Environmental change resulting from intensified human interventions and climate change has impacted the hydrological function of many large river systems, largely altering the production and transport of run‐off and sediment. It is thus vital to quantitatively evaluate the influence of climate change and human activities on streamflow and sediment discharge. Water balance equations, hydrological models, and comparative analyses are commonly used to fulfil this need. Double mass curves (DMCs), being one useful method for comparative analyses, are characterized by low data requirements and high transferability, and thus more practical than water balance equations and hydrological models for hydrologic benefit evaluations. However, the detailed derivation procedure of the DMC has, to date, yet been described in literature. Moreover, in previous studies, changing points of the DMC were determined either rather empirically or as the changing point of streamflow or sediment discharge (i.e., precipitation was not considered). Hence, the changing point detected may be subject to inaccuracies. This paper, for the first time, comprehensively detailed the derivation procedure of the DMC; a new way was proposed to quantitatively examine the changing point of the DMC; an example was also given to demonstrate the use of the DMC in the hydrologic benefit evaluation. It is hopeful that the method given in our paper will be widely adopted by future studies as a standard procedure to derive and use the DMC.

This paper presents a robust method designed to detect and track a road lane from images provided by an on-board monocular monochromatic camera. The proposed lane detection approach makes use of a deformable template model to the expected lane boundaries in the image, a maximum a posteriori formulation of the lane detection problem, and a Tabu search algorithm to maximize the posterior density. The model parameters completely determine the position of the host vehicle within the lane, its heading direction and the local structure of the lane ahead. Based on the lane detection result in the first frame of the image sequence, a particle filter, having multiple hypotheses capability and performing nonlinear filtering, is used to recursively estimate the lane shape and the vehicle position in the sequence of consecutive images. Experimental results reveal that the proposed lane detection and tracking method is robust against broken lane markings, curved lanes, shadows, strong distracting edges, and occlusions in the captured road images.

A series of oxacalix[2]arene[2]triazines bearing one anionic head such as carboxylate, sulfonate, sulfate, and phosphate were synthesized. With the anionic head and complementary V-shape electron-deficient cavity, these macrocycles can serve as dual building units, and their anion-π directed self-assembly was investigated. The formation of oligomeric aggregates in solution was revealed by nuclear magnetic resonance, dynamic light scattering, and mass spectroscopy. Crystal structures further confirmed chainlike assembly formation directed by anion-π interactions.

This paper explores the effectiveness of applying a deep learning based method to segment the amniotic fluid and fetal tissues in fetal ultrasound (US) images. The deeply learned model firstly encodes the input image into down scaled feature maps by convolution and pooling structures, then up-scale the feature maps to confidence maps by corresponded un-pooling and convolution layers. Additional convolution layers with 1×1 sized kernels are adopted to enhance the feature representations, which could be used to further improve the discriminative learning of our model. We effectively update the weights of the network by fine-tuning on part of the layers from a pre-trained model. By conducting experiments using clinical data, the feasibility of our proposed approach is compared and discussed. The result proves that this work achieves satisfied results for segmentation of specific anatomical structures from US images.

No abstract

No abstract

Detecting human bodies in highly crowded scenes is a challenging problem. Two main reasons result in such a problem: 1). weak visual cues of heavily occluded instances can hardly provide sufficient information for accurate detection; 2). heavily occluded instances are easier to be suppressed by Non-Maximum-Suppression (NMS). To address these two issues, we introduce a variant of two-stage detectors called PS-RCNN. PS-RCNN first detects slightly/none occluded objects by an R-CNN [1] module (referred as P-RCNN), and then suppress the detected instances by human-shaped masks so that the features of heavily occluded instances can stand out. After that, PS-RCNN utilizes another R-CNN module specialized in heavily occluded human detection (referred as S-RCNN) to detect the rest missed objects by P-RCNN. Final results are the ensemble of the outputs from these two R-CNNs. Moreover, we introduce a High Resolution RoI Align (HRRA) module to retain as much of fine-grained features of visible parts of the heavily occluded humans as possible. Our PS-RCNN significantly improves recall and AP by 4.49% and 2.92% respectively on CrowdHuman [2], compared to the baseline. Similar improvements on Widerperson [3] are also achieved by the PS-RCNN.

No abstract