Miracle-dz/UniFAR-FTUs huggingface dataset:
Road-constrained trajectory recovery via BERT-based multi-task learning

With the increasing popularity of GPS modules, the development of intelligent transportation applications based on trajectory data is also accelerating, such as traffic forecasting, vehicle navigation, and travel time estimation. These urban applications all require a large number of highly sampled trajectories as a basis. However, due to energy constraints and environmental interference, a large number of trajectories are collected at low sampling rates. In order to solve the uncertainty problem brought by low sampling rate trajectories, many trajectory recovery algorithms have been proposed. Currently, many existing works are based on sequence-to-sequence networks for trajectory recovery, that is, encoders are used to encode trajectories, and decoders are used to recover missing trajectory points. However, these works do not consider road network information and only use grid numbers to recover trajectories. Therefore, we propose a BERTbased multi-task learning framework, namely BTTrajRec, for trajectory recovery. BTTrajRec incorporates road information when encoding trajectories, and captures the bidirectional relationship between trajectory points, which can better characterize trajectory data. In addition, a constraint mask matrix is used to improve the recovery accuracy. We used multiple taxi data sets in the real world to verify the validity of the proposed method.
An 𝓵p-nonconvex regularization method for image smoothing

Image smoothing techniques are widely used in computer vision and graphics applications, such as detail enhancement, artifact removal, image denoising and high dynamic range (HDR) tone mapping. In this paper, an 𝓵p-nonconvex minimization model is presented to achieve diverse smoothness of edges. To induce sparsity more strongly than the 𝓵1 norm regularization, we take the nonconvex arctangent penalty function of the image gradient as the regularization term. To make the model more flexible and effective, we use the 𝓵p norm function as the fidelity term. The majorization-minimization (MM) algorithm is employed for the proposed nonconvex optimization model. We discuss the convergence of the resulting MM algorithm. Comprehensive experiments and comparisons show that the proposed method is effective in a variety of image processing tasks.
Metal surface defects segmentation method using cycle generative adversarial networks on small datasets

Aiming at the problems of small metal surface defect samples in industrial production and the difficulty of data annotation in supervised segmentation algorithms, a background reconstruction method based on Cycle generative adversarial networks is proposed to realize metal surface defect segmentation in combination with the traditional threshold segmentation algorithm. Firstly, the corresponding defect-free template is reconstructed from the defect image using Cycle generative adversarial networks, and the defect image and the reconstructed template are subjected to the differential subjected to eliminate the influence of the background texture of the defect sample. Finally, the segmentation process is performed using the adaptive thresholding segmentation method. In order to adapt to the small sample training as well as to improve the performance of background reconstruction of the generative network, the U-Net network structure is used as the generator, and the attention mechanism is also introduced. Meanwhile, the L1 loss and Multi-scale SSIM loss are combined to design the cycle consistency loss for training. The experimental results show that the method in this paper can accomplish good defect segmentation results using a small number of defective samples.
Research and exploration of SLAM solutions based on mobile robots

SLAM (Simultaneous Localization and Mapping) refers to the robot in the unknown environment through the sensor to detect the environment, and combined with its own movement to gradually build a map of the environment, and in this process to determine its own posture information, that is, positioning and map construction at the same time, is a more complex coupling problem. With the development of artificial intelligence and computer technology, there are a rich variety of solutions to the problem of simultaneous positioning and mapping of mobile robots. Firstly, the basic classification and principle of the existing SLAM scheme are introduced, and the laser SLAM is used as an example to detail the basic working framework of the system. Then, the mainstream laser and visual SLAM algorithms are described, and the current application and future development trend of SLAM are analyzed.
Personified multi-ship collision avoidance navigation on knowledge distillation

In systems where unmanned navigation and manned behavior of ships coexist, the intelligence-driven unmanned ships lack the adaptability to complex environments that humans exhibit when faced with realistic navigation environments and will affect the decision judgment of manned ships. To solve the problem, a knowledge distillation-based multi-intelligent body maritime collision avoidance navigation method for human-like behavior is proposed. The method first preprocesses the AIS data, and the core technique is to use the knowledge distillation method to combine the expert strategy with the reinforcement learning method, which introduces the human piloting ship habits and makes the ship’s automatic navigation exhibit human characteristics. The test results show that the unmanned ship trained by the method can learn human driving ship habits outside the reinforcement learning reward function setting, and enhance the training efficiency of reinforcement learning.
Research on multi-source data fusion method in power public service platform construction

