49 research outputs found
The influence of risk culture on the performance of international joint-venture securities
With the development of economic globalization, culture is a key factor supporting the sustainability of foreign direct investment (FDI), especially for multinational enterprises. This paper takes the Chinese capital market as a sample and, combined with interviews with managers of international joint-venture securities (IJVS), finds that the culture of participants formed in developed and emerging capital market has a significant impact on the performance of IJVS. Using the degree of price fluctuation to measure the risk culture of each capital market, this paper observes that the risk culture in the Chinese capital market is significantly stronger than that of developed countries. This paper also finds that the stronger the risk culture IJVS shareholders have, the better they can adapt to the environment of the Chinese capital market and the better the performance they can achieve. Furthermore, risk culture distance, calculated by the risk culture differences between foreign shareholders and Chinese capital market, are significantly negatively correlated with IJVS performance and efficiency
MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings
Head detection in the indoor video is an essential component of building
occupancy detection. While deep models have achieved remarkable progress in
general object detection, they are not satisfying enough in complex indoor
scenes. The indoor surveillance video often includes cluttered background
objects, among which heads have small scales and diverse poses. In this paper,
we propose Motion-aware Pseudo Siamese Network (MPSN), an end-to-end approach
that leverages head motion information to guide the deep model to extract
effective head features in indoor scenarios. By taking the pixel-wise
difference of adjacent frames as the auxiliary input, MPSN effectively enhances
human head motion information and removes the irrelevant objects in the
background. Compared with prior methods, it achieves superior performance on
the two indoor video datasets. Our experiments show that MPSN successfully
suppresses static background objects and highlights the moving instances,
especially human heads in indoor videos. We also compare different methods to
capture head motion, which demonstrates the simplicity and flexibility of MPSN.
Finally, to validate the robustness of MPSN, we conduct adversarial experiments
with a mathematical solution of small perturbations for robust model selection.
Code is available at https://github.com/pl-share/MPSN
SEABO: A Simple Search-Based Method for Offline Imitation Learning
Offline reinforcement learning (RL) has attracted much attention due to its
ability in learning from static offline datasets and eliminating the need of
interacting with the environment. Nevertheless, the success of offline RL
relies heavily on the offline transitions annotated with reward labels. In
practice, we often need to hand-craft the reward function, which is sometimes
difficult, labor-intensive, or inefficient. To tackle this challenge, we set
our focus on the offline imitation learning (IL) setting, and aim at getting a
reward function based on the expert data and unlabeled data. To that end, we
propose a simple yet effective search-based offline IL method, tagged SEABO.
SEABO allocates a larger reward to the transition that is close to its closest
neighbor in the expert demonstration, and a smaller reward otherwise, all in an
unsupervised learning manner. Experimental results on a variety of D4RL
datasets indicate that SEABO can achieve competitive performance to offline RL
algorithms with ground-truth rewards, given only a single expert trajectory,
and can outperform prior reward learning and offline IL methods across many
tasks. Moreover, we demonstrate that SEABO also works well if the expert
demonstrations contain only observations. Our code is publicly available at
https://github.com/dmksjfl/SEABO.Comment: To appear in ICLR202
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
Offline goal-conditioned RL (GCRL) offers a way to train general-purpose
agents from fully offline datasets. In addition to being conservative within
the dataset, the generalization ability to achieve unseen goals is another
fundamental challenge for offline GCRL. However, to the best of our knowledge,
this problem has not been well studied yet. In this paper, we study
out-of-distribution (OOD) generalization of offline GCRL both theoretically and
empirically to identify factors that are important. In a number of experiments,
we observe that weighted imitation learning enjoys better generalization than
pessimism-based offline RL method. Based on this insight, we derive a theory
for OOD generalization, which characterizes several important design choices.
