STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction task is an essential component of intelligent systems, and its applications include but are not limited to autonomous driving, robot navigation, and anomaly detection of monitoring systems. Due to the diversity of motion behaviors and the complex social interactions among pedestrians, accurately forecasting the future trajectory of pedestrians is challenging. Existing approaches commonly adopt GANs or CVAEs to generate diverse trajectories. However, GAN-based methods do not directly model data in a latent space, which makes them fail to have full support over the underlying data distribution; CVAE-based methods optimize a lower bound on the log-likelihood of observations, causing the learned distribution to deviate from the underlying distribution. The above limitations make existing approaches often generate highly biased or unnatural trajectories. In this paper, we propose a novel generative flow based framework with dual graphormer for pedestrian trajectory prediction (STGlow). Different from previous approaches, our method can more accurately model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors. Besides, our method has clear physical meanings to simulate the evolution of human motion behaviors, where the forward process of the flow gradually degrades the complex motion behavior into a simple behavior, while its reverse process represents the evolution of a simple behavior to the complex motion behavior. Further, we introduce a dual graphormer combining with the graph structure to more adequately model the temporal dependencies and the mutual spatial interactions. Experimental results on several benchmarks demonstrate that our method achieves much better performance compared to previous state-of-the-art approaches.Comment: 12 pages, 8 figure

Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision

Predicting human motion behavior in a crowd is important for many applications, ranging from the natural navigation of autonomous vehicles to intelligent security systems of video surveillance. All the previous works model and predict the trajectory with a single resolution, which is rather inefficient and difficult to simultaneously exploit the long-range information (e.g., the destination of the trajectory), and the short-range information (e.g., the walking direction and speed at a certain time) of the motion behavior. In this paper, we propose a temporal pyramid network for pedestrian trajectory prediction through a squeeze modulation and a dilation modulation. Our hierarchical framework builds a feature pyramid with increasingly richer temporal information from top to bottom, which can better capture the motion behavior at various tempos. Furthermore, we propose a coarse-to-fine fusion strategy with multi-supervision. By progressively merging the top coarse features of global context to the bottom fine features of rich local context, our method can fully exploit both the long-range and short-range information of the trajectory. Experimental results on several benchmarks demonstrate the superiority of our method.Comment: 9 pages, 5 figure

Indocyanine Green Loaded Reduced Graphene Oxide for In Vivo Photoacoustic/Fluorescence Dual-Modality Tumor Imaging

Multimodality imaging based on multifunctional nanocomposites holds great promise to fundamentally augment the capability of biomedical imaging. Specifically, photoacoustic and fluorescence dual-modality imaging is gaining much interest because of their non-invasiveness and the complementary nature of the two modalities in terms of imaging resolution, depth, sensitivity, and speed. Herein, using a green and facile method, we synthesize indocyanine green (ICG) loaded, polyethylene glycol (PEG) ylated, reduced nano-graphene oxide nanocomposite (rNGO-PEG/ICG) as a new type of fluorescence and photoacoustic dual-modality imaging contrast. The nanocomposite is shown to have minimal toxicity and excellent photoacoustic/fluorescence signals both in vitro and in vivo. Compared with free ICG, the nanocomposite is demonstrated to possess greater stability, longer blood circulation time, and superior passive tumor targeting capability. In vivo study shows that the circulation time of rNGO-PEG/ICG in the mouse body can sustain up to 6 h upon intravenous injection; while after 1 day, no obvious accumulation of rNGO-PEG/ICG is found in any major organs except the tumor regions. The demonstrated high fluorescence/photoacoustic dual contrasts, together with its low toxicity and excellent circulation life time, suggest that the synthesized rNGO-PEG/ICG can be a promising candidate for further translational studies on both the early diagnosis and image-guided therapy/surgery of cancer.11248Ysciescopu

Tumor-Targeted Fluorescence Imaging and Mechanisms of Tumor Cell-Derived Carbon Nanodots

An ideal cancer diagnostic probe should possess precise tumor-targeted accumulation with negligible sojourn in normal tissues. Herein, tumor cell-derived carbon nanodots (C-CNDU87 and C-CNDHepG2) about 3~7 nm were prepared by a solvothermal method with stable fluorescence and negligible cytotoxicity. More interestingly, due to the differences in gene expression of cancers, C-CND structurally mimicked the corresponding precursors during carbonization in which carbon nanodots were functionalized with α-amino and carboxyl groups with different densities on their edges. With inherent homology and homing effect, C-CND were highly enriched in precursor tumor tissues. Mechanistic studies showed that under the mediation of the original configuration of α-amino and carboxyl groups, C-CND specifically bound to the large neutral amino acid transporter 1 (LAT1, overexpressed in cancer cells), achieving specific tumor fluorescence imaging. This work provided a new vision about tumor cell architecture-mimicked carbon nanodots for tumor-targeted fluorescence imaging

Tuzoia specimens studied in this paper

Tuzoia specimens studied in this pape

L-H

Bivariate graph showing size distribution of Tuzoia spp. from the Balang and Kaili biotas (T. sinensis Pan, 1957 in purple; T. lazizhaiensis sp. nov. in blue, and T. bispinosa Yuan & Zhao, 1999 in red); valve length (L) on x-axis, and valve height (H) on y-axis

Performance of one-way carsharing systems under combined strategy of pricing and relocations

A bilevel nonlinear mathematical programing model is formulated to determine the optimal pricing and operator-based relocations in a one-way station-based carsharing system in competition with private cars. In the upper level, the carsharing operator determines the vehicle fleet, prices, and relocation operations with the objective of maximizing profits, considering the potential reaction of travelers. In the lower level, travelers choose travel modes from a cost-minimization perspective. Travel utilities are calculated through a logit model. The Karush–Kuhn–Tucker conditions are used to transform the bilevel model into a single-level model and then a genetic algorithm is proposed to solve it. Computational tests in four different scenarios show the combined strategy is the best one. The four scenarios are base, relocations, dynamic pricing, and a combination of relocations and pricing separately. The combined strategy can make the best trade-offs between the operator’s profit and the travelers’ cost.Accepted author manuscriptTransport and Plannin