Xuefeng Peng

Zongshun Zhang, Yao Liu, Qiao Liu et al.

Video-based lie detection aims to identify deceptive behaviors from visual cues. Despite recent progress, its core challenge lies in learning sparse yet discriminative representations. Deceptive signals are typically subtle and short-lived, easily overwhelmed by redundant information, while individual and contextual variations introduce strong identity-related noise. To address this issue, we propose GenLie, a Global-Enhanced Lie Detection Network that performs local feature modeling under global supervision. Specifically, sparse and subtle deceptive cues are captured at the local level, while global supervision and optimization ensure robust and discriminative representations by suppressing identity-related noise. Experiments on three public datasets, covering both high- and low-stakes scenarios, show that GenLie consistently outperforms state-of-the-art methods. Source code is available at https://github.com/AliasDictusZ1/GenLie.

Xuefeng Peng, Yi Ding, David Wihl et al.

Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework to personalize sepsis treatment. The mixture model selectively alternates between neighbor-based (kernel) and deep reinforcement learning (DRL) experts depending on patient's current history. On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.

Omer Gottesman, Fredrik Johansson, Joshua Meier et al.

Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare. Reinforcement learning (RL) is a sub-field within machine learning that is concerned with learning how to make sequences of decisions so as to optimize long-term effects. Already, RL algorithms have been proposed to identify decision-making strategies for mechanical ventilation, sepsis management and treatment of schizophrenia. However, before implementing treatment policies learned by black-box algorithms in high-stakes clinical decision problems, special care must be taken in the evaluation of these policies. In this document, our goal is to expose some of the subtleties associated with evaluating RL algorithms in healthcare. We aim to provide a conceptual starting point for clinical and computational researchers to ask the right questions when designing and evaluating algorithms for new ways of treating patients. In the following, we describe how choices about how to summarize a history, variance of statistical estimators, and confounders in more ad-hoc measures can result in unreliable, even misleading estimates of the quality of a treatment policy. We also provide suggestions for mitigating these effects---for while there is much promise for mining observational health data to uncover better treatment policies, evaluation must be performed thoughtfully.

Xuefeng Peng, Li-Kai Chi, Jiebo Luo

From reducing stress and loneliness, to boosting productivity and overall well-being, pets are believed to play a significant role in people's daily lives. Many traditional studies have identified that frequent interactions with pets could make individuals become healthier and more optimistic, and ultimately enjoy a happier life. However, most of those studies are not only restricted in scale, but also may carry biases by using subjective self-reports, interviews, and questionnaires as the major approaches. In this paper, we leverage large-scale data collected from social media and the state-of-the-art deep learning technologies to study this phenomenon in depth and breadth. Our study includes four major steps: 1) collecting timeline posts from around 20,000 Instagram users, 2) using face detection and recognition on 2-million photos to infer users' demographics, relationship status, and whether having children, 3) analyzing a user's degree of happiness based on images and captions via smiling classification and textual sentiment analysis, 3) applying transfer learning techniques to retrain the final layer of the Inception v3 model for pet classification, and 4) analyzing the effects of pets on happiness in terms of multiple factors of user demographics. Our main results have demonstrated the efficacy of our proposed method with many new insights. We believe this method is also applicable to other domains as a scalable, efficient, and effective methodology for modeling and analyzing social behaviors and psychological well-being. In addition, to facilitate the research involving human faces, we also release our dataset of 700K analyzed faces.

Xuefeng Peng, Jiebo Luo, Catherine Glenn et al.

The complexities of fatigue have drawn much attention from researchers across various disciplines. Short-term fatigue may cause safety issue while driving; thus, dynamic systems were designed to track driver fatigue. Long-term fatigue could lead to chronic syndromes, and eventually affect individuals physical and psychological health. Traditional methodologies of evaluating fatigue not only require sophisticated equipment but also consume enormous time. In this paper, we attempt to develop a novel and efficient method to predict individual's fatigue rate by scrutinizing human facial cues. Our goal is to predict fatigue rate based on a selfie. To associate the fatigue rate with user behaviors, we have collected nearly 1-million timeline posts from 10,480 users on Instagram. We first detect all the faces and identify their demographics using automatic algorithms. Next, we investigate the fatigue distribution by weekday over different age, gender, and ethnic groups. This work represents a promising way to assess sleep-deprived fatigue, and our study provides a viable and efficient computational framework for user fatigue modeling in large-scale via social media.