ESVIO: Event-based Stereo Visual Inertial Odometry

Event cameras that asynchronously output low-latency event streams provide great opportunities for state estimation under challenging situations. Despite event-based visual odometry having been extensively studied in recent years, most of them are based on monocular and few research on stereo event vision. In this paper, we present ESVIO, the first event-based stereo visual-inertial odometry, which leverages the complementary advantages of event streams, standard images and inertial measurements. Our proposed pipeline achieves temporal tracking and instantaneous matching between consecutive stereo event streams, thereby obtaining robust state estimation. In addition, the motion compensation method is designed to emphasize the edge of scenes by warping each event to reference moments with IMU and ESVIO back-end. We validate that both ESIO (purely event-based) and ESVIO (event with image-aided) have superior performance compared with other image-based and event-based baseline methods on public and self-collected datasets. Furthermore, we use our pipeline to perform onboard quadrotor flights under low-light environments. A real-world large-scale experiment is also conducted to demonstrate long-term effectiveness. We highlight that this work is a real-time, accurate system that is aimed at robust state estimation under challenging environments

BIG TRANSITIONS IN A SMALL FISHING VILLAGE:Late Preceramic Life in Huaca Negra, Virú Valley, Peru

Focusing on the data unearthed from the 2015 excavation in Huaca Negra, this dissertation aims to illustrate early human occupation in the Virú Valley to answer three research questions. (1) How can we add to our knowledge of the Late Preceramic Period and its transition to the Initial Period in the north coast of Peru, mainly in the Virú Valley and neighboring valleys? (2) Are there diachronic changes in economic activities? How do they shed light on possible social change? (3) How does an analytical perspective at the scope of the community help to address overlaps between public and domestic aspects of social life, and to enable a better understanding of early Andean societies? The fruitful results from the work at Huaca Negra provide new evidence for answering the abovementioned questions. First of all, the dating confirms that this site was continuously occupied between 5,000 to 3,200 CalBP, forming a rare case of uninterrupted cultural deposits from the Late Preceramic to Initial Period. Absolute dates and a detailed study of stratigraphy enable the reconstruction of four occupation phases, the foundation for diachronic comparison. Secondly, three interconvertible forms of “capital,” economic, cultural, and social capital, constitute the framework for analyzing unearthed materials and for assessing the nature of activities in Huaca Negra. Current data suggests that economic capital, in the form of subsistence, witnesses the most dramatic change: the importance of fishing activity declines while shellfish collecting becomes more significant over time. Subtle changes can also be discerned in the other two categories. Both the importance of cultural capital, in the form of craft production, and social capital, in the form of exotic goods, increase slightly, and there are more exotic goods being incorporated into people’s daily life in the later context. Through the examination of material remains and archaeological contexts, I suggest that two traditionally dichotomized social spheres, the public and the domestic, are juxtaposed in the same spatial contexts at Huaca Negra. This dissertation thus takes a “community” scope that encompasses both spheres in order to reveal the overall lifeways in this long-term occupied fishing village

Digital Nudging for Online Social Sharing: Evidence from A Randomized Field Experiment

This study investigates the effectiveness of digital nudging for users’ social sharing of online platform content. In collaboration with a leading career and education online platform, we conducted a large-scale randomized experiment of digital nudging using website popups. Grounding on the Social Capital Theory and the individual motivation mechanism, we proposed and tested four kinds of nudging messages: simple request, monetary incentive, relational capital, and cognitive capital. We find that nudging messages with monetary incentive, relational and cognitive capital framings lead to increase in social sharing behavior, while nudging message with simple request decreases social sharing, comparing to the control group without nudging. This study contributes to the prior research on digital nudging by providing causal evidence of effective nudging for online social sharing behavior. The findings of this study also provide valuable guidelines for the optimal design of online platforms to effectively nudge/encourage social sharing in practice

PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features

Event cameras are motion-activated sensors that capture pixel-level illumination changes instead of the intensity image with a fixed frame rate. Compared with the standard cameras, it can provide reliable visual perception during high-speed motions and in high dynamic range scenarios. However, event cameras output only a little information or even noise when the relative motion between the camera and the scene is limited, such as in a still state. While standard cameras can provide rich perception information in most scenarios, especially in good lighting conditions. These two cameras are exactly complementary. In this paper, we proposed a robust, high-accurate, and real-time optimization-based monocular event-based visual-inertial odometry (VIO) method with event-corner features, line-based event features, and point-based image features. The proposed method offers to leverage the point-based features in the nature scene and line-based features in the human-made scene to provide more additional structure or constraints information through well-design feature management. Experiments in the public benchmark datasets show that our method can achieve superior performance compared with the state-of-the-art image-based or event-based VIO. Finally, we used our method to demonstrate an onboard closed-loop autonomous quadrotor flight and large-scale outdoor experiments. Videos of the evaluations are presented on our project website: https://b23.tv/OE3QM6

