Speed estimation using single loop detector outputs

Flow speed describes general traffic operation conditions on a segment of roadway. It is also used to diagnose special conditions such as congestion and incidents. Accurate speed estimation plays a critical role in traffic management or traveler information systems. Data from loop detectors have been primary sources for traffic information, and single loop are the predominant loop detector type in many places. However, single loop detectors do not produce speed output. Therefore, speed estimation using single loop outputs has been an important issue for decades. This dissertation research presents two methodologies for speed estimation using single loop outputs. Based on findings from past studies and examinations in this research, it is verified that speed estimation is a nonlinear system under various traffic conditions. Thus, a methodology of using Unscented Kalman Filter (UKF) is first proposed for such a system. The UKF is a parametric filtering technique that is suitable for nonlinear problems. Through an Unscented Transformation (UT), the UKF is able to capture the posterior mean and covariance of a Gaussian random variable accurately for a nonlinear system without linearization. This research further shows that speed estimation is a nonlinear non-Gaussian system. However, Kalman filters including the UKF are established based on the Gaussian assumption. Thus, another nonlinear filtering technique for non-Gaussian systems, the Particle Filter (PF), is introduced. By combining the strengths of both the PF and the UKF, the second speed estimation methodology—Unscented Particle Filter (UPF) is proposed for speed estimation. The use of the UPF avoids the limitations of the UKF and the PF. Detector data are collected from multiple freeway locations and the microscopic traffic simulation program CORSIM. The developed methods are applied to the collected data for speed estimation. The results show that both proposed methods have high accuracies of speed estimation. Between the UKF and the UPF, the UPF has better performance but has higher computation cost. The improvement of speed estimation will benefit real-time traffic operations by improving the performance of applications such as travel time estimation using a series of single loops in the network, incident detection, and large truck volume estimation. Therefore, the work enables traffic analysts to use single loop outputs in a more cost-effective way

Delving into Crispness: Guided Label Refinement for Crisp Edge Detection

Learning-based edge detection usually suffers from predicting thick edges. Through extensive quantitative study with a new edge crispness measure, we find that noisy human-labeled edges are the main cause of thick predictions. Based on this observation, we advocate that more attention should be paid on label quality than on model design to achieve crisp edge detection. To this end, we propose an effective Canny-guided refinement of human-labeled edges whose result can be used to train crisp edge detectors. Essentially, it seeks for a subset of over-detected Canny edges that best align human labels. We show that several existing edge detectors can be turned into a crisp edge detector through training on our refined edge maps. Experiments demonstrate that deep models trained with refined edges achieve significant performance boost of crispness from 17.4% to 30.6%. With the PiDiNet backbone, our method improves ODS and OIS by 12.2% and 12.6% on the Multicue dataset, respectively, without relying on non-maximal suppression. We further conduct experiments and show the superiority of our crisp edge detection for optical flow estimation and image segmentation.Comment: Accepted by TI

Mining the Virgin Land of Neurotoxicology: A Novel Paradigm of Neurotoxic Peptides Action on Glycosylated Voltage-Gated Sodium Channels

Voltage-gated sodium channels (VGSCs) are important membrane protein carrying on the molecular basis for action potentials (AP) in neuronal firings. Even though the structure-function studies were the most pursued spots, the posttranslation modification processes, such as glycosylation, phosphorylation, and alternative splicing associating with channel functions captured less eyesights. The accumulative research suggested an interaction between the sialic acids chains and ion-permeable pores, giving rise to subtle but significant impacts on channel gating. Sodium channel-specific neurotoxic toxins, a family of long-chain polypeptides originated from venomous animals, are found to potentially share the binding sites adjacent to glycosylated region on VGSCs. Thus, an interaction between toxin and glycosylated VGSC might hopefully join the campaign to approach the role of glycosylation in modulating VGSCs-involved neuronal network activity. This paper will cover the state-of-the-art advances of researches on glycosylation-mediated VGSCs function and the possible underlying mechanisms of interactions between toxin and glycosylated VGSCs, which may therefore, fulfill the knowledge in identifying the pharmacological targets and therapeutic values of VGSCs

