244 research outputs found
Monitoring the Coastal Environment Using Remote Sensing and GIS Techniques
The coastal zone has been of importance for economic development and ecological restoration due to their rich natural resources and vulnerable ecosystems. Remote sensing techniques have proven to be powerful tools for the monitoring of the Earthâs surface and atmosphere on a global, regional, and even local scale, by providing important coverage, mapping and classification of land cover features such as vegetation, soil, water and forests. This chapter introduced the methods for monitoring the coastal environment using remote sensing and GIS techniques. Case studies of port expansion monitoring in typical coastal regions, together with the coastal environment changes analysis were also presented
Data-driven approach for modeling Reynolds stress tensor with invariance preservation
The present study represents a data-driven turbulent model with Galilean
invariance preservation based on machine learning algorithm. The fully
connected neural network (FCNN) and tensor basis neural network (TBNN) [Ling et
al. (2016)] are established. The models are trained based on five kinds of flow
cases with Reynolds Averaged Navier-Stokes (RANS) and high-fidelity data. The
mappings between two invariant sets, mean strain rate tensor and mean rotation
rate tensor as well as additional consideration of invariants of turbulent
kinetic energy gradients, and the Reynolds stress anisotropy tensor are
trained. The prediction of the Reynolds stress anisotropy tensor is treated as
user's defined RANS turbulent model with a modified turbulent kinetic energy
transport equation. The results show that both FCNN and TBNN models can provide
more accurate predictions of the anisotropy tensor and turbulent state in
square duct flow and periodic flow cases compared to the RANS model. The
machine learning based turbulent model with turbulent kinetic energy gradient
related invariants can improve the prediction precision compared with only mean
strain rate tensor and mean rotation rate tensor based models. The TBNN model
is able to predict a better flow velocity profile compared with FCNN model due
to a prior physical knowledge.Comment: 23 page
Running with a Mask? The Effect of Air Pollution on Marathon Runnersâ Performance
Using a sample of over 0.3 million marathon runners in 37 cities and 55 races in China in 2014 and 2015, we estimate the air pollution elasticity of finish time to be 0.041. Our causal identification comes from the exogeneity of air pollution on the race day because runners are required to register a race a few months in advance and we control for city fixed effects, seasonal effects, and weather condition on the race day. Including individual fixed effects also provides consistent evidence. Our study contributes to the emerging literature on the effect of air pollution on short-run productivity, particularly on the performance of athletes engaging outdoor sports and other workers whose jobs require intensive physical activities
Running with a Mask? The Effect of Air Pollution on Marathon Runnersâ Performance
Using a sample of over 0.3 million marathon runners in 37 cities and 55 races in China in 2014 and 2015, we estimate the air pollution elasticity of finish time to be 0.041. Our causal identification comes from the exogeneity of air pollution on the race day because runners are required to register a race a few months in advance and we control for city fixed effects, seasonal effects, and weather condition on the race day. Including individual fixed effects also provides consistent evidence. Our study contributes to the emerging literature on the effect of air pollution on short-run productivity, particularly on the performance of athletes engaging outdoor sports and other workers whose jobs require intensive physical activities
Active Implicit Object Reconstruction using Uncertainty-guided Next-Best-View Optimziation
Actively planning sensor views during object reconstruction is essential to
autonomous mobile robots. This task is usually performed by evaluating
information gain from an explicit uncertainty map. Existing algorithms compare
options among a set of preset candidate views and select the next-best-view
from them. In contrast to these, we take the emerging implicit representation
as the object model and seamlessly combine it with the active reconstruction
task. To fully integrate observation information into the model, we propose a
supervision method specifically for object-level reconstruction that considers
both valid and free space. Additionally, to directly evaluate view information
from the implicit object model, we introduce a sample-based uncertainty
evaluation method. It samples points on rays directly from the object model and
uses variations of implicit function inferences as the uncertainty metrics,
with no need for voxel traversal or an additional information map. Leveraging
the differentiability of our metrics, it is possible to optimize the
next-best-view by maximizing the uncertainty continuously. This does away with
the traditionally-used candidate views setting, which may provide sub-optimal
results. Experiments in simulations and real-world scenes show that our method
effectively improves the reconstruction accuracy and the view-planning
efficiency of active reconstruction tasks. The proposed system is going to open
source at https://github.com/HITSZ-NRSL/ActiveImplicitRecon.git.Comment: 8 pages, 10 figures, Submitted to IEEE Robotics and Automation
Letters (RA-L
Phase Fluctuation Analysis in Functional Brain Networks of Scaling EEG for Driver Fatigue Detection
The characterization of complex patterns arising from electroencephalogram (EEG) is an important problem with significant applications in identifying different mental states. Based on the operational EEG of drivers, a method is proposed to characterize and distinguish different EEG patterns. The EEG measurements from seven professional taxi drivers were collected under different states. The phase characterization method was used to calculate the instantaneous phase from the EEG measurements. Then, the optimization of driversâ EEG was realized through performing common spatial pattern analysis. The structures and scaling components of the brain networks from optimized EEG measurements are sensitive to the EEG patterns. The effectiveness of the method is demonstrated, and its applicability is articulated.</p
Negative Frames Matter in Egocentric Visual Query 2D Localization
The recently released Ego4D dataset and benchmark significantly scales and
diversifies the first-person visual perception data. In Ego4D, the Visual
Queries 2D Localization task aims to retrieve objects appeared in the past from
the recording in the first-person view. This task requires a system to
spatially and temporally localize the most recent appearance of a given object
query, where query is registered by a single tight visual crop of the object in
a different scene.
