Miracle-dz/UniFAR-FTUs huggingface dataset:
ClearPose: Large-scale Transparent Object Dataset and Benchmark

Transparent objects are ubiquitous in household settings and pose distinct challenges for visual sensing and perception systems. The optical properties of transparent objects leave conventional 3D sensors alone unreliable for object depth and pose estimation. These challenges are highlighted by the shortage of large-scale RGB-Depth datasets focusing on transparent objects in real-world settings. In this work, we contribute a large-scale real-world RGB-Depth transparent object dataset named ClearPose to serve as a benchmark dataset for segmentation, scene-level depth completion and object-centric pose estimation tasks. The ClearPose dataset contains over 350K labeled real-world RGB-Depth frames and 5M instance annotations covering 63 household objects. The dataset includes object categories commonly used in daily life under various lighting and occluding conditions as well as challenging test scenarios such as cases of occlusion by opaque or translucent objects, non-planar orientations, presence of liquids, etc. We benchmark several state-of-the-art depth completion and object pose estimation deep neural networks on ClearPose. The dataset and benchmarking source code is available at https://github.com/opipari/ClearPose.
A Lightweight and Detector-Free 3D Single Object Tracker on Point Clouds

Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking. However, it is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. In this paper, we address this issue by explicitly leveraging temporal motion cues and propose DMT, a Detector-free Motion-prediction-based 3D Tracking network that completely removes the usage of complicated 3D detectors and is lighter, faster, and more accurate than previous trackers. Specifically, the motion prediction module is first introduced to estimate a potential target center of the current frame in a point-cloud-free manner. Then, an explicit voting module is proposed to directly regress the 3D box from the estimated target center. Extensive experiments on KITTI and NuScenes datasets demonstrate that our DMT can still achieve better performance ( $\sim $ 10% improvement over the NuScenes dataset) and a faster tracking speed (i.e., 72 FPS) than state-of-the-art approaches without applying any complicated 3D detectors. Our code is released at https://github.com/jimmy-dq/DMT.
Follow‐up observations of the binary system
 γ
 Cep

We present new imaging and spectroscopic observations of the exoplanet host star $\gamma$ Cep A, and of its low-mass stellar companion $\gamma$ Cep B. We used AstraLux/CAHA to follow the orbital motion of the companion around its primary, whose radial velocity was determined with spectra of the star, taken with the spectrograph FLECHAS at the University Observatory Jena. We measured the astrometry of $\gamma$ Cep B relative to its primary in all AstraLux images and determined its apparent SDSS i'-band photometry, for which we obtained i'=9.84$\pm$0.17 mag. Using stellar evolutionary models and the Gaia parallax of the exoplanet host star, we derived the mass of $\gamma$ Cep B to be 0.39$\pm$0.03 M${_\odot}$. This is in good agreement with the mass of the companion, derived from its NIR photometry, given in the literature. With the detection limit, reached in our AstraLux images, we explored the detection space of potential additional companions in the $\gamma$ Cep binary system. In the background limited region at angular separations larger then 5 '' (or 69 au of proj. separation) companions down to 0.11 M$_\odot$ are detectable around $\gamma$ Cep A. The radial FoV, fully covered in our AstraLux images, exhibits a radius of 11.2 ''. This allows the detection of companions with proj. separations up to 155 au. However, except for $\gamma$ Cep B no additional companions could be imaged around the exoplanet host star. We redetermined the orbital solution of the $\gamma$ Cep binary system with the new AstraLux astrometry of $\gamma$ Cep B and the additional radial velocities of $\gamma$ Cep A, obtained from our FLECHAS spectroscopy of the star, combined with astrometric and radial velocity data from the literature. The determined orbital elements were used to derive the system parameters and to calculate specific future ephemeris for this intriguing exoplanet host binary star system.
Self-supervised Social Relation Representation for Human Group Detection

Human group detection, which splits crowd of people into groups, is an important step for video-based human social activity analysis. The core of human group detection is the human social relation representation and division. In this paper, we propose a new two-stage multi-head framework for human group detection. In the first stage, we propose a human behavior simulator head to learn the social relation feature embedding, which is self-supervisely trained by leveraging the socially grounded multi-person behavior relationship. In the second stage, based on the social relation embedding, we develop a self-attention inspired network for human group detection. Remarkable performance on two state-of-the-art large-scale benchmarks, i.e., PANDA and JRDBGroup, verifies the effectiveness of the proposed framework. Benefiting from the self-supervised social relation embedding, our method can provide promising results with very few (labeled) training data. We will release the source code to the public.
Lane Detection with Versatile AtrousFormer and Local Semantic Guidance

