arXiv subjects

Lu Feng

Publications and source records attributed to Lu Feng.

At least 73 records · Page 4Linked to original sources

arXiv2022

Cosmological search for sterile neutrinos after Planck 2018

Sterile neutrinos can affect the evolution of the universe, and thus using the cosmological observations can search for sterile neutrinos. In this work, we use the cosmic microwave background (CMB) anisotropy data from the Planck 2018 release, combined with the latest baryon acoustic oscillation (BAO), type Ia supernova (SN), and Hubble constant ($H_0$) data, to constrain the cosmological models with considering sterile neutrinos. In order to test the influences of the properties of dark energy on the {results} of searching for sterile neutrinos, in addition to the $Λ$ cold dark matter ($Λ$CDM) model, we also consider the $w$CDM model and the holographic dark energy (HDE) model. We find that the existence of sterile neutrinos {is not preferred} when the $H_0$ local measurement is not included in the data combination. When the $H_0$ measurement is included in the joint constraints, it is found that $ΔN_{\rm eff}>0$ is {favored} at about 2.7$σ$ level for the $Λ$CDM model and at about 1-1.7$σ$ level for the $w$CDM model. However, $m_{ν,{\rm{sterile}}}^{\rm{eff}}$ still cannot be well constrained and only upper limits can be given. In addition, we find that the HDE model is definitely ruled out by the current data. We also discuss the issue of the Hubble tension, and we conclude that involving sterile neutrinos in the cosmological models cannot truly resolve the Hubble tension.

astro-ph.CO↗

arXiv2021

Exploration of interacting dynamical dark energy model with interaction term including the equation-of-state parameter: alleviation of the $H_{0}$ tension

We explore a scenario of interacting dynamical dark energy model with the interaction term $Q$ including the varying equation-of-state parameter $w$. Using the data combination of the cosmic microwave background, the baryon acoustic oscillation, and the type Ia supernovae, to global fit the interacting dynamical dark energy model, we find that adding a factor of the varying $w$ in the function of $Q$ can change correlations between the coupling constant $β$ and other parameters, and then has a huge impact on the fitting result of $β$. In this model, the fitting value of $H_{0}$ is lower at the $3.54 σ$ level than the direct measurement value of $H_{0}$ . Comparing to the case of interacting dynamical dark energy model with $Q$ excluding $w$, the model with $Q$ including the constant $w$ is more favored by the current mainstream observation. To obtain higher fitting values of $H_{0}$ and narrow the discrepancy of $H_{0}$ between different observations, additional parameters including the effective number of relativistic species, the total neutrino mass, and massive sterile neutrinos are considered in the interacting dynamical dark energy cosmology. We find that the $H_{0}$ tension can be further reduced in these models, but is still at the about $3 σ$ level.

gr-qc↗

arXiv2021

SaSTL: Spatial Aggregation Signal Temporal Logic for Runtime Monitoring in Smart Cities

We present SaSTL -- a novel Spatial Aggregation Signal Temporal Logic -- for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.

cs.CY↗

arXiv2021

MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning

Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting the visual attention of drivers in accident-prone situations. MEDIRL predicts fixation locations that lead to maximal rewards by learning a task-sensitive reward function from eye fixation patterns recorded from attentive drivers. Additionally, we introduce EyeCar, a new driver attention dataset in accident-prone situations. We conduct comprehensive experiments to evaluate our proposed model on three common benchmarks: (DR(eye)VE, BDD-A, DADA-2000), and our EyeCar dataset. Results indicate that MEDIRL outperforms existing models for predicting attention and achieves state-of-the-art performance. We present extensive ablation studies to provide more insights into different features of our proposed model.

cs.CV↗

arXiv2021

Photon return on-sky test for by-product Rayleigh plume of sodium laser guide star

