formula> proof, an observer could easily recognize the exchange-owned address when a transaction spending from it appears on the blockchain. This is detrimental for an exchange’s privacy and becomes a natural reason for exchanges to not adopt <inline-formula> <tex-math notation="LaTeX">$\textsf{MProve}$ </tex-math></inline-formula>. To this end, we propose <inline-formula> <tex-math notation="LaTeX">$\textsf{MProve}$ </tex-math></inline-formula>+, a Bulletproofs-based (S&P, 2018) NIZK protocol, which unlinks the key images and the addresses, thus alleviating the drawback of <inline-formula> <tex-math notation="LaTeX">$\textsf{MProve}$ </tex-math></inline-formula>. Furthermore, <inline-formula> <tex-math notation="LaTeX">$\textsf{MProve}$ </tex-math></inline-formula>+ presents a promising alternative to <inline-formula> <tex-math notation="LaTeX">$\textsf{MProve}$ </tex-math></inline-formula> due to an order of magnitude smaller proof sizes along with practical proof generation and verification times.
Design Effect in Multilevel Settings: A Commentary on a Latent Variable Modeling Procedure for Its Evaluation

A latent variable modeling-based procedure is discussed that permits to readily point and interval estimate the design effect index in multilevel settings using widely circulated software. The method provides useful information about the relationship of important parameter standard errors when accounting for clustering effects relative to conducting single-level analyses. The approach can also be employed as an addendum to point and interval estimation of the intraclass correlation coefficient in empirical research. The discussed procedure makes it easily possible to evaluate the design effect in two-level studies by utilizing the popular latent variable modeling methodology and is illustrated with an example.
Modeling Heterogeneous Traffic Mixing Regular, Connected, and Connected-Autonomous Vehicles Under Connected Environment

As inter-vehicle communication and automatic driving technology continue to develop, but are not yet popular, regular vehicles, connected vehicles and connected autonomous vehicles (CAVs) will coexist on the road for a long time. This mixed traffic environment highlights the need to theoretically analyze the impacts of some connected and autonomous technologies (i.e., accurate detection technology, inter-vehicle communication technology, data storage technology and inter-vehicle cooperation technology) on the stable operation of heterogeneous traffic. According to the characteristics of the vehicles equipped with different technologies, this paper extends the corresponding car-following models based on the optimal velocity model. Through these analytical models, these connected and autonomous technologies are quantified and the linear stability analyses are conducted. Numerical simulation shows that the inter-vehicle communication between three vehicles, and two previous time-step data storage or two future time-step inter-vehicle cooperation are sufficient to stabilize the mixed traffic. As CAV penetration rates increase, the stability of heterogeneous traffic is improved. Furthermore, the stability of heterogeneous traffic is weakened when the size of the largest single fleet increases. These theoretical results can serve as a quantitative tool for scholars and vehicle designers before drawing any qualitative conclusions of related technologies on heterogeneous fleet stability to avoid wasting resources such as data storage capacity and inter-vehicle communication ranges.