A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation

Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping regions contain similar structures. In this paper, we present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation to fulfill pairwise registration and loop closure. Feature description is performed by a sparse UNet-like network based on BEV representation, and 3D keypoints are extracted by a detection head for 2D locations, and a regression head for heights. For overlap detection, a cross-attention module is applied for interacting contextual information of input point clouds, followed by a classification head to estimate the overlapping region. We evaluate our unified model extensively on the KITTI dataset and Apollo-SouthBay dataset. The experiments demonstrate that our method significantly outperforms existing methods on overlap estimation, especially in scenes with small overlaps. It also achieves top registration performance on both datasets in terms of translation and rotation errors.Comment: 8 pages. Accepted by ICRA-202

Best Management Practices and Nutrient Reduction: An Integrated Economic-Hydrological Model of the Western Lake Erie Basin

We develop the first spatially integrated economic-hydrological model of the western Lake Erie basin that explicitly links economic models of farmers\u27 field-level Best Management Practice (BMP) adoption choices with the Soil and Water Assessment Tool (SWAT) model to evaluate the cost-effectiveness of nutrient management policies. We quantify the tradeoffs between phosphorus reduction and policy costs and find that a hybrid policy that couples a fertilizer tax with cost-share payments for subsurface placement is the most cost-effective. We also find that economic adoption models can overstate the potential for nutrient reduction by ignoring biophysical complexities and thus demonstrate the importance of coupling economic and biophysical models for efficient policy design

Knowledge-driven Meta-learning for CSI Feedback

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of wireless channel, so that the DL model can achieve higher performance with small actually collected dataset and short training time. In addition, we provide analyses of rationale for the improvement yielded by the knowledge in both phases. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.Comment: arXiv admin note: text overlap with arXiv:2301.1347

Oxaliplatin but Not Irinotecan Impairs Posthepatectomy Liver Regeneration in a Murine Model

Introduction. We examined the murine hepatectomy model of liver regeneration (LR) in the setting of neoadjuvant chemotherapy. Methods. C57BL/6 mice were randomized to receive neoadjuvant intraperitoneal (IP) injections of a control, oxaliplatin (15 mg/kg), or irinotecan (100 mg/Kg or 250 mg/Kg) solution. Hepatectomy (70%) was performed 14 days after the final IP treatment. Animals were sacrificed at postoperative day (D) 0, 1, 2, 3, and 7. Liver remnants and serum were collected for analysis. T-tests for independent samples were used for statistical comparisons. Results. For oxaliplatin, percent LR did not differ at D1 or D2 but was significantly less at D3 (89.0% versus 70.0%, P = 0.048) with no difference on D7 (P = 0.21). Irinotecan-treated mice at both dose levels (100 mg/Kg and 250 mg/Kg) showed no significant differences in LR. BrdU incorporation was significantly decreased in oxaliplatin-treated animals (D1,2,3). Conclusions. Neoadjuvant oxaliplatin but not irinotecan impairs early LR in a posthepatectomy murine model which correlates with decreased DNA synthesis

Impairment of attention network function in posterior circulation ischemia-evidence from the Attention Network Test

ObjectiveThis study aimed to investigate the effect of posterior circulation ischemia (PCI) on attention network function and to determine whether PCI is holistic or selective attention network deficit and which attention network is affected.MethodsThirty-six PCI patients aged 30 to 75 were assessed using the Attention Network Test and the Mini-Mental State Examination (MMSE). There were no significant differences in age, sex, and education between PCI group and the control group (n = 32). All data were statistically analyzed by SPSS 23.0 software.ResultThere were no significant difference in the MMSE scores between the two groups. Compared with the control group, the PCI group had significantly shorter response time for alerting and orienting network. The executive control network response time was significantly longer in PCI group than in the control group. The overall mean response time was also significantly longer in PCI group than in normal control group. There was no significant difference in mean accuracy between the two groups.ConclusionThe alerting, orienting, and executive control networks were significantly less efficient in PCI group than in the control group (P < 0.01). This indicates impaired attention network in PCI patients. Since transient nerve seizures caused by vertebrobasilar ischemia may precede posterior circulation stroke, early assessment of cognitive function in patients with PCI is particularly important, and ANT is an excellent tool for this assessment

A Novel Interdigital Capacitor Pressure Sensor Based on LTCC Technology

A novel passive wireless pressure sensor is proposed based on LTCC (low temperature cofired ceramic) technology. The sensor employs a passive LC circuit, which is composed of a variable interdigital capacitor and a constant inductor. The inductor and capacitor were fabricated by screen-printing. Pressure measurement is tested using a wireless mutual inductance coupling method. The experimental sensitivity of the sensor is about 273.95 kHz/bar below 2 bar. Experimental results show that the sensor can be read out wirelessly by external antenna at 600°C. The max readout distance is 3 cm at room temperature. The sensors described can be applied for monitoring of gas pressure in harsh environments, such as environment with high temperature and chemical corrosion