SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration

Point cloud registration is to estimate a transformation to align point clouds collected in different perspectives. In learning-based point cloud registration, a robust descriptor is vital for high-accuracy registration. However, most methods are susceptible to noise and have poor generalization ability on unseen datasets. Motivated by this, we introduce SphereNet to learn a noise-robust and unseen-general descriptor for point cloud registration. In our method, first, the spheroid generator builds a geometric domain based on spherical voxelization to encode initial features. Then, the spherical interpolation of the sphere is introduced to realize robustness against noise. Finally, a new spherical convolutional neural network with spherical integrity padding completes the extraction of descriptors, which reduces the loss of features and fully captures the geometric features. To evaluate our methods, a new benchmark 3DMatch-noise with strong noise is introduced. Extensive experiments are carried out on both indoor and outdoor datasets. Under high-intensity noise, SphereNet increases the feature matching recall by more than 25 percentage points on 3DMatch-noise. In addition, it sets a new state-of-the-art performance for the 3DMatch and 3DLoMatch benchmarks with 93.5\% and 75.6\% registration recall and also has the best generalization ability on unseen datasets.Comment: 15 pages, under review for IEEE Transactions on Circuits and Systems for Video Technolog

Neutrino Masses and the LHC: Testing Type II Seesaw

We demonstrate how to systematically test a well-motivated mechanism for neutrino mass generation (Type-II seesaw) at the LHC, in which a Higgs triplet is introduced. In the optimistic scenarios with a small Higgs triplet vacuum expectation value vd < 10^{-4} GeV, one can look for clean signals of lepton number violation in the decays of doubly charged and singly charged Higgs bosons to distinguish the Normal Hierarchy (NH), the Inverted Hierarchy (IH) and the Quasi-Degenerate (QD) spectrum for the light neutrino masses. The observation of either H+ --> tau+ nubar or H+ --> e+ nubar will be particularly robust for the spectrum test since they are independent of the unknown Majorana phases. The H++ decays moderately depend on a Majorana phase Phi2 in the NH, but sensitively depend on Phi1 in the IH. In a less favorable scenario vd > 2 10^{-4} GeV, when the leptonic channels are suppressed, one needs to observe the decays H+ --> W+ H_1 and H+ --> t bbar to confirm the triplet-doublet mixing which in turn implies the existence of the same gauge-invariant interaction between the lepton doublet and the Higgs triplet responsible for the neutrino mass generation. In the most optimistic situation, vd approx 10^{-4} GeV, both channels of the lepton pairs and gauge boson pairs may be available simultaneously. The determination of their relative branching fractions would give a measurement for the value of vd.Comment: 50 pages, 51 figures, minor corrections, one reference added, to appear in Physical Review

Care burden on family caregivers of patients with dementia and affecting factors in China: A systematic review

BackgroundDementia is a chronic and progressive illness characterized by severe impairment and high dependencies. Under the influence of Chinese traditional culture, 80% of patients with dementia are watched over at home by family caregivers as primary caregivers. However, long-term care brings formidable burdens to them and reduces the quality of their life. It is necessary to find out the influencing factors of caregivers’ burden.MethodsA scoping search was conducted on eight electronic databases from 1 January 2010 to 14 June 2022: PubMed, Embase, the Cochrane Library, Web of Science, China National Knowledge Infrastructure, China VIP Database, China Biomedical Literature Database, and Wanfang Data Knowledge Service Platform. Research articles included in this review discussed the factors affecting Chinese dementia family caregivers’ care burden or stress, and the level of care burden was evaluated by a standardized care burden scale.ResultsA total of 1,888 related articles were found and 23 cross-sectional studies were eventually included. After quality assessment, 12 were of good quality and 11 were of fair quality. A total of 32 factors were identified that were associated with caregiver burden, and the results were grouped into three categories: patient, caregiver, and society. The severity of disease, poor self-care ability, neuropsychiatric symptoms, care time, number of helpers, poor health status, economic stress, poor psychological status, social support, and age were reported in many previous studies.ConclusionIn this review, the factors that affect the caregiver burden for people with dementia were clarified. By identifying these factors, hospitals, decision-makers, and communities can carry out special projects for these populations, provide appropriate assistance, or design corresponding intervention measures to reduce the caregiver burden and improve the quality of care for patients with dementia.Systematic review registration[https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD42022347816]

Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars

The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. However, classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses. In this paper, the deep learning classification network, IICnet (Infrared Image Classification network), is designed to classify sources from WISE images to achieve a more accurate classification goal. IICnet shows good ability on the feature extraction of the WISE sources. Experiments demonstrates that the classification results of IICnet are superior to some other methods; it has obtained 96.2% accuracy for galaxies, 97.9% accuracy for quasars, and 96.4% accuracy for stars, and the Area Under Curve (AUC) of the IICnet classifier can reach more than 99%. In addition, the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16, GoogleNet, ResNet34, MobileNet, EfficientNetV2, and RepVGG-fewer parameters and faster inference. The above proves that IICnet is an effective method to classify infrared sources

Impact of Water Scarcity on the Fenhe River Basin and Mitigation Strategies

This study produced a drought map for the Fenhe River basin covering the period from 150 BC to 2012 using regional historical drought records. Based on meteorological and hydrological features, the characteristics and causes of water scarcity in the Fenhe River basin were examined, along with their impact on the national economy and ecological environment. The effects of water scarcity in the basin on the national economy were determined from agricultural, industrial, and domestic perspectives. The impact on aquatic ecosystems was ascertained through an evolution trend analysis of surface water systems, including rivers, wetlands, and slope ecosystems, and subterranean water systems, including groundwater and karst springs. As a result of these analyses, strategies are presented for coping with water scarcity in this basin, including engineering countermeasures, such as the construction of a water network in Shanxi, and the non-engineering approach of groundwater resource preservation. These comprehensive coping strategies are proposed with the aim of assisting the prevention and control of water scarcity in the arid and semi-arid areas of China