PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms

Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases

Improving behavior based authentication against adversarial attack using XAI

In recent years, machine learning models, especially deep neural networks, have been widely used for classification tasks in the security domain. However, these models have been shown to be vulnerable to adversarial manipulation: small changes learned by an adversarial attack model, when applied to the input, can cause significant changes in the output. Most research on adversarial attacks and corresponding defense methods focuses only on scenarios where adversarial samples are directly generated by the attack model. In this study, we explore a more practical scenario in behavior-based authentication, where adversarial samples are collected from the attacker. The generated adversarial samples from the model are replicated by attackers with a certain level of discrepancy. We propose an eXplainable AI (XAI) based defense strategy against adversarial attacks in such scenarios. A feature selector, trained with our method, can be used as a filter in front of the original authenticator. It filters out features that are more vulnerable to adversarial attacks or irrelevant to authentication, while retaining features that are more robust. Through comprehensive experiments, we demonstrate that our XAI based defense strategy is effective against adversarial attacks and outperforms other defense strategies, such as adversarial training and defensive distillation

Mitigation mechanism of longitudinal ribs on rain-wind induced vibrations of stay cables

For cable stayed bridges rain-wind induced vibrations of stay cables are probably the most widespread and controversial phenomenon. Aerodynamic countermeasures have been implemented to tackle such vibrations, but there is still not sufficient insight on the inherent mitigation mechanisms. To this goal, a numerical model, based on lubrication theory, was employed in order to study the coupled cable vibration response, aerodynamic forces, and formation and oscillation of rivulets for stay cables equipped with longitudinal ribs. Coupled equations governing the synchronous cable motion and water film evolution were established in order to understand the effects of several key parameters associated with the vibration mitigation performance of the ribs. Such parameters include the cable inclination angle, the wind yaw angle, the number and the height of the ribs. Computed results were successfully validated against experimental data. For the various studied cases, it was apparent that the ribs did not stop the formation of rivulets, but they could affect both their position and oscillation ranges. Through such a control action they could further affect the oscillation range and frequency content of aerodynamic forces, mitigating or not cable vibrations

A Comprehensive and Reliable Feature Attribution Method: Double-sided Remove and Reconstruct (DoRaR)

The limited transparency of the inner decision-making mechanism in deep neural networks (DNN) and other machine learning (ML) models has hindered their application in several domains. In order to tackle this issue, feature attribution methods have been developed to identify the crucial features that heavily influence decisions made by these black box models. However, many feature attribution methods have inherent downsides. For example, one category of feature attribution methods suffers from the artifacts problem, which feeds out-of-distribution masked inputs directly through the classifier that was originally trained on natural data points. Another category of feature attribution method finds explanations by using jointly trained feature selectors and predictors. While avoiding the artifacts problem, this new category suffers from the Encoding Prediction in the Explanation (EPITE) problem, in which the predictor's decisions rely not on the features, but on the masks that selects those features. As a result, the credibility of attribution results is undermined by these downsides. In this research, we introduce the Double-sided Remove and Reconstruct (DoRaR) feature attribution method based on several improvement methods that addresses these issues. By conducting thorough testing on MNIST, CIFAR10 and our own synthetic dataset, we demonstrate that the DoRaR feature attribution method can effectively bypass the above issues and can aid in training a feature selector that outperforms other state-of-the-art feature attribution methods. Our code is available at https://github.com/dxq21/DoRaR.Comment: 16 pages, 22 figure

PVP: Pre-trained Visual Parameter-Efficient Tuning

Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models for downstream tasks due to their high computational and storage costs. Recently, Parameter-Efficient Tuning (PETuning) techniques, e.g., Visual Prompt Tuning (VPT) and Low-Rank Adaptation (LoRA), have significantly reduced the computation and storage cost by inserting lightweight prompt modules into the pre-trained models and tuning these prompt modules with a small number of trainable parameters, while keeping the transformer backbone frozen. Although only a few parameters need to be adjusted, most PETuning methods still require a significant amount of downstream task training data to achieve good results. The performance is inadequate on low-data regimes, especially when there are only one or two examples per class. To this end, we first empirically identify the poor performance is mainly due to the inappropriate way of initializing prompt modules, which has also been verified in the pre-trained language models. Next, we propose a Pre-trained Visual Parameter-efficient (PVP) Tuning framework, which pre-trains the parameter-efficient tuning modules first and then leverages the pre-trained modules along with the pre-trained transformer backbone to perform parameter-efficient tuning on downstream tasks. Experiment results on five Fine-Grained Visual Classification (FGVC) and VTAB-1k datasets demonstrate that our proposed method significantly outperforms state-of-the-art PETuning methods