Multi source data fusion plays a great role in promoting the application of basic geographic information in various industries, and this method makes up for the limitations of surveying and mapping data attribute collection. This paper expounds and analyzes the problems faced by the data integration of multi scale, multi format and multi topic settings in the construction of electric power public service platform, puts forward the research on multi-source data fusion methods in the construction of electric power public service platform, and discusses and studies the key technologies.
Resurrection of Tectaria cadieri from T. ingens (Tectariaceae) Based on Morphological and Molecular Data

Abstract We demonstrate that Tectaria cadieri (Tectariaceae) is a distinct species in Vietnam after long being placed under the synonymy of T. ingens. Morphologically, T. cadieri is similar to T. ingens, T. setulosa, and T. trichotoma in their large fronds (up to 0.8–2 m long), 3-pinnate laminae, fully free veins, and sori in two rows on ultimate segments. However, our observations show that T. cadieri differs from these species in the presence of linear, membranaceous stipe scales. Our analyses of five plastid regions (atpB, ndhF plus ndhF-trnN, rbcL, rps16-matK plus matK, and trnL-trnF) show that T. cadieri is not closely related to T. ingens, T. setulosa, nor T. trichotoma. Rather, T. cadieri is closely related to T. multicaudata, a morphologically distinct species with anastomosing veins. Along with a detailed description and illustrations for T. cadieri, we provide morphological comparisons and comments on the relationships between T. cadieri and morphologically similar species.
(Re)considering equity, inclusion and belonging in the updating of the Early Years Learning Framework for Australia: The potential and pitfalls of book sharing

Few would dispute the importance of equity, inclusion and belonging in early childhood education and care, yet translation into meaningful practice rarely centres the priorities of historically divested communities. The national learning framework for early childhood in Australia is the Early Years Learning Framework, positioning the child as a capable agent and describing inclusive, culturally competent practice. This article presents part of a larger study investigating educators’ beliefs and practices when using culturally diverse literature to address the Early Years Learning Framework’s diversity principles. A critical theoretical framework enables a robust examination of how the Early Years Learning Framework constructs, maintains, legitimises and/or disaffirms social inequities, implicitly probing how literacy education mediate/s messages children receive about their identity, cultures and roles in society. The findings suggest that instead of pursuing anti-racism and transformative justice, educators’ pedagogical practices were likely to legitimise existing racist structures. The findings are discussed in relation to 20 recommendations published by a consortium of experts in the updating of the Early Years Learning Framework. The implementation of the updated Early Years Learning Framework must act on questions of justice for whom and according to whom. To move to ideologies, methodologies and pedagogies of potentiality, it is necessary to interrogate and reject oppressive and harmful practices, inaccurate and insensitive portrayals, and pedagogies damaging to Black, Indigenous, and other communities of Color which this study shows have beenevident in the EYLF to date.
Synthesis and electrical characterizations of (Sn0.8Ti0.2)O2 electronic material

ABSTRACT The ceramic composite electronic material of (Sn0.8Ti0.2)O2 has been synthesized through a cost-effective solid-state ceramic approach. The crystallographic tetragonal structure from the XRD spectrum, hydrophilic porous structure from SEM micrograph, dielectric, conductivity, and electrical modulus in addition to impedance spectroscopy over an extensive range of temperature and frequency have been elucidated. Hydrophilicity, superior dielectric response with significantly low dielectric loss, and enhanced capacitive behaviour draw the major attraction of this ceramic-based material system. The temperature-dependent conductivity spectrum evinces Arrhenius’s behaviour. The grain and grain boundary effects in the synthesized sample has been displayed through the Nyquist spectrum. The non-Debye type of relaxation mechanism has been established through an electric modulus study. The extensive study sketched out this composite as a potential capacitive electronic component for humidity sensor device applications. The investigated electrical parameters associated with the ceramic composite may enlighten the development of functional electronic devices.
Insights into the friction stir processing of fused filament fabricated polymers with multiple reinforcements

ABSTRACT Friction stir processing (FSP) is a most widely used severe plastic deformation (SPD) technique used for enhancement of mechanical properties and refinement of microstructure in metals, alloys and also in surface alteration, surface composites, and metal matrix composites. This work deals with the integration of FSP with fused filament fabrication (FFF), a popular additive manufacturing (AM) method. The research work comprises of four parts, first being 3d printing of poly lactic acid (PLA) and acrylonitrile butadiene styrene (ABS) parts through FFF, then multiple reinforcements like multi-walled carbon nanotubes (MWCNTs), aluminum oxide (Al2O3) and silicon dioxide (SiO2) are filled into the grooves, then in the third step FSP is performed on the 3D specimens with variations in rotational and travel speed of the tool with threaded pin profile. Finally, tensile strength is examined and the effect of different reinforcements is studied. The results indicate that MWCNTs reinforced PLA is found to have the highest strength (37.41 MPa) which is 72.6% more than the lowest strength (21.67 MPa) obtained for SiO2 reinforced PLA. However, in the case of other polymer, SiO2 reinforced ABS exhibited maximum strength (14.71 MPa) amongst other reinforcements for ABS. The ductility is decreased for all the FFF specimens after FSP and PLA composites exhibited improved surface profiling parameters as compared with ABS composites after FSP. Also, fractography is performed to understand the failure modes of specimens filled with different reinforcements.
Growing among Trees: a 12-month process evaluation of school based outdoor learning interventions

