Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying interaction patterns of the ego vehicle with other nearby vehicles. Our model relaxes dependency on the number of surrounding vehicles by developing an acceleration-sensitive velocity field based on Gaussian processes. The experiment results demonstrate that the velocity field can represent the spatial interactions between the ego vehicle and its surroundings. Then, a discrete Bayesian nonparametric model, integrating Dirichlet processes and hidden Markov models, is developed to learn the interaction patterns over the temporal space by segmenting and clustering the sequential interaction data into interpretable granular patterns automatically. We then evaluate our approach in the highway lane-change scenarios using the highD dataset collected from real-world settings. Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships. Our proposed approach sheds light on efficiently analyzing other kinds of multi-agent interactions, such as vehicle-pedestrian interactions. View the demos via https://youtu.be/z_vf9UHtdAM.Comment: for the supplements, see https://chengyuan-zhang.github.io/Multivehicle-Interaction

EqCo: Equivalent Rules for Self-supervised Contrastive Learning

In this paper, we propose a method, named EqCo (Equivalent Rules for Contrastive Learning), to make self-supervised learning irrelevant to the number of negative samples in InfoNCE-based contrastive learning frameworks. Inspired by the InfoMax principle, we point that the margin term in contrastive loss needs to be adaptively scaled according to the number of negative pairs in order to keep steady mutual information bound and gradient magnitude. EqCo bridges the performance gap among a wide range of negative sample sizes, so that we can use only a few negative pairs (e.g. 16 per query) to perform self-supervised contrastive training on large-scale vision datasets like ImageNet, while with almost no accuracy drop. This is quite a contrast to the widely used large batch training or memory bank mechanism in current practices. Equipped with EqCo, our simplified MoCo (SiMo) achieves comparable accuracy with MoCo v2 on ImageNet (linear evaluation protocol) while only involves 4 negative pairs per query instead of 65536, suggesting that large quantities of negative samples might not be a critical factor in InfoNCE loss

MaNGA DynPop – III. Stellar dynamics versus stellar population relations in 6000 early-type and spiral galaxies: Fundamental Plane, mass-to-light ratios, total density slopes, and dark matter fractions

We present dynamical scaling relations, combined with the stellar population properties, for a subsample of about 6000 nearbygalaxies with the most reliable dynamical models extracted from the full Mapping Nearby Galaxies at Apache Point Observatory(MaNGA) sample of 10 000 galaxies. We show that the inclination-corrected mass plane for both early-type galaxies (ETGs) andlate-type galaxies (LTGs), which links dynamical mass, projected half-light radius Re, and the second stellar velocity momentσe within Re, satisfies the virial theorem and is even tighter than the uncorrected one. We find a clear parabolic relation betweenlg(M/L)e, the total mass-to-light ratio (M/L) within a sphere of radius Re, and lg σe, with the M/L increasing with σe andfor older stellar populations. However, the relation for ETGs is linear and the one for the youngest galaxies is constant. Weconfirm and improve the relation between mass-weighted total density slopes γT and σe: γT become steeper with increasingσe until lg(σe/km s−1) ≈ 2.2 and then remain constant around γT ≈ 2.2. The γT –σe variation is larger for LTGs than ETGs. Atfixed σe the total density profiles steepen with galaxy age and for ETGs. We find generally low dark matter fractions, medianfDM(<Re) = 8 per cent, within a sphere of radius Re. However, we find that fDM(<Re) depends on σe better than stellar mass:dark matter increases to a median fDM(<Re) = 33 per cent for galaxies with σe 100 km s−1. The increased fDM(<Re) at lowσe explains the parabolic lg(M/L)e– lg σe relation

SDSS-IV MaNGA: the inner density slopes of nearby galaxies

We derive the mass-weighted total density slopes within the effective (half-light) radius, γ′, for more than 2000 nearby galaxies from the SDSS-IV (Sloan Digital Sky Survey IV) MaNGA survey using Jeans-anisotropic-models applied to integral field unit observations. Our galaxies span a wide range of the stellar mass (109 M⊙ 100 km s−1, the density slope has a mean value 〈γ′〉 = 2.24 and a dispersion σγ = 0.22, almost independent of velocity dispersion, consistent with previous lensing and stellar dynamical analysis. We also quantitatively confirm with high accuracy a turnover in the γ′–σv relation is present at σ ∼ 100 km s−1, below which the density slope decreases rapidly with σv, consistent with the results reported by previous analysis of ATLAS3D survey. Our analysis shows that a large fraction of dwarf galaxies (below M* = 1010 M⊙) have total density slopes shallower than 1, which implies that they may reside in cold dark matter haloes with shallow density slopes. We compare our results with that of galaxies in hydrodynamical simulations of EAGLE, Illustris, and IllustrisTNG projects, and find all simulations predict shallower density slopes for massive galaxies with high σv. Finally, we explore the dependence of γ′ on the positions of galaxies in haloes, namely centrals versus satellites, and find that for the same velocity dispersion, the amplitude of γ′ is higher for satellite galaxies by about 0.1

The Combination of Homocysteine and C-Reactive Protein Predicts the Outcomes of Chinese Patients with Parkinson's Disease and Vascular Parkinsonism

