Building BROOK: A multi-modal and facial video database for Human-Vehicle Interaction research

With the growing popularity of Autonomous Vehicles, more opportunities have bloomed in the context of Human-Vehicle Interactions. However, the lack of comprehensive and concrete database support for such specific use case limits relevant studies in the whole design spaces. In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterise drivers' affective states and driving styles. We first explain how we over-engineer such database in details, and what we have gained through a ten-month study. Then we showcase a Neural Network-based predictor, leveraging BROOK, which supports multi-modal prediction (including physiological data of heart rate and skin conductance and driving status data of speed) through facial videos. Finally we discuss related issues when building such a database and our future directions in the context of BROOK. We believe BROOK is an essential building block for future Human-Vehicle Interaction Research. More details and updates about the project BROOK is online at https: //unnc-idl-ucc.github.io/BROOK/

Juvenile hormone and insulin regulate trehalose homeostasis in the red flour beetle, Tribolium castaneum

Insulin/IGF-1 signaling (IIS) has been well studied for its role in the control of life span extension and resistance to a variety of stresses. The Drosophila melanogaster insulin-like receptor (InR) mutant showed extended life span due to reduced juvenile hormone (JH) levels. However, little is known about the mechanism of cross talk between IIS and JH in regulation of life span extension and resistance to starvation. In the current study, we investigated the role of IIS and JH signaling in regulation of resistance to starvation. Reduction in JH biosynthesis, JH action, or insulin-like peptide 2 (ILP2) syntheses by RNA interference (RNAi)-aided knockdown in the expression of genes coding for juvenile hormone acid methyltransferase (JHAMT), methoprene-tolerant (Met), or ILP2 respectively decreased lipid and carbohydrate metabolism and extended the survival of starved beetles. Interestingly, the extension of life span could be restored by injection of bovine insulin into JHAMT RNAi beetles but not by application of JH III to ILP2 RNAi beetles. These data suggest that JH controls starvation resistance by regulating synthesis of ILP2. More importantly, JH regulates trehalose homeostasis, including trehalose transport and metabolism, and controls utilization of stored nutrients in starved adults

Pricing European options using Monte Carlo methods

European-style options are quite popular nowadays. Calculating their theo- retical price is not an easy task because there are many sources of uncertainty. However, we can model these uncertainties with random numbers. In this pa- per I discuss my implemention of two options-pricing programs using Monte Carlo methods, one for a CPU and the other for a GPU. I also optimize them to reduce their running time. Finally I compare the performance of those two programs

An in-depth analysis of system-level techniques for Simultaneous Multi-threaded Processors in Clouds

To improve the overall system utilization, Simultaneous Multi-Threading (SMT) has become a norm in clouds. Usually, Hardware threads are viewed and deployed directly as physical cores for attempts to improve resource utilization and system throughput. However, context switches in virtualized systems might incur severe resource waste, which further led to significant performance degradation. Worse, virtualized systems suffer from performance variations since the rescheduled vCPU may affect other hardware threads on the same physical core. In this paper, we perform an in-depth experimental study about how existing system software techniques improves the utilization of SMT Processors in Clouds. Considering the default Linux hypervisor vanilla KVM as the baseline, we evaluated two update-to-date kernel patches IdlePoll and HaltPoll through the combination of 14 real-world workloads. Our results show that mitigating they could significantly mitigate the number of context switches, which further improves the overall system throughput and decreases its latency. Based on our findings, we summarize key lessons from the previous wisdom and then discuss promising directions to be explored in the future

Building BROOK: A multi-modal and facial video database for Human-Vehicle Interaction research

With the growing popularity of Autonomous Vehicles, more opportunities have bloomed in the context of Human-Vehicle Interactions. However, the lack of comprehensive and concrete database support for such specific use case limits relevant studies in the whole design spaces. In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterise drivers' affective states and driving styles. We first explain how we over-engineer such database in details, and what we have gained through a ten-month study. Then we showcase a Neural Network-based predictor, leveraging BROOK, which supports multi-modal prediction (including physiological data of heart rate and skin conductance and driving status data of speed) through facial videos. Finally we discuss related issues when building such a database and our future directions in the context of BROOK. We believe BROOK is an essential building block for future Human-Vehicle Interaction Research. More details and updates about the project BROOK is online at https: //unnc-idl-ucc.github.io/BROOK/

AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn

Data-driven companies use AI models extensively to develop products and intelligent business solutions, making the health of these models crucial for business success. Model monitoring and alerting in industries pose unique challenges, including a lack of clear model health metrics definition, label sparsity, and fast model iterations that result in short-lived models and features. As a product, there are also requirements for scalability, generalizability, and explainability. To tackle these challenges, we propose AlerTiger, a deep-learning-based MLOps model monitoring system that helps AI teams across the company monitor their AI models' health by detecting anomalies in models' input features and output score over time. The system consists of four major steps: model statistics generation, deep-learning-based anomaly detection, anomaly post-processing, and user alerting. Our solution generates three categories of statistics to indicate AI model health, offers a two-stage deep anomaly detection solution to address label sparsity and attain the generalizability of monitoring new models, and provides holistic reports for actionable alerts. This approach has been deployed to most of LinkedIn's production AI models for over a year and has identified several model issues that later led to significant business metric gains after fixing

A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions

In the era of growing developments in Autonomous Vehicles, the importance of Human-Vehicle Interaction has become apparent. However, the requirements of retrieving in-vehicle drivers’ multi- modal data trails, by utilizing embedded sensors, have been consid- ered user unfriendly and impractical. Hence, speculative designs, for in-vehicle multi-modal data retrieval, has been demanded for future personalized and intelligent Human-Vehicle Interaction. In this paper, we explore the feasibility to utilize facial recog- nition techniques to build in-vehicle multi-modal data retrieval. We first perform a comprehensive user study to collect relevant data and extra trails through sensors, cameras and questionnaire. Then, we build the whole pipeline through Convolution Neural Net- works to predict multi-model values of three particular categories of data, which are Heart Rate, Skin Conductance and Vehicle Speed, by solely taking facial expressions as input. We further evaluate and validate its effectiveness within the data set, which suggest the promising future of Speculative Designs for Multi-modal Data Retrieval through this approach

Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions

Towards intelligent Human-Vehicle Interaction systems and innovative Human-Vehicle Interaction designs, in-vehicle drivers' physiological data has been explored as an essential data source. However, equipping multiple biosensors is considered the limited extent of user-friendliness and impractical during the driving procedure. The lack of a proper approach to access physiological data has hindered wider applications of advanced biosignal-driven designs in practice (e.g. monitoring systems and etc.). Hence, the demand for a user-friendly approach to measuring drivers' body statuses has become more intense. In this Work-In-Progress, we present Face2Multi-modal, an In-vehicle multi-modal Data Streams Predictors through facial expressions only. More specifically, we have explored the estimations of Heart Rate, Skin Conductance, and Vehicle Speed of the drivers. We believe Face2Multi-modal provides a user-friendly alternative to acquiring drivers' physiological status and vehicle status, which could serve as the building block for many current or future personalized Human-Vehicle Interaction designs. More details and updates about the project Face2Multi-modal is online at https://github.com/unnc-ucc/Face2Multimodal/