Effects of acidification on nitrification and associated nitrous oxide emission in estuarine and coastal waters

In the context of an increasing atmospheric carbon dioxide (CO2) level, acidification of estuarine and coastal waters is greatly exacerbated by land-derived nutrient inputs, coastal upwelling, and complex biogeochemical processes. A deeper understanding of how nitrifiers respond to intensifying acidification is thus crucial to predict the response of estuarine and coastal ecosystems and their contribution to global climate change. Here, we show that acidification can significantly decrease nitrification rate but stimulate generation of byproduct nitrous oxide (N2O) in estuarine and coastal waters. By varying CO2 concentration and pH independently, an expected beneficial effect of elevated CO2 on activity of nitrifiers (“CO2-fertilization” effect) is excluded under acidification. Metatranscriptome data further demonstrate that nitrifiers could significantly up-regulate gene expressions associated with intracellular pH homeostasis to cope with acidification stress. This study highlights the molecular underpinnings of acidification effects on nitrification and associated greenhouse gas N2O emission, and helps predict the response and evolution of estuarine and coastal ecosystems under climate change and human activities.publishedVersio

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with arXiv:2306.16636 by other author

Magnetic Control of Valley Pseudospin in Monolayer WSe2

Local energy extrema of the bands in momentum space, or valleys, can endow electrons in solids with pseudo-spin in addition to real spin. In transition metal dichalcogenides this valley pseudo-spin, like real spin, is associated with a magnetic moment which underlies the valley-dependent circular dichroism that allows optical generation of valley polarization, intervalley quantum coherence, and the valley Hall effect. However, magnetic manipulation of valley pseudospin via this magnetic moment, analogous to what is possible with real spin, has not been shown before. Here we report observation of the valley Zeeman splitting and magnetic tuning of polarization and coherence of the excitonic valley pseudospin, by performing polarization-resolved magneto-photoluminescence on monolayer WSe2. Our measurements reveal both the atomic orbital and lattice contributions to the valley orbital magnetic moment; demonstrate the deviation of the band edges in the valleys from an exact massive Dirac fermion model; and reveal a striking difference between the magnetic responses of neutral and charged valley excitons which is explained by renormalization of the excitonic spectrum due to strong exchange interactions

On-Road Bus Emission Comparison for Diverse Locations and Fuel Types in Real-World Operation Conditions

Urban buses have energy and environmental impacts because they are mostly equipped with heavy-duty diesel engines, having higher emission factors and pollution levels. This study proposed a mean distribution deviation (MDD) method to identify bus pollutant emissions including CO, CO2, HC, and NOX at road sections, intersections, and bus stops for different fuel types; and explore the changes in emissions for different locations in the road sections, bus stops, and intersection influence areas. Bus speed, acceleration, and emissions data were collected from four fuel types in China. For different locations and fuel types, the differences in emissions were all statistically significant. MDD values for different locations indicated that there were more obvious differences in emissions between road sections and intersections. In addition, heat maps were applied in this study to better understand changes in bus emissions for different locations in the bus stop influence areas, intersection influence areas, and road sections

Nonlinear effects of built environment on intermodal transit trips considering spatial heterogeneity

Understanding intermodal transit trip generation is essential to increase the share of long-distance transit trips among urban transportation systems. Although many studies have investigated trip generation, the existing literature still has limited evidence about intermodal transit trips and their nonlinear associations with the built environment over space. This study proposes a decision framework to identify the mean relative importance of socioeconomic attributes and built environment elements as well as their effective ranges and threshold effects at the spatial scale. An empirical study was conducted using large-scale smart card data in Nanjing, China. The modeling results indicate the proposed hybrid model can significantly enhance the predictive power, as compared to traditional models. The mean relative importance of the distance to the nearest metro station ranks the highest among all attributes studied, followed by bus route and land use mix. The effective ranges and thresholds of most built environment elements vary spatially with the upper quartile zones being the largest