The sodium laser guide star (sLGS) adaptive optics (AO) system has become an essential component for large ground-based optical/infrared telescope. The performance of the AO system can be significantly hindered if the on-sky spot profile of its sLGS is degraded by the turbulence along the laser launching uplink path. One effective method to overcome this problem is to perform real-time pre-correction on the laser before it is launched onto the sky to counter this turbulence effect especially for the lower altitude where turbulence is stronger. The by-product Rayleigh backscatter generated by large molecule and aerosols at low altitude when projecting sLGS is a perfect candidate for real time detection of low altitude turbulence. It is therefore important to evaluate whether this by-product of a sodium laser guide star could achieve suitable performance for wavefront sensing. In this paper, we attempt to answer the question regarding to the achievable photon return of such Rayleigh backscattered plume. A comparison between results from our field test and theoretical model of MSISE-90 was presented. We showed that the greatest differences is less than 20% which hints the applicability of using the defocused Rayleigh plume as wavefront sensing signal for different turbulence strengths. The result also showed that in case of strong (Fried parameter (r0) of 5 cm) or moderate (r0 of 10 cm) turbulence, the optimum center for range gating is 9 km, and the corresponding value of range gate depths are 3 km, 1.1 km, 0.56 km and 0.28 km when subaperture sizes are 3 cm, 5 cm, 7 cm and 10 cm, with the maximum pulse repetition frequency at 1500 Hz.

astro-ph.IM↗

arXiv2021

Short term minutes-time scale temporal variation statistics of sodium layer dynamics

The brightness and height of the sodium laser guide star of adaptive optics could vary significantly due to the temporal dynamics of sodium column density and the mean height of sodium layer. To measure these dynamics, an independent sodium Lidar is a necessity. Without such an instrument, it is almost impossible to discern the cause of the brightness variation of laser guide star from the sodium layer's dynamics or other factors from the laser itself. For applications such as characterizing the performance of sodium laser for sodium laser guide star generation, minutes scale short term statistics of the sodium layers' abundance and height is extremely helpful for estimating the contribution of sodium layer's variation to the variation of laser guide star's brightness. In this paper, we analyzed our previous measurement of sodium layer dynamics that has been gathered in two winters, and presented the temporal variation statistics of sodium column density and mean height within minute time scale based on our measurements.

astro-ph.IM↗

arXiv2021

Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems

Predictive monitoring -- making predictions about future states and monitoring if the predicted states satisfy requirements -- offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named \emph{Signal Temporal Logic with Uncertainty} (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on if all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines.

cs.LG↗

arXiv2021

Numerical study of a 20W class QCW pulsed sodium guide star laser's performances at five sites in China

In the past few years, Chinese astronomical community is actively testing astronomical sites for several new optical/infrared ground-based telescopes. These site testing campaigns conducted were mainly focused on fundamental performances of the site, such as cloud coverage, seeing, temperature, etc. With increasing interests in sodium laser guide star adaptive optics for these new telescopes in the Chinese astronomical community, it is interesting to investigate the performance of the laser guide star at these sites, especially considering that the sodium laser guide star's on-sky performance is significantly influenced by sites' local performances, such as geomagnetic field, sodium layer dynamics, density of air molecule, etc. In this paper, we studied sodium laser guide star's performance of a 20W class Quasi-CW pulsed laser developed by TIPC with numerical simulation for five selected sites in China.

astro-ph.IM↗

arXiv2021

A Novel Spatial-Temporal Specification-Based Monitoring System for Smart Cities

With the development of the Internet of Things, millions of sensors are being deployed in cities to collect real-time data. This leads to a need for checking city states against city requirements at runtime. In this paper, we develop a novel spatial-temporal specification-based monitoring system for smart cities. We first describe a study of over 1,000 smart city requirements, some of which cannot be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop SaSTL -- a novel Spatial Aggregation Signal Temporal Logic -- for the efficient runtime monitoring of safety and performance requirements in smart cities. We develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We define Boolean and \newcontent{quantitative semantics}~for SaSTL in support of the analysis of city performance across different periods and locations. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city). Additionally, we build a SaSTL-based monitoring tool to support decision making of different stakeholders to specify and runtime monitor their requirements in smart cities. We evaluate our SaSTL monitor by applying it to three case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one study). The results show that SaSTL has a much higher coverage expressiveness than other spatial-temporal logic, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor improves the safety and performance of smart cities via simulated experiments.

