The Narrowing of Charge Balance Function and Hadronization Time in Relativistic Heavy Ion Collisions

The widths of charge balance function in high energy hadron-hadron and relativistic heavy ion collisions are studied using the Monte Carlo generators PYTHIA and AMPT, respectively. The narrowing of balance function as the increase of multiplicity is found in both cases. The mean parton-freeze-out time of a heavy-ion-collision event is used as the characteristic hadronization time of the event. It turns out that for a fixed multiplicity interval the width of balance function is consistent with being independent of hadronization time.Comment: 4 pages, 7 figure

AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline

An open-source Mandarin speech corpus called AISHELL-1 is released. It is by far the largest corpus which is suitable for conducting the speech recognition research and building speech recognition systems for Mandarin. The recording procedure, including audio capturing devices and environments are presented in details. The preparation of the related resources, including transcriptions and lexicon are described. The corpus is released with a Kaldi recipe. Experimental results implies that the quality of audio recordings and transcriptions are promising.Comment: Oriental COCOSDA 201

A Sarcoplasmic Reticulum Localized Protein Phosphatase Regulates Phospholamban Phosphorylation and Promotes Ischemia Reperfusion Injury in the Heart.

Phospholamban (PLN) is a key regulator of sarcolemma calcium uptake in cardiomyocyte, its inhibitory activity to SERCA is regulated by phosphorylation. PLN hypophosphorylation is a common molecular feature in failing heart. The current study provided evidence at molecular, cellular and whole heart levels to implicate a sarcolemma membrane targeted protein phosphatase, PP2Ce, as a specific and potent PLN phosphatase. PP2Ce expression was elevated in failing human heart and induced acutely at protein level by β -adrenergic stimulation or oxidative stress in cardiomyocytes. PP2Ce expression in mouse heart blunted β-adrenergic response and exacerbated ischemia/reperfusion injury. Therefore, PP2Ce is a new regulator for cardiac function and pathogenesis

Universal scaling of strange particle pTp_{\rm T} spectra in pp collisions

As a complementary study to that performed on the transverse momentum ($p_{\rm T}$) spectra of charged pions, kaons and protons in proton-proton (pp) collisions at LHC energies 0.9, 2.76 and 7 TeV, we present a scaling behaviour in the $p_{\rm T}$ spectra of strange particles ($K_{S}^{0}$, $\rm \Lambda$, $\rm \Xi$ and $\phi$) at these three energies. This scaling behaviour is exhibited when the spectra are expressed in a suitable scaling variable $z=p_{\rm T}/K$, where the scaling parameter $K$ is determined by the quality factor method and increases with the center of mass energy ($\sqrt{s}$). The rates at which $K$ increases with $\mathrm{ln}\sqrt{s}$ for these strange particles are found to be identical within errors. In the framework of the colour string percolation model, we argue that these strange particles are produced through the decay of clusters that are formed by the colour strings overlapping. We observe that the strange mesons and baryons are produced from clusters with different size distributions, while the strange mesons (baryons) $K_{S}^{0}$ and $\phi$ ($\rm \Lambda$ and $\rm \Xi$) originate from clusters with the same size distributions. The cluster's size distributions for strange mesons are more dispersed than those for strange baryons. The scaling behaviour of the $p_{\rm T}$ spectra for these strange particles can be explained by the colour string percolation model in a quantitative way.Comment: 8 pages, 10 figures, accepted by EPJ

Psychophysiological responses to takeover requests in conditionally automated driving

