4DRVO-Net: Deep 4D Radar-Visual Odometry Using Multi-Modal and Multi-Scale Adaptive Fusion

Four-dimensional (4D) radar--visual odometry (4DRVO) integrates complementary information from 4D radar and cameras, making it an attractive solution for achieving accurate and robust pose estimation. However, 4DRVO may exhibit significant tracking errors owing to three main factors: 1) sparsity of 4D radar point clouds; 2) inaccurate data association and insufficient feature interaction between the 4D radar and camera; and 3) disturbances caused by dynamic objects in the environment, affecting odometry estimation. In this paper, we present 4DRVO-Net, which is a method for 4D radar--visual odometry. This method leverages the feature pyramid, pose warping, and cost volume (PWC) network architecture to progressively estimate and refine poses. Specifically, we propose a multi-scale feature extraction network called Radar-PointNet++ that fully considers rich 4D radar point information, enabling fine-grained learning for sparse 4D radar point clouds. To effectively integrate the two modalities, we design an adaptive 4D radar--camera fusion module (A-RCFM) that automatically selects image features based on 4D radar point features, facilitating multi-scale cross-modal feature interaction and adaptive multi-modal feature fusion. In addition, we introduce a velocity-guided point-confidence estimation module to measure local motion patterns, reduce the influence of dynamic objects and outliers, and provide continuous updates during pose refinement. We demonstrate the excellent performance of our method and the effectiveness of each module design on both the VoD and in-house datasets. Our method outperforms all learning-based and geometry-based methods for most sequences in the VoD dataset. Furthermore, it has exhibited promising performance that closely approaches that of the 64-line LiDAR odometry results of A-LOAM without mapping optimization.Comment: 14 pages,12 figure

UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding

This paper explores the development of UniFolding, a sample-efficient, scalable, and generalizable robotic system for unfolding and folding various garments. UniFolding employs the proposed UFONet neural network to integrate unfolding and folding decisions into a single policy model that is adaptable to different garment types and states. The design of UniFolding is based on a garment's partial point cloud, which aids in generalization and reduces sensitivity to variations in texture and shape. The training pipeline prioritizes low-cost, sample-efficient data collection. Training data is collected via a human-centric process with offline and online stages. The offline stage involves human unfolding and folding actions via Virtual Reality, while the online stage utilizes human-in-the-loop learning to fine-tune the model in a real-world setting. The system is tested on two garment types: long-sleeve and short-sleeve shirts. Performance is evaluated on 20 shirts with significant variations in textures, shapes, and materials. More experiments and videos can be found in the supplementary materials and on the website: https://unifolding.robotflow.aiComment: CoRL 202

Unlocking the enigma: unraveling multiple cognitive dysfunction linked to glymphatic impairment in early Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is one of the world’s well-known neurodegenerative diseases, which is related to the balance mechanism of production and clearance of two proteins (amyloid-β and tau) regulated by the glymphatic system. Latest studies have found that AD patients exhibit impairments to their glymphatic system. However, the alterations in the AD disease continuum, especially in the early stages, remain unclear. Moreover, the relationship between the glymphatic system and cognitive dysfunction is still worth exploring.MethodsA novel diffusion tensor image analysis method was applied to evaluate the activity of the glymphatic system by an index for diffusivity along the perivascular space (ALPS-index). Based on this method, the activity of the glymphatic system was noninvasively evaluated in 300 subjects, including 111 normal controls (NC), 120 subjects with mild cognitive impairment (MCI), and 69 subjects with AD. Partial correlation analysis was applied to explore the association between glymphatic system and cognitive impairment based on three domain-general scales and several domain-specific cognitive scales. Receiver operating characteristic curve analysis was used to evaluate the classification performance of ALPS-index along the AD continuum.ResultsALPS-index was significantly different among NC, MCI and AD groups, and ALPS-index decreased with cognitive decline. In addition, ALPS-index was significantly correlated with the scores of the clinical scales (p<0.05, FDR corrected), especially in left hemisphere. Furthermore, combination of ALPS and fractional anisotropy (FA) values achieved better classification results (NC vs. MCI: AUC = 0.6610, NC vs. AD: AUC = 0.8214).ConclusionHere, we show that the glymphatic system is closely associated with multiple cognitive dysfunctions, and ALPS-index can be used as a biomarker for alterations along the AD continuum. This may provide new targets and strategies for the treatment of AD, and has the potential to assist clinical diagnosis

Pre‐symptomatic transmission of novel coronavirus in community settings

We used contact tracing to document how COVID‐19 was transmitted across 5 generations involving 10 cases, starting with an individual who became ill on January 27. We calculated the incubation period of the cases as the interval between infection and development of symptoms. The median incubation period was 6.0 days (interquartile range, 3.5‐9.5 days). The last two generations were infected in public places, 3 and 4 days prior to the onset of illness in their infectors. Both had certain underlying conditions and comorbidity. Further identification of how individuals transmit prior to being symptomatic will have important consequences.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163478/2/irv12773.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163478/1/irv12773_am.pd

Human and animal exposure to newly discovered sand fly viruses, China

IntroductionThe Hedi virus (HEDV) and Wuxiang virus (WUXV) are newly discovered Bunyaviruses transmitted by sandflies. The geographical distribution of isolation of these two viruses continues to expand and it has been reported that WUXV causes neurological symptoms and even death in suckling mice. However, little is known about the prevalence of the two viruses in mammalian infections.MethodsIn order to understand the infection status of HEDV and WUXV in humans and animals from regions where the viruses have been isolated, this study used Western blotting to detect the positive rates of HEDV and WUXV IgG antibodies in serum samples from febrile patients, dogs, and chickens in the forementioned regions.ResultsThe results showed that of the 29 human serum samples, 17.24% (5/29) tested positive for HEDV, while 68.96% (20/29) were positive for WUXV. In the 31 dog serum samples, 87.10% (27/31) were positive for HEDV and 70.97% (22/31) were positive for WUXV, while in the 36 chicken serum samples, 47.22% (17/36) were positive for HEDV, and 52.78% (19/36) were positive for WUXV.DiscussionThese findings suggest there are widespread infections of HEDV and WUXV in mammals (dogs, chickens) and humans from the regions where these viruses have been isolated. Moreover, the positive rate of HEDV infections was higher in local animals compared to that measured in human specimens. This is the first seroepidemiological study of these two sandfly-transmitted viruses. The findings of the study have practical implications for vector-borne viral infections and related zoonotic infections in China, as well as providing an important reference for studies on the relationship between sandfly-transmitted viruses and zoonotic infections outside of China

Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star.Comment: publishe