VehSense: Slippery Road Detection Using Smartphones

This paper investigates a new application of vehicular sensing: detecting and reporting the slippery road conditions. We describe a system and associated algorithm to monitor vehicle skidding events using smartphones and OBD-II (On board Diagnostics) adaptors. This system, which we call the VehSense, gathers data from smartphone inertial sensors and vehicle wheel speed sensors, and processes the data to monitor slippery road conditions in real-time. Specifically, two speed readings are collected: 1) ground speed, which is estimated by vehicle acceleration and rotation, and 2) wheel speed, which is retrieved from the OBD-II interface. The mismatch between these two speeds is used to infer a skidding event. Without tapping into vehicle manufactures' proprietary data (e.g., antilock braking system), VehSense is compatible with most of the passenger vehicles, and thus can be easily deployed. We evaluate our system on snow-covered roads at Buffalo, and show that it can detect vehicle skidding effectively.Comment: 2017 IEEE 85th Vehicular Technology Conference (VTC2017-Spring

GATA binding protein 2 mediates leptin inhibition of PPARγ1 expression in hepatic stellate cells and contributes to hepatic stellate cell activation

AbstractHepatic stellate cell (HSC) activation is a crucial step in the development of liver fibrosis. Peroxisome-proliferator activated receptor γ (PPARγ) exerts a key role in the inhibition of HSC activation. Leptin reduces PPARγ expression in HSCs and plays a unique role in promoting liver fibrosis. The present studies aimed to investigate the mechanisms underlying leptin regulation of PPARγ1 (a major subtype of PPARγ) in HSCs in vivo and in vitro. Results revealed a leptin response region in mouse PPARγ1 promoter and indicated that the region included a GATA binding protein binding site around position −2323. GATA binding protein-2 (GATA-2) could bind to the site and inhibit PPARγ1 promoter activity in HSCs. Leptin induced GATA-2 expression in HSCs in vitro and in vivo. GATA-2 mediated leptin inhibition of PPARγ1 expression by its binding site in PPARγ1 promoter in HSCs and GATA-2 promoted HSC activation. Leptin upregulated GATA-2 expression through β-catenin and sonic hedgehog pathways in HSCs. Leptin-induced increase in GATA-2 was accompanied by the decrease in PPARγ expression in HSCs and by the increase in the activated HSC number and liver fibrosis in vivo. Our data might suggest a possible new explanation for the promotion effect of leptin on liver fibrogenesis

The relationship between marital adjustment and personality characteristics, medical coping style of infertile patients

目的  探讨不孕不育患者婚姻调适状况与人格特征和医学应对方式的关系，为临床开展心理健康干预提供依据。方法  选取2012年8月—2015年1月某三级甲等医院生殖医学中心治疗的156例已婚不孕不育症患者。采用艾森克人格问卷表（EPQ）、Locke-Wollance婚姻调适测定量表和医学应对问卷（MCMQ），对患者进行调查，分别测评患者婚姻调适状况、人格特征和医学应对方式及其相关性。结果  患者婚姻调适状况与EPQ的P、N及MCMQ屈服呈显著负相关，与EPQ 的E和MCMQ面对则呈显著正相关；EPQ的P、N与MCMQ面对呈负相关，与MCMQ的屈服呈正相关；EPQ的E则与MCMQ面对呈显著正相关。EPQ的P、E、N和MCMQ面对、屈服在婚姻调适状况高中低分组中的比较，差异均有统计学意义（均P＜0.01）。多元逐步回归分析：面对、回避，屈服及精神质（P）等4个因素共解释了不孕不育症患者婚姻调适总变异的26.4%。结论  人格特征、医学应对方式是影响不孕不育症患者婚姻调适的重要因素。Objective: To explore the relationship between marital adjustment and personality characteristics and coping styles of patients with infertility, and to provide evidence for clinical intervention. Methods: A total of 156 patients with infertility were selected from August 2012 to January 2015 in a grade a hospital of reproductive medicine center. This research is a cross - sectional survey. The Eysenck Personality Questionnaire (EPQ), Locke-Wollance marital adjustment test scale and Medical Coping Questionnaire (MCMQ), were investigated, respectively, to evaluate the patient status, marital adjustment and personality characteristics and coping style and their relationship. Results: Patients marital adjustment status and EPQ P, N and MCMQ yield was significantly negative correlated, and EPQ E and MCMQ was significantly positively related; EPQ P, N and MCMQ showed a negative correlation, and MCMQ yield positively correlated; EPQ E, and MCMQ face was significantly positively related, EPQ P, E, N and MCMQ, yield in the marital adjustment the conditions of grouping, the differences were statistically significant (all P < 0.01). Multiple stepwise regression analysis: 4 factors, such as confrontation, avoidance, yield and psychoticism (P), were used to explain the total variation of the adjustment of marriage in infertile patients (26.4%). Conclusion: Personality characteristics, medical coping styles are the important factors influencing the marital adjustment for infertility patients