EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting

Capturing high-dimensional social interactions and feasible futures is essential for predicting trajectories. To address this complex nature, several attempts have been devoted to reducing the dimensionality of the output variables via parametric curve fitting such as the B\'ezier curve and B-spline function. However, these functions, which originate in computer graphics fields, are not suitable to account for socially acceptable human dynamics. In this paper, we present EigenTrajectory ($\mathbb{ET}$), a trajectory prediction approach that uses a novel trajectory descriptor to form a compact space, known here as $\mathbb{ET}$ space, in place of Euclidean space, for representing pedestrian movements. We first reduce the complexity of the trajectory descriptor via a low-rank approximation. We transform the pedestrians' history paths into our $\mathbb{ET}$ space represented by spatio-temporal principle components, and feed them into off-the-shelf trajectory forecasting models. The inputs and outputs of the models as well as social interactions are all gathered and aggregated in the corresponding $\mathbb{ET}$ space. Lastly, we propose a trajectory anchor-based refinement method to cover all possible futures in the proposed $\mathbb{ET}$ space. Extensive experiments demonstrate that our EigenTrajectory predictor can significantly improve both the prediction accuracy and reliability of existing trajectory forecasting models on public benchmarks, indicating that the proposed descriptor is suited to represent pedestrian behaviors. Code is publicly available at https://github.com/inhwanbae/EigenTrajectory .Comment: Accepted at ICCV 202

Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction

Language models have demonstrated impressive ability in context understanding and generative performance. Inspired by the recent success of language foundation models, in this paper, we propose LMTraj (Language-based Multimodal Trajectory predictor), which recasts the trajectory prediction task into a sort of question-answering problem. Departing from traditional numerical regression models, which treat the trajectory coordinate sequence as continuous signals, we consider them as discrete signals like text prompts. Specially, we first transform an input space for the trajectory coordinate into the natural language space. Here, the entire time-series trajectories of pedestrians are converted into a text prompt, and scene images are described as text information through image captioning. The transformed numerical and image data are then wrapped into the question-answering template for use in a language model. Next, to guide the language model in understanding and reasoning high-level knowledge, such as scene context and social relationships between pedestrians, we introduce an auxiliary multi-task question and answering. We then train a numerical tokenizer with the prompt data. We encourage the tokenizer to separate the integer and decimal parts well, and leverage it to capture correlations between the consecutive numbers in the language model. Lastly, we train the language model using the numerical tokenizer and all of the question-answer prompts. Here, we propose a beam-search-based most-likely prediction and a temperature-based multimodal prediction to implement both deterministic and stochastic inferences. Applying our LMTraj, we show that the language-based model can be a powerful pedestrian trajectory predictor, and outperforms existing numerical-based predictor methods. Code is publicly available at https://github.com/inhwanbae/LMTrajectory .Comment: Accepted at CVPR 202

SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model

There are five types of trajectory prediction tasks: deterministic, stochastic, domain adaptation, momentary observation, and few-shot. These associated tasks are defined by various factors, such as the length of input paths, data split and pre-processing methods. Interestingly, even though they commonly take sequential coordinates of observations as input and infer future paths in the same coordinates as output, designing specialized architectures for each task is still necessary. For the other task, generality issues can lead to sub-optimal performances. In this paper, we propose SingularTrajectory, a diffusion-based universal trajectory prediction framework to reduce the performance gap across the five tasks. The core of SingularTrajectory is to unify a variety of human dynamics representations on the associated tasks. To do this, we first build a Singular space to project all types of motion patterns from each task into one embedding space. We next propose an adaptive anchor working in the Singular space. Unlike traditional fixed anchor methods that sometimes yield unacceptable paths, our adaptive anchor enables correct anchors, which are put into a wrong location, based on a traversability map. Finally, we adopt a diffusion-based predictor to further enhance the prototype paths using a cascaded denoising process. Our unified framework ensures the generality across various benchmark settings such as input modality, and trajectory lengths. Extensive experiments on five public benchmarks demonstrate that SingularTrajectory substantially outperforms existing models, highlighting its effectiveness in estimating general dynamics of human movements. Code is publicly available at https://github.com/inhwanbae/SingularTrajectory .Comment: Accepted at CVPR 202

Genome-based species-specific primers for rapid identification of six species of Lactobacillus acidophilus group using multiplex PCR.