We then propose a new offline GCRL method, Generalizable Offline
goAl-condiTioned RL (GOAT), by combining the findings from our theoretical and
empirical studies. On a new benchmark containing 9 independent identically
distributed (IID) tasks and 17 OOD tasks, GOAT outperforms current
state-of-the-art methods by a large margin.Comment: Accepted by Proceedings of the 40th International Conference on
Machine Learning, 202
Enabling qualitative research data sharing using a natural language processing pipeline for deidentification: Moving beyond HIPAA Safe Harbor identifiers
OBJECTIVE: Sharing health research data is essential for accelerating the translation of research into actionable knowledge that can impact health care services and outcomes. Qualitative health research data are rarely shared due to the challenge of deidentifying text and the potential risks of participant reidentification. Here, we establish and evaluate a framework for deidentifying qualitative research data using automated computational techniques including removal of identifiers that are not considered HIPAA Safe Harbor (HSH) identifiers but are likely to be found in unstructured qualitative data.
MATERIALS AND METHODS: We developed and validated a pipeline for deidentifying qualitative research data using automated computational techniques. An in-depth analysis and qualitative review of different types of qualitative health research data were conducted to inform and evaluate the development of a natural language processing (NLP) pipeline using named-entity recognition, pattern matching, dictionary, and regular expression methods to deidentify qualitative texts.
RESULTS: We collected 2 datasets with 1.2 million words derived from over 400 qualitative research data documents. We created a gold-standard dataset with 280K words (70 files) to evaluate our deidentification pipeline. The majority of identifiers in qualitative data are non-HSH and not captured by existing systems. Our NLP deidentification pipeline had a consistent F1-score of ∼0.90 for both datasets.
CONCLUSION: The results of this study demonstrate that NLP methods can be used to identify both HSH identifiers and non-HSH identifiers. Automated tools to assist researchers with the deidentification of qualitative data will be increasingly important given the new National Institutes of Health (NIH) data-sharing mandate
Uncertainty-driven Trajectory Truncation for Model-based Offline Reinforcement Learning
Equipped with the trained environmental dynamics, model-based offline
reinforcement learning (RL) algorithms can often successfully learn good
policies from fixed-sized datasets, even some datasets with poor quality.
Unfortunately, however, it can not be guaranteed that the generated samples
from the trained dynamics model are reliable (e.g., some synthetic samples may
lie outside of the support region of the static dataset). To address this
issue, we propose Trajectory Truncation with Uncertainty (TATU), which
adaptively truncates the synthetic trajectory if the accumulated uncertainty
along the trajectory is too large. We theoretically show the performance bound
of TATU to justify its benefits. To empirically show the advantages of TATU, we
first combine it with two classical model-based offline RL algorithms, MOPO and
COMBO. Furthermore, we integrate TATU with several off-the-shelf model-free
offline RL algorithms, e.g., BCQ. Experimental results on the D4RL benchmark
show that TATU significantly improves their performance, often by a large
margin
Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration
The generative adversarial imitation learning (GAIL) has provided an
adversarial learning framework for imitating expert policy from demonstrations
in high-dimensional continuous tasks. However, almost all GAIL and its
extensions only design a kind of reward function of logarithmic form in the
adversarial training strategy with the Jensen-Shannon (JS) divergence for all
complex environments. The fixed logarithmic type of reward function may be
difficult to solve all complex tasks, and the vanishing gradients problem
caused by the JS divergence will harm the adversarial learning process. In this
paper, we propose a new algorithm named Wasserstein Distance guided Adversarial
Imitation Learning (WDAIL) for promoting the performance of imitation learning
(IL). There are three improvements in our method: (a) introducing the
Wasserstein distance to obtain more appropriate measure in the adversarial
training process, (b) using proximal policy optimization (PPO) in the
reinforcement learning stage which is much simpler to implement and makes the
algorithm more efficient, and (c) exploring different reward function shapes to
suit different tasks for improving the performance. The experiment results show
that the learning procedure remains remarkably stable, and achieves significant
performance in the complex continuous control tasks of MuJoCo.Comment: M. Zhang and Y. Wang contribute equally to this wor