BACKGROUND: The elevation of plasma homocysteine (Hcy) and C-reactive protein (CRP) has been correlated to an increased risk of Parkinson's disease (PD) or vascular diseases. The association and clinical relevance of a combined assessment of Hcy and CRP levels in patients with PD and vascular parkinsonism (VP) are unknown. METHODOLOGY/PRINCIPAL FINDINGS: We performed a cross-sectional study of 88 Chinese patients with PD and VP using a clinical interview and the measurement of plasma Hcy and CRP to determine if Hcy and CRP levels in patients may predict the outcomes of the motor status, non-motor symptoms (NMS), disease severity, and cognitive declines. Each patient's NMS, cognitive deficit, disease severity, and motor status were assessed by the Nonmotor Symptoms Scale (NMSS), Mini-Mental State Examination (MMSE), the modified Hoehn and Yahr staging scale (H&Y), and the unified Parkinson's disease rating scale part III (UPDRS III), respectively. We found that 100% of patients with PD and VP presented with NMS. The UPDRS III significantly correlated with CRP (P = 0.011) and NMSS (P = 0.042) in PD patients. The H&Y was also correlated with Hcy (P = 0.002), CRP (P = 0.000), and NMSS (P = 0.023) in PD patients. In VP patients, the UPDRS III and H&Y were not significantly associated with NMSS, Hcy, CRP, or MMSE. Strong correlations were observed between Hcy and NMSS as well as between CRP and NMSS in PD and VP. CONCLUSIONS/SIGNIFICANCE: Our findings support the hypothesis that Hcy and CRP play important roles in the pathogenesis of PD. The combination of Hcy and CRP may be used to assess the progression of PD and VP. Whether or not anti-inflammatory medication could be used in the management of PD and VP will produce an interesting topic for further research

Simvastatin Prevents Dopaminergic Neurodegeneration in Experimental Parkinsonian Models: The Association with Anti-Inflammatory Responses

Background: In addition to their original applications to lowering cholesterol, statins display multiple neuroprotective effects. N-methyl-D-aspartate (NMDA) receptors interact closely with the dopaminergic system and are strongly implicated in therapeutic paradigms of Parkinson’s disease (PD). This study aims to investigate how simvastatin impacts on experimental parkinsonian models via regulating NMDA receptors. Methodology/Principal Findings: Regional changes in NMDA receptors in the rat brain and anxiolytic-like activity were examined after unilateral medial forebrain bundle lesion by 6-hydroxydopamine via a 3-week administration of simvastatin. NMDA receptor alterations in the post-mortem rat brain were detected by [3H]MK-801(Dizocilpine) binding autoradiography. 6-hydroxydopamine treated PC12 was applied to investigate the neuroprotection of simvastatin, the association with NMDA receptors, and the anti-inflammation. 6-hydroxydopamine induced anxiety and the downregulation of NMDA receptors in the hippocampus, CA1(Cornu Ammonis 1 Area), amygdala and caudate putamen was observed in 6- OHDA(6-hydroxydopamine) lesioned rats whereas simvastatin significantly ameliorated the anxiety-like activity and restored the expression of NMDA receptors in examined brain regions. Significant positive correlations were identified between anxiolytic-like activity and the restoration of expression of NMDA receptors in the hippocampus, amygdala and CA1 following simvastatin administration. Simvastatin exerted neuroprotection in 6-hydroxydopamine-lesioned rat brain and 6- hydroxydopamine treated PC12, partially by regulating NMDA receptors, MMP9 (matrix metalloproteinase-9), and TNF-a (tumour necrosis factor-alpha). Conclusions/Significance: Our results provide strong evidence that NMDA receptor modulation after simvastatin treatment could partially explain its anxiolytic-like activity and anti-inflammatory mechanisms in experimental parkinsonian models. These findings contribute to a better understanding of the critical roles of simvastatin in treating PD via NMDA receptors

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

The 13th Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-IV Survey Mapping Nearby Galaxies at Apache Point Observatory

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) began observations in July 2014. It pursues three core programs: APOGEE-2,MaNGA, and eBOSS. In addition, eBOSS contains two major subprograms: TDSS and SPIDERS. This paper describes the first data release from SDSS-IV, Data Release 13 (DR13), which contains new data, reanalysis of existing data sets and, like all SDSS data releases, is inclusive of previously released data. DR13 makes publicly available 1390 spatially resolved integral field unit observations of nearby galaxies from MaNGA,the first data released from this survey. It includes new observations from eBOSS, completing SEQUELS. In addition to targeting galaxies and quasars, SEQUELS also targeted variability-selected objects from TDSS and X-ray selected objects from SPIDERS. DR13 includes new reductions ofthe SDSS-III BOSS data, improving the spectrophotometric calibration and redshift classification. DR13 releases new reductions of the APOGEE-1data from SDSS-III, with abundances of elements not previously included and improved stellar parameters for dwarf stars and cooler stars. For the SDSS imaging data, DR13 provides new, more robust and precise photometric calibrations. Several value-added catalogs are being released in tandem with DR13, in particular target catalogs relevant for eBOSS, TDSS, and SPIDERS, and an updated red-clump catalog for APOGEE.This paper describes the location and format of the data now publicly available, as well as providing references to the important technical papers that describe the targeting, observing, and data reduction. The SDSS website, http://www.sdss.org, provides links to the data, tutorials and examples of data access, and extensive documentation of the reduction and analysis procedures. DR13 is the first of a scheduled set that will contain new data and analyses from the planned ~6-year operations of SDSS-IV.PostprintPeer reviewe

The 16th Data Release of the Sloan Digital Sky Surveys: First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra

This paper documents the 16th data release (DR16) from the Sloan Digital Sky Surveys (SDSS), the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the Southern Hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey and new data from the SPectroscopic IDentification of ERosita Survey programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library "MaStar"). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17)