Lane detection is one of the core functions in autonomous driving and has aroused widespread attention recently. The networks to segment lane instances, especially with bad appearance, must be able to explore lane distribution properties. Most existing methods tend to resort to CNN-based techniques. A few have a try on incorporating the recent adorable, the seq2seq Transformer \cite{transformer}. However, their innate drawbacks of weak global information collection ability and exorbitant computation overhead prohibit a wide range of the further applications. In this work, we propose Atrous Transformer (AtrousFormer) to solve the problem. Its variant local AtrousFormer is interleaved into feature extractor to enhance extraction. Their collecting information first by rows and then by columns in a dedicated manner finally equips our network with stronger information gleaning ability and better computation efficiency. To further improve the performance, we also propose a local semantic guided decoder to delineate the identities and shapes of lanes more accurately, in which the predicted Gaussian map of the starting point of each lane serves to guide the process. Extensive results on three challenging benchmarks (CULane, TuSimple, and BDD100K) show that our network performs favorably against the state of the arts.
The Archival Discovery of a Strong Lyα and [C ii] Emitter at z = 7.677

We report the archival discovery of Lyα emission from the bright ultraviolet galaxy Y002 at z = 7.677, spectroscopically confirmed by its ionized carbon [C ii] 158 μm emission line. The Lyα line is spatially associated with the rest-frame UV stellar emission (M UV ∼ −22, 2× brighter than MUV⋆ ), and it appears offset from the peak of the extended [C ii] emission at the current ∼1″ spatial resolution. We derive an estimate of the unobscured SFRUV = (22 ± 1) M ⊙ yr−1 and set an upper limit of SFRIR < 15 M ⊙ yr−1 from the far-infrared (FIR) wavelength range, which globally places Y002 on the SFR(UV+IR)–L [C II] correlation observed at lower redshifts. In terms of velocity, the peak of the Lyα emission is redshifted by Δv Lyα ∼ 500 km s−1 from the systemic redshift set by [C ii] and a high-velocity tail extends up to ∼1000 km s−1. The velocity offset is up to ∼3.5× higher than the average estimate for similarly UV-bright emitters at z ∼ 6–7, which might suggest that we are witnessing the merging of two clumps. A combination of strong outflows and the possible presence of an extended ionized bubble surrounding Y002 would likely facilitate the escape of copious Lyα light, as indicated by the large equivalent width EW0(Lyα) =24−6+5 Å. Assuming that [C ii] traces the neutral hydrogen, we estimate a H i gas fraction of M H I/M ⋆ ≳ 8 for Y002 as a system and speculate that patches of high H i column densities could contribute to explaining the observed spatial offsets between Lyα- and [C ii]-emitting regions. The low dust content, implied by the nondetection of the FIR continuum emission at rest frame ∼160 μm, would be sufficient to absorb any potential Lyα photons produced within the [C ii] clump as a result of large H i column densities.
Testing for treatment effect twice using internal and external controls in clinical trials

Abstract Leveraging external controls – relevant individual patient data under control from external trials or real-world data – has the potential to reduce the cost of randomized controlled trials (RCTs) while increasing the proportion of trial patients given access to novel treatments. However, due to lack of randomization, RCT patients and external controls may differ with respect to covariates that may or may not have been measured. Hence, after controlling for measured covariates, for instance by matching, testing for treatment effect using external controls may still be subject to unmeasured biases. In this article, we propose a sensitivity analysis approach to quantify the magnitude of unmeasured bias that would be needed to alter the study conclusion that presumed no unmeasured biases are introduced by employing external controls. Whether leveraging external controls increases power or not depends on the interplay between sample sizes and the magnitude of treatment effect and unmeasured biases, which may be difficult to anticipate. This motivates a combined testing procedure that performs two highly correlated analyses, one with and one without external controls, with a small correction for multiple testing using the joint distribution of the two test statistics. The combined test provides a new method of sensitivity analysis designed for data fusion problems, which anchors at the unbiased analysis based on RCT only and spends a small proportion of the type I error to also test using the external controls. In this way, if leveraging external controls increases power, the power gain compared to the analysis based on RCT only can be substantial; if not, the power loss is small. The proposed method is evaluated in theory and power calculations, and applied to a real trial.
Recent Formation of a Spiral Disk Hosting Progenitor Globular Clusters at the Center of the Perseus Brightest Cluster Galaxy. II. Progenitor Globular Clusters