Our study is based on the three-stage baseline introduced in the Episodic
Memory benchmark. The baseline solves the problem by detection and tracking:
detect the similar objects in all the frames, then run a tracker from the most
confident detection result. In the VQ2D challenge, we identified two
limitations of the current baseline. (1) The training configuration has
redundant computation. Although the training set has millions of instances,
most of them are repetitive and the number of unique object is only around
14.6k. The repeated gradient computation of the same object lead to an
inefficient training; (2) The false positive rate is high on background frames.
This is due to the distribution gap between training and evaluation. During
training, the model is only able to see the clean, stable, and labeled frames,
but the egocentric videos also have noisy, blurry, or unlabeled background
frames. To this end, we developed a more efficient and effective solution.
Concretely, we bring the training loop from ~15 days to less than 24 hours, and
we achieve 0.17% spatial-temporal AP, which is 31% higher than the baseline.
Our solution got the first ranking on the public leaderboard. Our code is
publicly available at https://github.com/facebookresearch/vq2d_cvpr.Comment: First place winning solution for VQ2D task in CVPR-2022 Ego4D
Challenge. Our code is publicly available at
https://github.com/facebookresearch/vq2d_cvp
Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization
This paper deals with the problem of localizing objects in image and video
datasets from visual exemplars. In particular, we focus on the challenging
problem of egocentric visual query localization. We first identify grave
implicit biases in current query-conditioned model design and visual query
datasets. Then, we directly tackle such biases at both frame and object set
levels. Concretely, our method solves these issues by expanding limited
annotations and dynamically dropping object proposals during training.
Additionally, we propose a novel transformer-based module that allows for
object-proposal set context to be considered while incorporating query
information. We name our module Conditioned Contextual Transformer or
CocoFormer. Our experiments show the proposed adaptations improve egocentric
query detection, leading to a better visual query localization system in both
2D and 3D configurations. Thus, we are able to improve frame-level detection
performance from 26.28% to 31.26 in AP, which correspondingly improves the VQ2D
and VQ3D localization scores by significant margins. Our improved context-aware
query object detector ranked first and second in the VQ2D and VQ3D tasks in the
2nd Ego4D challenge. In addition to this, we showcase the relevance of our
proposed model in the Few-Shot Detection (FSD) task, where we also achieve SOTA
results. Our code is available at
https://github.com/facebookresearch/vq2d_cvpr.Comment: We ranked first and second in the VQ2D and VQ3D tasks in the 2nd
Ego4D challeng
The genomic and bulked segregant analysis of \u3ci\u3eCurcuma alismatifolia\u3c/i\u3e revealed its diverse bract pigmentation
Compared with most flowers where the showy part comprises specialized leaves (petals) directly subtending the reproductive structures, most Zingiberaceae species produce showy ââflowersââ through modifications of leaves (bracts) subtending the true flowers throughout an inflorescence. Curcuma alismatifolia, belonging to the Zingiberaceae family, a plant species originating from Southeast Asia, has become increasingly popular in the flower market worldwide because of its varied and esthetically pleasing bracts produced in different cultivars. Here, we present the chromosome-scale genome assembly of C. alismatifolia ââChiang Mai Pinkââ and explore the underlying mechanisms of bract pigmentation. Comparative genomic analysis revealed C. alismatifolia contains a residual signal of wholegenome duplication. Duplicated genes, including pigment-related genes, exhibit functional and structural differentiation resulting in diverse bract colors among C. alismatifolia cultivars. In addition, we identified the key genes that produce different colored bracts in C. alismatifolia, such as F3\u275âH, DFR, ANS and several transcription factors for anthocyanin synthesis, as well as chlH and CAO in the chlorophyll synthesis pathway by conducting transcriptomic analysis, bulked segregant analysis using both DNA and RNA data, and population genomic analysis. This work provides data for understanding the mechanism of bract pigmentation and will accelerate breeding in developing novel cultivars with richly colored bracts in C. alismatifolia and related species. It is also important to understand the variation in the evolution of the Zingiberaceae family
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