Laser facilitates vaccination

Development of novel vaccine deliveries and vaccine adjuvants is of great importance to address the dilemma that the vaccine field faces: to improve vaccine efficacy without compromising safety. Harnessing the specific effects of laser on biological systems, a number of novel concepts have been proposed and proved in recent years to facilitate vaccination in a safer and more efficient way. The key advantage of using laser technology in vaccine delivery and adjuvantation is that all processes are initiated by physical effects with no foreign chemicals administered into the body. Here, we review the recent advances in using laser technology to facilitate vaccine delivery and augment vaccine efficacy as well as the underlying mechanisms

Research on Passenger Flow Control Plans for a Metro Station Based on Social Force Model

To better utilise the service capacity of the limited facilities of a metro station, as well as ensure safety and transport efficiency during peak hours, a large passenger flow control plan is studied through theoretical analysis and numerical simulation. Firstly, by passenger data collection and data analysis, the characteristics of the inbound and outbound passenger flow of a T metro station are analysed. Secondly, AnyLogic evacuation simulation models for the T Station during peak hours, peak hours without/with passenger flow control are established based on real passenger flow data as well as the station structures and layouts by using the AnyLogic software. The results show that there are no obvious congestions in the station hall, and the travel delay is significantly reduced when effective passenger flow control measures are taken. By controlling the speed, direction and movement path of passengers, as well as adjusting the operation of escalators, entrances and automatic ticket-checking machines, passenger flow can become more orderly, transport efficiency can also be improved, and congestion in the station can be well mitigated

HuMiTar: A sequence-based method for prediction of human microRNA targets

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR's seed region and implementation of cross-species conservation of the target site, and machine learning (ML) methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved.</p> <p>Results</p> <p>We present HuMiTar, a computational method for identifying common targets of miRs, which is based on a scoring function that considers base-pairing for both seed and non-seed positions for human miR-mRNA duplexes. Our design shows that certain non-seed miR nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of Watson-Crick pairing. We contrasted HuMiTar with several representative competing methods on two sets of human miR targets and a set of ten glioblastoma oncogenes. Comparison with the two best performing traditional methods, PicTar and TargetScanS, and a representative ML method that considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of the predictions of the other three methods. At the same time, the proposed method is also capable of finding more true positive targets as a trade-off for an increased number of predictions. Genome-wide predictions show that the proposed method is characterized by 1.99 signal-to-noise ratio and linear, with respect to the length of the mRNA sequence, computational complexity. The ROC analysis shows that HuMiTar obtains results comparable with PicTar, which are characterized by high true positive rates that are coupled with moderate values of false positive rates.</p> <p>Conclusion</p> <p>The proposed HuMiTar method constitutes a step towards providing an efficient model for studying translational gene regulation by miRs.</p

Leaching resistance of hazardous waste cement solidification after accelerated carbonation

When cement-based materials are carbonated, some of their physicochemical properties are changed, which includes reductions of porosity by 20% and pH from 12-13 to 8–9. These changes can enhance the retention ability of cementitious solids containing hazard waste. This research studied the effect of carbonation on the leaching resistance of hazardous waste cement solidification. The finite element software COMSOL Multiphysics was used to simulate the process of accelerated carbonation and the effect of carbonation on leaching. Laboratory tests were conducted to validate the numerical models. Parametric studies from the numerical simulations revealed that carbonation could significantly improve leaching retention capabilities of cementitious solids containing hazardous wastes

A Novel Deep Learning based Automatic Auscultatory Method to Measure Blood Pressure

Background: It is clinically important to develop innovative techniques that can accurately measure blood pressures (BP) automatically. Objectives: This study aimed to present and evaluate a novel automatic BP measurement method based on deep learning method, and to confirm the effects on measured BPs of the position and contact pressure of stethoscope. Methods: 30 healthy subjects were recruited. 9 BP measurements (from three different stethoscope contact pressures and three repeats) were performed on each subject. The convolutional neural network (CNN) was designed and trained to identify the Korotkoff sounds at a beat-by-beat level. Next, a mapping algorithm was developed to relate the identified Korotkoff beats to the corresponding cuff pressures for systolic and diastolic BP (SBP and DBP) determinations. Its performance was evaluated by investigating the effects of the position and contact pressure of stethoscope on measured BPs in comparison with reference manual auscultatory method. Results: The overall measurement errors of the proposed method were 1.4 ± 2.4 mmHg for SBP and 3.3 ± 2.9 mmHg for DBP from all the measurements. In addition, the method demonstrated that there were small SBP differences between the 2 stethoscope positions, respectively at the 3 stethoscope contact pressures, and that DBP from the stethoscope under the cuff was significantly lower than that from outside the cuff by 2.0 mmHg (P < 0.01). Conclusion: Our findings suggested that the deep learning based method was an effective technique to measure BP, and could be developed further to replace the current oscillometric based automatic blood pressure measurement method