Crossing at a Red Light: Behavior of Cyclists at Urban Intersections

To investigate the relationship between cyclist violation and waiting duration, the red-light running behavior of nonmotorized vehicles is examined at signalized intersections. Violation waiting duration is collected by video cameras and it is assigned as censored and uncensored data to distinguish between normal crossing and red-light running. A proportional hazard-based duration model is introduced, and variables revealing personal characteristics and traffic conditions are used to describe the effects of internal and external factors. Empirical results show that the red-light running behavior of cyclist is time dependent. Cyclist’s violating behavior represents positive duration dependence, that the longer the waiting time elapsed, the more likely cyclists would end the wait soon. About 32% of cyclists are at high risk of violation and low waiting time to cross the intersections. About 15% of all the cyclists are generally nonrisk takers who can obey the traffic rules after waiting for 95 seconds. The human factors and external environment play an important role in cyclists’ violation behavior. Minimizing the effects of unfavorable condition in traffic planning and designing may be an effective measure to enhance traffic safety

NOC: High-Quality Neural Object Cloning with 3D Lifting of Segment Anything

With the development of the neural field, reconstructing the 3D model of a target object from multi-view inputs has recently attracted increasing attention from the community. Existing methods normally learn a neural field for the whole scene, while it is still under-explored how to reconstruct a certain object indicated by users on-the-fly. Considering the Segment Anything Model (SAM) has shown effectiveness in segmenting any 2D images, in this paper, we propose Neural Object Cloning (NOC), a novel high-quality 3D object reconstruction method, which leverages the benefits of both neural field and SAM from two aspects. Firstly, to separate the target object from the scene, we propose a novel strategy to lift the multi-view 2D segmentation masks of SAM into a unified 3D variation field. The 3D variation field is then projected into 2D space and generates the new prompts for SAM. This process is iterative until convergence to separate the target object from the scene. Then, apart from 2D masks, we further lift the 2D features of the SAM encoder into a 3D SAM field in order to improve the reconstruction quality of the target object. NOC lifts the 2D masks and features of SAM into the 3D neural field for high-quality target object reconstruction. We conduct detailed experiments on several benchmark datasets to demonstrate the advantages of our method. The code will be released

Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning

Deception detection in conversations is a challenging yet important task, having pivotal applications in many fields such as credibility assessment in business, multimedia anti-frauds, and custom security. Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively. To address this issue, we introduce DOLOS\footnote {The name ``DOLOS" comes from Greek mythology.}, the largest gameshow deception detection dataset with rich deceptive conversations. DOLOS includes 1,675 video clips featuring 213 subjects, and it has been labeled with audio-visual feature annotations. We provide train-test, duration, and gender protocols to investigate the impact of different factors. We benchmark our dataset on previously proposed deception detection approaches. To further improve the performance by fine-tuning fewer parameters, we propose Parameter-Efficient Crossmodal Learning (PECL), where a Uniform Temporal Adapter (UT-Adapter) explores temporal attention in transformer-based architectures, and a crossmodal fusion module, Plug-in Audio-Visual Fusion (PAVF), combines crossmodal information from audio-visual features. Based on the rich fine-grained audio-visual annotations on DOLOS, we also exploit multi-task learning to enhance performance by concurrently predicting deception and audio-visual features. Experimental results demonstrate the desired quality of the DOLOS dataset and the effectiveness of the PECL. The DOLOS dataset and the source codes are available at https://github.com/NMS05/Audio-Visual-Deception-Detection-DOLOS-Dataset-and-Parameter-Efficient-Crossmodal-Learning/tree/main.Comment: 11 pages, 6 figure

Revisit to the yield ratio of triton and 3^3He as an indicator of neutron-rich neck emission

The neutron rich neck zone created in heavy ion reaction is experimentally probed by the production of the $A=3$ isobars. The energy spectra and angular distributions of triton and $^3$He are measured with the CSHINE detector in $^{86}$Kr +$^{208}$Pb reactions at 25 MeV/u. While the energy spectrum of $^{3}$He is harder than that of triton, known as "$^{3}$He-puzzle", the yield ratio $R({\rm t/^3He})$ presents a robust rising trend with the polar angle in laboratory. Using the fission fragments to reconstruct the fission plane, the enhancement of out-plane $R({\rm t/^3He})$ is confirmed in comparison to the in-plane ratios. Transport model simulations reproduce qualitatively the experimental trends, but the quantitative agreement is not achieved. The results demonstrate that a neutron rich neck zone is formed in the reactions. Further studies are called for to understand the clustering and the isospin dynamics related to neck formation

The substitution financing effect of suppliers’ trade credit on customers’ trade credit in China

This study investigates the substitution financing effect of suppliers’ trade credit on customers’ trade-credit using Chinese listed firms from 2009 to 2018. Results verify the substitution financing effect of suppliers’ trade credit on customers’ trade credit, indicating that firms with higher suppliers’ trade credit have lower customers’ trade credit. Moreover, suppliers’ trade-credit substitutes customers’ trade credit by alleviating financing constraints. Customer concentration weakens the substitution financing relation. Finally, the substitution financing effect of customers’ trade credit on bank credit is more pronounced than that of suppliers’ trade credit. As exogenous policy shock, the capital market liberalization has no significant impact on the substitution financing relation between heterogeneous trade credits. This study reveals that trade credit is heterogeneous rather than homogeneous. The substitution financing effect also exists in trade credit inside, which expands the existing literature’s understanding of trade credit and the substitution financing theory’s connotation

The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection

Some fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low. In response to the above issue, this study proposes a distributed Gaussian mixture cardinality jumping Markov-cardinalized probability hypothesis density (GM-JMNS-CPHD) filter based on a generalized inverse covariance intersection. The state estimation of the JMNS-CPHD filter combines the state evaluation of traditional CPHD filters with the state estimation of jump Markov systems, estimating the target state of multiple motion models without knowing the current motion models. The performances of the generalized covariance intersection (GCI)GCI-GM-JMNS-CPHD and generalized inverse covariance intersection (GICI)GICI-GM-JMNS-CPHD methods are evaluated via simulation results. The simulation results show that, compared with algorithms such as Sensor1, Sensor2, GCI-GM-CPHD, and GICI-GM-CPHD, this algorithm has smaller optimal subpattern assignment (OSPA) errors and a higher fusion accuracy

Natural Product 2-Oxokolavenol Is a Novel FXR Agonist

Acetaminophen (APAP) toxicity is a common cause of hepatic failure, and the development of effective therapy is still urgently needed. Farnesoid X receptor (FXR), a member of the nuclear receptor superfamily, has been identified as a master gene for regulating enterohepatic metabolic homeostasis and has proven to be a promising drug target for various liver diseases. Through high-throughput chemical screening, the natural product 2-oxokolavenol was identified as a novel and selective FXR agonist. Further investigations revealed that 2-oxokolavenol exerts therapeutic efficacy against APAP-induced hepatocyte damage in an FXR-dependent manner. Mechanistically, 2-oxokolavenol forms two hydrogen bonds with M265 and Y369 of human FXR to compatibly fit into the ligand binding pocket of FXR, which potently leads to the recruitment of multiple co-regulators and selectively induces the transcriptional activity of FXR. Our findings thus not only reveal the direct target of natural product 2-oxokolavenol, but also provide a promising hit compound for the design of new FXR modulators with potential clinical value