Financing higher education in Vietnam: Student loan reform

Working Paper 51 by Dung Doan, Jiacheng Kang and Yanran Zhu examines the potential of reforming the higher education student loan system in Vietnam – a rapidly aging middle-income country – as a solution to further invest in human capital and meet the country’s evolving demand for skilled labour. The paper finds that Vietnam’s current loan scheme not only supports a negligible number of credit-constrained students amidst rising tuition fees but can also create excessive repayment burden to debtors. The paper then explores three potential income-contingent loan schemes and analyses how they might perform in Vietnam with respect to government subsidies and debtor’s repayment experience. Using data from the Vietnam Household Living Standard Survey 2012-2016 and the Labor Force Survey 2016 and a recent econometric innovation that involves Copula functions to project graduate lifetime earnings, the paper concludes that it is feasible to design an income-contingent loan scheme that is both gentle on the fisca

A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV). However, the heterogeneities of databases in size, structure and driving context make existing datasets practically ineffective due to a lack of uniform frameworks and searchable indexes. In order to overcome these limitations on existing public datasets, this paper proposes a data unification framework based on traffic primitives with ability to automatically unify and label heterogeneous traffic data. This is achieved by two steps: 1) Carefully arrange raw multidimensional time series driving data into a relational database and then 2) automatically extract labeled and indexed traffic primitives from traffic data through a Bayesian nonparametric learning method. Finally, we evaluate the effectiveness of our developed framework using the collected real vehicle data.Comment: 6 pages, 7 figures, 1 table, ITSC 201

Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying interaction patterns of the ego vehicle with other nearby vehicles. Our model relaxes dependency on the number of surrounding vehicles by developing an acceleration-sensitive velocity field based on Gaussian processes. The experiment results demonstrate that the velocity field can represent the spatial interactions between the ego vehicle and its surroundings. Then, a discrete Bayesian nonparametric model, integrating Dirichlet processes and hidden Markov models, is developed to learn the interaction patterns over the temporal space by segmenting and clustering the sequential interaction data into interpretable granular patterns automatically. We then evaluate our approach in the highway lane-change scenarios using the highD dataset collected from real-world settings. Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships. Our proposed approach sheds light on efficiently analyzing other kinds of multi-agent interactions, such as vehicle-pedestrian interactions. View the demos via https://youtu.be/z_vf9UHtdAM.Comment: for the supplements, see https://chengyuan-zhang.github.io/Multivehicle-Interaction

A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos

Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions. This paper presents a general framework to gain insights into intricate multi-vehicle interaction patterns from bird's-eye view traffic videos. We adopt a Gaussian velocity field to describe the time-varying multi-vehicle interaction behaviors and then use deep autoencoders to learn associated latent representations for each temporal frame. Then, we utilize a hidden semi-Markov model with a hierarchical Dirichlet process as a prior to segment these sequential representations into granular components, also called traffic primitives, corresponding to interaction patterns. Experimental results demonstrate that our proposed framework can extract traffic primitives from videos, thus providing a semantic way to analyze multi-vehicle interaction patterns, even for cluttered driving scenarios that are far messier than human beings can cope with.Comment: 2019 IEEE Intelligent Transportation Systems Conference (ITSC

Co2N nanoparticles embedded N-doped mesoporous carbon as efficient electrocatalysts for oxygen reduction reaction

Co-N-C electrocatalysts have attracted great attention in electrocatalytic ORR (oxygen reduction reaction) field. In this work, we propose to prepare Co 2 N nanoparticles embedded N-doped mesoporous carbon by a facile method including in situ copolymerization and pyrolysis under NH 3 atmosphere. The results show that more N atoms can be doped in carbon framework by NH 3 pyrolysis, it is also found that pyrolysis temperature and Co content can influence the ORR performance of samples. The sample prepared by adding Co precursor and pyrolysis at 700 °C has high N content (11.86 at.%) and relative large specific surface area (362 m 2 g −1 ), and it also exhibited superior electrocatalytic ORR performance in terms of E onset (−0.038 V vs. SCE), E 1/2 (−0.126 V vs. SCE) and large current density (5.22 mA cm −2 ). Additionally, the sample also shows better stability and resistance to methanol poisoning than Pt/C catalyst. The synergistic effect of Co-N active centers and hierarchical porous structures contribute the excellent electrocatalytic activity, which are considering as alternative catalysts for ORR in full cells

Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge

Deep transfer learning (DTL) is a fundamental method in the field of Intelligent Fault Detection (IFD). It aims to mitigate the degradation of method performance that arises from the discrepancies in data distribution between training set (source domain) and testing set (target domain). Considering the fact that fault data collection is challenging and certain faults are scarce, DTL-based methods face the limitation of available observable data, which reduces the detection performance of the methods in the target domain. Furthermore, DTL-based methods lack comprehensive uncertainty analysis that is essential for building reliable IFD systems. To address the aforementioned problems, this paper proposes a novel DTL-based method known as Neural Processes-based deep transfer learning with graph convolution network (GTNP). Feature-based transfer strategy of GTNP bridges the data distribution discrepancies of source domain and target domain in high-dimensional space. Both the joint modeling based on global and local latent variables and sparse sampling strategy reduce the demand of observable data in the target domain. The multi-scale uncertainty analysis is obtained by using the distribution characteristics of global and local latent variables. Global analysis of uncertainty enables GTNP to provide quantitative values that reflect the complexity of methods and the difficulty of tasks. Local analysis of uncertainty allows GTNP to model uncertainty (confidence of the fault detection result) at each sample affected by noise and bias. The validation of the proposed method is conducted across 3 IFD tasks, consistently showing the superior detection performance of GTNP compared to the other DTL-based methods