Depth classification of underwater targets based on complex acoustic intensity of normal modes

In order to solve the problem of depth classification of the underwater target in a very low frequency acoustic field, the active component of cross spectra of particle pressure and horizontal velocity (ACCSPPHV) is adopted to distinguish the surface vessel and the underwater target. According to the effective depth of a Pekeris waveguide, the placing depth forecasting equations of passive vertical double vector hydrophones are proposed. Numerical examples show that when the sum of depths of two hydrophones is the effective depth, the sign distribution of ACCSPPHV has nothing to do with horizontal distance; in addition, the sum of the first critical surface and the second critical surface is equal to the effective depth. By setting the first critical surface less than the difference between the effective water depth and the actual water depth, that is, the second critical surface is greater than the actual depth, the three positive and negative regions of the whole ocean volume are equivalent to two positive and negative regions and therefore the depth classification of the underwater target is obtained. Besides, when the 20 m water depth is taken as the first critical surface in the simulation of underwater targets (40 Hz, 50 Hz, and 60 Hz respectively), the effectiveness of the algorithm and the correctness of relevant conclusions are verified, and the analysis of the corresponding forecasting performance is conducted.National Natural Science Foundation (China) (Grants 1404406, 51179034, 41072176 and 11204109)Public Science and Technology Research Funds Projects of Ocean (Grant 201405036-4)China Postdoctoral Science Foundation (Grant 2013M531015)Defense Technology Research (Grant JSJC2013604C012

Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving

Closed-set 3D perception models trained on only a pre-defined set of object categories can be inadequate for safety critical applications such as autonomous driving where new object types can be encountered after deployment. In this paper, we present a multi-modal auto labeling pipeline capable of generating amodal 3D bounding boxes and tracklets for training models on open-set categories without 3D human labels. Our pipeline exploits motion cues inherent in point cloud sequences in combination with the freely available 2D image-text pairs to identify and track all traffic participants. Compared to the recent studies in this domain, which can only provide class-agnostic auto labels limited to moving objects, our method can handle both static and moving objects in the unsupervised manner and is able to output open-vocabulary semantic labels thanks to the proposed vision-language knowledge distillation. Experiments on the Waymo Open Dataset show that our approach outperforms the prior work by significant margins on various unsupervised 3D perception tasks.Comment: ICCV 202

Clinical characteristics of 4,520 paediatric patients infected with the SARS-CoV-2 omicron variant, in Xi'an, China

Background and objectiveSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has broad tissue tropism and high transmission, which are likely to perpetuate the pandemic. The study aim to analyze the clinicopathogenic characteristics in paediatric patients.MethodsIn this single-centre study, we retrospectively included all confirmed cases infected by SARS-CoV-2 infection at Xi’an Children's Hospital, China, from 1 December to 31 December 2022. The demographic, clinical, laboratory, and radiological features of the patients were analysed.ResultsA total of 4,520 paediatric patients with SARS-CoV-2 omicron variant infections were included. Of these, 3,861 (85.36%) were outpatients, 659 (14.64%) were hospitalised patients, and nine patients (0.20%) died. Of the nine patients who died, five were diagnosed with acute necrotising encephalopathy (ANE). The most common symptoms were fever in 4,275 (94.59%) patients, cough in 1,320 (29.20%) patients, convulsions in 610 (13.50%) patients, vomiting in 410 (9.07%) patients, runny nose/coryza in 277 (6.13%) patients, hoarseness of voice in 273 (6.04%) patients. A blood cell analysis showed a slight elevation of monocytes (mean: 11.14 ± 0.07%). The main diagnoses for both outpatients and inpatients were respiratory infection with multisystem manifestations.ConclusionsA high incidence of convulsions is a typical characteristic of children infected with SARS-CoV-2. Five of the nine COVID-19 fatalities were associated with ANE. This indicates that nervous system damage in children with SARS-CoV-2 infection is more significant

Quantum Computing for MIMO Beam Selection Problem: Model and Optical Experimental Solution

Massive multiple-input multiple-output (MIMO) has gained widespread popularity in recent years due to its ability to increase data rates, improve signal quality, and provide better coverage in challenging environments. In this paper, we investigate the MIMO beam selection (MBS) problem, which is proven to be NP-hard and computationally intractable. To deal with this problem, quantum computing that can provide faster and more efficient solutions to large-scale combinatorial optimization is considered. MBS is formulated in a quadratic unbounded binary optimization form and solved with Coherent Ising Machine (CIM) physical machine. We compare the performance of our solution with two classic heuristics, simulated annealing and Tabu search. The results demonstrate an average performance improvement by a factor of 261.23 and 20.6, respectively, which shows that CIM-based solution performs significantly better in terms of selecting the optimal subset of beams. This work shows great promise for practical 5G operation and promotes the application of quantum computing in solving computationally hard problems in communication.Comment: Accepted by IEEE Globecom 202

PCC0208057 as a small molecule inhibitor of TRPC6 in the treatment of prostate cancer

Prostate cancer (PCa) is a common malignant tumor, whose morbidity and mortality keep the top three in the male-related tumors in developed countries. Abnormal ion channels, such as transient receptor potential canonical 6 (TRPC6), are reported to be involved in the carcinogenesis and progress of prostate cancer and have become potential drug targets against prostate cancer. Here, we report a novel small molecule inhibitor of TRPC6, designated as PCC0208057, which can suppress the proliferation and migration of prostate cancer cells in vitro, and inhibit the formation of Human umbilical vein endothelial cells cell lumen. PCC0208057 can effectively inhibit the growth of xenograft tumor in vivo. Molecular mechanism studies revealed that PCC0208057 could directly bind and inhibit the activity of TRPC6, which then induces the prostate cancer cells arrested in G2/M phase via enhancing the phosphorylation of Nuclear Factor of Activated T Cells (NFAT) and Cdc2. Taken together, our study describes for the first time that PCC0208057, a novel TRPC6 inhibitor, might be a promising lead compound for treatment of prostate cancer