cs.SE↗

arXiv2021

Towards Personalized Explanation of Robot Path Planning via User Feedback

Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaboration. In this paper, we present a system for generating personalized explanations of robot path planning via user feedback. We consider a robot navigating in an environment modeled as a Markov decision process (MDP), and develop an algorithm to automatically generate a personalized explanation of an optimal MDP policy, based on the user preference regarding four elements (i.e., objective, locality, specificity, and corpus). In addition, we design the system to interact with users via answering users' further questions about the generated explanations. Users have the option to update their preferences to view different explanations. The system is capable of detecting and resolving any preference conflict via user interaction. The results of an online user study show that the generated personalized explanations improve user satisfaction, while the majority of users liked the system's capabilities of question-answering and conflict detection/resolution.

cs.RO↗

arXiv2021

Safe Multi-Agent Reinforcement Learning via Shielding

Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e.g., no unsafe states are ever visited) during the learning process.Unfortunately, current MARL methods do not have safety guarantees. Therefore, we present two shielding approaches for safe MARL. In centralized shielding, we synthesize a single shield to monitor all agents' joint actions and correct any unsafe action if necessary. In factored shielding, we synthesize multiple shields based on a factorization of the joint state space observed by all agents; the set of shields monitors agents concurrently and each shield is only responsible for a subset of agents at each step.Experimental results show that both approaches can guarantee the safety of agents during learning without compromising the quality of learned policies; moreover, factored shielding is more scalable in the number of agents than centralized shielding.

cs.LG↗

arXiv2021

DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data

Automated vehicles promise a future where drivers can engage in non-driving tasks without hands on the steering wheels for a prolonged period. Nevertheless, automated vehicles may still need to occasionally hand the control back to drivers due to technology limitations and legal requirements. While some systems determine the need for driver takeover using driver context and road condition to initiate a takeover request, studies show that the driver may not react to it. We present DeepTake, a novel deep neural network-based framework that predicts multiple aspects of takeover behavior to ensure that the driver is able to safely take over the control when engaged in non-driving tasks. Using features from vehicle data, driver biometrics, and subjective measurements, DeepTake predicts the driver's intention, time, and quality of takeover. We evaluate DeepTake performance using multiple evaluation metrics. Results show that DeepTake reliably predicts the takeover intention, time, and quality, with an accuracy of 96%, 93%, and 83%, respectively. Results also indicate that DeepTake outperforms previous state-of-the-art methods on predicting driver takeover time and quality. Our findings have implications for the algorithm development of driver monitoring and state detection.

cs.LG↗

arXiv2020

Probabilistic Conditional System Invariant Generation with Bayesian Inference

Invariants are a set of properties over program attributes that are expected to be true during the execution of a program. Since developing those invariants manually can be costly and challenging, there are a myriad of approaches that support automated mining of likely invariants from sources such as program traces. Existing approaches, however, are not equipped to capture the rich states that condition the behavior of autonomous mobile robots, or to manage the uncertainty associated with many variables in these systems. This means that valuable invariants that appear only under specific states remain uncovered. In this work we introduce an approach to infer conditional probabilistic invariants to assist in the characterization of the behavior of such rich stateful, stochastic systems. These probabilistic invariants can encode a family of conditional patterns, are generated using Bayesian inference to leverage observed trace data against priors gleaned from previous experience and expert knowledge, and are ranked based on their surprise value and information content. Our studies on two semi-autonomous mobile robotic systems show how the proposed approach is able to generate valuable and previously hidden stateful invariants.

cs.RO↗

arXiv2020

Constraints on active and sterile neutrinos in an interacting dark energy cosmology