We address the nature and origin of super star clusters (SSCs) discovered by Holtzman et al. within a radius of ∼5 kpc from the center of NGC 1275, the giant elliptical galaxy at the center of the Perseus Cluster. We show that, in contrast with the much more numerous population of SSCs subsequently discovered up to ∼30 kpc from the center of this galaxy, the central SSC population have maximal masses an order of magnitude higher and a mass function with a shallower power-law slope. Furthermore, whereas the outer SSC population have ages spanning a few Myr to at least ∼1 Gyr, the central SSC population have ages strongly concentrated around ∼500 Myr with a 1σ dispersion of ∼100 Myr. These SSCs share a close spatial and temporal relationship with the “central spiral,” which also has a radius ∼5 kpc centered on NGC 1275 and a characteristic stellar age of ∼150 Myr. We argue that both the central SSC population and the central spiral formed from gas deposited by a residual cooling flow, with the SSCs forming first followed by the formation of the stellar body of the central spiral ∼300–400 Myr later. The ages of the central SSC population imply that they are able to withstand very strong tidal fields near the center of NGC 1275, making them genuine progenitor globular clusters. Evidently, a spiral disk hosting progenitor globular clusters has recently formed at the center of a giant elliptical galaxy.
RAPTEE: Leveraging trusted execution environments for Byzantine-tolerant peer sampling services

Peer sampling is a first-class abstraction used in distributed systems for overlay management and information dissemination. The goal of peer sampling is to continuously build and refresh a partial and local view of the full membership of a dynamic, large-scale distributed system. Malicious nodes under the control of an adversary may aim at being over-represented in the views of correct nodes, increasing their impact on the proper operation of protocols built over peer sampling. State-of-the-art Byzantine resilient peer sampling protocols reduce this bias as long as Byzantines are not overly present. This paper studies the benefits brought to the resilience of peer sampling services when considering that a small portion of trusted nodes can run code whose authenticity and integrity can be assessed within a trusted execution environment, and specifically Intel’s software guard extensions technology (SGX). We present RAPTEE, a protocol that builds and leverages trusted gossip-based communications to hamper an adversary’s ability to increase its system-wide representation in the views of all nodes. We apply RAPTEE to BRAHMS, the most resilient peer sampling protocol to date. Experiments with 10,000 nodes show that with only 1% of SGX-capable devices, RAPTEE can reduce the proportion of identifiers of Byzantine nodes in the view of honest ones by up to 17%, when the system contains 10% of Byzantine nodes. In addition, the security guarantees of RAPTEE hold even in the presence of a powerful attacker attempting to identify trusted nodes and injecting view-poisoned trusted nodes.
Safety Effects of Street Illuminance at Urban Signalized Intersections in Florida

Nighttime crashes are overrepresented on United States and state roadway systems. Nighttime safety at signalized intersections in urban areas receives more attention because of frequent and serious vehicle-to-vehicle, vehicle-to-pedestrian, and vehicle-to-bicyclist traffic conflicts at night. Street illumination has proved to be an effective countermeasure to increase visibility at intersections and to reduce nighttime crashes. However, the safety effects of illuminance at urban signalized intersections are not well documented. This study investigated the impacts of illuminance on nighttime crash occurrence and nighttime crash injury severity at urban signalized intersections. Illuminance data and crash data for a sample of 91 signalized intersections were collected from the Tampa Bay region in Florida. The negative binomial model and the probit model were developed to examine the safety effects of intersection illuminance in terms of crash frequency and the risk of fatality and severe injury, respectively. The models showed that an increase in intersection illuminance from low (<0.2 fc) to medium (≥0.2 fc and <1.1 fc) could reduce nighttime crash frequency and night-to-day crash ratios by approximately 50%. When illuminance was kept at 0.9 fc or higher, the risk of fatality and severe injury decreased significantly, especially in crashes that involved pedestrians and bicycles, head-on crashes, and angle crashes.
Generative Multi-purpose Sampler for Weighted M-estimation

To overcome the computational bottleneck of various data perturbation procedures such as the bootstrap and cross validations, we propose the Generative Multiple-purpose Sampler (GMS), which constructs a generator function to produce solutions of weighted M-estimators from a set of given weights and tuning parameters. The GMS is implemented by a single optimization without having to repeatedly evaluate the minimizers of weighted losses, and is thus capable of significantly reducing the computational time. We demonstrate that the GMS framework enables the implementation of various statistical procedures that would be unfeasible in a conventional framework, such as the iterated bootstrap, bootstrapped cross-validation for penalized likelihood, bootstrapped empirical Bayes with nonparametric maximum likelihood, etc. To construct a computationally efficient generator function, we also propose a novel form of neural network called the \emph{weight multiplicative multilayer perceptron} to achieve fast convergence. Our numerical results demonstrate that the new neural network structure enjoys a few orders of magnitude speed advantage in comparison to the conventional one. An R package called GMS is provided, which runs under Pytorch to implement the proposed methods and allows the user to provide a customized loss function to tailor to their own models of interest.
Application of Principal Component Analysis in Evaluation of Cigarette Quality and its Consistence