ABSTRACT A wealth of evidence shows that exposure to the natural environment and nature-based learning can benefit children’s physical, mental, social, and emotional health. Despite this, children are spending less time in nature and nature-based learning remains an underutilised pedagogical tool. Several barriers are frequently reported by teachers, including curriculum and time pressures, confidence, safety, cost and access to training and resources. There is a need to better understand and address these common concerns if nature-based learning is to be implemented more widely. Growing among Trees was a 12-month pilot project where community forest and woodland outreach interventions were delivered in urban schools. This process evaluation gathered data from various stakeholders (including pupils, teachers, and intervention providers) before, during and following the interventions delivered at each school. This study provides important insight into how school-based outdoor learning interventions could be implemented to increase the likelihood that they are replicable and sustainable.
A Lagrangian Analysis of the Sources of Rainfall Over the Horn of Africa Drylands

The Horn of Africa drylands (HAD) are among the most vulnerable regions to hydroclimatic extremes. The two rainfall seasons—long and short rains—exhibit high intraseasonal and interannual variability. Accurately simulating the long and short rains has proven to be a significant challenge for the current generation of weather and climate models, revealing key gaps in our understanding of the drivers of rainfall in the region. In contrast to existing climate modeling and observation‐based studies, here we analyze the HAD rainfall from an observationally‐constrained Lagrangian perspective. We quantify and map the region's major oceanic and terrestrial sources of moisture. Specifically, our results show that the Arabian Sea (through its influence on the northeast monsoon circulation) and the southern Indian Ocean (via the Somali low‐level jet) contribute ∼80% of the HAD rainfall. We see that moisture contributions from land sources are very low at the beginning of each season, but supply up to ∼20% from the second month onwards, that is, when the oceanic‐origin rainfall has already increased water availability over land. Further, our findings suggest that the interannual variability in the long and short rains is driven by changes in circulation patterns and regional thermodynamic processes rather than changes in ocean evaporation. Our results can be used to better evaluate, and potentially improve, numerical weather prediction and climate models, and have important implications for (sub‐)seasonal forecasts and long‐term projections of the HAD rainfall.
Sensitivity of Nocturnal Warm Sector Rainfall Simulation to the Configuration of Initial and Lateral Boundary Conditions: A Case Study in Southern China Based on the Operational TRAMS Model

A heavy nocturnal warm sector rainfall occurred over the western coastal region of South China in June 2020, of which the precipitation magnitude was seriously underestimated by the operational TRAMS (Tropical Regional Atmosphere Model System) model. In this study, by improving the configuration of initial conditions (ICs) and lateral boundary conditions (LBCs) in offline nesting, the simulation of the convection initiation (CI) process can be well improved. The CI simulation is found to be sensitive to the vertical resolution in ICs in this case. When the low‐level vertical resolution of ICs is increased, the nocturnal near‐surface cold layer caused by inland mountains can be resolved better, which is necessary to form the convergence line along the coastline. Another important impact of increasing the vertical resolution of ICs is the successful simulation of horizontal convective rolls (HCRs) over the northern South China Sea. The HCRs apparently increase the depth of the warm‐moist marine boundary layer jet (MBLJ), and finally lead to the CI along the coastline. The increased vertical resolutions of LBCs can reduce the discontinuity of simulated moist tongue across the southern lateral boundary of the TRAMS model, which provides more moisture for the warm sector rainfall through MBLJ. LBCs with Higher temporal frequency help to alleviate the western position bias of rain belt by reducing the temporal interpolation error. Our study highlights the importance of finer ICs and LBCs in offline nesting for regional operational NWP systems in the South China region.
Unfolding Edges: Adding Context to Edges in Multivariate Graph Visualization