In SAE Level 3 automated driving, taking over control from automation raises significant safety concerns because drivers out of the vehicle control loop have difficulty negotiating takeover transitions. Existing studies on takeover transitions have focused on drivers' behavioral responses to takeover requests (TORs). As a complement, this exploratory study aimed to examine drivers' psychophysiological responses to TORs as a result of varying non-driving-related tasks (NDRTs), traffic density and TOR lead time. A total number of 102 drivers were recruited and each of them experienced 8 takeover events in a high fidelity fixed-base driving simulator. Drivers' gaze behaviors, heart rate (HR) activities, galvanic skin responses (GSRs), and facial expressions were recorded and analyzed during two stages. First, during the automated driving stage, we found that drivers had lower heart rate variability, narrower horizontal gaze dispersion, and shorter eyes-on-road time when they had a high level of cognitive load relative to a low level of cognitive load. Second, during the takeover transition stage, 4s lead time led to inhibited blink numbers and larger maximum and mean GSR phasic activation compared to 7s lead time, whilst heavy traffic density resulted in increased HR acceleration patterns than light traffic density. Our results showed that psychophysiological measures can indicate specific internal states of drivers, including their workload, emotions, attention, and situation awareness in a continuous, non-invasive and real-time manner. The findings provide additional support for the value of using psychophysiological measures in automated driving and for future applications in driver monitoring systems and adaptive alert systems.University of Michigan McityPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162593/1/AAP_physiological_responses_HF_template.pdfSEL

Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving

In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving-related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers’ takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers’ takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not

Predicting Driver Takeover Performance and Designing Alert Systems in Conditionally Automated Driving

With the Society of Automotive Engineers Level 3 automation, drivers are no longer required to actively monitor driving environments, and can potentially engage in non-driving related tasks. Nevertheless, when the automation reaches its operational limits, drivers will have to take over control of vehicles at a moment’s notice. Drivers have difficulty with takeover transitions, as they become increasingly decoupled from the operational level of driving. In response to the takeover difficulty, existing literature has investigated various factors affecting takeover performance. However, not all the factors were studied comprehensively, and the results of some factors were mixed. Meanwhile, there is a lack of research on the development of computational models that predict drivers’ takeover performance using their physiological and driving environment data. Furthermore, current research on the design of in-vehicle alert systems suffers from methodological shortcomings and presents identical takeover warnings regardless of event criticality. To address these shortcomings, the goals of this dissertation were to (1) examine the effects of drivers' cognitive load, emotions, traffic density, and takeover request lead time on their driving behavioral (takeover timeliness and quality) and psychophysiological responses (eye movements, galvanic skin responses, and heart rate activities) to takeover requests; (2) develop computational models to predict drivers’ takeover performance using their physiological and driving environment data via machine learning algorithms; and (3) design in-vehicle alert systems with different display modalities and information types and evaluate the displays in different event criticality conditions via human-subject experiments. The results of three human-subject experiments showed that positive emotional valence led to smoother takeover behaviors. Only when drivers had low cognitive load, they had shorter takeover reaction time in high oncoming traffic conditions. High oncoming traffic led to higher collision risk. High speed led to higher collision risk and harsher takeover behaviors in lane changing scenarios, but engendered longer takeover reaction time and smoother takeover behaviors in lane keeping scenarios. Meanwhile, we developed a random forest model to predict drivers' takeover performance with an accuracy of 84.3% and an F1-score of 64.0%. Our model had finer granularity than and outperformed other machine learning models used in prior studies. The findings of alert system design studies showed that drivers had more anxiety with the why only information compared to the why + what will information when information was presented in the speech modality. They felt more prepared to take over control of the vehicle and had more preference for the combination of augmented reality and speech conditions than others when drivers were in high event criticality situations. This dissertation can add to the knowledge base about takeover response investigation, takeover performance prediction, and in-vehicle alert system design. The results will enhance the understanding of how drivers’ emotions, cognitive load, traffic density, and scenario type influence their takeover responses. The computational models for takeover performance prediction are underlying algorithms of in-vehicle monitoring systems in real-world applications. The findings will provide design recommendations to automated vehicle manufacturers on in-vehicle alert systems. This will ultimately enhance the interaction between drivers and automated vehicles and improve driving safety in intelligent transportation systems.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169727/1/nadu_1.pd