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

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/

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

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

The Gut-Microglia Connection: Implications for Central Nervous System Diseases

The importance of the gut microbiome in central nervous system (CNS) diseases has long been recognized; however, research into this connection is limited, in part, owing to a lack of convincing mechanisms because the brain is a distant target of the gut. Previous studies on the brain revealed that most of the CNS diseases affected by the gut microbiome are closely associated with microglial dysfunction. Microglia, the major CNS-resident macrophages, are crucial for the immune response of the CNS against infection and injury, as well as for brain development and function. However, the current understanding of the mechanisms controlling the maturation and function of microglia is obscure, especially regarding the extrinsic factors affecting microglial function during the developmental process. The gut microflora has been shown to significantly influence microglia from before birth until adulthood, and the metabolites generated by the microbiota regulate the inflammation response mediated by microglia in the CNS; this inspired our hypothesis that microglia act as a critical mediator between the gut microbiome and CNS diseases. Herein, we highlight and discuss current findings that show the influence of host microbiome, as a crucial extrinsic factor, on microglia within the CNS. In addition, we summarize the CNS diseases associated with both the host microbiome and microglia and explore the potential pathways by which the gut bacteria influence the pathogenesis of CNS diseases. Our work is thus a comprehensive theoretical foundation for studies on the gut-microglia connection in the development of CNS diseases; and provides great potential for researchers to target pathways associated with the gut-microglia connection and overcome CNS diseases

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

Mining users' activity on large Twitter text data

Online social network makes people interact with each other frequently. There comes an important question: at what time users always use twitter? How about users' relationship with others? How do the information flow in the network? In this paper, we conducted an experiment on a large twitter dataset, and some interesting user activity patterns have been discovered. We find that people always use twitter at night in a day. People tweet less on weekends than from Monday to Friday. We verify the power-law distribution of the degree in the network. And we propose a text-based user dividing method. We mine users' text data according to this method and divide them into different categories. Finally, we discover the information flow between different categories. ? 2013 ACM.EI

Cable Fault Location in VSC-HVDC System Based on Improved Local Mean Decomposition

Aiming at the problem of low positioning accuracy caused by modal aliasing and noise interference in DC cable fault location analysis of a VSC-HVDC system, a double-ended fault location method for flexible DC cables based on improved local mean decomposition is proposed. Firstly, the local mean decomposition (LMD) is used to decompose the six-mode voltage signal to obtain the product function (PF) component; then, to overcome the problem that the instantaneous frequency function of the LMD is limited by the extreme value, the Hilbert transform is performed on the PF1 to obtain the instantaneous frequency curve, and the arrival time of the voltage traveling wave head is determined from the mutation information. Finally, the fault distance is obtained by using the principle of double-ended traveling wave fault location. Different fault conditions are simulated, analyzed, and compared with wavelet transform and Hilbert–Huang transform. The results show that the proposed method has a positioning error within 1%, and it is less affected by interference noise and transition resistance

Fault Location Study of Overhead Line–Cable Lines with Branches

A new fault location method based on the three-terminal travelling wave method is proposed for the fault location problem of multi-branch overhead line–cable transmission lines. Firstly, the process of fault travelling wave propagation in overhead transmission lines and the phenomenon of refraction are analysed, and an improved phase-mode transformation is introduced to decouple the electromagnetic coupling and perform fault phase selection. Secondly, the Pearson correlation coefficient is introduced to compare the similarity of the current travelling waveforms at different measurement points in order to implement fault segmentation. To solve the problems of the complexity of the fault travelling wave propagation process and the difficulty of identifying the travelling wavehead, the Hilbert–Huang transform is used to extract the fault signal characteristics, and the travelling wave arrival moment is accurately calculated by the sampling error correction method to determine the fault location. Finally, the accuracy and stability of the method are verified via a simulation test on the MATLAB/Simulink platform. The results show that the proposed positioning method combining the three-terminal travelling wave method with HHT and sampling error correction can locate the fault location more accurately, and it has good potential for application in the engineering field. It provides a new technical means for fault location in overhead transmission lines, which is expected to become one of the most important technologies in the future power system