Existing work on visualizing multivariate graphs is primarily concerned with representing the attributes of nodes. Even though edges are the constitutive elements of networks, there have been only few attempts to visualize attributes of edges. In this work, we focus on the critical importance of edge attributes for interpreting network visualizations and building trust in the underlying data. We propose ‘unfolding of edges’ as an interactive approach to integrate multivariate edge attributes dynamically into existing node‐link diagrams. Unfolding edges is an in‐situ approach that gradually transforms basic links into detailed representations of the associated edge attributes. This approach extends focus+context, semantic zoom, and animated transitions for network visualizations to accommodate edge details on‐demand without cluttering the overall graph layout. We explore the design space for the unfolding of edges, which covers aspects of making space for the unfolding, of actually representing the edge context, and of navigating between edges. To demonstrate the utility of our approach, we present two case studies in the context of historical network analysis and computational social science. For these, web‐based prototypes were implemented based on which we conducted interviews with domain experts. The experts' feedback suggests that the proposed unfolding of edges is a useful tool for exploring rich edge information of multivariate graphs.
Determining the Flow State of Channels Under Vegetation With Airborne Lidar

Headwater channels are vital to ecological health, water quality, and watershed connectivity. However, the geographic extent and the temporal wetting and drying dynamics of headwater catchments are not thoroughly understood, primarily due to occlusive vegetation. Light Detection and Ranging (lidar) data can be utilized to address this knowledge gap. Airborne lidar is capable of penetrating vegetation and has been used as a spatial and spectral tool for water body delineation. In the work presented here we: (a) develop a novel means of normalizing lidar ground return intensity to allow for comparison across data sets; (b) demonstrate a statistically significant reduction in median return intensity of wet stream reaches under dense vegetation relative to dry channel reaches; and (c) leverage this reduction to create classified maps of wet and dry channel networks in densely vegetated drainages. Across four study basins spanning over 100 km2 we observed an average reduction in median intensity of 41.7% and 72.2% for areas with and without dense vegetation, respectively. Optimal thresholds for delineating wet from dry stream reaches were determined using probability density functions. Resulting classified maps yielded overall accuracies ranging from 87.3% to 95.3% when compared to the National Hydrography Dataset via stratified random sampling. This study demonstrates that remote delineation of channel flow states in densely vegetated areas is possible, thus allowing for better consideration, designation, and conservation of headwater channels.
Impact of Tibetan Plateau Spring Snow on the Summer Heat Wave Frequency Over the Indochina Peninsula

In this work, we investigated the impact of the interannual variation in Tibetan Plateau snow cover (TPSC) on the summer heat wave frequency (HWF) over the Indochina Peninsula (ICP) (HWF_ICP) for 1981–2020. This study shows that the spring TPSC variation over the central Tibetan Plateau (TP) is positively correlated with the summer HWF_ICP and can explain up to 30% of the summer HWF_ICP variance. Analysis of the apparent heat source shows that more‐than‐normal spring snow cover over the central TP has a cooling effect on the above atmosphere, which induces negative geopotential height anomalies in the upper troposphere. The anomalous spring TPSC and its cooling effect can persist until summer. In summer, the TPSC‐associated low anomalies propagate eastward to the East Asia‐Japan Sea area. The anomalous westerly winds along the south flank of these low anomalies strengthen the climatological westerly jet. Changes in the westerlies are accompanied by anomalous ascending and descending air motion north and south of 30°N over the coast of East Asia and the western Pacific. The anomalous descending air motion south of 30°N causes an enhanced subtropical high, less cloud cover, and more downward solar radiation, which are favorable conditions for a greater occurrence of heat waves over the ICP. The results of current study may provide useful information for improving the seasonal forecast skill of the summer HWF_ICP variation.
Quantifying Groundwater's Contribution to Regional Environmental‐Flows in Diverse Hydrologic Landscapes

Increasing recognition of the importance of ecosystem services in water resources management has accelerated the development and application of environmental‐flows requirements for lotic ecosystems. However, most environmental‐flows management focuses on water infrastructure, such as dams or diversions, without explicitly taking groundwater into account and ignoring the importance of groundwater environmental flow contribution. In this study two methods for estimating groundwater environmental flow contributions are presented: (a) a groundwater‐centric method (based on the Sustainability Boundary Approach), which proposes that high levels of ecological protection are maintained if 90% of groundwater discharge is preserved, and (b) a surface water‐centric method (novel method), which quantifies groundwater environmental flow contributions from streamflow using region‐specific streamflow sensitivity metrics and local environmental‐flows policies. The two methods were tested in British Columbia, Canada, which has a diverse, complex, and highly coupled groundwater‐surface water system. The two methods gave comparable results in various hydro‐geoclimatic settings. Although British Columbia was used as a case study, this framework can be implemented across various spatial and temporal scales for different regions and globally, in data‐scarce, hydrologically complex landscapes. Application of these methods can aid in a robust and holistic assessment of environmental‐flows, taking into account the often‐missing groundwater component.