Many Lactobacillus species are frequently isolated from dairy products, animal guts, and the vaginas of healthy women. However, sequencing-based identification of isolated Lactobacillus strain is time/cost-consuming and lobor-intensive. In this study, we developed a multiplex PCR method to distinguish six closely related species in the Lactobacillus acidophilus group (L. gasseri, L. acidophilus, L. helveticus, L. jensenii, L. crispatus, and L. gallinarum), which is based on species-specific primer sets. Altogether, 86 genomes of 9 Lactobacillus species from the National Center of Biotechnology Information (NCBI) database were compared to detect species-specific genes and design six species-specific primer sets. The PCR conditions of the individual primer sets were optimized via gradient PCR methods. A final multiplex PCR condition was also optimized for a mixture of all six primer sets mixed. When identifying a single strain, the optimized multiplex PCR method can specifically detect one of the six species, but no band was amplified at least from the other Lactobacillus and Enterococcus species. These results indicated that species-specific primer sets designed from the genome comparison could identify one strain within the six Lactobacillus species by a single PCR reaction. Using the method described here, we will be able to save time, cost, and labor during species identification and screening of commercially important probiotic lactobacilli

Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is one of the important tasks required for autonomous navigation and social robots in human environments. Previous studies focused on estimating social forces among individual pedestrians. However, they did not consider the social forces of groups on pedestrians, which results in over-collision avoidance problems. To address this problem, we present a Disentangled Multi-Relational Graph Convolutional Network (DMRGCN) for socially entangled pedestrian trajectory prediction. We first introduce a novel disentangled multi-scale aggregation to better represent social interactions, among pedestrians on a weighted graph. For the aggregation, we construct the multi-relational weighted graphs based on distances and relative displacements among pedestrians. In the prediction step, we propose a global temporal aggregation to alleviate accumulated errors for pedestrians changing their directions. Finally, we apply DropEdge into our DMRGCN to avoid the over-fitting issue on relatively small pedestrian trajectory datasets. Through the effective incorporation of the three parts within an end-to-end framework, DMRGCN achieves state-of-the-art performances on a variety of challenging trajectory prediction benchmarks

A Set of Control Points Conditioned Pedestrian Trajectory Prediction

Predicting the trajectories of pedestrians in crowded conditions is an important task for applications like autonomous navigation systems. Previous studies have tackled this problem using two strategies. They (1) infer all future steps recursively, or (2) predict the potential destinations of pedestrians at once and interpolate the intermediate steps to arrive there. However, these strategies often suffer from the accumulated errors of the recursive inference, or restrictive assumptions about social relations in the intermediate path. In this paper, we present a graph convolutional network-based trajectory prediction. Firstly, we propose a control point prediction that divides the future path into three sections and infers the intermediate destinations of pedestrians to reduce the accumulated error. To do this, we construct multi-relational weighted graphs to account for their physical and complex social relations. We then introduce a trajectory refinement step based on a spatio-temporal and multi-relational graph. By considering the social interactions between neighbors, better prediction results are achievable. In experiments, the proposed network achieves state-of-the-art performance on various real-world trajectory prediction benchmarks

Functional Characterization of Cinnamyl Alcohol Dehydrogenase during Developmental Stages and under Various Stress Conditions in Kenaf (Hibiscus cannabinus L.)

In this study, the entire gene encoding cinnamyl alcohol dehydrogenase in kenaf (HcCAD2) was cloned and characterized. CAD is a key enzyme in the last step of lignin biosynthesis. The full-length HcCAD ortholog is composed of a 1,074-bp open reading frame (ORF) encoding 357 amino acids (KM044582). BlastP and a phylogenetic study revealed that the deduced amino acid sequences share the highest similarity with Gossypium hirsutum (ABZ01817) (89%). Upon real-time PCR analysis, HcCAD1 (HM151380) and HcCAD2 were highly up-regulated in 4-week-old stem and mature flower tissues, which was matched with histochemical staining and lignin component analysis. The expression patterns of the two genes differed in response to wound, cold, NaCl, SA, H2O2, ABA, MeJA, and drought. CAD enzyme activity was measured with various aldehydes as substrates to form corresponding alcohols. The results indicated that the preferred substrates were coniferyl and sinapyl aldehydes with high catalytic efficiency