We investigate the impacts of dark energy on constraining massive (active/sterile) neutrinos in interacting dark energy (IDE) models by using the current observations. We employ two typical IDE models, the interacting $w$ cold dark matter (I$w$CDM) model and the interacting holographic dark energy (IHDE) model, to make an analysis. To avoid large-scale instability, we use the parameterized post-Friedmann approach to calculate the cosmological perturbations in the IDE models. The cosmological observational data used in this work include the Planck cosmic microwave background (CMB) anisotropies data, the baryon acoustic oscillation data, the type Ia supernovae data, the direct measurement of the Hubble constant, the weak lensing data, the redshift-space distortion data, and the CMB lensing data. We find that the dark energy properties could influence the constraint limits of active neutrino mass and sterile neutrino parameters in the IDE models. We also find that the dark energy properties could influence the constraints on the coupling strength parameter $β$, and a positive coupling constant, $β>0$, can be detected at the $2.5σ$ statistical significance for the IHDE+$ν_s$ model by using the all-data combination. In addition, we also discuss the "Hubble tension" issue in these scenarios. We find that the $H_0$ tension can be effectively relieved by considering massive sterile neutrinos, and in particular in the IHDE+$ν_s$ model the $H_0$ tension can be reduced to be at the $1.28σ$ level.

astro-ph.CO↗

arXiv2020

Optical turbulence at Ali, China -- Results from the first year of lunar scintillometer observations

The location of an astronomical observatory is a key factor that affects its scientific productivity. The best astronomical sites are generally those found at high altitudes. Several such sites in western China and the Tibetan plateau are presently under development for astronomy. One of these is Ali, which at over 5000 m is one of the highest astronomical sites in the world. In order to further investigate the astronomical potential of Ali, we have installed a lunar scintillometer, for the primary purpose of profiling atmospheric turbulence. This paper describes the instrument and technique, and reports results from the first year of observations. We find that ground layer (GL) turbulence at Ali is remarkably weak and relatively thin. The median seeing, from turbulence in the range 11- 500 m above ground is 0.34 arcsec, with seeing better than 0.26 arcsec occurring 25 per cent of the time. Under median conditions, half of the GL turbulence lies below a height of 62 m. These initial results, and the high altitude and relatively low temperatures, suggest that Ali could prove to be an outstanding site for ground-based astronomy.

astro-ph.IM↗

arXiv2020

Site-testing at Muztagh-ata site I: Ground Meteorology and Sky Brightness

Site-testing is crucial for achieving the goal of scientific research and analysis of meteorological and optical observing conditions is one of the basic tasks of it. As one of three potential sites to host 12-meter Large Optical/infrared Telescope (LOT), Muztagh-ata site which is located on the Pamirs Plateau in west China's Xinjiang began its site-testing task in the spring of 2017. In this paper, we firstly start with an introduction to the site and then present a statistical analysis of the ground-level meteorological properties such as air temperature, barometric pressure, relative humidity, wind speed and direction, recorded by automatic weather station with standard meteorological sensors for two-year long. We also show the monitoring results of sky brightness during this period.

astro-ph.IM↗

arXiv2020

Site-testing at Muztagh-ata site II: Seeing statistics

In this article, we present a detailed analysis of the statistical properties of seeing for the Muztagh-ata site which is the candidate site for hosting future Chinese Large Optical/infrared Telescope (LOT) project. The measurement was obtained with Differential Image Motion Monitor (DIMM) from April 2017 to November 2018 at different heights during different periods. The median seeing at 11 meters and 6 meters are very close but different significantly from that on the ground. We mainly analyzed the seeing at 11 meters monthly and hourly, having found that the best season for observing was from late autumn to early winter and seeing tended to improve during the night only in autumn. The analysis of the dependence on temperature inversion, wind speed, direction also was made and the best meteorological conditions for seeing is given.

astro-ph.IM↗

arXiv2020

Towards Transparent Robotic Planning via Contrastive Explanations

Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust. Social sciences suggest that the best explanations are contrastive, explaining not just why one action is taken, but why one action is taken instead of another. We formalize the notion of contrastive explanations for robotic planning policies based on Markov decision processes, drawing on insights from the social sciences. We present methods for the automated generation of contrastive explanations with three key factors: selectiveness, constrictiveness, and responsibility. The results of a user study with 100 participants on the Amazon Mechanical Turk platform show that our generated contrastive explanations can help to increase users' understanding and trust of robotic planning policies while reducing users' cognitive burden.

cs.RO↗