In order to effectively evaluate cigarette quality and its consistence,the physical indexes and mainstream smoke indexes of cigarette samples A,B1 and B2 were analyzed with Principal Component Analysis(PCA),and their principal components were determined.According to characteristic matrix,the principal component score diagrams of cigarette samples were established,and cluster analyses were conducted.By using principal component load graphs,the importance of cigarette physical indexes and mainstream smoke indexes and their correlations were analyzed respectively.The results showed that: 1) The samples of different cigarette brands,A and B1,could be distinguished by cluster analysis,which indicated that they possessed different principal components.While the samples of the same brand but from different batches,B1 and B2 were not differentiable,which indicated that the cigarette brand B was in good quality consistence.2) The load vector angle of 0.86 degrees between puff number of cigarette and CO in mainstream smoke suggested that there existed stronger correlation between the two.The load vectors of puff number with moisture content in mainstream smoke and that of draw resistance with nicotine in mainstream smoke were in an orthogonal state,it indicated their weak correlations.The load vector angle was 148.09 degrees between draw resistance of cigarette and moisture content in mainstream smoke,it indicated a certain negative correlation between them.The correlation analysis between each index could provide reference and theoretical basis for the product design and quality control of cigarette.
Emotion diffusion effect: Negative sentiment COVID-19 tweets of public organizations attract more responses from followers

Coronavirus disease 2019 (COVID-19) has triggered an enormous number of discussion topics on social media Twitter. It has an impact on the global health system and citizen responses to the pandemic. Multiple responses (replies, favorites, and retweets) reflect the followers’ attitudes and emotions towards these tweets. Twitter data such as these have inspired substantial research interest in sentiment and social trend analyses. To date, studies on Twitter data have focused on the associational relationships between variables in a population. There is a need for further discovery of causality, such as the influence of sentiment polarity of tweet response on further discussion topics. These topics often reflect the human perception of COVID-19. This study addresses this exact topic. It aims to develop a new method to unveil the causal relationships between the sentiment polarity and responses in social media data. We employed sentiment polarity, i.e., positive or negative sentiment, as the treatment variable in this quasi-experimental study. The data is the tweets posted by nine authoritative public organizations in four countries and the World Health Organization from December 1, 2019, to May 10, 2020. Employing the inverse probability weighting model, we identified the treatment effect of sentiment polarity on the multiple responses of tweets. The topics with negative sentiment polarity on COVID-19 attracted significantly more replies (69±49) and favorites (688±677) than the positive tweets. However, no significant difference in the number of retweets was found between the negative and positive tweets. This study contributes a new method for social media analysis. It generates new insight into the influence of sentiment polarity of tweets about COVID-19 on tweet responses.
The value of combining individual and small area sociodemographic data for assessing and handling selective participation in cohort studies: Evidence from the Swedish CardioPulmonary bioImage Study

Objectives To study the value of combining individual- and neighborhood-level sociodemographic data to predict study participation and assess the effects of baseline selection on the distribution of metabolic risk factors and lifestyle factors in the Swedish CardioPulmonary bioImage Study (SCAPIS). Methods We linked sociodemographic register data to SCAPIS participants (n = 30,154, ages: 50–64 years) and a random sample of the study’s target population (n = 59,909). We assessed the classification ability of participation models based on individual-level data, neighborhood-level data, and combinations of both. Standardized mean differences (SMD) were used to examine how reweighting the sample to match the population affected the averages of 32 cardiopulmonary risk factors at baseline. Absolute SMDs >0.10 were considered meaningful. Results Combining both individual-level and neighborhood-level data gave rise to a model with better classification ability (AUC: 71.3%) than models with only individual-level (AUC: 66.9%) or neighborhood-level data (AUC: 65.5%). We observed a greater change in the distribution of risk factors when we reweighted the participants using both individual and area data. The only meaningful change was related to the (self-reported) frequency of alcohol consumption, which appears to be higher in the SCAPIS sample than in the population. The remaining risk factors did not change meaningfully. Conclusions Both individual- and neighborhood-level characteristics are informative in assessing study selection effects. Future analyses of cardiopulmonary outcomes in the SCAPIS cohort can benefit from our study, though the average impact of selection on risk factor distributions at baseline appears small.