Non-Probability Sampling Network for Stochastic Human Trajectory Prediction

Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to infer a finite set of future trajectories. The inferred trajectories are based on observation paths and the latent vectors of potential decisions of pedestrians in the inference step. However, stochastic approaches provide varying results for the same data and parameter settings, due to the random sampling of the latent vector. In this paper, we analyze the problem by reconstructing and comparing probabilistic distributions from prediction samples and socially-acceptable paths, respectively. Through this analysis, we observe that the inferences of all stochastic models are biased toward the random sampling, and fail to generate a set of realistic paths from finite samples. The problem cannot be resolved unless an infinite number of samples is available, which is infeasible in practice. We introduce that the Quasi-Monte Carlo (QMC) method, ensuring uniform coverage on the sampling space, as an alternative to the conventional random sampling. With the same finite number of samples, the QMC improves all the multimodal prediction results. We take an additional step ahead by incorporating a learnable sampling network into the existing networks for trajectory prediction. For this purpose, we propose the Non-Probability Sampling Network (NPSN), a very small network (~5K parameters) that generates purposive sample sequences using the past paths of pedestrians and their social interactions. Extensive experiments confirm that NPSN can significantly improve both the prediction accuracy (up to 60%) and reliability of the public pedestrian trajectory prediction benchmark. Code is publicly available at https://github.com/inhwanbae/NPSN .Comment: Accepted at CVPR 202

An Urban Autodriving Algorithm Based on a Sensor-Weighted Integration Field with Deep Learning

This paper proposes two algorithms for adaptive driving in urban environments: The first uses vision deep learning, which is named the sparse spatial convolutional neural network (SSCNN); and the second uses a sensor integration algorithm, named the sensor-weighted integration field (SWIF). These algorithms utilize three kinds of sensors, namely vision, Light Detection and Range (LiDAR), and GPS sensors, and decide critical motions for autonomous vehicle, such as steering angles and vehicle speed. SSCNN, which is used for lane recognition, has 2.7 times faster processing speed than the existing spatial CNN method. Additionally, the dataset for SSCNN was constructed by considering both normal and abnormal driving in 7 classes. Thus, lanes can be recognized by extending lanes for special characteristics in urban settings, in which the lanes can be obscured or erased, or the vehicle can drive in any direction. SWIF generates a two-dimensional matrix, in which elements are weighted by integrating both the object data from LiDAR and waypoints from GPS based on detected lanes. These weights are the integers, indicating the degree of safety. Based on the field formed by SWIF, the safe trajectories for two vehicles’ motions, steering angles, and vehicle speed are generated by applying the cost field. Additionally, to flexibly follow the desired steering angle and vehicle speed, the Proportional-Integral-Differential (PID) control is moderated by an integral anti-windup scheme. Consequently, as the dataset considers characteristics of the urban environment, SSCNN is able to be adopted for lane recognition on urban roads. The SWIF algorithm is also useful for flexible driving owing to the high efficiency of its sensor integration, including having a resolution of 2 cm per pixel and speed of 24 fps. Thus, a vehicle can be successfully maneuvered with minimized steering angle change, without lane or route departure, and without obstacle collision in the presence of diverse disturbances in urban road conditions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.TRU

Multiple Reaction Monitoring Mode Based Liquid Chromatography-Mass Spectrometry Method for Simultaneous Quantification of Brassinolide and Other Plant Hormones Involved in Abiotic Stresses

Plant hormones are the key regulators of adaptive stress response. Abiotic stresses such as drought and salt are known to affect the growth and productivity of plants. It is well known that the levels of plant hormones such as zeatin (ZA), abscisic acid (ABA), salicylic acid (SA), jasmonic acid (JA), and brassinolide (BR) fluctuate upon abiotic stress exposure. At present, there is not any single suitable liquid chromatography-mass spectrometry (LC-MS) method for simultaneous analysis of BR and other plant hormones involved in abiotic stresses. In the present study, we developed a simple, sensitive, and rapid method for simultaneous analysis of five major plant hormones, ZA, ABA, JA, SA, and BR, which are directly or indirectly involved in drought and salt stresses. The optimized extraction procedure was simple and easy to use for simultaneous measurement of these plant hormones in Arabidopsis thaliana. The developed method is highly reproducible and can be adapted for simultaneous measurement of changes in plant hormones (ZA, ABA, JA, SA, and BR) in response to abiotic stresses in plants like A. thaliana and tomato